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

This dataset was created on 2020-01-10 22:52:11.461 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1930 households: This dataset includes all households from the 1930 US census.

IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.

IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

Section 2

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.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 1930 census data was collected in April 1930. 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 IPUMS 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, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. 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 edite

This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

This dataset includes all individuals from the 1930 US census.

This dataset includes all households from the 1930 US census.

This crosswalk consists of individuals matched between the 1920 and 1930 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, ABE-EI exact (conservative and standard) method, and the ABE-EI NYSIIS (conservative and standard) method, with variants in which race is used as a matching variable. 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.

The Home Owners’ Loan Corporation (HOLC) was a U.S. federal agency that graded mortgage investment risk of neighborhoods across the U.S. between 1935 and 1940. HOLC residential security maps standardized neighborhood risk appraisal methods that included race and ethnicity, pioneering the institutional logic of residential “redlining.” The Mapping Inequality Project digitized the HOLC mortgage security risk maps from the 1930s. We overlaid the HOLC maps with 2010 and 2020 census tracts for 142 cities across the U.S. using ArcGIS and determined the proportion of HOLC residential security grades contained within the boundaries. We assigned a numerical value to each HOLC risk category as follows: 1 for “A” grade, 2 for “B” grade, 3 for “C” grade, and 4 for “D” grade. We calculated a historic redlining score from the summed proportion of HOLC residential security grades multiplied by a weighting factor based on area within each census tract. A higher score means greater redlining of the census tract. Continuous historic redlining score, assessing the degree of “redlining,” as well as 4 equal interval divisions of redlining, can be linked to existing data sources by census tract identifier allowing for one form of structural racism in the housing market to be assessed with a variety of outcomes. The 2010 files are set to census 2010 tract boundaries. The 2020 files use the new census 2020 tract boundaries, reflecting the increase in the number of tracts from 12,888 in 2010, to 13,488 in 2020. Use the 2010 HRS with decennial census 2010 or ACS 2010-2019 data. As of publication (10/15/2020) decennial census 2020 data for the P1 (population) and H1 (housing) files are available from census.

For 75 parts of town of greater Berlin election results of the city representative elections of 1929, the Reichstag elections of 1930 and 1932 (November), the city representatives elections of 1946 as well as census data on population status, religious denomination and sex of 1933 and 1946. For 40 West Berlin parts of town election results of the city representatives elections of 1948 and the House of Representatives elections of 1950, 1954, 1958 and 1963, as well as census data of 1950 on population status, religious denomination, sex, age, occupation and number of residences.

This data collection contains electoral and demographic data at several levels of aggregation (kreis, land/regierungsberzirk, and wahlkreis) for Germany in the Weimar Republic period of 1919-1933. Two datasets are available. Part 1, 1919 Data, presents raw and percentagized election returns at the wahlkreis level for the 1919 election to the Nationalversammlung. Information is provided on the number and percentage of eligible voters and the total votes cast for parties such as the German National People's Party, German People's Party, Christian People's Party, German Democratic Party, Social Democratic Party, and Independent Social Democratic Party. Part 2, 1920-1933 Data, consists of returns for elections to the Reichstag, 1920-1933, and for the Reichsprasident elections of 1925 and 1932 (including runoff elections in each year), returns for two national referenda, held in 1926 and 1929, and data pertaining to urban population, religion, and occupations, taken from the German Census of 1925. This second dataset contains data at several levels of aggregation and is a merged file. Crosstemporal discrepancies, such as changes in the names of the geographical units and the disappearance of units, have been adjusted for whenever possible. Variables in this file provide information for the total number and percentage of eligible voters and votes cast for parties, including the German Nationalist People's Party, German People's Party, German Center Party, German Democratic Party, German Social Democratic Party, German Communist Party, Bavarian People's Party, Nationalist-Socialist German Workers' Party (Hitler's movement), German Middle Class Party, German Business and Labor Party, Conservative People's Party, and other parties. Data are also provided for the total number and percentage of votes cast in the Reichsprasident elections of 1925 and 1932 for candidates Jarres, Held, Ludendorff, Braun, Marx, Hellpach, Thalman, Hitler, Duesterburg, Von Hindenburg, Winter, and others. Additional variables provide information on occupations in the country, including the number of wage earners employed in agriculture, industry and manufacturing, trade and transportation, civil service, army and navy, clergy, public health, welfare, domestic and personal services, and unknown occupations. Other census data cover the total number of wage earners in the labor force and the number of female wage earners employed in all occupations. Also provided is the percentage of the total population living in towns with 5,000 inhabitants or more, and the number and percentage of the population who were Protestants, Catholics, and Jews.

