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ipums_bpl.xml
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<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="ipums-ddi-xslt.xsl"?>
<codeBook ID="ddi2-640e0990-8d51-0136-a169-005056a35405-usa_00007.dat-usa.ipums.org" version="2.5" xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 http://www.ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd">
<docDscr>
<citation>
<titlStmt>
<titl>Codebook for an IPUMS USA Data Extract</titl>
<subTitl>DDI 2.5 metadata describing the extract file 'ipums_bpl.dat'</subTitl>
<IDNo>ddi2-640e0990-8d51-0136-a169-005056a35405-usa_00007.dat-usa.ipums.org</IDNo>
</titlStmt>
<rspStmt>
<AuthEnty affiliation="University of Minnesota">IPUMS</AuthEnty>
</rspStmt>
<prodStmt>
<producer abbr="IPUMS" affiliation="University of Minnesota" role="Documentation">IPUMS</producer>
<prodDate date="2024-07-15">July 15, 2024</prodDate>
<prodPlac>IPUMS, 50 Willey Hall, 225 - 19th Avenue South, Minneapolis, MN 55455</prodPlac>
</prodStmt>
<distStmt>
<contact URI="https://ipums.org" affiliation="University of Minnesota">IPUMS</contact>
</distStmt>
</citation>
</docDscr>
<stdyDscr>
<citation>
<titlStmt>
<titl>User Extract ipums_bpl.dat</titl>
</titlStmt>
<rspStmt>
<AuthEnty affiliation="University of Minnesota">IPUMS</AuthEnty>
</rspStmt>
<prodStmt>
<producer abbr="IPUMS" affiliation="University of Minnesota" role="Documentation">IPUMS</producer>
<prodDate date="2024-07-15">July 15, 2024</prodDate>
<prodPlac>IPUMS, 50 Willey Hall, 225 - 19th Avenue South, Minneapolis, MN 55455</prodPlac>
</prodStmt>
<distStmt>
<contact URI="https://ipums.org" affiliation="University of Minnesota">IPUMS</contact>
</distStmt>
<serStmt>
<serName abbr="usa">IPUMS USA</serName>
<serInfo>DOI:10.18128/D010.V15.0</serInfo>
</serStmt>
<verStmt>
<version date="2024-07-15"/>
</verStmt>
</citation>
<stdyInfo>
<subject>
<topcClas vocab="IPUMS">Technical Variables -- HOUSEHOLD</topcClas>
<topcClas vocab="IPUMS">Geographic Variables -- HOUSEHOLD</topcClas>
<topcClas vocab="IPUMS">Group Quarters Variables -- HOUSEHOLD</topcClas>
<topcClas vocab="IPUMS">Technical Variables -- PERSON</topcClas>
<topcClas vocab="IPUMS">Race, Ethnicity, and Nativity Variables -- PERSON</topcClas>
<topcClas vocab="IPUMS">Historical Technical Variables -- PERSON</topcClas>
</subject>
<sumDscr ID="sdatref-2551">
<timePrd date="1850" event="single">1850</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<sumDscr ID="sdatref-2553">
<timePrd date="1860" event="single">1860</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<sumDscr ID="sdatref-2555">
<timePrd date="1870" event="single">1870</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<sumDscr ID="sdatref-2630">
<timePrd date="1880" event="single">1880</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<sumDscr ID="sdatref-2663">
<timePrd date="1900" event="single">1900</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<sumDscr ID="sdatref-2665">
<timePrd date="1910" event="single">1910</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<sumDscr ID="sdatref-2577">
<timePrd date="1920" event="single">1920</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<sumDscr ID="sdatref-2640">
<timePrd date="1930" event="single">1930</timePrd>
<nation abbr="us">United States</nation>
</sumDscr>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1850 1%
Density of the full data file: 1.0%
Density of this extract: 1.0%
]]></notes>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1860 1% sample with black oversample
Density of the full data file: 1.2%
Density of this extract: 1.2%
]]></notes>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1870 1% sample with black oversample
