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The 1915 Iowa State Census Project

Version
v0
Resource Type
Dataset : census/enumeration data
Creator
  • Goldin, Claudia
  • Katz, Lawrence
Other Title
  • Archival Version (Subtitle)
Publication Date
2010-12-14
Publication Place
Ann Arbor, Michigan
Publisher
  • Inter-University Consortium for Political and Social Research
Language
English
Free Keywords
Schema: ICPSR
census data; family structure; fertility; health; households; immigration; migration; mortality; population characteristics
Description
  • Abstract

    The 1915 Iowa State Census is a unique document. It was the first census in the United States to include information on education and income prior to the United States Federal Census of 1940. It contains considerable detail on other aspects of individuals and households, e.g., religion, wealth and years in the United States and Iowa. The Iowa State Census of 1915 was a complete sample of the residents of the state and the returns were written by census takers (assessors) on index cards. These cards were kept in the Iowa State Archives in Des Moines and were microfilmed in 1986 by the Genealogical Society of Salt Lake City. The census cards were sorted by county, although large cities (those having more than 25,000 residents) were grouped separately. Within each county or large city, records were alphabetized by last name and within last name by first name. This data set includes individual-level records for three of the largest Iowa cities (Des Moines, Dubuque, and Davenport; the Sioux City films were unreadable) and for ten counties that did not contain a large city. (Additional details on sample selection are available in the documentation). Variables include name, age, place of residence, earnings, education, birthplace, religion, marital status, race, occupation, military service, among others. Data on familial ties between records are also included.
  • Abstract

    Historical state census data for residents of Iowa in 1915.
  • Methods

    Variables include name (for the rural subsample), age, place of residence, earnings, education, birthplace, church affiliation, marital status, race, occupation, military service, among others. Data on familial relationships within households can also be inferred.
  • Methods

    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: Created variable labels and/or value labels.; Standardized missing values.; Checked for undocumented or out-of-range codes..
  • Methods

    Presence of Common Scales: Occupations were coded using the 1940 Census of Population Codes (see U.S. Department of Commerce, Bureau of the Census, "Alphabetical Index of Occupations and Industries," 16th Census, Washington, D.C.: GPO, 1940).
  • Methods

    Response Rates: The urban sample represents 5.5 percent (n = 26,768 observations) of Iowa's population in large cities in 1914. The rural sample represents 1.8 percent (n = 33,305) of the population in counties without large cities.
  • Abstract

    Datasets:

