description:
This dataset traces net changes in terms of volume in the growing stock of standing wood on forest land. Forest depletion and growth describe balances or imbalances in different types of forests. The intensity of use of forest resources reflects various forest management methods and their sustainability. These data should be read in connection with other indicators, in particular land use changes and forest quality (species diversity, forest degradation), and be complemented with data on forest management practices and protection measures. Please bear in mind that definitions and estimation methods vary for some countries. For details see:

Data source(s): OECD Annual Quality Assurance (AQA) questionnaire.

Contact: ENV.Stat@oecd.org

Dataset release date: March, 2024

Definitions

Country metadata

[A] Annual

A10: Decennial

A2: Biennial

A20: Bidecennial

A3: Triennial

A30: Tridecennial

A4: Quadrennial

A5: Quinquennial

A_3: Three times a year

B: Daily – business week

D: Daily

D_2: Twice a day

[H] Hourly

H2: Bihourly

H3: Trihourly

I: Irregular

M: Monthly

M2: Bimonthly

M_2: Semimonthly

M_3: Three times a month

N: Minutely

OA: Occasional annual

OM: Occasional monthly

Q: Quarterly

S: Half-yearly, semester

W: Weekly

W2: Biweekly

W3: Triweekly

W4: Four-weekly

W_2: Semiweekly

W_3: Three times a week

_O: Other

_U: Unspecified

CHANGE: Net change

FELL_SALV: Volumes salvaged

FELL_TOT: Fellings

GINCR: Gross Increment

INT_USE: Intensity of use of forest resources

LOSSES: Natural losses

NET_FELL: Net fellings

NINCR: Net Increment

A2_A3_X: Northern and Central America unspecified

A5_X: Central America and the Caribbean unspecified

A9_X: Latin America and the Caribbean unspecified

ANNEX1: Annex I of the United Nations Framework Convention on Climate Change Parties

ANNEX2: Annex II of the United Nations Framework Convention on Climate Change Parties

ANNEX2A2: Annex II of the United Nations Framework Convention on Climate Change North America Parties

ANNEX2E: Annex II of the United Nations Framework Convention on Climate Change Europe Parties

ANNEX2OS: Annex II of the United Nations Framework Convention on Climate Change Asia Oceania Parties

CENSAD: Community of Sahel–Saharan States (CEN–SAD)

COMESA: Common Market for Eastern and Southern Africa (COMESA)

EA00_X: Euro area (evolving composition) unspecified

ECCAS: Economic Community of Central African States (ECCAS)

ECOWAS: Economic Community of West African States (ECOWAS)

EU00_X: European Union (evolving composition) unspecified

EU22OECD_2020: European Union (22 countries) in OECD (from 01/02/2020)

EU25_REP: European Union (25 countries) average country

EU27_2020: European Union (27 countries from 01/02/2020)

EU28XEU15: European Union 28 excluding European Union 15 countries

FSCAC: Fragile states and Conflict-affected countries

HICSXA9: High income countries (excluding Latin American and Caribbean countries)

IGAD: Intergovernmental Authority on Development (IGAD)

INCWB_X: Countries not classified by the World Bank

IPAC: International Programme for Action on Climate Parties

LDCWXF: World outside Africa, least developed countries

LLDCF: Africa, land locked developing countries

LLDCWXF: World outside Africa, landlocked developing countries

LMICWB: Lower-middle income countries (World Bank)

LMICWBF: Africa, lower-middle income countries (World Bank)

LMICWBWXF: World outside Africa, lower-middle income countries (World Bank)

MADCT: More advanced developed countries and territories

MADCT_X: More advanced developed countries and territories unspecified

NRRCFWXF: World outside Africa, non-resource-rich countries

OECDEXEU: Non-European Union countries in OECD Europe

OECDXUSAJPNEA17: OECD countries except USA, JPN and euro area

OLICWBF: Africa, low income countries (World Bank)

OLICWBWXF: World outside Africa, low income countries (World Bank)

PALOP: Portuguese-speaking African countries (PALOP)

PCI_F: Former Pacific Islands (Trust Territory)

PSE: Palestinian Authority or West Bank and Gaza Strip

RRCFWXF: World outside Africa, resource-rich countries

S3: Western Asia / Near and Middle East countries

S3_O: Other Western Asia / Near and Middle East countries

S3_OXOECD: Non-OECD other Western Asia / Near and Middle East countries

SADC: Southern African Development Community (SADC)

SGS: South Georgia and the South Sandwich Islands

SIDSWXF: World outside Africa, small island developing states

UMICWB: Upper-middle income countries (World Bank)

UMICWBF: Africa, upper-middle income countries (World Bank)

UMICWBWXF: World outside Africa, upper-middle income countries (World Bank)

USMCA: United States - Mexico - Canada Agreement (USMCA)

WXEU00_X: Extra-EU (evolving composition) unspecified

WXEU27_2020: Extra-European Union (27 countries from 01/02/2020)

10P3EMP_MS: Per 1 000 employment in the market sector

10P3HB_AGE: Per 1 000 inhabitants in the same age group

10P3PHYS_PRF_HLTH: Per 1 000 practising physicians in the same health profession

DDD_10P3HB: Defined daily doses per 1 000 inhabitants per day

EUR_EXC_PS: Euros, exchange rate converted, per person

FCTR_EARN_FT_SIM: Factor of earnings for full-time, full-year similarly educated workers

FCTR_EARN_FT_TER: Factor of earnings for full-time, full-year tertiary-educated workers

FCTR_FAS_S1: Factor of financial assets of total economy

FCTR_LI_F5: Factor of equity and investment fund shares liabilities

FCTR_LI_S1S: Factor of liabilities of total economy, except financial corporations

FCTR_P6_M: Factor of export market for goods and services

FCTR_Q1_SCR_LIFE_SATISFACTION: Factor of lowest life satisfaction scores quintile

FCTR_Q1_SCR_PERS_REL_SATISFACTION: Factor of lowest personal relationship satisfaction scores quintile

FCTR_Q1_SCR_TIME_USE: Factor of lowest time use scores quintile

FCTR_SAL_EXP0: Factor of salary at the start of the career

FCTR_SAL_ISCED11_1: Factor of salary per net teaching hour of teachers in primary education

FCTR_WG_SAL_G_I_INC: Factor of gross individual wage at the same income level

FCTR_WG_SAL_N_I_INC: Factor of net individual wage at the same income level

G_CO2E_KWH: Grammes of CO2-equivalent per kilowatt hour

HB_LAU: Inhabitants per local administrative unit

KGOE_PS: Kilogrammes of oil equivalent per person

KG_CO2E_10P3USD: Kilogrammes of CO2-equivalent per 1 000 US dollars

KG_CO2E_KG: Kilogrammes of CO2-equivalent per kilogramme

KG_CO2E_PS: Kilogrammes of CO2-equivalent per person

LAU_10P5HB: Local administrative units per 100 000 inhabitants

PASKM_10P3USD: Passenger-kilometres per 1 000 US dollars

PC_DT_A: Percentage change from base period average deaths

PD_EMP_PA: Percentage point difference from prime-age workers

PRC_10P3HB_Y_GE65: Procedures per 1 000 inhabitants aged 65 years old or over

PRC_10P3POP_Y_GE65: [Deprecated] Procedures per 1 000 population aged 65 years old and over

PT_ADLSC_EDU: Percentage of adolescent students at the same education level

PT_APD: Percentage of official development assistance

PT_APD_SEC: Percentage of sector-allocable official development assistance

PT_AS_PENS_OCC: Percentage of occupational pension assets

PT_AS_S1M: Percentage of household and NPISH assets

PT_B1GQ_POP_PPP_OECD: Percentage of PPP converted GDP per capita of OECD

PT_B1G_MS: Percentage of gross value added in the market sector

PT_B2A3G: Percentage of gross operating surplus and mixed income

PT_B5G_S14: Percentage of balance of household gross primary income

PT_B5G_S1M: Percentage of balance of household and NPISH gross primary income

PT_B5N_S14: Percentage of balance of household net primary incomes

PT_B6GA_S1M: Percentage of household and NPISH gross disposable income adjusted for the net change in pension entitlements

PT_B6G_A_POP_Y_GT65: Percentage of gross disposable income of the population aged over 65 years

PT_B6G_S14: Percentage of household gross disposable income

PT_B6G_S1M: Percentage of household and NPISH gross disposable income

PT_B6NA_S1M: Percentage of household and NPISH net disposable income adjusted for the net change in pension entitlements