Density of the full data file: 1.2%
Density of this extract: 1.2%
]]></notes>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1880 10%
Density of the full data file: 10.0%
Density of this extract: 10.0%
]]></notes>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1900 1% sample with oversamples
Density of the full data file: 1.2%
Density of this extract: 1.2%
]]></notes>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1910 1.4% sample with oversamples
Density of the full data file: 1.4%
Density of this extract: 1.4%
]]></notes>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1920 1%
Density of the full data file: 1.0%
Density of this extract: 1.0%
]]></notes>
<notes><![CDATA[Additional notes on a sample that is part of this study: 1930 5%
Density of the full data file: 5.0%
Density of this extract: 5.0%
]]></notes>
</stdyInfo>
<dataAccs>
<useStmt>
<confDec required="no"><![CDATA[None]]></confDec>
<contact URI="http://usa.ipums.org" affiliation="IPUMS">IPUMS USA</contact>
<citReq><![CDATA[Publications and research reports based on the IPUMS USA database must cite it appropriately. The citation should include the following:
Steven Ruggles, Sarah Flood, Matthew Sobek, Daniel Backman, Annie Chen, Grace Cooper, Stephanie Richards, Renae Rodgers, and Megan Schouweiler. IPUMS USA: Version 15.0 [dataset]. Minneapolis, MN: IPUMS, 2024. https://doi.org/10.18128/D010.V15.0
The licensing agreement for use of IPUMS USA data requires that users supply us with the title and full citation for any publications, research reports, or educational materials making use of the data or documentation. Please add your citation to the IPUMS bibliography at http://bibliography.ipums.org/.]]></citReq>
<conditions><![CDATA[Users of IPUMS USA data must agree to abide by the conditions of use. A user's license is valid for one year and may be renewed. Users must agree to the following conditions:
(1) No fees may be charged for use or distribution of the data.
(2) Cite IPUMS appropriately. For information on proper citation, refer to the citation requirement section of this DDI document.
(3) Tell us about any work you do using the IPUMS. Publications, research reports, or presentations making use of IPUMS USA should be added to our Bibliography. Continued funding for the IPUMS depends on our ability to show our sponsor agencies that researchers are using the data for productive purposes.
(4) The IPUMS cannot be used for genealogical research
(5) It is difficult to use the IPUMS to study small geographic areas. In the IPUMS census samples for years 1940-present, no places having a population of fewer than 100,000 persons can be identified.
(6) Use it for GOOD -- never for EVIL.
(7) Please notify [email protected] regarding errors in the data or documentation.]]></conditions>
<disclaimer>The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.</disclaimer>
</useStmt>
</dataAccs>
<notes><![CDATA[User-provided description: Revision of (1850-1930 Asian Names), by Birthplace]]></notes>
<notes>This extract is a revision of the user's previous extract, ID 22862546.</notes>
</stdyDscr>
<fileDscr ID="ExtractData">
<fileTxt>
<fileName>ipums_bpl.dat</fileName>
<fileCont>Microdata records</fileCont>
<fileStrc type="rectangular"/>
<fileType charset="ISO-8859-1">ISO-8859-1 data file</fileType>
<format>fixed length fields</format>
<filePlac>IPUMS, 50 Willey Hall, 225 - 19th Avenue South, Minneapolis, MN 55455</filePlac>
</fileTxt>
</fileDscr>
<dataDscr><var ID="YEAR" dcml="0" files="ExtractData" intrvl="discrete" name="YEAR">
<location EndPos="4" StartPos="1" width="4"/>
<labl>Census year</labl>
<txt><![CDATA[YEAR reports the four-digit year when the household was enumerated or included in the census, the ACS, and the PRCS.
