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 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.

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 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 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.

The study’s theme: The development of infant mortality in Germany for the 19th century is only poorly documented. Especially for the period prior to 1871 only small area statistics are available. With the preparation of the information collected by the authorities of the former German States the author tries to create a new statistical basis. The reconstructed national series of birth and infant mortality (from 1826) documents relatively high infant mortality rates with little progress (i.e. improvement of the situation) until the beginning of the 20th Century. Considering the influence of urbanization the evaluation of the different regional patterns and trends leads to a new weighting of the problem. Thus the living and working conditions in the country were of considerable importance. Overall, the prevailing habits and attitudes are considered crucial to the survival of small children (Gehrmann 2011, S. 807)

Data and data preparation, source problems: The federal structure of the Empire leads to the problem that the printed statistics on infant mortality before 1901 remained incomplete. In some German states, information concerning infant mortality was not collected from the beginning of the registry offices. However, the ‘Kaiserliches Statistisches Amt’ (Imperial Statistical Office) was able to create despi9te the difficult situation a life table, which represented 97,3% of live birth for the period of 1872 to 1880. Hence, the annual infant mortality rate in 1872 is known. „Die föderale Struktur des Kaiserreichs hatte (…) zur Folge, dass die gedruckte Statistik zur Säuglingssterblichkeit vor 1901 lückenhaft blieb. Mehr noch: es wurden offensichtlich in einigen Staaten diesbezügliche Angaben gar nicht oder zumindest nicht von Anfang an bei den Standesämtern abgefragt. Als das Kaiserliche Statistische Amt in den 1880er Jahren die erste Sterbetafel für das Deutsche Reich erstellen wollte, musste es deshalb konstatieren, dass in den Einzelstaaten „fast alle in der Statistik überhaupt üblichen Arten und Grade der Spezialisierung vertreten“ (Kaiserliches Statistisches Amt 1887: 21) waren, aus manchen aber trotzdem keine geeigneten Unterlagen beschafft werden konnten. Immerhin repräsentierte die Sterbetafel am Ende doch 96,8% der Reichsbevölkerung im Jahre 1885 und 97,3% der Lebendgeborenen 1872 bis 1880. Damit ist auch die jährliche Säuglingssterblichkeitsrate ab 1872 bekannt. (…) Mit Hilfe des Sterbetafel-Materials kann die statistische Reihe aus „Bevölkerung und Wirtschaft“ also um fast 30 Jahre nach hinten verlängert werden. (…) Komplizierter stellt sich die Sachlage für weiter zurückliegende Zeitabschnitte dar. „ (S. 812-813) Although in most German states statistical collection on population movement has been carried out, the statistics vary considerably in quality. In the first step therefore, the author reject the procedure of simply extrapolating the birth rates because of the qualitative differences of the early statistics are too fundamental. Especially, in this approach of simply summing up, the values of the undocumented areas would equate with the values of the other well documented regions. Therefore, the author chose a complex way to estimate the lacking values: The missing values in small territories are estimated on the basis of the values of neighboring regions. Finally, it can be seen, that the data for the period from 1828 to 1871, calculated by the complex procedure of filling in missing data does not lead to significantly different results comparing to the data row calculated by the simple sum of the different sources. Per year, the difference between the two series (the series calculated in the complex way and the series calculated by summing up the values of the available statistics) is not more than 0,9 percent points, which can be seen as a slight difference between the two series in relation to the former level of infant mortality. The indeterminate values of those German states lacking a birth statistics may not being significantly different to those calculated on the basis of the complex procedure, because even unexpected, extreme runaway values in individual states can not realistically assumed to be so large that they could have a sufficient impact on the overall values. Thus, the presented row is a solid basis for the assessment of the overall development of the German Empire’s birth development. „Vielmehr empfiehlt es sich, zunächst in kleinen Schritten für die einzelnen Territor...