PT_B6N_S14: Percentage of household net disposable income

PT_B6N_S1M: Percentage of household and NPISH net disposable income

PT_B7G_S14: Percentage of household gross adjusted disposable income

PT_B7N_S14: Percentage of household net adjusted disposable income

PT_B7N_S1M: Percentage of household and NPISH net adjusted disposable income

PT_B90_S1M: Percentage of household and NPISH wealth

PT_BD_AV_ICU: Percentage of available intensive care unit (ICU) beds

PT_BERD: Percentage of business enterprise expenditure on R&D

PT_BF90_S14: Percentage of household financial wealth

PT_BF90_S1M: Percentage of household and NPISH financial wealth

PT_CONS_MAT_D: Percentage of domestic material consumption

PT_CONS_VA: Percentage of value added in consumption

PT_COS_WLFR_DT_PRMTR: Percentage of welfare cost of premature deaths

PT_CSE_AGR_COM: Percentage of consumption expenditure on agricultural commodities at farm gate prices, excluding taxpayer transfers to consumers

PT_DALY: Percentage of disability-adjusted life years

PT_DDD_PRSC: Percentage of defined daily doses prescribed

PT_DEPEND_SUB: Percentage of dependents in the subgroup

PT_DFD_FVA: Percentage of foreign value added in domestic final demand

PT_DT_A_PRECVD19: Percentage of average deaths in pre-COVID years

PT_EARN_G_PRE_RET_INC: Percentage of gross pre-retirement earnings at the same income level

PT_EARN_N_PRE_RET_INC: Percentage of net pre-retirement earnings at the same income level

PT_EARN_PRVS_LST_SUB: Percentage of previous earnings lost in the same subgroup

PT_EARN_WR_ISCED11A_0T2: Percentage of earnings of workers with below upper secondary educational attainment

PT_EARN_WR_ISCED11A_3_3_4: Percentage of earnings of workers with upper secondary educational attainment, or workers with upper secondary or post-secondary non-tertiary education if the former is not available

PT_EARN_WR_ISCED11A_3_4: Percentage of earnings of workers with upper secondary or post-secondary non-tertiary educational attainment

PT_EARN_WR_ISCED11A_5T8: Percentage of earnings of workers with tertiary educational attainment

PT_EARN_WR_NB: Percentage of earnings of native-born workers

PT_EARN_WR_Y25T34: Percentage of earnings of workers aged 25-34 years

PT_EMP_FT_HSPTL: Percentage of full-time hospital employment

PT_EMP_PT_INV: Percentage of involuntary part-time employment

PT_EMP_S13: Percentage of general government employment

PT_EMP_SEX_AGE: Percentage of employment in the same age and sex

PT_EMP_SUB: Percentage of employment in the same subgroup

PT_EM_CO2_TRAN: Percentage of CO2 emissions from transport

PT_EXGR_DVA: Percentage of domestic value added in gross exports

PT_EXGR_INT_DVA: Percentage of domestic value added in gross exports of intermediates

PT_EXGR_T: Percentage of total economy gross exports

PT_EXP_CUR_EDU_INST: Percentage of current expenditure on educational institutions

PT_EXP_EDU_INST: Percentage of expenditure on educational institutions

PT_EXP_EDU_INST_EXP_TP: Percentage of expenditure on educational institutions in the same expenditure type

PT_EXP_FNC: Percentage of expenditure in the same function

PT_EXP_FS: Percentage of expenditure in the same financing scheme

PT_EXP_OUT_S13: Percentage of general government outsourcing expenditures

PT_EXP_PRV: Percentage of expenditure in the same provider

PT_EXP_S14_SUB: Percentage of household expenditure in the same subgroup

PT_EXP_S14_TRAN: Percentage of household expenditure in transport

PT_EXP_SECTOR: Percentage of expenditure in the same institutional sector

PT_EXP_TOUR_INT: Percentage of international tourism expenditure

PT_FAS_S12L: Percentage of financial assets of investment funds

PT_FAS_S12Q: Percentage of financial assets of insurance corporations and pension funds

PT_FAS_S14: Percentage of household financial assets

PT_FAS_S1M: Percentage of household and NPISH financial assets

PT_FD4_S13M_INST: Percentage of state and local government debt in the same instrument

PT_FD4_S13_INST: Percentage of general government debt in the same instrument

PT_FD_VA: Percentage of value added in final demand

PT_FFD_DCE: Percentage of domestic compensation of employees in foreign final demand

PT_FFD_DEM: Percentage of domestic employment in foreign final demand

PT_FFD_DVA: Percentage of domestic value added in foreign final demand

PT_GBARD: Percentage of government allocations for R&D

PT_GBARD_C: Percentage of civil government allocations for R&D

PT_GERD: Percentage of gross domestic expenditure on R&D

PT_GFCF_VA: Percentage of value added in gross fixed capital formation

PT_GOVERD: Percentage of government intramural expenditure on R&D

PT_GPROC_S13: Percentage of general government procurement

PT_HERD: Percentage of higher education expenditure on R&D

PT_HH_PS_GE1_PENS: Percentage of households with at least one member aged 65+ that benefits from a pension in the same subgroup

PT_HH_WR_LT_INT_PVL: Percentage of households with workers living with less than the international poverty line amount

PT_H_W_USA_PS: Percentage of USA hours worked per person

PT_IMGR_INT: Percentage of gross imports of intermediates

PT_IM_CIF: Percentage of imports (valued at the CIF price)

PT_INC_DISP_HH: Percentage of disposable household income

PT_INC_DISP_HH_BJL: Percentage of disposable household income before job loss

PT_INC_DISP_HH_MEDIAN: Percentage of median disposable household income

PT_INC_DISP_HH_TP_REF_WG_A: Percentage of disposable income in the same household type and with reference person working at average wage

PT_INC_DISP_HH_TP_REF_WG_MEDIAN: Percentage of disposable income in the same household type and with reference person working at median wage

PT_INFR_MAI: Percentage of infrastructure maintenance

PT_INST_COM: Percentage of compulsory instruction time

PT_INV_W_ENV: Percentage of environment related worldwide inventions

PT_LF_SUB: Percentage of labour force in the same subgroup

PT_LI_F2: Percentage of currency and deposits liabilities

PT_LI_S12L: Percentage of liabilities of investment funds

PT_LI_S12Q: Percentage of liabilities of insurance corporations and pension funds

PT_LI_S13: Percentage of general government liabilities

PT_LND_NTR_SNTR_1992: Percentage of natural and semi-natural land in 1992

PT_LND_NTR_SNTR_2004: Percentage of natural and semi-natural land in 2004

PT_LND_NTR_SNTR_BP: Percentage of natural and semi-natural land in base period

PT_LSC_FRST_INT_2000: Percentage of intact forest landscape in 2000

PT_MTH_CH_Y_LE15: Percentage of mothers with at least one child under 15 years

PT_NST_SUB: Percentage of non-students in the same subgroup

PT_ODA_S13: [Deprecated] Percentage of tax revenue / total tax receipt

PT_OTE_S13: Percentage of general government expenditure

PT_OTE_S13M_FNC: Percentage of state and local government expenditure in the same function

PT_OTE_S13M_TRANSACT: Percentage of state and local government expenditure in the same transaction

PT_OTE_S13_EDU: Percentage of general government expenditure on education

PT_OTE_S13_FNC: Percentage of general government expenditure in the same function

PT_OTE_S13_TRANSACT: Percentage of general government expenditure in the same transaction

PT_OTR_REV_CAT: Percentage of revenues in the same revenue category

PT_OTR_S13: Percentage of general government revenues

PT_OTR_S13M_REV_CAT: Percentage of state and local government revenues in the same revenue category

PT_OTR_S13_CAT: Percentage of general government revenues in the same category

PT_OTR_SECTOR: Percentage of revenues in the same institutional sector

PT_P2_ACT: Percentage of intermediate consumption in the same activity

PT_P2_ACT_PROD: Percentage of intermediate consumption in the same activity and product

PT_P2_PROD: Percentage of intermediate consumption in the same product

PT_P3: Percentage of final consumption expenditure

PT_P3_POP_PPP_OECD: Percentage of PPP converted final consumption expenditure per capita of OECD

PT_P3_S14: Percentage of final household consumption expenditure

PT_P3_S14_O_PROD: Percentage of household final consumption expenditure at purchasers' prices in the same product

PT_P3_S14_PROD: Percentage of household final consumption expenditure in the same product

PT_P41_POP_PPP_OECD: Percentage of PPP converted actual individual consumption per capita of OECD