For the multi-year ACS/PRCS samples, YEAR indicates the last year of data included (e.g., 2007 for the 2005-2007 3-year ACS/PRCS; 2008 for the 2006-2008 3-year ACS/PRCS; and so on). For the actual year of survey in these multi-year data, see MULTYEAR.]]></txt>
<catgry>
<catValu>1850</catValu>
<labl>1850</labl>
</catgry>
<catgry>
<catValu>1860</catValu>
<labl>1860</labl>
</catgry>
<catgry>
<catValu>1870</catValu>
<labl>1870</labl>
</catgry>
<catgry>
<catValu>1880</catValu>
<labl>1880</labl>
</catgry>
<catgry>
<catValu>1900</catValu>
<labl>1900</labl>
</catgry>
<catgry>
<catValu>1910</catValu>
<labl>1910</labl>
</catgry>
<catgry>
<catValu>1920</catValu>
<labl>1920</labl>
</catgry>
<catgry>
<catValu>1930</catValu>
<labl>1930</labl>
</catgry>
<catgry>
<catValu>1940</catValu>
<labl>1940</labl>
</catgry>
<catgry>
<catValu>1950</catValu>
<labl>1950</labl>
</catgry>
<catgry>
<catValu>1960</catValu>
<labl>1960</labl>
</catgry>
<catgry>
<catValu>1970</catValu>
<labl>1970</labl>
</catgry>
<catgry>
<catValu>1980</catValu>
<labl>1980</labl>
</catgry>
<catgry>
<catValu>1990</catValu>
<labl>1990</labl>
</catgry>
<catgry>
<catValu>2000</catValu>
<labl>2000</labl>
</catgry>
<catgry>
<catValu>2001</catValu>
<labl>2001</labl>
</catgry>
<catgry>
<catValu>2002</catValu>
<labl>2002</labl>
</catgry>
<catgry>
<catValu>2003</catValu>
<labl>2003</labl>
</catgry>
<catgry>
<catValu>2004</catValu>
<labl>2004</labl>
</catgry>
<catgry>
<catValu>2005</catValu>
<labl>2005</labl>
</catgry>
<catgry>
<catValu>2006</catValu>
<labl>2006</labl>
</catgry>
<catgry>
<catValu>2007</catValu>
<labl>2007</labl>
</catgry>
<catgry>
<catValu>2008</catValu>
<labl>2008</labl>
</catgry>
<catgry>
<catValu>2009</catValu>
<labl>2009</labl>
</catgry>
<catgry>
<catValu>2010</catValu>
<labl>2010</labl>
</catgry>
<catgry>
<catValu>2011</catValu>
<labl>2011</labl>
</catgry>
<catgry>
<catValu>2012</catValu>
<labl>2012</labl>
</catgry>
<catgry>
<catValu>2013</catValu>
<labl>2013</labl>
</catgry>
<catgry>
<catValu>2014</catValu>
<labl>2014</labl>
</catgry>
<catgry>
<catValu>2015</catValu>
<labl>2015</labl>
</catgry>
<catgry>
<catValu>2016</catValu>
<labl>2016</labl>
</catgry>
<catgry>
<catValu>2017</catValu>
<labl>2017</labl>
</catgry>
<catgry>
<catValu>2018</catValu>
<labl>2018</labl>
</catgry>
<catgry>
<catValu>2019</catValu>
<labl>2019</labl>
</catgry>
<catgry>
<catValu>2020</catValu>
<labl>2020</labl>
</catgry>
<catgry>
<catValu>2021</catValu>
<labl>2021</labl>
</catgry>
<catgry>
<catValu>2022</catValu>
<labl>2022</labl>
</catgry>
<concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
<varFormat schema="other" type="numeric"/>
</var>
<var ID="SAMPLE" dcml="0" files="ExtractData" intrvl="discrete" name="SAMPLE">
<location EndPos="10" StartPos="5" width="6"/>
<labl>IPUMS sample identifier</labl>
<txt><![CDATA[SAMPLE identifies the IPUMS sample from which the case is drawn. Each sample receives a unique 6-digit code. The codes are structured as follows:
The first four digits are the year of the census/survey.
The next two digits identify the sample within the year.
For most censuses, IPUMS has multiple datasets which were constructed using different sampling techniques (i.e. size/demographic of the sample population, geographic coverage level or location, or duration of the sampling period for the ACS/PRCS samples).
The availability table for each variable indicates whether that variable is available in only certain samples for a given year. For further discussion of sample differences, see "Sample Designs.".
