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<codeBook ID="ddi2-ef0cf890-f532-0138-e5de-0242ac1d0007-cps_00159.dat-cps.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-CPS Data Extract</titl>
        <subTitl>DDI 2.5 metadata describing the extract file  'cps_00159.dat'</subTitl>
        <IDNo>ddi2-ef0cf890-f532-0138-e5de-0242ac1d0007-cps_00159.dat-cps.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="2023-07-10">July 10, 2023</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 cps_00159.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="2023-07-10">July 10, 2023</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="cps">IPUMS CPS</serName>
        <serInfo>DOI:10.18128/D030.V10.0</serInfo>
      </serStmt>
      <verStmt>
        <version date="2023-07-10"/>
      </verStmt>
    </citation>
    <stdyInfo>
      <subject>
        <topcClas vocab="IPUMS">Technical Variables -- HOUSEHOLD</topcClas>
        <topcClas vocab="IPUMS">Geographic Variables -- HOUSEHOLD</topcClas>
        <topcClas vocab="IPUMS">Technical Variables -- PERSON</topcClas>
        <topcClas vocab="IPUMS">Income Variables -- PERSON</topcClas>
      </subject>
      <sumDscr ID="sdatref-2320">
        <timePrd date="1962-03" event="single">1962-03</timePrd>
        <nation abbr="us">United States</nation>
      </sumDscr>
      <sumDscr ID="sdatref-2321">
        <timePrd date="1963-03" event="single">1963-03</timePrd>
        <nation abbr="us">United States</nation>
      </sumDscr>
      <notes><![CDATA[Additional notes on a sample that is part of this study:  IPUMS-CPS, ASEC 1962
            Note: persons under age 14 not included in sample
]]></notes>
      <notes><![CDATA[Additional notes on a sample that is part of this study:  IPUMS-CPS, ASEC 1963
            Note: persons under age 14 not included in sample
]]></notes>
    </stdyInfo>
    <dataAccs>
      <useStmt>
        <confDec required="no"><![CDATA[None]]></confDec>
        <contact URI="http://cps.ipums.org/" affiliation="IPUMS">IPUMS-CPS</contact>
        <citReq><![CDATA[Publications and research reports based on the IPUMS-CPS database must cite it appropriately. The citation should include the following:

Sarah Flood, Miriam King, Renae Rodgers, Steven Ruggles, J. Robert Warren and Michael Westberry. Integrated Public Use Microdata Series, Current Population Survey: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2022. https://doi.org/10.18128/D030.V10.0

The licensing agreement for use of IPUMS-CPS 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: http://bibliography.ipums.org/]]></citReq>
        <conditions><![CDATA[Users of IPUMS-CPS 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.  All persons are granted a limited license to use these data, but you may not charge a fee for the data if you distribute it to others.

(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-CPS 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) Use it for GOOD -- never for EVIL.]]></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:  Reproducing cps00010]]></notes>
  </stdyDscr>
  <fileDscr ID="ExtractData">
    <fileTxt>
      <fileName>cps_00159.dat</fileName>
      <fileCont>Microdata records</fileCont>
      <fileStrc type="hierarchical">
        <recGrp keyvar="SERIAL" recidvar="RECTYPE" rectype="P">
          <labl>Person Record</labl>
        </recGrp>
        <recGrp recidvar="RECTYPE" rectype="H">
          <labl>Household Record</labl>
        </recGrp>
      </fileStrc>
      <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="RECTYPE" dcml="0" files="ExtractData" intrvl="contin" name="RECTYPE" rectype="H P">
      <location EndPos="1" StartPos="1" width="1"/>
      <labl>Record type</labl>
      <txt><![CDATA[RECTYPE assigns all household records the alphabetic string "H" and all person records a "P".  This allows researchers using the hierarchical data format to discriminate between household and person records.  Each household record is followed by one or more person records, and all data pertaining to any household record usually pertain to all of the persons within it.

