The Arithmetic of Global Predation

What the World Happiness Report 2026

actually measures

Jeffrey Sachs, in the introduction to the inaugural World Happiness Report in 2012, made this compelling statement: “The realities of poverty, anxiety, environmental degradation, and unhappiness in the midst of great plenty should not be regarded as mere curiosities. They require our urgent attention, and especially so at this juncture in human history.” He continued by pointing out how affluent populations are “so separated from those they are imperiling that there is little recognition, practical or moral, of the adverse spillovers (or ‘externalities’) from their own behavior.”⁴

It is striking that the report’s senior editor was already, in 2012, naming a pattern that would continue to be documented for the next fourteen years.

For example, the US Department of Labor has reported not only forced labor conditions in cobalt mining in the Democratic Republic of the Congo (cobalt is used to produce lithium-ion batteries used in consumer electronics and an increasing share of electric vehicles)³⁷ but also child labor on cocoa plantations for European chocolate.³⁸

In Brazil’s Cerrado, there are deforestation and ecosystem problems driven in part by soy production for livestock feed essential to EU meat and dairy supply chains³⁹ and sweatshop and labor-rights abuses that produce products for international fashion supply chains documented in Dhaka-area garment factories (Bangladesh).⁴⁰

Three different parts of the world have attempted to address these problems.

In March 2017, France enacted the Loi relative au devoir de vigilance (Duty of Vigilance Law), requiring its largest companies to monitor and prevent human rights and environmental harms throughout their global supply chains.¹

Germany followed with the Lieferkettensorgfaltspflichtengesetz (Supply Chain Due Diligence Act), adopted in 2021 and in force since January 1, 2023.²

California’s Transparency in Supply Chains Act was enacted in 2010 (effective 2012), while the European Union’s Corporate Sustainability Due Diligence Directive (Directive (EU) 2024/1760) established a bloc-wide due-diligence framework for companies covered by the directive.³

These laws share an assumption that remains largely unspoken: prosperity in wealthy countries is structurally entangled with harm in poorer ones.

The legislators of Berlin, Paris, and Sacramento are not naïve about where their constituents’ standard of living comes from. They are, however, quietly conceding in legal language that one part of humanity lives well by extracting from another.

The supply-chain laws are not a dismantling of the global problem of inequality and predatory well-being…they are the first steps in an admission that these problems exist, an attempt to manage it rather than end it.

They are the modern equivalent of the eighteenth-century slave-trade reform debates that, at first, attempted to regulate slavery rather than abolish it.⁷¹

For reasons discussed further below, abolishing these and other such inequalities is a threat to the social status and standard of living of people whose political support is necessary for existing political and economic institutions to survive. As opposed to abolishing these and other predatory inequities, the ability to make these system-justified harms appear right and good mediates the institutional threat, but only if the persuasive and coercive cost of maintaining that legitimacy is sustainable.

A Quality Life Extraction Pattern

The World Happiness Report 2026 ranks 147 nations on the Cantril life-evaluation ladder.⁵ Finland is first for the ninth consecutive year, with a score of 7.764. Afghanistan is last at 1.446. Most readers see the ranking and move on. A few note that the happiest countries cluster in a particular corner of the world. Fewer still ask the obvious question: what is actually being measured when 147 nations are sorted by “life evaluation”?

What is being measured is an extraction pattern.

The happiness gradient across 147 countries is the population-scale signature of a predatory pattern of social hierarchy that has dogged human civilizations since the first cities and states appeared some 5,000+ years ago.⁶ This pattern has a biological mechanism: human differences in cognition, behavior, moral judgments and social preferences emerge from the complex interplay between inherited DNA differences (genetic propensities) and environmental conditions.⁷

The result is a gene-environment paradox where internal valuations of what is considered “right” or “good” vary across individuals and groups due to biological variation.⁹ Consequently, dominant groups continuously engineer social environments that optimize their own genetic propensities and well-being while simultaneously transferring environmental and biological costs to descending levels of the global hierarchy.

The gene-environment paradox expresses itself through system-justified harms and Equality Exclusions (EQEXs) embedded in the norms and institutions of every social hierarchy in the world.⁸

The World Happiness Report 2026 provides a quantitative, diagnostic record of this mechanism affirmed by a comparison of the report’s top 20% to its bottom 20%.

Equal Slices of Humanity, Opposite Ends of Every Indicator

Splitting the 147 countries at the natural 20% boundary reveals a stark demographic reality. The top 20% (29 countries), ranging from Finland (7.764) to the United Kingdom (6.694), contains approximately 926 million people.^41,59

The bottom quintile of the 2026 World Happiness Report ranking (29 of 147 countries) spans scores from Jordan (4.478) to Afghanistan (1.446); using United Nations World Population Prospects 2024 mid-year 2025 estimates, these countries total approximately 1.02 billion people.^41,59

The top and bottom quintiles are within about 10% of each other in size: approximately equal slices of humanity.^41,59

Their life evaluations are not.

Population-weighted, the top cohort’s mean Cantril score is approximately 6.87. The bottom cohort’s mean is approximately 3.91. The gap of approximately 2.96 points…let’s call it 3.0… puts it among the largest stable inequalities measured in social science.^41,59

The Genome, the Exposome, and What “Life Evaluation” Actually Measures

Genetic epidemiologist Margot van de Weijer and colleagues make the point that individual differences in well-being decompose to approximately 30-40% genome and 60-70% exposome, with the latter being “the totality of…environmental exposures…influencing variation in well-being from conception onwards.”¹⁰

Geneticist Meike Bartels summarizes decades of behavioral-genetic work, writing that “individuals create and choose their own environments based on genetically informed preferences.”¹¹

This sorting mechanism, however, operates along the axis of individual-level genetic variation that exists within populations, not across them. Population genetics has consistently shown that for complex behavioral and health-relevant traits, genetic variance between continental populations is small relative to the variance found among individuals within any given population.^69,70

Here, it is vital to avoid the ecological fallacy: the gene-environment sorting process is not a genetic sorting process. It does not imply that national populations in the top quintile possess superior genetic endowments compared to those in the bottom quintile.