    • DS1: Dataset
Temporal Coverage
  • Time period: 1915
  • Collection date: 1914
Geographic Coverage
  • Iowa
  • United States
Sampled Universe
The 1915 Iowa State Census is a complete survey of Iowa residents in 1914. This data set, however, takes as its universe only three of the largest Iowa cities (Des Moines, Dubuque, and Davenport; the Sioux City films were unreadable) and ten counties that did not contain a large city. The counties were chosen by grouping the ninety-nine counties in Iowa into four equal units by the mean educational levels of their adult population and then randomly taking three from each of the four groups. None of the counties contained a large city. The ten resulting counties were determined by the quality of the microfilm. These rural counties span the geography of the state: Clay and Lyon in the northwest, Mitchell in the north central, Johnson and Buchanan in the east central, Marshall in the central, Wayne in the south central, Adair and Montgomery in the southwest, and Carroll in the west central. In the urban sample, we sampled every-other family name on each roll of microfilm chosen for the sample. This means that ALL of the Jones would be done and all of the Goldschlagers, should those names appear. When a roll of microfilm is started, it is begun with the SECOND name on the film. The reason is that we do not know whether the first name on the film was left over from the previous film. The same thing applies to the LAST name on a film -- it was not taken. The underlying rationale for this is that the names are alphabetically arranged. With some care, family ties can be established (using name, age, sex, marital status, address and card number), but only if we know that we have all persons in the city (or county) with the same last name. On occasion the cards do not go in alphabetical order and we attempted to re-alphabetize the order, continuing to take all persons having every other last name. For the rural sample, we sampled all names on each roll of microfilm chosen for the sample, and inferred familial relations as described for the urban sample. Altogether, the urban sample contains 26,768 observations or 5.5 percent of Iowa's population in large cities. The rural sample contains 33,305 observations or 1.8 percent of the population in counties without large cities. Smallest Geographic Unit: town/ward
Sampling
Data set consists of an urban and rural sample. As described, urban sample includes residents of three of the largest Iowa cities: Davenport, Des Moines, and Dubuque. For the urban sample, we sampled every-other family name one each relevant roll of microfilm. This means that ALL of the Jones would be done and all off the Goldschlagers, should those names appear. When a roll of microfilm is started, it is begun with the SECOND name on the film. The reason is that we do not know whether the first name on the film was left over from the previous film. The same thing applies to the LAST name on a film -- it was not taken. The underlying rationale for this is that the names are alphabetically arranged. With some care, family ties can be established (using name, age, sex, marital status, address and card number), but only if we know that we have all persons in the city (or county) with the same last name. On occasion the cards do not go in alpha order and we attempted to re-alpha order, continuing to take all persons having every other last name. The total urban sample contains 26,768 observations or 5.5 percent of Iowa's large cities (25,000+) population. The rural sample includes records from ten counties that did not contain a large city. These counties were chosen by grouping the ninety-nine counties in Iowa into four equal units by the mean educational levels of their adult population and then randomly taking three from each of the foru groups. The ten resulting counties were determined by the quality of the microfilm. The ten resulting counties were determined by the quality of the microfilm. These rural counties span the geography of the state: Clay and Lyon in the northwest, Mitchell in the north central, Johnson and Buchanan in the east central, Marshall in the central, Wayne in the south central, Adair and Montgomery in the southwest, and Carroll in the west central. The rural sample includes 33,305 observations or 1.8 percent of the population in counties without large cities.
Collection Mode
  • coded on-site observation
  • on-site questionnaire
Availability
Download
This version of the study is no longer available on the web. If you need to acquire this version of the data, you have to contact ICPSR User Support (ICPSR-help@umich.edu).
Alternative Identifiers
  • 28501 (Type: ICPSR Study Number)
Relations
  • Is previous version of
    DOI: 10.3886/ICPSR28501.v1
Publications
  • Autor, David, Goldin, Claudia, Katz, Lawrence F.. Extending the Race between Education and Technology. National Bureau of Economic Research Working Paper Series.26705, Cambridge, MA: National Bureau of Economic Research. 2020.
    • ID: 10.3386/w26705 (DOI)
  • Saavedra, Martin, Twinam, Tate. A machine learning approach to improving occupational income scores. Explorations in Economic History.75, (101304), 2020.
    • ID: 10.1016/j.eeh.2019.101304 (DOI)
  • Santavirta, Torsten, Stuhler, Jan. Name-Based Estimators of Intergenerational Mobility: Evidence from Finnish Veterans. . 2019.
