Economic Indicators

This page provides a tutorial for getting started using the obb.economy.indicators endpoint, with the openbb-econdb provider extension. The command provides access to over 100 standardized indicator symbols, covering countries around the world.

note

OpenBB is not affiliated with EconDB. All service is provided "AS IS" and without warranty.

Authorization

This command requires an API key. You can create an account here, and enter the key assigned.

Leaving the key as None will attempt to retrieve a temporary token that is assigned when downloading the data as a CSV file from a browser. This is a workaround and can be blocked by Cloudflare. In this instance, the temporary token can be viewed by opening any indicator in your browser, then selecting 'Python' from the 'Export' drop-down.

The best method is to support the service by registering an account.

from openbb import obb
obb.user.credentials.econdb_api_key = REPLACE_WITH_KEY

See the API keys page for more information on credentials.

Important Considerations

Carefully inspect data before conducting analysis and arriving at conclusions.

EconDB sources data directly from national agencies, or other reputable sources such as the IMF, but the data itself may not be directly comparable between collections of countries. For example, some countries will have GDP data as 'annualized' (North America) while others report the values for the period.

These factors can be considered as "known-unknowns", the metadata will not contain this information.

important

• This function will not attempt to normalize the values for cross-country comparison. Some values should be manually adjusted, post-request, to accommodate.

• Transforming levels as USD, or % of GDP, may have unexpected results where values are not appropriate for this conversion. Do not attempt to apply these transformations universally.

• Values that are scaled to, or transformed as, a percent are returned as a normalized value - i.e, 1% = 0.01. It will have a multiplier value of 1, which is not indicative of display values.

• Contextual information is contained within metadata, returned under the "extra" attribute of the function response.

Available Indicators

EconDB has a list of "main" indicators, which are standardized base symbols across the available countries for each.

from openbb import obb

indicators = obb.economy.available_indicators(provider="econdb").to_df()

indicators.iloc[0]

symbol_root	POLIR
symbol	POLIREA
country	Euro area
iso	EA
description	Policy rate - short term
frequency	D
currency	PERCENT
scale	Units
multiplier	1
transformation	Period level
source	European Central Bank
first_date	1999-01-01
last_date	2024-04-08
last_insert_timestamp	2024-04-08 12:27:05.089860

tip

Metadata is returned in the command response to the 'extra' attribute.

data = obb.economy.indicators("POLIR", country="EA")
data.extra["results_metadata"]

{'POLIREA': {'title': 'Euro area - Policy rate - short term',
  'country': 'Euro area',
  'frequency': 'D',
  'dataset': 'ECBFM',
  'transformation': None,
  'units': 'PERCENT',
  'scale': 'Units',
  'multiplier': 1,
  'additional_info': {'FREQ:Frequency': 'D:Daily',
   'REF_AREA:Reference area': 'U2:Euro area (Member States and Institutions of the Euro Area) changing composition',
   'CURRENCY:Currency': 'EUR:Euro',
   'PROVIDER_FM:Financial market provider': '4F:ECB',
   'INSTRUMENT_FM:Financial market instrument': 'KR:Key interest rate',
   'PROVIDER_FM_ID:Financial market provider identifier': 'MRR_FR:ECB Main refinancing operations - fixed rate tenders (fixed rate) (date of changes)',
   'DATA_TYPE_FM:Financial market data type': 'LEV:Level'}}}

Basic Descriptions

Basic descriptions of the base symbols can be imported from within the extension module helpers.

from openbb_econdb.utils.helpers import INDICATORS_DESCRIPTIONS

important

This list should not be considered as the absolute source of truth. The metadata for the indicator will be returned in the command response, under the extras attribute.