PT_P51G: Percentage of gross fixed capital formation

PT_P51G_PROD: Percentage of gross fixed capital formation in the same product

PT_P5L_S1: Percentage of investment of total economy

PT_P5L_S13: Percentage of general government investment

PT_P5L_SECTOR: Percentage of investment in the same institutional sector

PT_P6_PROD: Percentage of exports in the same product

PT_PARL_ELECT_OFF_LSH: Percentage of elected officials in the national lower or single houses of parliament

PT_PATN_FM_T: Percentage of world triadic patent families

PT_PAT_DRUG_ANTCOAG: Percentage of patients receiving anticoagulating drugs

PT_PAT_DSC_D_MD: Percentage of discharged patients diagnosed with a mental disorder

PT_PAT_WAIT_M_GT3: Percentage of patients waiting more than 3 months

PT_PBL_SC_E: Percentage of expected scientific publications

PT_PBL_SC_SUB: Percentage of scientific publications in the same subgroup

PT_PCOS_S13: Percentage of general government production costs

PT_POP_AGE: Percentage of population in the same age

PT_POP_COND: Percentage of population who have condition

PT_POP_LT_INT_PVL: Percentage of population living with less than the international poverty line amount

PT_POP_SEX: Percentage of population in the same sex

PT_POP_SEX_AGE: Percentage of population in the same sex and age

PT_POP_SUB: Percentage of population in the same subgroup

PT_POP_Y15T24: Percentage of population aged 15-24 years

PT_POP_Y15T24_SUB: Percentage of population aged 15-24 years in the same subgroup

PT_POP_Y15T29: Percentage of population aged 15-29 years

PT_POP_Y15T64: Percentage of population aged 15-64 years

PT_POP_Y15T74: Percentage of population aged 15-74 years

PT_POP_Y16T65: Percentage of population aged 16-65 years

PT_POP_Y16T65_SUB: Percentage of population aged 16-65 in the same subgroup

PT_POP_Y20T64: Percentage of population aged 20-64 years

PT_POP_Y25T34: Percentage of population aged 25-34 years

PT_POP_Y25T54: Percentage of population aged 25-54 years

PT_POP_Y25T64: Percentage of population aged 25-64 years

PT_POP_Y25T64_SUB: Percentage of population aged 25-64 years in the same subgroup

PT_POP_Y_GE15: Percentage of population aged 15 years or over

PT_POP_Y_GE15_SUB: Percentage of population aged 15 years or over in the same subgroup

PT_POP_Y_GE16: Percentage of population aged 16 years or over

PT_POP_Y_GE16_SUB: Percentage of population aged 16 years or over in the same subgroup

PT_POP_Y_GE65: Percentage of population aged 65 years or over

PT_POP_Y_GE75: Percentage of population aged 75 years or over

PT_POP_Y_LT1: Percentage of children aged less than 1 years

PT_REC_FRM_COM: Percentage of commodity specific gross farm receipts

PT_REC_TOUR_INT: Percentage of international tourism receipts

PT_RSC_REN_INT: Percentage of internal renewable resources

PT_RSC_REN_INT_WAT_FRSH: Percentage of internal renewable fresh water resources

PT_RSC_REN_WAT: Percentage of available renewable water resources

PT_SAL_SUB: Percentage of employees in the same subgroup

PT_SELF_SUB: Percentage of self-employed in the same subgroup

PT_SL_VAL_M: Percentage of sales value (turnover) in the same market

PT_SL_VOL_M: Percentage of sales volume in the same market

PT_SME: Percentage of small and medium-sized enterprises

PT_SNGU_SECTOR: Percentage of subnational government units in the same institutional sector

PT_ST_SUB: Percentage of students in the same subgroup

PT_SUPP_E_ALL: Percentage of total support estimate for all countries

PT_SUPP_E_GN_SRV: Percentage of general services support estimate

PT_SUPP_E_GN_SRV_ALL: Percentage of general services support estimate for all countries

PT_SUPP_E_PRDC_ALL: Percentage of producer support estimate for all countries

PT_SUPP_FOS_FL_OC: Percentage of ocean-related fossil fuel support

PT_SUR_ART_BP: Percentage of artificial surfaces in base period

PT_SUR_WAT_PER_1984: Percentage of permanent water surface in 1984

PT_TAX_REV_ENV: Percentage of environment related tax revenue

PT_TAX_REV_NRG: Percentage of energy related tax revenue

PT_TCAR_BP: Percentage of tree-covered areas in base period

PT_TECH_COL: Percentage of collaborations in all technologies

PT_TECH_ENV: Percentage of environment related technologies

PT_TRD_INT_G: Percentage of trade in intermediate goods

PT_TS_O_PROD: Percentage of total supply at purchasers' prices in the same product

PT_UNEMPT_SUB: Percentage of unemployment in the same subgroup

PT_VOTES_SUB: Percentage of votes in the same subgroup

PT_WAP_SUB: Percentage of working age population in the same subgroup

PT_WG_A_SAL_FT: Percentage of average wage of full-time employees

PT_WG_SAL_M_D: Percentage of wages of men in the same decile

PT_WR_HLTH_INS_SUB: Percentage of workers with health insurance in the same subgroup

PT_WR_INF_DEG_URB: Percentage of informal workers in the same degree of urbanisation

PT_WR_INF_EMP_ST: Percentage of informal workers in the same employment status

PT_WR_INF_SEX: Percentage of informal workers in the same sex

PT_WR_INF_SUB: Percentage of informal workers in the same subgroup

PT_WR_LT_INT_PVL: Percentage of workers living with less than the international poverty line amount

PT_WR_LT_NAT_PVL: Percentage of workers living with less than the national poverty line amount

PT_WR_PENS_SUB: Percentage of workers contributing to pensions in the same subgroup

PT_WR_PRF_HLTH: Percentage of workers in the same health profession

PT_WR_PRF_HLTH_SEX: Percentage of workers in the same health profession and sex

PT_WR_SEX_AGE_EMP_ST: Percentage of workers in the same age, sex and employment status

PT_WR_SUB: Percentage of workers in the same subgroup

PT_W_SUB: Percentage of women in the same subgroup

TOE_10P3USD: Tonnes of oil equivalent per 1 000 US dollars

T_NMVOCE_USD: Tonnes of NMVOC equivalent per US dollar

USD_EXC_H: US dollars per hour, exchange rate converted

USD_EXC_PS: US dollars per person, exchange rate converted

USD_HHE_PPP: US dollars per equivalised household, PPP converted

USD_KG_CO2E: US dollars per kilogramme of CO2-equivalent

USD_PPP_KWH: US dollars per kilowatt hour, PPP converted

XDC_HH_EQ: National currency per equivalised household

XDC_INP: National currency per unit of total inputs

XDC_TCO2E: National currency per tonne of CO2-equivalent

name: Depletion and growth of forest resources

provider_name: Organisation for Economic Co-operation and Development


CONF_STATUS: Confidentiality status

CURRENCY: Currency

DECIMALS: Decimals

OBS_STATUS: Observation status

UNIT_MULT: Unit multiplier

[A] Primary confidentiality due to small counts

C: Confidential statistical information

D: Secondary confidentiality set by the sender, not for publication

E: Not for publication, restricted for internal use only (equivalent to the code N) until the embargo time elapses; Free for publication (equivalent to the code F) after the embargo time elapses.

