Economics
The Economics module gives insights for 60+ countries into key economic indicators such as the Consumer Price Index (CPI), Gross Domestic Product (GDP), Unemployment Rates and 3-month and 10-year Government Interest Rates. This is done through the economics module and can be used as a standalone module as well.
To install the FinanceToolkit it simply requires the following:
pip install financetoolkit -UIf you are looking for documentation regarding the toolkit, discovery, ratios, models, technicals, fixed income, risk and performance, please have a look below:
init
Initializes the Economics Controller Class.
Args:
- quarterly (bool | None, optional): Parameter that defines if the default data returned is quarterly or yearly. Defaults to None.
- start_date (str | None, optional): The start date to retrieve data from. Defaults to None.
- end_date (str | None, optional): The end date to retrieve data from. Defaults to None.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
As an example:
from financetoolkit import Toolkit
toolkit = Toolkit(["AMZN", "ASML"])
cpi = toolkit.economics.get_consumer_price_index(period='yearly')
cpi.loc['2015':, ['United States', 'Netherlands', 'Japan']]Which returns:
| United States | Netherlands | Japan | |
|---|---|---|---|
| 2015 | 100 | 100 | 100 |
| 2016 | 101.262 | 100.317 | 99.8727 |
| 2017 | 103.419 | 101.703 | 100.356 |
| 2018 | 105.945 | 103.435 | 101.349 |
| 2019 | 107.865 | 106.159 | 101.824 |
| 2020 | 109.195 | 107.51 | 101.799 |
| 2021 | 114.325 | 110.387 | 101.561 |
| 2022 | 123.474 | 121.427 | 104.098 |
get_gross_domestic_product
Get the Gross Domestic Product for a variety of countries over time from the OECD. The Gross Domestic Product is the total value of goods produced and services provided in a country during one year.
The data is available in two forms: compared to the previous year’s value or compared to the previous period. The year on year data is the GDP compared to the same quarter in the previous year. The quarter on quarter data is the GDP compared to the previous quarter.
See definition: https://data.oecd.org/gdp/gross -domestic -product -gdp.htm
Args:
- inflation_adjusted (bool, optional): Whether to return the inflation adjusted data or the nominal data.
- per_capita (bool, optional): Whether to return the per capita data or the total data.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Gross Domestic Product
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2015-01-01', end_date='2021-01-01')
real_gdp = economics.get_gross_domestic_product(inflation_adjusted=True)
real_gdp.loc[:, ['Netherlands', 'Germany', 'China']]Which returns:
| Netherlands | Germany | China | |
|---|---|---|---|
| 2015 | 851994 | 3.88475e+06 | 1.77968e+07 |
| 2016 | 870238 | 3.96797e+06 | 1.9007e+07 |
| 2017 | 896473 | 4.08338e+06 | 2.03184e+07 |
| 2018 | 917246 | 4.13627e+06 | 2.16798e+07 |
| 2019 | 932198 | 4.16067e+06 | 2.29806e+07 |
| 2020 | 897261 | 3.94717e+06 | 2.35091e+07 |
| 2021 | 921282 | 4.07756e+06 | 2.55147e+07 |
get_gross_domestic_product_growth
Get the Gross Domestic Product growth rate for a variety of countries over time from the OECD. The Gross Domestic Product is the total value of goods produced and services provided in a country during one year.
It is possible to view the growth rate on a quarterly or annual basis, the default is dependent on the quarterly parameter. The growth rate is the percentage change in the GDP compared to the previous period.
