5/06/11 DRAFT
World Suffering - Conceptualization, Measurement, and Findings
Ronald Anderson rea@umn.edu
(Paper presented at the 2011 annual meetings of the American Association for Public
Opinion Research (AAPOR) in Phoenix, Arizona, May 13, 2011)
Abstract
Rarely have researchers asked people if they were suffering. Meanwhile research on
well-being has flourished. Yet, there appears to be broad agreement that suffering takes
a great psychological and social toll. The Gallup-Healthways surveys of well-being in
100+ countries included the Cantril Ladder scale to measure life well-being, and
categorized those choosing a rung near "worst possible life" as "suffering." This study
explores the validity of a subjective suffering metric and what role demographic factors
and social conflicts play in perpetuating suffering. First, using the 123 countries
common to both the Gallup-Healthways surveys and the UN’s Human Development
data, we found that the Gallup poll suffering category to be more problematic than the
life satisfaction scale. Second, indictors of the prevalence of discrete life events like HIV
illness, homicides, and suicides did not always predict the prevalence of ill-being or
subjective suffering. On the other hand, festering conditions like lack of human
development, corruption, and gender inequality help explain suffering. In addition,
religion, religiosity, and social support help explain variation in national suffering. The
analysis discovered that the distribution of religions along with differences in social
support among nations helps explain social suffering levels. This pattern is pronounced
among African countries where world suffering is most severe. This project helps locate
suffering and its magnitude around the world. In so doing, it shows how public policy
can more effectively reduce suffering of individuals and their societies.
The Concept of Suffering
Colloquial definitions of suffering emphasize pain, distress, sorrow, and grief, primarily
from a psychological point of view. In common and scholarly usage, suffering
encompasses mild unpleasantness up to excruciating torture and intense agony.
Sociologist Wilkinson (2010) open’s his book on the sociology of suffering with this
observation: “The problem with suffering is that it involves us in far too much
pain….Suffering destroys our bodies, ruins our minds, and smashes our ‘spirit’.” He
continues by arguing that social science researchers have been unable to understand
human suffering because it raises so many unsettling questions about the nature of
humanity, meaning, and morality. Most scholars of suffering tend to focus on a narrow
dimension such as aging (Black, 2005), children (Lauredan 2010), mental health
(Fancher, 2003), nursing (Ferrell & Coyle, 2008), cancer (Gregory & Russell, 1999), or
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on different parts of the world: Asia (Nagappan, 2005), North America (Parish, 2008) or
Africa (Chabal, 2009). In what is perhaps a growing trend, researchers are addressing
the bigger picture and the underlying ethics (Hirata, 2011).
Suffering unfolds an array of deeply human ironies. Every major religion calls for
compassion and aid for our fellow humans who suffer, yet the number who struggle with
severe suffering continues to enlarge. Those who reach out to others who suffer,
themselves encounter subjective suffering, even if they feel joy from having reduced
someone’s suffering. Arguably, the noblest human emotion, compassion, cannot exist
without suffering. Without suffering, would we have humanitarian action and charitable
giving?
Conceptual Framework for Suffering
In common usage, pain is physical discomfort, while suffering refers to experienced
discomfort. Pain is a neurological signal, while suffering is the meaning or interpretation
given to the signal. The First Noble Truth of Buddhism is: "Pain is inevitable; suffering is
optional". Hundreds of book titles advertise religious techniques for avoiding suffering,
yet it persists.
The diagram above makes not only a major distinction between personal and social
suffering, and stresses that the sources of suffering are social and psychological
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traumas that generally produce major, if not extreme, suffering. Some of these trauma
sources are listed in the outer circle. In the smaller circle, labeled “personal suffering”
are mental processes that magnify or sustain the suffering that the traumas can
produce. Yet, these mental processes like ego-preoccupation, self-pity, hopelessness,
and non-acceptance of the inevitability of traumas can all be reversed or avoided by
mindfulness training, presence training and other kinds of cognitive re-orientation
(Siegel, 2010 ; Dalai Lama & Goleman, 2003). Fortunately, the severe traumas of
everyday life do not have to incapacitate either individuals or societies. But resilience in
the face of suffering requires cultivation. Viktor Frankl (1984) who nearly died in a Nazi
prison camp put the challenge more philosophically: “When a man finds that it is his
destiny to suffer, he will have to accept his suffering as his task; his single and unique
task.”
Underlying the principles of most religions, but especially Buddhism, is the premise that
the path to nirvana or salvation consists of accepting pain and distress without self-pity
(Dalai Lama, 2011). The premise of “self-pity suffering” is that anger, resentment,
retribution and such negative states of mind are justified, when in fact they arise from
self-pity. The eradication of self-pity makes it also possible to accept life’s painful
episodes with much less suffering.
Social suffering” is represented by a circle surrounding “personal suffering.” While social
suffering in one sense is just the aggregation of suffering across a collectivity of persons
suffering, since someone in a community is generally suffering, social suffering is not
something that can be shed or disappear as readily as personal suffering, particularly
under dire social conditions .
Social suffering, in contrast to psychological suffering, refers to the pain and distress of
a social system and its consequences. Bourdieu’s (1993) book The Weight of the World
– Social Suffering in Contemporary Society exemplifies this perspective by elaborating
many themes of social suffering across multiple societies. More recently, Vollmann
(2007) in Poor People also conveys the anguish of the destitute and community
climates of fear, violence and victimization. Both social analysts build a large body of
evidence on how the social dimensions of suffering produce intractable cultures making
individuals’ escape from suffering nearly impossible.
Thus, social suffering adds up to more than just the aggregate of individual suffering.
The quantitative measurement of social suffering as an attribute of social systems has
not previously been attempted. In fact, research on suffering at the individual level has
been neglected as well, although numerous empirical studies have included pain
measurement at both physiological and subjective levels.
While the term “subjective suffering” will be used sometimes to refer to suffering, we
replace the term “objective suffering” with “trauma” because in an important sense all
suffering is subjective (Mayerfeld, 1999). Traumatic events consist primarily in terms of
physiologically pain or major loss such as death, but any such events qualify as trauma.
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Another dimension of suffering is time. Both pain and suffering can be chronic, lasting
for long periods of time, in which case both will likely be considered severe. Suffering
can continue after the physical pain has stopped. This type of extended suffering may
be due to many things including the anticipation that the pain may return or that it
signals a life-threatening result.
Although suffering is generally considered undesirable, if not evil, it is sometimes
considered advantageous or educative because it has the potential for educating either
the recipient or the observer. The notion of redemptive suffering goes one step further
by considering additional benefits from suffering. In specific religions, suffering is
believed to assist moral regeneration by pointing to the advantage of corrective action
or a “change of heart.” Criminal punishment is sometimes grounded on the belief that
suffering not only has an educative function but a redemptive one as well. Because of
differences among religions and societies on the meaning and value of suffering, we
would expect that the awareness of suffering, if not the degree of suffering, might be
related to religion and religiosity. Any beliefs in the educative or redemptive character of
suffering might heighten the degree of correlation between suffering and these belief
systems.