An individual’s inherited DNA sequences do not change over time. What changes is the exposome (environmental conditions) such as global supply chains, legal, political, economic and educational structures as well as housing and climate conditions. The exposome’s share of well-being variance across the 147-country ranking is subject to the universal and pervasive effects of institutional engineering.

Within these institutional environments, children and adults are then sorted at the individual level. As Sophie von Stumm and colleagues note, “children are assorted to environments in line with their genetic propensities.”¹² Institutions then reward specific gene-environment alignments over others, biologically embedding social advantages. Frank Mann and Colin DeYoung extend the point: “…individuals are not randomly assigned to social-relational environments. Rather, individuals select into and evoke responses from environments based on their heritable characteristics.”¹³

The result is that two children with similar genetic endowments can experience radically different biological trajectories depending on which institutional environment surrounds them, and two children with different endowments can experience similar trajectories when institutions are designed to compensate rather than amplify differences.

The Indicators Behind the Score

The Cantril gradient is reflected in nearly every objective indicator the global statistical system produces.

GDP per capita.

Population-weighted, World Happiness Report (WHR) top-20% residents live in countries averaging roughly $59,500 per year of GDP per capita. Bottom-20% residents live in countries averaging roughly $1,760. The gap is approximately 34-fold.^15,60

Research has shown that socioeconomic inequality can become biologically embedded through DNA methylation, telomere shortening, and inflammatory load.¹⁷ Socioeconomic predation leaves measurable molecular fingerprints on its prey, and those fingerprints transmit, partially, to subsequent generations through epigenetic mechanisms.¹⁸

Life expectancy.

Switzerland, with a Cantril score 7.018, has a life expectancy at birth of about 84 years.¹⁶ Chad’s life expectancy at birth is about 55 years,¹⁶ its Cantril score is 4.385. Peer-reviewed work over the past two decades has revealed the biological nature of this gap. The roughly 30-year longevity gap between a Swiss child and a Chadian child is not a difference in genetic endowment; it is a difference in what environments created by institutions have been permitted to do to a genome over a lifetime.

Caloric supply and dietary inequality.

The population-weighted average caloric supply of the WHR top 20% is approximately 3,560 kcal per person per day, while the bottom-quintile average is approximately 2,500 kcal per person per day (based on the Food and Agriculture Organization of the United Nations Food Balance Sheet data (FAOSTAT) for 2022, the latest year for which FAO has published a complete balanced release, and population weights from the United Nations World Population Prospects 2024 (mid-2025 estimates)).^45,65,100

A roughly 19-year average lifespan advantage of high-income populations relative to low-income populations (the standard comparisons reported by the World Bank and Our World in Data)⁴⁷ is built, in part, on this dietary surplus.

However, per-capita caloric supply is the smaller part of the story. The larger part is what those calories are made of and what producing them costs the planet.

The Universalizability Test: Diet and Footprint

If the bottom 80% of humanity adopted the dietary composition of the top 20% (as ranked by the World Happiness Report), the agricultural footprint could not be sustained by the planet. Population-weighted across the top cohort using the country-level Human Appropriation of Land for Food (HALF) values published by Our World in Data on a habitable-land basis, the implied HALF index is approximately 121% of the world’s habitable land.^51,66

If the bottom 80% of humanity (as ranked by the World Happiness Report) adopted the dietary composition of the top 20%, the agricultural land required would equal approximately 121% of the world’s habitable land.^51,66

Obviously, the agricultural footprint could not be sustained by the planet.

To be clear, this is not 121% of current agricultural land. It is 121% of habitable land–requiring every forest, grassland, wetland, and city on Earth to be converted to agriculture, and still falling 21% short.^48,49,50,52

This same dynamic is mirrored in the Ecological Footprint. Drawing on the National Ecological Footprint and Biocapacity Accounts, 2025 Edition, with 2022 as the reference year, the population-weighted footprint of the top 20% is approximately 6.10 global hectares per person.^53,67

Measured against an Earth biocapacity of 1.51 global hectares per person, universalizing this cohort’s consumption implies the productive capacity of approximately 4.0 Earths.^54,67

The top-20% diet is, in the most literal possible sense, non-universalizable. It is a standard of consumption whose physical structure depends on the rest of humanity not having it.

Why Composition Outweighs Volume

What necessitates this massive spatial footprint? The constraint is shaped not by sheer caloric volume, but by the interplay of diet composition and agricultural land use. Animal protein is the load-bearing variable in this dynamic. The land cost of a top-cohort calorie is several times that of a bottom-cohort calorie not because the calorie is larger, but because it is systematically routed through livestock.

The global asymmetry is stark: livestock occupies roughly 77% of the world’s agricultural land, yet delivers only 18% of global calories and 37% of protein.^55,56 Because of this inefficiency, modeled scenarios suggest that a universal shift to a plant-based diet would reduce global agricultural land use by 75%–freeing an area equivalent to North America and Brazil combined.⁵⁷

Ultimately, what a population eats matters far more than how much it eats. As one striking illustration of this disparity, the land area required merely to absorb the food waste in a U.S.-style consumption pattern is roughly twice the area required to produce the entire average Indian diet.⁴⁸

Predatory extraction.

And there is more to the story. The land cost of the top-cohort diet is not borne where the diet is consumed. Alexander and colleagues note that “…land use and the environmental impacts associated with agricultural production are also increasingly displaced from the country of consumption, through international trade of food commodities,” citing the literature on virtual-land trade and global-displacement flows.⁵⁸ The U.S. Department of Labor’s identification of soy-driven Cerrado conversion in Brazil as embedded in EU meat and dairy supply chains is the visible legal acknowledgment of this geography. Soy from the Brazilian Cerrado, palm oil from Indonesia, and beef from cleared Amazon pasture flow through global feed and food systems into the diets of the WHR top 20%. The land that disappears is in the Global South. The calories that arrive are eaten in the Global North.

