✨ birth rate monthly improvements #3693
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Login: chart-diff: ✅No charts for review.data-diff: ❌ Found differences= Dataset garden/hmd/2024-12-03/hmd_country
= Table birth_rate_month_max
~ Dim country
+ + New values: 61 / 3998 (1.53%)
year country
1924 Bulgaria
1860 France
1955 Greece
1898 Japan
1945 West Germany
~ Dim year
+ + New values: 61 / 3998 (1.53%)
country year
Bulgaria 1924
France 1860
Greece 1955
Japan 1898
West Germany 1945
~ Column birth_rate_per_day_max (changed metadata, new data, changed data)
- - title: Peak daily birth rate
+ + title: Peak birth rate per day, on a monthly basis
- - description_short: The highest average daily number of births, per 1,000 people, recorded in the given year.
? ^^^^^
+ + description_short: The highest average daily number of births, per million people, recorded in the given year.
? ^^^^^^^
- - unit: births per 1,000 people
? ^^^^^
+ + unit: births per million people
? ^^^^^^^
+ + New values: 61 / 3998 (1.53%)
country year birth_rate_per_day_max
Bulgaria 1924 <NA>
France 1860 <NA>
Greece 1955 <NA>
Japan 1898 <NA>
West Germany 1945 <NA>
~ Changed values: 42 / 3998 (1.05%)
country year birth_rate_per_day_max - birth_rate_per_day_max +
Bulgaria 2021 24.578798 24.764009
England and Wales 2021 30.864176 30.850918
New Zealand 2020 32.752079 32.464931
Portugal 2022 24.763571 24.690685
Sweden 2023 28.571289 28.543976
~ Column birth_rate_per_day_max_lead_9months (changed metadata, new data, changed data)
- - {}
+ + title: Peak birth rate per day, on a monthly basis, in 9 months
+ + description_short: The highest average daily number of births, per million people, recorded in the given year.
+ + origins:
+ + - producer: Human Mortality Database
+ + title: Human Mortality Database
+ + description: |-
+ + The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
+ +
+ +
+ + # Scope and basic principles
+ +
+ + The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
+ +
+ + The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
+ +
+ +
+ + # Computing death rates and life tables
+ +
+ + Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
+ +
+ + 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
+ + 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
+ + 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
+ + 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
+ + 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
+ + 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
+ +
+ +
+ + # Corrections to the data
+ +
+ + The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
+ +
+ + Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
+ +
+ +
+ + # Age misreporting
+ +
+ + Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
+ +
+ + In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
+ +
+ + Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
+ +
+ +
+ + # Uniform set of procedures
+ +
+ + A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
+ +
+ + It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
+ +
+ + Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
+ + citation_full: |-
+ + HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
+ +
+ + See also the methods protocol:
+ + Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). [Available online](https://www.mortality.org/File/GetDocument/Public/Docs/MethodsProtocolV6.pdf) (needs log in to mortality.org).
+ + attribution_short: HMD
+ + url_main: https://www.mortality.org/Data/ZippedDataFiles
+ + date_accessed: '2024-11-27'
+ + date_published: '2024-11-13'
+ + license:
+ + name: CC BY 4.0
+ + url: https://www.mortality.org/Data/UserAgreement
+ + - producer: Human Mortality Database
+ + title: Human Mortality Database, by country
+ + description: |-
+ + The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
+ +
+ +
+ + # Scope and basic principles
+ +
+ + The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
+ +
+ + The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
+ +
+ +
+ + # Computing death rates and life tables
+ +
+ + Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
+ +
+ + 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
+ + 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
+ + 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
+ + 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
+ + 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
+ + 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
+ +
+ +
+ + # Corrections to the data
+ +
+ + The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
+ +
+ + Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
+ +
+ +
+ + # Age misreporting
+ +
+ + Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
+ +
+ + In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
+ +
+ + Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
+ +
+ +
+ + # Uniform set of procedures
+ +
+ + A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
+ +
+ + It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
+ +
+ + Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
+ + description_snapshot: |-
+ + HMD data by country. This contains the raw data, including their "input data", which HMD defines as:
+ +
+ + The Input Database houses the raw data that are the basis for all HMD calculations. Input data files for each population are accessible from the country page.
+ + citation_full: |-
+ + HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
+ +
+ + See also the methods protocol:
+ + Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). [Available online](https://www.mortality.org/File/GetDocument/Public/Docs/MethodsProtocolV6.pdf) (needs log in to mortality.org).