One of unique aspects of suffering or trauma is that it can be experienced identically
whether the event happens to oneself or to an object of caring. Through empathy,
suffering can be equally stressful when the trauma is experienced by a close friend or
bystander. If the pain or suffering of another person is seen as deserved however,
empathy or compassion may be highly constrained and problematic. Interpretations of
responsibility and blame for suffering are ways that individuals negate their religious or
other moral responsibility for attending to the suffering of others.
Religions and other ethical systems generally accept the premise that suffering calls for
moral responsibility (Mayerfeld, 1999; Bowker, 1970). Thus, suffering is the spark that
energizes the compassion of the sympathetic bystander. For those believing in
universal moral responsibility for suffering human beings, everyone is a global
bystander. The Fourteenth Dalai Lama (2011) said “We must recognize that the
suffering of one person or one nation is the suffering of humanity; that the happiness of
one person or nation is the happiness of humanity.” And according to Thomas Merton
(2011), “It is through suffering that we grow into the beings that we are born to be, and
cultivate compassion for ourselves and for others.”
The link between suffering and religious commitments has many facets. One facet is the
tendency for religious tenants to explain suffering with the notion of evil or evil behavior.
Pruett (1987) argues that the Buddhist claim of craving as the root of suffering is
equivalent to Freud’s claim that neurosis is the root of suffering. Many Christians and
other religious faithful believe that less sinful or evil behavior yields greater happiness.
This is supported by the research finding that belief in the importance of religion is
associated with higher self-reported happiness.
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Well-Being, Happiness and Suffering
Research on happiness, public health and human welfare increasingly has become
organized under the labels of “quality of life” and well-being. The publication of the
academic journal, Social Indicators Research, in 1974 marks the crystallization of
research on these topics. In 1995, the professional association International Society for
Quality-of-Life Studies (ISQOLS) was formed and it continues with a bi-annual
conference and publication of several academic journals. While dominated by
economists, this social movement tends to oppose the assumption that wealth or
income is the primary determinant of well-being (Diener, Kahneman, Tov, & Arora,
2009; Diener, Lucas, Schimmack, & Helliwell. 2009). Well-being research, which more
and more is conducted under the banner of “quality of life” research encompasses both
individuals and societies and explores a wide range of contexts including the built
environment, physical and mental health, education, recreation and leisure time, and
social belonging (Sirgy, Phillips, & Rahtz, 2011).
Well-being and quality of life are measured in two principal ways, one is the subjective
“life satisfaction” such as the Cantril Ladder instrument described in the next section.
The second major approach depends on official statistics and builds a composite index
or indicator. This is the approach taken by the UNDP (2010) Human Development Index
and variants of it. Researchers generally assume that well-being is a unitary concept,
but some have pointed out that the negative end of the continuum may not be a simple
instance of the absence of positive well-being but ill-being instead (Headey, Holmstrom,
& Wearing (1984). As ill-being is not a colloquial word, this semantic label has not
caught on.
Strangely, well-being and happiness research have become intertwined, perhaps
because they sometimes use the same measurement strategy. The emerging
consensus is that happiness is a more temporal mental state than well-being. So,
typically happiness is measured by asking about respondents’ moods at the moment or
during the previous day. Life satisfaction, which is sometimes called well-being or
subjective well-being, is based upon respondents’ evaluation of their life as a whole at
the present time, during the last five years, or during the next, projected five years.
The availability of happiness data has sparked quite a few popular and scholarly books
on happiness. For example, Bok (2010) argues that the American (and other)
governments could benefit from using happiness indicators in formulating public policy.
Bormans (2010) and Feldman (2010) also argue for using happiness research to shape
our own lives and improving society. The pioneer of positive psychology and much
happiness research, Seligman (2011), in his latest book argues happiness lacks the
meaning needed for individual and social purposefulness, but that considerations of
well-being are needed as well. None of these illustrious writers and scholars have yet
recognized that quality of life research will continue to be handicapped until it is
embellished with the suffering dimension.
This paper will show how life satisfaction can also be used to measure suffering as well.
The following figure (Figure 1) illustrates how well-being, happiness and suffering are
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intertwined, but conceptually distinct. Specific nations will be used as examples of each
cell or block. Selection of illustrative countries was based upon the UNDP (2010) HDI
report. Specifically, the Human Development Index was used to measure well-being,
reverse of Gallup World Poll’s life satisfaction scale was used for suffering, and a more
temporal measure of satisfaction was used for happiness, namely the emotional mood
of the previous day. Keep in mind that the view of happiness assumed here is that of
absence of negative emotion rather than euphoria.
Figure 1. A Continuum from Bliss to Despair -- Tabular Representation of the Relation
among the Concepts of Suffering, Well-Being, and Happiness
Happiness, perhaps the easiest to define and identify is the most superficial and selfcentered of the three concepts. Well-being and the absence of suffering apply equally to
both others and oneself. Exemplars and dystopias are straightforward polar opposites,
with all three concepts aligned. The transition from positive to negative quality of life,
from exemplars (block 1) to dystopias (block 8), is depicted in the cells of the figure
above. In this progression in the blocks, well-being changes the most rapidly, followed
by happiness and finally suffering.
Beginning with exemplar nations (block 1), the top of the list includes countries like
Norway and Switzerland because they are so high on all three dimensions. Replacing
well-being with ill-being (block 2) are countries like Panama, Saudi Arabia, and
Mauritania because except for low scores on well-being, these nations have high
happiness and low suffering ratings. Block 3 has been labeled “perfectionists” because
despite well-being and non-suffering, these countries (e.g., Australia and the United
States) have very low scores on happiness. Block 4 in the figure is labeled “optimists”
because despite unhappiness and ill-being, they are suffering free. Guatemala and El
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Salvador are examples of this category, as are many other Central and South American
countries. Block 5, which is labeled “survivors”, has exactly the opposite levels as block
4, namely happiness, well-being, and non-suffering. Good examples of block 5
countries are Latvia and Ukraine. India, Haiti, and Liberia are representative of block 6,
labeled “stoics” because their happiness is moderate despite both ill-being and
suffering. Bulgaria and Georgia are representative of block 7, which is labeled
“pessimists” because for these countries, unhappiness and suffering persist despite
well-being. Finally, block 8 represents the negative pole of all three concepts and is
considered “dystopia.” Representative dystopias are Afghanistan, Ethiopia and Niger
and all of the major failed states. The reader may quibble with the labels of the blocks,
but the main point is that the three concepts are distinct and their interplay suggests
interesting variations in either individual or social behavior.
The question remains: to what extent does the concept and measurement of well-being
capture the essence and ramifications of suffering? This paper addresses this question
in several ways. The empirical portion of this investigation focuses on word suffering
with countries (nations) as the unit of analysis.
Methods
The Cantril Self-Anchoring Striving Scale (Cantril, 1965) has been included in several
Gallup research initiatives, including Gallup's World Poll in 150 countries, and in
Gallup's in-depth daily poll of America's wellbeing (Gallup-Healthways Well-Being Index;
Harter & Gurley, 2008). The Cantril Scale measures wellbeing closer to the end of the
continuum representing judgments of life or life evaluation (Diener, Kahneman, Tov, &
Arora, 2009). Research conducted across countries around the world (Deaton, 2008)
indicates substantial correlations between the Cantril Scale and income.
The Cantril Self-Anchoring Scale is typically administered with the following instructions:
Please imagine a ladder with steps numbered from zero at the bottom to 10 at the top.