The child mortality rate in Canada, for children under the age of five, was 333 deaths per thousand births in the year 1830. This means that one third of all children born in 1830 did not make it to their fifth birthday. Child mortality remained above 25 percent for the remainder of the nineteenth century, before falling at a much faster rate throughout the 1900s. By the year 2020, Canada's child mortality rate is expected to be just five deaths per thousand births.

In the mid-19th century, the urban milk supply in the United States was regularly skimmed or diluted with water, reducing its nutritional value. At the urging of public health experts, cities across the country hired milk inspectors, who were tasked with collecting and analyzing milk samples with the goal of preventing adulteration and skimming. Using city-level data for the period 1880-1910, we explore the effects of milk inspections on infant mortality and mortality among children under the age of 5. Event-study estimates are small and statistically insignificant, providing little evidence of post-treatment reductions in either infant or child mortality.

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 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.

ICD10h2024.2 (this version, published June 2025) incorporates the following changes: - Manual: new preface to list changes to files; changes relating to code changes; a small number of other corrections and improvements. - Masterlist (see 2025MasterlistChanges): 9 new codes added, 5 codes deleted, and 17 changes made to ICD10 or ICD10h descriptions. - Transferfile (see 2025TransferfileChanges): 47 errors were fixed. - InfantCat (see 2025InfantCatChanges): 17 ICD10h codes were updated. - Historic Strings English (see 2025HistoricStringsEnglishChanges): 14 changes were made to ICD10h or ICD10hInjury codes were made. ICD10hDescription and ICD10hInjuryDescription columns were deleted.

This file contains manual for ICD10h: a historic cause of death coding and classification scheme for individual-level causes of death. ICD10h has been designed by the authors to aid the coding and classification of causes of death recorded on historic individual death records. Input data for the scheme consisted of mortality records for Scottish town of Kilmarnock and Isle of Skye (derived from the project Determining the Demography of Victorian Scotland Through Record Linkage, ESRC RES-000-23-0128 held at the Cambridge Group for the History of Population and Social Structure, University of Cambridge), and the island of Tasmania (P. Gunn and R. Kippen, ‘Household and Family Formation in Nineteenth-Century Tasmania, Dataset of 195 Thousand Births, 93 Thousand Deaths and 51 Thousand Marriages Registered in Tasmania, 1838-1899’, 2008) for the late 19th century. Although ICD10h contains exemplar causes of death based on these datasets, it does not contain any information about any deceased person apart from cause of death, and does not provide counts of deaths from particular causes. The data were hand-coded and subject to stringent algorithm-assisted tests. The ICD10h system is based on the 10th revision of the International Classification of Diseases - 2016 version (ICD10 - 2016), and combines ICD10 codes (without modification) with new codes for archaic/historic terms. A general historical classification of deaths, HistCat, is also provided, as well as a historical classification for infant deaths, InfantCat. The manual presents the background to the development of the system, and explains how to use the system for the coding and classification of causes in both English and other languages. It is associated with additional datasets (available in both .xlsx and .csv) containing 1) the lists of codes and their descriptions which constitute the coding and classification system, together with the HistCat classification [https://doi.org/10.17863/CAM.109961]; 2) a set of exemplar historic strings in the English language [https://doi.org/10.17863/CAM.109962]; and 3) the classification for infant deaths [https://doi.org/10.17863/CAM.109963]. ICD10h has been developed in association with the following research projects: Digitising Scotland/Scottish Health Informatics Project (funded by the ESRC); Studying Health in Port Cities (funded by The Netherlands Organisation for Scientific Research); the Great Leap (funded by COST-Action CA22116). Please note that ICD10h is a research tool created to facilitate the study of historical cause of death records and should not be used for any official purpose. It is based on the International Classification of Diseases, 10th Revision (ICD-10) version 2016 (Geneva: World Health Organization 2016) but is not a recognised version or extension of ICD-10 and is not authorised by WHO. However we have consulted with WHO: they recognise that ICD10h is a useful academic methodology and have not raised any objections to its creation. Data coded using ICD10h are not directly comparable with data coded in ICD-10, and the underlying or primary cause of death derived using the ICD10h methodology may be different from the underlying cause derived in ICD-10 according to the WHO rules. Please note that ICD-10 version 2016 is not the most recent version of ICD-10; and that WHO now recommend the use of ICD-11; a more advanced and detailed classification.

