Alternative Poverty Measurements
– A Multidimensional Approach to Wellbeing in Illinois, US
Daniel Auer
Vienna University of Economics and Business Administration
NEURUS exchange program at University of Illinois at Urbana-Champaign
Spring 2012
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Abstract
“Poverty measurement and research has made important progress by moving from
unidimensional to multidimensional approaches. [...] [The latter] assesses the state of
human well-being by focusing on ‘what one has,’ ‘how much prospect one has’, and
‘how much advantaged or disadvantaged one is in society’ toward improving such
prospect with all contributing to ‘what one can have.’ Although ‘how much one has’ is
important, as it is the means by which one can acquire human well-being, poverty is
a more complex social phenomenon and incorporating more information is necessary
to draw its accurate picture.” (Waglé, 2007, p. 16)
Based on the debate on finding more accurate ways of measuring poverty than the
current income proxies, this paper provides an alternative multidimensional approach
by investigating five specific dimensions of wellbeing (capability, income,
employment, housing & facilities, and health) through interpreting the results of their
aggregated overall index of wellbeing as well as its decomposition. While different
approaches during the process of creating such an index are discussed briefly, the
paper stresses the importance of a more comprehensive picture of poverty than the
conventional income-based measures can provide.
By converting indicators of wellbeing into ratios, an alternative method of poverty
thresholds can be established, basing on the percentage difference to an overall
benchmark. This simple yet powerful approach implies a significant value added for
policy planning and the investigation of quality of life in general.
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Content
1. Introduction ........................................................................................................ 4
1.1. Research Goal ............................................................................................. 7
2. Multidimensional Measures of Wellbeing ........................................................ 8
3. An Alternative Approach ................................................................................. 14
4. Results .............................................................................................................. 26
4.1. Deprivation Count ..................................................................................... 26
4.2. The Aggregated Index of Wellbeing ......................................................... 30
4.3. Decomposing the Index of Wellbeing ...................................................... 34
4.4. Comparison with Conventional Poverty Measures ................................ 38
4.5. Introducing ‘Natural Weights’................................................................... 40
5. Suggested Further Research .......................................................................... 43
6. Conclusion ....................................................................................................... 45
Sources ................................................................................................................... 47
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1. Introduction
According to recent estimations conducted by the U.S. Census Bureau, almost
50 million Americans live in poverty today. (International Herald Tribune, 2011)
Numbers of “children growing in families officially designated as the poor” rise up to
20 percent. (Waglé, 2008, p. 131) But what defines a person or a family as poor?
The conventional practice is to incorporate consumption and income in order to
derive easily understood poverty thresholds. The process involves specifying and
valuing basic needs and expressing the value as the poverty threshold in terms of
income such that those without sufficient income are categorized as the poor.
Consequently poverty is immediately understood as income deficiency. (Waglé,
2008)
The U.S. Census Bureau states that “[f]ollowing the Office of Management and
Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of
money income thresholds that vary by family size and composition to determine
who is in poverty. If a family's total income is less than the family's threshold,
then that family and every individual in it is considered in poverty. The official
poverty thresholds do not vary geographically, but they are updated for inflation
using Consumer Price Index (CPI-U). The official poverty definition uses money
income before taxes and does not include capital gains or noncash benefits
(such as public housing, Medicaid, and food stamps).” (U.S. Census Bureau,
2011b)
This definition’s origins date back to the 1960s where poverty thresholds were
calculated as three times the cost of a minimum diet. Accordingly, a person or a
family that spends over one-third of its household income on food is considered to be
in poverty. (Fisher, 1992) Derived income-based poverty lines are appealing to
governments all over the world, especially because of their “simplicity, accessibility,
and comparability over time and across societies.” (Citro & Michael, 1995; cited
Waglé, 2008, p. 17)
On the other hand, several scientific sources consider this unidimensional approach
as a limitation for stating a person’s quality of life. (e.g. Betti, Cheli, Lemmi, & Verma,
5
2006) For example, measuring income alone can be insufficient for explaining an
individual’s living condition as a relatively low level of income could be more than
compensated for the fact that its recipient owns his house. (Miceli, 2006) This leads
back to the question mentioned above: What defines someone being poor?
Nobel laureate Amartya Sen1, who can be considered as one of the most influential
pioneers trying to change the view of poverty in modern society, argues for two major
aspects: That there is an existence of irreducible absolute human needs such as
food and shelter on the one hand (Sen, 1979), and that poverty in general is a
product of inequality on the other hand.
Referring to Sen, Vero stresses the following: “Poverty is a difficult notion and it
may be defined in various ways which correspond to different philosophical
approaches. The general idea is that poverty is a consequence of an inequality,
between individuals, in the control of certain things, i.e., the result of an unequal
distribution between those who have something and those who are more or less
deprived by it. Poverty is then a situation in which certain individuals are
deprived of this something. Thus, according to Sen, the central question to
define and measure inequality, as well as poverty can be resumed as follows:
"equality of what?" (Sen, 1980; Sen, 1987) Thus, in order to define and
measure poverty one has to formulate a value judgment on what must be the
objects of value. Discussions on normative economics have offered us a wide
menu in answer to this question "equality of what?": for example, income,
wealth, rights, freedom, etc.” (Vero, 2006, p. 211)
Hence, a solely income-based approach excludes non-economic aspects and
therefore creates an imperfect picture of people’s quality of life.
According to numerous critics, inequality plays a minor role in the United States to
understand poverty compared to other countries in the developed world. (e.g. Brady,
2003; Glennerster, 2002; Townsend & Gordon, 2000; Waglé, 2008) As a result, rising
inequality and miss-targeted social policies can be observed. (e.g. Glennerster, 2002;
Gilbert, 2008; Smeeding, 2005) The ‘2010 Report on Illinois Poverty’ (Terpstra & et
1 “The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1998 was awarded
to Amartya Sen ‘for his contributions to welfare economics‘.“ (The Nobel Prize, 2012)
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al., 2010), for example, estimates the percentage of people living in poverty at 12.2
percent whereas the Corporation for Enterprise Development numbers 26.4 percent
as “[…] ’asset poor’, meaning they don’t have enough money tucked away to cover
basic expenses for three months in case of a layoff or emergency that saps income
[…]” (Yerak, 2012, p. 1f) There should be no doubt about the fact that asset poor are
in a state of deprivation as well.
“Because the definition of poverty has been invariably focused on lowness of
income to meet basic consumption, [policy makers] have never looked outside
of the box to understand what actually constitutes poverty and what causes and
perpetuates it.” (Waglé, 2008, p. 131)
Yet, in a wider scientific community, the understanding of poverty as a
multidimensional phenomenon has already been debated and does not present a
fundamentally new approach. (e.g. Brady, 2003; Lister, 2004; Ravallion, 1996; Sen,
2000; Waglé, 2002) By pinpointing focus areas in order to implement efficient poverty
alleviation policies, it is important to deal with deprivations on a multifaceted level.
There is no doubt that available income is of high importance for determining a
person’s social-economic status. However, various forms of deprivation including
social and environmental aspects have to be considered as well. Only a reciprocal
evaluation of monetary and complementing non-monetary indicators can guarantee a
significant picture of poverty in a certain region. (Coromaldi & Zoli, 2011; Nolan &
Whelan, Rseources, Deprivation and Poverty, 1996; Perry, 2002; Townsend P. ,
Poverty in the United Kingdom, 1979) Non-monetary elements are usually
understood as a lack of access to resources, facilities and individual attributes. (Betti,
Cheli, Lemmi, & Verma, 2006)
The World Bank, for example, stresses in its Development Report 2000/2001: “This
report accepts the now traditional view of poverty […] as encompassing not only
material deprivation (measured by an appropriate concept of income or
consumption), but also low achievements in education and health.” (World Bank,
2001, p. 15)
While the generally accepted multidimensional wellbeing approach reaches slowly
outside of academia into policy-oriented debates as the above-mentioned or the
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Laeken European Council (Atkinson, Cantillon, Marlier, & Nolan, 2002), the United
States are lacking behind in dealing with quality of life contemporarily. (Waglé, 2008)
Poverty should be considered as a shortfall in specific dimensions of a person’s
wellbeing. A quality of life index therefore could provide policy planners and
governments with a more accurate picture of an individual’s or household’s situation
reaching beyond income.