+ + attribution_short: HMD
+ + url_main: https://www.mortality.org/Data/ZippedDataFiles
+ + date_accessed: '2024-11-27'
+ + date_published: '2024-11-13'
+ + license:
+ + name: CC BY 4.0
+ + url: https://www.mortality.org/Data/UserAgreement
+ + unit: births per million people
+ + display:
+ + name: Maximum birth rate, per day
+ + presentation:
+ + topic_tags:
+ + - Fertility Rate
+ + New values: 61 / 3998 (1.53%)
country year birth_rate_per_day_max_lead_9months
Bulgaria 1924 97.067017
France 1860 82.915085
Greece 1955 54.558788
Japan 1898 59.609818
West Germany 1945 41.749641
~ Changed values: 3937 / 3998 (98.47%)
country year birth_rate_per_day_max_lead_9months - birth_rate_per_day_max_lead_9months +
Denmark 2022 NaN 28.423363
Estonia 2007 NaN 35.612888
Iceland 1937 NaN 65.086006
Portugal 1988 NaN 34.387554
Sweden 1963 NaN 51.30164
~ Column month_max (changed metadata, new data)
- - title: Month ordinal with the peak daily birth rate
? ----
+ + title: Month ordinal with peak daily birth rate
+ + New values: 61 / 3998 (1.53%)
country year month_max
Bulgaria 1924 <NA>
France 1860 <NA>
Greece 1955 <NA>
Japan 1898 <NA>
West Germany 1945 <NA>
~ Column month_max_lead_9months (changed metadata, new data, changed data)
- - {}
+ + title: Month ordinal with peak daily birth rate in 9 months
+ + description_short: Number corresponding to the month with the highest daily birth rate.
+ + origins:
+ + - producer: Human Mortality Database
+ + title: Human Mortality Database, by country
+ + description: |-
+ + The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
+ +
+ +
+ + # Scope and basic principles
+ +
+ + The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
+ +
+ + The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
+ +
+ +
+ + # Computing death rates and life tables
+ +
+ + Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
+ +
+ + 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
+ + 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
+ + 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
+ + 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
+ + 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
+ + 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
+ +
+ +
+ + # Corrections to the data
+ +
+ + The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
+ +
+ + Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
+ +
+ +
+ + # Age misreporting
+ +
+ + Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
+ +
+ + In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
+ +
+ + Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
+ +
+ +
+ + # Uniform set of procedures
+ +
+ + A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
+ +
+ + It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
+ +
+ + Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
+ + description_snapshot: |-
+ + HMD data by country. This contains the raw data, including their "input data", which HMD defines as:
+ +
+ + The Input Database houses the raw data that are the basis for all HMD calculations. Input data files for each population are accessible from the country page.
+ + citation_full: |-
+ + HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
+ +
+ + See also the methods protocol:
+ + Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). [Available online](https://www.mortality.org/File/GetDocument/Public/Docs/MethodsProtocolV6.pdf) (needs log in to mortality.org).
+ + attribution_short: HMD
+ + url_main: https://www.mortality.org/Data/ZippedDataFiles
+ + date_accessed: '2024-11-27'
+ + date_published: '2024-11-13'
+ + license:
+ + name: CC BY 4.0
+ + url: https://www.mortality.org/Data/UserAgreement
+ + unit: ''
+ + presentation:
+ + topic_tags:
+ + - Fertility Rate
+ + New values: 61 / 3998 (1.53%)
country year month_max_lead_9months
Bulgaria 1924 5
France 1860 7
Greece 1955 4
Japan 1898 4
West Germany 1945 9
~ Changed values: 3937 / 3998 (98.47%)
country year month_max_lead_9months - month_max_lead_9months +
Denmark 2022 NaN 10
Estonia 2007 NaN 10
Iceland 1937 NaN 1
Portugal 1988 NaN 8
Sweden 1963 NaN 7
~ Column month_max_name (changed metadata, new data)
- - title: Month name with the peak daily birth rate
? ----
+ + title: Month name with peak daily birth rate
+ + New values: 61 / 3998 (1.53%)
country year month_max_name
Bulgaria 1924 NaN
France 1860 NaN
Greece 1955 NaN
Japan 1898 NaN
West Germany 1945 NaN
~ Column month_max_name_lead_9months (changed metadata, new data, changed data)
- - {}
+ + title: Month name with peak daily birth rate in 9 months
+ + description_short: Month with the highest daily birth rate.