Sources: Conscription lists of the birth cohorst 1813-1842, Department II, - of the capital and royal seat Munich, - of the royal county court Toelz and - of the public records office of Munich. Reichenhall: conscription lists are available only for the birth cohort 1840. Districts Miesbach, Toelz, Wasserburg and Reichenhall: all available conscription lists of the public records office are evaluated and all inductees of the birth cohorts 1813 to 1842 are collected.

Study question: Do the mothers of twins and singletons differ regarding post-partum and old-age mortality?

Summary answer: Mothers of twins had twice as high post-partum mortality as mothers of singletons; survival of twinners was higher than survival of the mothers of singletons after the 67^th lifespan percentile.

What is known already: Twinning is typically associated with higher post-partum maternal mortality. The evidence about whether twinning incurs long-term survival costs of reproduction or is a trait pertinent to long-lived women is scarce and contradictory.

Study design, size, duration: The study is based on the data of the Estonian Family Register (operating from 1926-43) and involves 5 565 mothers of twins and 119 613 mothers of singletons born between 1850-99. The subset for comparing maternal lifespans included 1 703 – 1 884 mothers of twins and 19 747 – 36 690 mothers of singletons.

Participants/materials, setting, methods: Post-partum maternal mortality was analysed in the whole sample (including mothers of a single child) by logistic regression. Most of the analyses were performed in samples where each mother of twins was matched against mothers of singletons based on parity, urban versus rural origin, whether their lifespan was known, date of birth and age at first birth. Quantile regression was used to analyse age-dependent variations in maternal mortality rates. Lifespans were compared in linear mixed models. All models were adjusted for relevant biodemographic covariates.

Main results and the role of chance: The twinning rate in the whole sample was 4.4%. During the year after giving birth, maternal mortality for multiple gestations was 0.75% (17/2 273) and 0.37% (449/122 750) for single gestations (OR = 2.05, 95% CI = 1.21 – 3.23). The association between twinning and post-natal maternal mortality remained significant in a model controlling for parity and age of first and last birth. The life spans of the mothers of twins and singletons did not differ in matched samples. Past the 67^th lifespan percentile, the odds of survival were significantly higher for mothers of twins than mothers of singletons, as indicated by non-overlapping 95% confidence intervals.

Limitations, reasons for caution: Relatively low number of individuals (22 802) with known age at death due to discontinuation of the register after 1943.

This data set contains estimated teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) by county and year. DEFINITIONS Estimated teen birth rate: Model-based estimates of teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates “borrow strength” across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information—including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables—to provide a stable estimate of the county teen birth rate. Median teen birth rate: The middle value of the estimated teen birth rates for the age group 15–19 for counties in a state. Bayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model. NOTES Data on the number of live births for women aged 15–19 years were extracted from the National Center for Health Statistics’ (NCHS) National Vital Statistics System birth data files for 2003–2015 (5). Population estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used. Hierarchical Bayesian space–time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003–2015 (1,2,3,4). The Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-α)% Bayesian credible interval for an unknown parameter vector θ and observed data vector y is a subset C of parameter space Ф such that 1-α≤P({C│y})=∫p{θ │y}dθ, where integration is performed over the set and is replaced by summation for discrete components of θ. The probability that θ lies in C given the observed data y is at least (1- α) (6). County borders in Alaska changed, and new counties were formed and others were merged, during 2003–2015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003–2015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005–2007 bridged-race population file geographies (7). SOURCES National Center for Health Statistics. Vital statistics data available online, Natality all-county files. Hyattsville, MD. Published annually. For details about file release and access policy, see NCHS data release and access policy for micro-data and compressed vital statistics files, available from: http://www.cdc.gov/nchs/nvss/dvs_data_release.htm. For natality public-use files, see vital statistics data available online, available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. National Center for Health Statistics. U.S. Census populations with bridged race categories. Estimated population data available. Postcensal and intercensal files. Hyattsville, MD