See definition: https://data.oecd.org/gdp/quarterly -gdp.htm
Args:
- quarterly (bool, optional): Whether to return the quarterly data or the annual data.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Gross Domestic Product growth rates.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2021-01-01', end_date='2022-01-01')
gdp_growth = economics.get_gross_domestic_product_growth(quarterly=True)
gdp_growth.loc[:, ['United Kingdom', 'United States', 'Belgium']]Which returns:
| United Kingdom | United States | Belgium | |
|---|---|---|---|
| 2021Q1 | -0.0102 | 0.0129 | 0.0181 |
| 2021Q2 | 0.0733 | 0.0152 | 0.0193 |
| 2021Q3 | 0.0172 | 0.0081 | 0.0219 |
| 2021Q4 | 0.0152 | 0.017 | 0.0076 |
| 2022Q1 | 0.0053 | -0.005 | 0.0012 |
get_gross_domestic_product_forecast
Get the Gross Domestic Product growth rate for a variety of countries over time from the OECD. The Gross Domestic Product is the total value of goods produced and services provided in a country during one year.
It is possible to view the growth rate on a quarterly or annual basis, the default is dependent on the quarterly parameter. The growth rate is the percentage change in the GDP compared to the previous period.
See definition: https://data.oecd.org/gdp/real -gdp -long -term -forecast.htm
Args:
- quarterly (bool, optional): Whether to return the quarterly data or the annual data.
- inflation_adjusted (bool, optional): Whether to return the inflation adjusted data or the nominal data.
- long_term (bool, optional): Whether to return the long term forecast or the short term forecast.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Gross Domestic Product forecast growth rates.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2021-01-01')
gdp_growth_forecast = economics.get_gross_domestic_product_forecast(long_term=False)
gdp_growth_forecast.loc[:, ['Indonesia', 'China', 'India']]Which returns:
| Indonesia | China | India | |
|---|---|---|---|
| 2021 | 0.037 | 0.0845 | 0.0905 |
| 2022 | 0.0531 | 0.0299 | 0.0724 |
| 2023 | 0.0488 | 0.0516 | 0.0626 |
| 2024 | 0.0519 | 0.047 | 0.0606 |
| 2025 | 0.0519 | 0.0424 | 0.0648 |
get_consumer_confidence_index
This consumer confidence indicator provides an indication of future developments of households consumption and saving, based upon answers regarding their expected financial situation, their sentiment about the general economic situation, unemployment and capability of savings.
An indicator above 100 signals a boost in the consumers’ confidence towards the future economic situation, as a consequence of which they are less prone to save, and more inclined to spend money on major purchases in the next 12 months. Values below 100 indicate a pessimistic attitude towards future developments in the economy, possibly resulting in a tendency to save more and consume less.
See definition: https://data.oecd.org/leadind/consumer -confidence -index -cci.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Consumer Confidence Index.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2008-09-01', end_date='2009-03-01')
consumer_confidence_index = economics.get_consumer_confidence_index()
consumer_confidence_index.loc[:, ['Germany', 'France', 'Portugal']]Which returns:
| Germany | France | Portugal | |
|---|---|---|---|
| 2008-09 | 98.4042 | 97.4657 | 97.8598 |
| 2008-10 | 98.2065 | 97.4716 | 97.748 |
| 2008-11 | 97.9886 | 97.5514 | 97.3693 |
| 2008-12 | 97.7184 | 97.5094 | 96.9437 |
| 2009-01 | 97.5575 | 97.4412 | 96.6658 |
| 2009-02 | 97.4573 | 97.3785 | 96.658 |
| 2009-03 | 97.4165 | 97.4899 | 96.9339 |
get_business_confidence_index
This business confidence indicator provides information on future developments, based upon opinion surveys on developments in production, orders and stocks of finished goods in the industry sector. It can be used to monitor output growth and to anticipate turning points in economic activity.
Numbers above 100 suggest an increased confidence in near future business performance, and numbers below 100 indicate pessimism towards future performance.