F: Free (free for publication)

G: Primary confidentiality due to dominance by one or two units

M: Primary confidentiality due to data declared confidential based on other measures of concentration

O: Primary confidentiality due to dominance by one unit

S: Secondary confidentiality set and managed by the receiver, not for publication

T: Primary confidentiality due to dominance by two units

X: Confidentiality due to military secrecy

ADF: Andorran franc (1-1 peg to the French franc)

ADP: Andorran peseta (1-1 peg to the Spanish peseta)

AED: United Arab Emirates dirham

AFA: Afghanistan afghani (old)

AFN: Afghani

ALL: Lek

AMD: Armenian dram

ANG: Netherlands Antillian guilder

AOA: Kwanza

AON: Angolan kwanza (old)

AOR: Angolan kwanza readjustado (old)

ARS: Argentine peso

ATS: Austrian schilling

AUD: Australian dollar

AWG: Aruban guilder

AZM: Azerbaijanian manat (old)

AZN: Azerbaijanian manat

BAM: Bosnia and Herzegovina convertible mark

BBD: Barbados dollar

BDT: Taka

BEF: Belgian franc

BEL: Belgian franc (financial)

BGL: Bulgarian lev (old)

BGN: Bulgarian lev

BHD: Bahraini dinar

BIF: Burundi franc

BMD: Bermudian dollar

BND: Brunei dollar

BOB: Boliviano

BOV: Mvdol

BRL: Brazilian real

BSD: Bahamian dollar

BTN: Ngultrum

BWP: Pula

BYB: Belarussian ruble (old)

BYN: Belarussian ruble

BYR: Belarus ruble

BZD: Belize dollar

CAD: Canadian dollar

CDF: Congolese franc

CENT: Euro cent

CFA: CFA franc

CHE: WIR euro

CHF: Swiss franc

CHW: WIR franc

CLF: Unidad de formento (UF)

CLP: Chilean peso

CNY: Chinese yuan renminbi

COP: Colombian peso

COU: Unidad de Valor real

CRC: Costa Rican colon

CSD: Serbian dinar (old)

CUP: Cuban peso

CVE: Cape Verde escudo

CYP: Cypriot pound

CZK: Czech koruna

DEM: German mark

DJF: Djibouti franc

DKK: Danish krone

DOP: Dominican peso

DZD: Algerian dinar

ECS: Ecuador sucre (old)

ECV: Unidad de valor constante

EEK: Estonian kroon

EGP: Egyptian pound

ERN: Nakfa

ESP: Spanish peseta

ETB: Ethiopian birr

EUR: Euro

FIM: Finnish markka

FJD: Fiji dollar

FKP: Falkland Islands pound

FRF: French franc

GBP: Pound sterling

GEL: Lari

GGP: Guernsey pounds

GHC: Ghanaian cedi (old)

GHS: Ghana cedi

GIP: Gibraltar pound

GMD: Dalasi

GNF: Guinea franc

GRD: Greek drachma

GTQ: Quetzal

GWP: Guinea-Bissau peso

GYD: Guyana dollar

HKD: Hong Kong dollar

HKQ: Hong Kong dollar (old)

HNL: Lempira

HRK: Croatian kuna

HTG: Gourde

HUF: Forint

IDR: Rupiah

IEP: Irish pound

ILS: New Israeli sheqel

L: Missing value; data exist but were not collected

description:
Benefits represent one of the sources of outflows from pension plans while contributions are one of the sources of inflows. Benefits are payments made to plan members (or dependants) after retirement, and can take the form of a lump sum or pension. Contributions are payments that can be made to a plan by the sponsors or members.

          Users can find the amount of benefits and contributions in several units (national currency, USD and as a percentage of GDP), as well as the split of contributions between employees and employers (in national currency, USD and as a percentage of total contributions) in this dataset.

Assets overseas issued by entities located abroad

Assets overseas issued in foreign currencies

Cash and deposits in entities located abroad

Bills and bonds issued by entities located abroad

Equity issued by entities located abroad

Market or fair value of derivatives held

Bills and bonds issued by public and private sector

Bills and bonds issued by public administration

Assets transferred to a third party for the pay-out phase

GEO: Geographical distribution of pension fund assets

PER_EMPLYMT_LINKED: Personal pension plans, to which access can be linked to employment or professional activity

PER_NOT_EMPLYMT_LINKED: Personal pension plans, to which access is not linked to employment or professional activity

WXANNEX1:
Non-Annex I of the United Nations Framework Convention on Climate

            Change Parties

PT_EARN_WR_ISCED11A_3: Percentage of earnings of workers with upper secondary educational attainment

name: Benefit payments and contributions to asset-backed pensions


Asset-backed pensions - main database

N: Not for publication, restricted for internal use only

description:
This database on asset-backed pensions is based on data collected through the OECD Global Pension Statistics exercise. Data come from various administrative sources, mainly: pension supervisory authorities, financial market authorities, ministries of finance, or national statistical offices. Data cover all asset-backed pension arrangements where assets are accumulated to back future benefit payments, except reserves of public (pay-as-you-go) pension arrangements. Asset-backed pension plans may be financed through different vehicles (such as pension funds, pension insurance contracts, bank or investment company managed funds), publicly or privately administered, mandatory or voluntary, occupational or personal, defined benefit (DB) or defined contribution (DC), for public or private-sector workers. Employers’ book reserves are also in the scope.

          This database includes various statistics and indicators on asset-backed pensions such as the amount of assets earmarked for retirement, the liabilities of pension providers, their revenues, their expenditure, the number of funds and plans, as well as plan membership. These statistics and indicators can be split by type of financing vehicle and type of plan.

WXANNEX1:
Non-Annex I of the United Nations Framework Convention on Climate
            Change Parties


Pension plan membership

description:
Members of pension plans are those who are either active (working or contributing, and hence actively accumulating assets), passive (retired, and hence receiving benefits) or deferred (holding deferred benefits) participants in a pension plan.

          This dataset shows the proportion of active, deferred and passive members, as well as the proportion of men and women participant in a plan, as a percentage of total members. These indicators are available by type of pension plan (occupational or personal, defined benefit (DB) or defined contribution (DC)) and financing vehicle (e.g. pension funds, pension insurance contracts, other financing vehicle).


description:
Operating costs include all costs of administration and investment management involved in the process of transforming pension contributions into retirement benefits. Indicators on operating costs also reflect the extent to which data are available in each jurisdiction for the different types of costs pension providers (such as pension funds, insurance companies, banks or investment funds offering pension arrangements) incur.

          This dataset shows the investment expenses and administrative expenses of pension providers expressed as a percentage of total assets. These indicators are available by type of pension plan (occupational or personal, defined benefit (DB) or defined contribution (DC)) and financing vehicle (e.g. pension funds, pesnion insurance contracts, other financing vehicle).

name: Operating costs of asset-backed pensions


Assets earmarked for retirement

description:
Assets earmarked for retirement are assets accumulating to back future benefit payments. These assets may accumulate in pension funds, pension insurance contracts, bank or investment company managed funds. They may be in occupational or personal plans, defined benefit (DB) or defined contribution (DC) plans.

          This dataset shows the amount of assets - expressed in national currency, USD and as a percentage of GDP - by type of vehicle.

          Data cover employers’ book reserves but do not cover reserves of public (pay-as-you-go) pension arrangements.


Structure of asset-backed pensions

description:
The pension landscape includes various types of plans around the world. These plans may finance the future benefits of retirees through different vehicles (such as pension funds, reserves in employers' book, pension insurance contracts, bank or investment company managed funds). They may be publicly or privately administered, mandatory or voluntary, occupational or personal, defined benefit (DB) or defined contribution (DC), for public or private-sector workers.

          This dataset shows the split of assets by type of vehicle. It also includes the split of occupational pension assets between DB and DC plans. Users can also find the amount of assets in occupational and personal plans, as well as assets in DB and DC plans expressed as a percentage of GDP.


description:
The way assets earmarked for retirement are invested in financial markets varies across jurisdictions. The OECD Global Pension Statistics exercise collects both direct investments in various instruments and vehicles and indirect investments through collective investment schemes.

          This dataset shows the asset allocation of pension providers (e.g. pension funds, insurance companies offering pension insurance contratcs, and other providers) by type of asset class or instrument, expressed in national currency, USD, as a percentage of total assets (and as a percentage of total holdings of a given instrument in some cases).

name: Asset allocation of pension funds and other pension providers


Full-time and part-time employment

Zero

One

Ten

Eleven

Twelve

Thirteen

Fourteen

Fifteen

Two

Three

Four

Five

Six

Seven

Eight

Nine

[A] Normal value

B: Time series break

D: Definition differs

E: Estimated value

F: Forecast value

G: Experimental value

[H] Missing value; holiday or weekend

I: Value imputed by a receiving agency

J: Derogation

K: Data included in another category

M: Missing value; data cannot exist

N: Not significant

O: Missing value

P: Provisional value

Q: Missing value; suppressed

S: Strike and other special events

U: Low reliability

V: Unvalidated value

W: Includes data from another category

Units

Tens

Tens of billions

Hundreds of billions

Trillions

Tens of trillions

Hundreds of trillions

Quadrillions

Hundreds

Thousands

Tens of thousands

Hundreds of thousands

Millions

Tens of millions

Hundreds of millions

Billions

description:
This table contains data on full-time and part-time employment according to:

a common definition of 30-usual weekly hours of work in the main job

national definitions

          Data are broken down by professional status - employees, total employment - sex and standardised age groups (15-24, 25-54, 55+, total)

In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates.