This file contains the infant death categorisations associated with the ICD10h (Historic cause of death coding and classification scheme for individual-level causes of death). ICD10h has been designed by the authors to aid the coding and classification of causes of death recorded on historic individual death records and associated files include a manual, the list of codes, descriptions and values of a general categorisation, and exemplar list of historic strings together with the ICD10h codes. The ICD10h system is based on the 10th revision of the International Classification of Diseases - 2016 version (ICD10 - 2016), and combines ICD10 codes (without modification) with new codes for archaic/historic terms.

The data was derived from the following projects/deposited data: Determining the Demography of Victorian Scotland Through Record Linkage, ESRC RES-000-23-0128 held at the Cambridge Group for the History of Population and Social Structure, University of Cambridge; P. Gunn and R. Kippen, ‘Household and Family Formation in Nineteenth-Century Tasmania, Dataset of 195 Thousand Births, 93 Thousand Deaths and 51 Thousand Marriages Registered in Tasmania, 1838-1899’, 2008.

The resource creation was supported by the following projects: Digitising Scotland/Scottish Health Informatics Project (funded by the ESRC); Studying Health in Port Cities (funded by The Netherlands Organisation for Scientific Research); The Great Leap (funded by COST-Action CA22116).

SHARING/ACCESS INFORMATION

This resource is available under a CC BY licence.

Recommended citation for this dataset: Alice Reid, Eilidh Garrett, Maria Hiltunen Maltesdotter, Angelique Janssens, 2024, ICD10h: Historic cause of death coding and classification scheme for individual-level causes of death – Infant Categorisations [https://doi.org/10.17863/CAM.109963]

Please see the associated resources: Historic cause of death coding and classification scheme for individual-level causes of death – manual [https://doi.org/10.17863/CAM.109960] Historic cause of death coding and classification scheme for individual-level causes of death – Codes [https://doi.org/10.17863/CAM.109961] Historic cause of death coding and classification scheme for individual-level causes of death – English language historic strings [https://doi.org/10.17863/CAM.109962]

ICD10h is a research tool created to facilitate the study of historical cause of death records and should not be used for any official purpose. It is based on the International Classification of Diseases, 10th Revision (ICD-10) version 2016 (Geneva: World Health Organization 2016) but is not a recognised version or extension of ICD-10 and is not authorised by WHO. However we have consulted with WHO: they recognise that ICD10h is a useful academic methodology and have not raised any objections to its creation. Data coded using ICD10h are not directly comparable with data coded in ICD-10, and the underlying or primary cause of death derived using the ICD10h methodology may be different from the underlying cause derived in ICD-10 according to the WHO rules. Please note that ICD-10 version 2016 is not the most recent version of ICD-10; and that WHO now recommend the use of ICD-11; a more advanced and detailed classification.

DATA & FILE OVERVIEW

ICD10h_InfantCat.xlsx Excel file consisting of 2 worksheets: 1) ReadMe sheet 2) InfantCat

Separate csv file for 2) containing the same information.

This file builds on a previous, unpublished version of ICD10h (dating from 2020). InfantCat2024 provides an updated version of the previous categorisation (InfantCat2020). Please see the Manual for detail of the changes.

METHODOLOGICAL INFORMATION

The data were hand-coded and subject to stringent algorithm-assisted tests.

DATA-SPECIFIC INFORMATION FOR: InfantCat

Number of variables: 4

Number of cases/rows: 14088

Variable List: IDMasterlist (unique ID number, same as Masterlist table) ICD10h (ICD10h code ) Infantcat2024 (Infantcat2024 category) Infantcat2020 (Infantcat2020 category)

This depository includes the replication package for Interest Rates, Sanitation Infrastructure, and Mortality Decline in Nineteenth-Century England and Wales, to be published in the Journal of Economic History in March 2022. Abstract: This paper investigates whether high borrowing costs deterred investment in sanitation infrastructure in late nineteenth-century Britain. Town councils had to borrow to fund investment, with considerable variation in interest rates across towns and over time. Panel regressions, using annual data from more than 800 town councils, indicate that higher interest rates were associated with lower levels of infrastructure investment between 1887 and 1903. Instrumental variable regressions show that falling interest rates after 1887 stimulated investment and led to lower infant mortality. These findings suggest that Parliament could have expedited mortality decline by subsidizing loans or facilitating private borrowing.

