Historical chart and dataset showing U.K. life expectancy by year from 1950 to 2025.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

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 ...).

Historical chart and dataset showing World life expectancy by year from 1950 to 2025.

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

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

Analysis of ‘Life Expectancy vs GDP, 1950-2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/luxoloshilofunde/life-expectancy-vs-gdp-19502018 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Life expectancy at birth is defined as the average number of years that a newborn could expect to live if he or she were to pass through life subject to the age-specific mortality rates of a given period. The years are from 1950 to 2018.

Content

For regional- and global-level data pre-1950, data from a study by Riley was used, which draws from over 700 sources to estimate life expectancy at birth from 1800 to 2001.

Riley estimated life expectancy before 1800, which he calls "the pre-health transition period". "Health transitions began in different countries in different periods, as early as the 1770s in Denmark and as late as the 1970s in some countries of sub-Saharan Africa". As such, for the sake of consistency, we have assigned the period before the health transition to the year 1770. "The life expectancy values employed are averages of estimates for the period before the beginning of the transitions for countries within that region. ... This period has presumably the weakest basis, the largest margin of error, and the simplest method of deriving an estimate."

For country-level data pre-1950, Clio Infra's dataset was used, compiled by Zijdeman and Ribeira da Silva (2015).

For country-, regional- and global-level data post-1950, data published by the United Nations Population Division was used, since they are updated every year. This is possible because Riley writes that "for 1950-2001, I have drawn life expectancy estimates chiefly from various sources provided by the United Nations, the World Bank’s World Development Indicators, and the Human Mortality Database".

For the Americas from 1950-2015, the population-weighted average of Northern America and Latin America and the Caribbean was taken, using UN Population Division estimates of population size.

Acknowledgements

Life expectancy:

Data publisher's source: https://www.lifetable.de/RileyBib.pdf Data published by: James C. Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Issue Population and Development Review. Population and Development Review. Volume 31, Issue 3, pages 537–543, September 2005., Zijdeman, Richard; Ribeira da Silva, Filipa, 2015, "Life Expectancy at Birth (Total)", http://hdl.handle.net/10622/LKYT53, IISH Dataverse, V1, and UN Population Division (2019) Link: https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/LKYT53, http://onlinelibrary.wiley.com/doi/10.1111/j.1728-4457.2005.00083.x/epdf, https://population.un.org/wpp/Download/Standard/Population/ Dataset: https://ourworldindata.org/life-expectancy

GDP per capita:

Data publisher's source: The Maddison Project Database is based on the work of many researchers that have produced estimates of economic growth for individual countries. Data published by: Bolt, Jutta and Jan Luiten van Zanden (2020), “Maddison style estimates of the evolution of the world economy. A new 2020 update”. Link: https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020 Dataset: https://ourworldindata.org/life-expectancy

Inspiration

The life expectancy vs GDP per capita analysis.

--- Original source retains full ownership of the source dataset ---

This table contains 2754 series, with data for years 2005/2007 - 2012/2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (153 items: Canada; Newfoundland and Labrador; Eastern Regional Integrated Health Authority, Newfoundland and Labrador; Central Regional Integrated Health Authority, Newfoundland and Labrador; ...); Age group (2 items: At birth; At age 65); Sex (3 items: Both sexes; Males; Females); Characteristics (3 items: Life expectancy; Low 95% confidence interval, life expectancy; High 95% confidence interval, life expectancy).

This indicator measures inequalities in life expectancy at birth within England as a whole, each English region, and each local authority. Life expectancy at birth is calculated for each deprivation decile of lower super output areas within each area and then the slope index of inequality (SII) is calculated based on these figures.

The SII is a measure of the social gradient in life expectancy, i.e., how much life expectancy varies with deprivation. It takes account of health inequalities across the whole range of deprivation within each area and summarises this in a single number. This represents the range in years of life expectancy across the social gradient from most to least deprived, based on a statistical analysis of the relationship between life expectancy and deprivation across all deprivation deciles.

Life expectancy at birth is a measure of the average number of years a person would expect to live based on contemporary mortality rates. For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.

The SII for England and for regions have been presented alongside the local authority figures in order to improve the display of the indicators on the overview page. However, they should not be considered as comparators for the local authority figures. The SII for England takes account of the full range of deprivation and mortality across the whole country. This does not therefore provide a suitable benchmark with which to compare local authority results, which take into account the range of deprivation and mortality within much smaller geographies.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:

Breakdown by race/ethnicity in pop-up:

There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.

Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

People in different subgroups age at different rates. Surveys containing biomarkers can be used to assess these subgroup differences. We illustrate this using hand-grip strength to produce an easily interpretable, physical-based measure that allows us to compare characteristic-based ages across educational subgroups in the United States. Hand-grip strength has been shown to be a good predictor of future mortality and morbidity, and therefore a useful indicator of population aging. Data from the Health and Retirement Survey (HRS) were used. Two education subgroups were distinguished, those with less than a high school diploma and those with more education. Regressions on hand-grip strength were run for each sex and race using age and education, their interactions and other covariates as independent variables. Ages of identical mean hand-grip strength across education groups were compared for people in the age range 60 to 80. The hand-grip strength of 65 year old white males with less education was the equivalent to that of 69.6 (68.2, 70.9) year old white men with more education, indicating that the more educated men had aged more slowly. This is a constant characteristic age, as defined in the Sanderson and Scherbov article “The characteristics approach to the measurement of population aging” published 2013 in Population and Development Review. Sixty-five year old white females with less education had the same average hand-grip strength as 69.4 (68.2, 70.7) year old white women with more education. African-American women at ages 60 and 65 with more education also aged more slowly than their less educated counterparts. African American men with more education aged at about the same rate as those with less education. This paper expands the toolkit of those interested in population aging by showing how survey data can be used to measure the differential extent of aging across subpopulations.

Historical chart and dataset showing Nigeria life expectancy by year from 1950 to 2025.

Historical chart and dataset showing South Korea life expectancy by year from 1950 to 2025.

Context:

The World Happiness Report is a landmark survey of the state of global happiness . The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

Content:

Here's a brief explanation of each column in the dataset: 1. Country name: Name of the country. 2. Regional indicator: Region to which the country belongs. 3. Ladder score: The happiness score for each country, based on responses to the Cantril Ladder question that asks respondents to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. 4. Upper whisker: Upper bound of the happiness score. 5. Lower whisker: Lower bound of the happiness score. 6. Log GDP per capita: The natural logarithm of the country's GDP per capita, adjusted for purchasing power parity (PPP) to account for differences in the cost of living between countries. 7. Social support: The national average of binary responses(either 0 or 1 representing No/Yes) to the question about having relatives or friends to count on in times of trouble. 8. Healthy life expectancy: The average number of years a newborn infant would live in good health, based on mortality rates and life expectancy at different ages. 9. Freedom to make life choices: The national average of responses to the question about satisfaction with freedom to choose what to do with one's life. 10. Generosity: The residual of regressing the national average of responses to the question about donating money to charity on GDP per capita. 11. Perceptions of corruption: The national average of survey responses to questions about the perceived extent of corruption in the government and businesses. 12. Dystopia + residual: Dystopia is an imaginary country with the world’s least-happy people, used as a benchmark for comparison. The dystopia + residual score is a combination of the Dystopia score and the unexplained residual for each country, ensuring that the combined score is always positive. Each of these factors contributes to the overall happiness score, but the Dystopia + residual value is a benchmark that ensures no country has a lower score than the hypothetical Dystopia. 13. Positive affect: The national average of responses to questions about positive emotions experienced yesterday. 14. Negative affect: The national average of responses to questions about negative emotions experienced yesterday.