See definition: https://data.oecd.org/leadind/business -confidence -index -bci.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Business Confidence Index.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2022-09-01', end_date='2023-03-01')
business_confidence_index = economics.get_business_confidence_index()
business_confidence_index.loc[:, ['Brazil', 'Canada', 'Costa Rica']]Which returns:
| Brazil | Canada | Costa Rica | |
|---|---|---|---|
| 2022-09 | 100.196 | 100.381 | 101.157 |
| 2022-10 | 99.7735 | 99.9799 | 101.145 |
| 2022-11 | 99.4016 | 99.6322 | 101.141 |
| 2022-12 | 99.2565 | 99.3052 | 101.161 |
| 2023-01 | 99.2264 | 98.9732 | 101.222 |
| 2023-02 | 99.2644 | 98.6224 | 101.35 |
| 2023-03 | 99.3837 | 98.2617 | 101.553 |
get_composite_leading_indicator
The composite leading indicator (CLI) is designed to provide early signals of turning points in business cycles showing fluctuation of the economic activity around its long term potential level. CLIs show short -term economic movements in qualitative rather than quantitative terms.
See definition: https://data.oecd.org/leadind/composite -leading -indicator -cli.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Composite Leading Indicator.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2023-06-01', end_date='2023-12-01')
composite_leading_indicator = economics.get_composite_leading_indicator()
composite_leading_indicator.loc[:, ['United States', 'United Kingdom', 'Japan']]Which returns:
| United States | United Kingdom | Japan | |
|---|---|---|---|
| 2023-06 | 99.1511 | 99.9353 | 100.023 |
| 2023-07 | 99.2797 | 100.196 | 100.037 |
| 2023-08 | 99.3826 | 100.419 | 100.055 |
| 2023-09 | 99.4504 | 100.622 | 100.067 |
| 2023-10 | 99.4863 | 100.806 | 100.075 |
| 2023-11 | 99.5104 | 100.998 | 100.085 |
get_consumer_price_index
Inflation measured by consumer price index (CPI) is defined as the change in the prices of a basket of goods and services that are typically purchased by specific groups of households. Inflation is measured in terms an index, 2015 base year with a breakdown for food, energy and total.
Inflation measures the erosion of living standards. A consumer price index is estimated as a series of summary measures of the period -to -period proportional change in the prices of a fixed set of consumer goods and services of constant quantity and characteristics, acquired, used or paid for by the reference population.
Each summary measure is constructed as a weighted average of a large number of elementary aggregate indices. Each of the elementary aggregate indices is estimated using a sample of prices for a defined set of goods and services obtained in, or by residents of, a specific region from a given set of outlets or other sources of consumption goods and services.
This indicator uses index 2015 = 100.
See definition: https://data.oecd.org/price/inflation -cpi.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Consumer Price Index.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2015-01-01', end_date='2023-12-31')
consumer_price_index = economics.get_consumer_price_index()
consumer_price_index.loc[:, ['United States', 'South Africa', 'China']]Which returns:
| United States | South Africa | China | |
|---|---|---|---|
| 2015 | 100 | 100 | 100 |
| 2016 | 101.262 | 106.571 | 102 |
| 2017 | 103.419 | 112.096 | 103.625 |
| 2018 | 105.945 | 117.16 | 105.775 |
| 2019 | 107.865 | 121.987 | 108.842 |
| 2020 | 109.195 | 125.903 | 111.475 |
| 2021 | 114.325 | 131.709 | 112.569 |
| 2022 | 123.474 | 140.981 | 114.79 |
get_producer_price_index
Producer price indices in manufacturing measure the rate of change in prices of products sold as they leave the producer. They exclude any taxes, transport and trade margins that the purchaser may have to pay. PPIs provide measures of average movements of prices received by the producers of various commodities. hey are often seen as advanced indicators of price changes throughout the economy, including changes in the prices of consumer goods and services.
Manufacturing covers the production of semi -processed goods and other intermediate goods as well as final products such as consumer goods and capital equipment. A variety of price indices may be used to measure inflation in an economy. These include consumer price indices (CPI), price indices relating to specific goods and/or services, GDP deflators and producer price indices (PPI).
This indicator is presented for total industry and uses index 2015 = 100.