By the early 1870s, the child mortality rate of the area of modern-day Bangladesh was estimated to be just over five hundred deaths per thousand live births, meaning that more than half of all infants born in these years would not survive past their fifth birthday. Child mortality would steadily climb towards the end of the 19th century, to a rate of almost 57 percent, as a series of famines would result in significant declines in access to nutrition and the increased displacement of the population. However, after peaking at just over 565 deaths per thousand births at the turn of the century, the British colonial administration partitioned the Bengal region (a large part of which lies in present-day India), which would begin to bring some bureaucratic stability to the region, improving healthcare and sanitation.

Child mortality would largely decline throughout the 20th century, with two temporary reversals in the late 1940s and early 1970s. The first of these can be attributed in part to disruptions in government services and mass displacement of the country’s population in the partitioning of India and Pakistan following their independence from the British Empire; during which time, present-day Bangladesh became East Pakistan. The second reversal would occur in the early 1970s, as a side effect for the Bangladesh Liberation War, the famine of 1974, and the subsequent transition to independence. Outside of these reversals, child mortality would decline significantly in the 20th century, and by the turn of the century, child mortality in Bangladesh would fall below one hundred deaths per thousand births; less than a fifth of the rate at the beginning of the century. In the past two decades, Bangladesh's child mortality has continued its decline to roughly a third of this rate, due to improvements in healthcare access and quality in the country; in 2020, it was estimated that for every thousand children born in Bangladesh, almost 97 percent will survive past the age of five years.

This file provides the necessary input data (crude vital rates) and shows the calculations for the indirect estimation of life expectancy at birth (e0) for males and females combined, using the method developed in McCann, J. 1976. 'A Technique for Estimating Life Expectancy with Crude Vital Rates', Demography, 13(2): pp. 259-272.

Coverage: Sweden (1736-1750), Norway (1735-1845), Denmark (1800-1834), Iceland (1735-1837), and Finland (1751-1877).

The annual estimates end in the year before estimates in the Human Mortality Database become available.

For a detailed description see Torres, C. and Oeppen, J. 2019. The Health Transition in the Nordic Countries (Working paper, available upon request: ctorres@sdu.dk).

This dataset contains a variety of demographic measures (related to fertility, marriage, mortality and migration), plus a range of socio-economic indicators (related to households, age structure, and social class) for the 2000+ Registration Sub Districts (RSDs) in England and Wales for each census year between 1851 and 1911, and for the 600+ Registration Districts of Scotland 1851-1901. The measures have mainly been derived from the computerised individual level census enumerators' books (and household schedules for 1911) enhanced under the I-CeM project. I-CeM does not currently include data for England and Wales 1871, although the project has been able to access a version of the data for that year it does not contain information necessary to calculate many of the variables presented here. Scotland 1911 is also not available. Users should therefore beware that 1871 does not contain data for many of the variables. Additional data has been derived from the tables summarising numbers of births and deaths by year and areas, which were published by the Registrar General of England and Wales in his quarterly, annual and decennial reports of births, deaths and marriages. Data from the decennial reports was obtained from Woods (SN 3552) and we transcribed data from the quarterly and annual reports ourselves. Counts of births and deaths for Scottish Registration Districts were obtained from the Digitising Scotland project at the University of Edinburgh. The dataset builds on SN 8613 and SN 853547 which provide data for a more limited set of variables and for England and Wales only (the same dataset also has two UKDS SN numbers as it was re-routed by UKDS during the deposit process).

