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    <citation>
      <titlStmt>
        <titl>
          WLD_2010_GCD_v01_M_v01_A_ESS
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        <IDNo>
          DDI_WLD_2010_GCD_v01_M_v01_A_ESS_FAO
        </IDNo>
      </titlStmt>
      <prodStmt>
        <producer abbr="DECDG" affiliation="World Bank" role="Metadata producer">
          Development Data Group
        </producer>
        <producer abbr="ESS" affiliation="Food and Agriculture Organization of the United Nations" role="Metadata adapted for FAM">
          Statistics Division
        </producer>
        <software version="4.0.10" date="2018-05-02">
          Nesstar Publisher
        </software>
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    </citation>
  </docDscr>
  <stdyDscr>
    <citation>
      <titlStmt>
        <titl>
          Global Consumption Database 2010 (version 2014-03)
        </titl>
        <IDNo>
          WLD_2010_GCD_v01_M_v01_A_ESS
        </IDNo>
      </titlStmt>
      <rspStmt>
        <AuthEnty>
          World Bank, Development Data Group (DECDG)
        </AuthEnty>
      </rspStmt>
      <prodStmt>
        <software version="4.0.10" date="2018-05-02">
          Nesstar Publisher
        </software>
      </prodStmt>
      <distStmt>
        <contact affiliation="World Bank" URI="https://data.worldbank.org/about/contact" email="data@worldbank.org">
          Data helpdesk
        </contact>
      </distStmt>
      <serStmt>
        <serName>
          Other Household Survey [hh/oth]
        </serName>
      </serStmt>
      <verStmt>
      </verStmt>
    </citation>
    <stdyInfo>
      <subject>
        <keyword>
          Consumption
        </keyword>
        <keyword>
          Expenditure
        </keyword>
        <keyword>
          Consumption pattern
        </keyword>
        <keyword>
          Consumption profile
        </keyword>
        <keyword>
          Consumption share
        </keyword>
        <keyword>
          Icp basic heading
        </keyword>
      </subject>
      <abstract>
        <![CDATA[The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
- Sample Size by Country, Area and Consumption Segment (Number of Households)
- Population 2010 by Country, Area and Consumption Segment
- Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
- Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
- Population 2010 by Country, Age Group, Sex and Consumption Segment
- Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
- Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
- Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
- Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
- Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
- Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
- Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
- Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
- Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
- Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
- Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
- Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
- Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
- Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
- Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
- Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
- Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
- Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
- Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
- Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
- Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
- Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)]]>
      </abstract>
      <sumDscr>
        <collDate date="2010" event="start"/>
        <collDate date="2010" event="end"/>
        <nation abbr="AFG">
          Afghanistan
        </nation>
        <nation abbr="ALB">
          Albania
        </nation>
        <nation abbr="ARM">
          Armenia
        </nation>
        <nation abbr="AZE">
          Azerbaijan
        </nation>
        <nation abbr="BGD">
          Bangladesh
        </nation>
        <nation abbr="BLR">
          Belarus
        </nation>
        <nation abbr="BEN">
          Benin
        </nation>
        <nation abbr="BTN">
          Bhutan
        </nation>
        <nation abbr="BOL">
          Bolivia (Plurinational State of)
        </nation>
        <nation abbr="BIH">
          Bosnia and Herzegovina
        </nation>
        <nation abbr="BRA">
          Brazil
        </nation>
        <nation abbr="BGR">
          Bulgaria
        </nation>
        <nation abbr="BFA">
          Burkina Faso
        </nation>
        <nation abbr="BDI">
          Burundi
        </nation>
        <nation abbr="KHM">
          Cambodia
        </nation>
        <nation abbr="CMR">
          Cameroon
        </nation>
        <nation abbr="CPV">
          Cabo Verde
        </nation>
