The child mortality rate in the United States, for children under the age of five, was 462.9 deaths per thousand births in 1800. This means that for every thousand babies born in 1800, over 46 percent did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, and the rate has dropped to its lowest point ever in 2020 where it is just seven deaths per thousand births. Although the child mortality rate has decreased greatly over this 220 year period, there were two occasions where it increased; in the 1870s, as a result of the fourth cholera pandemic, smallpox outbreaks, and yellow fever, and in the late 1910s, due to the Spanish Flu pandemic.

The infant mortality rate in Sweden, for children under the age of one year old, was 240 deaths per thousand births in 1800. This meant that for all babies born in 1800, approximately one quarter did not survive past their first birthday. There were some brief periods in the early and mid nineteenth century where this rate increased, but overall, Sweden's infant mortality rate has decreased steadily throughout it's recorded history. Nowadays, the figure is at it's lowest point ever, with just two deaths per thousand births recorded, meaning that 99.8 percent of children would live past their first birthday.

The child mortality rate in the United Kingdom, for children under the age of five, was 329 deaths per thousand births in 1800. This means that approximately one in every three children born in 1800 did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, particularly in the first half of the twentieth century, and the rate has dropped to its lowest point ever in 2020 where it is just four deaths per thousand births.

The child mortality rate in France, for children under the age of five, was 412 deaths per thousand births in 1800. This means that more than forty percent of all children born in 1800 did not make it to their fifth birthday. Child mortality remained high in the nineteenth century, before falling at a much faster rate throughout the 1900s. Despite falling consistently during the last 130 years, there were two occasions where child mortality actually increased, which can be attributed to both World Wars and the Spanish Flu Pandemic. In 2020, the child mortality rate in France is expected to be just four deaths per thousand births.

This collection contains five modified data sets with mortality, population, and other demographic information for five American cities (Baltimore, Maryland; Boston, Massachusetts; New Orleans, Louisiana; New York City (Manhattan only), New York; and Philadelphia, Pennsylvania) from the early 19th century to the early 20th century. Mortality was represented by an annual crude death rate (deaths per 1000 population per year). The population was linearly interpolated from U.S. Census data and state census data (for Boston and New York City). All data sets include variables for year, total deaths, census populations, estimated annual linearly interpolated populations, and crude death rate. The Baltimore data set (DS0001) also provides birth and death rate variables based on race and slave status demographics, as well as a variable for stillbirths. The Philadelphia data set (DS0005) also includes variables for total births, total infant deaths, crude birth rate, and infant deaths per 1,000 live births.

The child mortality rate in Austria, for children under the age of five, was 387 deaths per thousand births in 1800. This means that just under forty percent of all children born in 1860 did not make it to their fifth birthday. Child mortality increased to over forty percent for most of the nineteenth century, as the country became more industrialized and urbanized, which allowed diseases to spread much faster. From 1900 onwards, the child mortality rate in Austria dropped consistently until today, (apart from a small increase during the Second World War) and it is expected to fall to just four deaths per thousand births in 2020.

The child mortality rate in Afghanistan (for children under the age of five) was around 475 deaths per 1000 births during the course of 19th century. Given as a percentage, this means that 47.5% of children born would not make it to their 5th birthday. After 1950, the child morality rate dropped significantly due to considerable medical advancements, falling to 68 deaths per thousand in 2020. Despite this considerable decline in recent decades, Afghanistan still has one of the highest child mortality rates in the world. Afghanistan's infant mortality rate (among those aged below one year) in 2020 is 52 deaths per thousand births, meaning that the majority of child deaths occur during infancy.

The child mortality rate in Germany, for children under the age of five, was 340 deaths per thousand births in 1800. This means that more than one in every three children born in 1800 did not make it to their fifth birthday. Child mortality increased to almost fifty percent in the mid-nineteenth century, as the country industrialized and urbanized rapidly, which allowed diseases to spread much faster. This changed however, with the introduction of mandatory vaccination in 1874, which kickstarted a gradual decline in child mortality in Germany. The decline was most rapid in the first half of the twentieth century, and by the year 2020 child mortality in Germany is expected to be as low as four deaths per thousand births.

The infant mortality rate in Russia, for children under the age of one year old, was over 266 deaths per thousand births in 1870. This means that for all babies born in 1870, over one quarter did not survive past their first birthday. Unfortunately some information is missing in the early twentieth century, during Russia's revolutionary period and again during the Second World War, however it is noticeable that Russia's infant mortality rate fell to one death for every ten babies born in 1955, and from this point the rate has fallen to just six deaths per thousand births today.

The infant mortality rate in the United States, for children under the age of one (twelve months), was 60 deaths per thousand births in 1935. Approximately six percent of children born in 1935 did not survive past their first birthday. Over the course of the next 85 years, this number has dropped significantly, and the rate has reached its lowest point ever in the period between 2015 and 2020, at six deaths per thousand births. Figures have been below ten since the 1990s.