For detailed information on labour force surveys for all countries please see the attached file: http://www.oecd.org/els/employmentpoliciesanddata/LFSNOTES_SOURCES.pdf

AGE: Age

FREQ: Frequency of observation

JOB_COVERAGE: Job coverage

LABOUR_FORCE_STATUS: Labour force status

MEASURE: Measure

METHODOLOGY: Methodology

REF_AREA: Reference area

SEX: Sex

UNIT_MEASURE: Unit of measure

WORKER_STATUS: Worker status

WORK_TIME_ARNGMNT: Working time arrangement

BIRTH: At birth

D29T60: From 29 to 60 days

D91T365: From 91 to 365 days

DEPENDENT: Dependent

M12T23: From 12 to 23 months

M24T35: From 24 to 35 months

M24T59: From 24 to 59 months

M36T47: From 36 to 47 months

M36T59: From 36 to 59 months

M48T59: From 48 to 59 months

MID: Middle-aged

M_LT1: Less than 1 month

OLD: Old

RETIRE: National retirement age

Y0: 0 years

Y1: 1 years

Y10: 10 years

Y100: 100 years

Y101: 101 years

Y102: 102 years

Y103: 103 years

Y104: 104 years

Y105: 105 years

Y106: 106 years

Y107: 107 years

Y108: 108 years

Y109: 109 years

Y10T14: From 10 to 14 years

Y11: 11 years

Y12: 12 years

Y12T24: From 12 to 24 years

Y13: 13 years

Y14: 14 years

Y15: 15 years

Y15T17: From 15 to 17 years

NSNAW: Not seeking, not available, willing (willing non-jobseekers)

NSNW: Not seeking, not willing (unwilling non-jobseekers)

PLF_NSA: Not seeking, available (available potential jobseekers)

PLF_NSA_D: Not seeking, available, because of discouragement

PLF_SNA: Seeking, not available (unavailable jobseekers)


description:
This table based on so-called OECD harmonized definition contains data on full-time and part-time employment according to a common definition of 30-usual weekly hours of work in the main job. Data are broken down by professional status - employees, total employment - sex and standardised age groups (15-24, 25-54, 55+, total)

In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates.

For detailed information on labour force surveys for all countries please see the attached file: http://www.oecd.org/els/employmentpoliciesanddata/LFSNOTES_SOURCES.pdf

name: Full-time and part-time employment based on OECD-harmonized definition


description:
This table contains incidences and gender composition of part-time employment with standardised (15-24, 25-54, 55-64, 65+, total) and detailed age groups. Data are further broken down by professional status - employees, total employment. Part-time employment is based on a common 30-usual-hour cut-off in the main job.

In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates.

For detailed information on labour force surveys for all countries please see the attached file: http://www.oecd.org/els/employmentpoliciesanddata/LFSNOTES_SOURCES.pdf

name: Incidence of full-time and part-time employment based on OECD-harmonized definition


Gender share of part-time employment

description:
This table contains gender composition of part-time employment with standardised (15-24, 25-54, 55-64, 65+, total) and detailed age groups. Data are further broken down by professional status - employees, total employment.

In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates.

For detailed information on labour force surveys for all countries please see the attached file: http://www.oecd.org/els/employmentpoliciesanddata/LFSNOTES_SOURCES.pdf


description:
National definitions of part-time work vary considerably across OECD countries. Essentially three main approaches can be distinguished: i) a classification based on the worker’s perception of his/her employment situation; ii) a cut-off (generally 30 or 35 hours per week) based on usual working hours, with persons usually working fewer hours being considered part-timers; iii) a comparable cut-off based on actual hours worked during the reference week. A criterion based on actual hours will generally yield a part-time rate higher than one based on usual hours, particularly if there are temporary reductions in working time as a result of holidays, illness, short-timing, etc. On the other hand, it is not entirely clear whether a classification based on the worker’s perception will necessarily yield estimates of part-time work that are higher or lower than one based on a fixed cut-off. In one country (France) which changed from 1981 to 1982 from a definition based on an actual hours cut-off (30 hours) to one based on the respondent’s perception, the latter criterion appeared to produce slightly higher estimates.

Other factors as well affect the international comparability of the estimates. In some countries, the hours’ cut-off is based on hours for the main job, in others on total hours for all jobs. Certain countries do not consider unpaid family workers to be employed unless they work more than a minimum number of hours, so that such workers do not enter into counts for part-time workers.

This table contains data on full-time and part-time employment based on national definition. Data are broken down by professional status - employees, total employment - sex and standardised age groups (15-24, 25-54, 55+, total).

In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates.

For detailed information on labour force surveys for all countries please see the attached file: http://www.oecd.org/els/employmentpoliciesanddata/LFSNOTES_SOURCES.pdf

name: Full-time and part-time employment based on national definition


description:
This table contains incidences and gender composition of part-time employment with standardised (15-24, 25-54, 55-64, 65+, total) and detailed age groups. Data are further broken down by professional status - employees, total employment. Part-time employment is based on national definitions.

The definition of part-time work varies considerably across OECD countries Essentially three main approaches can be distinguished: i) a classification based on the worker's perception of his/her employment situation; ii) a cut-off (generally 30 or 35 hours per week) based on usual working hours, with persons usually working fewer hours being considered part-timers; iii) a comparable cut-off based on actual hours worked during the reference week. A criterion based on actual hours will generally yield a part-time rate higher than one based on usual hours, particularly if there are temporary reductions in working time as a result of holidays, illness, short-timing, etc. On the other hand, it is not entirely clear whether a classification based on the worker's perception will necessarily yield estimates of part-time work that are higher or lower than one based on a fixed cut-off. In one country (France) which changed from 1981 to 1982 from a definition based on an actual hours cut-off (30 hours) to one based on the respondent's perception, the latter criterion appeared to produce slightly higher estimates. Other data characteristics

In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates.

For detailed information on labour force surveys for all countries please see the attached file: http://www.oecd.org/els/employmentpoliciesanddata/LFSNOTES_SOURCES.pdf

name: Incidence of full-time and part-time employment based on national definition


Coastal flooding - Cities and FUAs

description:
This dataset provides estimates of population and built-up exposure to coastal floods. These indicators are based on the extent of coastal flood events of different return periods (10 to 100 years). A return period corresponds to the estimated time interval between floods of similar intensity.

Data sources and methodology

Coastal flooding indicators are estimated using the Global coastal flood hazard maps (Muis, S. et al., 2016). This dataset provides the extent of coastal floods for different return periods (10 to 100 years) with a 1-kilometre resolution. Built-up area exposure to coastal floods is computed by intersecting this map with Copernius annual land cover data (Copernicus Climate Change Service, Climate Data Store, 2019) and population exposure is computed using the Global Human Settlement Population layer (European Commission, GHSL Data Package 2023).

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

HEAT_STRESS: Heat stress threshold

RET_PERIOD: Return period

TERRITORIAL_LEVEL: Territorial level

_Z: Not applicable

GE32: Strong heat stress (> 32°C)

GE38: Very strong heat stress (> 38°C)

GE46: Extreme heat stress (> 46°C)

AES_AL_FNFAET12: Participation in formal and/or non-formal training - 12-month reference period

B5N: Primary incomes, net

BB_ACC: Share of households with internet broadband access

CF_BUILT_EXP: Built-up area exposure to coastal flooding

EARLY_LEAVERS_RATE: Early leavers from education and training

ELEC_FOSSIL: Gross electricity generation from fossil fuels

ELEC_HYDRO: Gross electricity generation from hydro power

ELEC_NUCL: Gross electricity generation from nuclear

ELEC_OTHER: Gross electricity generation from other sources

ELEC_OTHERRES: Gross electricity generation from other renewable sources (biomass, geothermal, waste, wave and tidal)

ELEC_RES: Gross electricity generation from renewable energy sources

ELEC_SOLAR: Gross electricity generation from solar power

ELEC_WIND: Gross electricity generation from wind power

EMP_RATIO_SEXDIF: Employment to population ratio, gender difference

GHG_AGRICULTURE: Green house gas emissions from agriculture

GHG_BUILDINGS: Green house gas emissions from buildings

GHG_INDUSTRY: Green house gas emissions from industries

GHG_POWER: Green house gas emissions from the power sector

GHG_ROAD_TRANSPORT: Green house gas emissions from road transport

GHG_TRANSPORT: Green house gas emissions from transport

INMIG: New residents in the region coming from another country

INMOB: New residents in the region coming from another region of the same country

LAU_POP: Mean local administrative unit population

LFEXPDFM: Life expectancy difference (female-male)

LF_RATE_SEXDIF: Labour force participation rate, gender difference

MORT_ICDI_CRUDE_RATIO: Circulatory system diseases mortality crude rate

MORT_ICDJ_CRUDE_RATIO: Respiratory system diseases mortality crude rate

MORT_INFANT_DFM: Infant mortality rate difference (female-male)

NETB: Net enterprises population growth/shrink

NETMIG: Net international migration (inflows minus outflows)

NETMOB: Net inter-regional mobility (inflows minus outflows)

OUTMIG: Persons who left the region to reside in another country

OUTMOB: Persons who left the region to reside in another region of the same country

PIAAC_AL_FNFAET4: Participation in formal and/or non-formal training - 4-week reference period

POP_WATER_2ND_TREAT: Population connected to at least secondary wastewater treatment

RF_BUILT_EXP: Built-up area exposure to river flooding

TRANS_SHARE_EDULABOUR_NE_U_I: Population not in education and unemployed or inactive (NEET)

UNE_RATE_SEXDIF: Long-term unemployment rate, gender difference

VEH_NEW_ELECTRIC: New registered vehicles, electric power engine

VEH_NEW_HYBRID: New registered vehicles, hybrid power engine

ES069C: Castellón de la Plana/Castelló de la Plana

ES098C: San Vicente del Raspeig/Sant Vicent del Raspeig

UK586C: Derry & Strabane Local Government District


Wildfires - Cities and FUAs

description:
This dataset provides measures of population and land exposure to wildfires.

