Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Documentation

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1920 census data was collected in January 1920. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

• Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

• Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, MORTGAGE, FARM, CLASSWKR, OCC1950, IND1950, MARST, RACE, SEX, RELATE, MTONGUE. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

• Most inconsistent information was not edited for this release, thus there are observations outside of the universe for some variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next release.

%3C!-- --%3E

Section 2

This dataset was created on 2020-01-10 18:46:34.647 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1920 households: This dataset includes all households from the 1920 US census.

IPUMS 1920 persons: This dataset includes all individuals from the 1920 US census.

IPUMS 1920 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1920 datasets.

This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1920 datasets.

1920 United States Federal Census contains records from Philadelphia, Pennsylvania, USA by Fourteenth Census of the United States, 1920. (NARA microfilm publication T625, 2076 rolls). Records of the Bureau of the Census, Record Group 29. National Archives, Washington, D.C. Year: 1920; Census Place: Philadelphia Ward 42, Philadelphia, Pennsylvania; Roll: T625_1643; Page: 13A; Enumeration District: 1564 - .

This dataset includes all individuals from the 1920 US census.

This dataset includes all households from the 1920 US census.

This crosswalk consists of individuals matched between the 1900 and 1920 complete-count US Censuses. Within the crosswalk, users have the option to select the linking method with which these matches were created. This version of the crosswalk contains links made by the ABE-exact (conservative and standard) method, the ABE-NYSIIS (conservative and standard) method and the ABE-NYSIIS (conservative and standard) method where race is used as a matching variable. This crosswalk also includes Census Tree Links created by Joseph Price, Kasey Buckles and Mark Clement at the Brigham Young University (BYU) Record Linking Lab. More detail on these links can be found in the census_tree_links_BYU_readme. For any chosen method, users can merge into this crosswalk a wide set of individual- and household-level variables provided publicly by IPUMS, thereby creating a historical longitudinal dataset for analysis.

This dataset is made up of images containing handwritten 3-digit occupation codes from the Norwegian population census of 1950. The occupation codes were added to the census sheets by Statistics Norway after the census was concluded for the purpose of creating aggregated occupational statistics for the entire population. The coding standard used in the 1950 census is, according to Statistics Norway’s official publications (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1950, booklet 4, page 81), very similar to the standards used in the census for 1920. Cf. the 13th booklet published for the 1920 census (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1920, note that this booklet is only available in Norwegian). In short, an occupation code is a 3-digit number that corresponds to a given occupation or type of occupation. According to the official list of occupation codes provided by Statistics Norway there are 339 unique codes. These are not all necessarily sequential or hierarchical in general, but some subgroupings are. This list can be found under Files. It is also worth noting that these images were extracted from the original census sheet images algorithmically. This process was not flawless and lead to additional images being extracted, these can contain written occupation titles or be left entirely blank. The dataset consists of 90,000 unique images, and 9,000 images that were randomly selected and copied from the unique images. These were all used for a research project (link to preprint article: https://doi.org/10.48550/arXiv.2306.16126) where we (author list can be found in preprint) tried to find a more efficient way of reviewing and correcting classification results from a Machine Learning model, where the results did not pass a pre-set confidence threshold. This was a follow-up to our previous article where we describe the initial project and creating of our model in more detail, if it is of interest (“Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes”, https://doi.org/10.51964/hlcs11331).

Abstract (en): These data on 19th- and early 20th-century police department and arrest behavior were collected between 1975 and 1978 for a study of police and crime in the United States. Raw and aggregated time-series data are presented in Parts 1 and 3 on 23 American cities for most years during the period 1860-1920. The data were drawn from annual reports of police departments found in the Library of Congress or in newspapers and legislative reports located elsewhere. Variables in Part 1, for which the city is the unit of analysis, include arrests for drunkenness, conditional offenses and homicides, persons dismissed or held, police personnel, and population. Part 3 aggregates the data by year and reports some of these variables on a per capita basis, using a linear interpolation from the last decennial census to estimate population. Part 2 contains data for 267 United States cities for the period 1880-1890 and was generated from the 1880 federal census volume, REPORT ON THE DEFECTIVE, DEPENDENT, AND DELINQUENT CLASSES, published in 1888, and from the 1890 federal census volume, SOCIAL STATISTICS OF CITIES. Information includes police personnel and expenditures, arrests, persons held overnight, trains entering town, and population. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. 2006-01-12 All files were removed from dataset 4 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 4 and flagged as study-level files, so that they will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics.

