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Appendix

References & Notes for:

The World Happiness Report 2026: The Arithmetic of Global Inequality

Please note the following: In the context of the interplay of genes and environments–unless the article states otherwise–cultural, historical, political, economic, natural and biophysical factors are viewed as dynamic and interwoven environmental conditions and not as isolated variables.

In addition, the use of the word ‘interplay’ in the context of ‘gene-environment interplay’ and other similar sentences is defined according the following article: Allegrini, A. G., Karhunen, V., Coleman, J. R., Selzam, S., Rimfeld, K., von Stumm, S., … & Plomin, R. (2020). Multivariable GE interplay in the prediction of educational achievement. PLoS Genetics, 16(11), e1009153.

“Quantitative genetic theory distinguishes two types of interplay between genetic and environmental effects, genotype-environment correlation (rGE) and genotype-environment interaction (GxE).”

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:

The Gene-Environment Roots of Inequality

Four Fundamental Drivers of Social Hierarchy

The Predatory Stresses of Hierarchy and Socioeconomic Inequality

9. References for: “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.”

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 EA, Vazquez AY, Pearson AL, Klump KL, Burt SA. Neighborhood features moderate genetic and environmental influences on children’s social information processing. Dev Psychol. 2024 Apr;60(4):610-623. doi: 10.1037/dev0001690. Epub 2024 Feb 29. PMID: 38421787.

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.

Greene, J. D., Nystrom, L. E., Engell, A. D., Darley, J. M., & Cohen, J. D. (2004). The neural bases of cognitive conflict and control in moral judgment. Neuron, 44(2), 389-400.

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.

Costa, C., Scarpazza, C., & Filippini, N. (2025). The anterior insula engages in feature- and context-level predictive coding processes for recognition judgments. The Journal of Neuroscience, 45(5).

Moral Pluralism—Why People Disagree About Right and Good:

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.

Haidt, J. (2012). The righteous mind: Why good people are divided by politics and religion. Pantheon Books.

Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., & Ditto, P. H. (2013). Moral foundations theory: The pragmatic validity of moral pluralism. Advances in Experimental Social Psychology, 47, 55-130.

Shweder, R. A., Much, N. C., Mahapatra, M., & Park, L. (1997). The “big three” of morality (autonomy, community, divinity) and the “big three” explanations of suffering. In A. M. Brandt & P. Rozin (Eds.), Morality and health (pp. 119-169). Routledge.

Twin and Genetic Studies of Morality and Prosociality

Zakharin, M., & Bates, T. C. (2023). Testing heritability of moral foundations: Common pathway models support strong heritability for the five moral foundations. European Journal of Personality, 37(4), 485-497.

Knafo, A., & Plomin, R. (2006). Prosocial behavior from early to middle childhood: Genetic and environmental influences on stability and change. Developmental Psychology, 42(5), 771–786.

Limone, P., & Toto, G. A. (2022). Origin and development of moral sense: A systematic review. Frontiers in Psychology, 13, 887537.

Israel, S., Hasenfratz, L., & Knafo-Noam, A. (2015). The genetics of morality and prosociality. Current Opinion in Psychology, 6, 55-59.

Tielbeek, J. J., et al. (2022). Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis. Nature Human Behaviour, 6, 1711–1721.

Economic Preferences and Altruism

Cesarini, D., Dawes, C. T., Johannesson, M., Lichtenstein, P., & Wallace, B. (2009). Genetic variation in preferences for giving and risk taking

10. van de Weijer, M. P., Pelt, D. H., Baselmans, B. M., Ligthart, L., Huider, F., Hottenga, J. J., … & Bartels, M. (2024). Capturing the well-being exposome in poly-environmental scores. Journal of Environmental Psychology, 93, 102208.