Data sources and methodology

Exposure to wildfire indicators are derived from JRC's Global wildfire dataset for the analysis of fire regimes and fire behaviours (Artés, T. et al., 2019), which provides monthly individual fire perimeters for 2001-2020. Burnt areas are aggregated by year to obtain the total burnt land for each year. Built-up area exposure to wildfire is computed by intersecting the total burnt area with Copernius annual land cover data (Copernicus Climate Change Service, Climate Data Store, 2019). For population exposure, a 5-km buffer was added to the wildfire perimeters before intersecting with the Global Human Settlement Population layer (European Commission, GHSL Data Package 2023).

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

ES069F: Castellón de la Plana/Castelló de la Plana

UK586F: Derry & Strabane Local Government District


Heat stress - Cities and FUAs

description:
This dataset provides measures of population exposure to heat stress, estimated using the Universal Thermal Comfort Index (UTCI). The UTCI enables to assess the impact of atmospheric conditions on the human body by considering air temperature, wind, radiation and humidity. UTCI values from 32°C to 38°C are considered as strong heat stress, from 38°C to 46°C as very strong heat stress, and above 46°C as extreme heat stress.

Data sources and methodology

Population exposure to heat stress was computed using the hourly thermal comfort indices grids from the Copernicus Climate Data Store (Di Napoli C. et al.). For each 0.25° by 0.25° grid cell, the number of days for which the maximum UTCI value is above each heat stress threshold (32°C, 38°C and 46°C) is computed. FUA and city level indicators correspond to the population-weighted average number of days above each heat stress threshold, obtained by intersecting the computed grid with the Global Human Settlement Population layer (European Commission, GHSL Data Package 2023).

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.


River flooding - Cities and FUAs

description:
This dataset provides estimates of population and built-up exposure to river floods. These indicators are based on the extent of river flood events of different return periods (10 to 100 years). A return period corresponds to the estimated time interval between floods of similar intensity.

Data sources and methodology

River flooding indicators are estimated using the River Flood Hazard Maps at European and Global Scales (Dottori, F. et al., 2021). These datasets provide the extent of river floods for different return periods (10 to 100 years). For OECD countries located in Europe and the Mediterranean Basin, the regional 250-metre resolution map was used. For the remaining OECD countries, the global map with a spatial granularity of 1 km was used. Built-up area exposure to river floods is computed by intersecting this map with Copernius annual land cover data (Copernicus Climate Change Service, Climate Data Store, 2019) and population exposure is computed using the Global Human Settlement Population layer (European Commission, GHSL Data Package 2023).

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.


description:
This dataset provides population counts by 5-year age groups and sex for OECD Functional Urban Areas (FUAs) and cities.

Data sources and methodology

          Population by age and sex data is collected at the level of small administrative units (e.g. municipalities) and aggregated at the FUA and city level. The correspondence table between SAUs and FUAs/cities is available at this link.

          When population by age and sex data is not available at such granular level, FUA and city level indicators are estimated by adjusting the regional (OECD TL3 regions) values to the FUA and city boundaries, based on the population distribution in each region. Regional values (population by age and sex) in TL3 regions are used as data inputs and combined with gridded total population data (European Commission, GHSL Data Package 2023). FUA and city boundaries are intersected with TL3 borders and coefficients are computed for each region, based on the share of the regional population that lives within the FUA/city. These coefficients are then applied to the variables of interest (e.g. population by age groups) and allocated to the FUA/city. In case several regions intersect the FUA/city, the adjusted values of intersecting regions are summed. This approach assumes that the variables of interest have the same spatial distribution as the total population. Therefore, the modelled indicators should be interpreted with caution.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

ORIGIN: Origin

Y15T19: From 15 to 19 years

Y15T24: From 15 to 24 years

Y15T29: From 15 to 29 years

FB_EU: Foreign-born from inside EU-27 countries

FB_NONEU: Foreign-born from outside EU-27 countries

name: Population by age and sex - Cities and FUAs


Dependency ratio - Cities and FUAs

description:
This dataset provides dependency ratios for OECD Functional Urban Areas (FUAs) and cities.

Data sources and methodology

          Dependency ratios are derived from population by age and sex data collected at the level of small administrative units (e.g. municipalities) and aggregated at the FUA and city level. The correspondence table between SAUs and FUAs/cities is available at this link.

          When population by age and sex data is not available at such granular level, FUA and city level indicators are estimated by adjusting the regional (OECD TL3 regions) values to the FUA and city boundaries, based on the population distribution in each region. Regional values (population by age and sex) in TL3 regions are used as data inputs and combined with gridded total population data (European Commission, GHSL Data Package 2023). FUA and city boundaries are intersected with TL3 borders and coefficients are computed for each region, based on the share of the regional population that lives within the FUA/city. These coefficients are then applied to the variables of interest (e.g. population by age groups) and allocated to the FUA/city. In case several regions intersect the FUA/city, the adjusted values of intersecting regions are summed. This approach assumes that the variables of interest have the same spatial distribution as the total population. Therefore, the modelled indicators should be interpreted with caution.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.


description:
This dataset provides population counts for native and foreign-born population in OECD Functional Urban Areas (FUAs) and cities.

Data sources and methodology

          Population by origin data is collected at the level of small administrative units (e.g. municipalities) and aggregated at the FUA and city level. The correspondence table between SAUs and FUAs/cities is available at this link.

          For more details on data sources for each country, please see here.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

name: Foreign-born population - Cities and FUAs


Internet speed - Cities and FUAs

description:
This dataset provides estimates of internet speed (downloading and uploading speeds) in FUAs and cities compared to the national values.

Data sources and methodology

Internet speed indicators are computed using Ookla quarterly data (Speedtest® by Ookla® Global Fixed and Mobile Network Performance Maps, based on analysis by Ookla of Speedtest Intelligence® data for 2019-2023). Indicators are derived from internet speed tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, ethernet).

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

B6N: Disposable income, net

B7G: Adjusted disposable income, gross

B7N: Adjusted disposable income, net

CDD: Cooling degree days

CF_CROP_EXP: Cropland exposure to coastal flooding

CF_LAND_EXP: Land exposure to coastal flooding


Economy - FUAs

description:
This dataset provides estimates of economic indicators for FUAs of more than 250 000 inhabitants. Indicators include GDP, GDP per capita, jobs and labour productivity.

Data sources and methodology

          FUA-level indicators are estimated by adjusting the regional (OECD TL2 and TL3 regions) values to the FUA boundaries, based on the population distribution in each region. Regional values (GDP and jobs) in TL3 regions are used as data inputs and combined with gridded population data (European Commission, GHSL Data Package 2023). FUA boundaries are intersected with TL3 borders and a coefficient is computed for each region, based on the share of the regional population that lives within the FUA. This coefficient is then applied to the variable of interest (e.g. GDP) and allocated to the FUA. In case several regions intersect the FUA, the adjusted values of intersecting regions are summed. For countries where TL3-level data is not available, data for TL2 regions is used. This approach assumes that the variable of interest has the same spatial distribution as population. Therefore, the modelled indicators should be interpreted with caution.