Note: SAMPLE replaces DATANUM. Though the last two digits in SAMPLE do not correlate exactly with the now-deprecated DATANUM, the variable serves the same purpose of assigning a unique id to all cases that belong to the same dataset.]]></txt>
<catgry>
<catValu>202204</catValu>
<labl>2018-2022, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>202203</catValu>
<labl>2018-2022, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>202202</catValu>
<labl>2022 PRCS</labl>
</catgry>
<catgry>
<catValu>202201</catValu>
<labl>2022 ACS</labl>
</catgry>
<catgry>
<catValu>202104</catValu>
<labl>2017-2021, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>202103</catValu>
<labl>2017-2021, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>202102</catValu>
<labl>2021 PRCS</labl>
</catgry>
<catgry>
<catValu>202101</catValu>
<labl>2021 ACS</labl>
</catgry>
<catgry>
<catValu>202004</catValu>
<labl>2016-2020, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>202003</catValu>
<labl>2016-2020, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>202001</catValu>
<labl>2020 ACS</labl>
</catgry>
<catgry>
<catValu>201904</catValu>
<labl>2015-2019, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201903</catValu>
<labl>2015-2019, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201902</catValu>
<labl>2019 PRCS</labl>
</catgry>
<catgry>
<catValu>201901</catValu>
<labl>2019 ACS</labl>
</catgry>
<catgry>
<catValu>201804</catValu>
<labl>2014-2018, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201803</catValu>
<labl>2014-2018, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201802</catValu>
<labl>2018 PRCS</labl>
</catgry>
<catgry>
<catValu>201801</catValu>
<labl>2018 ACS</labl>
</catgry>
<catgry>
<catValu>201704</catValu>
<labl>2013-2017, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201703</catValu>
<labl>2013-2017, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201702</catValu>
<labl>2017 PRCS</labl>
</catgry>
<catgry>
<catValu>201701</catValu>
<labl>2017 ACS</labl>
</catgry>
<catgry>
<catValu>201604</catValu>
<labl>2012-2016, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201603</catValu>
<labl>2012-2016, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201602</catValu>
<labl>2016 PRCS</labl>
</catgry>
<catgry>
<catValu>201601</catValu>
<labl>2016 ACS</labl>
</catgry>
<catgry>
<catValu>201504</catValu>
<labl>2011-2015, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201503</catValu>
<labl>2011-2015, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201502</catValu>
<labl>2015 PRCS</labl>
</catgry>
<catgry>
<catValu>201501</catValu>
<labl>2015 ACS</labl>
</catgry>
<catgry>
<catValu>201404</catValu>
<labl>2010-2014, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201403</catValu>
<labl>2010-2014, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201402</catValu>
<labl>2014 PRCS</labl>
</catgry>
<catgry>
<catValu>201401</catValu>
<labl>2014 ACS</labl>
</catgry>
<catgry>
<catValu>201306</catValu>
<labl>2009-2013, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201305</catValu>
<labl>2009-2013, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201304</catValu>
<labl>2011-2013, PRCS 3-year</labl>
</catgry>
<catgry>
<catValu>201303</catValu>
<labl>2011-2013, ACS 3-year</labl>
</catgry>
<catgry>
<catValu>201302</catValu>
<labl>2013 PRCS</labl>
</catgry>
<catgry>
<catValu>201301</catValu>
<labl>2013 ACS</labl>
</catgry>
<catgry>
<catValu>201206</catValu>
<labl>2008-2012, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201205</catValu>
<labl>2008-2012, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201204</catValu>
<labl>2010-2012, PRCS 3-year</labl>
</catgry>
<catgry>
<catValu>201203</catValu>