11. Bartels, M., Nes, R. B., Armitage, J. M., van de Weijer, M. P., de Vries, L. P., & Haworth, C. (2022). Exploring the biological basis for happiness. In World happiness Report 2022, Ch. 5 (Sustainable Development Solutions Network)

12. von Stumm, S., Kandaswamy, R., & Maxwell, J. (2023). Gene-environment interplay in early life cognitive development. Intelligence, 98, 101748.

13. Mann, F. D., DeYoung, C. G., Tiberius, V., & Krueger, R. F. (2019). Social-relational exposures and well-being. Journal of Research in Personality, 83, 103880.

15. World Bank. (2026). GDP per capita (current US$) [Data set, series NY.GDP.PCAP.CD]. World Development Indicators. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD. Population weights: UN DESA, World Population Prospects 2024 (medium variant), mid-2025 estimates. Cohort definitions follow Helliwell et al. (eds.), WHR 2026: top 20% = ranks 1–29; bottom 20% = ranks 119–147. Population-weighted mean: Σ(popᵢ × GDPpcᵢ) ÷ Σ popᵢ. Inputs and computation in Methodological Appendix A.3.

16. World Bank. (2025). Life expectancy at birth, total (years), series SP.DYN.LE00.IN. https://data.worldbank.org/indicator/SP.DYN.LE00.IN. (Switzerland 84.4 years, 2024; Chad 55.1 years, 2023.)

17. Fiorito, G., Polidoro, S., Dugué, P. A., Kivimaki, M., Ponzi, E., Matullo, G., … & Vineis, P. (2017). Social adversity and epigenetic aging: a multi-cohort study on socioeconomic differences in peripheral blood DNA methylation. Scientific reports, 7(1), 1-12.

Stringhini, S., Polidoro, S., Sacerdote, C., Kelly, R. S., Van Veldhoven, K., Agnoli, C., … & Vineis, P. (2015). Life-course socioeconomic status and DNA methylation of genes regulating inflammation. International journal of epidemiology, 44(4), 1320-1330.

18. Zhou, A., & Ryan, J. (2023). Biological embedding of early-life adversity and a scoping review of the evidence for intergenerational epigenetic transmission of stress and trauma in humans. Genes, 14(8), 1639.

19. van de Werfhorst, H. G. (2026). The Sociogenomics of Social Stratification and General Theories of Inequality. The British journal of sociology.

20. Tian, N., et al. (2025). Trends in world military expenditure, 2024 (SIPRI Fact Sheet, April 2025). Stockholm International Peace Research Institute. https://www.sipri.org/publications/2025/sipri-fact-sheets/trends-world-military-expenditure-2024

21. Le Billon, P. (2014). Wars of plunder: Conflicts, profits and the politics of resources. Oxford University Press.

22. Kristensen, H. M., Korda, M., Johns, E., & Knight-Boyle, M. (2025). Status of world nuclear forces. Federation of American Scientists, Nuclear Information Project. https://fas.org/initiative/status-world-nuclear-forces/

Kristensen, H., Korda, M., Johns, E., Knight, M., & Kohn, K. (2017). Status of world nuclear forces. Federation of American Scientists. Available: https://fas. org/initiative/status-world-nuclear-forces/[Accessed 18 Mar 2025].

23. Xia, L., Robock, A., Scherrer, K., Harrison, C. S., Bodirsky, B. L., Weindl, I., … & Heneghan, R. (2022). Global food insecurity and famine from reduced crop, marine fishery and livestock production due to climate disruption from nuclear war soot injection. Nature Food, 3(8), 586-596.

24. 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.

25. Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., … & Esko, T. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature genetics, 54(4), 437-449.

26. Morris, T. T., von Hinke, S., Pike, L., Ingram, N. R., Davey Smith, G., Munafò, M. R., & Davies, N. M. (2024). Implications of the genomic revolution for education research and policy. British Educational Research Journal, 50(3), 923-943.

27. Torvik, F. A., et al. (2022). Modeling assortative mating and genetic similarities between partners, siblings, and in-laws. Nature Communications, 13(1), 1108. https://doi.org/10.1038/s41467-022-28732-w

28. Milanovic, B. (2021). What happened to social mobility in America? A new aristocracy has a lock on capital and jobs. Foreign Affairs.