          When economic statistics are available at a more granular level (e.g. municipal level), data is aggregated for each FUA. For example in the United States, GDP estimates are available at the county-level (US Bureau of Economic Analysis), and then aggregated by FUA.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.


description:
This dataset provides estimates of Cooling and Heating Degree Days (CDDs and HDDs) for FUAs and cities. CDDs measure the intensity (in °C) and the length (in days) of cooling needs. More precisely, annual CDDs are the sum over a year of the differences between the daily mean outdoor air temperature and the threshold temperature (e.g. 22°C) when the outdoor temperature is above the threshold temperature. Similarly, HDDs measure heating needs, and correspond to the sum over a year of the differences between the threshold temperature and the daily mean outdoor air temperature when the outdoor temperature is below the threshold temperature.

Data sources and methodology

          CDDs and HDDs are computed at the FUA and city level using a 0.25 degree resolution global gridded dataset of monthly and annual cooling and heating degree-days (1970-2018) based on GLDAS data (Mistry, 2019). The threshold temperatures are 22°C for CDDs and 15°C for HDDs.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

name: Cooling and heating degree days - Cities and FUAs


Green areas - FUAs

description:
This dataset provides measures of the availability of green areas in FUAs' urban centres.

Data sources and methodology

The share of green areas in FUAs is estimated using ESA Worldcover data (Zanaga et al., 2021), which provides worldwide land cover data for 2020 at a 10 metre resolution. Green areas are defined by the following classes: trees, shrublands and grasslands. Only the urban centres (as defined by the degree of urbanization) located within each FUA are taken into account for a higher comparability across FUAs.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

PM_CONCENTRATION: PM concentration level

TIME_SEASON: Time of the day and season


Air pollution - Cities and FUAs

description:
This dataset provides measures of PM2.5 (particulate matter) air pollution in FUAs and cities. Indicators provide an estimate of the population-weighted average of PM2.5 concentration, and of the shares of population that live above certain thresholds of PM2.5 concentration.

Data sources and methodology

PM2.5 air pollution indicators are computed using geospatial data from the Global Burden of Disease (GBD) 2019 project. The available 0.1° by 0.1° resolution grids provide the estimated mean concentration of PM2.5 at the cell level. GBD estimates are derived by integrating satellite observations, chemical transport models and measurements from ground monitoring station networks.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.


Urban heat island - FUAs

description:
This dataset provides measures of the urban heat island intensity in FUAs. Cities often record higher temperatures than their surrounding areas due to the urban heat island effect. This phenomenon results from high building density, heat generated by human activities, building materials and limited vegetation.

Data sources and methodology

The urban heat island is defined for a FUAs as the difference in land surface temperature between the built-up area and its surroundings. Urban and non-urban lands are delineated for each FUA using MODIS yearly land cover data. Urban lands refer to the "urban and built-up lands" class in the International Geoshpere-Biosphere Programme (IGBP) classification, and non-urban lands correspond to the remaing classes, except "water bodies". Land surface temperature is derived from MODIS Terra and Aqua land surface temperature daily datasets. The mean temperature is computed for both urban and non-urban lands for the whole year and for summer only (1 June to 31 August for the Northern Hemisphere and 1 December to 28 February for the Southern hemisphere). The urban heat island intensity corresponds to the difference between the mean temperature for urban lands and the mean temperature for non-urban lands. For more details, see here.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.


Labour market - FUAs

description:
This dataset provides estimates of labour market indicators for FUAs of more than 250 000 inhabitants. Indicators include labour force, participation rate, employment, employment to population ratio, unemployment and unemployment rate.

Data sources and methodology

          FUA-level indicators are estimated by adjusting the regional (OECD TL2 and TL3 regions) values to the FUA boundaries, based on the population distribution in each region. Regional values (labour force, employment and unemployment) in TL3 regions are used as data inputs and combined with gridded population data (European Commission, GHSL Data Package 2023). FUA boundaries are intersected with TL3 borders and a coefficient is computed for each region, based on the share of the regional population that lives within the FUA. This coefficient is then applied to the variable of interest (e.g. employment) and allocated to the FUA. In case several regions intersect the FUA, the adjusted values of intersecting regions are summed. For countries where TL3-level data is not available, data for TL2 regions is used. This approach assumes that the variable of interest has the same spatial distribution as population. Therefore, the modelled indicators should be interpreted with caution.

          When labour statistics are available at a more granular level (e.g. municipal level), data is aggregated for each FUA. For example in the United States, labour market data is available at the county-level (US Bureau of Labor Statistics), and then aggregated by FUA.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

Y15T44: From 15 to 44 years

Y15T49: From 15 to 49 years


description:
This dataset provides measures of population density and built-up area in FUAs and cities.

Data sources and methodology

          Built-up data is computed using GHS built-up surface grids (European Commission, GHSL Data Package 2023).
          Information on population is extracted from the Population by age and sex - FUAs and cities dataset and FUA and city surfaces are calculated from FUA and city shapefiles, using GIS tools.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

name: Population and built-up density - Cities and FUAs


description:
This dataset provides information on the fragmentation of FUAs and cities into local administrative units (LAUs).

Data sources and methodology

          Local administrative units (LAUs) are mapped to FUAs and cities using GIS techniques. The correspondence table between LAUs and FUAs/cities is available at this link.

          Information on population is extracted from the Population by age and sex - FUAs and cities dataset.

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

name: Local administrative units - Cities and FUAs


Access to public transport - FUAs

description:
This dataset provides measures of accessibility to public transport stops in the largest FUA of each country. Indicators provide an estimate of the share of FUA population that lives withing a certain travel time by foot from a public transport stop.

Data sources and methodology

Public transport accessibility indicators are computed using public transport stops' location from Open Street Map (OSM), a routing tool (Mapbox Isochrone API) and the Global Human Settlement Layer's population grid for 2015 (Schiavina, Freire and MacManus, 2019).

The FUA and city definitions

The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs). Being composed of a city and its commuting zone, FUAs encompass the economic and functional extent of cities based on daily people’s movements (OECD, 2012).

FUAs and cities are defined in several steps. First, urban centres defined by the degree of urbanisation are adapted to the closest local administrative units to define a city. Next, commuting flows are used to identify which of the surrounding, less densely populated local units are part of the city’s labour market (commuting zone). Commuting zones are defined as all local units with at least 15% of their employed residents working in a certain city. Municipalities surrounded by a single functional urban area are included and non-contiguous municipalities are dropped.

Around 1 300 FUAs and 1400 cities were delineated in 37 OECD countries.

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

For any question or comment, please write to RegionStat@oecd.org

FUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.

TRAN_MODE: Mode of transport

TRAVEL_TIME: Travel time


G20 - Consumer price indices, all items

BASE_PER: Base period

DURABILITY: Durability

D: Durable

ND: Non durable

S: Services

SD: Semi durable

_T: Total

description:
This dataset contains statistics on Consumer Price Indices - all items, for G20 countries and for the G20 as a whole.

The G20 area consists of the following economies: Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Korea, Mexico, the Russian Federation, Saudi Arabia, South Africa, Türkiye, the United Kingdom, the United States, the African Union and the European Union.

The CPI aggregate for G20 area is calculated for the headline indicator only (CPI/HICP All items). It reflects national CPIs for all G20 countries (with the exception of Türkiye and the United Kingdom) that are not part of the EU while it reflects the HICPs for the EU, its Member States, for Türkiye and the United Kingdom. It is an annual chain-linked Laspeyres-type indices, calculated taking the sixteen individual country members of the G20 (other than France, Germany, and Italy) plus the EU as an aggregate. The G20 aggregate does not include African Union countries, except for South Africa.

dimensions_codes_order: REF_AREA FREQ METHODOLOGY MEASURE UNIT_MEASURE EXPENDITURE ADJUSTMENT TRANSFORMATION

ADJUSTMENT: Adjustment

EXPENDITURE: Expenditure

TRANSFORMATION: Transformation

AA: Amplitude adjusted

C: Trend-cycle, calendar adjusted

[H] Seasonally adjusted and weather normalised

I: Irregular component

K: Calendar component

M: Seasonal and calendar component

N: Neither seasonally adjusted nor calendar adjusted

NOR: Normalized

R: Trend-cycle, not calendar adjusted

RT: Ratio to trend

S: Seasonally adjusted, not calendar adjusted

T: Trend

TR: Trend restored

W: Calendar adjusted, not seasonally adjusted

X: Seasonal component

Y: Calendar and seasonally adjusted

CP01: Food and non-alcoholic beverages

CP011: Food

CP0111: Bread and cereals

CP01111: Rice

CP01112: Flours and other cereals

CP01113: Bread

CP01114: Other bakery products

CP01115: Pizza and quiche

CP01116: Pasta products and couscous

CP01117: Breakfast cereals

CP01118: Other cereal products

CP0112: Meat

CP01121: Beef and veal

CP01122: Pork

CP01123: Lamb and goat

CP01124: Poultry

CP01125: Other meats

CP01135: Dried, smoked or salted fish and seafood

CP01136: Other preserved or processed fish and seafood and fish and seafood preparations