The original CPS data did not have household records until 1976.  Because the IPUMS system is designed to handle hierarchical records consisting of a household record followed by person records, IPUMS-CPS created a new household record by copying the person record of the household head for earlier years.]]></txt>
      <catgry>
        <catValu>H</catValu>
        <labl>Household</labl>
      </catgry>
      <catgry>
        <catValu>P</catValu>
        <labl>Person</labl>
      </catgry>
      <concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
      <varFormat schema="other" type="character"/>
    </var>
    <var ID="YEAR" dcml="0" files="ExtractData" intrvl="contin" name="YEAR" rectype="H P">
      <location EndPos="5" StartPos="2" width="4"/>
      <labl>Survey year</labl>
      <txt><![CDATA[YEAR reports the year in which the survey was conducted.  YEARP is repeated on person records.]]></txt>
      <codInstr><![CDATA[CodesYEAR is a 4-digit numeric value.]]></codInstr>
      <concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
      <varFormat schema="other" type="numeric"/>
    </var>
    <var ID="SERIAL" dcml="0" files="ExtractData" intrvl="contin" name="SERIAL" rectype="H P">
      <location EndPos="10" StartPos="6" width="5"/>
      <labl>Household serial number</labl>
      <txt><![CDATA[SERIAL is an identifying number unique to each household in a given survey month and year.  All person records are assigned the same serial number as the household record they follow.  A combination of YEAR, MONTH, and SERIAL provides a within-sample unique identifier for every household in IPUMS-CPS; YEAR, MONTH, SERIAL, and PERNUM uniquely identify every person within a single sample.

SERIAL is a new value generated for IPUMS-CPS and should not be confused with the household serial number created by the Census Bureau and included in the original CPS data.]]></txt>
      <codInstr><![CDATA[CodesSERIAL is a 5-digit numeric variable.]]></codInstr>
      <concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
      <varFormat schema="other" type="numeric"/>
    </var>
    <var ID="MONTH" dcml="0" files="ExtractData" intrvl="discrete" name="MONTH" rectype="H">
      <location EndPos="12" StartPos="11" width="2"/>
      <labl>Month</labl>
      <txt><![CDATA[MONTH indicates the calendar month of the CPS interview.]]></txt>
      <catgry>
        <catValu>01</catValu>
        <labl>January</labl>
      </catgry>
      <catgry>
        <catValu>02</catValu>
        <labl>February</labl>
      </catgry>
      <catgry>
        <catValu>03</catValu>
        <labl>March</labl>
      </catgry>
      <catgry>
        <catValu>04</catValu>
        <labl>April</labl>
      </catgry>
      <catgry>
        <catValu>05</catValu>
        <labl>May</labl>
      </catgry>
      <catgry>
        <catValu>06</catValu>
        <labl>June</labl>
      </catgry>
      <catgry>
        <catValu>07</catValu>
        <labl>July</labl>
      </catgry>
      <catgry>
        <catValu>08</catValu>
        <labl>August</labl>
      </catgry>
      <catgry>
        <catValu>09</catValu>
        <labl>September</labl>
      </catgry>
      <catgry>
        <catValu>10</catValu>
        <labl>October</labl>
      </catgry>
      <catgry>
        <catValu>11</catValu>
        <labl>November</labl>
      </catgry>
      <catgry>
        <catValu>12</catValu>
        <labl>December</labl>
      </catgry>
      <concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
      <varFormat schema="other" type="numeric"/>
    </var>
    <var ID="ASECWTH" dcml="4" files="ExtractData" intrvl="contin" name="ASECWTH" rectype="H">
      <location EndPos="23" StartPos="13" width="11"/>
      <labl>Annual Social and Economic Supplement Household weight</labl>
      <txt><![CDATA[ASECWTH is a household-level weight that should be used to generate statistics about households in March Annual Social and Economic (ASEC) Supplement data. The CPS uses a complex stratified sampling scheme, and ASECWTH must be used to produce unbiased household-level statistics from the IPUMS-CPS ASEC data. For analyses of non-ASEC data, researchers should use HWTFINL. For individual-level analyses, researchers should use WTFINL, ASECWT, or EARNWT. 