ACPOP	Active population
CA	Current account balance
CAR	Passenger car sales
CBAL	Commercial balance (goods + services)
CI	Change in inventories
CKA	Net foreign investment
CLAIMS	Weekly unemployment insurance claims
CLI	OECD CLI
CON	Total consumption
CONF	Consumer confidence index
CORE	Core consumer price index
CP	Construction production
CPI	Consumer price index
CRED	Domestic credit
CREDEA	Domestic credit
DWPE	Dwelling permits
ELE	Production electricity
EMP	Total employment
EMRATIO	Employment to working age population
EQYCAP	Market capitalization
EXP	Exports of goods and services
EXPMON	Monthly exports
GASDEM	Gas demand
GASODEM	Gasoline demand
GASOPROD	Gasoline production
GASPROD	Gas production
GBAL	Government balance
GCF	Gross capital formation
GDEBT	Government debt
GDEBTN	Government net debt
GDP	Gross domestic product
GDPDEF	GDP deflator
GDPPC	GDP per capita
GFCF	Gross fixed capital formation
GREV	General government total revenue
GSPE	General government total expenditure
HHDIR	Household debt to income ratio
HHS	Household saving
HOU	House price
IBD1	Interbank lending overnight rate
IIPA	International investment position: Assets
IIPL	International investment position: Liabilities
IMP	Imports of goods and services
IMPMON	Monthly imports
INVER	Investment rate
IP	Industrial production
JHR	Job hires rate
JLR	Job layoffs rate
JQR	Job quits rate
JVR	Job vacancy rate
KA	Capital account
LE00	Life expectancy at birth
LMICS	Low and Middle Income Commodity Index (World Bank)
M3	Money supply
M3YD	3 month yield
MB	Monetary base
NCT	Net current transfers (Secondary Income)
NFCI	Non-financial corporations investment rate
NFCLOAN	Lending to non-financial corporations
NIIP	Net international investment position
NPL	Non performing loans
NY	Net income from abroad (Primary Income)
OILDEM	Oil demand
OILPROD	Oil production
PALUM	Aluminum
PAPPLE	Non-Citrus Fruit, Apple
PART	Participation rate
PBANSOP	Bananas
PBARL	Barley
PBEEF	Beef
PCE	Personal consumption expenditure price index
PCHANA	Legumes, Chickpea
PCHROM	Chromium
PCOALAU	Coal, Australia
PCOALSA	Coal, South Africa
PCOBA	Cobalt
PCOCO	Cocoa
PCOFFOTM	Coffee, Other Mild Arabica
PCOFFROB	Coffee, Robustas
PCOIL	Coconut Oil
PCOPP	Copper
PCOTTIND	Cotton
PDAP	Diammonium phosphate
PFSHMEAL	Fish Meal
PGASO	Gasoline
PGNUTS	Groundnuts
PGOLD	Gold
PHEATOIL	Heating Oil
PHIDE	Hides
PIORECR	Iron
PLAMB	Lamb
PLEAD	Lead
PLITH	Lithium
PLMMODY	Molybdenum
PLOGORE	Soft Logs
PLOGSK	Hard Logs, Import Price Japan
PMAIZMT	Corn
PMANGELE	Manganese
PMILK	Dairy Products, Milk
PNGASEU	Natural gas, EU
PNGASJP	LNG, Asia
PNGASUS	Natural Gas, US Henry Hub Gas
PNICK	Nickel
POATS	Oats
POILAPSP	APSP crude oil($/bbl)
POILBRE	Brent Crude
POILDUB	Dubai Crude
POILWTI	WTI Crude
POLIR	Policy rate - short term
POLVOIL	Olive Oil
POP	Population
PORANG	Orange
PPALLA	Palladium
PPI	Producer price index
PPLAT	Platinum
PPOIL	Palm Oil
PPORK	Swine
PPOTASH	Potassium Fertilizer
PPOULT	Poultry
PPROPANE	Propane
PRC	Private consumption
PREODOM	Rare Earth Elements
PRICENPQ	Rice, Thailand
PRIDEBT	Private debt
PROIL	Rapeseed Oil
PRUBB	Rubber
PSALM	Fish
PSAWMAL	Hard Sawnwood, Dark Red Meranti
PSAWORE	Soft Sawnwood, Average of Softwoods,
PSHRI	Shrimp
PSILLUMP	Silicon
PSILVER	Silver
PSMEA	Soybean Meal
PSOIL	Soybeans Oil
PSORG	Sorghum
PSOYB	Soybeans
PSUGAISA	Sugar, No. 11, World
PSUGAUSA	Sugar, No. 16, US
PSUNO	Sunflower Oil
PTEA	Tea, Kenyan
PTEAINDIA	Tea, Kolkata
PTEAMOM	Tea, Mombasa
PTEASL	Tea, Colombo
PTIN	Tin
PTOMATO	Vegetables, Tomato
PUC	Public consumption
PURAN	Uranium
PUREA	Urea
PVANPENT	Vanadium
PWHEAMT	Wheat
PWOOLC	Wool, Coarse
PWOOLF	Wool, Fine
PZINC	Zinc
RCI	Real change in inventories
RCON	Real total consumption
REER	Real effective exchange rate
REEREA	Real effective exchange rate
RETA	Retail trade
REXP	Real exports of goods and services
RGCF	Real gross capital formation
RGDP	Real gross domestic product
RGDPPC	Real GDP per capita
RGFCF	Real gross fixed capital formation
RIMP	Real imports of goods and services
RPRC	Real private consumption
RPUC	Real public consumption
SEI	Stock exchange index
SENT	Sentiment index
TB	Trade balance
URATE	Unemployment
UTIL	Utilization rate
WAGE	Wages/Earnings
WAGEMAN	Hourly wage manufacturing
Y10YD	Long term yield