1.1. Research Goal
Effective policy making requires not only administrational and financial resources, but
also valuable information. Regarding policies targeting a society’s quality of life and
hereby especially poverty alleviation programs, the ability to investigate people’s
wellbeing in a more detailed way is fundamental for cost minimization and impact
maximization.
“When poverty statuses are erroneously identified, the consequence will not
only be inappropriate policy prescriptions with ineffective measures to deal with
poverty. It will also be inaccurately identified target population and thus
inaccurately targeted policies.” (Waglé, 2008, p. 67)
A solely monetary – and therefore imperfect - definition of poverty cannot provide as
detailed information as multidimensional measures do. “The role of income and
wealth [...] has to be integrated into a broader and fuller picture of success and
deprivation.” (Sen, 1999, p. 20) Consequently, the goal of this research is to create a
tool which reflects poverty in a more comprehensive and accurate way by
pinpointing the various forms of possible deprivation.
Based on scientific literature as well as empirical studies, this paper proofs the value
added for policy making by broadening the focus from solely income-based poverty
measurements as provided by the U.S. Census to a dimension-based investigation of
people’s quality of life.
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2. Multidimensional Measures of Wellbeing
The International Labor Organization (ILO, 1976) as well as the United States’
President’s Commission on Income Maintenance (1969; cited Spicker, Alvarez
Leguizamón, & Gordon, 2006) stressed that poverty is a state of deprivation
regarding basic human needs like food, shelter, but also in general an acceptable
level of maintaining life. So far, studies like these did not intend to implement
multidimensional measures into policy planning.
Eventually, in 2010, the Oxford Poverty and Human Development Initiative (OPHI) of
the Oxford University and the Human Development Report Office of the United
Nations Development Programme (UNDP) implemented a Multidimensional Poverty
Index (MPI) which replaced the previous Human Poverty Index. (United Nations,
2012) This MPI – based on the works of Sabina Alkire and James Foster (Oxford
Poverty & Human Development Initiative, 2012) – is composed of three dimensions
(health, education, and standard of living) which in turn consist of ten specific
indicators to represent each dimension adequately. Using Alkire and Foster’s
methodology (2007; 2009) with equally weighted indicators and further also equally
weighted dimensions, eventually an aggregated measure is created. Its idea is
considerably based on Maslow’s hierarchy of needs (Maslow, 1943) and should
address basic physical needs referring to fundamental human rights. Although this
reflection of rudimentary services and core human functionings is deeply constrained
by data limitations, “the MPI reveals a different pattern of poverty than income
poverty”. (Alkire & Santos, 2010, p. 7) Similar to the MPI, the established United
Nation Health Development Index (HDI) evoked critiques, mainly referring to
assumed redundancy, meaning that there is no value added in adding non-income
dimensions to existing income measurements. However, several authors (e.g. Lustig,
2011) are rejecting this critique by pinpointing significant differences after comparing
ranks of per capita incomes and HDI.
Basically, multidimensional measures (of poverty or wellbeing in general) rely on a
vector I of K variables, with K > 1. The variables represent indicators of wellbeing and
are usually heterogeneous in their nature; thus, both quantitative and qualitative.
(Asselin, 2009)
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While the United Nation’s MPI is probably the most visual product of recent studies
on the field of multidimensional measures of poverty and quality of life, there have
been several earlier efforts towards more axiomatic changes to this relatively new
field of science: “[…] Adelman and Morris (1967; 1973) and Morris (1979) […] sought
to quantify poverty and well-being under such guises as economic development and
physical quality of life index using infant mortality, life expectancy, adult literacy, and
other macro indicators.” (Waglé, 2008, p. 56f) While the more recent works of
Bourguignon and Chakravarty (2003) and Tsui (2002) provide fundamental
theoretical information, Dewilde (2004) was able to show important conclusions
regarding the higher plausibility of multidimensional approaches compared to
income-based ones when he elaborated a cross-country analysis between the United
Kingdom and Belgium, operationalizing three poverty dimensions (housing, financial
stress, and limited financial means). A central argument of multidimensional
measurements of poverty and well-being is stressed by Moisio (2004) using relative
income poverty, subjective poverty, and housing deprivation measures: “Although the
assumed unidimensionality of the indicators may not be highly consistent with the
multidimensional approach, results using some theoretically justified indicators were
highly encouraging thus providing richer understanding of the quality of deprivation.”
(Moisio, 2004; cited Waglé, 2008, p. 56f)
While the multidimensional approach is more or less stringently considered as
enrichment for poverty alleviation programs and wellbeing measures in general,
regarding the appropriate methodology for these measures a broad consensus is not
existent.
Apriori, a central aspect in the scientific debate is the definition of wellbeing itself.
Separating the various approaches into two major mindsets, one can identify those
who focus on material aspects or resources (e.g. Citro & Michael, 1995) and those
who consider the standard of living including for example an individual’s capabilities
or health as the preferred area of investigation (e.g. Nolan & Whelan, Rseources,
Deprivation and Poverty, 1996). Eventually, some try to combine both approaches.
(e.g. Ringen, 1987) In general, a person’s quality of life will always be a reflection of
needs, available resources, and last but not least of lifestyle choices.
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Inevitable, any approach that requires a certain initial definition of wellbeing is
normative to a certain extent and one has to accept that there may always be several
possible definitions. However, considering measurements of quality of life as
necessary, three basic strategies can be chosen:
(i) The above mentioned absolute approach (e.g. Sen, 1980) takes some basic
needs into account and thereof defines certain thresholds which separate a
population according to their status of wellbeing respectively into poor and non-poor.
(ii) An alternative approach is to define quality of life and potential deprivations
relatively, “by comparing the situation of each individual with the standard of living
prevailing at a certain point in time in a given [region].” (Miceli, 2006, p. 195f) This
method is widely applied in today’s poverty research. (Waglé, 2008) Several
European countries implemented a 60 percent threshold of a benchmarking amount
of income (e.g. Glennerster, 2002; Immervoll, Levy, Lietz, Mantovani, & Sutherland,
2006)
(iii) Eventually, one could evaluate a population’s self-perceived status of wellbeing
which evolves as a subjective index. (e.g. Goedhart, Halberstadt, Kapteyn, & Van
Praag, 1977)
Ultimately, the choice of an approach and further the “important conceptual step”
(Asselin, 2009, p. 7) of which dimensions and indicators are chosen, depends on the
purpose the measurement should serve.
“Money-metric (income or consumption based) poverty measures have been
useful […] in estimating approximately the magnitude of global poverty as well
as individual regions’ and countries’ progress towards the reduction of poverty.