+ + origins:
+ + - producer: Human Mortality Database
+ + title: Human Mortality Database, by country
+ + description: |-
+ + The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
+ +
+ +
+ + # Scope and basic principles
+ +
+ + The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
+ +
+ + The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
+ +
+ +
+ + # Computing death rates and life tables
+ +
+ + Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
+ +
+ + 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
+ + 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
+ + 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
+ + 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
+ + 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
+ + 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
+ +
+ +
+ + # Corrections to the data
+ +
+ + The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
+ +
+ + Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
+ +
+ +
+ + # Age misreporting
+ +
+ + Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
+ +
+ + In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
+ +
+ + Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
+ +
+ +
+ + # Uniform set of procedures
+ +
+ + A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
+ +
+ + It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
+ +
+ + Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
+ + description_snapshot: |-
+ + HMD data by country. This contains the raw data, including their "input data", which HMD defines as:
+ +
+ + The Input Database houses the raw data that are the basis for all HMD calculations. Input data files for each population are accessible from the country page.
+ + citation_full: |-
+ + HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
+ +
+ + See also the methods protocol:
+ + Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). [Available online](https://www.mortality.org/File/GetDocument/Public/Docs/MethodsProtocolV6.pdf) (needs log in to mortality.org).
+ + attribution_short: HMD
+ + url_main: https://www.mortality.org/Data/ZippedDataFiles
+ + date_accessed: '2024-11-27'
+ + date_published: '2024-11-13'
+ + license:
+ + name: CC BY 4.0
+ + url: https://www.mortality.org/Data/UserAgreement
+ + unit: ''
+ + presentation:
+ + topic_tags:
+ + - Fertility Rate
+ + New values: 61 / 3998 (1.53%)
country year month_max_name_lead_9months
Bulgaria 1924 May
France 1860 July
Greece 1955 April
Japan 1898 April
West Germany 1945 September
~ Changed values: 3937 / 3998 (98.47%)
country year month_max_name_lead_9months - month_max_name_lead_9months +
Denmark 2022 NaN October
Estonia 2007 NaN October
Iceland 1937 NaN January
Portugal 1988 NaN August
Sweden 1963 NaN July
= Table birth_rate_month
~ Column birth_rate (changed metadata, changed data)
- - title: Birth rate (monthly) - << month >>
+ + title: Birth rate, in << month >>
~ Changed values: 517 / 47243 (1.09%)
country year month birth_rate - birth_rate +
Finland 2023 March 0.658883 0.658097
Hong Kong 2020 July 0.45487 0.455102
Hungary 2020 September 0.870945 0.873273
New Zealand 2020 December 0.936682 0.925427
Slovenia 2019 March 0.761227 0.760313
~ Column birth_rate_per_day (changed metadata, changed data)
- - title: Daily birth rate (average in month) - << month >>
+ + title: Birth rate per day, in << month >>
- - description_short: The average daily number of births, per 1,000 people, calculated for <<month>>.
? ^^^^^
+ + description_short: The average daily number of births, per million people, calculated for <<month>>.
? ^^^^^^^
- - unit: births per 1,000 people
? ^^^^^
+ + unit: births per million people
? ^^^^^^^
~ Changed values: 517 / 47243 (1.09%)
country year month birth_rate_per_day - birth_rate_per_day +
Finland 2023 March 21.254305 21.228937
Hong Kong 2020 July 14.673223 14.680702
Hungary 2020 September 29.031488 29.109112
New Zealand 2020 December 30.215536 29.85248
Slovenia 2019 March 24.555696 24.526213
= Table birth_rate
~ Dim country
+ + New values: 47792 / 47792 (100.00%)
date country
2019-04-30 Germany
1959-11-30 Greece
1915-04-30 Italy
2020-12-31 Norway
1978-07-31 United States
- - Removed values: 47243 / 47792 (98.85%)
date country
1994-10-01 Croatia
1961-11-01 Denmark
2010-12-01 Estonia
2001-04-01 Luxembourg
2017-11-01 New Zealand
~ Dim date
+ + New values: 47792 / 47792 (100.00%)
country date
Germany 2019-04-30
Greece 1959-11-30
Italy 1915-04-30
Norway 2020-12-31
United States 1978-07-31
- - Removed values: 47243 / 47792 (98.85%)
country date
Croatia 1994-10-01
Denmark 1961-11-01
Estonia 2010-12-01
Luxembourg 2001-04-01
New Zealand 2017-11-01
~ Column birth_rate (changed metadata, new data, changed data)
- - title: Birth rate (monthly)
? ^^ ^
+ + title: Birth rate, on a monthly basis
? ^^^^^^^ ^^^^^^
+ + New values: 47792 / 47792 (100.00%)
country date birth_rate
Germany 2019-04-30 0.754137
Greece 1959-11-30 1.266592
Italy 1915-04-30 2.590419
Norway 2020-12-31 0.677254
United States 1978-07-31 1.327256
- - Removed values: 47243 / 47792 (98.85%)
country date birth_rate
Croatia 1994-10-01 0.939606
Denmark 1961-11-01 1.305436
Estonia 2010-12-01 0.91209
Luxembourg 2001-04-01 1.036519
New Zealand 2017-11-01 1.028617
~ Column birth_rate_lead_9months (changed metadata, new data)
- - {}
+ + title: Birth rate, on a monthly basis, by estimated month of conception
+ + description_short: The total number of births per 1,000 people in a given month.