See definition: https://data.oecd.org/price/producer -price -indices -ppi.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Consumer Price Index.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2020-01-01', end_date='2023-12-31')
producer_price_index = economics.get_producer_price_index(period='quarterly')
producer_price_index.loc[:, ['Germany', 'Poland', 'Belgium']]Which returns:
| Germany | Poland | Belgium | |
|---|---|---|---|
| 2020Q1 | 103.9 | 105.193 | 110.167 |
| 2020Q2 | 102.9 | 103.926 | 105.1 |
| 2020Q3 | 102.833 | 104.026 | 107.9 |
| 2020Q4 | 102.967 | 104.693 | 108.233 |
| 2021Q1 | 104.567 | 107.26 | 112.9 |
| 2021Q2 | 106.9 | 110.594 | 118.233 |
| 2021Q3 | 109.567 | 114.362 | 123.033 |
| 2021Q4 | 111.933 | 118.663 | 130.3 |
| 2022Q1 | 117 | 124.298 | 138.433 |
| 2022Q2 | 123 | 135 | 148.533 |
| 2022Q3 | 125.2 | 137.534 | 150.167 |
| 2022Q4 | 125.8 | 138.601 | 149.933 |
get_house_prices
In most cases, the nominal house price index covers the sales of newly -built and existing dwellings, following the recommendations from the RPPI (Residential Property Prices Indices) manual.
The real house price index is given by the ratio of the nominal house price index to the consumers’ expenditure deflator in each country from the OECD national accounts database. Both indices are seasonally adjusted.
Both are based on an 2015 = 100 as an index.
See definition: https://data.oecd.org/price/housing -prices.htm
Args:
- quarterly (bool | None, optional): Whether to return the quarterly data or the annual data.
- inflation_adjusted (bool, optional): Whether to return the inflation adjusted data or the nominal data.
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the House Prices.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2015-01-01', end_date='2023-12-31')
real_house_prices = economics.get_house_prices(quarterly=False, inflation_adjusted=True)
real_house_prices.loc[:, ['Japan', 'Netherlands', 'Ireland']]Which returns:
| Japan | Netherlands | Ireland | |
|---|---|---|---|
| 2015 | 100 | 100 | 100 |
| 2016 | 102.559 | 104.447 | 106.77 |
| 2017 | 104.762 | 110.795 | 116.608 |
| 2018 | 106.054 | 118.658 | 126.275 |
| 2019 | 107.256 | 124.074 | 126.897 |
| 2020 | 106.991 | 131.814 | 126.311 |
| 2021 | 112.714 | 147.149 | 131.669 |
| 2022 | 118.827 | 156.422 | 138.298 |
get_rent_prices
The price to rent ratio is the nominal house price index divided by the housing rent price index and can be considered as a measure of the profitability of house ownership.
This is based on an 2015 = 100 as an index.
See definition: https://data.oecd.org/price/housing -prices.htm
Args:
- quarterly (bool | None, optional): Whether to return the quarterly data or the annual data.
- inflation_adjusted (bool, optional): Whether to return the inflation adjusted data or the nominal data.
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the House Prices.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2015-01-01', end_date='2023-12-31')
rent_prices = economics.get_rent_prices(quarterly=False)
rent_prices.loc[:, ['Turkey', 'United States', 'United Kingdom']]Which returns:
| Turkey | United States | United Kingdom | |
|---|---|---|---|
| 2015 | 100 | 100 | 100 |
| 2016 | 108.667 | 103.773 | 101.725 |
| 2017 | 118.586 | 107.731 | 102.699 |
| 2018 | 130.05 | 111.627 | 103.174 |
| 2019 | 143.192 | 115.765 | 103.924 |
| 2020 | 156.58 | 119.382 | 105.399 |
| 2021 | 172.63 | 122.062 | 107.148 |
| 2022 | 221.225 | 129.426 | 110.897 |
| 2023 | 398.003 | 139.543 | 117.179 |
get_share_prices
Share price indices are calculated from the prices of common shares of companies traded on national or foreign stock exchanges. They are usually determined by the stock exchange, using the closing daily values for the monthly data, and normally expressed as simple arithmetic averages of the daily data.