<labl>2010-2012, ACS 3-year</labl>
</catgry>
<catgry>
<catValu>201202</catValu>
<labl>2012 PRCS</labl>
</catgry>
<catgry>
<catValu>201201</catValu>
<labl>2012 ACS</labl>
</catgry>
<catgry>
<catValu>201106</catValu>
<labl>2007-2011, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201105</catValu>
<labl>2007-2011, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201104</catValu>
<labl>2009-2011, PRCS 3-year</labl>
</catgry>
<catgry>
<catValu>201103</catValu>
<labl>2009-2011, ACS 3-year</labl>
</catgry>
<catgry>
<catValu>201102</catValu>
<labl>2011 PRCS</labl>
</catgry>
<catgry>
<catValu>201101</catValu>
<labl>2011 ACS</labl>
</catgry>
<catgry>
<catValu>201008</catValu>
<labl>2010 Puerto Rico 10%</labl>
</catgry>
<catgry>
<catValu>201007</catValu>
<labl>2010 10%</labl>
</catgry>
<catgry>
<catValu>201006</catValu>
<labl>2006-2010, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>201005</catValu>
<labl>2006-2010, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>201004</catValu>
<labl>2008-2010, PRCS 3-year</labl>
</catgry>
<catgry>
<catValu>201003</catValu>
<labl>2008-2010, ACS 3-year</labl>
</catgry>
<catgry>
<catValu>201002</catValu>
<labl>2010 PRCS</labl>
</catgry>
<catgry>
<catValu>201001</catValu>
<labl>2010 ACS</labl>
</catgry>
<catgry>
<catValu>200906</catValu>
<labl>2005-2009, PRCS 5-year</labl>
</catgry>
<catgry>
<catValu>200905</catValu>
<labl>2005-2009, ACS 5-year</labl>
</catgry>
<catgry>
<catValu>200904</catValu>
<labl>2007-2009, PRCS 3-year</labl>
</catgry>
<catgry>
<catValu>200903</catValu>
<labl>2007-2009, ACS 3-year</labl>
</catgry>
<catgry>
<catValu>200902</catValu>
<labl>2009 PRCS</labl>
</catgry>
<catgry>
<catValu>200901</catValu>
<labl>2009 ACS</labl>
</catgry>
<catgry>
<catValu>200804</catValu>
<labl>2006-2008, PRCS 3-year</labl>
</catgry>
<catgry>
<catValu>200803</catValu>
<labl>2006-2008, ACS 3-year</labl>
</catgry>
<catgry>
<catValu>200802</catValu>
<labl>2008 PRCS</labl>
</catgry>
<catgry>
<catValu>200801</catValu>
<labl>2008 ACS</labl>
</catgry>
<catgry>
<catValu>200704</catValu>
<labl>2005-2007, PRCS 3-year</labl>
</catgry>
<catgry>
<catValu>200703</catValu>
<labl>2005-2007, ACS 3-year</labl>
</catgry>
<catgry>
<catValu>200702</catValu>
<labl>2007 PRCS</labl>
</catgry>
<catgry>
<catValu>200701</catValu>
<labl>2007 ACS</labl>
</catgry>
<catgry>
<catValu>200602</catValu>
<labl>2006 PRCS</labl>
</catgry>
<catgry>
<catValu>200601</catValu>
<labl>2006 ACS</labl>
</catgry>
<catgry>
<catValu>200502</catValu>
<labl>2005 PRCS</labl>
</catgry>
<catgry>
<catValu>200501</catValu>
<labl>2005 ACS</labl>
</catgry>
<catgry>
<catValu>200401</catValu>
<labl>2004 ACS</labl>
</catgry>
<catgry>
<catValu>200301</catValu>
<labl>2003 ACS</labl>
</catgry>
<catgry>
<catValu>200201</catValu>
<labl>2002 ACS</labl>
</catgry>
<catgry>
<catValu>200101</catValu>
<labl>2001 ACS</labl>
</catgry>
<catgry>
<catValu>200008</catValu>
<labl>2000 Puerto Rico 1%</labl>
</catgry>
<catgry>
<catValu>200007</catValu>
<labl>2000 1%</labl>
</catgry>
<catgry>
<catValu>200006</catValu>
<labl>2000 Puerto Rico 1% sample (old version)</labl>
</catgry>
<catgry>
<catValu>200005</catValu>
<labl>2000 Puerto Rico 5%</labl>
</catgry>
<catgry>
<catValu>200004</catValu>
<labl>2000 ACS</labl>
</catgry>
<catgry>
<catValu>200003</catValu>
<labl>2000 Unweighted 1%</labl>
</catgry>
<catgry>
<catValu>200002</catValu>
<labl>2000 1% sample (old version)</labl>
</catgry>
<catgry>
<catValu>200001</catValu>
<labl>2000 5%</labl>
</catgry>
<catgry>
<catValu>199007</catValu>
<labl>1990 Puerto Rico 1%</labl>
</catgry>
<catgry>
<catValu>199006</catValu>
<labl>1990 Puerto Rico 5%</labl>
</catgry>
<catgry>
<catValu>199005</catValu>
<labl>1990 Labor Market Area</labl>