Data base of results of Reichstag elections between 1920 and 1933 as well as data on economic and social structure at various geographic aggregate levels (municipalities and districts); cross section analysis and longitudinal analysis possible.

Topics: Identification variables for the area units: name of survey unit, constituency affiliation, aggregate codes; Reichstag election results (6.6.1920, 4.5.1924, 7.12.1924, 20.5.1928, 14.9.1930, 31.7.1932, 6.11.1932, 5.3.1933); social-structural collective characteristics of area units: number of residents, denominational structure, structure of the population according to economic divisions and professional position, unemployment.

Content

The data provides the population of Japan as collected by the official Japanese government from 1920 to 2015. It is given by year, prefecture, age range, and gender.

Inspiration

Can the data be used to answer questions such as the following?

• When and how did mass movement of population occurred from rural to urban areas?
• Which prefectures are rural? Which are urban?
• Were there gender and/or age differences in the movement of population?
• In what manner is the population shifting toward aged people?
• And much more.

Method of Editing and Translation

The following script written by the dataset owner was used:

import pandas as pd
import numpy as np
import re

japan_census = pd.read_csv('~/Downloads/c03.csv', encoding = 'SJIS')
# Eliminate a note
japan_census = japan_census.iloc[:-1]
# Eliminate the sums across prefectures
japan_census = japan_census[japan_census['年齢5歳階級'] != '総数']

def prefecture(japanese):
 return {
  '北海道': 'Hokkaido',
  '青森県': 'Aomori Prefecture',
  '岩手県': 'Iwate Prefecture',
  '宮城県': 'Miyagi Prefecture',
  '秋田県': 'Akita Prefecture',
  '山形県': 'Yamagata Prefecture',
  '福島県': 'Fukushima Prefecture',
  '茨城県': 'Ibaraki Prefecture',
  '栃木県': 'Tochigi Prefecture',
  '群馬県': 'Gunma Prefecture',
  '埼玉県': 'Saitama Prefecture',
  '千葉県': 'Chiba Prefecture',
  '東京都': 'Tokyo Metropolis',
  '神奈川県': 'Kanagawa Prefecture',
  '新潟県': 'Niigata Prefecture',
  '富山県': 'Toyama Prefecture',
  '石川県': 'Ishikawa Prefecture',
  '福井県': 'Fukui Prefecture',
  '山梨県': 'Yamanashi Prefecture',
  '長野県': 'Nagano Prefecture',
  '岐阜県': 'Gifu Prefecture',
  '静岡県': 'Shizuoka Prefecture',
  '愛知県': 'Aichi Prefecture',
  '三重県': 'Mie Prefecture',
  '滋賀県': 'Shiga Prefecture',
  '京都府': 'Kyoto Prefecture',
  '大阪府': 'Osaka Prefecture',
  '兵庫県': 'Hyogo Prefecture',
  '奈良県': 'Nara Prefecture',
  '和歌山県': 'Wakayama Prefecture',
  '鳥取県': 'Tottori Prefecture',
  '島根県': 'Shimane Prefecture',
  '岡山県': 'Okayama Prefecture',
  '広島県': 'Hiroshima Prefecture',
  '山口県': 'Yamaguchi Prefecture',
  '徳島県': 'Tokushima Prefecture',
  '香川県': 'Kagawa Prefecture',
  '愛媛県': 'Ehime Prefecture',
  '高知県': 'Kochi Prefecture',
  '福岡県': 'Fukui Prefecture',
  '佐賀県': 'Saga Prefecture',
  '長崎県': 'Nagasaki Prefecture',
  '熊本県': 'Kumamoto Prefecture',
  '大分県': 'Oita Prefecture',
  '宮崎県': 'Miyazaki Prefecture',
  '鹿児島県': 'Kagoshima Prefecture',
  '沖縄県': 'Okinawa Prefecture',
 }.get(japanese)