The top of the ladder represents the best possible life for you and the bottom of the ladder
represents the worst possible life for you.
On which step of the ladder would you say you personally feel you stand at this time? (Ladderpresent)
On which step do you think you will stand about five years from now? (Ladder-future)
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Table 1. The Life Evaluation Index – The Gallup Poll Version of the Cantril Ladder Scale
The Gallup adaptation of the Life Evaluation Index includes a self-evaluation of two items (present life situation and
anticipated life situation five years from now) using the Cantril Self-Anchoring Striving Scale with steps from 0 to 10,
where "0" represents the worst possible life and "10" represents the best possible life. Taken together, respondents
are then classified as "thriving," "struggling," or "suffering," with "thriving" respondents evaluating their current state
as a "7" or higher and their future state as a "8" or higher, while "suffering" respondents provide a "4" or lower to both
evaluations. Retrieved on 4/28/2011 from http://www.well-beingindex.com/methodology.asp
Based on statistical studies of the ladder-present and ladder future scale and how each
relates to other items and dimensions as outlined above, Gallup categorized
respondents into three distinct groups:
Thriving -- wellbeing that is strong, consistent, and progressing. These
respondents have positive views of their present life situation (7+) and have
positive views of the next five years (8+). They report significantly fewer health
problems, fewer sick days, less worry, stress, sadness, anger, and more
happiness, enjoyment, interest, and respect.
Struggling -- wellbeing that is moderate or inconsistent. These respondents have
moderate views of their present life situation OR moderate OR negative views of
their future. They are either struggling in the present, or expect to struggle in the
future. They report more daily stress and worry about money than the "thriving"
respondents, and more than double the amount of sick days. They are more likely
to smoke, and are less likely to eat healthy.
Suffering -- wellbeing that is at high risk. These respondents have poor ratings of
their current life situation (4 and below) AND negative views of the next five years
(4 and below). They are more likely to report lacking the basics of food and shelter,
more likely to have physical pain, a lot of stress, worry, sadness, and anger. They
have less access to health insurance and care, and more than double the disease
burden, in comparison to "thriving" respondents.
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The findings from this instrument were compiled and report in the book Well Being by
Rath and Harter (2010). While the book also describes the nature and results of other
measures of well-being used by Gallup polls, the results are given for the three groups
above (thriving, struggling and suffering) for over 130 countries. Indicators of the
percent of persons in each of these three groups for each country were merged with the
data published with the United Nations Human Development Report 2010 (2010).
The resulting dataset contained statistical data for 123 countries. The total world
population in mid-2010 was estimated at 6,852,000. The total population of the UNDP
2010 database of 169 countries added up to 6,804,000, less than a 1% loss. Reducing
the countries to 123 (46 fewer) in order to match or harmonize to the Gallup poll data
dropped the total population by 208 million or 3%. The population of the countries
analyzed in this study total to 6,596,000 million or over 96.5% of the world population.
Validity of the Suffering Index
Two methods for estimating subjective suffering were derived from the Gallup World
Poll data. One is to take the percentage Gallup classified as “Suffering” for each
country, and that is referred to here as “Suffering Threshold.” The second method uses
the full range of the Cantril Ladder scale, but we reverse coded it by subtracting every
country’s average life satisfaction score from 11, which is the total number of categories
in the scale.
These two methods for operationalizing subjective suffering are represented in the
following table’s columns. Table 2 gives the result of comparing the “face validity” of the
two approaches. The 123 countries were separating ranked from high to low for each
scale. Then the 20 countries at the top and bottom of each ranking were scanned for
countries that did not seem to fit as countries very high or very low on suffering. The
countries listed in the table below did not seem valid, based upon their known
demographics, especially their poverty, development level, and war or disaster status.
The remainder of the countries in the top and bottom of the lists did seem valid and
were not listed in the table.
From Table 2, it is apparent that the threshold method had over twice as many invalid
categorizations as the scale method. Face validity is not foolproof. It may be that
Hungarians responded to the Cantril Ladder consistent with high perceived or felt
suffering. Never-the-less, it is unlikely that the people of Nigeria, Guinea, and Mexico
are among the least suffering peoples of the world, particularly as they have in recent
years had serious, large-scale civil violence. The “scale” rather than the “threshold”
method of deriving a suffering measure from Gallup’s Cantril scale demonstrates much
better face validity.
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Table 2: Questionable (poor face-validity) Country Rankings within Top 20 and Bottom 20 Nations
Based upon either the Suffering Scale or the Suffering Threshold*
Suffering Scale
Suffering Threshold
Twenty countries with highest
suffering levels
Georgia
Hungary
Ukraine
Georgia
Twenty countries with lowest
suffering levels
Columbia
Costa Rica
Nicaragua
Nigeria
Guinea
Mexico
Guyana
Jamaica
Kazakhstan
*The “Suffering Scale” was derived from the formula: [11 – X], where X was the Cantril Ladder score
national average. The “Suffering Threshold” was based upon the Gallup Poll assignment to “Suffering” of
any one’s answer of rung 4 or lower for satisfaction with life currently and satisfaction with life in the “next
5 years”. A national score for the latter scheme was the percent of a country’s respondents that fell into
the suffering category.
The validity of the suffering scale alternatives was checked in another way.
Comparisons were made between the correlations between the two scales and each of
about 30 different indicators of trauma or negative social conditions. While these
correlations are not listed here, we found that the correlations of the “scale threshold”
type were about half the size as those using the “suffering scale.” These correlations
include the 10 types of trauma listed in Table 3.
The ten “trauma” indicators from the UNDP Human Development Report, in Table 3
seem like they might indicate the possibility of associated suffering. The deaths or
displacements represented by these body counts not only imply suffering for the
individual victims, but for family and friends who were survivors of the death or covictims of the event. In table 3, the statistics represent total populations or counts of
victims, whereas for the correlational analyses, these totals or counts were converted to
rates or percentages, so that the indicators were not contaminated by the variation in
population sizes across countries. Appendix A contains a description of each of these
indicators.
Table 3. Ten Trauma Indicators from official World Statistics*
Indicator
World
Child Deaths (Under-age-5)
10,530,830
Pollution-related Deaths
5,030,203
HIV Prevalence
32,446,568
Homicides
302,093
Hunger (Nutrition deprived)
743,915,108
Natural Disaster (Deaths & homelessness)
3,381,851
Refugees (out-migration)
12,757,786
Internally Displaced Persons
25,297,883
Civil war deaths
103,437
Suicides
576,133
Total Estimated Suffering
833,765,759
10
Total Population
6,595,955,575
*World statistics were based upon 123 countries, which
included 96.7% of the world population. Statistics were
obtained from the 2010 UNDP Human Development Report.
All indicators in this table are population totals rather than
rates or percentages.
In summary, the suffering scale has higher validity than the suffering threshold. This
may be a consequence of setting the threshold too high on the ladder such that fewer
persons were categorized as suffering. Without the individual level data from each
country poll, it was impossible to test this hypothesis. It is noteworthy that the “Thriving”
threshold created by Gallup encompassed fewer respondents and did not have the
problem of relatively low correlations with external attributes.