ASECWTH generally has the same value as WTSUPP for the household head or reference person. Vacant housing units and households that could not be interviewed due to residents' absence or refusal to participate have a value of zero in HWTSUPP; such sampled units were included in the public use CPS data beginning in 1988. 

Estimates on the entire population are prepared by projecting forward the resident population from the last available census. These projections are derived by updating the demographic census data from a number of other data sources that account for death, births and net migration. About 3 years after every census (i.e. 2003 for the 2000 Census and 2013 for the 2010 Census), the Census Bureau updates its independent population control and provides a new weight for the relevant years.

Two important points should be noted here. First, the lag between when the Census is conducted and when the CPS weights are updated is about 3 years. While the Census data are being processed, the CPS files are made available using the weighting scheme from the US Census prior to the latest Census. Second, once the files are updated, the old weights become obsolete and are replaced in the IPUMS data extract system. Published estimates from the lag years that use the old weights are not always updated. For example, 2010 poverty estimates were released in ASEC using the 2000 population controls. Once the 2010 population controls were made available, IPUMS-CPS replaced the ASEC 2010, 2011, and 2012 weights that are based on the 2000 population control with weights that are based on the 2010 population controls.

IPUMS-CPS makes available only the most up-to-date weights. The old values are available here: Old SPM and Weights Values.]]></txt>
      <codInstr><![CDATA[CodesASECWTH is a 10-digit numeric variable with four implied decimals.  That is, 1234567890 should be interpreted as 123456.7890.  The IPUMS command files automatically divide ASECWTH by 10,000, so no further adjustment is needed.]]></codInstr>
      <concept vocab="IPUMS">Technical Variables -- HOUSEHOLD</concept>
      <varFormat schema="other" type="numeric"/>
    </var>
    <var ID="STATEFIP" dcml="0" files="ExtractData" intrvl="discrete" name="STATEFIP" rectype="H">
      <location EndPos="25" StartPos="24" width="2"/>
      <labl>State (FIPS code)</labl>
      <txt><![CDATA[STATEFIP identifies the household's state of residence, using the Federal Information Processing Standards (FIPS) coding scheme, which orders the states alphabetically.