Countries

Details

The country parameter will accept the ISO country code, or the country name. Regional groups listed below are also valid:

• all
• africa
• central_asia
• east_asia
• europe
• g7
• g20
• latin_america
• middle_east
• north_america
• oceania
• south_asia
• southeast_asia

note

Some symbols do not have a country - e.g., commodity items - and they will ignore any supplied values to the parameter. Not every indicator has data for every country. Items with no results will be communicated via the warnings attribute of the response object.

obb.economy.indicators("POLIR", country="southeast_asia")

OBBject

id: 0661ac07-ab9c-7ebf-8000-849fef1202bc
results: [{'date': datetime.date(2013, 4, 29), 'symbol': 'POLIRSG', 'country': 'Sin...
provider: econdb
warnings: [{'category': 'UserWarning', 'message': "Invalid country code for...
chart: None
extra: {'results_metadata': {'POLIRID': {'title': 'Indonesia - Policy rate - short ...

Countries By Indicator

In addition to filtering the available_indicators data locally, countries by indicator can be imported as a utility function from the openbb_econdb.utils.helpers module.

from openbb_econdb.utils.helpers import get_indicator_countries

get_indicator_countries("GDPPC")  # returns a list of two-letter ISO country codes

How To Enter Symbols

Details

The three parameters - symbol, country, transform - all work together.

• Symbol (base symbol)
  • CPI, GDP, CORE, etc
  • Multiple items allowed
• Country
  • ISO country code, or name
  • Multiple items allowed
• Transform
  • tpop: Percent change from previous period
  • toya: Percent change from year ago
  • tusd: Values as US dollars
  • tpgp: Values as a percent of GDP

The transform will apply to all combinations of symbol and country.

warning

If you attempt to pass a base symbol (excluding commodity and world indicators) with no country, it will raise an error.

Example - One Indicator & Country

M3 Money Supply

data = obb.economy.indicators("M3", country="us")

data.to_df().tail(12)

date	symbol_root	symbol	country	value
2023-03-01	M3	M3US	United States	21027
2023-04-01	M3	M3US	United States	20843
2023-05-01	M3	M3US	United States	20711
2023-06-01	M3	M3US	United States	20749
2023-07-01	M3	M3US	United States	20724
2023-08-01	M3	M3US	United States	20695
2023-09-01	M3	M3US	United States	20669
2023-10-01	M3	M3US	United States	20636
2023-11-01	M3	M3US	United States	20723
2023-12-01	M3	M3US	United States	20890
2024-01-01	M3	M3US	United States	20862
2024-02-01	M3	M3US	United States	20788

data.extra.get("results_metadata")