For purposes of mapping, the suffering scale values were truncated to whole numbers
and then the two highest numbers were collapsed because there were only 4 countries
in the highest category. This process yielded 5 categories, which we call levels with the
highest being level 5 and the lowest suffering being level 1. These “level” scores were
used in producing the following world map (Figure 2).
Figure 2. Subjective Suffering Levels Worldwide (123 countries)
Suffering severity is represented by darker shades. The yellow color indicates missing
data. Level 5 (most suffering) has a reddish-brown color, level 4 is dark orange, level 3 is
medium orange, level 2 is a light orange, and level 1 is beige.
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Among the countries with the greatest suffering are Togo, Benin, Afghanistan and Haiti;
level 4 nations include South Africa, Turkey and India; level 3 include Egypt, China, and
Chile; level 2 include Argentine, the UK, and Japan; level 1 include the USA, Saudi
Arabia, and Brazil.
Development of an Objective Suffering Measure
In an attempt to create a less subjective measure of suffering, it seemed plausible to
scale the trauma indicators reported by the UN. The relative version of each of the ten
trauma indicators listed in Table 3 was examined statistically in relation to the subjective
suffering scale from the Gallup polls. The result was disappointing as the majority of the
trauma indicators either correlated with the suffering scale around zero (no association)
or had a modest negative correlation. It is possible that subjective suffering is not
closely related to human traumas and casualties, but more than likely the problem is
due to data weaknesses. For many of the trauma indicators, the data were missing for
many countries. There is also the problem that progress on uniform reporting
categories for international statistical indicators is poor and depends upon both the
country and type of indicator or reporting agency.
Never-the-less, the trauma indicators with the highest correlations was used in linear
regression modeling and the best model produced is given in Tables 4a and 4b. The
definition of the variables can be found in Appendix A. With the exception of the
correlation between child (under 5) deaths and deaths due to pollution, the intercorrelations among the three items are not particularly high. Child deaths clearly have
the greatest independent association with subjective suffering. The correlation between
pollution deaths and suffering is high, but the effect of pollution deaths nearly
disappears when HIV prevalence and child mortality are controlled. These three
variables explain 55% of the variance in the suffering scale, which is not bad for only
three variables, but disappointing overall in terms of creating an objective suffering
scale.
Table 4a. Descriptives and Correlations for Trauma Indicators Model
Correlations
1. Suffering Scale
2. HIV Prevalence
3. Child Deaths
4. Pollution Deaths
Mean
5.1
0.01
35.5
85.3
Std.
Deviation
1.5
0.02
36.0
147.5
1 Suffering
1.00
0.34
0.72
0.66
2. HIV
3. Child
Deaths
1.00
0.28
0.25
1.00
0.56
Table 4b. Linear regression of three Trauma indicators on Suffering Scale
Model
B
SE
Stand. B (Beta)
Significance
Constant
2. HIV Prevalence
3. Child Deaths
4. Pollution Deaths
4.06
9.41
0.16
0.00
.13
3.99
0.00
0.00
R-square = 0.55; N=122 countries
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0.16
0.66
0.18
.00
0.02
0.00
0.10
It would be possible to construct a 3-variable weighted scale from HIV prevalence, child
mortality and pollution deaths, however, at this point it seems wiser to first try to improve
the quality of data first.
Socio-Economic Determinants of Suffering
In this and subsequent sections, as a measure of subjective suffering we used the
suffering scale derived from the Gallup/Cantril life satisfaction scale. The research
questions explored in this section include general sociological and economic variables.
The next section explores indictors of religion and social support.
A large body of research has found that both individual and national measures of
income account for considerable variation in well-being and happiness. However,
increasingly social scientists have been calling for broader and better measures of the
social forces that underlie well-being and happiness. This trend accounts for the latest
Human Development Report in 2010, which includes a variation of the well-known
Human Development Index (HDI) that adjusts for income inequality. The 2010 HDI is a
composite of life expectancy, average years of schooling, expected years of schooling
and Gross National Income (GNI) per capita. A measure of income equality for each
nation was subtracted from the HDI to obtain the “inequality-adjusted HDI” (HDI 2010; p.
219). In countries with high income inequality, larger amounts are subtracted from the
HDI for that nation. The amount adjusted from HDI scores was constrained so that
income, and education and life expectancy, remain the dominant elements of the
income-inequality-adjusted HDI indicator (referred to as IHDI). Tables 5a and 5b
demonstrate a relatively strong association of IHDI with (and effect on) subjective
suffering.
Table 5a. Descriptives & Correlations on Socio-Economic Determinants of Subjective Suffering
Correlations
1. Suffering Scale
2. Inequality-adjusted HDI
3. Corruption
4. Gender Inequality Index
Mean
5.13
0.49
14.21
0.54
Std.
Deviation
1.47
0.23
8.56
0.18
1. Suffering
2. Inequalityadjusted HDI
3. Corruption
1.00
-0.64
0.54
0.62
1.00
-0.40
-0.81
1.00
0.43
4. Gender Inequality Index
Table 5b. Linear Regression of Socio-Economic Determinants of Subjective Suffering
Model
B
SE
Stand. B (Beta)
Significance
Constant
2. Inequality-adjusted HDI
3. Corruption
4. Gender Inequality Index
4.56
-2.22
0.05
1.70
0.86
0.76
0.01
0.98
-0.35
0.30
0.21
0.00
0.00
0.00
0.05
R-squared = 0.52; N=110 countries
The other measures in Tables 5a and 5b showing strong associations with and effects
on subjective suffering are corruption and gender inequality. These two variables were
included in this model because they capture social dimensions quite distinct from
income, wealth, and other factors included in the HDI. There are two considerations for
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using distinctively non-economic measures; one is conceptual and the other technical1.
Conceptually we know that “money does not buy happiness” nor free one from
suffering, but neither theory nor research have identified many other strong predictors
related to well-being. Corruption and gender inequality are two phenomena that have
been conceptually identified as important but which are difficult to measure.
The corruption indicator came from the HDI 2010 report but its source was the Gallup
World Poll database. The Gallup polls asked the question of citizens of each country if
they had “faced a bribe situation this past year”. The national indicator was simply the
percent who has faced such a situation.
The measure of gender inequality was a composite measure including the following
components: (1) maternal mortality ratio, (2) adolescent fertility rate, and (3) the share
of parliamentary seats held by each nation. These three sub-indicators were combined
into a single variable by calculating the geometric mean of each of the three indicators
for each gender and then by combining them statistically (HDI 2010; p. 219). The index
reflects the loss in human development resulting from women’s disadvantage in
reproductive health, empowerment, and the labor market. The country scores range
from 1 (complete gender equality) to 0 (worse possible women’s advantage).
What is most significant about the model shown in Table 5b is that all three factors,
inequality-adjusted HDI, Corruption, and gender inequality, play solid, independent roles
in predicting or explaining subjective suffering. Together these three factors explain
52% of the variation in suffering. The common role that these three factors play is
illustrated by Figure 3, which shows the scatterplot between the weighted average of
these three variables and subjective suffering. The scatterplot confirms the linear
relationship by showing most of the countries clustering around the implicit regression
line.
1The technical rationale is that many available social indicators at the national level are correlated with income and
wealth. The result is that most available indicators have common elements or correlations with income and other
elements of the HDI, producing a problem called multicollinearity. This condition of modest or high correlations
among one’s predictor variables produces unstable models that distort the true, independent effects of individual
predictor variables.