This is the precise legal-economic mechanism that the French Duty of Vigilance Law, the German Supply Chain Act, and the EU Corporate Sustainability Due Diligence Directive were written to address, and the precise mechanism that population-weighted Cantril scores measure as a roughly 3.0-point gap.

The Arithmetic, Restated

Population-weighted, the WHR top 20% consumes approximately 1,065 additional kcal/day per person relative to the bottom 20%,^65,100 with that excess weighted heavily toward animal products whose land footprint is several times higher per calorie than plant equivalents.⁴⁶ The longevity premium attached to that diet, roughly 19 additional years of expected life on average between high-income and low-income cohorts,⁴⁷ is built on a dietary composition that the other ~7 billion people on Earth cannot adopt without exceeding the carrying capacity of the biosphere.

The HALF index makes the inequality structural rather than rhetorical. A diet that requires 121% of habitable land if universalized is, mathematically, a diet that depends on its own non-universalization.⁶⁶The caloric supply to the top 20% is justified by hierarchy elites who argue that these disparities are merited–that free markets determine who deserves more and who deserves less.

However, what this really shows is what equality exclusions (EQEXs) look like on a planetary scale: a system-justified harm dressed up in the language of choice, freedom, food preference, and market outcomes, whose physical signature is the impossibility of extending it to the people excluded from it.

Tertiary educational attainment.

Roughly 43% of adults aged 25–64 in the WHR top 20% versus approximately 10% in the bottom 20% have completed tertiary education (ISCED 5–8).^61,68

Large education gradients in mortality are documented across countries: people with more schooling tend to live longer, and this pattern is observed not only in high‑income settings but also across middle‑ and lower‑income contexts.⁴³

This difference frames a severe structural and institutional problem regarding how educational outcomes and longevity are distributed.

However, this disparity also points to a much deeper biological reality regarding human individual differences in happiness and well-being.

These differences can be explained by the interplay between genes and environments where the alignment (or misalignment) of a person’s inherited DNA differences (genetic propensities) and environmental conditions from childhood through adulthood, particularly the norms and institutions that regulate educational attainment and resource distribution, is a key determinant of life course outcomes and well-being.⁴²

Military expenditure (% of GDP).

When analyzing population-weighted cohorts from the World Happiness Report using SIPRI Military Expenditure Database 2024 figures, the military burden of the top 20% is roughly 2.7% of GDP, compared to 1.6% for the bottom 20%.^20,62

Within the context of Predatory Well-Being, this elevated military burden is not economic waste; rather, it serves as the enforcement infrastructure of global extractive geography. As Le Billon demonstrates, the political ecology of global resources requires armed enforcement.²¹ Naval chokepoint control (Hormuz, Malacca, Suez), basing networks across Africa and the Indian Ocean, sanctions, and arms-transfer pipelines are examples of international Equality Exclusions (EQEXs). They are the system-justified means by which the immense resource flows required to sustain the top cohort’s highly optimized exposome are physically defended and extracted from descending levels of the global hierarchy.

Nuclear weapons.

Three of the 29 top-20% countries–the United States, the United Kingdom, and Israel–have nuclear arsenals. None of the 29 bottom-cohort countries do.²²

Xia and colleagues’ 2022 Nature Food analysis estimates that a full US–Russia nuclear exchange would produce roughly 5 billion deaths from soot-induced agricultural collapse within two years.²³ The civilization-level tail risk is borne disproportionately by the cohort whose institutions did not produce the weapons.

Meritocracy and the domestic mirror of the gradient.

Case and Deaton’s analyses of U.S. data document a separation within the wealthiest country in the WHR top 20% between the life expectancy of adults who hold a bachelor’s degree and adults who do not. The gap was 2.5 years in 1992; by 2021 it was 8.5 years.^24,97

“Without a 4-yr college diploma,” they conclude, “it is increasingly difficult to build a meaningful and successful life in the United States.” Expected years lived between 25 and 75 have been declining for non-BA Americans since roughly 2010.⁹⁷

The mechanism is biological as well as institutional.

While educational attainment is influenced by the complexity of the gene-environment interplay, recent analyses of over 3 million individuals show it is one of the most powerful polygenic scores ever measured in the social sciences, capable of predicting significant variance in educational outcomes.²⁵

The offspring of parents with high educational-attainment polygenic scores tend themselves to have high polygenic scores, both through direct genetic inheritance and through the genetic-nurture environments those parents construct.²⁶

Assortative mating based on educational attainment further concentrates these genetic alignments across generations.²⁷ Meritocracy as a social system thus selects on a trait whose biological distribution is being shaped, in real time, by the very system doing the selecting. Sociologically, the result is what economist Branko Milanovic calls “a deeply entrenched new class structure” and a “self-sustaining upper-class.”²⁸ Biologically, however, this structure is more than just economic; it is the direct product of the genetics of assortative mating embedded within meritocratic institutions.

In the framework’s terms, meritocracy is an EQEX. Educational attainment, partially heritable and environmentally amplified, is used to justify radically unequal life outcomes, and the extraction of quality life-years from non-BA Americans is framed as fair and just because it reflects ‘merit,’ ‘effort,’ and ‘choice.’ Nobel laureate Angus Deaton put it precisely: “Inequality in health reinforces inequality in income, and perhaps even a longer life is for sale.”²⁹

It is critical to recognize that this extraction does not require conscious malice on the part of hierarchy elites. Rather, individuals and groups on top of a social hierarchy try to optimize, through the design and enforcement of their social and institutional preferences, their own gene-environment alignments and well-being. Attempts to optimize their power and control over generations results in rigid institutions that systematically exclude those whose inherited DNA sequences are mismatched and poorly aligned.

The U.S. internal gradient and the global cohort gradient are not coincidental parallels. They are the same pattern expressed at different scales, the same biological-institutional logic operating internationally between the top 20% and the bottom 20% of the World Happiness Report, and domestically, between the top and bottom of every social hierarchy on Earth.