{'M3US': {'title': 'United States - Money supply',
  'country': 'United States',
  'frequency': 'M',
  'dataset': 'FRB_H6_M2',
  'transform': None,
  'units': 'DOMESTIC',
  'scale': 'Billions',
  'multiplier': 1000000000,
  'additional_info': {'ADJUSTED:Seasonal Adjustment': 'NSA:Not seasonally adjusted',
   'CURRENCY:Currency': 'USD:United States / United States Dollar',
   'FREQ:Frequency': '129:Monthly',
   'SERIES_NAME:Series name (FRB)': 'M2_N.M:M2_N.M',
   'UNIT:Units': 'CURRENCY:Currency'}}}

Example - One Indicator & Country With Transform

US PPI - Change from one year ago.

data = obb.economy.indicators("PPI", country="us", transform="toya")

data.to_df().tail(12)

date	symbol_root	symbol	country	value
2023-04-01	PPI	PPIUS~TOYA	United States	0.022995
2023-05-01	PPI	PPIUS~TOYA	United States	0.011822
2023-06-01	PPI	PPIUS~TOYA	United States	0.001912
2023-07-01	PPI	PPIUS~TOYA	United States	0.008722
2023-08-01	PPI	PPIUS~TOYA	United States	0.016003
2023-09-01	PPI	PPIUS~TOYA	United States	0.021542
2023-10-01	PPI	PPIUS~TOYA	United States	0.013481
2023-11-01	PPI	PPIUS~TOYA	United States	0.008465
2023-12-01	PPI	PPIUS~TOYA	United States	0.00948
2024-01-01	PPI	PPIUS~TOYA	United States	0.008222
2024-02-01	PPI	PPIUS~TOYA	United States	0.015153
2024-03-01	PPI	PPIUS~TOYA	United States	0.020896

data.extra.get("results_metadata")

{'PPIUS~TOYA': {'title': 'United States - Producer price index',
  'country': 'United States',
  'frequency': 'M',
  'dataset': 'BLS_PPI00',
  'transform': 'Change from one year ago',
  'units': 'INDEX',
  'scale': 'PERCENT',
  'multiplier': 1,
  'additional_info': {'DATA_DOMAIN:Data Domain': 'PPI:Producer price indices',
   'REF_AREA:Reference country or area': 'US:United States',
   'INDICATOR:Economic Indicator': 'PPPI_SA_IX:Prices, Producer Price Index, All Commodities, Seasonally adjusted, Index',
   'COUNTERPART_AREA:Counterpart country or area': '_Z:Not applicable',
   'FREQ:Frequency': 'M:Monthly',
   'UNIT_MULT:Unit multiplier': '0:Units'}}}

Example - Commodity Indicator

Values are always in USD.

lead = obb.economy.indicators("plead")

lead.to_df().tail(4)

date	symbol_root	symbol	country	value
2023-10-01	PLEAD	PLEAD	World	2131.4
2023-11-01	PLEAD	PLEAD	World	2188.5
2023-12-01	PLEAD	PLEAD	World	2027.2
2024-01-01	PLEAD	PLEAD	World	2087.4

lead.extra["results_metadata"]

{'PLEAD': {'title': 'World - Lead',
  'country': 'World',
  'frequency': 'M',
  'dataset': 'IMF_PCPS',
  'transform': None,
  'units': 'USD',
  'scale': 'Units',
  'multiplier': 1,
  'additional_info': {'FREQ:Frequency': 'M:Monthly',
   'REF_AREA:Reference Area': 'W00:All Countries, excluding the IO',
   'COMMODITY:Commodity': 'PLEAD:Primary Commodity Prices, Lead',
   'UNIT_MEASURE:Unit of Measure': 'USD:US Dollars',
   'UNIT_MULT:Scale': '0:Units'}}}

