This dataset contains replication files for "The Association Between Income and Life Expectancy in the United States, 2001-2014" by Augustin Bergeron, Raj Chetty, David Cutler, Benjamin Scuderi, Michael Stepner, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/lifeexpectancy/. A summary of the related publication follows. How can we reduce socioeconomic disparities in health outcomes? Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals. Our findings show that disparities in life expectancy are not inevitable. There are cities throughout America — from New York to San Francisco to Birmingham, AL — where gaps in life expectancy are relatively small or are narrowing over time. Replicating these successes more broadly will require targeted local efforts, focusing on improving health behaviors among the poor in cities such as Las Vegas and Detroit. Our findings also imply that federal programs such as Social Security and Medicare are less redistributive than they might appear because low-income individuals obtain these benefits for significantly fewer years than high-income individuals, especially in cities like Detroit. Going forward, the challenge is to understand the mechanisms that lead to better health and longevity for low-income individuals in some parts of the U.S. To facilitate future research and monitor local progress, we have posted annual statistics on life expectancy by income group and geographic area (state, CZ, and county) at The Health Inequality Project website. Using these data, researchers will be able to study why certain places have high or improving levels of life expectancy and ultimately apply these lessons to reduce health disparities in other parts of the country.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

This statistic shows the average life expectancy in North America for those born in 2022, by gender and region. In Canada, the average life expectancy was 80 years for males and 84 years for females.

Life expectancy in North America

Of those considered in this statistic, the life expectancy of female Canadian infants born in 2021 was the longest, at 84 years. Female infants born in America that year had a similarly high life expectancy of 81 years. Male infants, meanwhile, had lower life expectancies of 80 years (Canada) and 76 years (USA).

Compare this to the worldwide life expectancy for babies born in 2021: 75 years for women and 71 years for men. Of continents worldwide, North America ranks equal first in terms of life expectancy of (77 years for men and 81 years for women). Life expectancy is lowest in Africa at just 63 years and 66 years for males and females respectively. Japan is the country with the highest life expectancy worldwide for babies born in 2020.

Life expectancy is calculated according to current mortality rates of the population in question. Global variations in life expectancy are caused by differences in medical care, public health and diet, and reflect global inequalities in economic circumstances. Africa’s low life expectancy, for example, can be attributed in part to the AIDS epidemic. In 2019, around 72,000 people died of AIDS in South Africa, the largest amount worldwide. Nigeria, Tanzania and India were also high on the list of countries ranked by AIDS deaths that year. Likewise, Africa has by far the highest rate of mortality by communicable disease (i.e. AIDS, neglected tropics diseases, malaria and tuberculosis).

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

These test data files were used to debug the code used in the following study: "Is the Gini Coefficient Enough? A Microeconomic Data Decomposition Study."

List of test data: 1. it14ih.dta - household-level dataset for Italy. 2. it14ip.dta - person-level dataset for Italy. 3. mx16ih.dta - household-level dataset for Mexico. 4. mx16ip.dta - person-level dataset for Mexico. 5. us18ih.dta - household-level dataset for the USA. 6. us18ip.dta - person-level dataset for the USA.

All files can be used for testing/debugging of the following scripts: lis_theil.R, lis_scv.R, lis_theil_functions.R, lis_scv_functions.R.

These datasets were donloaded from the following website. https://www.lisdatacenter.org/resources/self-teaching/.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

BackgroundThe gap between the highest and lowest life expectancies for race-county combinations in the United States is over 35 y. We divided the race-county combinations of the US population into eight distinct groups, referred to as the “eight Americas,” to explore the causes of the disparities that can inform specific public health intervention policies and programs. Methods and FindingsThe eight Americas were defined based on race, location of the county of residence, population density, race-specific county-level per capita income, and cumulative homicide rate. Data sources for population and mortality figures were the Bureau of the Census and the National Center for Health Statistics. We estimated life expectancy, the risk of mortality from specific diseases, health insurance, and health-care utilization for the eight Americas. The life expectancy gap between the 3.4 million high-risk urban black males and the 5.6 million Asian females was 20.7 y in 2001. Within the sexes, the life expectancy gap between the best-off and the worst-off groups was 15.4 y for males (Asians versus high-risk urban blacks) and 12.8 y for females (Asians versus low-income southern rural blacks). Mortality disparities among the eight Americas were largest for young (15–44 y) and middle-aged (45–59 y) adults, especially for men. The disparities were caused primarily by a number of chronic diseases and injuries with well-established risk factors. Between 1982 and 2001, the ordering of life expectancy among the eight Americas and the absolute difference between the advantaged and disadvantaged groups remained largely unchanged. Self-reported health plan coverage was lowest for western Native Americans and low-income southern rural blacks. Crude self-reported health-care utilization, however, was slightly higher for the more disadvantaged populations. ConclusionsDisparities in mortality across the eight Americas, each consisting of millions or tens of millions of Americans, are enormous by all international standards. The observed disparities in life expectancy cannot be explained by race, income, or basic health-care access and utilization alone. Because policies aimed at reducing fundamental socioeconomic inequalities are currently practically absent in the US, health disparities will have to be at least partly addressed through public health strategies that reduce risk factors for chronic diseases and injuries.