29. Deaton, A. (2016). On death and money: History, facts, and explanations. JAMA, 315(16), 1703-1705. https://doi.org/10.1001/jama.2016.4224

30. Institute for Crime & Justice Policy Research. (2024). World Prison Brief: Prison population rates. https://www.prisonstudies.org/

31. United Nations Office on Drugs and Crime. (2024). Global study on homicide.

32. Wacquant, L. (2009). Punishing the poor. Duke University Press.

33. Hsiang, S. M., Burke, M., & Miguel, E. (2013). Quantifying the influence of climate on human conflict. Science, 341(6151), 1235367.

34. World Bank Group. (2025). World Bank country and lending groups, fiscal year 2026 classification.

35. Herrera-Ulloa, M. (2024). Yes, Costa Rica is Different: A Successful Experience at Risk. ReVista (Cambridge), 23(3), 1-13.

36. IPCC. (2021). Climate change 2021: The physical science basis. Cambridge University Press;

Chancel, L. (2022). Global carbon inequality over 1990–2019. Nature Sustainability, 5(11), 931–938.

Diffenbaugh, N. S., & Burke, M. (2019). Global warming has increased global economic inequality. Proceedings of the National Academy of Sciences, 116(20), 9808-9813.

Kenner, D. (2019). Carbon inequality: The role of the richest in climate change. Routledge.

Wiedmann, T., Lenzen, M., Keyßer, L. T., & Steinberger, J. K. (2020). Scientists’ warning on affluence. Nature Communications, 11(1), 3107.

Otto, I. M., Kim, K. M., Dubrovsky, N., & Lucht, W. (2019). Shift the focus from the super-poor to the super-rich. Nature Climate Change, 9(2), 82-84.

37. U.S. Department of Labor. List of Goods Produced by Child Labor or Forced Labor; Lithium-ion batteries supply chains. https://www.dol.gov/agencies/ilab/reports/child-labor/list-of-goods/supply-chains/lithium-ion-batteries

38. U.S. Department of Labor, Bureau of International Labor Affairs. (2024). List of goods produced by child labor or forced labor (11th ed.). https://www.dol.gov/agencies/ilab/reports/child-labor/list-of-goods.

39. Green, J. M. H., Croft, S. A., Durán, A. P., Balmford, A. P., Burgess, N. D., Fick, S., … West, C. D. (2019). Linking global drivers of agricultural trade to on-the-ground impacts on biodiversity. Proceedings of the National Academy of Sciences, 116(46), 23202–23208.

40. Human Rights Watch. (2015). Whoever Raises Their Head Suffers the Most: Workers’ Rights in Bangladesh’s Garment Factories.

41. Happier Lives Institute. (2026). World Happiness Report 2026: Full country rankings. https://www.happierlivesinstitute.org/2026/03/19/world-happiness-report-2026-full-country-rankings-our-favourite-finding/ Cohort populations and cohort-level Cantril means computed by the author from this WHR 2026 ranking and UN WPP 2024 mid-2025 population estimates; computation given in Methodological Appendix A.1–A.2.

42. von Stumm, S., Kandaswamy, R., & Maxwell, J. (2023). Gene–environment interplay in early life cognitive development. Intelligence, 98, 101748

“Children’s differences in early life cognitive development are driven by the interplay of genetic and environmental factors.”

“By the time they start formal education, children’s differences in cognitive ability are powerful predictors of their contemporaneous and future academic achievement.”

von Stumm, S., Smith-Woolley, E., Ayorech, Z., et al. (2020). Predicting educational achievement from genomic measures and socioeconomic status. Developmental Science, 23(3), e12925.

“…SES is often assumed to represent solely environmental advantages of wealth and privilege, but it is actually just as heritable as most other complex traits, with estimates from twin studies of about 50%. The main ingredients in most SES scores are parents’ educational attainment and occupational status, both of which are substantially heritable.”