14
Figure 3. Scatterplot Showing Prediction of Subjective Suffering with Three Factors
Standardized and weighted predictors: IHDI, Corruption, and Gender Inequality
It was not surprising to find that the HDI, adjusted for income inequality, was related to
subjective suffering because very low income, lack of education, and short live spans
tend to be associated with inability to escape suffering. However, the significant role of
corruption and gender inequity were less predictable. It is conventional wisdom in both
development and political circles that corruption impedes economic growth. To find that
greater corruption in nations can predict greater subjective suffering, suggests that
corruption and its real effects on peoples’ lives are pervasive across quite a few
countries.
The role of gender inequality in suffering is not conventional wisdom in most
development and political circles. Martha Nussbaum (2011) clarifies how unequal
treatment of women, especially within developing societies, in so many ways undercuts
development initiatives. By making it extremely difficult for girls and women to contribute
their capabilities to productive work, much less community decision making, such
cultures develop slowly and erratically much like a jet airplane operating with only one
engine running. Nussbaum’s “capabilities approach” to development calls for eliminating
the violence, the health disadvantages, the education deficits, and so forth, that keep
women, racial minorities, and other social groups from applying their potential for
progress that helps to reduce suffering. The fact that gender inequality appears as a
significant statistical predictor of greater suffering in this analysis suggests that gender
15
inequality is indeed a significant cultural barrier to human well-being and the reduction
of suffering.
Religious Determinants of Suffering Reduction
Below is a world map showing the distribution of Christian majorities and Muslim
majorities versus mixed religion countries around the world. If a country had 50% or
more Christian, than it was coded as such and shown as dark brown on the map.
Whereas, countries with 50% or more Muslims were colored red or gray; and all others
were colored lavender or light gray and labeled “mixed” religion.
Figure 4. Religious Majorities
Black or dark brown represents countries with 50% or more Christians; dark gray or red indicates
50% or more Muslims; and light gray or lavender is for remaining countries, most of which have
two or more popular religions. Yellow or white indicates missing data.
Christian majority countries (coded black or brown) include all of North and South
America, South Africa and Australia; Muslim majority countries (coded dark gray or red)
include Indonesia, Pakistan, and Algeria; mixed-relgion countries include Russia, China,
and Madagascar. It should be noted that the “mixed-religion” category includes India,
which is predominantly Hindu, Thailand, which is predominantly Buddhist, and Israel,
which is predominantly Jewish. There are so few countries with majorities of these
religions, that they were included with “mixed” for ease of comparison.
16
The correlation matrix in Table 6a shows a positive association (0.46) between
subjective suffering in a country and the degree of importance assigned to religion. As
we do not know the direction of influence, this could mean that religiosity increases in
response to suffering or that religious commitment leads to greater suffering, which is
less likely, although conceivable because the most popular religions give certain types
of suffering special respect and rewards.
Table 6a. Descriptives and Correlations for Subjective Suffering and Religion Model
Correlations
1. Suffering Scale
2. Importance of Religion
3. Christianity majority
4. Muslim majority
5. Social Support Network
Mean
5.1
0.01
0.54
0.23
79.0
Std.
Deviation
1.47
0.28
0.50
0.42
13.5
1. Suffering
1.00
0.46
-0.27
0.19
-0.71
2. Importance
of Religion
3. Christian
majority
4. Muslim
majority
1.00
-0.09
0.40
-0.49
1.00
-0.58
-.20
-0.23
Table 6b. Linear Regression of Religion Indicators on Subjective Suffering
Model
B
SE
Stand. B (Beta)
Significance
Constant
2. Importance of Religion
3. Christian majority
4. Muslim majority
4.40
2.58
-1.38
-1.10
0.46
0.46
0.38
0.44
0.49
-0.47
-0.32
0.00
0.00
0.00
0.01
R-squared = 0.30; N=122 countries
Table 6c. Linear Regression of Religion Indicators plus Social Support on
Subjective Suffering
Model
B
SE
Stand. B (Beta)
Significance
Constant
2. Importance of Religion
3. Christian majority
4. Muslim majority
5. Social Support Network
9.958
1.080
-.581
-.497
-.065
0.79
0.42
0.23
0.30
0.01
0.20
-0.20
-0.14
-0.60
0.00
0.01
0.01
0.10
0.00
R-squared = 0.54; N=122 countries
The correlations also show a smaller, but negative correlation between suffering and
Christian majorities. This means that countries with Christian majorities in their
population tend to possess or express less suffering. One possibility is that countries
with Christian majorities tend to work harder to alleviate suffering.
Table 6b gives the linear regression model of these three variables predicting
(regressed on) subjective suffering. The Beta coefficients indicate that strongest
predictor of suffering at the country level is the perceived importance of religion and it is
positive indicating that with greater suffering, religious commitment may increase. Yet,
at the same time, the effect coefficients (Betas) between suffering and Christian
majorities and Muslim majorities are negative. These results probably tell us that merely
having a majority of either Christians or Muslims helps to reduce suffering, because
without these majorities, consensus would be less likely on actions and policies that
could reduce suffering and social conflicts like civil wars.
17
Next, we look at the role of social support. Figure 5 contains a map of the world with
greater social support shaded darker colors.
Figure 5. Satisfaction with Social Support Network
Key: Darker colors indicate higher average satisfaction with one’s social support network. Yellow
colored countries indicate missing data.
Among the countries with the highest social support are the United States, Brazil,
Russia, and South Africa; below that are Mexico, Algeria, and Ukraine; China, Egypt,
and Peru form the next tier; the lowest social support groups include India, Turkey,
Chad, and Afghanistan.
When the percent of people satisfied with their “social support network,” is included in
the regression model, which is shown in Table 6c, not only does social support have a
strong negative effect on suffering, but the importance of both perceived importance of
religion and religious majorities have less effect. This almost certainly means that a
large part of the positive effect of religion on reducing suffering is actually due to the
social support networks that is a byproduct of religion.
The pattern of these relationships may vary by region, and to explore that we examined
the same correlations and regression model but just for the 37 African countries. The
results are presented in Tables 7a and 7b. The patterns in the African countries have
basically the same structure as the entire set of 123 countries except that the
18
“importance of religion” on suffering disappeared but the effects of Christian majorities
and Muslim majorities are substantially magnified.
Table 7a. Descriptives and Correlations for Suffering and Religion Model – Africa Only
Correlations
1. Suffering Scale
2. Importance of Religion
3. Christianity majority
4. Muslim majority
5. Social Support Network
Mean
6.69
0.93
0.42
0.39
70.00
Std.