In 1973-1975 ASEC samples, all households in the Anaheim-Santa Ana-Garden Grove, CA METAREA are coded as Michigan-Wisconsin for STATEFIP in the original data. As there is insufficient geographic information in the public use data to determine which variable is in error, this mistake has been left un-recoded.]]></txt>
      <catgry>
        <catValu>01</catValu>
        <labl>Alabama</labl>
      </catgry>
      <catgry>
        <catValu>02</catValu>
        <labl>Alaska</labl>
      </catgry>
      <catgry>
        <catValu>04</catValu>
        <labl>Arizona</labl>
      </catgry>
      <catgry>
        <catValu>05</catValu>
        <labl>Arkansas</labl>
      </catgry>
      <catgry>
        <catValu>06</catValu>
        <labl>California</labl>
      </catgry>
      <catgry>
        <catValu>08</catValu>
        <labl>Colorado</labl>
      </catgry>
      <catgry>
        <catValu>09</catValu>
        <labl>Connecticut</labl>
      </catgry>
      <catgry>
        <catValu>10</catValu>
        <labl>Delaware</labl>
      </catgry>
      <catgry>
        <catValu>11</catValu>
        <labl>District of Columbia</labl>
      </catgry>
      <catgry>
        <catValu>12</catValu>
        <labl>Florida</labl>
      </catgry>
      <catgry>
        <catValu>13</catValu>
        <labl>Georgia</labl>
      </catgry>
      <catgry>
        <catValu>15</catValu>
        <labl>Hawaii</labl>
      </catgry>
      <catgry>
        <catValu>16</catValu>
        <labl>Idaho</labl>
      </catgry>
      <catgry>
        <catValu>17</catValu>
        <labl>Illinois</labl>
      </catgry>
      <catgry>
        <catValu>18</catValu>
        <labl>Indiana</labl>
      </catgry>
      <catgry>
        <catValu>19</catValu>
        <labl>Iowa</labl>
      </catgry>
      <catgry>
        <catValu>20</catValu>
        <labl>Kansas</labl>
      </catgry>
      <catgry>
        <catValu>21</catValu>
        <labl>Kentucky</labl>
      </catgry>
      <catgry>
        <catValu>22</catValu>
        <labl>Louisiana</labl>
      </catgry>
      <catgry>
        <catValu>23</catValu>
        <labl>Maine</labl>
      </catgry>
      <catgry>
        <catValu>24</catValu>
        <labl>Maryland</labl>
      </catgry>
      <catgry>
        <catValu>25</catValu>
        <labl>Massachusetts</labl>
      </catgry>
      <catgry>
        <catValu>26</catValu>
        <labl>Michigan</labl>
      </catgry>
      <catgry>
        <catValu>27</catValu>
        <labl>Minnesota</labl>
      </catgry>
      <catgry>
        <catValu>28</catValu>
        <labl>Mississippi</labl>
      </catgry>
      <catgry>
        <catValu>29</catValu>
        <labl>Missouri</labl>
      </catgry>
      <catgry>
        <catValu>30</catValu>
        <labl>Montana</labl>
      </catgry>
      <catgry>
        <catValu>31</catValu>
        <labl>Nebraska</labl>
      </catgry>
      <catgry>
        <catValu>32</catValu>
        <labl>Nevada</labl>
      </catgry>
      <catgry>
        <catValu>33</catValu>
        <labl>New Hampshire</labl>
      </catgry>
      <catgry>
        <catValu>34</catValu>
        <labl>New Jersey</labl>
      </catgry>
      <catgry>
        <catValu>35</catValu>
        <labl>New Mexico</labl>
      </catgry>
      <catgry>
        <catValu>36</catValu>
        <labl>New York</labl>
      </catgry>
      <catgry>
        <catValu>37</catValu>
        <labl>North Carolina</labl>
      </catgry>
      <catgry>
        <catValu>38</catValu>
        <labl>North Dakota</labl>
      </catgry>
      <catgry>
        <catValu>39</catValu>
        <labl>Ohio</labl>
      </catgry>
      <catgry>
        <catValu>40</catValu>
        <labl>Oklahoma</labl>
      </catgry>
      <catgry>
        <catValu>41</catValu>
        <labl>Oregon</labl>
      </catgry>
      <catgry>
        <catValu>42</catValu>
        <labl>Pennsylvania</labl>
      </catgry>
      <catgry>
        <catValu>44</catValu>
        <labl>Rhode Island</labl>
      </catgry>
      <catgry>
        <catValu>45</catValu>
        <labl>South Carolina</labl>
      </catgry>
      <catgry>
        <catValu>46</catValu>
        <labl>South Dakota</labl>
      </catgry>
      <catgry>
        <catValu>47</catValu>
        <labl>Tennessee</labl>
      </catgry>
      <catgry>
        <catValu>48</catValu>
        <labl>Texas</labl>
      </catgry>
      <catgry>
        <catValu>49</catValu>
        <labl>Utah</labl>
      </catgry>
      <catgry>
        <catValu>50</catValu>
        <labl>Vermont</labl>
      </catgry>
      <catgry>