A share price index measures how the value of the stocks in the index is changing, a share return index tells the investor what their “return” is, meaning how much money they would make as a result of investing in that basket of shares.
A price index measures changes in the market capitalisation of the basket of shares in the index whereas a return index adds on to the price index the value of dividend payments, assuming they are re -invested in the same stocks. Occasionally agencies such as central banks will compile share indices.
This uses 2015 as the base year (= 100)
See definition: https://data.oecd.org/price/share -prices.htm
Args:
- period (str | None, optional): Whether to return the monthly, quarterly or the annual data.
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Exchange Rates.
As an example:
from financetoolkit import Economics
economics = Economics()
share_prices = economics.get_share_prices()
share_prices.loc[:, ['Turkey', 'Belgium', 'Australia']]Which returns:
| Turkey | Belgium | Australia | |
|---|---|---|---|
| 2013 | 96.6029 | 74.3936 | 92.3054 |
| 2014 | 93.2354 | 87.8382 | 98.611 |
| 2015 | 100 | 100 | 100 |
| 2016 | 95.6644 | 95.2324 | 96.0699 |
| 2017 | 122.746 | 101.514 | 105.648 |
| 2018 | 126.263 | 96.5515 | 109.205 |
| 2019 | 123.056 | 92.6847 | 117.326 |
| 2020 | 140.511 | 77.8758 | 111.188 |
| 2021 | 187.146 | 91.6789 | 130.475 |
| 2022 | 369.298 | 93.0484 | 128.367 |
get_long_term_interest_rate
Long -term interest rates refer to government bonds maturing in ten years. Rates are mainly determined by the price charged by the lender, the risk from the borrower and the fall in the capital value. Long -term interest rates are generally averages of daily rates, measured as a percentage. These interest rates are implied by the prices at which the government bonds are traded on financial markets, not the interest rates at which the loans were issued.
In all cases, they refer to bonds whose capital repayment is guaranteed by governments. Long -term interest rates are one of the determinants of business investment. Low long term interest rates encourage investment in new equipment and high interest rates discourage it. Investment is, in turn, a major source of economic growth
See definition: https://data.oecd.org/interest/long -term -interest -rates.htm
Args:
- period (str | None, optional): Whether to return the monthly, quarterly or the annual data.
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Long Term Interest Rate.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2023-05-01', end_date='2023-12-31')
long_term_interest_rate = economics.get_long_term_interest_rate(period='monthly')
long_term_interest_rate.loc[:, ['Japan', 'United States', 'Brazil']]Which returns:
| Japan | United States | Brazil | |
|---|---|---|---|
| 2023-05 | 0.0043 | 0.0357 | 0.0728 |
| 2023-06 | 0.004 | 0.0375 | 0.0728 |
| 2023-07 | 0.0059 | 0.039 | 0.07 |
| 2023-08 | 0.0064 | 0.0417 | 0.07 |
| 2023-09 | 0.0076 | 0.0438 | 0.07 |
| 2023-10 | 0.0095 | 0.048 | 0.0655 |
| 2023-11 | 0.0066 | 0.045 | 0.0655 |
get_short_term_interest_rate
Short -term interest rates are the rates at which short -term borrowings are effected between financial institutions or the rate at which short -term government paper is issued or traded in the market. Short -term interest rates are generally averages of daily rates, measured as a percentage.
Short -term interest rates are based on three -month money market rates where available. Typical standardised names are “money market rate” and “treasury bill rate”.
See definition: https://data.oecd.org/interest/short -term -interest -rates.htm
Args:
- period (str | None, optional): Whether to return the monthly, quarterly or the annual data.