Deviation
1.08
0.05
0.50
0.49
15.30
1. Suffering
1.00
-0.21
-0.01
-0.40
-0.46
2. Importance
of Religion
3. Christianity
majority
4. Muslim
majority
1.00
-0.20
0.37
0.02
1.00
-0.67
-0.03
0.28
Table 7b. Linear regression of religion indicators on Suffering Scale - Africa Only
Model
B
SE
Stand. B (Beta)
Significance
Constant
2. Importance of Religion
3. Christianity majority
4. Muslim majority
5. Social Support Network
10.73
-1.71
-0.90
-1.22
-0.02
3.10
3.22
0.43
0.48
0.01
-0.08
-0.41
-0.56
-0.32
0.00
0.60
0.04
0.02
0.04
R-squared = 0.40; N=35 countries
Figure 6. Prediction of Subjective Suffering within African Nations
*”Standardized predictive value” refers to the four predictors (weighted and standardized) from
the regression model in Table 6c (importance of religion, Christian majority, Muslim majority, and
social support network). The blue
19
Figure 6 helps us understand the puzzle inherent in all of these findings with respect to
religion. The scatterplot in the figure shows the 37 African countries in our sample
where the horizontal axis is the degree of subjective suffering and the vertical axis is the
predicted value based upon the model (shown in Table 7b.) using the following
standardized and weighted independent variables: satisfaction with social support,
Christian majority, and Muslim majority. The country circles are color coded as
following: countries with a Christian majority are colored blue; countries with a Muslim
majority are colored green; and all other countries are colored a light yellow.
Significantly, all of the “other” countries are clustered in the upper right quadrant,
whereas the Muslim majority countries are all on the left hand side of the grid, and the
Christian majorities are in between. There are eight countries in the “other” category:
Togo, Benin, Tanzania, Burundi, Madagascar, Mozambique, Cameroon, and Botswana.
All eight of these countries have 30 to 49% Christian majorities, but their populations
include a sizable share of Muslims, and in some cases, those subscribing to other
religion systems. These mixed-religion countries tend to have the highest suffering in
Africa, perhaps because of conflicts or the difficulty of arriving at common institutions
and national policies. Coinciding with the problem of consensus may be the lack of
strong social support networks, which are more typical in countries where the majority of
citizens are either Christian or Muslim.
Figure 7. Suffering by Social Support among African Countries
Social Support Level
20
The above graph (Figure 7) is just like Figure 6 except it displays “social support level”
alone on the horizontal, X axis, instead of the combined values of support and religious
majorities. Figure 7 shows a pattern to Figure 6, but it is easier to visualize that the
Muslim countries have an edge over the Christian countries in terms of a small, but
higher level of subjective suffering. Except for Burundi, all of the countries with both
extreme suffering and very low social support are those like Togo, which are mixed
religion. These separate analyses of African countries show that Muslim countries have
a higher level of satisfaction with their social support networks than do Christian
countries and both are substantially higher than the remaining eight countries that lack a
majority religion.
To further elaborate the role of region in the relationship between support, religion and
suffering, Figure 8 shows the same variables as does the previous figure, but for the
Asian countries only. (Asian includes middle-eastern as well as countries traditionally
identified as Asian. Like the African countries, those Asian countries with Muslim or
Christian majorities have higher social support networks than the “mixed” religion
countries.
Figure 8. Support by Social Support for Asian Countries only
Social Support Level
21
The overall pattern of Asia are largely different from Africa. First, the high suffering
countries with low social support are all Muslim majorities except for Armenia, which is
mostly Christian. Other Christian countries in Asia include Cyprus, Philippines,
Australia, and New Zealand. Among the countries categorized as “mixed” are Sri Lanka,
Cambodia, Nepal, Vietnam, India, Japan, China, Korea, and Israel. The majority of
these countries have religious majorities, but they are less common religions like
Judaism, Buddhism, and Hinduism. In Asia, religion does not help understand the
relationship between social support and suffering. The main conclusion that can be
drawn is that in Asia a strong association exists between higher social support and
lower suffering.
Implications of this Study for Policies to Reduce Suffering
One important consequence of the subjective suffering scale used throughout this
report is that the scale lends itself to creating five levels (ordered categories) of suffering
with level five being the most severe suffering. The 28 nations in level five are listed in
Table 8. All the countries reside in Africa except for Afghanistan, Bulgaria, Georgia, and
Haiti. Clearly, the dominant source of severe national suffering lies in Africa.
Table 8. The 28 Nations in Level Five (severe suffering) of the Suffering Scale
__________________
Guinea
Afghanistan, Asia
Comoros
Senegal
Cameroon
Sierra Leone
Sudan
Haiti, Americas
Liberia
Uganda
Mali
Benin
Bulgaria, Europe
Mozambique
Burundi
Angola
Niger
Zimbabwe
Georgia, Europe
Nigeria
Togo
Zambia
Kenya
Tanzania
Ethiopia
Madagascar
Rwanda
Burkina Faso
_____________________________________________________________________________________________
These high suffering countries are sorted in order of greatest severity of suffering. All countries in this list
are located in the African region except for the four countries identified from another region. The total
population of these 28 countries adds to 747 million, which was 11% of the world population in 2010.
The population across these 28 countries with severe suffering adds up to 747 million.
Several “failed states” were not included in level five countries because many failed
states were too dangerous to survey. The following countries, which are rated high on
the Failed States Index, were not included: Somalia, Sudan, Iraq, Burma, North Korea,
Yemen, Libya, and Iran. These eight nations have a combined population of 276.8
million, or slightly less than 5% of the world population. If these nations are combined
with the level 5 suffering states, their combined populations is over 16% or slightly over
one billion people. This number is similar to Collier’s (2007) estimate of the world’s
population most seriously trapped by poverty.
22
Another perspective on the severity of the suffering among the level five countries is
given by estimating the sever traumas from world statistical databases. Table 9 gives
such a view, showing the trauma counts for the world, level five countries, and for the
United States alone.
Table 9. Indicators of Severe Traumas from official Statistics for World, for Level Five
Countries and United States*
Severe Traumas
World
Level Five (severest
Suffering) Nations
United States
Child Deaths (Under-age-5)
10,530,830
51,331
5,618,875
Pollution-related Deaths
5,030,203
43,100
1,757,783
HIV Prevalence
32,446,568
1,200,863
14,046,547
Homicides
302,093
16,517
20,401
Hunger (Nutrition deprived)
743,915,108
NA
106,823,190
Natural Disaster (Deaths & homeless)
3,381,851
13,042
1,024,098
Refugees (out-migration)
12,757,786
4,212
8,932,361
Internally Displaced Persons
25,297,883
NA
18,201,852
Civil war deaths
103,437
0
61,477
Suicides
9,138
576,133
13,641
Total Estimated Severe Traumas
833,765,759
1,329,065
156,486,584
Total Population
6,595,955,575
317,641,087
746,791,047
Ratio of Severe Traumas to Population
12.6
21
0.4
*World statistics were based upon 123 countries, which included 96.7% of the world population. Both
world and United States statistics were obtained from the 2010 UNDP Human Development Report. All
indicator statistics in this table are population totals rather than rates or percentages.
The “total estimated severe traumas” is the sum across all ten types of trauma in the
first 10 rows of the table. The ratio of “total estimated severe traumas” to population
totals gives a basis for comparing the severity of human tragedies across countries or
groups of countries. The ratios in the above table are calculated by dividing the total
traumas by the total population and multiplying by 100. The ratio is equivalent to the
percent of the population that experiences a trauma, not considering overlapping
trauma types. From the above table, this ratio for all countries in the world is 12.6%,
whereas for the level 5 suffering- severity nations it is 21%. For the United States, it is
only 0.4%. Trachtenberg (2007) argued that “Americans have the peculiar delusion that
they’re exempt from suffering.” While the USA has much less suffering than most of the
world, it certainly is not suffering-free. In fact, compared to other high-income nations, it
is among the lowest in terms of well-being or quality of life per capita.
