Social determinants of health

Annual pre-tax income received by all members of a household, inflation-adjusted. Primary measure of material resources for health-relevant consumption.

Highest level of formal schooling completed, operationalized as years of schooling or credential level.

Degree of dispersion in income distribution, most commonly measured by the Gini coefficient (0-1 scale).

Share of respondents answering 'yes' to 'If you were in trouble, do you have relatives or friends you can count on?' Gallup World Poll. Ranges 0-1.

Objective lack of social contacts or relationships, measured by network size or frequency of social interaction.

Average number of years a newborn is expected to live given current age-specific mortality rates.

Microcredit access has no significant effect on average household consumption, indicating microcredit does not broadly raise living standards in the short to medium term

Expanding microcredit access in the Philippines produces modest positive effects on business profits and scale but does not translate into higher household income

Savings products may be more important than credit for the poor, as they provide consumption smoothing and asset building without the risks of over-indebtedness

A consistent social gradient in health runs from top to bottom of the occupational hierarchy; higher social position predicts better health outcomes.

Income, insurance, and background account for ~30% of the education-health gradient; knowledge and cognitive ability ~30%; social networks ~10%.

Gap in life expectancy between richest 1% and poorest 1% of Americans is 14.6 years for men and 10.1 years for women.

Neighborhood-level characteristics in childhood predict adult income rank with substantial explanatory power (R-squared = 0.5 at census tract level), demonstrating that geography is a powerful determinant of economic mobility.

Educational attainment is the strongest individual-level predictor of income, with a correlation of r=0.55 between bachelor's degree attainment rates and median household income across counties.

The rural-urban income divide continues to widen: metropolitan areas have approximately 2x higher median household income than non-metropolitan areas, with the gap growing over the past two decades.

Upward mobility varies dramatically by race and geography: Black boys raised in the same census tract as white boys earn substantially less as adults, suggesting neighborhood effects interact with race to produce divergent outcomes.

Top 1% income share in the United States roughly doubled from approximately 8% in 1970 to approximately 17% by 2000, representing a dramatic U-shaped pattern over the 20th century with high concentration pre-WWII, compression mid-century, and reconcentration since the 1980s.

Global inequality is driven by two forces moving in opposite directions: between-country inequality is declining (primarily due to China and India's growth) while within-country inequality is rising in most nations, producing complex distributional patterns.

The 'elephant curve' of global income growth (1988-2008) shows strong gains for the global middle class (percentiles 30-60, mainly Asian) and the global top 1%, but stagnation for the lower-middle class of rich countries (percentiles 75-90), explaining populist discontent in developed nations.

Inequality follows 'Kuznets waves' — cyclical patterns of rising and falling inequality driven by technological change, globalization, and policy responses, rather than the single inverted-U curve originally proposed by Kuznets.

Areas with less residential segregation, less income inequality, better schools, stronger social capital, and more stable families have significantly higher intergenerational mobility. These five factors are the strongest correlates of upward mobility across US commuting zones.

Social support is a strong positive predictor of life satisfaction. A one-unit increase in the social support proportion is associated with approximately +1.4 points on the Cantril ladder.

People with fewest social ties had age-adjusted relative mortality risks of 2.3 (men) and 2.8 (women) over nine years, independent of SES and health behaviors.

Meta-analysis of 70 studies: social isolation (OR=1.29), loneliness (OR=1.26), and living alone (OR=1.32) each predicted elevated all-cause mortality.

Health spending has strong positive effect on life expectancy — each 10% increase in health spending associated with approximately 0.3 year gain in life expectancy at birth.

Returns to health spending exhibit strong diminishing returns — high-income countries get less mortality reduction per additional dollar spent.

Health spending has strong positive effect on life expectancy — each 10% increase associated with ~0.3 year gain

Returns to health spending exhibit strong diminishing returns at high income levels

Challenge to finding #755: OLS regression on 50 country-year observations (2000-2020) shows health_expenditure_per_capita has a strong positive effect on life_expectancy (β=+0.76 standardized, p<.001, R²=0.58), contradicting the null finding | Reasoning: Filmer & Pritchett controlled for income and education which absorb most of the health spending variation. Without those controls, the raw relationship is strongly positive. The discrepancy reflects an endogeneity debate: richer countries spend more on health AND have better health outcomes, so the causal effect of spending alone is unclear. | Data comparison: different_data | Method comparison: different_method | Method difference: Bivariate OLS without income/education controls vs multivariate with controls

Mortality decline in the 20th century was driven primarily by public health measures, nutrition, and income growth — not by medical spending.

Healthy life expectancy positively predicts life satisfaction. Each additional year of healthy life expectancy at birth is associated with approximately +0.03 points on the Cantril ladder.

Basic analysis workflow for the social_determinants_of_health domain.

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