Employment by activities and status (ALFS) ARCHIVE - ALFS_EMP_ARCHIVE

Data - OECD

Author

François Geerolf


Code 
load_data("oecd/ALFS_EMP_ARCHIVE_var.RData")
load_data("oecd/ALFS_EMP_ARCHIVE.RData")

Nobs - Javascript

Code 
ALFS_EMP_ARCHIVE %>%
  left_join(ALFS_EMP_ARCHIVE_var %>% pluck("SUBJECT"), by = c("SUBJECT" = "id")) %>%
  rename(`SUBJECT Description` = label) %>%
  group_by(SUBJECT, `SUBJECT Description`, SEX) %>%
  summarise(nobs = n()) %>%
  arrange(-nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Data Structure

Code 
ALFS_EMP_ARCHIVE_var %>%
  pluck("VAR_DESC") %>%
  {if (is_html_output()) print_table(.) else .}
id description
LOCATION Country
SUBJECT Subject
SEX Sex
FREQUENCY Frequency
TIME Time
OBS_VALUE Observation Value
TIME_FORMAT Time Format
OBS_STATUS Observation Status
UNIT Unit
POWERCODE Unit multiplier
REFERENCEPERIOD Reference period

SUBJECT

Code 
ALFS_EMP_ARCHIVE_var %>%
  pluck("SUBJECT") %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

SEX

Code 
ALFS_EMP_ARCHIVE_var %>%
  pluck("SEX") %>%
  {if (is_html_output()) print_table(.) else .}
id label
MA Males
FE Females
TT All persons

FREQUENCY

Code 
ALFS_EMP_ARCHIVE_var %>%
  pluck("FREQUENCY") %>%
  {if (is_html_output()) print_table(.) else .}
id label
A Annual

TIME_FORMAT

Code 
ALFS_EMP_ARCHIVE_var %>%
  pluck("TIME_FORMAT") %>%
  {if (is_html_output()) print_table(.) else .}
id label
P1Y Annual
P1M Monthly
P3M Quarterly
P6M Half-yearly
P1D Daily