The Human Development Index has provided the development community with
a broader measure of well-being — adding health and educational dimensions
to income — and demonstrating that the correlation between the monetary and
non-monetary dimensions of poverty within and across countries was far from
perfect. Finally, multidimensional indicators such as the Alkire-Foster (A-F)
measure respond to an effective demand for scalar estimates of poverty that
could be used by policymakers in the allocation of funds to reduce poverty in an
efficient and equitable way. Hence, notwithstanding their shortcomings, each of
these indicators fulfills somewhat different yet important functions.” (Thorbecke,
2011, p. 485)
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The variety of dealing with certain dimensions and their indicators is as
encompassing as the literature on multidimensional measures itself. Shall
dimensions be aggregated to an overall index of wellbeing? Consequently, the
measurement allows a complete ordering of the obtained results. (e.g. Alkire &
Sarwar, 2009) At the same time, this approach of creating one index and therefore
one general threshold maneuvers the particular study again towards
unidimensionality. Having a unidimensional index consistent of several dimensions
can still explain poverty/wellbeing more plausible than measurements based on a
single indicator. Due to a relatively easy measurement process and wieldy
applicability these kinds of multidimensional indices enjoy certain popularity in the
scientific community.
However, by incorporating information to an overall index of poverty (whether it is
monetary or non-monetary) a loss of important explanatory value has to be accepted.
Allowing an index to be split into its original describing dimensions can increase the
value of the measurement fundamentally. Consequently when poverty is defined as
deprivation in multiple (simultaneous) dimensions the notion of identifying the poor
changes. Considering several dimensions of wellbeing which are independent from
other dimensions to a certain extent, but correlated with each other under to notion of
general wellbeing, creates a new set of possibilities of identifying the poor: Several
authors stress the so called ‘union approach’ where an individual is considered as
poor when she or he is deprived in at least one of the possible dimensions. In
contrast to that the ‘intersection method’ identifies those as poor who are deprived in
all possible dimensions. Claiming a lack of explanatory value for these
measurements (either too many or too few are classified as poor) the above
mentioned Alkire-Foster Method (Alkire & Foster, 2011) - on which for example the
UNDP Multidimensional Poverty Index is based - embodies a widely used
methodological approach as it is regarded as a useful general framework which
allows various modifications regarding the choice of indicators, measurement of
cutoffs, etc.
Considering union and intersection approach as the two extremes the newly
created index “’[uses] a methodology for measuring poverty in the sense of Sen
(1979) that first identifies who is poor, then aggregates to obtain overall
measures of poverty that reflect the multiple deprivations experienced by the
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poor.’ The multi-dimensional poverty measures derived from applying the M0
methodology — as the authors call it — fulfill desirable axioms, are
decomposable, and include discrete and qualitative data as well as continuous
and cardinal data. The construction of multi-dimensional poverty measures
using the M0 methodology involves the selection of dimensions, dimensional
cutoffs (to determine when a person is deprived in a particular dimension),
dimensional weights (to indicate the relative importance of the different
deprivations), and a poverty cutoff (to determine when a person experiences
enough deprivations to be considered poor). The essence of identifying the poor
in that multi-dimensional framework is to use a ‘dual cutoff’ method. The
‘deprivation cutoff’ is used to determine whether a person is poor in a particular
dimension. It is analogous to the poverty line in the unidimensional analysis.
The second cutoff is called the ‘poverty cutoff.’ It is the number of deprivations
(or the weighted sum if unequal weights are used) that defines a person as
poor. If the number is equal to 1, the multi-dimensional index will be the union of
deprivations. If it equals the number of dimensions, the index will be the
intersection of deprivations. There exists an array of possibilities in between
these two extremes.’ (Lustig, 2011, p. 229)
Eventually, whereas several authors seek to provide a political or philosophical
rationale argue for the use of multidimensional measures (e.g. Lister, 2004;
Ravallion, 1996; Sen, 2000; Waglé, 2002) the critical question is not ‘whether’ to use
this new approach, but rather ‘how’ to create a value added for potential policies.
The connotation of wellbeing is subjective and altered by individual and cultural
preferences as well as it is dependent of time and space. (Waglé, 2008)
Consequently, poverty itself is an ever-changing concept. When voices in the United
States stress that people fall below the poverty threshold while they can afford color
television or a mobile (e.g. Paulin, 2011), they often ignore the fact that today’s
common understanding of poverty cannot be compared to early 1960s. Likewise
space plays a similar crucial role. In the United States the current official poverty
threshold for a single household is 11.491 $ per year (U.S. Census Bureau, 2012b)
while the United Nations define extreme poverty in the world’s most struck regions by
less than 1 $ a day. (United Nations, 2012)
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For poverty measures it is necessary to evolve an understanding of wellbeing in
terms of the ability to realize a certain quality of life as it is deemed appropriate and
therefore measure the availability of certain resources and individual achievements
which allow an individual to reach the same. (Waglé, 2008)
“Because one’s needs are conditioned by the level of overall well-being in
society, those focusing on relative poverty see distributional issues to be central
to developing poverty thresholds. While this invokes broader issues of whether
or not inequality in the distribution of economic resources is justified in a well-
ordered society (Friedman, 1982; Friedman & Friedman, 1980; Nozick, 1980;
Rawls, 1971; 2005), this also has implications for establishing needs as a
reasonable basis for demarcation of the quality of life of the poor relative to
those of the non-poor. Since the purpose of determining who is poor and who is
not is to identify the population that needs policy resources to improve the
quality of life to an acceptable minimum level, this process is essentially
political.” (Waglé, 2008, p. 24f)
Thus, creating a tool which reflects the dynamic nature of wellbeing (Waglé, 2008) as
well as the fact that individual needs are conditioned by their – social – environment
is vital for improving the understanding of poverty and its manifold connections
between a person and the surrounding society.
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3. An Alternative Approach
The multidimensional wellbeing measurement tool established hereinafter integrates
the relative understanding of poverty as well as the basic elements of a so called
fuzzy set approach into the Alkire-Foster Method’s theoretical fundament.
Additionally, a new approach of converting deprivations into percentage-based
differences to certain regional benchmarks will be implemented. Thereof results a
tool which will be able to pinpoint more accurately the particular dimensions of
deprivations and thus obtaining a more powerful instrument regarding poverty
alleviation programs.
“The process of identifying the population in poverty can go wrong in a number
of ways. One is the use of information itself. The availability and use of a more
comprehensive set of information likely result in more accurate measurement
outcomes. The purpose is to thoroughly understand people’s living conditions,
how they operate in day-to-day lives, and what are the challenges facing them
in efforts to improve quality of life. More information on people’s behavior and
activities would help better understand their living conditions. At the same time,
there always exist data constraints in that collecting comprehensive sets of
information will be expensive and operationally infeasible in many policy
circumstances. The goal, therefore, is to create an accurate understanding
given data and resource constraints. The resource constraint further indicates
that policy-makers cannot afford to aimlessly wander around with irrelevant
information. The theoretical framework also needs to be accurate and pertinent
to the given policy context.” (Waglé, 2008, p. 67)
According to Coromaldi and Zoli (2011) specific hypothesis relating to four main
aspects have to be established in order to derive this indicator: (i) The choice of
dimensions and indicators, (ii) establishing a weighting structure, (iii) developing an
aggregation strategy, and (iv) the implementation of thresholds.
(i) The choice of dimensions and their explanatory indicators which should guarantee
an accurate reflection of an individual’s quality of life is a crucial and probably the
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most controversial aspect of multidimensional measures. Considering every possible
element that depicts wellbeing is simply impossible and would always be susceptible
to severe critiques regarding individual preferences. Thus, the challenge is to
introduce indicators that are representatives of a certain dimension and not to cover
every single possible aspect of wellbeing. It is undeniable that any approach has to
be based on normative assumptions and judgments on what should be considered
as central for a desirable quality of life.