Dividing the world ratio by the US ratio, we can conclude that the world nations have 30
times more severe trauma events than does the USA. And the level 5 (severe suffering)
countries have 50 times more severe trauma than the United States. These estimates of
traumas have been on the extreme conservative side, e.g., there are no hunger traumas
listed for the United States; also, no counts of chronic illnesses or political imprisonment
are included for any of these groupings. Given this, it is safe to say that suffering level
five nations have well over 50 times as much suffering as the United States. And the
world nations as a whole have over 30 times as much suffering as the United States.
23
These estimates of suffering error on the part of being too low in another way, which is
that some of the most seriously failed states were not included in the study’s sample, as
mentioned earlier. If these nations are combined with the level 5 suffering states, the
ratio of USA suffering to level five and world suffering would be considerably higher than
50 times and 30 times respectively. Failed states should not be overlooked even though
it is not feasible to collect survey data nor health and other administrative statistics from
them.
Evan though the total trauma in the world is at least 30 times greater than the total
trauma in the United States, the ratio of US spending for world aid is 1% compared to
60% for social services within the United States. Private philanthropy from US donors
also goes primarily to US suffering rather than world suffering. The US government and
philanthropic organizations tend to give, not in response to the distribution of suffering,
but in response to internal politics.
The Center for Global Development’s Commitment to Development Index puts the
United States near the bottom of the 22 richest countries in terms of per capita aid for
developing countries. United States policy has been to give relatively little to the poorest
countries but more to the oil-rich countries of the Middle East. In the context of
contemporary political trends against tax increases, against cuts in social security, and
against spending on foreign aid, could it be that concern for humanitarian reduction in
suffering has given way to self-centered protection of our personal wealth and social
benefits? Whatever happened to the Christian ethic of caring and “loving thy neighbor
as thyself”?
It appears that this study is the first to quantify the distribution of suffering around the
world. Now it is possible to make systematic comparisons regarding the degree of
suffering. Should suffering not be taken into account in public policy considerations? For
instance, when we occupy another country with the potential for millions of people being
displaced or killed, should not that quality and quantity of suffering be weighed against
the potential political benefits of going into military conflict? The same applied to
security benefits.
Clearly, careful reconsideration is urgently needed on policy agendas for reduction of
suffering in failed states and other nations with extreme suffering. The challenges are
enormous including environmental sustainability, political and economic stability, ethnic
and social integration, preparedness for disasters, healthcare, and population control.
With a combination of resources and an international “peace corps,” major inroads to
suffering are possible unless a climate of violence becomes pervasive; this makes a
spiral of disintegration inevitable.
24
Conclusions
Suffering unfolds an array of deeply human ironies. Every major religion calls for
compassion and aid for our fellow humans who suffer, yet the number who struggle with
severe suffering continues to enlarge. Those who reach out to others who suffer,
themselves encounter subjective suffering, even if they feel joy from having reduced
someone’s suffering.
Another irony is that the powerful contemporary institutions established to ostensibly
reduce suffering, primarily address poverty and economic development rather than
suffering. While economic resources help to reduce suffering, they also increase
suffering by increasing expectations. Perhaps the biggest tragedy is that in an age of
globalized media, people with charitable resources have become largely desensitized to
horror and suffering, especially when it is outside their neighborhood or national
boundaries (Cohen, 2001). Still, suffering statistics, as compared to poverty statistics,
have more potential for arousing public interest and mobilizing actions to improve the
conditions of those in severe suffering.
One finding from this study is that social support networks play a very large role in
diminishing suffering. Yet, few philanthropic or other aid organizations have policies
directed toward building social support systems or enhancing social cohesion,
especially in developing countries.
Another finding here was that Muslim and Christian majority nations have a suffering
advantage over those with a mixture of religious persuasions. In Africa, Muslim majority
countries have a suffering advantage over Christian majority countries, probably
because Muslims have more effective social support networks. Ironically, whatever
advantage religious majorities may offer, if violence breaks out along religious lines, this
advantage turns into gigantic failure because of the spike in pain and suffering. This is
just one example of how a research-grounded focus on suffering can add great value to
policy analysis and decision-making.
Finally, the fact that “quality of life” researchers have focused all of their attention on
happiness and well-being instead of suffering is a puzzling irony. Are the research
institutions of the world so blinded by power and economics that they facilitate only
topics of interest to the advantaged? Is it not possible for researchers to take the role of
those undergoing extreme suffering and see solutions to world suffering from those in
the depths of despair?
25
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Appendix A. Indicators Descriptions
A.1. Indicators of suffering-producing Traumas from the UNDP HRI Report 2010
For each indicator of objective suffering, both a relative and a total or absolute measure
were constructed. The relative indicator is adjusted for the total population of each
country, whereas the total measure is a frequency count of the total number of people
with a particular type of suffering. The relative indicators are percentages, proportions,
rates per thousand, per hundred thousand or per million. Thus, the relative indicators
remove the variation in population among countries, making comparison across
countries possible without the size of country affecting the country estimates. The
relative measures are best for country comparisons, the total measures are best for
estimating regional or global or the total amount of an attribute in each country.
Since each type of suffering indicator had some unique issues, each will be discussed
in term. All the country statistics were taken from the data contained in the UNDP
Human Development Report 2010, except the data on suicides, which was obtained
from the World Health Organization, 2009.
Child Deaths (Under-age-5)
This indicator is analogous to infant mortality, which refers to child deaths before the
child’s first birthday. What we call Child Deaths is child mortality before the fifth birthday.
This health indicator is reported in UNDP (2010) and other statistical reports as deaths
in the first 5 years per 1,000 live births. To calculate a total number of child deaths per
country, an adjustment was made to take into account the fertility rates and the total
population in order to estimate the total under-age-five child deaths per year.
Pollution-related Deaths
This estimate of death was provided by the UNDP (2010) HDI report. It include known
deaths in millions of population for only those deaths that could be attributed to
pollution, both indoor and outdoor. Such deaths included those due to unsanitary water,
air pollution, including lung diseases, and cardiovascular diseases due to unclean air.
HIV Prevalence
The UNDP data tables provided the percent of the persons in the 15-49 age range with
HIV in 2007. To obtain a relative measure of HIV for each population, that percent was
multiplied by an age group’s proportion of the population, which on average across
countries was 63% for those in the 15-49 age group. As this does not take the
prevalence of HIV in those outside this age range, it underestimates the shares of
populations having HIV, it does give us a measure for the entire population. The total
HIV estimate was calculated by multiplying or weighting the relative HIV measure by the
ratio of the age group population to the total population for each country. The sum HIV
prevalence across 169 countries was about 34 million, which is nearly identical to that
estimated by UNAIDS and WHO in 2008.
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Homicides
The relative indicator of homicide used was the number of homicides per 100,000
persons in 2008. Using each country’s population, the total homicides were calculated
for each country. The sum of homicides across 169 countries was 307,083. The
Geneva Declaration on Armed Violence and Development estimated the worldwide
“intentional homicides” per year were between 400,000 and 500,000 during the past 20
years.