        <nation abbr="TCD">
          Chad
        </nation>
        <nation abbr="CHN">
          China
        </nation>
        <nation abbr="COL">
          Colombia
        </nation>
        <nation abbr="COG">
          Congo
        </nation>
        <nation abbr="CIV">
          Côte d'Ivoire
        </nation>
        <nation abbr="COD">
          Democratic Republic of the Congo
        </nation>
        <nation abbr="DJI">
          Djibouti
        </nation>
        <nation abbr="EGY">
          Egypt
        </nation>
        <nation abbr="SLV">
          El Salvador
        </nation>
        <nation abbr="SWZ">
          Eswatini
        </nation>
        <nation abbr="ETH">
          Ethiopia
        </nation>
        <nation abbr="FJI">
          Fiji
        </nation>
        <nation abbr="GAB">
          Gabon
        </nation>
        <nation abbr="GMB">
          Gambia
        </nation>
        <nation abbr="GHA">
          Ghana
        </nation>
        <nation abbr="GTM">
          Guatemala
        </nation>
        <nation abbr="GIN">
          Guinea
        </nation>
        <nation abbr="HND">
          Honduras
        </nation>
        <nation abbr="IND">
          India
        </nation>
        <nation abbr="IDN">
          Indonesia
        </nation>
        <nation abbr="IRQ">
          Iraq
        </nation>
        <nation abbr="JAM">
          Jamaica
        </nation>
        <nation abbr="JOR">
          Jordan
        </nation>
        <nation abbr="KAZ">
          Kazakhstan
        </nation>
        <nation abbr="KEN">
          Kenya
        </nation>
        <nation abbr="KGZ">
          Kyrgyzstan
        </nation>
        <nation abbr="LAO">
          Lao People's Democratic Republic
        </nation>
        <nation abbr="LVA">
          Latvia
        </nation>
        <nation abbr="LSO">
          Lesotho
        </nation>
        <nation abbr="LBR">
          Liberia
        </nation>
        <nation abbr="LTU">
          Lithuania
        </nation>
        <nation abbr="MKD">
          North Macedonia
        </nation>
        <nation abbr="MDG">
          Madagascar
        </nation>
        <nation abbr="MWI">
          Malawi
        </nation>
        <nation abbr="MYS">
          Malaysia
        </nation>
        <nation abbr="MDV">
          Maldives
        </nation>
        <nation abbr="MLI">
          Mali
        </nation>
        <nation abbr="MRT">
          Mauritania
        </nation>
        <nation abbr="MUS">
          Mauritius
        </nation>
        <nation abbr="MEX">
          Mexico
        </nation>
        <nation abbr="MNG">
          Mongolia
        </nation>
        <nation abbr="MNE">
          Montenegro
        </nation>
        <nation abbr="MAR">
          Morocco
        </nation>
        <nation abbr="MOZ">
          Mozambique
        </nation>
        <nation abbr="NAM">
          Namibia
        </nation>
        <nation abbr="NPL">
          Nepal
        </nation>
        <nation abbr="NER">
          Niger
        </nation>
        <nation abbr="NGA">
          Nigeria
        </nation>
        <nation abbr="PAK">
          Pakistan
        </nation>
        <nation abbr="PNG">
          Papua New Guinea
        </nation>
        <nation abbr="PRY">
          Paraguay
        </nation>
        <nation abbr="PER">
          Peru
        </nation>
        <nation abbr="PHL">
          Philippines
        </nation>
        <nation abbr="MDA">
          Republic of Moldova
        </nation>
        <nation abbr="ROU">
          Romania
        </nation>
        <nation abbr="RUS">
          Russian Federation
        </nation>
        <nation abbr="RWA">
          Rwanda
        </nation>
        <nation abbr="STP">
          Sao Tome and Principe
        </nation>
        <nation abbr="SEN">
          Senegal
        </nation>
        <nation abbr="SRB">
          Serbia
        </nation>
        <nation abbr="SLE">
          Sierra Leone
        </nation>
        <nation abbr="ZAF">
          South Africa
        </nation>
        <nation abbr="LKA">
          Sri Lanka
        </nation>
        <nation abbr="TJK">
          Tajikistan
        </nation>
        <nation abbr="TZA">
          Tanzania
        </nation>
        <nation abbr="THA">
          Thailand
        </nation>
        <nation abbr="TLS">
          Timor-Leste
        </nation>
        <nation abbr="TGO">
          Togo
        </nation>
        <nation abbr="TUR">
          Türkiye
        </nation>
        <nation abbr="UGA">
          Uganda
        </nation>
        <nation abbr="UKR">
          Ukraine
        </nation>
        <nation abbr="VNM">
          Viet Nam
        </nation>
        <nation abbr="YEM">
          Yemen
        </nation>
        <nation abbr="ZMB">
          Zambia
        </nation>
        <geogCover>
          <![CDATA[All surveys used have a nationwide coverage.
		  