japan_census_translated = pd.DataFrame()
japan_census_translated['Year'] = japan_census['西暦（年）'].astype('int')
japan_census_translated['Prefecture'] = japan_census['都道府県名'].map(lambda x: prefecture(x))
japan_census_translated[['Age Lower Bound', 'Age Upper Bound']] = [
 [m.group(1), m.group(2)] for m in japan_census['年齢5歳階級'].map(lambda x: re.search('(\d+)\D+(\d+)?', x))
]
japan_census_translated = pd.DataFrame(
 np.repeat(japan_census_translated.values, 2, axis = 0),
 columns = japan_census_translated.columns
)
japan_census_translated[['Gender', 'Population']] = [
 x for _, row in japan_census.iterrows() for x in [
  ['Male', int(row.loc['人口（男）'])],
  ['Female', int(row.loc['人口（女）'])],
 ]
]
print(japan_census_translated)
japan_census_translated.to_csv('japanese_census.csv')

Block-level census coverage of early Central Phoenix for 1920, 1930, and 1940, including population, race/ethnicity, household ownership and rentership, and temporary residency. This dataset was designed for use in combination with parcel-level land-use data derived from Sanborn Fire Insurance Maps to assess environmental justice issues in Phoenix’s early 20th Century development.

Abstract (en): This collection includes county-level data from the United States Censuses of Agriculture for the years 1840 to 2012. The files provide data about the number, types, output, and prices of various agricultural products, as well as information on the amount, expenses, sales, values, and production of machinery. Most of the basic crop output data apply to the previous harvest year. Data collected also included the population and value of livestock, the number of animals slaughtered, and the size, type, and value of farms. Part 46 of this collection contains data from 1980 through 2010. Variables in part 46 include information such as the average value of farmland, number and value of buildings per acre, food services, resident population, composition of households, and unemployment rates. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Not applicable. Datasets:DS0: Study-Level FilesDS1: Farm Land Value Data Set (County and State) 1850-1959DS2: 1840 County and StateDS3: 1850 County and StateDS4: 1860 County and StateDS5: 1870 County and StateDS6: 1880 County and StateDS7: 1890 County and StateDS8: 1900 County and StateDS9: 1910 County and StateDS10: 1920 County and State, Dataset 1DS11: 1920 County and State, Dataset 2DS12: 1925 County and StateDS13: 1930 County and State, Dataset 1DS14: 1930 County and State, Dataset 2DS15: 1935 County and StateDS16: 1940 County and State, Dataset 1DS17: 1940 County and State, Dataset 2DS18: 1940 County and State, Dataset 3DS19: 1940 County and State, Dataset 4 (Water)DS20: 1945 County and StateDS21: 1950 County and State, Dataset 1DS22: 1950 Crops, County and State, Dataset 2DS23: 1950 County, Dataset 3DS24: 1950 County and State, Dataset 4DS25: 1954 County and State, Dataset 1DS26: 1954 Crops, County and State, Dataset 2DS27: 1959 County and State, Dataset 1DS28: 1959 Crops, County and State, Dataset 2DS29: 1959 County, Dataset 3DS30: 1964 Dataset 1DS31: 1964 Crops, County and State, Dataset 2DS32: 1964 County, Dataset 3DS33: 1969 All Farms, County and State, Dataset 1DS34: 1969 Farms 2500, County and State, Dataset 2DS35: 1969 Crops, County and State, Dataset 3DS36: 1974 All Farms, County and State, Dataset 1DS37: 1974 Farms 2500, County and State, Dataset 2DS38: 1974 Crops, County and State, Dataset 3DS39: 1978 County and StateDS40: 1982 County and StateDS41: 1987 County and StateDS42: 1992 County and StateDS43: 1997 County and StateDS44: 2002 County and StateDS45: 2007 County and StateDS46: State and County Data, United States, 1980-2010DS47: 2012 County and State Farms within United States counties and states. Smallest Geographic Unit: FIPS code The sample was the universe of agricultural operating units. For 1969-2007, data were taken from computer files from the Census Bureau and the United States Department of Agriculture. 2018-08-20 The P.I. resupplied data and documentation for 1935 County and State (dataset 15) and 1997 County and State (dataset 43). Additionally, documentation updates and variable label revisions have been incorporated in datasets 22, 26, 28, 31, 35, and 38 at the request of the P.I.2016-06-29 The data and documentation for 2012 County and State (data set 47) have been added to this collection. The collection and documentation titles have been updated to reflect the new year.2015-08-05 The data, setup files, and documentation for 1964 Dataset 1 have been updated to reflect changes from the producer. Funding insitution(s): National Science Foundation (NSF-SES-0921732; 0648045). United States Department of Health and Human Services. National Institutes of Health (R01 HD057929).