        <catValu>51</catValu>
        <labl>Virginia</labl>
      </catgry>
      <catgry>
        <catValu>53</catValu>
        <labl>Washington</labl>
      </catgry>
      <catgry>
        <catValu>54</catValu>
        <labl>West Virginia</labl>
      </catgry>
      <catgry>
        <catValu>55</catValu>
        <labl>Wisconsin</labl>
      </catgry>
      <catgry>
        <catValu>56</catValu>
        <labl>Wyoming</labl>
      </catgry>
      <catgry>
        <catValu>61</catValu>
        <labl>Maine-New Hampshire-Vermont</labl>
      </catgry>
      <catgry>
        <catValu>65</catValu>
        <labl>Montana-Idaho-Wyoming</labl>
      </catgry>
      <catgry>
        <catValu>68</catValu>
        <labl>Alaska-Hawaii</labl>
      </catgry>
      <catgry>
        <catValu>69</catValu>
        <labl>Nebraska-North Dakota-South Dakota</labl>
      </catgry>
      <catgry>
        <catValu>70</catValu>
        <labl>Maine-Massachusetts-New Hampshire-Rhode Island-Vermont</labl>
      </catgry>
      <catgry>
        <catValu>71</catValu>
        <labl>Michigan-Wisconsin</labl>
      </catgry>
      <catgry>
        <catValu>72</catValu>
        <labl>Minnesota-Iowa</labl>
      </catgry>
      <catgry>
        <catValu>73</catValu>
        <labl>Nebraska-North Dakota-South Dakota-Kansas</labl>
      </catgry>
      <catgry>
        <catValu>74</catValu>
        <labl>Delaware-Virginia</labl>
      </catgry>
      <catgry>
        <catValu>75</catValu>
        <labl>North Carolina-South Carolina</labl>
      </catgry>
      <catgry>
        <catValu>76</catValu>
        <labl>Alabama-Mississippi</labl>
      </catgry>
      <catgry>
        <catValu>77</catValu>
        <labl>Arkansas-Oklahoma</labl>
      </catgry>
      <catgry>
        <catValu>78</catValu>
        <labl>Arizona-New Mexico-Colorado</labl>
      </catgry>
      <catgry>
        <catValu>79</catValu>
        <labl>Idaho-Wyoming-Utah-Montana-Nevada</labl>
      </catgry>
      <catgry>
        <catValu>80</catValu>
        <labl>Alaska-Washington-Hawaii</labl>
      </catgry>
      <catgry>
        <catValu>81</catValu>
        <labl>New Hampshire-Maine-Vermont-Rhode Island</labl>
      </catgry>
      <catgry>
        <catValu>83</catValu>
        <labl>South Carolina-Georgia</labl>
      </catgry>
      <catgry>
        <catValu>84</catValu>
        <labl>Kentucky-Tennessee</labl>
      </catgry>
      <catgry>
        <catValu>85</catValu>
        <labl>Arkansas-Louisiana-Oklahoma</labl>
      </catgry>
      <catgry>
        <catValu>87</catValu>
        <labl>Iowa-N Dakota-S Dakota-Nebraska-Kansas-Minnesota-Missouri</labl>
      </catgry>
      <catgry>
        <catValu>88</catValu>
        <labl>Washington-Oregon-Alaska-Hawaii</labl>
      </catgry>
      <catgry>
        <catValu>89</catValu>
        <labl>Montana-Wyoming-Colorado-New Mexico-Utah-Nevada-Arizona</labl>
      </catgry>
      <catgry>
        <catValu>90</catValu>
        <labl>Delaware-Maryland-Virginia-West Virginia</labl>
      </catgry>
      <catgry>
        <catValu>99</catValu>
        <labl>State not identified</labl>
      </catgry>
      <concept vocab="IPUMS">Geographic Variables -- HOUSEHOLD</concept>
      <varFormat schema="other" type="numeric"/>
      <notes>Case selections:  27 Minnesota, 19 Iowa, 55 Wisconsin, 46 South Dakota, 38 North Dakota</notes>
    </var>
    <var ID="PERNUM" dcml="0" files="ExtractData" intrvl="contin" name="PERNUM" rectype="P">
      <location EndPos="12" StartPos="11" width="2"/>
      <labl>Person number in sample unit</labl>
      <txt><![CDATA[PERNUM numbers all persons within each household consecutively (starting with "1") in the order in which they are listed in the original CPS data.  When combined with YEAR , MONTH, and SERIAL, PERNUM uniquely identifies each person within IPUMS-CPS samples, though not across IPUMS-CPS samples.]]></txt>
      <codInstr><![CDATA[CodesPERNUM is a 2-digit numeric variable.]]></codInstr>
      <concept vocab="IPUMS">Technical Variables -- PERSON</concept>
      <varFormat schema="other" type="numeric"/>
    </var>
    <var ID="ASECWT" dcml="4" files="ExtractData" intrvl="contin" name="ASECWT" rectype="P">
      <location EndPos="23" StartPos="13" width="11"/>
      <labl>Annual Social and Economic Supplement Weight</labl>
      <txt><![CDATA[ASECWT is a person-level weight that should be used in analyses of individual-level CPS supplement data. Since the CPS relies on a complex stratified sampling scheme, it is essential to use one of the provided weighting variables.