For all countries, estimates are provided at the national level and at the urban/rural levels.
		  
For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1):
- Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins
- India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal
- South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape]]>
        </geogCover>
        <anlyUnit>
          Households
        </anlyUnit>
        <universe>
          The universe of each survey is composed of ordinary households only. Institutional households (prisons, military barracks, hospitals, convents, and others) are not covered by household surveys. Homeless and nomadic populations and visitors present in a country during a survey are also excluded from the sample.
        </universe>
        <dataKind>
          Data derived from survey microdata
        </dataKind>
      </sumDscr>
      <notes>
        <![CDATA[The questionnaires collected information on the consumption patterns of products and services such as:
- Bread and cereals
- Meat
- Fish and seafood
- Milk, cheese, and eggs
- Vegetables
- Clothing
- Water supply and miscellaneous services relating to the dwelling

For more information on the questionnaire coverage by country and category see the summary of survey questionnaire coverage of ICP/COICOP categories document in the downloads section.]]>
      </notes>
    </stdyInfo>
    <method>
      <dataColl>
        <sampProc>
          The sample size for individual surveys ranges from less than 2000 households to more than 100 000.
        </sampProc>
        <sources/>
        <collSitu>
          <![CDATA[Few developing countries conduct household consumption or expenditure surveys on an annual basis. International organizations recommend conducting such surveys every three or four years. The surveys used in the database were conducted between 2000 and 2010 (except the one for Djibouti, which was conducted in 1996); most were conducted during the period 2007-10. All data presented in the Global Consumption Database are as of 2010. When based on a survey conducted before 2010, the estimates were obtained by extrapolation, as described in the notes on the standardization of data (see Step 4).

Household survey datasets are complemented by data on population, purchasing power parity (PPP) conversion factors, and average exchange rates obtained from the World Bank's World Development Indicators database.

Since of the diversity of methods and instruments used by the surveys, comparability across countries is limited. Survey questionnaires are provided below as an important metadata component. Links are also provided to the microdata when available.

Furthermore, since household surveys differ across countries in design, methodology, and timing, there are limits to the extent to which household data can be standardized after they have been collected. Comparisons of household data across countries and over time must therefore be done with caution.

The Global Consumption Database uses multiple types of surveys, depending on data availability-including household budget surveys, living standards measurement surveys, and various kinds of country-specific socioeconomic surveys. All these surveys measure consumption or expenditure at the household (not individual) level. But because the surveys are designed for different purposes (such as to measure poverty or to update the consumption basket used to compile consumption price indices), they may differ substantially in design and methodology.