In 1800, the population of Luxembourg was estimated to be 127,000, a figure which would rise steadily through the early 19 th century as the country would become an increasingly prominent city in the region. Luxembourg’s population would see its first major period of growth following the defeat of Napoleon in 1815, which would result in the previously-French occupied Luxembourg being granted formal autonomy in the subsequent Congress of Vienna. As a largely agrarian state at this time, the population of Luxembourg would see several periods of growth and decline throughout the remainder of the 20th century, as many residents emigrated abroad to countries such as the United States in search of work. Nevertheless, the population of Luxembourg would rise to over 235,000 by the turn of the century, as Dutch modernization and the removal of the city’s fortifications in the 1867 Treaty of London would allow for a greater expansion of the city proper.

The first half of the 20 th century would largely be a period of stagnation for the country, as the country would see large periods of stagnation in the 1910s and throughout the 1930s and 1940s, as occupation in both World Wars and the 1918 Spanish Flu epidemic) would see massive damage to the city in both human and economic terms. Luxembourg’s population would see significant growth in the country’s population, particularly so following the creation of the European Union in 1958 (Luxembourg was one of the six founding members of the union). Growth would accelerate even further following the 1980s, as increases in industrialization and accompanying economic growth would lead to an increasingly growing immigrant population from other EU nations in Luxembourg, which by 2015 would account for nearly half the citizens in Luxembourg. As a result of this growth, in 2020, Luxembourg is estimated to have a population of 626,000.

In 1800, the population of the area of modern-day Bangladesh was estimated to be just over 19 million, a figure which would rise steadily throughout the 19th century, reaching over 26 million by 1900. At the time, Bangladesh was the eastern part of the Bengal region in the British Raj, and had the most-concentrated Muslim population in the subcontinent's east. At the turn of the 20th century, the British colonial administration believed that east Bengal was economically lagging behind the west, and Bengal was partitioned in 1905 as a means of improving the region's development. East Bengal then became the only Muslim-majority state in the eastern Raj, which led to socioeconomic tensions between the Hindu upper classes and the general population. Bengal Famine During the Second World War, over 2.5 million men from across the British Raj enlisted in the British Army and their involvement was fundamental to the war effort. The war, however, had devastating consequences for the Bengal region, as the famine of 1943-1944 resulted in the deaths of up to three million people (with over two thirds thought to have been in the east) due to starvation and malnutrition-related disease. As the population boomed in the 1930s, East Bengal's mismanaged and underdeveloped agricultural sector could not sustain this growth; by 1942, food shortages spread across the region, millions began migrating in search of food and work, and colonial mismanagement exacerbated this further. On the brink of famine in early-1943, authorities in India called for aid and permission to redirect their own resources from the war effort to combat the famine, however these were mostly rejected by authorities in London. While the exact extent of each of these factors on causing the famine remains a topic of debate, the general consensus is that the British War Cabinet's refusal to send food or aid was the most decisive. Food shortages did not dissipate until late 1943, however famine deaths persisted for another year. Partition to independence Following the war, the movement for Indian independence reached its final stages as the process of British decolonization began. Unrest between the Raj's Muslim and Hindu populations led to the creation of two separate states in1947; the Muslim-majority regions became East Pakistan (now Bangladesh) and West Pakistan (now Pakistan), separated by the Hindu-majority India. Although East Pakistan's population was larger, power lay with the military in the west, and authorities grew increasingly suppressive and neglectful of the eastern province in the following years. This reached a tipping point when authorities failed to respond adequately to the Bhola cyclone in 1970, which claimed over half a million lives in the Bengal region, and again when they failed to respect the results of the 1970 election, in which the Bengal party Awami League won the majority of seats. Bangladeshi independence was claimed the following March, leading to a brutal war between East and West Pakistan that claimed between 1.5 and three million deaths in just nine months. The war also saw over half of the country displaced, widespread atrocities, and the systematic rape of hundreds of thousands of women. As the war spilled over into India, their forces joined on the side of Bangladesh, and Pakistan was defeated two weeks later. An additional famine in 1974 claimed the lives of several hundred thousand people, meaning that the early 1970s was one of the most devastating periods in the country's history. Independent Bangladesh In the first decades of independence, Bangladesh's political hierarchy was particularly unstable and two of its presidents were assassinated in military coups. Since transitioning to parliamentary democracy in the 1990s, things have become comparatively stable, although political turmoil, violence, and corruption are persistent challenges. As Bangladesh continues to modernize and industrialize, living standards have increased and individual wealth has risen. Service industries have emerged to facilitate the demands of Bangladesh's developing economy, while manufacturing industries, particularly textiles, remain strong. Declining fertility rates have seen natural population growth fall in recent years, although the influx of Myanmar's Rohingya population due to the displacement crisis has seen upwards of one million refugees arrive in the country since 2017. In 2020, it is estimated that Bangladesh has a population of approximately 165 million people.