Example - Multiple Indicators & Countries With Transform

params = {"symbol": "core,cpi", "country": "us,de,jp", "transform": "toya"}
data = obb.economy.indicators(**params)
df = data.to_df().filter(like="2024", axis=0)

df

date	symbol_root	symbol	country	value
2024-01-01	CORE	COREDE~TOYA	Germany	0.03394
2024-01-01	CORE	COREJP~TOYA	Japan	0.03523
2024-01-01	CORE	COREUS~TOYA	United States	0.03875
2024-01-01	CPI	CPIDE~TOYA	Germany	0.02887
2024-01-01	CPI	CPIJP~TOYA	Japan	0.021012
2024-01-01	CPI	CPIUS~TOYA	United States	0.03106
2024-02-01	CORE	COREDE~TOYA	Germany	0.03454
2024-02-01	CORE	COREJP~TOYA	Japan	0.03216
2024-02-01	CORE	COREUS~TOYA	United States	0.03762
2024-02-01	CPI	CPIDE~TOYA	Germany	0.02517
2024-02-01	CPI	CPIJP~TOYA	Japan	0.02788
2024-02-01	CPI	CPIUS~TOYA	United States	0.03166
2024-03-01	CORE	COREUS~TOYA	United States	0.03797
2024-03-01	CPI	CPIDE~TOYA	Germany	0.021533
2024-03-01	CPI	CPIUS~TOYA	United States	0.03475

Example - All Countries

Setting the country to "all" will retrieve data for all available countries.

data = obb.economy.indicators("ny", country="all", transform="tusd")

data.to_df().filter(like="2024", axis=0)

date	symbol_root	symbol	country	value
2024-01-01	NY	NYBE~TUSD	Belgium	1132.6
2024-01-01	NY	NYBG~TUSD	Bulgaria	-78.79
2024-01-01	NY	NYHR~TUSD	Croatia	31.99
2024-01-01	NY	NYCZ~TUSD	Czechia	-676.3
2024-01-01	NY	NYDK~TUSD	Denmark	1323.6
2024-01-01	NY	NYEE~TUSD	Estonia	-131.74
2024-01-01	NY	NYFI~TUSD	Finland	626.4
2024-01-01	NY	NYFR~TUSD	France	8570
2024-01-01	NY	NYDE~TUSD	Germany	13656
2024-01-01	NY	NYGR~TUSD	Greece	451.5
2024-01-01	NY	NYHU~TUSD	Hungary	-528.7
2024-01-01	NY	NYIT~TUSD	Italy	-509.5
2024-01-01	NY	NYLV~TUSD	Latvia	53.57
2024-01-01	NY	NYLT~TUSD	Lithuania	-111.62
2024-01-01	NY	NYLU~TUSD	Luxembourg	-3791
2024-01-01	NY	NYMT~TUSD	Malta	-218.54
2024-01-01	NY	NYPL~TUSD	Poland	-3029
2024-01-01	NY	NYPT~TUSD	Portugal	28.42
2024-01-01	NY	NYRO~TUSD	Romania	-410.1
2024-01-01	NY	NYSK~TUSD	Slovakia	-342.7
2024-01-01	NY	NYSI~TUSD	Slovenia	-82.76
2024-01-01	NY	NYSE~TUSD	Sweden	2334.1

data.extra.get("results_metadata")["NYSE~TUSD"]

{'title': 'Sweden - Net income from abroad (Primary Income)',
 'country': 'Sweden',
 'frequency': 'M',
 'dataset': 'BOP_C6_M',
 'transform': 'Values as US dollars',
 'units': 'USD',
 'scale': 'Units',
 'multiplier': 1000000,
 'additional_info': {'GEO:Geopolitical entity (reporting)': 'SE:Sweden',
  'PARTNER:Geopolitical entity (partner)': 'WRL_REST:Rest of the world',
  'SECTPART:Sector (ESA 2010)': 'S1:Total economy',
  'SECTOR10:Sector (ESA 2010)': 'S1:Total economy',
  'FREQ:Frequency': 'M:Monthly',
  'STK_FLOW:Stock or flow': 'BAL:Balance',
  'CURRENCY:Currency': 'MIO_NAC:Million units of national currency',
  'BOP_ITEM:BOP_item': 'IN1:Primary income'}}

Advanced Symbols

Details

The grouping behaviour can be overridden. This will allow multiple transformations, or for a specific symbol to ignore the supplied country and transform parameters.