This project will present the first historic population geography of Great Britain during the late nineteenth century. This was a period of unprecedented demographic change, when both mortality and fertility started the dramatic secular declines of the first demographic transition. National trends are well established: mortality decline started in childhood and early adulthood, with infant mortality lagging behind, particularly in urban-industrial areas. The fall in fertility was led by the middle classes but quickly spread throughout society. Urban growth was fuelled by movement from the countryside to the city, but there was also considerable migration overseas, particularly from Scotland, although to some extent outmigration was offset by immigration. There was local and regional variation in these patterns, and a contrast between the demographic experiences of Scotland and of England and Wales. Marriage was later in Scotland but fertility within marriage higher, and the improvement in Scottish mortality was slower than that south of the border. However, while there has been research on local and regional patterns within each country, these have mainly been pursued separately, and it is therefore unclear whether there were real national differences or whether there were local demographic continuities across borders, and if so whether they followed economic, occupational, cultural or even linguistic lines. Understanding population processes involves a holistic appreciation of the interaction between the basic demographic components of fertility, mortality, nuptiality and migration, and how they come together, interacting with economic and cultural processes, to create a specific demographic system via the spread of people and ideas. This project is the first to consider a historical population geography of the whole of Great Britain across the first demographic transition, drawing together measures of nuptiality, fertility, mortality and migration for small geographic areas and unpacking how they interacted to produce the more readily available broad-brush national patterns for Scotland and for England and Wales.

We will build on our immensely successful project on the fertility of Victorian England and Wales, which used complete count census data for England and Wales to calculate more detailed fertility measures than ever previously possible for some 2000 small geographic areas and 8 social groups, allowing the investigation of intra-urban as well as urban-rural differences in fertility. The new measures allowed us to examine age patterns of fertility across the two countries for the first time. We were also able to calculate contextual variables from the census data which allowed us to undertake spatial analysis of the influences on fertility over time. As well as academic papers, our previous project presented summary data at a fine spatial resolution in an interactive online atlas, populationspast.org, a major new resource which is already being widely used as a teaching tool in both schools and universities.

In this new project we will calculate comparable measures of fertility and contextual variables using the full count census data for Scotland, 1851 to 1901 inclusive, to complement those for England and Wales. However, our new project will go considerably further and will integrate place-specific measures of mortality and migration, for both Scotland and for England and Wales. We will provide new age-specific data on fertility, mortality and migration for the whole of Great Britain using existing datasets, at a finer geographic level than has previously been possible, and will analyse these spatially and temporally to gain a panoramic understanding of the forces driving this crucial period of demographic and social change. We will expand populationspast.org to bring our new findings to a wide academic and non-academic audience and will provide the data for others to explore interactively.

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT . Abstract: In this study the constantly rising human life expectancy since the beginning of the 18th century is analysed in some regions of Germany in comparative point of view. On the basis of worldwide singular sources in terms of clan registers of villages and localities as well as flow sheets the researcher Arthur E. Imhof and his research group of the ‘Freie Universität Berlin’ analysed more than 130.000 individual biografies from the 17th till the 19th century in six regions of northern, southern and central Germany. Aim of this research project was to compile area life-tables and to compute the life-expectancy. To enable comparisons with life-expectancy-calculations of today, all data originally prepared by generations are transformed into period-tables according to modern demografic methods. Topics Regional and national datafiles on populationstructure, development of mortality, historical demography, family structure, date of birth, marriages, number of birth, date of death, cause of death, locality of death, occupation, occupation of the parents. This study is available as SPSS-Data file as well as a downloadable EXCEL-Data-File, offered via the online-downloadsystem HISTAT (Historical Statistics). In HISTAT timeseries data are available. Categorisation in HISTAT:In HISTAT an excerpt of the archived total data stock is offered. The total data stock can be ordered as individual personal data at GESIS, Data Archive and Data Analysis. A. Datatables about mortality (14 tables, timeseries)B. Synoptical mortality tables (14 tables, timeseries)C. Datatables about life expectancy (14 tables, timeseries)D. Synoptical tables: all regions (without Hamburg) by sex in periodical presentation. (14 tables, timeseries)