UNIT

Code 
ALFS_EMP_ARCHIVE_var %>%
  pluck("UNIT") %>%
  {if (is_html_output()) print_table(.) else .}
id label
1 RATIOS
GRWH Growth rate
AVGRW Average growth rate
IDX Index
P50P10 Interdecile ratio P50/P10
P90P10 Interdecile ratio P90/P10
P90P50 Interdecile ratio P90/P50
AVSCORE Average score
NATUSD National currency per US dollar
OECDIDX OECD=100
PC Percentage
RATIO Ratio
TOEUSD To be deleted-Toe per 1 000 US dollars
TOE_1000USD TOE per 1 000 US dollars
USD_KG US dollars per kilogram
USD_L US dollars per litre
USD_CO2 US dollars per unit of CO2
USD_BAR US dollars per barrel
USD_HAB To be deleted
EUIDX European Union = 100
NATEU National currency per Euro
0_EXIST_1_DONTEXIST 0 = Data do not exist / 1 = Data exist
DEATH_1000BIRTH Deaths per 1 000 live births
EMPLOYED Employed
ENTR Enterprises
ESTB Establishments
1000000HAB Per 1 000 000 inhabitants
VKM Vehicle-kilometres
FTE Full time equivalent
_1000 Per thousand
SE Standard-error
0_TO_1 0-1 scale
2 NUMBERS
CHLD Children
DEATH1000 To be deleted-Deaths per 1 000 live births
EMPLOYEE Employee
HAB Inhabitants
JOB Jobs
NBR Number
PKM Passenger-kilometres
1000HAB Per 1 000 inhabitants
100000HAB Per 100 000 inhabitants
100HAB Per 100 inhabitants
PC_PNT Percentage points
PER Persons
3 TIME
DAY Days
HOUR Hours
HPYPP Hours per year per person
MONTH Months
YR Years
MPD Minutes per day
WEEK Weeks
DPYPP Days per year per person
LT_HAB Litres per capita
KCAL_HAB Kilocalories per capita
G_HAB Grams per capita
BAR_DAY Barrels per day
BAR Barrels
TONNE_ANIMAL Tonnes per animal
TONNE_HA Tonnes per hectare
KOE_HAB Kilograms of oil equivalent per capita
MICRO_M3 Micrograms per cubic metre
TONNE_1000USD Tonnes per 1 000 US dollars
GWH Gigawatt hours
KG_1000USD Kilograms per 1 000 US dollars
MICRO_L Micrograms per litre
MILLI_L Milligrams per litre
T_CO2_EQVT Tonnes of CO2 equivalent
4 PHYSICAL MEASURES
CAR Carats
CUR Curies
KG Kilograms
KG_HAB Kilograms per capita
KM Kilometres
KM2 Square kilometres
HA Hectares
KW Kilowatts
KWH Kilowatt hours
LT Litres
M2 Square metres
M3 Cubic metres
M3CAP Cubic metres per capita
METRE Metres
TWH Terawatt hours
TONNE Tonnes
TONNEKM Tonnes-kilometres
TOE Tonnes of oil equivalent (toe)
5 CURRENCIES
AED Dirham from the United Arab Emirates
AFA Afghani (old)
AFN Afghani
ALL Lek
AMD Armenian Dram
ANG Netherlands Antillian Guilder
AOA Kwanza
ARS Argentine Peso
ATS Austrian Schilling
AUD Australian Dollar
AWG Aruban Guilder
AZM Azerbaijanian Manat (old)
AZN Azerbaijanian Manat
BAM Convertible Marks
BBD Barbados Dollar
BDT Taka
BEF Belgian Franc
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
BYR Belarussian Ruble (before Jul.2016)
BZD Belize Dollar
CAD Canadian Dollar
CDF Franc Congolais
CFA CFA Franc
CHE WIR Euro
CHF Swiss Franc
CHW WIR Franc
CLF Unidad de formento (UF)
CLP Chilean Peso
CNY Yuan Renminbi
COP Colombian Peso
COU Unidad de valor real
CRC Costa Rican Colon
CSD Dinar of Serbia and Montenegro
CUP Cuban Peso
CVE Cape Verde Escudo
CYP Cyprus Pound
CZK Czech Koruna
DEM German Mark
DJF Djibouti Franc
DKK Danish Krone
DOP Dominican Peso
DZD Algerian Dinar
ECV Unidad de Valor Constante
EEK 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
GHC Ghana 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
HNL Lempira
HRK Croatian Kuna
HTG Gourde
HUF Forint
IDR Rupiah
IEP Irish Pound
ILS New Israeli Sheqel
INR Indian Rupee
IQD Iraqi Dinar
IRR Iranian Rial
ISK Iceland Krona
ITL Italian Lira
JMD Jamaican Dollar
JOD Jordanian Dinar
JPY Yen
KES Kenyan Shilling
KGS Som
KHR Riel
KMF Comoro Franc
KPW North Korean Won
KRW Won
KWD Kuwaiti Dinar
KYD Cayman Islands Dollar
KZT Tenge
LAK Kip
LBP Lebanese Pound
LKR Sri Lanka Rupee
LRD Liberian Dollar
LSL Loti
LTL Lithuanian Litas
LUF Luxembourg Franc
LVL Latvian Lats
LYD Libyan Dinar
MAD Moroccan Dirham
MDL Moldovan Leu
MGA Malagasy Ariary
MGF Malagasy Franc
MKD Denar
MMK Kyat
MNT Tugrik
MOP Pataca
MRO Ouguiya
MTL Maltese Lira
MUR Mauritius Rupee
MVR Rufiyaa
MWK Kwacha
MXN Mexican Peso
MXP Mexican peso (old)
MXV Mexican Unidad de Inversion (UDI)
MYR Malaysian Ringgit
MZM Metical (old)
MZN Metical
NAD Namibia Dollar
NGN Naira
NIO Cordoba Oro
NLG Netherlands Guilder
NOK Norwegian Krone
NPR Nepalese Rupee
NZD New Zealand Dollar
OMR Rial Omani
PAB Balboa
PEN Nuevo Sol
PGK Kina
PHP Philippine Peso
PKR Pakistan Rupee
PLN Zloty
PTE Portugese Escudo
PYG Guarani
QAR Qatari Rial
ROL Romanian Leu (old)
RON Romanian Leu
RSD Serbian Dinar
RUB Russian Ruble
RWF Rwanda Franc
SAR Saudi Riyal
SBD Solomon Islands Dollar
SCR Seychelles Rupee
SDD Sudanese Dinar
SDG Sudanese Pound
SEK Swedish Krona
SGD Singapore Dollar
SHP Saint Helena Pound
SIT Slovenian Tolar
SKK Slovak Koruna
SLL Leone
SOS Somali Shilling
SRD Surinam Dollar
SSP South-Sudanese Pound
STD Dobra
SVC El Salvador Colon
SYP Syrian Pound
SZL Lilangeni
THB Baht
TJS Somoni
TMM Turkmen Manat (old)
TMT Turkmen Manat
TND Tunisian Dinar
TOP Pa'anga
TRL Turkish Lira (old)
TRY Turkish Lira
TTD Trinidad and Tobago Dollar
TWD New Taiwan Dollar
TZS Tanzanian Shilling
UAH Hryvnia
UGX Uganda Shilling
USD US Dollar
USN Dollar (Next day)
USS Dollar (Same day)
UYI Uruguay Peso en Unidades Indexadas
UYU Peso Uruguayo
UZS Uzbekistan Sum
VEB Bolívar (old)
VEF Bolívar fuerte
VND Dong
VUV Vatu
WST Tala
XAF CFA Franc - BEAC
XAG Silver
XAU Gold
XBA Bond Markets Units European Composite Unit (EURCO)
XBB European Monetary Unit (E.M.U.-6)
XBC European Unit of Account 9 (E.U.A.-9)
XBD European Unit of Account 17 (E.U.A.-17)
XCD East Caribbean Dollar
XDR Special Drawing Rights
XEU European Currency Unit (ECU)
XFO Gold-Franc
XFU UIC-Franc
XOF CFA Franc - BCEAO
XPD Palladium
XPF CFP Franc
XPT Platinum
XTS Code specifically reserved for testing purposes
XXX Transactions whithout currency
YER Yemeni Rial
ZAR Rand
ZMK Kwacha (old)
ZMW Kwacha
ZWD Zimbabwe Dollar (before Sept.2008)
ZWL Zimbabwe Dollar (before Feb.2009)
ZWR Zimbabwe Dollar
NATCUR National currency
NATCUR_HL National currency per hectolitre
NATCUR_TONNE National currency per tonne
USD_HL US Dollar per hectolitre
USD_TONNE US Dollar per tonne
VES Bolívar Soberano
BYN Belarussian Ruble
SUBS Subscriptions
S90S40 S90/S40 decile share
S90S10 S90/S10 decile share
S80S20 S80/S20 quintile share
CODE English Name
TONNE_1000000USD Tonnes per USD million
EUR_TCO2 Euro per tonne of CO2