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Methods Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance.

Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071.

Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/.

Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067.

Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013.

Please see README.txt for further information

In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

BackgroundGun violence has shortened the average life expectancy of Americans, and better knowledge about the root causes of gun violence is crucial to its prevention. While some empirical evidence exists regarding the impacts of social and economic factors on violence and firearm homicide rates, to the author’s knowledge, there has yet to be a comprehensive and comparative lagged, multilevel investigation of major social determinants of health in relation to firearm homicides and mass shootings.Methods and findingsThis study used negative binomial regression models and geolocated gun homicide incident data from January 1, 2015, to December 31, 2015, to explore and compare the independent associations of key state-, county-, and neighborhood-level social determinants of health—social mobility, social capital, income inequality, racial and economic segregation, and social spending—with neighborhood firearm-related homicides and mass shootings in the United States, accounting for relevant state firearm laws and a variety of state, county, and neighborhood (census tract [CT]) characteristics. Latitude and longitude coordinates on firearm-related deaths were previously collected by the Gun Violence Archive, and then linked by the British newspaper The Guardian to CTs according to 2010 Census geographies. The study population consisted of all 74,134 CTs as defined for the 2010 Census in the 48 states of the contiguous US. The final sample spanned 70,579 CTs, containing an estimated 314,247,908 individuals, or 98% of the total US population in 2015. The analyses were based on 13,060 firearm-related deaths in 2015, with 11,244 non-mass shootings taking place in 8,673 CTs and 141 mass shootings occurring in 138 CTs. For area-level social determinants, lag periods of 3 to 17 years were examined based on existing theory, empirical evidence, and data availability. County-level institutional social capital (levels of trust in institutions), social mobility, income inequality, and public welfare spending exhibited robust relationships with CT-level gun homicide rates and the total numbers of combined non-mass and mass shooting homicide incidents and non-mass shooting homicide incidents alone. A 1–standard deviation (SD) increase in institutional social capital was linked to a 19% reduction in the homicide rate (incidence rate ratio [IRR] = 0.81, 95% CI 0.73–0.91, p < 0.001) and a 17% decrease in the number of firearm homicide incidents (IRR = 0.83, 95% CI 0.73–0.95, p = 0.01). Upward social mobility was related to a 25% reduction in the gun homicide rate (IRR = 0.75, 95% CI 0.66–0.86, p < 0.001) and a 24% decrease in the number of homicide incidents (IRR = 0.76, 95% CI 0.67–0.87, p < 0.001). Meanwhile, 1-SD increases in the neighborhood percentages of residents in poverty and males living alone were associated with 26%–27% and 12% higher homicide rates, respectively. Study limitations include possible residual confounding by factors at the individual/household level, and lack of disaggregation of gun homicide data by gender and race/ethnicity.ConclusionsThis study finds that the rich–poor gap, level of citizens’ trust in institutions, economic opportunity, and public welfare spending are all related to firearm homicide rates in the US. Further establishing the causal nature of these associations and modifying these social determinants may help to address the growing gun violence epidemic and reverse recent life expectancy declines among Americans.

These datasets contain aggregated expenditure and demographic variables, that are derived from the Family Expenditure Survey (GN 33057), the Expenditure and Food Survey/Living Costs and Food Survey (GN 33334), the General Household Survey (GN 33090) and the Health Survey for England (GN 33261). These files can be used to replicate the results in the paper Banks, J., Blundell, R., Levell, P. and Smith, J. "Life-Cycle Consumption Patterns at Older Ages in the US and the UK: Can Medical Expenditures Explain the Difference?", AEJ: Economic Policy (August, 2019) (see related resources).