In 1844, Romania had a population of just 3.6 million people. During the early entries in this data, Romania's borders were very different and much smaller than today, and control of this area often switched hands between the Austrian, Ottoman and Russian empires. The populations during this time are based on estimates made for incomplete census data, and they show that the population grows from 3.6 million in 1844, doubling to 7.2 million in 1912, part of this growth is due to a high natural birth rate during this period, but also partly due to the changing of Romania's borders and annexation of new lands. During this time Romania gained its independence from the Ottoman Empire as a result of the Russo-Turkish War in 1878, and experienced a period of increased stability and progress.

Between 1912 and 1930 the population of Romania grew by over 10 million people. The main reason for this is the huge territories gained by Romania in the aftermath of the First World War. During the war Romania remained neutral for the first two years, after which it joined the allies; however, it was very quickly defeated and overrun by the Central Powers, and in total it lost over 600 thousand people as a direct result of the war. With the collapse of the Austro-Hungarian and Russian empires after the war, Romania gained almost double it's territory, which caused the population to soar to 18.1 million in 1930. The population then decreases by 1941 and again by 1948, as Romania seceded territory to neighboring countries and lost approximately half a million people during the Second World War. From 1948 onwards the population begins to grow again, reaching it's peak at 23.5 million people in 1990.

Like many other Eastern European countries, there was very limited freedom of movement from Romania during the Cold War, and communist rule was difficult for the Romanian people. The Romanian Revolution in 1989 ended communist rule in the country, Romania transitioned to a free-market society and movement from the country was allowed. Since then the population has fallen each year as more and more Romanians move abroad in search of work and opportunities. The population is expected to fall to 19.2 million in 2020, which is over 4 million fewer people than it had in 1990.

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.

This is the index map, which will provide access to the map sheets that are part of the Volksumskarte der Slowakei series of maps. The Volkstumskarte der Slowakei is an ethnicity map of Slovakia, that was published in 1941, which visualize ethnicity data from the 1930 Czechoslovakia Census. To access the digital version of these maps, please use the Map Index below, zoom into your area of interest (blue box), and click on the Map Sheet that corresponds with your area of interest. Please note that these maps are in KMZ format and should be viewed in Google Earth.To find out more about the maps, please visit the Ethnicity Maps of Southeastern Europe Project, at emse.blog.yorku.ca. The print maps of these maps are found at University of Alberta Libraries.

The world's Jewish population has had a complex and tumultuous history over the past millennia, regularly dealing with persecution, pogroms, and even genocide. The legacy of expulsion and persecution of Jews, including bans on land ownership, meant that Jewish communities disproportionately lived in urban areas, working as artisans or traders, and often lived in their own settlements separate to the rest of the urban population. This separation contributed to the impression that events such as pandemics, famines, or economic shocks did not affect Jews as much as other populations, and such factors came to form the basis of the mistrust and stereotypes of wealth (characterized as greed) that have made up anti-Semitic rhetoric for centuries. Development since the Middle Ages The concentration of Jewish populations across the world has shifted across different centuries. In the Middle Ages, the largest Jewish populations were found in Palestine and the wider Levant region, with other sizeable populations in present-day France, Italy, and Spain. Later, however, the Jewish disapora became increasingly concentrated in Eastern Europe after waves of pogroms in the west saw Jewish communities move eastward. Poland in particular was often considered a refuge for Jews from the late-Middle Ages until the 18th century, when it was then partitioned between Austria, Prussia, and Russia, and persecution increased. Push factors such as major pogroms in the Russian Empire in the 19th century and growing oppression in the west during the interwar period then saw many Jews migrate to the United States in search of opportunity.

【対象期間】昭和5年10月1日国勢調査【注】関東州及満鉄附属地、南洋委任統治区ヲ含マズ【計数出所】外務省調査 / PERIOD: Population census on Oct. 1, 1930. NOTE: (Excluding Kwantung Province and South Manchuria Railway Zone, and, South Pacific Mandate). SOURCE: [Survey by the Ministry of Foreign Affairs]. / 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, 統計, 経済, Statistics, Economics, Censuses, 人口, Population【リソース】Fulltext

This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.

【対象期間】昭和5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, 1930. 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

【対象期間】昭和5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, 1930. 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, 統計, 経済, Statistics, Economics, Censuses, 人口, Population【リソース】Fulltext

【対象期間】昭和5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, 1930. 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, 家族生活と結婚, 統計, 経済, Statistics, Economics, Censuses, Family life and marriage, 人口, 世帯, Population, Households【リソース】Fulltext

【対象期間】昭和5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, 1930. 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, 統計, 経済, Statistics, Economics, Censuses, 人口, Population【リソース】Fulltext

【対象期間】昭和5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, 1930. 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, 家族生活と結婚, 統計, 経済, Statistics, Economics, Censuses, Family life and marriage, 人口, 世帯, Population, Households【リソース】Fulltext

【対象期間】大正14年10月1日国勢調査, 昭和5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, 1925 and Oct. 1, 1930. 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, 家族生活と結婚, 統計, 経済, Statistics, Economics, Censuses, Family life and marriage, 人口, 世帯, Population, Households【リソース】Fulltext