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Short Term Interest Rate.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2023-05-01')
short_term_interest_rate = economics.get_short_term_interest_rate(period='quarterly', forecast=True)
short_term_interest_rate.loc[:, ['Japan', 'United States', 'China']]Which returns:
| Japan | United States | China | |
|---|---|---|---|
| 2023Q2 | -0.0003 | 0.0513 | 0.0435 |
| 2023Q3 | -0.0003 | 0.0543 | 0.0435 |
| 2023Q4 | -0.0003 | 0.0543 | 0.0435 |
| 2024Q1 | 0.0007 | 0.0536 | 0.0435 |
| 2024Q2 | 0.0017 | 0.0513 | 0.043 |
| 2024Q3 | 0.0027 | 0.0488 | 0.043 |
| 2024Q4 | 0.0037 | 0.0468 | 0.0425 |
| 2025Q1 | 0.0047 | 0.0448 | 0.0425 |
| 2025Q2 | 0.0057 | 0.0423 | 0.0425 |
| 2025Q3 | 0.0067 | 0.0408 | 0.0425 |
| 2025Q4 | 0.0077 | 0.0398 | 0.0425 |
get_narrow_and_broad_money
M1 includes currency i.e. banknotes and coins, plus overnight deposits. M1 is expressed as a seasonally adjusted index based on 2015=100.
Broad money (M3) includes currency, deposits with an agreed maturity of up to two years, deposits redeemable at notice of up to three months and repurchase agreements, money market fund shares/units and debt securities up to two years. M3 is measured as a seasonally adjusted index based on 2015=100.
See definition: https://data.oecd.org/money/broad -money -m3.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Narrow and Broad Money.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2012-01-01', end_date='2020-01-01')
narrow_and_broad_money = economics.get_narrow_and_broad_money(growth=True)
narrow_and_broad_money.loc[:, 'Australia']Which returns:
| Narrow Money (M1) | Broad Money (M3) | |
|---|---|---|
| 2012 | 0.0462 | 0.0786 |
| 2013 | 0.1402 | 0.0667 |
| 2014 | 0.1279 | 0.0722 |
| 2015 | 0.1466 | 0.068 |
| 2016 | 0.0983 | 0.0594 |
| 2017 | 0.0941 | 0.0684 |
| 2018 | 0.0565 | 0.027 |
| 2019 | 0.1066 | 0.0301 |
| 2020 | 0.294 | 0.0835 |
get_purchasing_power_parity
Purchasing power parities (PPPs) are the rates of currency conversion that try to equalise the purchasing power of different currencies, by eliminating the differences in price levels between countries. The basket of goods and services priced is a sample of all those that are part of final expenditures: final consumption of households and government, fixed capital formation, and net exports.
This indicator is measured in terms of national currency per US dollar.
See definition: https://data.oecd.org/conversion/purchasing -power -parities -ppp.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Purchasing Power Parity.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2015-01-01', end_date='2023-01-01')
purchasing_power_parity = economics.get_purchasing_power_parity()
purchasing_power_parity.loc[:, ['China', 'Chile', 'France']]Which returns:
| China | Chile | France | |
|---|---|---|---|
| 2015 | 3.8707 | 391.179 | 0.8087 |
| 2016 | 3.9888 | 397.251 | 0.78 |
| 2017 | 4.1838 | 397.689 | 0.7701 |
| 2018 | 4.2292 | 396.229 | 0.7562 |
| 2019 | 4.2083 | 401.613 | 0.7163 |
| 2020 | 4.1787 | 418.446 | 0.7104 |
| 2021 | 4.1873 | 435.156 | 0.7187 |
| 2022 | 4.0219 | 443.416 | 0.701 |
get_exchange_rates
Exchange rates are defined as the price of one country’s’ currency in relation to another country’s currency. This indicator is measured in terms of national currency per US dollar.
See definition: https://data.oecd.org/conversion/exchange -rates.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Exchange Rates.