In Poland, the crude birth rate in 1800 was 43.6 live births per thousand people, meaning that approximately 4.4 percent of the population had been born in that year. Throughout Poland's history, including the area of modern-day Poland that did not exist as a state until 1918, the crude birth rate has gradually decreased over the past 220 years, however it did fluctuate greatly over this period. In the nineteenth century, modern-day Poland was a part of many other states and empires, such as Austria, Germany, Lithuania and Russia, and the crude birth rate fluctuated between 38 and 48 throughout this period. In the twentieth century, Poland's crude birth rate declined, particularly before and during both World Wars, and in both cases there was a baby boom in the aftermath of the war. The rate did grow in the 1970s and 80s, however the fall of communism in 1989 caused the birth rate to drop again, reaching its lowest point of 9.4 in 2005. Since 2005, the crude birth rate has increased above ten again, but is expected to drop to 9.9 in 2020.

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 ---

The child mortality rate in Finland, for children under the age of five, was 420 deaths per thousand births in 1800. This means that for every thousand babies born in 1800, roughly 42 percent did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, and the rate has dropped to its lowest point ever in 2020 where it is just seven deaths per thousand births, which is the lowest of any country n the world. Since 1800, the child mortality rate in Finland has dropped gradually, particularly since the turn of the twentieth century, and the only time since 1900 where the mortality rate increased was between 1915 and 1920, as a result of the Spanish Flu pandemic that swept across the globe.

The child mortality rate in Sweden, for children under the age of five, was 381 deaths per thousand births in 1800. This means that approximately 38 percent of all children born in 1800 did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, particularly from 1880 onwards, and the rate has dropped to its lowest point ever in 2020 where it is just two deaths per thousand births, which is the lowest in the world.

The child mortality rate in India, for children under the age of five, was 509 deaths per thousand births in 1880. This means that over half of all children born in 1880 did not survive past the age of five, and it remained this way until the twentieth century. From 1900 until today, the child mortality rate has fallen from over 53 percent in 1900, to under four percent in 2020. Since 1900, there were only two times where the child mortality rate increased in India, which were as a result of the Spanish Flu pandemic in the 1910s, and in the 1950s as India adjusted to its newfound independence.

The child mortality rate in Australia, for children under the age of five, was 391 deaths per thousand births in 1860. This means that just under forty percent of all children born in 1860 did not make it to their fifth birthday. This number dropped drastically over the next ten years, then it remained between 150 and two hundred for the remainder of the 1800s, before dropping consistently from 1900 until today. By 2020, child mortality in Australia is expected to be approximately four deaths per thousand births.

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth

Abstract

The 2011–2012 Multiple Indicator Cluster Survey (MICS) on Roma in BiH was conducted by the Ministry for Human Rights and Refugees of BiH (MHRR BiH) in cooperation with the Agency for Statistics of BiH (BHAS). Financial and technical support was provided by UNICEF, with additional financial support being provided by UNFPA and the UNHCR. The survey was undertaken as part of the fourth global round of MICS (MICS4).

The survey is based on a representative sample of 1,791 households, with a response rate of 86 percent. In these households, 1,380 women and 1,456 men aged 15-49 were interviewed and questionnaires completed for 748 children under age five.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

The survey covered household members in Roma settlements, all women aged between 15-49 years, all children under 5 living in the household, and all men aged 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the MICS survey on Roma in BiH was to produce statistically reliable estimates for most indicators at the BiH, FBiH and RS level.

A one-stage stratified sampling approach was used for the selection of the survey sample.

The target sample size for the Roma communities MICS was calculated as 1,800 households in 62 municipalities. Data from MICS3 on Roma in Serbia in 2005 was used to calculate specific indicators. At the time of the sample design the Serbian MICS3 was the only unique source of data on the Roma population in the sub-region (BiH, Montenegro, Croatia, the Former Yugoslav Republic of Macedonia and Serbia). The key indicator used for the calculation of the sample size was the immunisation coverage rate for the tuberculosis vaccine amongst children aged 18-29 months. Out of the 30 indicators 17 required a smaller sample size than 1,800 households and the remaining 13 required a larger sample size of households. Of the 13 indicators that required a larger sample size than that of 1,800 households the average size of the confidence interval for a sample of 1,800 households was calculated at 7.4 percent.

Sampling frames for the Roma population were non-existent in BiH until 2009 when MHRR BiH conducted an enumeration of Roma in BiH as part of activities within the Decade of Roma Inclusion 2005-2015.

The master sample frame was prepared using information from the 2009 enumeration. During the 2009 enumeration procedure data was collected on 4,307 Roma households living in Roma communities in 67 municipalities. The total number of municipalities in BiH is 142. Data from the remaining 75 municipalities was not collected due to a lack of information on the presence of Roma in these municipalities. Five municipalities with 1 Roma household were excluded from the master sample frame.

Municipalities in the FBiH, RS and BD were identified as the sample strata and a one-stage stratified sampling approach was used for the selection of the survey sample, with households defined as the primary sampling units (PSUs).

Households were selected from each of the sampling strata (municipalities) by using systematic pps sampling procedures, based on the estimated sizes of the strata from the 2009 enumeration of Roma in BiH.