</catgry>
<catgry>
<catValu>199004</catValu>
<labl>1990 Elderly</labl>
</catgry>
<catgry>
<catValu>199003</catValu>
<labl>1990 Unweighted 1%</labl>
</catgry>
<catgry>
<catValu>199002</catValu>
<labl>1990 1%</labl>
</catgry>
<catgry>
<catValu>199001</catValu>
<labl>1990 5%</labl>
</catgry>
<catgry>
<catValu>198007</catValu>
<labl>1980 Puerto Rico 1%</labl>
</catgry>
<catgry>
<catValu>198006</catValu>
<labl>1980 Puerto Rico 5%</labl>
</catgry>
<catgry>
<catValu>198005</catValu>
<labl>1980 Detailed metro/non-metro</labl>
</catgry>
<catgry>
<catValu>198004</catValu>
<labl>1980 Labor Market Area</labl>
</catgry>
<catgry>
<catValu>198003</catValu>
<labl>1980 Urban/Rural</labl>
</catgry>
<catgry>
<catValu>198002</catValu>
<labl>1980 1%</labl>
</catgry>
<catgry>
<catValu>198001</catValu>
<labl>1980 5%</labl>
</catgry>
<catgry>
<catValu>197009</catValu>
<labl>1970 Puerto Rico Neighborhood</labl>
</catgry>
<catgry>
<catValu>197008</catValu>
<labl>1970 Puerto Rico Municipio</labl>
</catgry>
<catgry>
<catValu>197007</catValu>
<labl>1970 Puerto Rico State</labl>
</catgry>
<catgry>
<catValu>197006</catValu>
<labl>1970 Form 2 Neighborhood</labl>
</catgry>
<catgry>
<catValu>197005</catValu>
<labl>1970 Form 1 Neighborhood</labl>
</catgry>
<catgry>
<catValu>197004</catValu>
<labl>1970 Form 2 Metro</labl>
</catgry>
<catgry>
<catValu>197003</catValu>
<labl>1970 Form 1 Metro</labl>
</catgry>
<catgry>
<catValu>197002</catValu>
<labl>1970 Form 2 State</labl>
</catgry>
<catgry>
<catValu>197001</catValu>
<labl>1970 Form 1 State</labl>
</catgry>
<catgry>
<catValu>196002</catValu>
<labl>1960 5%</labl>
</catgry>
<catgry>
<catValu>196001</catValu>
<labl>1960 1%</labl>
</catgry>
<catgry>
<catValu>195002</catValu>
<labl>1950 100% database</labl>
</catgry>
<catgry>
<catValu>195001</catValu>
<labl>1950 1%</labl>
</catgry>
<catgry>
<catValu>194002</catValu>
<labl>1940 100% database</labl>
</catgry>
<catgry>
<catValu>194001</catValu>
<labl>1940 1%</labl>
</catgry>
<catgry>
<catValu>193004</catValu>
<labl>1930 100% database</labl>
</catgry>
<catgry>
<catValu>193003</catValu>
<labl>1930 Puerto Rico</labl>
</catgry>
<catgry>
<catValu>193002</catValu>
<labl>1930 5%</labl>
</catgry>
<catgry>
<catValu>193001</catValu>
<labl>1930 1%</labl>
</catgry>
<catgry>
<catValu>192003</catValu>
<labl>1920 100% database</labl>
</catgry>
<catgry>
<catValu>192002</catValu>
<labl>1920 Puerto Rico sample</labl>
</catgry>
<catgry>
<catValu>192001</catValu>
<labl>1920 1%</labl>
</catgry>
<catgry>
<catValu>191004</catValu>
<labl>1910 100% database</labl>
</catgry>
<catgry>
<catValu>191003</catValu>
<labl>1910 1.4% sample with oversamples</labl>
</catgry>
<catgry>
<catValu>191002</catValu>
<labl>1910 1%</labl>
</catgry>
<catgry>
<catValu>191001</catValu>
<labl>1910 Puerto Rico</labl>
</catgry>
<catgry>
<catValu>190004</catValu>
<labl>1900 100% database</labl>
</catgry>
<catgry>
<catValu>190003</catValu>
<labl>1900 1% sample with oversamples</labl>
</catgry>
<catgry>
<catValu>190002</catValu>
<labl>1900 1%</labl>
</catgry>
<catgry>
<catValu>190001</catValu>
<labl>1900 5%</labl>
</catgry>
<catgry>
<catValu>188003</catValu>
<labl>1880 100% database</labl>
</catgry>
<catgry>
<catValu>188002</catValu>
<labl>1880 10%</labl>
</catgry>
<catgry>
<catValu>188001</catValu>
<labl>1880 1%</labl>
</catgry>
<catgry>
<catValu>187003</catValu>
<labl>1870 100% database</labl>
</catgry>
<catgry>
<catValu>187002</catValu>
<labl>1870 1% sample with black oversample</labl>
</catgry>
<catgry>
<catValu>187001</catValu>
<labl>1870 1%</labl>
</catgry>
<catgry>
<catValu>186003</catValu>
<labl>1860 100% database</labl>
</catgry>
<catgry>
<catValu>186002</catValu>
<labl>1860 1% sample with black oversample</labl>
</catgry>