    • ID: https://tsantavirta.com/wp-content/uploads/2019/09/Santavirta_Stuhler_Names_0911.pdf (URL)
  • Boustan, Leah, Bunten, Devin, Hearey, Owen. Urbanization in American economic history, 1800--2000. The Oxford Handbook of American Economic History, Volume 2.Oxford, United Kingdom: Oxford University Press. 2018.
  • Feigenbaum, James J.. Multiple measures of historical intergenerational mobility: Iowa 1915 to 1940. Economic Journal.128, (612), 446-481.2018.
    • ID: 10.1111/ecoj.12525 (DOI)
  • Saavedra, Martin Hugo, Twinam, Tate, A.. A Machine Learning Approach to Improving Occupational Income Scores. Social Science Research Network. 2018.
    • ID: 10.2139/ssrn.2944870 (DOI)
  • Dobis, Elizabeth A.. The Evolution of the American Urban System: History, Hierarchy, and Contagion. Dissertation, Purdue University. 2017.
  • Garcia, John A.. The race project: Researching race in the social sciences researchers, measures, and scope of studies. Journal of Race, Ethnicity, and Politics.2, (2), 300-346.2017.
    • ID: 10.1017/rep.2017.15 (DOI)
  • Feigenbaum, James J.. A Machine Learning Approach to Census Record Linking. . 2016.
    • ID: https://ranabr.people.stanford.edu/sites/g/files/sbiybj5391/f/machine_learning_approach.pdf (URL)
  • Feigenbaum, James Joseph. Essays on Intergenerational Mobility and Inequality in Economic History. Dissertation, Harvard University. 2016.
  • Bakker, Gerben, Crafts, Nicholas, Woltjer, Pieter. A Vision of the Growth Process in a Technologically Progressive Economy: The United States, 1899-1941. Department of Economic History Working Paper No. 226.London, UK: London School of Economics and Political Science. 2015.
  • Feigenbaum, James J.. A New Old Measure of Intergenerational Mobility: Iowa 1915 to 1940. . 2015.
    • ID: https://scholar.harvard.edu/files/jfeigenbaum/files/feigenbaum_-_intergenerational_mobility_-_10-21-14.pdf (URL)
  • Lafortune, Jeanne, Tessada, Jose, Lewis, Ethan. People and Machines: A Look at the Evolving Relationship Between Capital and Skill In Manufacturing 1860-1930 Using Immigration Shocks. Working Paper 21435.Cambridge, MA: National Bureau of Economic Research. 2015.
    • ID: 10.3386/w21435 (DOI)
  • Sohn, Kitae. The gender gap in earnings among teachers: The case of Iowa in 1915. Feminist Economics.21, (4), 175-196.2015.
    • ID: 10.1080/13545701.2014.936481 (DOI)
  • Boustan, Leah Platt, Bunten, Devin, Hearey, Owen. Urbanization in the United States, 1800-2000. NBER Working Paper 19041.Cambridge, MA: National Bureau of Economic Research (NBER). 2013.
    • ID: http://www.nber.org/papers/w19041.pdf (URL)
  • Parman, John. American mobility and the expansion of public education. Journal of Economic History.71, (1), 105-132.2011.
    • ID: 10.1017/S0022050711000040 (DOI)
  • Goldin, Claudia, Katz, Lawrence F.. The Race Between Education and Technology. Cambridge, MA: Harvard University Press. 2008.
  • Parman, John. The Private and Public Effects of School Reform: Educational Investment, Human Capital Spillovers and Intergenerational Mobility During the Expansion of Public Schools in the United States. Dissertation, Northwestern University. 2008.
  • Goldin, Claudia, Katz, Lawrence F.. Long-Run Changes in the U.S. Wage Structure: Narrowing, Widening, Polarizing. National Bureau of Economic Research Working Paper Series.13568, Cambridge, MA: National Bureau of Economic Research. 2007.
    • ID: 10.3386/w13568 (DOI)
  • Goldin, Claudia, Katz, Lawrence F.. The Race between Education and Technology: The Evolution of U.S. Educational Wage Differentials, 1890 to 2005. National Bureau of Economic Research Working Paper Series.12984, Cambridge, MA: National Bureau of Economic Research. 2007.
    • ID: 10.3386/w12984 (DOI)
  • DeLong, J. Bradford, Goldin, Claudia, Katz, Lawrence F.. Sustaining U.S. Economic Growth. . 2002.
    • ID: https://pdfs.semanticscholar.org/d172/8708e168b16af77022cd9a6a466fbf3bc227.pdf (URL)
  • Goldin, Claudia. The human-capital century and American leadership: Virtues of the past. Journal of Economic History.61, (2), 263-292.2001.
    • ID: 10.1017/S0022050701028017 (DOI)
  • Goldin, Claudia, Katz, Lawrence F.. Decreasing (and then increasing) inequality in America: A tale of two half-centuries. The Causes and Consequences of Increasing Inequality.Chicago, IL: University of Chicago Press. 2001.
  • Goldin, Claudia, Katz, Lawrence F.. Education and income in the early twentieth century: Evidence from the prairies. Journal of Economic History.60, (3), 782-818.2000.
    • ID: 10.1017/S0022050700000334 (DOI)
  • Goldin, Claudia, Katz, Lawrence F.. Human capital and social capital: The rise of secondary schooling in America, 1910-1940. Journal of Interdisciplinary History.29, (4), 683-723.1999.
    • ID: www.jstor.org/stable/206979 (URL)
  • Goldin, Claudia, Katz, Lawrence F.. Human Capital and Social Capital: The Rise of Secondary Schooling in America, 1910 to 1940. NBER Working Paper No. 6439.National Bureau of Economic Research, . 1998.
    • ID: http://www.nber.org/papers/w6439.pdf (URL)

Update Metadata: 2020-06-09 | Issue Number: 7 | Registration Date: 2015-06-16

Goldin, Claudia; Katz, Lawrence (2010): The 1915 Iowa State Census Project. Archival Version. Version: v0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/ICPSR28501