A conventional strategy for choosing dimensions and indicators is to conduct existing
scientific research and their elaborate set of indicators on the one hand or work
together with experts and focus groups in order to implement indicators especially
relevant for the actual research. (e.g. Guhathakurta, Pijawka, & Sadalla, 2010)
Regarding the present paper in particular, the choice of dimensions and indicators
was solely based on existing scientific research due to time-constraints. Thereby the
selection had to be tailored to requirements for a multidimensional index capable of
measuring wellbeing in an industrialized country. This conjuncture is especially
relevant regarding the fact that the majority of multidimensional (poverty) indices are
established for the context of developing countries. (e.g. Salahuddin & Zaman, 2011)
As mentioned, human needs and achievements are a dynamic concept which is
dependent of time and space. The access to clean water for example is an essential
good for a healthy life and its introduction into the United Nation’s Multidimensional
Poverty Index is the logical consequence. (United Nations, 2012) At the same time,
its consideration as an indicator for wellbeing in an industrialized country like the
United States may not be useful for any estimation, as the sweeping majority actually
has access to clean water. Thus, in order to obtain relevant information, poverty lines
and further the understanding of poverty itself, have to be relatively changing in the
same direction as the overall quality of life in a specific region. (e.g. Miller & Roby,
1970; Townsend, 1970)
Generally the content of possible dimensions is abundant, ranging from poverty as a
reflection of individual characteristics such as low educational achievement to
wellbeing influenced by social and economic structures such as a lack of jobs or
public health care2. (Crump, 1997, p. 99)
2 Martínez and Ruiz-Huerta (2000) for example introduce a distinction between a so-called “life-style
approach” that includes non-necessary goods, and a “necessities approach” that excludes the same.
16
Above all, the United Kingdom study ‘Monitoring Poverty and Social Exclusion 2010’
(Parekh, MacInnes, & Kenway, 2010), the European Union Indicators of Social
Exclusion (‘Laeken Indicators’) (Atkinson, Cantillon, Marlier, & Nolan, 2002; Dennis &
Guio, 2003; Guio, 2005), as well as Waglé’s works on capability deprivation in the
United States (Waglé, 2009) met particular repercussion in the present paper as they
have been adopted for a similar socio-economic context.
Eventually, five dimensions of wellbeing have been identified as playing a major role
regarding an individual’s quality of life: Capability, Income, Employment, Housing &
Facilities, and Health3. If nothing else, the decision for these dimensions is
underpinned by a broad consensus regarding their importance as aspects of
wellbeing.
Since dimensions in general cannot be observed and therefore measured directly,
the use of explanatory indicators is necessary. Potential indicators require as a
minimum to be ordinal. Asselin (2009) emphasizes that variables like ‘main
occupation of household head’ are not admissible poverty indicators. However, those
can still play an important role in poverty analysis. Despite the argument’s plausibility,
the present paper takes into account ordinal or metric variables only as the focus lies
on the creation of a tool for poverty analysis. Regarding further steps of interpretation
and eventually policy making, the information value added of cardinal variables
should be taken into consideration.
The alternative approach compared to existing multidimensional measurements is
the conversion of ordinal and metric variables into ratios from 0 to 1 to allow in further
steps a new way of identifying the poor by comparing the ratio-based scores with
regional benchmarks. Thereby, a value of 0 would be considered as total deprivation
of a specific indicator, whereas 1 equals no or 0 percent deprivation. This method
allows the index to benefit from a typical advantage of unidimensional measures:
Ranking the units of observation according to a complete ordering which is
particularly important for targeting policies and more sophisticated poverty analysis.
(Asselin, 2009) Table 3.1 provides an overview of the five dimensions, their
explanatory indicators as well as the indicators’ mathematical composition of ratios.
3 Several authors stress the consideration of a “social quality of life” (e.g. Asselin, 2009); a possible
introduction to the set of dimensions will be discussed in Chapter 5.
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Dimension Indicator Definition*
Capability
Pre-School enrollment pct of Children enrolled in Pre-School
Educational attainment 1 - pct of population ≥ 25 years with less than High
School Degree
Ability to speaking
English
1 - pct of population with not imputed ability to speak
English
Literacy 1 - pct of population lacking basic prose literacy skills
Income
Relative Income (Household income) / (Median IL-State income) ≤ 1**
GINI - Coefficient 1 - GINI coefficient
Jobless Households 1 - pct of population living in a jobless household
Children in Poverty 1 - pct of Children in Poverty
Employment
Unemployment 1 - pct (Unemployed 20-64 years) - (NAIRU 3%) ≤ 1**
Young Unemployment 1 - pct (Unemployment 16 years and over) - (NAIRU
4%) ≤ 1**
Housing
and
Facilities
Over occupancy 1 - pct of Rooms with more than 2 occupants per room
Mortgage Status 1 - pct of Housing units with second mortgage and
home equity loan
Kitchen Facilities 1 - pct of Households lacking Kitchen Facilities
Plumbing Facilities 1 - pct of Households lacking Plumbing Facilities
Phone availability 1 - pct of Households without Phone
Vehicle availability 1 - pct of Household without Vehicle available
Health
Health Insurance
Coverage 1 - pct of population uninsured
Life Expectancy (County Life Expectancy) / (Median IL-Life Expectancy)
≤ 1**
Table 3.1: Dimensions & Indicators, mathematical structure and geographic scale
* In order to retain ratios with 1 as the highest score, the majority of indicators
had to be measured as (1 – percentage of I).
** Four indicators (Relative Income, Unemployment, Young Unemployment, and
Life Expectancy) receive special treatment regarding their conversion into
ratios. In order to keep up equal weights for every indicator the resulting ratios
of these four indicators are capped at 1 or 100 percent. This method is plausible
insofar that with having for example a higher income than the regional
benchmark (≥1), one would assume a non-deprived situation.
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The indicators’ scale is based on the Census Tract Level, data is retrieved from the
American Community Survey – 5 year estimates 2006-20104. (U.S. Census Bureau,
2011a)
“Census tracts are small, relatively permanent statistical subdivisions of a
county delineated by local participants as part of the U.S. Census Bureau's
Participant Statistical Areas Program. The U.S. Census Bureau delineated
census tracts in situations where no local participant existed or where local or
tribal governments declined to participate. The primary purpose of census
tracts is to provide a stable set of geographic units for the presentation of
decennial census data. […] Census tracts generally have between 1,500 and
8,000 people, with an optimum size of 4,000 people. (Counties with fewer
people have a single census tract.) When first delineated, census tracts are
designed to be homogeneous with respect to population characteristics,
economic status, and living conditions. The spatial size of census tracts varies
widely depending on the density of settlement.” (U.S. Census Bureau, 2012a)
Only three exceptions - Literacy, Health Insurance Coverage, and Life Expectancy -
are county-based and had to be broken down to Census Tracts with the necessary
assumption that all Census Tracts in a specific county obtain the same indicator-
values.
The choice for Census Tracts as the preferred scale reflects a compromise between
data availability, maximization of details, and minimization of complexity in order to
keep up comparability as well as easy usage for potential policy planning.
Undoubtedly, the most detailed scale would be the individual level, including the
maximum information regarding quality of life and especially the connections of an
individual’s several dimensions of wellbeing. Having the individual as the unit of
analysis would allow comparisons across certain socio-economic groups of the
society. (Alkire & Santos, 2010) Although several authors stress the unprecedented
amount of available information especially in the United States (e.g. Alkire & Foster,
2011), it is a well-known fact that multidimensional measurements require
4 One has to be aware of binding constraints like estimation errors on a larger scale, caused using
data conducted by a survey. “[…] [A] key priority for future work on multidimensional poverty must be
gathering more and better data around core areas […].” (Alkire & Santos, 2010, p. 13)
19
comprehensive data on a large scale, which are often difficult to come by. (Ravallion,
2011; Waglé, 2008) Eventually, the necessary data for individual scale is not
available considering this research’s constraint of time and resources.