The two faces of institutional violence.

Two indicators must be read with care because they show that EQEXs operate inside the happy cohort as well as between cohorts.

Incarceration rates, weighted by cohort population, run at roughly 295 per 100,000 in the top cohort and 85 per 100,000 in the bottom cohort.^30,63

The number is higher in the happier top cohort. Almost the entire gap is produced by a single country: the United States, at 541 per 100,000–the highest incarceration rate of any high-income OECD member and one of the five highest rates in the world (after El Salvador, Cuba, Rwanda, and Turkmenistan) according to the World Prison Brief.^30,63,102

The U.S. carries a prison population of roughly 1.81 million people, which is approximately twice the combined prison population of all 29 countries in the WHR 2026 bottom 20%.^30,41,59,103

Intentional homicide rates show a similar fracture. Equal-weighted, the top cohort’s rate is roughly 3.5 per 100,000 and the bottom cohort’s is roughly 7.0 per 100,000, with the higher equal-weighted bottom number driven by a small number of high-violence states such as Lesotho (43.6) and Eswatini (18.6).^31,64

Population-weighted, the rates partially invert: the top cohort’s rate is approximately 6.3 per 100,000 versus the bottom cohort’s 5.5 per 100,000, because the top cohort contains Mexico (24.9), Belize (28.1), and the United States (5.8), while the bottom cohort contains low-violence high-poverty states like Bangladesh (2.3) and Egypt (1.3).

Read superficially, this looks like a counter-pattern. Read through the framework, it is more evidence, not less. The top cohort produces its life-evaluation averages partly by warehousing certain populations away from the average, a domestic system-justified harm operating inside the wealthiest countries.³² The bottom cohort produces its averages partly by absorbing the lethal interpersonal violence that follows from collapsed institutions, climate stress, and resource conflict, much of it in regions whose extractive geography is shaped by the upper end of the global hierarchy.³³

Costa Rica and the United States: The Same Cohort, Two Different Configurations

The framework predicts that the global gradient is alignment-dependent, not income-dependent, that two countries at the same income level can produce radically different life-evaluation outcomes depending on how their institutions distribute exposomes across their populations.

The 2026 World Happiness Report ranking provides the test. Both Costa Rica (rank 4, score 7.439) and the United States (rank 23, score 6.816) are in the top 20%. Both are classified as high-income by the World Bank.³⁴

They diverge sharply on every framework-relevant institutional variable.

Costa Rica dismantled its army in 1948 and redirected military expenditure into health and education.³⁵ It has near-universal health insurance. Its Gini coefficient is high, but its institutional commitments to public goods are dense. It has no nuclear weapons, no projection-of-force doctrine, no carceral system on the U.S. scale, and a homicide rate (~17 per 100,000) that is troubling but typical of the region rather than catastrophic.³⁵ Its score on the Cantril ladder is 0.62 points higher than that of the United States, despite the U.S. having roughly 4.6 times Costa Rica’s GDP per capita on a current-US$ basis (United States $85,810 vs. Costa Rica $18,587 in 2024).^15,60

The United States, by contrast, runs the world’s largest military ($997 billion in 2024, equal to 3.4% of GDP, and exceeding the combined 2024 military expenditure of the next nine biggest spenders (China, Russia, Germany, India, the United Kingdom, Saudi Arabia, Ukraine, France, and Japan), whose total was $984.4 billion,^20,62,104 holds the world’s largest nuclear arsenal,²² incarcerates more of its population than any peer country,³⁰ and has produced a non-BA mortality crisis that serves as a lethal symptom of the very trajectory Sachs warned about in 2012. Its life-evaluation score has fallen from rank 11 in the 2012 report to rank 23 in 2026–the trajectory Sachs warned about, played out in the data over fourteen years.

In the 2012 World Happiness Report, Jeffrey Sachs wrote: “The world’s economic superpower, the United States, has achieved striking economic and technological progress over the past half century without gains in the self-reported happiness of the citizenry. Instead, uncertainties and anxieties are high, social and economic inequalities have widened considerably, social trust is in decline, and confidence in government is at an all-time low. Perhaps for these reasons, life satisfaction has remained nearly constant during decades of rising Gross National Product (GNP) per capita.”⁴

The pair isolates the framework’s load-bearing claim. What separates Costa Rica from the United States is not income; it is the configuration of EQEXs and system-justified harms inside their institutions. Costa Rica has demilitarized many of the channels through which the predatory pattern would otherwise operate. The United States has institutionalized them.

What this means.

The framework’s core claim is that the happiness gradient illustrated by the World Happiness Report 2026 is not a failure of prosocial civilization. It is an expression of it, filtered through the gene-environment paradox.

What a dominant class considers “right and good” is biased by the inherited DNA differences of that class’s members and by the environments those differences have built.

The ranking of 147 countries by life evaluation is, read correctly, a ranking by degree of alignment between a country’s exposome and the genetic propensities of the population whose institutions designed it.

That configuration was assembled over the Industrial Age and its neoclassical-economic aftermath by populations whose gene-environment alignments happened to match the available technological opportunities and extractive vectors.

Once assembled, it extracts–through trade rules, financial rents, military force, meritocratic sorting, supply-chain coercion, dietary land-use displacement, and carbon emissions–from every population whose alignments differ.³⁶

This is not metaphor. It is what the World Happiness Report 2026 is measuring. The gap between Finland’s 7.764 and Afghanistan’s 1.446 is not a difference in the human stuff of those populations…it is a difference in what their exposomes have been permitted to do to their genomes over generations.

The arithmetic of predation is legible in every row of the report.

Once you know what you are looking at, you cannot unsee it.

Read: What you should know before you read the articles…

Feature Articles

The Arithmetic of Global Predation

Foundational Articles

Welcome to the Jungle!