Created with a 500 meter side hexagon grid, we undertook a regression analysis creating a correlation matrix utilising a number of demographic indicators from the Local Insight OCSI platform. This dataset is showing the distribution of the metrics that were found to have the strongest relationships, with the base comparison metric of Indices of Deprivation 2019 income deprivation affecting older people. This dataset contains the following metrics: IoD 2019 Income Deprivation Affecting Older People (IDAOPI) Score (rate) - The Indices of Deprivation (IoD) 2019 Income Deprivation Affecting Older People Index captures deprivation affecting older people defined as those adults aged 60 or over receiving Income Support or income-based Jobseekers Allowance or income-based Employment and Support Allowance or Pension Credit (Guarantee) or Universal Credit (in the 'Searching for work', 'No work requirements', 'Planning for work', 'Working with requirements' and 'Preparing for work' conditionality groups) or families not in receipt of these benefits but in receipt of Working Tax Credit or Child Tax Credit with an equivalised income (excluding housing benefit) below 60 per cent of the national median before housing costs. Asylum seekers aged 60 and over are not included in the Income Deprivation Affecting Older People Index. Rate calculated as = (ID 2019 Income Deprivation Affecting Older People Index (IDAOPI) numerator)/(ID 2019 Older population aged 60 and over: mid 2015 (excluding prisoners))*100.Pension Credit claimants who are single - Shows the proportion of people receiving Pension Credit who are single (as a % of all of pensionable age). Pension Credit provides financial help for people aged 60 or over whose income is below a certain level set by the law. Rate calculated as = (Pension Credit claimants, single)/(Population aged 65+)*100.Pension Credit claimants, Guarantee Element - Shows the proportion of people of retirement age receiving Pension Credit Guarantee Element. Pension Credit provides financial help for people aged 60 or over whose income is below a certain level set by the law. The Guarantee Element is payable to tops up incomes that are below a minimum threshold. Rate calculated as = (Pension Credit claimants, Guarantee Element)/(Population aged 65+)*100.Working-age DWP benefit claimants aged 50 and over - Shows the proportion of people aged 50-64 receiving DWP benefits. DWP Benefits are benefits payable to all people who need additional financial support due to low income, worklessness, poor health, caring responsibilities, bereavement or disability. The following benefits are included: Bereavement Benefit, Carers Allowance, Disability Living Allowance, Incapacity Benefit/Severe Disablement Allowance, Income Support, Jobseekers Allowance, Pension Credit and Widows Benefit. Figure are derived from 100% sample of administrative records from the Work and Pensions Longitudinal Study (WPLS), with all clients receiving more than one benefit counted only by their primary reason for interacting with the benefits system (to avoid double counting). Universal Credit (UC) and Personal Independence Payment (PIP) started to replace the benefits included in this measure from April 2013 when new Jobseeker's Allowance and Disability Living Allowance claimants started to move onto the new benefits in selected geographical areas. This rollout intensified from March 2016 onwards to capture all of the other Working age DWP Benefits. As UC and PIP are not included in this measure it no longer represent a complete count of working age people receiving DWP Benefits. As a result the measure was discontinued in November 2016. Rate calculated as = (Working-age DWP benefit claimants aged 50 and over) /(Population aged 50+)*100.People with numeracy skills at entry level 1 or below (2011) (%) - Shows the proportion of people with numeracy skills at entry level 1 or below. The Skills for Life Survey 2011 was commissioned by the Department for Business Innovation and Skills. The survey aimed to produce a national profile of adult literacy, numeracy and Information and Communication Technology (ICT) skills, and to assess the impact different skills had on people's lives. Each figure is a mean estimate of the number of adults with each skill level (or who do / do not speak English as a first language). The survey was conducted at regional level as a part interview part questionnaire. The interview comprised a background questionnaire followed by a pre-assigned random combination of two of the three skills assessments: literacy, numeracy and ICT. The background questionnaire was designed to collect a broad set of relevant demographic and behavioural data. This demographic data was used to model the information down to neighbourhood level using the neighbourhood characteristics of each MSOA to create a likely average skill level of the population within each MSOA. survey. Respondents who completed the questions allocated to the literacy and numeracy assessments were assigned to one of the five lowest levels of the National Qualifications Framework: Entry Level 1 or below; Entry Level 2; Entry Level 3; Level 1; or Level 2 or above. Each figure is a mean estimate of the number of adults with each skill level (or who do / do not speak English as a first language).IoD 2015 Housing affordability indicator -Social Grade (N-SEC): 8. Never worked and long-term unemployed - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 8. Never worked and long-term unemployed. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Never worked and long-term unemployed (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Female healthy life expectancy at birth - Female healthy life expectancy at birth. Healthy life expectancy (HLE) is the average number of years that an individual might expect to live in "good" health in their lifetime. The 'good' health state used for estimation of HLE was based on self-reports of general health at the 2011 Census; specifically those reporting their general health as 'very good' or 'good' were defined as in 'Good' health in this context. The HLE estimates are a snapshot of the health status of the population, based on self-reported health status and mortality rates for each area in that period. They are not a guide to how long someone will actually expect to live in "good" health, both because mortality rates and levels of health status are likely to change in the future, and because many of those born in an area will live elsewhere for at least part of their lives.Sport England Market Segmentation: Pub League Team Mates - Shows the proportion of people living in the area that are classified as Pub League Team Mates in the Sports Market Segmentation tool developed by Sport England. The Pub League Team Mates classification group are predominantly aged 36-45 are a mix of married/single child and childless and likely to be engaged in a vocational job. For more details about the characteristics of this group see http://segments.sportengland.org/pdf/penPortrait-9.pdf. Sports Market Segmentation is a web-based tool developed by Sport England to help all those delivering sport to better understand their local markets and target them more effectively.IoD 2010 Income Domain, score - The Indices of Deprivation (IoD) 2010 Income Deprivation Domain measures the proportion of the population in an area experiencing deprivation relating to low income. The definition of low income used includes both those people that are out-of-work, and those that are in work but who have low earnings (and who satisfy the respective means tests). The domain forms part of the overall Index of Multiple Deprivation (IMD) 2010. The IMD 2010 is the most comprehensive measure of multiple deprivation available. Drawn primarily from 2008 data and presented at small area level, the IMD 2010 is a unique and invaluable tool for measuring deprivation nationally and across local areas. The concept of multiple deprivation upon which the IMD 2010 is based is that separate types of deprivation exist, which are separately recognised and measurable.People over the age of 65 with bad or very bad health - Shows the proportion of people over the age of 65 that reported to have bad or very bad health. Figures are self-reported and taken from the 2011 Census. Rate calculated as = (Bad or very bad health (census LC3206)/(Population aged 65+)*100

Average age and maximum life span of breeding adult three-spined sticklebacks (Gasterosteus aculeatus) were determined in eight Fennoscandian localities with the aid of skeletochronology. The average age varied from 1.8 to 3.6 years, and maximum life span from three to six years depending on the locality. On average, fish from marine populations were significantly older than those from freshwater populations, but variation within habitat types was large. We also found significant differences in mean body size among different habitat types and populations, but only the population differences remained significant after accounting for variation due to age effects. These results show that generation length and longevity in three-spined sticklebacks can vary significantly from one locality to another, and that population differences in mean body size cannot be explained as a simple consequence of differences in population age structure. We also describe a nanistic population from northern Fi...