Capability dimensions can be defined as an individual’s ability to achieve certain
‘functionings’ in the sense of valued elements by a society. Capability is the freedom
to choose in order to realize these functionings. (Sen, 1999) “At a general level,
functionings conceived as the outcomes have constitutive values whereas the
capabilities taken as the means have instrumental values to make such functionings
happen.” (Waglé, 2009, p. 510) The indicators established to reflect a society’s
capability (see Table 3.1.) provide the “freedom to do the things that one wants to do
including earning an income needed to maintain a desired lifestyle. Income or the
type of life maintained with it constitutes the outcome or functioning with education
having an instrumental role in achieving it.” (Waglé, 2009, p. 510) In the present case
an additional indicator has been implemented in order to obtain a fitted value for the
context of the United States as the ‘ability to speak English’ is often a requirement for
– especially well-paid – jobs. Consequently the quality of life should be assessed in
terms of an individual’s capabilities because it is the capability that defines the choice
of alternative combinations of functionings a person can achieve. (Vero, 2006)
Further, the present research does not take into account the assumption of external
capabilities (Alkire & Santos, 2010) and effective literacy (Basu & Foster, 1998),
stating that for example other household members benefit from the abilities of one
literate person in the household. Above that, the data selection basing on Census
Tracts would not allow assumptions on the individual level at all.
Although it is widely recognized that deprivations are – as already mentioned – a
multidimensional phenomenon, monetary indicators still play a fundamental role as
one aspect of the multidimensionality. (Betti, D'Agostino, & Neri, 2003) The plausible
approach of measuring the monetary dimension would be the investigation of
consumption. However, this causes massive complexity, as consumption is not only
influenced by available income, but also by tastes and preferences all together
conditioned by time, place, culture and other factors. Thus, “income has been a
widely used proxy measure of consumption assuming that it can capture not only the
ability to consume but the actual consumption as well.” (Waglé, 2008, p. 17) The
20
income proxy implemented in the present paper is not adjusted for regional variations
in the cost of living. Crump stresses this constraint in particular. (Crump, 1997) A
possible correction for that is to introduce further indicators to the income dimension,
concretely the GINI-coefficient as well as the percentage of jobless households and
children in poverty.
The understanding of employment as a dimension of wellbeing is not only referring to
a certain security of income or available insurance, but also regarding the notion of
employment as one form of an individual’s connection to society. Additionally, special
attention is paid to young unemployment as it is considered as the most critical form
of unemployment. (e.g. Freeman & Wise, 1982) In order to obtain the scores for
unemployment and young unemployment, their specific ratios are corrected for full
employment considering the ‘Non Accelerating Inflation Rate of Unemployment’
(NAIRU) (Gordon, 1996) and capped with a value of 1. This expresses the
assumption that unemployment rates below 3 percent for 24-year-olds and over and
4 percent for 16-year-olds and over are considered as no deprivation.
The Housing & Facilities dimension is notably altered by the indicators’ requirements
for an industrialized country. Considering a lack of plumbing facilities as deprivation is
basically a reflection of a wealthy society at a certain point in time. In the same vein
over occupancy defined as housing units with more than 2 occupants per room is
primarily plausible in the present context5. Of course a major distinction has to be
drawn whether the housing unit is owned by the occupant or not. The economic
consequences of being fraught with both a second mortgage and a home equity loan
obviously can be devastating.
Although some authors question the possession of durable goods as a valuable
poverty indicator (Asselin, 2009), not obtaining these objects is evaluated as a form
of deprivation by the majority of scientific papers (e.g. Miceli, 2006). Though, the
interpretation of goods as poverty indicators has to be done with caution. One can
definitely argue that not possessing a vehicle is a reflection of taste and preferences.
However, the present paper considers this as a deprivation due to the extraordinarily
high value in America’s society.
5 A more detailed discussion about the integration of housing as indicators of wellbeing can be found
in Groh-Samberg (2004).
21
“A true indicator of the physical quality of life, for example, is the status of health as it
can accurately gauge the state of one’s physical life.” (Waglé, 2008, p. 16) Yet,
health was the most difficult dimension of wellbeing to measure. Comparable
information regarding a person’s health condition is missing on a large scale, mainly
due to privacy concerns. The general approach is to measure a long and healthy life,
whereby possible diseases are not taken into account as this would require an
unreasonable judgment over the ranking of certain illnesses. A plausible indicator
that deals with diseases would be child development in the sense of overweight,
malnutrition and disabilities, as shortfalls in child development often lay the
foundation of a general shortfall of wellbeing at adulthood. Eventually, shortfalls in life
expectancy as well as health insurance coverage (Alkire & Foster, 2011)
representing the security of acceptable treatment in case of illness are introduced as
indicators of an individual’s health quality.
(ii) Establishing a weighting structure for both dimensions and indicators describes
another key aspect of multidimensional measures. The scientific debate on how to
introduce weights yields various possibilities. By following the conventional approach
(e.g. Alkire & Santos, 2010; Alkire & Foster, 2011) this methodology implements
equally weighted dimensions and among them equally weighted indicators
(Equation 1).
Equation 1:
Hence, every dimension has an imputed equal importance for wellbeing.
(Townsend, 1979) However, the impact of introducing unevenly weighted indicators
based on statistical methods will be discussed in a subsequent chapter.
(iii) Having specific indicators built up, it is another controversial question whether to
aggregate them to an overall indicator or not. (Lustig, 2011; Brandolini, 2008)
Investigating an overall index postulates an understanding of poverty as a combined
22
shortfall based on multiple deprivations and further using a certain threshold to
identify the poor and non-poor (Equation 2). (Waglé, 2008)
Equation 2:
This is not only a simple, but also often useful interpretation of a multidimensional
context. (Brandolini, 2008) Despite the fact that a simple summary of a complex
phenomenon is better provided by an overall index, Ravallion (2011, p. 237) stresses
a vivid example for a situation in which an aggregated index might not be the first
choice: “Imagine you go for your annual medical checkup. Your doctor does all the
usual tests, but tells you that she will base her assessment solely on a single
composite index — rescaling and averaging all the test results. You would be well
advised to get a new doctor!” Considering the single dimensions of potential
deprivations rather than their aggregation reduces the inevitable loss of information
of aggregation-based approaches. (Coromaldi & Zoli, 2011; Brandolini, 2008)
An elegant way to benefit from the advantages of both approaches is provided by a
special version of comprehensive strategies6: What Ravallion calls a “deprivation
aggregation” (Ravallion, 2011) is basically the identification of the poverty status in
each of the dimensions and then the aggregation into a composite index. Methods
like this (e.g. Oxford Poverty & Human Development Initiative, 2012) allow
aggregation of an overall index as well as the latter’s decomposition in its
dimensions. Therefore they provide policy makers with a powerful tool to investigate
dimensional deprivations of specific groups by unpacking the overall index. “[This
method] embodies Sen's (1993) view of poverty as capability deprivation and is
6 “According to Brandolini (2008), two kinds of strategies can be distinguished: ‘supplementation’
strategies, which consider all well-being items one by one and in conjunction with income, and
‘comprehensive’ strategies, which make an attempt to reduce the multidimensionality into one or more
summary measures. Albeit undoubtedly simpler, the first solution prevents the researchers from
having a synthetic picture of people’s standard of living. Within the second category, a further
classification distinguishes between ‘aggregative’ strategies, where a single well-being indicator
summarizes all the information provided by several life domains, and ‘non-aggregative’ strategies,
which analyze deprivation in distinct dimensions separately, by constructing multidimensional indices
specific for each domain.” (Coromaldi & Zoli, 2011, p. 4)
23
motivated by Atkinson's (2003) discussion of counting methods for measuring
deprivations.” (Alkire & Foster, 2011, p. 477)
The present paper addresses this approach twofold: On the one hand its index
remains easy to be decomposed into the explanatory dimensions (see Table 3.2. and
Chapter 4.4.). Further, it considers ‘deprivation aggregation’ by introducing an
alternative ‘deprivation count’ that emerges being similar in its results (see Chapter
4.1.).