Humanity’s Most Dangerous Self-Inflicted Wound: Predatory Well-Being

The Paradox of the Right and the Good: A Gene-Environment Paradox

Predatory Well-Being: A Global Problem

Socioeconomic Inequality and Genetic Enhancement

Heritable Inequality: The Dark Legacy of Social Hierarchy

The Cognitive Super-Predator in the 21st Century

Quality Life Years Lost to Meritocracy

Health and Longevity in America

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References

1. Savourey, E., & Brabant, S. (2021). The French law on the duty of vigilance: Theoretical and practical challenges since its adoption. Business and Human Rights Journal, 6(1), 141-152.

2.Lieferkettensorgfaltspflichtengesetz (LkSG) (Germany), entry into force January 1, 2023.

3. California Transparency in Supply Chains Act (SB 657, 2010), effective 2012; European Parliament and Council. (2024). Directive (EU) 2024/1760 on Corporate Sustainability Due Diligence (CSDDD). Official Journal of the European Union.

4. Sachs, J. D. (2012). Introduction. In J. F. Helliwell, R. Layard, & J. D. Sachs (Eds.), World Happiness Report 2012 (Chapter 1, pp. 2–9). The Earth Institute, Columbia University.

5. Helliwell, J. F., Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B., & Wang, S. (Eds.). (2026). World Happiness Report 2026. University of Oxford: Wellbeing Research Centre.

6. Tilly, C. (2011). Cities, states, and trust networks: chapter 1 of Cities and States in World History. In Contention and trust in cities and states (pp. 1-16). Dordrecht: Springer Netherlands.

“No states existed anywhere in the world before 4000 BCE.”

“Cities, then, first appeared in the same periods and regions as states. Like cities, states can only exist in symbiosis with agriculture that produces enough to support significant non-agricultural populations. Cities differ from strictly agricultural settlements, furthermore, by virtue of substantial populations, differentiated and specialized activities, and location as nodes in far-reaching networks of trade and political coordination. Cities and states maintain ambivalent relations: urban merchants and intellectuals seek the protection that states can provide, but resist the extraction and control that states’ rulers impose on them. Rulers of states, on their side, commonly try to combat urbanites’ independence, but also seek to benefit from concentrations of resources in cities as well as from the relative defensibility of compact cities as compared with scattered rural populations.”

“What of the state? A state is a structure of power involving four distinctive elements: 1) major concentrated means of coercion, especially an army, 2) organization that is at least partly independent of kinship and religious relations, 3) a defined area of jurisdiction, and 4) priority in some regards over all other organizations operating within that area. Although the four elements had existed separately for some time, no one put all four of them together before the Middle East’s creation of both cities and states. No states existed anywhere in the world before 4000 BCE. By the era of Gilgamesh’s Uruk, however, full-fledged cities and states were flourishing across significant parts of the Middle East, and possibly forming in other parts of Eurasia as well.”

Pinker, S. (2012). The better angels of our nature. Penguin.

7. Briley, D. A., Livengood, J., & Derringer, J. (2018). Behaviour genetic frameworks of causal reasoning for personality psychology. European Journal of Personality, 32(3), 202–220

8. See these foundational articles from QualityLifeJungle.net:

Predatory Well-Being: A Global Problem

The Paradox of the Right and the Good

Humanity’s Most Dangerous Self-Inflicted Wound: Predatory Well-Being

Heritable Inequality: The Dark Legacy of Social Hierarchy

9. References for: “A Gene-Environment Paradox–The Paradox of the Right and the Good”

Gene-Environment Foundations, Evidence, and Neural Mechanisms

Foundations and the Gene-Environment Interplay

Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016). Behavioral genetics (7th ed.). New York: Worth Publishers.

Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype → environment effects. Child Development, 54(2), 424-435.

Mills, M. C., & Tropf, F. C. (2020). Sociology, genetics, and the coming of age of sociogenomics. Annual Review of Sociology, 46, 553-581.

Briley, D. A., Livengood, J., & Derringer, J. (2018). Behaviour genetic frameworks of causal reasoning for personality psychology. European Journal of Personality, 32(3), 202-220.

“…individuals actively create or select environmental experiences aligned with their genetically influenced preferences and desires.”

“People select themselves into, adapt to, and shape the environments that correspond to their genotypes.”

Vukasović, T., & Bratko, D. (2015). Heritability of personality: A meta-analysis of behavior genetic studies. Psychological Bulletin, 141(4), 769–785.

von Stumm, S., & d’Apice, K. (2022). From genome-wide to environment-wide: Capturing the environome. Perspectives on Psychological Science, 17(1), 30-40.

“People select themselves into, adapt to, and shape the environments that correspond to their genotypes.”

“There is broad consensus that people’s differences in affect, behavior, and cognition result from the interplay between genetic propensities and environmental conditions.”

Measuring the Mechanism: Polygenic Scores

Plomin, R., & von Stumm, S. (2022). Polygenic scores: Prediction versus explanation. Molecular Psychiatry, 27(1), 49-52.

Belsky, D. W., & Harden, K. P. (2019). Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries from Genome-Wide Association Studies from the Laboratory to the Population. Developmental Psychopathology, 31(4), 1309–1319.

Specific Examples of Gene-Environment Interaction

Tucker-Drob, E. M., & Bates, T. C. (2016). Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychological Science, 27(2), 138-149.

Beaver, K. M., & Belsky, J. (2012). Gene–environment interaction and the intergenerational transmission of parenting: Testing the differential susceptibility hypothesis. Psychiatric Quarterly, 83(1), 29–40.

Shewark, E. A., Burt, S. A., Klump, K. L., & Hyde, L. W. (2024). Neighborhood features moderate genetic and environmental influences on aggressive expectations. Developmental Psychology, 60(3), 498–511.

Domingue, B. W., & Boardman, J. D. (2014). Genetic and educational assortative mating among US adults. Proceedings of the National Academy of Sciences, 111(22), 7996-8000.

The Neural Architecture of “The Right and The Good”

Hsu, M., Anen, C., & Quartz, S. R. (2008). The right and the good: Distributive justice and neural encoding of equity and efficiency. Science, 320(5879), 1092-1095.

Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. H. (2011). Mapping the moral domain. Journal of Personality and Social Psychology, 101(2), 366-385.

Curry, O. S., Mullins, D. A., & Whitehouse, H. (2019). Is it good to cooperate? Testing the theory of morality-as-cooperation in 60 societies. Current Anthropology, 60(1), 47-69.

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Li, Y., Zhang, T., Li, W., et al. (2020). Linking brain structure and activation in anterior insula cortex to explain the trait empathy for pain. Human Brain Mapping, 41, 1030–1042.

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46. Cohort caloric averages (top 20% ≈ 3,564 kcal/day; bottom 20% ≈ 2,498 kcal/day) computed by the author as the population-weighted mean over the 29 top-cohort and 29 bottom-cohort countries identified in the WHR 2026 ranking, using FAOSTAT 2022 food-supply data per country and UN World Population Prospects 2024 mid-2025 population estimates. See Methodological Appendix A.4 for inputs and weighting procedure.

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51. Population-weighted HALF index for the WHR 2026 top-20% cohort (≈121%) computed by the author from country-level habitable-land HALF values published by Our World in Data (Ritchie 2017, citing Alexander et al. 2016) using UN World Population Prospects 2024 mid-2025 population weights. Coverage and arithmetic in Methodological Appendix A.6.

52. Ritchie, H., & Roser, M. (2019, November). Half of the world’s habitable land is used for agriculture. Our World in Data. https://ourworldindata.org/global-land-for-agriculture

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54. Cohort-level Ecological Footprint (≈6.10 gha/person) and Earth-equivalent figure (≈4.0 Earths) computed by the author as the population-weighted mean of country-level footprints from the National Ecological Footprint and Biocapacity Accounts, 2025 Edition (Lo et al. 2025), divided by the global biocapacity per person of 1.51 gha published in the same NFBA 2025 Edition (2022 reference year). Inputs and arithmetic in Methodological Appendix A.5.

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66. Alexander, P., Brown, C., Arneth, A., Finnigan, J., & Rounsevell, M. D. (2016). Human appropriation of land for food: The role of diet. Global environmental change, 41, 88-98.

Ritchie, H. (2017). How much of the world’s land would we need in order to feed the global population with the average diet of a given country? Our World in Data. https://ourworldindata.org/agricultural-land-by-global-diets

67. Lo, K., et al. (2025). National Ecological Footprint and Biocapacity Accounts, 2025 Edition; Global Footprint Network Open Data Platform.

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97. Case, A., & Deaton, A. (2021). Life expectancy in adulthood is falling for those without a BA degree, but as educational gaps have widened, racial gaps have narrowed. Proceedings of the National Academy of Sciences, 118(11), e2024777118.

100. Cohort caloric differential (≈1,065 kcal/person/day) computed by the author as the difference of the top- and bottom-cohort population-weighted caloric supplies in reference 46. See Methodological Appendix A.4.

101. Population-weighted HALF index for the WHR 2026 top-20% cohort with the United States excluded computed by the author from the same OWID/Alexander country-level habitable-land HALF values used in reference 51 and Appendix A.6, restricted to 17 of the 29 top-cohort countries present in the OWID published table (the 18 of A.6 minus the United States), with UN WPP 2024 mid-2025 population weights. Coverage and sensitivity ranges (≈95–100% of habitable land) reported in Methodological Appendix A.6.1

102. Institute for Crime & Justice Policy Research, World Prison Brief, “Highest to Lowest – Prison population rate,” Birkbeck, University of London, https://www.prisonstudies.org/highest-to-lowest/prison_population_rate, latest available country values; basis for the claim that the United States holds the highest incarceration rate of any high-income OECD member and is within the global top five overall (after El Salvador, Cuba, Rwanda, and Turkmenistan). Comparator-set definition (high-income OECD member) and country-by-country distribution reported in Methodological Appendix A.9.1

103. Combined bottom-20% prison population total of approximately 866,000 people computed by the author by applying the bottom-cohort population-weighted incarceration rate of 85.24 per 100,000 (Methodological Appendix A.9) to the bottom-cohort total population of 1,016.40 million (Methodological Appendix A.1): 85.24 × 10,164 ≈ 866,379. Cross-checked against a direct sum of World Prison Brief national prison-population totals across the 29 bottom-cohort countries, which yields a range of approximately 0.7–0.9 million people. The U.S. prison population is approximately 1.81 million (World Prison Brief, latest national total). See Methodological Appendix A.9.2

104. Stockholm International Peace Research Institute, Trends in World Military Expenditure, 2024, SIPRI Fact Sheet, April 2025 (Liang, Tian, Lopes da Silva, Scarazzato, Karim and Guiberteau Ricard), Table 1 (“The 40 countries with the highest military expenditure in 2024”). Table 1 reports U.S. military expenditure of $997 billion in 2024 and U.S. military expenditure as a share of GDP of 3.4% in 2024. Computation of “greater than the next nine combined” ($997.0B − $984.4B = +$12.6B, using the 2024 spending figures for ranks 2–10 in SIPRI Table 1) reported in Methodological Appendix A.8.1

Methodological Appendix

Author-Derived Computations: Inputs, Vintages, and Arithmetic

A.0 Single-Vintage Decisions

Every cohort-level number reported in the body of the article above is a population-weighted mean computed by the author from the public country-level inputs documented below. To prevent any inconsistency between body text and reference notes, a single edition of each underlying dataset has been chosen and is used for every claim that draws on it. The selection criteria were: (a) the most recent edition with substantially complete reporting, (b) preference for the original publisher of the data over secondary aggregators, and (c) for indicators with annual fluctuation, the most recent reference year for which the publisher has issued a fully balanced (not preliminary) release.