CP01164: Preserved fruit and fruit-based products

CP01171: Fresh or chilled vegetables other than potatoes and other tubers

CP01172: Frozen vegetables other than potatoes and other tubers

CP01173: Dried vegetables, other preserved or processed vegetables

CP01176: Other tubers and products of tuber vegetables

CP0118: Sugar, jam, honey, chocolate and confectionery

CP0122: Mineral waters, soft drinks, fruit and vegetable juices

CP02: Alcoholic beverages, tobacco and narcotics

CP03123: Garments for infants (0 to 2 years) and children (3 to 13 years)

CP0313: Other articles of clothing and clothing accessories

CP04: Housing, water, electricity, gas and other fuels

CP04121: Actual rentals paid by tenants for secondary residences

CP04122: Garage rentals and other rentals paid by tenants

CP041T043X042: Housing excluding imputed rentals for housing

CP0431: Materials for the maintenance and repair of the dwelling

CP0432: Services for the maintenance and repair of the dwelling

CP04323: Maintenance services for heating systems

CP04329: Other services for maintenance and repair of the dwelling

CP044: Water supply and miscellaneous services relating to the dwelling

CP0444: Other services relating to the dwelling n.e.c.

CP04441: Maintenance charges in multi-occupied buildings

CP04522: Liquefied hydrocarbons (butane, propane, etc.)

CP05: Furnishings, household equipment and routine household maintenance

CP051: Furniture and furnishings, carpets and other floor coverings

CP05123: Services of laying of fitted carpets and floor coverings

CP0513: Repair of furniture, furnishings and floor coverings

CP05130: Repair of furniture, furnishings and floor coverings

CP0531: Major household appliances whether electric or not

CP05311: Refrigerators, freezers and fridge-freezers

CP05312: Clothes washing machines, clothes drying machines and dish washing machines

CP0531_0532: Major household appliances whether electric or not and small electric household appliances

CP05322: Coffee machines, tea-makers and similar appliances

CP05329: Other small electric household appliances

CP054: Glassware, tableware and household utensils

CP0540: Glassware, tableware and household utensils

CP05401: Glassware, crystal-ware, ceramic ware and chinaware

CP05403: Non-electric kitchen utensils and articles

CP05404: Repair of glasseware, tableware and household ustensils

CP055: Tools and equipment for house and garden

CP05512: Repair, leasing and rental of major tools and equipment

CP0552: Small tools and miscellaneous accessories

CP05523: Repair of non-motorised small tools and miscellaneous accessories

CP056: Goods and services for routine household maintenance

CP05612: Other non-durable small household articles

CP0562: Domestic services and household services

CP05629: Other domestic services and household services

CP061: Medical products, appliances and equipment

CP0612_0613: Other medical products, therapeutic appliances and equipment

CP06131: Corrective eye-glasses and contact lenses

CP06133: Repair of therapeutic appliances and equipment

CP06139: Other therapeutic appliances and equipment

CP06231: Services of medical analysis laboratories and X-ray centres

CP06232: Thermal-baths, corrective-gymnastic therapy, ambulance and hire of therapeutic equipment

CP0712T0714: Motor cycles, bicycles and animal drawn vehicles

CP072: Operation of personal transport equipment

CP0721: Spare parts and accessories for personal transport equipment

CP07212: Spare parts for personal transport equipment

CP07213: Accessories for personal transport equipment

CP0722: Fuels and lubricants for personal transport equipment

CP07223: Other fuels for personal transport equipment

CP0723: Maintenance and repair of personal transport equipment

CP07230: Maintenance and repair of personal transport equipment

CP0724: Other services in respect of personal transport equipment

CP07241: Hire of garages, parking spaces and personal transport equipment

CP07243: Driving lessons, tests, licences and road worthiness tests

CP07312: Passenger transport by underground and tram

CP07322: Passenger transport by taxi and hired car with driver

CP0734: Passenger transport by sea and inland waterway

CP07361: Funicular, cable-car and chair-lift transport

CP07369: Other purchased transport services n.e.c.

CP08203: Other equipment of telephone and telefax equipment

CP08204: Repair of telephone or telefax equipment

CP082_083: Telephone and telefax equipment and services

CP091: Audio-visual, photographic and information processing equipment

CP0911: Equipment for the reception, recording and reproduction of sound and pictures

CP09112: Equipment for the reception, recording and reproduction of sound and vision

CP09119: Other equipment for the reception, recording and reproduction of sound and picture

CP0912: Photographic and cinematographic equipment and optical instruments

CP09122: Accessories for photographic and cinematographic equipment

CP09132: Accessories for information processing equipment

CP09134: Calculators and other information processing equipment

CP0915: Repair of audio-visual, photographic and information processing equipment

CP092: Other major durables for recreation and culture

CP09212: Aeroplanes, microlight aircraft, gliders, hang-gliders and hot-air balloons

CP09213: Boats, outboard motors and fitting out of boats

CP0921_0922: Major durables for indoor and outdoor recreation including musical instruments

CP0922: Musical instruments and major durables for indoor recreation

CP0923: Maintenance and repair of other major durables for recreation and culture

CP093: Other recreational items and equipment, gardens and pets

CP0932: Equipment for sport, camping and open-air recreation

CP09322: Equipment for camping and open-air recreation

CP09323: Repair of equipment for sport, camping and open-air recreation

CP0934_0935: Pets and related products; veterinary and other services for pets

CP09411: Recreational and sporting services - Attendance

CP09412: Recreational and sporting services - Participation

CP09423: Television and radio licence fees, subscriptions

CP09424: Hire of equipment and accessories for culture

CP0953_0954: Miscellaneous printed matter; stationery and drawing materials

CP11111: Restaurants, cafés and dancing establishments

CP11201: Hotels, motels, inns and similar accommodation services

CP11202: Holiday centres, camping sites, youth hostels and similar accommodation services

CP11203: Accommodation services of other establishments

CP1211: Hairdressing salons and personal grooming establishments

CP12122: Repair of electric appliances for personal care

CP1212_1213: Electrical appliances for personal care; other appliances, articles and products for personal care

CP1213: Other appliances, articles and products for personal care

CP12132: Articles for personal hygiene and wellness, esoteric products and beauty products

CP12402: Retirement homes for elderly persons and residences for disabled persons

CP12403: Services to maintain people in their private homes

CP12622: Fees and service charges of brokers, investment counsellors

SERVXCP041_0432: Services less housing (Housing excluding imputed rentals for housing)

_TXNRG_01_02: Overall index excluding energy, food, alcohol and tobacco

HICP: Eurostat harmonised index of consumer prices (HICP)

ACR: Annual rate of change corrected for the effect of methodological changes where applicable

C4G: Growth rate, period on period, over 4-period cumulated sum

D1: Differences, period on period, first order

D1D1: Differences, period on period, second order

D4: Difference period on 4 periods, first order

ECX: Monthly index, backdated, ECU (to 1989) and fixed euro conversion rate (from 1990) used for weights

EPX: Monthly index, backdated, PPP exchange rates used for weights

ERX: Monthly index, backdated, fixed euro conversion rate used for weights

F1: Growth rate, flow over stock, period on period

F12: Growth rate, flow over stock, over 12 periods

F3: Growth rate, flow over stock, over 3 periods

F4: Growth rate, flow over stock, over 4 periods

F6: Growth rate, flow over stock, over 6 periods

FO1: Contribution to growth rate, flow over stock, period on period

FO12: Contribution to growth rate, flow over stock, over 12  periods

FO16: Contribution to growth rate, flow over stock, over 16 periods

FO3: Contribution to growth rate, flow over stock, over 3 periods

FO4: Contribution to growth rate, flow over stock, over 4 periods

FO6: Contribution to growth rate, flow over stock, over 6 periods

FY: Growth rate, flow over stock, over 1 year

GCM: Cumulative growth rate since base period

GO1: Contribution to growth rate, period on period

GO3: Contribution to growth rate, over 3 periods

GO6: Contribution to growth rate, over 6 periods

GOY: Contribution to growth rate, over 1 year

GYA12: Growth rate, over 1 year, based on 12-period average

GYA3: Growth rate, over 1 year, based on 3-period average

R1: Revisions, difference to last transmission period