(iv) The fourth and last of the major steps is the implementation of a threshold
identifying the deprived and non-deprived. A large number of poverty measurements
including the income-based methods entail a major limitation: “[T]hey need to
dichotomize the population into the poor and the non-poor by means of the so called
poverty line.” (Betti, Cheli, Lemmi, & Verma, 2006, p. 115) Basing distinction between
poor and not poor on for example a certain exact amount of available income
guarantees a simple measurement process, but can be criticized as too rigorous. In
contrast ‘poor’ is rather a vague predicate with allowance for borderline cases, the
absence of sharp borderlines in general and susceptibility to a Sorites paradox.
Considering the paradox of the heap or so called Sorites paradox, giving a poor
person one dollar will not make that person rich. By repeatedly giving that person a
dollar she will become rich indeed. In that case, drawing a sharp line is impossible.
Likewise is wellbeing a continuum of living standards from poor to rich and therefore
making sharp cutoffs arbitrary. (Mack & Lansley, 1985) Consequently, the clearest
statement possible is to identify a person as “borderline poor” (Qizilbash, 2006), but
any dichotomy segmentation reflects reality in a too simplified and therefore defective
way. Hence, the so called ‘fuzzy set’ approach allows a continuous transition (for
example from 0 to 1) and can therefore eliminate abrupt changes like a single
threshold would reflect a leap between 0 and 17. (Vero, 2006) “In other words,
poverty should be considered as a matter of degree rather than as an attribute that is
simply present or absent for individuals in the population.” (Betti, Cheli, Lemmi, &
Verma, 2006, p. 115f)
However, because it is an easy-to-use method, the value of simple thresholds for
policy planning should be acknowledged. In the present methodology, any
implementation of thresholds or multiple cutoffs for every single dimension as well as
7 For a detailed discussion of the fuzzy approach see Qizilbash (2006).
24
for the aggregated index is left open. This methodological construction of not
dictating exact thresholds provides a maximum of flexibility for any policy making or
analytical approaches.
The present research introduces the median score of the state of Illinois (overall and
for every dimension) as the regional benchmark and therefore the deprivation
threshold (see Table 3.2.). In doing so, the boundary value between deprived and not
deprived is exactly defined. Yet, the existence of this line doesn’t make any point
about the categorization of poor and non-poor. Being deprived by a relatively small
percentage in a certain dimension is not necessarily resulting in being poor,
especially when additional dimensions are introduced into the equation. Thus, the
issue of poverty as a vague concept is addressed indirectly by introducing the
possibility of trade-offs among dimensions8. Standard approaches of
multidimensional measurements don’t consider values above a certain deprivation
threshold. Therefore, possible trade-offs among dimensions as an essential aspect of
wellbeing cannot be measured using a unidimensional indicators or the majority of
multidimensional indices of poverty and wellbeing. For instance, a person classified
as deprived because of the absence of health insurance coverage, but with a relative
income above a certain level could theoretically compensate for the lack of insurance
by drawing on his or her income surplus. Consequently, by ignoring information
above the deprivation threshold any substitutability or complementarity between
dimensions is ruled out. (Thorbecke, 2011)
Although it can be stressed that the estimation of trade-offs and complementary
relations is too complex that it would be inopportune from an operational standpoint,
this approximation represents a crucial value added regarding the understanding of
multidimensionality and beyond that it is able to attenuate the negative side-effects of
sharp thresholds.
Summarizing, the relative approach addresses the fact that the concept of poverty or
wellbeing is dynamic regarding time and place. Building upon ratios, a complete
ordering expressed as percentage difference to the regional benchmark is possible.
8 Due to massively differing rates of substitution across societies/economies (Lustig, 2011, p. 228f),
the introduction of possible trade-offs impedes any comparison attempts. Yet, it is already mentioned
above that the present measurements are intentionally tailored to the demands of the United States
and not subject to international comparisons.
25
Additionally, by using those ratios, the resulting indices for dimensions and indicators
guarantee strong flexibility for drawing poverty lines or creating corridors for policy
making as they are partly implemented in monetary indicators. (e.g. Terpstra & et al.,
2010) And finally, the introduction of estimating possible trade-offs weakens the
strong normative influence of the median as the deprivation threshold and considers
an important aspect of multidimensionality.
Table 3.2.: Measuring the Multidimensional Index
26
4. Results
The measurement outcomes can be rehashed in several possible ways, depending
on which information is required. Following this paper’s central argumentation that
the focus should lie specifically on the multidimensional perspective, the so called
deprivation count (Chapter 4.1.) as well as the overall index in dependence to its
decomposition (Chapter 4.2. and Chapter 4.3.) can be considered as the most
valuable ways of presenting the results. Further, Chapter 4.4. discusses the
measurement outcomes in relation to conventional unidimensional income-based
measures.
4.1. Deprivation Count
Simple counting of the number of dimensions in which a given region or individual is
regarded as deprived is not only a very elementary, but also widely used method for
visualizations because it provides an immediate picture of especially critical areas for
potential poverty alleviation programs.
Most importantly, one has to decide where to draw the line(s) to identify the poor.
Again, this step’s basic thinking is similar to any above-mentioned issues regarding
thresholds. However, building on Waglé’s fundamental approach of identifying
different cases of poverty (Waglé, 2008, p. 70), the introduction of poverty lines turns
out to be less arbitrary than given by the information available: Waglé’s scenario
deals with three dimensions (Economic-Wellbeing, Capability and Social Inclusion).
Being deprived in one of these dimensions ranks an individual as “poor”. Deprivation
in two dimensions is similar to “very poor” whereas being deprived in all three
dimensions is defined as “abject poor”.
Entering this down-to-earth approach into the present methodology with five
dimensions yields following plausible configuration (Definition 1).
27
Definition 1:
Number of dimensions in deprivation (n) = 0;1: Nonpoor
n = 2: Poor
n = 3: Very poor
n = 4; 5: Abject poor
This ranking pays especially attention to the rather rigorous definition of deprivation
by considering the state’s median as the benchmark for deprivation (see Chapter 3).
Accordingly, 808 of 3123 Census Tracts in the state of Illinois would be considered
as nonpoor, 636 as poor, 639 as very poor, and eventually 1038 as abject poor (see
Map 4.1.1.9). At first appearance the results seem exceptionally high. Yet, with due
regard to reminding that deprivation is present when falling below the state median,
the results become more plausible. Additionally, a principal accordance of multiply
deprived Census Tracts and areas with high income-based poverty rates (Terpstra &
et al., 2010) can be observed.
It is essential to take into account that a deprivation count cannot provide statements
regarding the severity of deprivation in a given dimension. Counting deprivations by
scaling dimensions in a dichtomous way fulfils the initial purpose of addressing areas
that deserve special attention only. Drawing detailed conclusions necessitates
deeper analysis.
A simple approach addresses the fact that poverty can feature different directions:
On the one hand – as it is measured with help of a deprivation count – poverty can
manifest itself as a more or less severe deprivation across several dimensions.
Additionally, the extent of deprivation occuring in a single dimension can justify the
term ‘poor’ as well. This plays an important role for policy making as poverty always
tends to be a combination of both vectors.
Consequently, this research defines each a vector regarding the ‘horizontal form of
poverty’ and the ‘vertical form of poverty’ which are shown in Graph 4.1.1.: The
intensitiy of deprivation can be measured with one composite index as well as with a
measure for every single dimension always as a negative percentage difference to
9 For a detailed discourse about poverty maps see Benson, Minot, and Epprecht (2007) as well as
Crump (1997).
28
the regional benachmark from 0 to 1. The volume of deprived dimensions is
represented by a simple deprivation count.
