WLD_GCD_2010_v2014-03_M
Global Consumption Database 2010 (version 2014-03)
GCD 2010
Name | Country code |
---|---|
Afghanistan | AFG |
Albania | ALB |
Armenia | ARM |
Azerbaijan | AZE |
Bangladesh | BGD |
Belarus | BLR |
Benin | BEN |
Bhutan | BTN |
Bolivia | BOL |
Bosnia and Herzegovina | BIH |
Brazil | BRA |
Bulgaria | BGR |
Burkina Faso | BFA |
Burundi | BDI |
Cambodia | KHM |
Cameroon | CMR |
Cape Verde | CPV |
Chad | TCD |
China | CHN |
Colombia | COL |
Congo, Democratic Republic | COD |
Congo, Rep. | COG |
Côte d'Ivoire | CIV |
Djibouti | DJI |
Egypt | EGY |
El Salvador | SLV |
Ethiopia | ETH |
Fiji | FJI |
Gabon | GAB |
Gambia (The) | GMB |
Ghana | GHA |
Guatemala | GTM |
Guinea | GIN |
Honduras | HND |
India | IND |
Indonesia | IDN |
Iraq | IRQ |
Jamaica | JAM |
Jordan | JOR |
Kazakhstan | KAZ |
Kenya | KEN |
Kyrgyz Republic | KGZ |
Lao PDR | LAO |
Latvia | LVA |
Lesotho | LSO |
Liberia | LBR |
Lithuania | LTU |
Macedonia (FYROM) | MKD |
Madagascar | MDG |
Malawi | MWI |
Malaysia | MYS |
Maldives | MDV |
Mali | MLI |
Mauritania | MRT |
Mauritius | MUS |
Mexico | MEX |
Moldova | MDA |
Mongolia | MNG |
Montenegro | MNE |
Morocco | MAR |
Mozambique | MOZ |
Namibia | NAM |
Nepal | NPL |
Nicaragua | NIC |
Niger | NER |
Nigeria | NGA |
Pakistan | PAK |
Papua New Guinea | PNG |
Paraguay | PRY |
Peru | PER |
Philippines | PHL |
Romania | ROU |
Russia | RUS |
Rwanda | RWA |
Sao Tome and Principe | STP |
Senegal | SEN |
Serbia | SRB |
Sierra Leone | SLE |
South Africa | ZAF |
Sri Lanka | LKA |
Swaziland | SWZ |
Tajikistan | TJK |
Tanzania | TZA |
Thailand | THA |
Timor Leste | TLS |
Togo | TGO |
Turkiye | TUR |
Uganda | UGA |
Ukraine | UKR |
Viet Nam | VNM |
Yemen | YEM |
Zambia | ZMB |
Type | Identifier |
---|---|
DOI | https://doi.org/10.48529/0s8s-xk76 |
Data derived from survey microdata
GCD v2014-03
2014-03
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):
Name | Affiliation |
---|---|
Development Data Group (DECDG) | World Bank |
Name | Affiliation |
---|---|
Olivier Dupriez | World Bank, Development Data Group (DECDG) |
Tefera Bekele | World Bank, Development Data Group (DECDG) |
Yuri Dikhanov | World Bank, Development Data Group (DECDG) |
Start date | End date |
---|---|
2010 | 2010 |
All surveys used have a nationwide coverage. Their sample size ranges from less than 2,000 households to more than 100,000. 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.
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.
Because 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.
Because 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. Data may be collected over a period of 12 months to account for seasonality or 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 nonfood consumption using diaries in which households or individuals report daily on what they spend. But most 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, and clothing and footwear for the past month or the past 4 weeks; and 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 do shorter recall periods.
- Level of detail. Some survey questionnaires include a long, detailed list of goods and services; 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 in 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 are not available in all surveys. This too affects the calculation of household consumption aggregates and the cross-country comparability of data.
See file Draft_Technical note on standardization process.pdf and questionnaire coverage by country.xlsx
Organization name | Affiliation | URL |
---|---|---|
World Bank Microdata Library | World Bank Group | http://microdata.worldbank.org |
Name | URL | |
---|---|---|
Data helpdesk | data@worldbank.org | https://data.worldbank.org/about/contact |
WLD_GCD_2010_v2014-03_M
Name | Affiliation |
---|---|
Development Data Group | World Bank |
2022-02-20
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