Variable	Dataset / Vintage	Reference year
Cantril life-evaluation	WHR 2026 (Helliwell et al. 2026, Wellbeing Research Centre, Oxford)	2023–2025 three-year average
Population weights	UN DESA, World Population Prospects 2024 (medium variant)	Mid-year 2025 estimate
GDP per capita (current US$)	World Bank WDI series NY.GDP.PCAP.CD	2024
Caloric supply (kcal/cap/day)	FAOSTAT Food Balance Sheets 2010–2022 (FAO Analytical Brief 91)	2022
Ecological Footprint (gha/p)	NFBA 2025 Edition (Lo et al. 2025)	2022
Earth biocapacity benchmark	Same NFBA 2025 Edition: global biocapacity per person	1.51 gha/person, 2022
HALF index (% habitable)	Alexander et al. 2016 country-diet HALF, on habitable-land basis (Ritchie/OWID 2017)	Diet snapshot ~2011 (latest available)
Tertiary attainment (ISCED 5–8, 25–64)	OECD Education at a Glance 2025 (Table A1.1); UNESCO UIS and World Bank WDI for non-OECD	Most recent year per country
Military expenditure (% GDP)	SIPRI Military Expenditure Database (Apr 2025 release)	2024
Incarceration rate	Institute for Crime & Justice Policy Research, World Prison Brief	Most recent year per country (≤2024)
Intentional homicide	UNODC Intentional Homicide Victims data set; Global Study on Homicide	Most recent year per country (typically 2022)
Life expectancy at birth	World Bank WDI / FRED (UN WPP underlying)	2023

A.1 Cohort Populations

A.2 Population-Weighted Cantril Score

Formula: x̄ = Σᵢ(popᵢ · scoreᵢ) / Σᵢ popᵢ, summed over the 29 cohort countries.

Top 20% pop-weighted Cantril: 6.869
Bottom 20% pop-weighted Cantril: 3.910
Gap: 2.959 (rounded to “approximately 3.0” in body).

A.3 Population-Weighted GDP per Capita

Inputs: World Bank WDI series NY.GDP.PCAP.CD, current US$, 2024 reference year, for each cohort country. Coverage: 29/29 in both cohorts.

Top 20% pop-weighted GDPpc: $59,491
Bottom 20% pop-weighted GDPpc: $1,759
Ratio: Top ÷ Bottom = 33.8 ≈ “34-fold”.
United States vs. Costa Rica: $85,810 ÷ $18,587 = 4.62 ≈ “roughly 4.6 times.”

A.4 Population-Weighted Caloric Supply

Inputs: FAOSTAT Food Balance Sheets 2010–2022, food supply (kcal/capita/day), 2022 reference year. Coverage: 27/29 in top cohort (Kosovo and Taiwan are not separately reported in FAOSTAT) covering 97.3% of cohort population; 29/29 in bottom cohort covering 100% of cohort population.

Top 20% pop-weighted: 3,564 kcal/person/day
Bottom 20% pop-weighted: 2,498 kcal/person/day
Differential: 3,564 − 2,498 = 1,066 kcal/person/day (rounded to “~1,065”).

A.5 Population-Weighted Ecological Footprint and Earth-Equivalents

Inputs: Lo et al. 2025, National Ecological Footprint and Biocapacity Accounts, 2025 Edition, Footprint Data Foundation / York University / University of Iceland; Ecological Footprint of Consumption (gha/person), 2022 reference year. Earth biocapacity benchmark from the same NFBA 2025 Edition: 1.51 gha/person (2022). Coverage: 27/29 in top cohort (Kosovo and Taiwan not separately reported), 29/29 in bottom cohort.

Top 20% pop-weighted EF: 6.10 gha/person
Bottom 20% pop-weighted EF: 1.12 gha/person
Earth-equivalents (top): 6.10 / 1.51 = 4.04, rounded to “approximately 4.0 Earths.”

A.6 Population-Weighted HALF Index

Inputs: Country-level habitable-land HALF index from Alexander et al. (2016) as published by Our World in Data (Ritchie 2017). Coverage: 18/29 of the top-cohort countries are present in the published OWID table, covering 90.2% of top-cohort population. Cohort countries not in the published table are excluded from the cohort weighted mean.

Top 20% pop-weighted HALF: 120.9% of habitable land (rounded to 121%).
Coverage caveat (disclosed in the body): the U.S. value (138%) is in the table; cohort countries without a published HALF value contribute neither to the numerator nor the denominator.
Bottom-cohort HALF is not reported as a cohort statistic because only one bottom-cohort country (Bangladesh, 38%) appears in the published Alexander/OWID table.

A.7 Population-Weighted Tertiary Attainment (ISCED 5–8, ages 25–64)

Inputs: OECD Education at a Glance 2025, Table A1.1; UNESCO UIS and World Bank WDI series SE.TER.CUAT.BA.ZS used to fill non-OECD countries. Coverage: 29/29 in both cohorts.

Top 20% pop-weighted: 43.5% (rounded to 43%).
Bottom 20% pop-weighted: 9.8% (rounded to 10%).
Definitional caveat: OECD reports 25–64; some non-OECD figures are reported for ages 25+. The mismatch is small but noted.

A.8 Population-Weighted Military Expenditure (% of GDP)

Inputs: SIPRI Military Expenditure Database, April 2025 release; military expenditure as % of GDP for 2024. Coverage: 29/29 in top cohort; 26/29 in bottom cohort (Comoros, Yemen, Afghanistan not reported by SIPRI), covering 91.4% of bottom-cohort population.

Top 20% pop-weighted: 2.66% (rounded to 2.7%).
Bottom 20% pop-weighted: 1.59% (rounded to 1.6%).

A.8.1 Country-Level Verification: U.S. Military Expenditure in 2024

Source: Stockholm International Peace Research Institute (SIPRI), Trends in World Military Expenditure, 2024, SIPRI Fact Sheet, April 2025 (Liang, Tian, Lopes da Silva, Scarazzato, Karim and Guiberteau Ricard). Table 1 (“The 40 countries with the highest military expenditure in 2024”) reports U.S. military expenditure of $997 billion in 2024 and U.S. military expenditure as a share of GDP of 3.4% for 2024.