Example - Bypass Group Parameters

The "~" character is used to separate the base symbol + 2-letter ISO country code, and the transformation. It works as a flag to exclude from the other parameters.

obb.economy.indicators("CPIUS~").to_df().tail(2)

date	symbol_root	symbol	country	value
2024-02-01	CPI	CPIUS	United States	311.1
2024-03-01	CPI	CPIUS	United States	312.2

Without "~", symbols are assumed to be a base symbol, and require a country parameter. An error (or warning where at least one symbol supplied was valid) is raised if the condition is not met.

obb.economy.indicators("CPIUS")

OpenBBError: No valid combination of indicator symbols and countries were supplied.
Valid countries for 'CPIUS' are: None
If the symbol - CPIUS - is missing a country code. Please add the two-letter country code or use the country parameter.
If already included, add '~' to the end of the symbol.

This example bypasses the transformation for US data, applying it only to France.

obb.economy.indicators(symbol=["CPIUS~","CPI"], country="fr", transform="toya").to_df().tail(2)

date	symbol_root	symbol	country	value
2024-03-01	CPI	CPIFR~TOYA	France	0.022947
2024-03-01	CPI	CPIUS	United States	312.2

Example - Non-Standard Symbols

The symbol parameter can also be used to access non-standard series. These are specific to reporting entities, like the Ministry of Finance, Japan. These symbols are not searchable, but the structure will be familiar if you have worked with the particular source before. For this purpose, enter each symbol ending with, "~".

important

Non-standard symbols will not have transformations, standardized metadata, or normalized percent values.

Japan Yield Curve

symbols = [
  "MFJP_IR.1Y.D.JP~",
  "MFJP_IR.2Y.D.JP~",
  "MFJP_IR.3Y.D.JP~",
  "MFJP_IR.4Y.D.JP~",
  "MFJP_IR.5Y.D.JP~",
  "MFJP_IR.6Y.D.JP~",
  "MFJP_IR.7Y.D.JP~",
  "MFJP_IR.8Y.D.JP~",
  "MFJP_IR.9Y.D.JP~",
  "MFJP_IR.10Y.D.JP~",
  "MFJP_IR.15Y.D.JP~",
  "MFJP_IR.20Y.D.JP~",
  "MFJP_IR.25Y.D.JP~",
  "MFJP_IR.30Y.D.JP~",
  "MFJP_IR.40Y.D.JP~",
]
data = obb.economy.indicators(symbol=symbols)

curve = data.to_df().filter(like="2024-04-11", axis=0)

curve

date	symbol_root	symbol	country	value
2024-04-11		MFJP_IR.1Y.D.JP	Japan	0.068
2024-04-11		MFJP_IR.2Y.D.JP	Japan	0.27
2024-04-11		MFJP_IR.3Y.D.JP	Japan	0.28
2024-04-11		MFJP_IR.4Y.D.JP	Japan	0.37
2024-04-11		MFJP_IR.5Y.D.JP	Japan	0.485
2024-04-11		MFJP_IR.6Y.D.JP	Japan	0.508
2024-04-11		MFJP_IR.7Y.D.JP	Japan	0.592
2024-04-11		MFJP_IR.8Y.D.JP	Japan	0.68
2024-04-11		MFJP_IR.9Y.D.JP	Japan	0.757
2024-04-11		MFJP_IR.10Y.D.JP	Japan	0.854
2024-04-11		MFJP_IR.15Y.D.JP	Japan	1.289
2024-04-11		MFJP_IR.20Y.D.JP	Japan	1.625
2024-04-11		MFJP_IR.25Y.D.JP	Japan	1.78
2024-04-11		MFJP_IR.30Y.D.JP	Japan	1.907
2024-04-11		MFJP_IR.40Y.D.JP	Japan	2.065

data.extra["results_metadata"].get("MFJP_IR.5Y.D.JP")

{'title': 'Japan - Japanese Government Bonds - 5Y yield',
 'country': 'Japan',
 'frequency': 'D',
 'dataset': 'MFJP_IR',
 'transform': None,
 'units': None,
 'scale': None,
 'multiplier': 1,
 'additional_info': {'3:Indicator': '8:Japanese Government Bonds - 5Y yield',
  'GEO:None': '107:None'}}