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

Avinun, R. (2020). The E is in the G: Gene–environment–trait correlations and findings from genome-wide association studies. Perspectives on Psychological Science, 15(1), 81–89.

Motsinger-Reif, A. A., Reif, D. M., Akhtari, F. S., House, J. S., Campbell, C. R., Messier, K. P., … & Woychik, R. (2024). Gene-environment interactions within a precision environmental health framework. Cell Genomics, 4(7).

Abdellaoui, A., Martin, H. C., Kolk, M., Rutherford, A., Muthukrishna, M., Tropf, F. C., Mills, M. C., Zietsch, B. P., Verweij, K. J. H., & Visscher, P. M. (2025). Socio-economic status is a social construct with heritable components and genetic consequences. Nature Human Behaviour, 9(5), 864–876.

Kweon, H., Burik, C., Karlsson Linnér, R., De Vlaming, R., Okbay, A., Martschenko, D., … & Koellinger, P. (2020). Genetic fortune: Winning or losing education, income, and health.

Belsky, D. W., Domingue, B. W., Wedow, R., Arseneault, L., Boardman, J. D., Caspi, A., … Moffitt, T. E. (2018). Genetic analysis of social‑class mobility in five longitudinal studies. Proceedings of the National Academy of Sciences, 115(31), E7275–E7284.

Conley, D., & Fletcher, J. (2017). The genome factor: What the social genomics revolution reveals about ourselves, our history, and the future. Princeton University Press.

43. Sudharsanan, N., Zhang, Y., Payne, C. F., Dow, W., & Crimmins, E. (2020). Education and adult mortality in middle-income countries: Surprising gradients in six nationally representative longitudinal surveys. SSM – Population Health, 12, Article 100649. https://doi.org/10.1016/j.ssmph.2020.100649  https://www.sciencedirect.com/science/article/pii/S2468266723003067

Balaj, M., Henson, C. A., Aronsson, A., Aravkin, A., Beck, K., Degail, C., Donadello, L., Eikemo, K., Friedman, J., Giouleka, A., Gradeci, I., Hay, S. I., Jensen, M. R., McLaughlin, S. A., Mullany, E. C., O’Connell, E. M., Sripada, K., Stonkute, D., Sorensen, R. J. D., Solhaug, S., Vonen, H. D., Westby, C., Zheng, P., Mohammad, T., Eikemo, T. A., & Gakidou, E. (2024). Effects of education on adult mortality: A global systematic review and meta-analysis. The Lancet Public Health, 9(3), e155-e165. https://doi.org/10.1016/S2468-2667(23)00306-7

https://www.sciencedirect.com/science/article/pii/S235282732030286X

Inequalities in Longevity by Education in OECD Countries, 2023. https://www.oecd.org/en/publications/inequalities-in-longevity-by-education-in-oecd-countries_6b64d9cf-en.html

44. Belsky, D. W., Caspi, A., Corcoran, D. L., Sugden, K., Poulton, R., Arseneault, L., … & Moffitt, T. E. (2022). DunedinPACE, a DNA methylation biomarker of the pace of aging. elife, 11, e73420.

45. Food and Agriculture Organization. (2024). FAOSTAT food balance sheets, food supply (kcal/capita/day) [Data set, 2010–2022 release]. https://www.fao.org/faostat/en/#data/FBS — As presented and analyzed by Ritchie, H., Rosado, P., & Roser, M. (2024). Daily caloric supply per person. Our World in Data. https://ourworldindata.org/grapher/food-supply-kcal

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.

47. Roser, M., Ortiz-Ospina, E., & Ritchie, H. (2024). Life expectancy. Our World in Data. https://ourworldindata.org/life-expectancy…The roughly 19-year (high-income vs. low-income) and ≈30-year (Switzerland–Chad) gaps are the standard comparisons reported by the World Bank and Our World in Data using United Nations Population Division life-table data; underlying source: UN DESA Population Division (2024), World Population Prospects 2024.