In 1800, the population of the area of modern-day Hungary was approximately 3.3 million, a figure which would steadily rise in the first two decades of the 19th century, as modernization driven by rising exports of cash crops resulting from the ongoing Napoleonic wars would see Hungary become a major exporter in Europe. The slowing in population growth in the 1920s can be attributed in part to the economic recession which hit Hungary in the years following Napoleon defeat, as a grain prices collapsed, and economic hardship intensified in the country. Hungary would see a small increase in population growth in the 1860s, as the country would merge with the Austria to form Austria-Hungary in 1967. As industrialization would continue to accelerate in Hungary, the country’s population rise even further, reaching just over seven million by 1900.

While Hungary had enjoyed largely uninterrupted growth throughout the 19th century, the first half of the 20th century would see several major disruptions to Hungary’s population growth. Growth would slow greatly in the First World War, as Austria-Hungary would find itself one of the largest combatants in the conflict, losing an estimated 1.8 to 2 million people to the war. Hungary’s population would flatline entirely in the 1940s, as the country would see extensive military losses in the country’s invasion of the Soviet Union alongside Germany, and further loss of civilian life in the German occupation of the country and subsequent deportation and mass-murder of several hundred thousand Hungarian Jews. As a result, Hungary’s population would remain stagnant at just over nine million until the early 1950s.

After remaining stagnant for over a decade, Hungary’s population would spike greatly in the early 1950s, as a combination of a tax on childlessness and strict contraception restrictions implemented by then-Minister of Public Welfare Anna Ratkó would lead to a dramatic expansion in births, causing Hungary’s population to rise by over half a million in just five years. However, this spike would prove only temporary, as the death of Stalin in 1953 and subsequent resignation of much of the Stalinist regime in Hungary would see an end to the pro-natalist policies driving the spike. From 1980 onward, however, Hungary’s population would begin to steadily decline, as a sharp reduction in birth rates, combined with a trend of anti-immigrant policies by the Hungarian government, both before and after the collapse of the Soviet bloc, has led Hungary’s population to fall steadily from its 10.8 million peak in 1980, and in 2020, Hungary is estimated to have a population of just over nine and a half million.

【対象期間】大正9年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1, 1920 . SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 人口センサス, 雇用, 統計, 経済, Statistics, Economics, Censuses, Employment, 人口, 労働力, Population, Labour Force【リソース】Fulltext

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

【対象期間】大正9年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1,1920. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 家族生活と結婚, 人口センサス, 統計, 社会, Statistics, Society, Family life and marriage, Censuses, 世帯, Households【リソース】Fulltext

【対象期間】大正9年10月1日国勢調査【注】【計数出所】内閣統計局調査 / PERIOD: Population census on Oct. 1, 1920. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet]. / 公的統計: 集計データ、統計表 / official statistics: aggregate data / 集計 / Aggregation / Keywords: 人口センサス, 雇用, 統計, 経済, Statistics, Economics, Censuses, Employment, 人口, 労働力, Population, Labour Force【リソース】Fulltext

In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.