Graph 4.1.1.: Horizontal & Vertical form of poverty
29
Map 4.1.1. Deprivation Count, State of Illinois
30
4.2. The Aggregated Index of Wellbeing
The aggregated index of wellbeing in turn presents the vertical form of poverty as it is
defined as the percentage difference of a Census Tract’s ratio to the regional
benchmark. Yet it cannot provide information regarding the horizontal form of
poverty.
Interpreting the aggregated index serves a similar purpose as counting the number of
deprived dimensions: It can provide an informative basis for further analysis. In
contrast to any deprivation count method, the aggregated index of multiple
dimensions - as it is measured in the present paper – contains information about the
extent of overall wellbeing and therefore allows for statements regarding the severity
of deprivations ergo the vertical extent of deprivation rather than the horizontal one.
Table 4.2.1. provides a summary of the overall deprivation scores for Census Tracts
in the state of Illinois. Showing a range of 0.2969 percentage points from 0.9496 to
the minimum of 0.6527, the median overall score of wellbeing is 0.8832. In other
words, the median Census Tract’s population in Illinois is deprived by 11.68 percent.
Table 4.2.1.: Wellbeing in Illinois, Census Tract Scores
0
100
200
300
400
0.65 0.70 0.75 0.80 0.85 0.90 0.95
Series: MPI_UW_OVERALL
Sample 1 3123
Observations 3123
Mean 0.873794
Median 0.883200
Maximum 0.949600
Minimum 0.652700
Std. Dev. 0.040682
Skewness -0.974774
Kurtosis 3.784237
Jarque-Bera 574.6014
Probability 0.000000
31
Map 4.2.1. presents this equally weighted index whereat the categorization is based
on the values’ standard deviation. Aligning Census Tracts (or any given entities)
according to their standard deviation is a useful method in order to gain a general
view of a region’s shape. Expectably, the majority of more severely deprived
individuals can be assumed in mainly urban areas; besides the existence of some
larger rural Census Tracts spread across the state. Map 4.2.2. provides a
visualization of the Chicago area where the most deprived Census Tracts are
located.
The results go along with the previous deprivation count; essentially producing an
overlap of more severely deprived and multiply deprived Census Tracts. Again,
Waglé’s definition of poverty (2008, p. 70) can be implemented - in an adapted form -
for every Census Tract and consequently its inhabitants (Definition 2).
Definition 2:
[percentage difference to state median (r) > -0.5 * Std. Dev.] = Nonpoor
[-0.5 * Std. Dev.; -1.5 * Std. Dev.] = Poor
[-1.5 * Std. Dev.; -2.5 * Std. Dev.] = Very poor
[r < -2.5 * Std. Dev.] = Abject poor
32
Map 4.2.1.: The aggregated index of multiple dimensions in the state Illinois
33
Map 4.2.2.: The aggregated index of multiple dimensions, clip of Chicago area
34
4.3. Decomposing the Index of Wellbeing
As Lustig (2011, p. 232) refers to Ravallion (2011): “What is the usefulness of
aggregating deprivations into a composite index if, for policy purposes, a
disaggregation will be indispensable?”
The value added of analyzing the aggregated index is limited indeed, although one
can already draw inferences about the situation of poor people in certain areas. This
paper stresses the argument that a more revealing procedure is to decompose the
index of wellbeing into its initial dimensions. (e.g. Nolan & Whelan, 2010)
Map 4.3.1. shows the area of Champaign County, IL and the selected Census Tract
representing the University of Illinois at Urbana-Champaign’s campus. According to
the overall index the campus area (overall score: -0.0689) would be considered as
very poor, being in numerous company of other Census Tract’s located in the urban
area of the twin city of Champaign-Urbana as well as the city of Rantoul in the
northern part of Champaign County. A similar conclusion can be found as well in the
official poverty statistics provided by the U.S. Census Bureau where the campus
area’s income-based poverty rate lies at around 44 percent. (U.S. Census Bureau,
2010) However, comparing unidimensional and multidimensional measures requires
special precaution which will be discussed in the upcoming chapter.
Decomposing the index offers important additional insights. In contrast to the two
previous approaches of counting deprivations and aggregating one index, this
method enables the interpretation of the horizontal as well as the vertical form of
poverty.
Interpreting Map 4.3.2. renders some important indications of how the different
dimensions affect the situation of the poor. Accordingly, the campus area shows
rather good or ‘acceptable’ values regarding employment and capability. In contrast
to that, the situation regarding housing and income is critical, even more as the
campus area stands out as the negative peak in both dimensions (together with a
very small number of other Census Tracts).
Table 4.3.1. offers a comparison of the example area’s dimension scores and its
state benchmarks.
35
Map 4.3.1.: The aggregated index of multiple dimensions, Champaign County, IL
36
Map 4.3.2.: Decomposition, Champaign County, IL
37
Table 4.3.1.: UIUC dimension scores compared to state benchmarks
38
4.4. Comparison with Conventional Poverty Measures
A very important aspect for any evaluation attempts of comparing uni- and
multidimensional measures is the fact that these measures differ not only in regard to
their philosophical definition of poverty itself, but also regarding what they actually
measure. (Asselin, 2009)
So far, this paper presented two different approaches of measuring multidimensional
poverty and interrelated their outcomes with unidimensional measures respectively
the income-based approach as a reference:
- The deprivation count consists of simply counting an individual’s number of
deprived dimensions. Its outcome can be considered – as mentioned above –
as the horizontal form of poverty. (Due to the unavailability of individual data,
the information had to be aggregated to larger scale.)
- An index as it is defined in this paper measures the extent of deprivation,
whether in one or in several dimensions, again referring to an individual or the
aggregation to a certain scale.
- The unidimensional income-based poverty rate expresses the ratio of
individuals being considered as poor in a certain area.
Thus, the main question in the case of official poverty rates is ‘How many individuals
are poor in a given region?’ whereas a multidimensional measure according to this
research asks for ‘How poor is every single person in a given region on average?’
Consequently, comparing the two main readings turns out to be inaccurate when
looking at the numbers only.
Nonetheless, it is possible to treat the official income-based poverty rate in the same
way as the multidimensional approach by measuring the percentage difference of a
Census Tract’s poverty rate to the state’s median poverty rate. The resulting values
contain the same information as their multidimensional counterpart.
The outcomes of comparing the values for every Census Tract presented in Table
4.4.1. show that - to a considerable extent - both measures identify the same
Census Tracts for example either as areas of high poverty rates or as severely
39
deprived areas. The mean difference of both measures is 0.018 percentage points
and therefore can be considered as arbitrary.
Table 4.4.1.: Comparison of uni- and multidimensional measure, percentage points
Thus, one could interpret the similar results as the ‘proof’ that the multidimensional
measure is correct to a certain extent. Or in other words: Either both the income-
based poverty rates and the multidimensional approach are correct or both fail to
capture poverty. However, the latter approach is more versatile as it provides
additional information regarding the composition of poverty and therefore enables to
measure poverty more accurately.
0
100
200
300
400
500
600
700
800
900
-0.2 -0.0 0.2 0.4 0.6 0.8
Series: PPDIFF_INDEX_POVSC
Sample 1 3123
Observations 3123
Mean 0.018405
Median -0.014200
Maximum 0.859400
Minimum -0.178100
Std. Dev. 0.113649
Skewness 1.972039
Kurtosis 8.793031
Jarque-Bera 6391.084
Probability 0.000000
40
4.5. Introducing ‘Natural Weights’
With reference to the above mentioned discussion about weighting strategies it is
legitimate to investigate the methodology after introducing an alternative weighting
system than that of equal weights. (Thorbecke, 2011)
Among the great variety of different methods, the present paper introduces what shall
be called ‘natural weights’ and is a determination of the optimal weight associated
with each attribute using conventional statistical methods. (e.g. Dewilde, 2004; Nolan
& Whelan, 1996) The explanatory values are obtained running a standard regression
analysis with the income-based poverty rate as the dependent variable. The specific
weights can be gathered from the coefficient column in Table 5.1. However, what is
immediately revealed is the fact that this table presents a drastically reduced set of
indicators. Due to insignificant variables for this configuration of regression analysis
as well as suppression of a smaller contingent of variables, the final model contains
of nine indicators representing the five dimensions10. The actual configuration of the
model presents only one of several possibilities. Most importantly the information
criteria show acceptable values.