This proposal sets out a major new programme of research that will lead to significant scientific progress and policy impact. Building on the expertise developed at the Centre and at IFS, we will use the developments in econometric techniques and data availability, including linked survey and administrative data, to push our research agenda in exciting new directions. The focus of the work will be on: a) Consumers and markets. We will use insights from behavioural economics and robust methods to understand within-household behaviour and we will explore the relationships between government policy, firm behaviour and outcomes for consumers. This work has the potential to transform our understanding of the effects of policy interventions that either change the relative prices of the goods consumers buy (e.g. taxes on alcohol, green levies, sugar taxes) or try to change consumers' preferences (e.g. through information campaigns or restrictions on advertising). b) Inequality, risk and insurance. Understanding the determinants of inequality is central to our agenda. We will focus on understanding inequality across the life cycle and across and within generations. We will explore the role of housing, of insurance and of market and non-market mechanisms in managing risk and uncertainty. The availability of new administrative data linked to existing surveys will allow us to examine the dynamics of inequality and the impact of alternative policies. In particular, we will focus on the role of wealth and bequests in generating within-cohort inequality among the younger generations and we will investigate how uncertainty is resolved over the life cycle and how this affects the degree of insurance provided by taxes and benefits at different ages. c) Public finances and taxation. Focusing on high earners and multinational companies, we will use newly-available data to throw new light on risks to the public finances in the UK from these vital but increasingly risky sources of revenue. We will also develop a programme of work that focuses on the particular issues facing tax design in middle-income countries. d) Evolution of human capital over the life cycle. We aim to make major strides in understanding the process of formation of human capital from the early years to young adulthood, how human capital is rewarded in the labour market, how it is linked to labour supply and productivity, and how the evolution of health and well-being interacts with labour supply and other outcomes in later years. These issues are intricately related and we envisage a joined-up programme of work that will provide new answers to some of the most important questions currently facing policymakers. How do people make decisions over savings, nutrition, education and labour supply and how can government influence those decisions? What is driving increased levels of income inequality and how might interventions in education and through the tax and welfare system ameliorate them, and at what cost? How should governments respond to the pressures on corporate and individual tax revenues created by increasing globalisation? What drives decisions over pension savings, health behaviours and retirement decisions and how should governments design policy in the face of an ageing population? In answering these questions, we will make use of the unique expertise and data resources brought together at the Centre. Crucially, our intention is also to take a consistent approach in which we will model the determinants of individual decisions over the life course and the interactions between economic actors; we will model behavioural 'biases' and market frictions; we will use a combination of available data, randomised controlled trials and structural modelling to understand not just the effect of policies but also what drives that effect and hence what might be the effect of other policies; and we will develop new data and measurement tools.

Over the past 30 years, the birth rate in the United States has been steadily declining, and in 2023, there were 10.7 births per 1,000 of the population. In 1990, this figure stood at 16.7 births per 1,000 of the population. Demographics have an impact The average birth rate in the U.S. may be falling, but when broken down along ethnic and economic lines, a different picture is painted: Native Hawaiian and other Pacific Islander women saw the highest birth rate in 2022 among all ethnicities, and Asian women and white women both saw the lowest birth rate. Additionally, the higher the family income, the lower the birth rate; families making between 15,000 and 24,999 U.S. dollars annually had the highest birth rate of any income bracket in the States. Life expectancy at birth In addition to the declining birth rate in the U.S., the total life expectancy at birth has also reached its lowest value recently. Studies have shown that the life expectancy of both men and women in the United States has been declining over the last few years. Declines in life expectancy, like declines in birth rates, may indicate that there are social and economic factors negatively influencing the overall population health and well-being of the country.

This dataset contains spatially explicit, annual socioeconomic indicator projections for Germany at the district (NUTS-3; "Landkreise") level from 2020 to 2100. It provides multidimensional trajectories covering human, social, financial, and manufactured capital, including demographic dynamics, education, income, employment, inequality, and social cohesion. Developed through a mixed-methods framework integrating historical trend analysis, participatory scenario building, and quantitative projection, the dataset supports integrated climate-land system modelling, with a focus on informing land use, climate mitigation, and adaptation strategies, particularly carbon dioxide removal.