48. 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.

49. World Wide Fund for Nature. (2016). Living Planet Report 2016. — Source for the 71% habitable / 10% glacial / 19% barren land partition used by Our World in Data to convert Alexander et al. (2016) total-land HALF to a habitable-land basis.

50. Ritchie, H. (2017, October 3). 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 — Reports the U.S.-diet HALF index at 138% of habitable land. The same article identifies multiple top-cohort WHR 2026 countries (New Zealand, Australia, Canada, and “several countries across Europe”) as exceeding 100% of habitable land on the OWID habitable-land scale; New Zealand is described as requiring “almost twice as much habitable land as we have” (≈190%).

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

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.

55. Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992.

56. Ritchie, H., & Roser, M. (2019). Half of the world’s habitable land is used for agriculture. Our World in Data.

57. Ritchie, H. (2021, March 4). If the world adopted a plant-based diet, we would reduce global agricultural land use from 4 to 1 billion hectares. Our World in Data.

58. Alexander et al. (2016), op. cit., p. 88, citing: Erb, K.-H., et al. (2009). Embodied HANPP. Ecological Economics, 69(2), 328–334; Weinzettel, J., et al. (2013). Affluence drives the global displacement of land use. Global Environmental Change, 23(2), 433–438; Yu, Y., Feng, K., & Hubacek, K. (2013). Tele-connecting local consumption to global land use. Global Environmental Change, 23(5), 1178–1186.

59. United Nations, Department of Economic and Social Affairs, Population Division. (2024). World Population Prospects 2024: Summary of Results (UN DESA/POP/2024/TR/NO. 9). https://population.un.org/wpp/ Mid-year 2025 total-population estimate, medium variant.

61. OECD. (2025). Education at a Glance 2025: OECD Indicators (Table A1.1: Educational attainment of 25–64 year-olds). https://doi.org/10.1787/c58fc9ae-en

64. United Nations Office on Drugs and Crime. (2024). Intentional Homicide Victims [Data set]. UNODC Statistics. https://data.unodc.org/datareport/hom-victim

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., Miller, E., Dworatzek, P., Basnet, N., Silva, J., Van Berkum, J. L., Halldórsdóttir, R. B., & Dyck, M. D. R. (2025). National Ecological Footprint and Biocapacity Accounts, 2025 Edition. Footprint Data Foundation, York University, and University of Iceland. https://footprint.info.yorku.ca/data/ ; mirrored at Global Footprint Network Open Data Platform, https://data.footprintnetwork.org/. Country-level Ecological Footprint of Consumption (gha/person), 2022 reference year.

68. OECD. (2025). Education at a Glance 2025; UNESCO Institute for Statistics. (2024). Educational attainment data; World Bank. (2024). Educational attainment, at least Bachelor’s or equivalent, population 25+, WDI series SE.TER.CUAT.BA.ZS.

69. Ding, Y., Hou, K., Xu, Z., Pimplaskar, A., Petter, E., Boulier, K., … & Pasaniuc, B. (2023). Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature, 1–8.

Polygenic scores are “estimates of an individual’s genetic predisposition for complex traits and diseases.”

70. Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019). The missing diversity in human genetic studies. Cell, 177(1), 26-31. “It is clear that patterns of genetic variation among populations can affect both disease risk and treatment efficacy and safety. Yet, a majority of studies still occur in European ancestry populations and the results can have limited utility across populations. This bias effectively translates into poorer disease prediction and treatment for individuals of under-represented ancestries. Importantly, studying diverse populations increases our ability to broadly understand genetic disease architectures that will, ultimately, lead to increased precision in medical care.”

71. Wyman-McCarthy, M. (2018). Perceptions of French and Spanish slave law in late eighteenth-century Britain. Journal of British Studies, 57(1), 29–52

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.

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

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.

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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