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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 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

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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 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.

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.

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 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

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Happiness, perhaps the easiest to define and identify is the most superficial and self-centered 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 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? (Ladder-present)

On which step do you think you will stand about five years from now? (Ladder-future)

Table 1. The Life Evaluation Index – The Gallup Poll Version of the Cantril Ladder Scale

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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

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.

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.

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 |Nigeria |

| |Costa Rica |Guinea |

| |Nicaragua |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 co-victims 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 |

|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)

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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.

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 inter-correlations 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 |

| |Mean |Std. Deviation|1 Suffering |2. HIV |3. Child Deaths |

|1. Suffering Scale |5.1 |1.5 |1.00 | | |

|2. HIV Prevalence |0.01 |0.02 |0.34 |1.00 | |

|3. Child Deaths |35.5 |36.0 |0.72 |0.28 |1.00 |

|4. Pollution Deaths |85.3 |147.5 |0.66 |0.25 |0.56 |

Table 4b. Linear regression of three Trauma indicators on Suffering Scale

|Model |B |SE |Stand. B (Beta) |Significance |

|Constant |4.06 |.13 | |.00 |

|2. HIV Prevalence |9.41 |3.99 |0.16 |0.02 |

|3. Child Deaths |0.16 |0.00 |0.66 |0.00 |

|4. Pollution Deaths |0.00 |0.00 |0.18 |0.10 |

R-square = 0.55; N=122 countries

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 |

| |Mean |Std. Deviation |1. Suffering |2. Inequality-adjusted |

| | | | |HDI |

|Constant |4.56 |0.86 | |0.00 |

|2. Inequality-adjusted HDI |-2.22 |0.76 |-0.35 |0.00 |

|3. Corruption |0.05 |0.01 |0.30 |0.00 |

|4. Gender Inequality Index |1.70 |0.98 |0.21 |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 using distinctively non-economic measures; one is conceptual and the other technical[1]. 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.

Figure 3. Scatterplot Showing Prediction of Subjective Suffering with Three Factors

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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 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

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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.

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 |

| |Mean |Std. Deviation |1. Suffering |2. Importance of |

| | | | |Religion |

|Constant |4.40 |0.46 | |0.00 |

|2. Importance of Religion |2.58 |0.46 |0.49 |0.00 |

|3. Christian majority |-1.38 |0.38 |-0.47 |0.00 |

|4. Muslim majority |-1.10 |0.44 |-0.32 |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 |9.958 |0.79 | |0.00 |

|2. Importance of Religion |1.080 |0.42 |0.20 |0.01 |

|3. Christian majority |-.581 |0.23 |-0.20 |0.01 |

|4. Muslim majority |-.497 |0.30 |-0.14 |0.10 |

|5. Social Support Network |-.065 |0.01 |-0.60 |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.

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

[pic]

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 “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 |

| |Mean |Std. Deviation |1. Suffering |2. Importance of |

| | | | |Religion |

|Constant |10.73 |3.10 | |0.00 |

|2. Importance of Religion |-1.71 |3.22 |-0.08 |0.60 |

|3. Christianity majority |-0.90 |0.43 |-0.41 |0.04 |

|4. Muslim majority |-1.22 |0.48 |-0.56 |0.02 |

|5. Social Support Network |-0.02 |0.01 |-0.32 |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

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

[pic]

Social Support Level

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

[pic]

Social Support Level

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

 Senegal

 Sudan

 Uganda

 Bulgaria, Europe

 Angola

 Georgia, Europe

 Zambia

 Ethiopia

 Rwanda

 Afghanistan, Asia

 Cameroon

 Haiti, Americas

 Mali

 Mozambique

 Niger

 Nigeria

 Kenya

 Madagascar

 Burkina Faso

 Comoros

 Sierra Leone

 Liberia

 Benin

 Burundi

 Zimbabwe

 Togo

 Tanzania

_____________________________________________________________________________________________

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.

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 |United States |

| | |Suffering) Nations | |

| Child Deaths (Under-age-5) |10,530,830 | 5,618,875 | 51,331 |

| Pollution-related Deaths |5,030,203 | 1,757,783 | 43,100 |

| HIV Prevalence |32,446,568 | 14,046,547 | 1,200,863 |

| Homicides |302,093 | 20,401 | 16,517 |

| Hunger (Nutrition deprived) |743,915,108 | 106,823,190 |NA  |

| Natural Disaster (Deaths & homeless) |3,381,851 | 1,024,098 | 13,042 |

| Refugees (out-migration) | 12,757,786 | 8,932,361 | 4,212 |

| Internally Displaced Persons | 25,297,883 | 18,201,852 |NA  |

| Civil war deaths | 103,437 | 61,477 |0  |

| Suicides | 576,133 | 13,641 |9,138  |

|Total Estimated Severe Traumas |833,765,759 |156,486,584 | 1,329,065 |

|Total Population | 6,595,955,575 | 746,791,047 | 317,641,087 |

|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.

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.

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?

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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.

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.

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 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.”

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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[1]The 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.

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