<catgry>
<catValu>186001</catValu>
<labl>1860 1%</labl>
</catgry>
<catgry>
<catValu>185002</catValu>
<labl>1850 100% database</labl>
</catgry>
<catgry>
<catValu>185001</catValu>
<labl>1850 1%</labl>
</catgry>
<concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
<varFormat schema="other" type="numeric"/>
</var>
<var ID="SERIAL" dcml="0" files="ExtractData" intrvl="contin" name="SERIAL">
<location EndPos="18" StartPos="11" width="8"/>
<labl>Household serial number</labl>
<txt><![CDATA[SERIAL is an identifying number unique to each household record in a given sample. All person records are assigned the same serial number as the household record that they follow. (Person records also have their own unique identifiers - see PERNUM.) A combination of SAMPLE and SERIAL provides a unique identifier for every household in the IPUMS; the combination of SAMPLE, SERIAL, and PERNUM uniquely identifies every person in the database.
For 1850-1930, households that are part of a multi-household dwelling can be identified by using the DWELLING and DWSEQ variables. See "Sample Designs" for further discussion of sampling from within multi-household dwellings.]]></txt>
<codInstr><![CDATA[CodesSERIAL is an 8-digit numeric variable which assigns a unique identification number to each household record in a given sample (See PERNUM for the analogous person record identifier). A combination of SAMPLE and SERIAL provides a unique identifier for every household in the IPUMS; the combination of SAMPLE, SERIAL, and PERNUM uniquely identifies every person in the database. SERIAL specific variable codes for missing, edited, or unidentified observations, observations not applicable (N/A), observations not in universe (NIU), top and bottom value coding, etc. are provided below if applicable by Census year (and data sample if specified).
SERIAL Specific Variable Codes]]></codInstr>
<concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
<varFormat schema="other" type="numeric"/>
</var>
<var ID="HHWT" dcml="2" files="ExtractData" intrvl="contin" name="HHWT">
<location EndPos="28" StartPos="19" width="10"/>
<labl>Household weight</labl>
<txt><![CDATA[HHWT indicates how many households in the U.S. population are represented by a given household in an IPUMS sample.
It is generally a good idea to use HHWT when conducting a household-level analysis of any IPUMS sample. The use of HHWT is optional when analyzing one of the "flat" or unweighted IPUMS samples. Flat IPUMS samples include the 1% samples from 1850-1930, all samples from 1960, 1970, and 1980, the 1% unweighted samples from 1990 and 2000, the 10% 2010 sample, and any of the full count 100% census datasets. HHWT must be used to obtain nationally representative statistics for household-level analyses of any sample other than those.
Users should also be sure to select one person (e.g., PERNUM = 1) to represent the entire household.
For further explanation of the sample weights, see "Sample Designs" and "Sample Weights". See also PERWT for a corresponding variable at the person level, and SLWT for a weight variable used with sample-line records in 1940 1% and 1950.]]></txt>
<codInstr><![CDATA[CodesHHWT is a 6-digit numeric variable which indicates how many households in the U.S. population are represented by a given household in an IPUMS sample and has two implied decimals. For example, a HHWT value of 010461 should be interpreted as 104.61. HHWT specific variable codes for missing, edited, or unidentified observations, observations not applicable (N/A), observations not in universe (NIU), top and bottom value coding, etc. are provided below if applicable by Census year (and data sample if specified).