Key differences between surveys include these:
- Duration of data collection. Some surveys collect data over a period of 12 months to account for seasonality and some over a shorter period (a few weeks or a few months).
- Method for household reporting on consumption. Some surveys collect data on food and some non-food consumption using diaries in which households or individuals report daily on what they spend. However, most studies rely on the recall method, asking households to report what they recall spending over a certain period. The recall period varies across surveys and categories. For example, data might be collected on: spending on food for the past 7 days, the past 2 weeks, or a typical month; on education for the past 12 months or the last academic year; on rent, outpatient health services, clothing, and footwear for the past month or the past 4 weeks; on durable goods and hospitalization for the past 6 or 12 months. The choice of recall period may have a substantial effect on the levels of consumption reported. Longer recall periods for frequently purchased items typically produce lower levels of reported spending than shorter recall periods do.
- Level of detail. Some survey questionnaires include a long, detailed list of goods and services while others provide a shorter, more aggregate list. Longer lists with a finer breakdown of categories typically generate higher estimates of consumption.
- Method for estimating rental value of dwellings. In some countries, surveys ask households that own their home or occupy it for free to provide an estimate of the rental value of the dwelling. In others, surveys collect data on the characteristics of dwellings that can be used to impute the rental value of owner-occupied dwellings through hedonic regressions. And for still other countries it is not possible to measure the rental value of owner-occupied dwellings because the rental market is too limited. Because this rental value represents a substantial share of household expenditure, these differences have major implications for the calculation of household consumption aggregates and for the comparability of data across countries.
- Method for estimating value of durable goods. Some surveys collect data on household expenditures on durable goods such as musical instruments. Others attempt to estimate the annual use value of these goods. Estimating the use value of a good requires data on its price and date of purchase or on its resale value, data that is not available in all surveys. This too affects the calculation of household consumption aggregates and the cross-country comparability of data.]]>
        </collSitu>
        <cleanOps>
          See the technical note on the standardization of survey data files for information on the standardization process employed to produce internationally comparable price levels, expenditure values, and Purchasing Power Parity (PPP) estimates.
        </cleanOps>
      </dataColl>
    </method>
    <dataAccs>
      <setAvail>
        <accsPlac/>
      </setAvail>
    </dataAccs>
  </stdyDscr>
  <fileDscr ID="F1" URI="WLD_2010_GCD_v01_M_v01_A_ESS.Nesstar?Index=0&amp;Name=WB_GCD_2010_v2014-03_survey_data">
    <fileTxt>
      <fileName>
        WB_GCD_2010_v2014-03_survey_data.NSDstat
      </fileName>
      <fileCont>
        Data file in MS-Excel, with all indicators (long format)
      </fileCont>
      <dimensns>
        <caseQnty>
          0
        </caseQnty>
        <varQnty>
          17
        </varQnty>
      </dimensns>
      <fileType>
        Nesstar 200801
      </fileType>
    </fileTxt>
  </fileDscr>
  <dataDscr>
    <var ID="V1" name="Countrycode" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Country code
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        Codes from World Bank World Development Indicators database.
      </notes>
    </var>
    <var ID="V2" name="Countryname" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Country name
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
    </var>
    <var ID="V3" name="Region" files="F1" dcml="0" intrvl="discrete">
      <labl>
        World Bank administrative region
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        See World Bank list of countries by region: https://data.worldbank.org/country
      </notes>
    </var>
    <var ID="V4" name="Exchangerate" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Exchange rate (average for 2010; LCU/USD)
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        Source: World Development Indicators (2014)
      </notes>
    </var>
    <var ID="V5" name="PPPconversionfactor" files="F1" dcml="0" intrvl="discrete">
      <labl>
        PPP 2005 conversion factor
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        Source: International Comparison Program (ICP) report
      </notes>
    </var>
    <var ID="V6" name="SurveyID" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Survey identifier
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        From the WB Microdata Library/IHSN catalog
      </notes>
    </var>
    <var ID="V7" name="Surveyyear" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Survey year
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
    </var>
    <var ID="V8" name="Year" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Year
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        2010 for all observations
      </notes>
    </var>
    <var ID="V9" name="Indicatorname" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Indicator name
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
    </var>
    <var ID="V10" name="Labelofproductorservice" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Product or service (label)
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        See Excel file ICP_basic_headings.xls
      </notes>
    </var>
    <var ID="V11" name="Codeofproductorservice" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Product or service (code)
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        The code corresponds to the basic headings of the International Comparison Program (close to COICOP). See Excel file ICP_basic_headings.xls
      </notes>
    </var>
    <var ID="V12" name="Sex" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Sex
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
    </var>
    <var ID="V13" name="Agegroup" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Age group
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
    </var>
    <var ID="V14" name="Measurementunit" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Measurement unit for the selected indicator
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
    </var>
    <var ID="V15" name="Area" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Area of residence (urban/rural)
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        Definition of urban/rural specific to each country (variable extracted from source datasets)
      </notes>
    </var>
    <var ID="V16" name="Incomegroup" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Income group
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
      <notes>
        Households in developing countries were categorized in four consumption segments for the Global Consumption Database: lowest, low, middle, and higher. Four levels of consumption are used to segment the market in each country: lowest, low, middle, and higher. They are based on global income distribution data, which rank the global population by income per capita. The lowest consumption segment corresponds to the bottom half of the global distribution, or the 50th percentile and below; the low consumption segment to the 51th-75th percentiles; the middle consumption segment to the 76th-90th percentiles; and the higher consumption segment to the 91st percentile and above. These thresholds were used to establish the four consumption segments: - Lowest-below $2.97 per capita a day - Low-between $2.97 and $8.44 per capita a day - Middle-between $8.44 and $23.03 per capita a day - Higher-above $23.03 per capita a day To convert a PPP$ threshold into annual expenditure in local currency (as of 2010), the following formula was applied: threshold * U.S. inflation rate for the period 2005-10 (1.117) * PPP conversion factor for 2010 * 365 For example, the PPP$2.97 threshold is equivalent to 25,702.22 Indian rupees (2.97 * 1.117 * 21.226 * 365). The U.S. inflation rate and PPP conversion factors are available in the World Bank World Development Indicators database. See Excel file WB_GCD_2010_v2014-03_thresholds.xlsx
      </notes>
    </var>
    <var ID="V17" name="Value" files="F1" dcml="0" intrvl="discrete">
      <labl>
        Value for the corresponding indicator
      </labl>
      <sumStat type="vald">
        0
      </sumStat>
      <sumStat type="invd">
        0
      </sumStat>
      <varFormat type="numeric" schema="other"/>
    </var>
  </dataDscr>
</codeBook>