Hunger (Nutrition Deprived)
The UNDP data tables provide a relative measure of hunger, the intensity of food
deprivation best described as protein-energy malnutrition. It is the average percent of
the population with malnutrition due to a “shortfall in minimum dietary energy
requirement.” In other words, it gives us an estimate of the share of the population
whose daily food intake was below their dietary required minimum energy level. (This
form of hunger leads to serious health problems and early death.) These estimates are
not available for most of the 42 countries categorized as “very high human
development.” To estimate the total hunger the relative estimates were multiplied by the
total population. The total estimated hungry across 169 countries summed to 766
million. By comparison, the World Hunger Organization and the UN Food and
Agriculture Organization both estimated 925 million hungry people in 2010, so this
estimate is conservative.
Natural Disaster victims
For each country, the UNDP report gives an estimate of the “population affected by
natural disasters. “Affected” is a loose term and we sought to limit the number to those
seriously harmed. The World Health Organization’s International Disaster Database
(EM-DAT) reports that over the past 35 years the average deaths and displacements
(those made homeless) per year were 50,000 and 4,550,000 respectively, for an
approximate total of 4.6 million per year averaged over the 35-year period beginning in
1975. The “total affected” counts per country for the years 2000 to 2009 were down
weighted to represent estimates of only those who died or were made homeless. The
resulting total across 169 countries adds up to 4,577,579 harmed (death or
homelessness) per year. The relative estimate of natural disaster victims was calculated
by dividing the total estimates by the country population.
Refugees Fled
The UNDP data provide an estimate by country of the number of refugees who fled from
any given country. The UN Refugee Agency (UNHCR) estimate of total refugees under
their responsibility or the UN Palestine relief agency totals to 15.2 million in 2008 (2008
Global Trends: Refugees, Asylum-seekers, Returnees, Internally Displaced and
Stateless Persons). This does not include those who are still in asylum-seeking
(pending) status. The total across 169 countries adds up to 14,057,778 for 2008. The
relative estimate of refugees was calculated by dividing the total estimates by the
country population.
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Internally Displaced Persons
The UNDP data also provide an estimate by country of the number of Internally
Displaced Persons (IDP) having fled from any given country. The UN Refugee Agency
(UNHCR) estimate of total refugees under their responsibility or the UN Palestine relief
agency totals to 15.2 million in 2008. The total across 169 countries adds up to
26,344,755 for 2008. The relative estimate of refugees was calculated by dividing the
total estimates by the country population.
Civil War Fatalities
The UNDP Report gives estimates by country of the fatalities from civil war based upon
the average of years of the conflict year during the years 1990-2008. The estimates are
for deaths per million persons. The total fatalities are calculated by multiplying these
relative estimates by the population in millions. The total across 169 countries is
131,244 for all civil wars for an average year over 19 years beginning in 1990.
Suicides
Statistics on annual suicides were obtained from the World Health organization, for the
most recent year available. It would be noted that suicide estimates were only available
for about 80 countries, so there this indicator has more than the usual missing data
points.
A.2. Other Indicators Used in the Study
Corruption
The corruption indicator came from the HDI 2010 report but its source was the Gallup
World Poll database. The Gallup polls asked the question of citizens of each country if
they had “faced a bribe situation this past year”. The country indicator was simply the
percent who has faced such a situation.
Gender Inequality
The measure of gender inequality was a composite measure including the following
components: (1) maternal mortality ratio, (2) adolescent fertility rate, and (3) the share
of parliamentary seats held by each nation. These three sub-indicators were combined
into a single variable by calculating the geometric mean of each of the three indicators
for each gender and then by combining them statistically (HDI 2010; p. 219). The index
reflects the loss in human development resulting from women’s disadvantage in
reproductive health, empowerment, and the labor market. The country scores range
from 1 (complete gender equality) to 0 (worse possible women’s advantage).
Happiness
Compared to life satisfaction, happiness is temporal. While life satisfaction is usually
assess in terms of one’s overall life quality, perhaps over the past five years, happiness
is generally measured in terms of yesterday’s moods. The index of happiness used here
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was from Gallup World polls and was based upon asking about whether or not they had
certain emotional states the day before.
Human Development Index (HDI)
The 2010 HDI is a composite of life expectancy, average years of schooling, expected
years of schooling and Gross National Income (GNI) per capita. For most of the
analysis reported here, we used the “inequality-adjusted HDI” (HDI 2010), which is
separately described below.
Inequality-Adjusted Human Development Index (IHDI)
The 2010 HDI is a composite of life expectancy, average years of schooling, expected
years of schooling and Gross National Income (GNI) per capita. A measure of income
equality for each nation was subtracted from the HDI to obtain the “inequality-adjusted
HDI” (HDI 2010; p. 219). In countries with high income inequality, larger amounts are
subtracted from the HDI for that nation. The amount adjusted from HDI scores was
constrained so that income, and education and life expectancy, remain the dominant
elements of the income-inequality-adjusted HDI indicator (referred to as IHDI).
Importance of Religion
The Gallup World Poll has asked a broad question: "Is religion important in your daily
life?" Any "yes”, answer counts toward the percentage of people in a country’s survey
who consider religion to be important. The data used were from 20076-2008.
Religious Majorities: Christian Majority, Muslim Majority, Mixed
To determine the percentage of a population who identified with each of the major
religions, a compilation from 2007 by Wikipedia’s article “Religions by Country” was
used as the starting point. The sum of these religious percentages was calculated for
each country, and when the sun was significantly less than or greater than 100%, then
additional sources were consulted. The source of greatest inconsistency was the
estimates of those who were listed in the category “nonreligious,” because some
surveys gave that category greater priority than other surveys. In resolving
discrepancies, the percent nonreligious was given less importance for present
purposes. From these data we created the dummy variables Christian, Muslim, Hindu,
Buddhist, Judaism, and other. If 50% or more of a country’s adult were listed as
Christian, the variable Christian was set equal to 1, otherwise it was zero. The same
was true for each of the other five religious categories. For purposes of the analysis
reported here, the Hindu, Buddhist, Jewish, and “other” religions were collapsed into
one category labeled “Mixed,” as only 5 or 6 countries had majorities in these other
religions. It should be noted that for the analysis done in this report, these dummy
(presence/absence) variables were more predictive of suffering than the actual
percentage of persons in each country belonging to each of the major religions.
Social Support
Gallup World Poll asked the question in their 2010 database, “Are you satisfied with
your social support network? The percentage of people from each country who
answered this question “yes” is the variable called “social support.”
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Suffering and Suffering Scale
The Cantril Ladder scale is described in an early section of this report. The “Suffering
Scale” was derived from the formula: [11 – X], where X was the Cantril Ladder score
national average. Each person’s score first was based upon the average of satisfaction
with life currently and satisfaction with life in the “next 5 years”. The national score was
the average of these averages, collapsed into 11 categories, ranging from zero to 10.
After reversing the coding scheme to make large code numbers associated with greater
levels of suffering, the national scores were truncated and then the top highest suffering
scores were collapsed. The resulting five scores or ordered categories are call levels of
suffering, with the greatest or most extreme suffering being level five suffering and the
least suffering being level one.
Well-Being
The well-being index used for sorting countries was the Human Development Index
(HDI), described above. Neither the HDI nor any other well-being measure was used in
the statistical analysis reported here.
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