Next-nine comparison (SIPRI Fact Sheet, Table 1, 2024 spending in current US$ billion):

Rank 2 China 314.0
Rank 3 Russia 149.0
Rank 4 Germany 88.5
Rank 5 India 86.1
Rank 6 United Kingdom 81.8
Rank 7 Saudi Arabia 80.3
Rank 8 Ukraine 64.7
Rank 9 France 64.7
Rank 10 Japan 55.3
Sum of ranks 2–10: $984.4 billion
U.S. (rank 1): $997.0 billion
U.S. − (next nine combined): +$12.6 billion

Interpretation: in 2024 the United States, on the SIPRI definition of military expenditure, spent more on the military than the next nine largest national military spenders combined ($997.0 billion vs. $984.4 billion). SIPRI notes that this margin narrowed sharply in 2025 because of large spending increases in other countries (Germany +24%, Ukraine +20%); the article’s claim is restricted to the 2024 reference year used throughout this Appendix.

A.9 Population-Weighted Incarceration

Inputs: Institute for Crime & Justice Policy Research, World Prison Brief, prison population per 100,000 of national population, latest available year per country (≤2024). Coverage: 29/29 in both cohorts.

Top 20% pop-weighted: 294.79 per 100,000 (rounded to 295).
Bottom 20% pop-weighted: 85.24 per 100,000 (rounded to 85).
Disclosed in body: the United States (541 per 100,000, ~1.81 million prisoners) dominates the top-cohort population-weighted figure.

A.9.1 Verification: U.S. Incarceration Rate in International Context

Definition of comparator set: “high-income OECD members” is the set of OECD Development Assistance Committee members classified by the World Bank as high-income, fiscal year 2026 lending-group classification, as used elsewhere in this Appendix (A.0, and reference 34 in the body). “Rate” is the prison-population rate per 100,000 of national population, as published by the World Prison Brief, latest reported year per country.

Data and source: Institute for Crime & Justice Policy Research, World Prison Brief, “Highest to Lowest – Prison population rate,” Birkbeck, University of London (https://www.prisonstudies.org/highest-to-lowest/prison_population_rate). As of the most recent WPB update accessed for this article:

United States: 541 per 100,000 (2022 figure used by WPB; ≈1.81 million prisoners). No other high-income OECD member is within a factor of three of this rate; representative high-income OECD comparators from the same WPB list include Turkey 408 (an OECD member but classified by the World Bank as upper-middle-income), Israel ≈234, Lithuania ≈185, New Zealand ≈183, Latvia ≈171, Estonia ≈165, Hungary ≈186, Poland ≈184, Czechia ≈161, Slovakia ≈195, UK (England & Wales) ≈146, Australia ≈160, Canada ≈104, Italy ≈104, Greece ≈105, Portugal ≈118, France ≈106, Belgium ≈101, Spain ≈96, Austria ≈97, Switzerland ≈76, Germany ≈68, Netherlands ≈54, Denmark ≈72, Sweden ≈82, Norway ≈54, Finland ≈52, Japan ≈36, South Korea ≈67. The U.S. rate is more than 2.5× the highest of the European high-income OECD members in this list and more than 5× the median of that group.
Top-five overall (WPB latest-available, in descending order, omitting territories with population <500,000): El Salvador (≈1,600+), Cuba (≈794), Rwanda (≈637), Turkmenistan (≈576), United States (541). None of the four countries above the U.S. is a high-income OECD member.

Conclusion: the U.S. incarceration rate is the highest of any high-income OECD member and is within the global top five overall by the most recent WPB ranking.

A.9.2 Cohort Prison Population Total: Bottom 20%

Method 1 — implied total from cohort-weighted rate. Using the appendix’s own figures: the bottom-20% population-weighted incarceration rate is 85.24 per 100,000 (Appendix A.9), and the bottom-20% cohort population is 1,016.40 million (Appendix A.1). The implied combined prison population is:

85.24 × (1,016,400,000 / 100,000) = 85.24 × 10,164 ≈ 866,379 people.

Method 2 — direct sum of country totals. Summing World Prison Brief latest-available national prison-population totals across the 29 bottom-cohort countries (with several countries reported as estimates rather than official figures, and Myanmar and Egypt contributing particularly large numbers) yields a combined total of roughly 700,000–880,000, depending on which reporting year is used for the largest contributors. The two methods triangulate on a bottom-cohort combined total of approximately 0.7–0.9 million people.

Comparison to the United States. The U.S. prison population reported by the World Prison Brief is approximately 1,808,100 (2022 figure used as latest WPB national total). Therefore:

U.S. ÷ bottom-cohort combined ≈ 1,808,100 ÷ 866,379 ≈ 2.09.

Conclusion: the U.S. alone incarcerates roughly twice as many people as the combined prison populations of every country in the WHR 2026 bottom 20%.

A.10 Cohort-Level Intentional Homicide Rates

Inputs: UNODC Intentional Homicide Victims data set, latest reported year per country. Coverage: 29/29 in both cohorts.

Equal-weighted: top ≈ 3.5/100k; bottom ≈ 7.0/100k (Lesotho 43.6 and Eswatini 18.6 are major contributors).
Population-weighted: top ≈ 6.3/100k; bottom ≈ 5.5/100k. Mexico (24.9), Belize (28.1), and U.S. (5.8) drive the top-cohort number; Bangladesh (2.3) and Egypt (1.3) drag the bottom-cohort number down.

A.11 Reproducibility

All country-level inputs and the Python script used to compute the cohort means above are available from the author on request and can be reproduced by any third party (including automated systems) by downloading the cited datasets at the vintages listed in A.0 and applying the formula Σ(pop·x) / Σ pop over the 29 countries in each cohort. Where coverage is less than 29/29, both the numerator and denominator are restricted to the same subset; the cohort coverage fraction is reported alongside each cohort statistic in the appendix entries above.

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