The dataset contains the following 10 socioeconomic indicators (short indicator name used in file names and data columns):

• Population density (pop)

• Life expectancy (life_exp)

• Secondary education (sec_edu)

• Household income (income)

• Employment rate (employment)

• Crimes per person (crime)

• Social cohesion (cohesion)

• Income inequality (inequality)

• Share of urban and traffic area (built_up)

• Gross value added (manufacturing) (gva_manu)

The indicator projections are provided for six national scenarios that area based on the Shared Socioeconomic Scenarios (SSPs) and tailored to a Germany-specific land use focus: SSP1a (Green Growth), SSP1b (Degrowth), SSP2 (Middle of the Road), SSP3 (Regional Rivalry), SSP4 (Inequality), and SSP5 (Fossil-fuelled Development). Here some short descriptions of the narratives (see Gulde et al., in review, for more detail):

• SSP1a (Green Growth): With strong government backing and sustainability standards, Germany becomes a leader in green economics. Traditional industries such as automotive and steel decarbonise successfully, while new markets thrive through innovation, research, and an equitable education system. Society values sustainability highly, with trust in democracy, strong welfare structures, and active civic participation. Consumption remains high but shifts towards environmentally friendly goods, while agriculture and forestry adapt early to sustainable practices.
• SSP1b (Degrowth): This path reflects a deliberate move away from growth, with well-being, ecological restoration, and social justice at its core. Bottom-up initiatives face resistance at first but eventually reshape society through equity-oriented policies, redistribution, and support for unpaid care work. Sufficiency-focused lifestyles and strict regulation drastically reduce consumption and strengthen the common good while prioritising opportunities for the Global South. Local and circular economies expand, traditional industries rapidly decarbonise, and unsustainable sectors are phased out, reducing dependence on global trade.
• SSP2 (Middle of the Road): Germany largely continues on its current trajectory, facing mounting demographic challenges, labour shortages, and rising inequalities. Policy decisions favour maintaining established structures, and technological advances concentrate on traditional industries, while education and digitalisation lag. Social cohesion weakens under growing polarisation, hampering effective reforms. Sustainability awareness grows but remains limited by resource-intensive lifestyles, bureaucratic inefficiencies, and slow progress in the energy transition.
• SSP3 (Regional Rivalry): Rising insecurity and inadequate political responses fuel distrust in democracy and growing nationalist sentiment. Protectionist policies restrict trade, leading to economic stagnation, declining prosperity, and worsening inequalities. Industry is re-shored to Germany, fossil fuels are reactivated, and investment priorities shift to defence and security. Consumption, services, and migration decline, while employment increasingly shifts towards agriculture, forestry, and industry in a fragmented, inward-looking society.
• SSP4 (Inequality): Rapid technological progress and automation create new markets, boosting Germany’s global competitiveness in sectors like robotics, renewables, and IT. However, economic elites and lobby groups increasingly dominate politics, weakening democracy and curbing public investment in education and infrastructure. Structural change brings job losses in traditional industries, eroding the middle class, widening inequalities, and driving rural depopulation. Advanced technologies reshape agriculture, meat becomes a luxury item, and Germany strengthens its position in globalised markets at the expense of the Global South.
• SSP5 (Fossil-fuelled Development): Initially hindered by energy and demographic pressures, Germany restores growth by abandoning environmental standards and investing heavily in fossil fuels. Subsidies, alongside a strong STEM focus in education, drive industrial innovation, while environmental awareness fades. Traditional industries benefit from deregulation, lifestyles remain resource-intensive, and consumption rises sharply, including demand for meat and luxury goods. Despite persisting inequalities, growing prosperity, robust institutions, and social spending sustain social stability and trust in democracy.

By capturing regional diversity and interdependencies among socioeconomic dimensions, these projections enable more nuanced assessments of equity, resilience, and sustainability under alternative Shared Socioeconomic Pathway (SSP)-aligned futures. The dataset advances beyond conventional aggregate GDP and population indicators by providing high-resolution, policy-relevant insights into persistent regional disparities and scenario-specific developmental pathways. It is intended for researchers, policymakers, and modelers working on climate action, land-use planning, and socioeconomic impacts of climate change.

The dataset is openly available under a CC-BY 4.0 license to facilitate reuse, validation, and integration into diverse climate and land system modelling frameworks. Accompanying documentation includes methodological details, indicator definitions, and scenario narratives to support proper application and interpretation.

Dataset is linked to a publication (currently in review):

Winkler, K., Witting, M., Gulde, F., Garschagen, M., Pongratz, P., Rounsevell, M. (in review). Spatiotemporal scenarios of socioeconomic futures in Germany. Frontiers in Climate.

County Buddy is a dataset detailing the presence, count, and institutions of special populations (incarcerated individuals, college students, military personnel, and Native Americans) at the U.S. county and census tract levels. It offers geographic and demographic context to help explain variation in socio-economic indicators like life expectancy, income, and education.