All households where the head of household declared himself or herself to be of Roma ethnicity were considered as Roma households.

Households were selected within each stratum based on the date of birth of the household head. If the date of birth of the household head was not available then the date of birth of the next oldest person in the household with an available date of birth was used. If none of the household members had an available date of birth then the date of birth of the interviewer was used as the reference.

The sampling procedures are more fully described in "Bosnia and Herzegovina Multiple Indicator Cluster Survey (MICS) 2011 - Roma Settlements - Final Report" pp.144-147.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, child discipline and hand washing.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, children under age five and men age 15-49. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's background, access to mass media and ICT, child mortality, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tobacco and alcohol use, life satisfaction and health care.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, immunisation and anthropometry.

The men's questionnaire includes man's background, access to mass media and ICT, child mortality, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tobacco and alcohol use, life satisfaction and health care.

The questionnaires were based on the MICS4 model questionnaire. From the MICS4 model English version the questionnaires were translated into the local languages used in BiH. The questionnaires were pre-tested in the FBiH in three municipalities in Sarajevo Canton (Centar, Ilijas and Novo Sarajevo) during September 2011. The plan provided for 18 households to be interviewed (9 each in urban and rural areas). These households were selected using a random selection method based on the date of birth of the household head. On the basis of the pre-test results, modifications were then made to the wording and translation of the questionnaires for the survey.

Cleaning operations

The data was entered and processed by the MHRR BiH. The data was entered using CSPro software into four microcomputers by 4 trained data entry operators; the process was supervised by data entry supervisors and a data entry coordinator. Data entry began ten days after the start of data collection (20 November 2011) and was completed on 26 April 2012.

The SPSS (Statistical Package for Social Sciences) software programme (Version 18) was used to analyse the data and model syntax and tabulation plans developed by UNICEF were also used for this purpose. In order to ensure quality control all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programmes developed under the global MICS4 programme and adapted to the questionnaires for the survey on Roma in BiH were used throughout.

Response rate

Of the 1,791 households selected for the sample 1,788 were found to be occupied. Of these, 1,544 households were successfully interviewed for a household response rate of 86 percent. In the interviewed households 1,457 women aged 15-49 were identified of which 1,380 were successfully interviewed, yielding a response rate of 95 percent. In addition, 1,559 men aged 15-49 were listed in the household questionnaire. Questionnaires were completed for 1,456 eligible men, which corresponds to a response rate of 93 percent. There were 760 children under age five listed in the household questionnaire and questionnaires were completed for 748 children, which corresponds to a response rate of 98 percent. The overall response rates for the women’s, men’s and children’s questionnaires were 82 percent, 81 percent and 85 percent respectively.

Sampling error estimates

The sample of respondents selected in the MICS survey on Roma in BiH was only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would have yielded results that differed somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly but can be estimated statistically from the survey data.

The simple one-stage stratified sample design for the MICS survey on Roma in BiH is reflected in the calculations of the sampling errors, whereby the strata are municipalities and the primary sampling units (PSUs) are households (clusters of persons).

Given the overall high sampling rate (1,800/3,784), sampling without replacement was used in order to apply a finite population correction factor. As part of the estimation procedure, the first stage sampling rate for each stratum (municipality) was specified. For strata with a sampling rate of 1, the finite population correction factor was zero (resulting in a zero variance component for the corresponding stratum).

The sampling error measures below are presented in this appendix for each of the selected indicators.

• Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor Linearization method was used for the estimation of standard errors.
• Coefficient of variation (se/r): is the ratio of the standard error to the value of the indicator and is a measure of the relative

This dataset provides a count by year of first names given to babies born in Sonoma County. Current year data is updated monthly. In the 1800’s, the processes for birth registration were not as established as they are today. Births could have been registered in other places, such as churches, and not all births were reported. Given this, the number of births recorded early on may only represent a subset of the actual births that occurred during that time period. Blank names mean that the birth certificate did not have a name listed. There is no requirement that the baby's name be determined prior to the birth certificate being registered. Birth certificates can be amended through the state at a later date to add the first name, if the individual desires to do so.

This data describes the average life expectancy at birth for various nations from 1543-2021 . Data Variable description: The average number of years that a newborn could expect to live, if he or she were to pass through life exposed to the sex- and age-specific death rates prevailing at the time of his or her birth, for a specific year, in a given country, territory, or geographic area. (Definition from the WHO) Data Variable time span: 1543 – 2021 Data published by : United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022, Online Edition; Zijdeman et al. (2015) (via clio-infra.eu); Riley, J. C. (2005). Estimates of Regional and Global Life Expectancy, 1800-2001. Population and Development Review, 31(3), 537–543. http://www.jstor.org/stable/3401478 Link https://population.un.org/wpp/Download/ ; https://clioinfra.eu/Indicators/LifeExpectancyatBirthTotal.html ; https://doi.org/10.1111/j.1728-4457.2005.00083.x;https://ourworldindata.org/health-meta License: Copyright © 2022 by United Nations, made available under a Creative Commons license CC BY 3.0 IGO: http://creativecommons.org/licenses/by/3.0/igo/