User Note: Users should also be sure to select one person (e.g., PERNUM = 1) to represent the entire household when using HHWT.
HHWT Specific Variable Codes]]></codInstr>
<concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
<varFormat schema="other" type="numeric"/>
</var>
<var ID="CLUSTER" dcml="0" files="ExtractData" intrvl="contin" name="CLUSTER">
<location EndPos="41" StartPos="29" width="13"/>
<labl>Household cluster for variance estimation</labl>
<txt><![CDATA[CLUSTER is designed for use with STRATA in Taylor series linear approximation for correction of complex sample design characteristics. See the STRATA variable description for more details.]]></txt>
<codInstr><![CDATA[CodesCLUSTER is an 11-digit numeric variable designed for use with STRATA in Taylor series linear approximation for correction of complex sample design characteristics (See the Description of STRATA for more details). CLUSTER specific variable codes for missing, edited, or unidentified observations, observations not applicable (N/A), observations not in universe (NIU), top and bottom value coding, etc. are provided below if applicable by Census year (and data sample if specified).
CLUSTER Specific Variable Codes]]></codInstr>
<concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
<varFormat schema="other" type="numeric"/>
</var>
<var ID="COUNTYICP" dcml="0" files="ExtractData" intrvl="discrete" name="COUNTYICP">
<location EndPos="45" StartPos="42" width="4"/>
<labl>County (ICPSR code, identifiable counties only)</labl>
<txt><![CDATA[IPUMS USA cannot identify most counties in recent samples.
COUNTYICP identifies the county where the household was enumerated, using the Inter-University Consortium for Political and Social Research (ICPSR) coding scheme.
COUNTYICP codes are state-dependent; they must be combined with state codes (see STATEICP or STATEFIP) to distinguish counties located in different states.
Many county boundaries and some county names have changed over time. IPUMS does not impose a uniform county boundary system on the data, so each county listed for a given year in IPUMS should be assumed to have the boundaries that it had in that year.
All counties are identified in 1850 to 1950 full count files. Counties are not identified in public-use microdata samples from 1950 onwards, so IPUMS instead identifies counties, where possible, from other low-level geographic identifiers. These include State Economic Areas (SEA) in the 1950 1% sample; county groups in 1970 samples (CNTYGP97) and 1980 samples (CNTYGP98); and Public Use Microdata Areas (PUMA) from 1990 onwards, including Super-PUMAs (PUMASUPR) in 2000.
In 1950 and later samples (excluding the 1950 full count), COUNTYICP identifies a county if and only if:
it was coterminous with a single SEA, county group, or PUMA; or
it contained multiple SEAs, county groups, or PUMAs, none of which extended into other counties.
List of counties identified in 1950 and later samples:
Identified Counties, 1950-Forward
For municipios, the Puerto Rican statistical equivalent of U.S. counties, see PRCOUNTA (alphabetic version) and PRCOUNTY (numeric version).
ICPSR county codes are generally ordered alphabetically by county name within states. With a few exceptions, ICPSR codes correspond to 3-digit FIPS codes (as identified by COUNTYFIP) followed by an added zero digit. The fourth digit is used to accommodate the complete history of U.S. County definitions. FIPS codes were instituted around the time of the 1970 census, and historical counties that were dissolved before then have no FIPS code. For such counties, ICPSR generally appends a fourth digit of 5.
Like STATEICP, COUNTYICP facilitates merging IPUMS data with ICPSR data. COUNTYICP also identifies areas that were not part of any county, including the independent cities of Virginia and some Indian lands.]]></txt>
<catgry>
<catValu>0010</catValu>
<labl/>
</catgry>
<catgry>
<catValu>0030</catValu>
<labl/>
</catgry>
<catgry>
<catValu>0050</catValu>
<labl/>
</catgry>
<catgry>
<catValu>0070</catValu>
<labl/>
</catgry>