Researchers should use WTFINL rather than ASECWT when they wish to conduct person-level analyses of non-ASEC data. EARNWT should be used for any analysis including a small number of person-level variables (EARNWEEK, HOURWAGE, PAIDHOUR, and UNION). Researchers should use ASECWTH for household-level analyses. ASECWTCVD is available for the 2020 ASEC to adjust for nonrandom nonresponse resulting from the COVID-19 pandemic.

User Caution: For analyses that include the 2014 ASEC sample, please see the comparability tab.

The ASEC CPS files include two groups of people who are not included in the production of published labor force statistics: (1) members of the armed services, and (2) members of the Hispanic oversample who were interviewed in months other than March. WTFINL and EARNWT assign these groups a value of 0. Both groups are assigned non-zero values in ASECWT.

ASECWT is based on the inverse probability of selection into the sample and adjustments for the following factors: failure to obtain an interview; sampling within large sample units; the known distribution of the entire population according to age, sex, and race; over-sampling Hispanic persons; to give husbands and wives the same weight; and an additional step to provide consistency with labor force estimates from the basic survey. 

Estimates on the entire population are prepared by projecting forward the resident population from the last available census. These projections are derived by updating the demographic census data from a number of other data sources that account for death, births and net migration. About 3 years after every census (i.e. 2003 for the 2000 Census and 2013 for the 2010 Census), the Census Bureau updates its independent population control and provides a new weight for the relevant years.

Two important points should be noted here. First, the lag between when the Census is conducted and when the CPS weights are updated is about 3 years. While the Census data are being processed, the CPS files are made available using the weighting scheme from the US Census prior to the latest Census. Second, once the files are updated, the old weights become obsolete and are replaced in the IPUMS data extract system. Published estimates from the lag years that use the old weights are not always updated. For example, 2010 poverty estimates were released in ASEC using the 2000 population controls. Once the 2010 population controls were made available, IPUMS-CPS replaced the ASEC 2010, 2011, and 2012 weights that are based on the 2000 population control with weights that are based on the 2010 population controls. IPUMS-CPS makes available only the most up-to-date weights.]]></txt>
      <codInstr><![CDATA[CodesASECWT is a 10-digit numeric variable with four implied decimal places. That is, values of 0012345600 should be interpreted as 1,234.56.  The IPUMS command files automatically divide ASECWT by 10,000, so no further adjustment is needed.]]></codInstr>
      <concept vocab="IPUMS">Technical Variables -- PERSON</concept>
      <varFormat schema="other" type="numeric"/>
    </var>
    <var ID="INCTOT" dcml="0" files="ExtractData" intrvl="contin" name="INCTOT" rectype="P">
      <location EndPos="32" StartPos="24" width="9"/>
      <labl>Total personal income</labl>
      <txt><![CDATA[INCTOT indicates each respondent's total pre-tax personal income or losses from all sources for the previous calendar year.  Amounts are expressed as they were reported to the interviewer; users must adjust for inflation using Consumer Price Index adjustment factors.]]></txt>
      <codInstr><![CDATA[Codes999999999 = N.I.U.
999999998 = Missing. (1962-1964 only)
Values can be negative.

The Census Bureau applies different disclosure avoidance measures across time for individuals with high income in this variable. Detailed explanations of these methods, topcodes, and replacement value and swap value thresholds are available here.]]></codInstr>
      <concept vocab="IPUMS">Income Variables -- PERSON</concept>
      <varFormat schema="other" type="numeric"/>
    </var>
  </dataDscr>
</codeBook>