+ + origins:
+ + - producer: Human Mortality Database
+ + title: Human Mortality Database
+ + description: |-
+ + The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
+ +
+ +
+ + # Scope and basic principles
+ +
+ + The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
+ +
+ + The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
+ +
+ +
+ + # Computing death rates and life tables
+ +
+ + Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
+ +
+ + 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
+ + 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
+ + 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
+ + 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
+ + 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
+ + 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
+ +
+ +
+ + # Corrections to the data
+ +
+ + The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
+ +
+ + Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
+ +
+ +
+ + # Age misreporting
+ +
+ + Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
+ +
+ + In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
+ +
+ + Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
+ +
+ +
+ + # Uniform set of procedures
+ +
+ + A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
+ +
+ + It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
+ +
+ + Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
+ + citation_full: |-
+ + HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
+ +
+ + See also the methods protocol:
+ + Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality database (v6). [Available online](https://www.mortality.org/File/GetDocument/Public/Docs/MethodsProtocolV6.pdf) (needs log in to mortality.org).
+ + attribution_short: HMD
+ + url_main: https://www.mortality.org/Data/ZippedDataFiles
+ + date_accessed: '2024-11-27'
+ + date_published: '2024-11-13'
+ + license:
+ + name: CC BY 4.0
+ + url: https://www.mortality.org/Data/UserAgreement
+ + - producer: Human Mortality Database
+ + title: Human Mortality Database, by country
+ + description: |-
+ + The Human Mortality Database (HMD) contains original calculations of all-cause death rates and life tables for national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources.
+ +
+ +
+ + # Scope and basic principles
+ +
+ + The database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included here are relatively wealthy and for the most part highly industrialized.
+ +
+ + The main goal of the Human Mortality Database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. As much as possible, the authors of the database have followed four guiding principles: comparability, flexibility, accessibility, reproducibility.
+ +
+ +
+ + # Computing death rates and life tables
+ +
+ + Their process for computing mortality rates and life tables can be described in terms of six steps, corresponding to six data types that are available from the HMD. Here is an overview of the process:
+ +
+ + 1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
+ + 2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
+ + 3. Population size. Annual estimates of population size on January 1st are either obtained from another source or are derived from census data plus birth and death counts.
+ + 4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
+ + 5. Death rates. Death rates are always a ratio of the death count for a given age-time interval divided by an estimate of the exposure-to-risk in the same interval.
+ + 6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.
+ +
+ +
+ + # Corrections to the data
+ +
+ + The data presented here have been corrected for gross errors (e.g., a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected). However, the authors have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events).
+ +
+ + Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, the authors did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.
+ +
+ +
+ + # Age misreporting
+ +
+ + Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, there is evidence of both age heaping (overreporting ages ending in "0" or "5") and age exaggeration in these data.
+ +
+ + In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases, it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement.
+ +
+ + Age exaggeration, on the other hand, is a more insidious problem. The authors' approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, the authors derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.
+ +
+ +
+ + # Uniform set of procedures
+ +
+ + A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that the authors have not introduced biases by the authors' own manipulations. The desire of the authors for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). The authors' general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, they compute death rates and life tables in a variety of age-time configurations.
+ +
+ + It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, the authors' uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what they considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because the authors have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.
+ +
+ + Although the authors adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to an individual researcher, who takes responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks the authors' data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!
+ + description_snapshot: |-
+ + HMD data by country. This contains the raw data, including their "input data", which HMD defines as:
+ +
+ + The Input Database houses the raw data that are the basis for all HMD calculations. Input data files for each population are accessible from the country page.
+ + citation_full: |-
+ + HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.
+ +
+ + See also the methods protocol:
+ + Wilmoth, J. R., Andreev, K., Jdanov, D., Glei, D. A., Riffe, T., Boe, C., Bubenheim, M., Philipov, D., Shkolnikov, V., Vachon, P., Winant, C., & Barbieri, M. (2021). Methods protocol for the human mortality da
...diff too long, truncated...Automatically updated datasets matching weekly_wildfires|excess_mortality|covid|fluid|flunet|country_profile|garden/ihme_gbd/2019/gbd_risk are not included Edited: 2024-12-04 19:42:21 UTC |
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mergeoperation that was leading to data loss (due to using INNER join)/schedule