10
In order to guarantee at least one representational indicator per dimension the estimates for life
expectancy have been retained in the model, despite their assumed 5-percent-acceptance for H0.
41
Table 5.1.: Introducing an alternative weighting system: regression analysis
Now, the aspect in question is whether the alternative weighting system alters the
outcomes for the multidimensional index in a significant way. A comparison of the
equally weighted index and that composed of ‘natural weights’ (Table 5.2.) shows a
normal distribution of percentage points differences with an average difference of
0.003 percentage points. Consequently, regarding their aggregated indices and using
the present set up for both methods, their outcomes are basically identical.
42
Table 5.2.: Comparing indices composed of equal- and natural weights
A significant difference might be concealed behind the shift in the dimension
composition which implicates different interpretations for single dimension scores and
therefore causes altered results regarding the interpretation of the decomposed
index. However, the major impetus for not implementing a weighting structure such
as the model above is serious consideration concerning wrong causality. Although
the literature is in general not dealing with the issue at all, it seems to be a plausible
conclusion that for example not obtaining a vehicle can be explained due to the fact
of poverty and not vice versa as the actual model would suggest.
0
100
200
300
400
500
-0.10 -0.05 -0.00 0.05 0.10 0.15 0.20
Series: DIFF_MEDIAN_UW_W
Sample 1 3123
Observations 3123
Mean -0.003153
Median -0.011100
Maximum 0.191500
Minimum -0.105700
Std. Dev. 0.042436
Skewness 1.092241
Kurtosis 4.731567
Jarque-Bera 1011.108
Probability 0.000000
43
5. Suggested Further Research
During the process of creating an alternative measure of wellbeing based on
multidimensional implications, several occurrences evoke issues of various kinds.
Some have already been discussed in this paper. The following chapter seeks to
present the most fervent topics, less to criticize multidimensional indices, but rather to
spur further research.
On the very beginning it is the choice of dimensions and indicators which is subjected
to widespread discussion. While the general debate11 has already been dealt with in
this chapter the focus rests on the actual approach.
First, indicators for the possession of durable goods cannot distinct between not
having these goods because one cannot afford them and not possessing as a matter
of taste. (Brandolini, 2008) Thus, it would be informative to be able to control for the
intentions of consumption or non-consumption. However, this task reaches far
beyond this paper’s ambitions.
Further, as the notion of poverty shifts towards overall quality of life or human-
wellbeing, this tendency should be reflected in the choice of indicators too. The
present paper tries to emphasize quality of life mainly by measuring health (besides
alternative definitions of employment etc.). The index could be enriched with more
social aspects of wellbeing (Waglé, 2008) such as gender and racial equality, child
development, or other politically loaded vocabularies such as choice, opportunities,
and freedom. This would also alter the way poverty is understood in general towards
a more comprehensive notion. (Waglé, 2009) In its initial structure, this approach
included a sixth dimension of ‘social quality of life’. Ultimately it had to be excluded
from the set of indicators due to severe data restrictions. One possible way of gaining
such information would be conducting surveys asking about self-perceived quality of
life. (e.g. Goedhart, Halberstadt, Kapteyn, & Van Praag, 1977; Guhathakurta,
Pijawka, & Sadalla, 2010)
Another aspect which could be taken into consideration is more extensive spatial
analyses such as gravity modeling. For example introducing a proxy such as the
access to goods or markets could provide valuable information not only for the choice
11
“The need for selection and discrimination is neither an embarrassment, nor a unique difficulty, for
conceptualizing functionings and capabilities.” (Sen, 2008; cited Alkire & Santos, 2010, p. 11)
44
of indicators in the first place, but also for explaining interrelations between deprived
areas and their environment in terms of discrepancies to neighboring areas,
employment centers and so on.
As mentioned above, information for this research is gathered or aggregated to
Census Tract level because of data restrictions on the one hand and lucidity
regarding policy making. It has also been mentioned that scaling data on the
individual level would add very important information. On the individual level it is
possible to make clear statements regarding a person’s situation of wellbeing across
the whole set of dimensions. Right now, having obtained Census Tract data, the
wellbeing status can only be estimated as a Census Tract’s inhabitants’ average.
Consequently, one can make rough statements, yet it remains unclear whether
deprivations are distributed rather evenly across a population or whether a smaller
number of individuals are severely deprived across several dimensions and others
are not deprived at all.
Finally, considering longitudinal measures can be undoubtedly vital for any kind of
policy making. “Persistence and movement over time is an equally important aspect
of the intensity of deprivation, requiring longitudinal study at the micro level and in the
aggregate.” (Betti, Cheli, Lemmi, & Verma, 2006, p. 115)
45
6. Conclusion
The possibilities for a science-based treatment of poverty as a multidimensional
phenomenon are abundant, likewise is the debate. Undoubtedly, alternative
measures of wellbeing receive growing attention (Brandolini, 2008), yet
comprehensive integration into policy-oriented analysis is still to come. (Brady, 2003;
Glennerster, 2002) The United States in particular “do not even recognize the
relevance of incorporating this approach in their official poverty measurement
attempts.” (Waglé, 2009, p. 528)
The impression is that multidimensional analysis is sometimes reduced to
bunching together a number of indicators of living standard through some
multivariate technique. But neglecting the role of underlying assumptions may
be extremely misleading. It is of the utmost importance to develop a close link
between analytical characterization and practical application of measurement
tools.” (Brandolini, 2008, p. 28)
A more comprehensive application of wellbeing would guarantee a more sustained
impact. For example, alleviating educational or health-related deprivations tends to
improve other dimensions or capabilities as well and therefore renders a larger
impact than targeting exclusively based on low income. (Sen, 1995)
“It is this direct relationship between the empirical findings and policy planning
that gives appropriate meaning to the application of the multidimensional
approach to poverty.” (Waglé, 2008, p. 134)
This research tried to proof the applicability of alternative measures of wellbeing for
more accurate policy making. Based on the understanding of wellbeing as a dynamic
phenomenon, individual poverty is relatively put in the context of a person’s
environment. The derived measurements are corrected for the society of the United
States offering sophisticated lifestyle choices. Hence, the method should be regarded
as a useful instrument rather than a strict paradigm of how to interpret poverty.
(Bourguignon F. , 1999)
46
Interpreting multidimensional measures can add a very important information gain to
any poverty alleviation programs. The findings go along with existing studies related
to the field of multidimensional poverty measures. (Coromaldi & Zoli, 2011) Whereas
the aggregated results are very similar to those provided by income-based poverty
measurements, a decomposition of the overall index can explain wellbeing and
consequently poverty more accurately. It is necessary not to exclude monetary
indicators, but to broaden their explanatory value by shifting the focus to capability
aspects.
“[Multidimensional Poverty Measurements assess] the state of human well-
being by focusing on ‘what one has,’ ‘how much prospect one has’, and ‘how
much advantaged or disadvantaged one is in society’ toward improving such
prospect with all contributing to ‘what one can have.’ Although ‘how much one
has’ is important, as it is the means by which one can acquire human well-
being, poverty is a more complex social phenomenon and incorporating more
information is necessary to draw its accurate picture.” (Waglé, 2007, p. 16)
47
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