Ex

Code 
ALFS_EMP_ARCHIVE %>%
  # YW992IL1_ST: Employees in Industry (ISIC rev.2, 2-5)
  # YS99TTL2_ST: Employees in all activities
  filter(SUBJECT %in% c("YW992IL1_ST", "YS99TTL2_ST"), 
         SEX == "TT") %>%
  select(LOCATION, SUBJECT, obsTime, obsValue) %>%
  spread(SUBJECT, obsValue) %>%
  mutate(share = (100*YW992IL1_ST/YS99TTL2_ST) %>% round(., digits = 1)) %>%
  left_join(ALFS_EMP_ARCHIVE_var %>% pluck("LOCATION"), by = c("LOCATION" = "id")) %>%
  rename(LOCATION_desc = label) %>%
  filter(!is.na(share)) %>%
  mutate(share = paste0(share, " %")) %>%
  group_by(LOCATION_desc) %>%
  summarise(`Year 1` = first(obsTime),
            `Year 2` = last(obsTime),
            `Share 1` = first(share),
            `Share 2` = last(share)) %>%
  {if (is_html_output()) print_table(.) else .}
LOCATION_desc Year 1 Year 2 Share 1 Share 2
Australia 1964 2005 44 % 20.5 %
Austria 1969 2001 49.6 % 33.2 %
Belgium 1956 1999 56.9 % 25.1 %
Canada 1975 1998 31.2 % 24.6 %
Czech Republic 1992 1998 46.6 % 45.7 %
Denmark 1960 2008 42.8 % 22.5 %
Finland 1978 1998 40.5 % 29.4 %
France 1956 2006 49.1 % 21.9 %
Germany 1957 1998 56.6 % 35.9 %
Greece 1961 1997 43.2 % 28.2 %
Hungary 1994 1998 38.4 % 36.4 %
Iceland 1991 2014 26.7 % 17.6 %
Ireland 1961 2014 37.4 % 18.1 %
Italy 1993 2010 37.5 % 31 %
Japan 1956 2003 43.7 % 30.4 %
Korea 1985 2008 49.3 % 31 %
Luxembourg 1991 1994 31.4 % 29.8 %
Mexico 1990 2014 34 % 27.6 %
Netherlands 1975 2002 38 % 20.7 %
New Zealand 1956 1998 39.4 % 24.1 %
Norway 1956 2000 43.9 % 22.4 %
Poland 1994 2012 42.7 % 35.3 %
Portugal 1960 2008 33.7 % 33.8 %
Spain 1960 1999 42 % 31.4 %
Sweden 1970 2008 41 % 21.8 %
Turkey 1955 2000 39.4 % 40 %
United Kingdom 1959 2013 49.8 % 17.5 %
United States 1956 2002 41.2 % 21.8 %