We conduct a novel investigation into the effects of uncertain health shocks and medical costs on the life cycle consumption, housing, and saving decisions. Our model aids in understanding the role of health shocks and medical costs after age 70 in explaining the lack of wealth and housing decumulation during retirement. We utilize a comprehensive life-cycle model that includes housing, as well as shocks to house price, labor income, and health. Our model could be useful for policy evaluation and future studies concerning older adults. Our first contribution to the previous literature is modeling the whole adult life-cycle. This enables us to determine whether decisions in youth and middle age are influenced by anticipated health shocks in old age. Our second contribution is modeling housing explicitly with health shocks. Conclusions regarding the savings puzzle may be significantly influenced by the explicit modeling of housing. We develop a more realistic model by relaxing some of the assumptions made in previous studies. We find that health shocks motivate the household to accumulate higher wealth before retirement. Moreover, as health shocks become more severe, individuals reduce their consumption and decumulate less wealth in old age. Health shocks help explain the flat trends observed in the housing and wealth profiles of older adults. The possible health shocks after age 70 affect the decisions in young and middle ages only marginally. The wealth profile in middle and old age is affected by health shocks.

The rising preference for community and home care services has contributed significantly to industry growth and performance. Baby boomers entering the later phase of adulthood, increased life expectancy and the greater incidence of disabilities in individuals of advancing age have contributed to a higher demand for long-term care services such as adult day care and nonmedical home care services. Home aid has become the dominant sector in long-term care since it provides independence and comfort, and adult day care centers offer a place for community interaction. The growth in homecare services weathered the impact of the pandemic, and industry-wide revenue has been growing at a CAGR of 2.0% through 2025 to total $80.1 billion, when revenue will climb by an estimated 3.4%. The industry has faced challenges as a fragmented market. Out-of-market competition from residential care providers and the increased presence of franchises challenge industry pricing and high service offerings for many needing services. However, telemedicine and wearable technology have changed the scope and quality of services. They can abate the need for residential services by providing remote health monitoring, offering virtual consultations and ensuring continuous care, enabling seniors to receive support at home. Their adoption will depend on the costs of the technology and continued funding support by Medicaid and Medicare. The continued need and preference for nonmedical home aid services will be a significant future demand driver; however, with rising wages, industry revenue will be significantly impacted by the level of funding for older adults, children and individuals with disabilities. The changes to Medicaid, Medicare funding and, in particular, State Home and Community-Based Services waivers that help reduce costs of home services compared to residential facilities will impact future funding for services and industry revenue. A healthy economy will support the payment for services not covered by government programs, and forecasts for strong per capita disposable income growth will support out-of-pocket service payments. While government funding cutbacks and staff layoffs could hamper the sector's future growth and profitability, industry revenue is forecast to strengthen at a CAGR of 2.2% through 2030 to reach $89.4 billion, with profit remaining stable.

In 2024, Switzerland led the ranking of countries with the highest average wealth per adult, with approximately ******* U.S. dollars per person. The United States was ranked second with an average wealth of around ******* U.S. dollars per adult, followed by Hong Kong SAR. However, the figures do not show the actual distribution of wealth. The Gini index shows wealth disparities in countries worldwide. Does wealth guarantee a longer life? As the adage goes, “money can’t buy you happiness,” yet wealth and income are continuously correlated to the quality of life of individuals in different countries around the world. While greater levels of wealth may not guarantee a higher quality of life, it certainly increases an individual’s chances of having a longer one. Although they do not show the whole picture, life expectancy at birth is higher in the wealthier world regions. Does money bring happiness? A number of the world’s happiest nations also feature in the list of those countries for which average income was highest. Finland, however, which was the happiest country worldwide in 2022, is missing from the list of the top twenty countries with the highest wealth per adult. As such, the explanation for this may be the fact that a larger proportion of the population has access to a high-income relative to global levels. Measures of quality of life Criticism of the use of income or wealth as a proxy for quality of life led to the creation of the United Nations’ Human Development Index. Although income is included within the index, it also has other factors taken into account, such as health and education. As such, the countries with the highest human development index can be correlated to those with the highest income levels. That said, none of the above measures seek to assess the physical and mental environmental impact of a high quality of life sourced through high incomes. The happy planet index demonstrates that the inclusion of experienced well-being and ecological footprint in place of income and other proxies for quality of life results in many of the world’s materially poorer nations being included in the happiest.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.