As an example:
from financetoolkit import Economics
economics = Economics()
exchange_rates = economics.get_exchange_rates()
exchange_rates.loc[:, ['Netherlands', 'Japan', 'Indonesia']]Which returns:
| Netherlands | Japan | Indonesia | |
|---|---|---|---|
| 2013 | 0.7529 | 97.5957 | 10461.2 |
| 2014 | 0.7527 | 105.945 | 11865.2 |
| 2015 | 0.9013 | 121.044 | 13389.4 |
| 2016 | 0.9034 | 108.793 | 13308.3 |
| 2017 | 0.8852 | 112.166 | 13380.8 |
| 2018 | 0.8468 | 110.423 | 14236.9 |
| 2019 | 0.8933 | 109.01 | 14147.7 |
| 2020 | 0.8755 | 106.775 | 14582.2 |
| 2021 | 0.8455 | 109.754 | 14308.1 |
| 2022 | 0.9496 | 131.498 | 14849.9 |
get_renewable_energy
Renewable energy is defined as the contribution of renewables to total primary energy supply (TPES). Renewables include the primary energy equivalent of hydro (excluding pumped storage), geothermal, solar, wind, tide and wave sources.
Energy derived from solid biofuels, biogasoline, biodiesels, other liquid biofuels, biogases and the renewable fraction of municipal waste are also included. Biofuels are defined as fuels derived directly or indirectly from biomass (material obtained from living or recently living organisms).
This includes wood, vegetal waste (including wood waste and crops used for energy production), ethanol, animal materials/wastes and sulphite lyes. Municipal waste comprises wastes produced by the residential, commercial and public service sectors that are collected by local authorities for disposal in a central location for the production of heat and/or power.
This indicator in percentage of total primary energy supply.
See definition: https://data.oecd.org/energy/renewable -energy.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Renewable Energy Percentage.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2010-01-01', end_date='2020-01-01')
renewable_energy = economics.get_renewable_energy()
renewable_energy.loc[:, ['Zambia', 'Albania', 'Austria']]Which returns:
| Zambia | Albania | Austria | |
|---|---|---|---|
| 2010 | 0.9038 | 0.4049 | 0.2742 |
| 2011 | 0.8882 | 0.2581 | 0.2696 |
| 2012 | 0.8726 | 0.3121 | 0.307 |
| 2013 | 0.874 | 0.3489 | 0.3011 |
| 2014 | 0.8627 | 0.2722 | 0.3068 |
| 2015 | 0.8486 | 0.3433 | 0.2985 |
| 2016 | 0.8241 | 0.4209 | 0.3034 |
| 2017 | 0.8097 | 0.273 | 0.2984 |
| 2018 | 0.8081 | 0.4322 | 0.2943 |
| 2019 | 0.8089 | 0.3172 | 0.3006 |
| 2020 | 0.818 | 0.3388 | 0.3202 |
get_environmental_tax
Environmentally related taxes are an important instrument for governments to shape relative prices of goods and services.
The characteristics of such taxes included in the database (e.g. revenue, tax base, tax rates, exemptions, etc.) are used to construct the environmentally related tax revenues with a breakdown by environmental domain:
- Energy products (including vehicle fuels);
- Motor vehicles and transport services;
- Measured or estimated emissions to air and water, ozone depleting substances, certain non -point sources of water pollution, waste management and noise, as well as management of water, land, soil, forests, biodiversity, wildlife and fish stocks.
The data have been cross -validated and complemented with Revenue statistics from the OECD Tax statistics database and official national sources.
See definition: https://data.oecd.org/envpolicy/environmental -tax.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Environmental Tax.
As an example:
from financetoolkit import Economics
economics = Economics(start_date="2010-01-01", end_date="2020-01-01")
environmental_tax = economics.get_environmental_tax()
environmental_tax.loc[:, 'Netherlands']Which returns:
| Total | Energy | Transport | Resource | Pollution | |
|---|---|---|---|---|---|
| 2010 | 3.63 | 1.88 | 1.14 | 0.37 | 0.24 |
| 2011 | 3.45 | 1.85 | 1.1 | 0.27 | 0.23 |
| 2012 | 3.28 | 1.78 | 1.02 | 0.25 | 0.23 |
| 2013 | 3.29 | 1.9 | 0.95 | 0.26 | 0.19 |
| 2014 | 3.35 | 1.88 | 1 | 0.28 | 0.19 |
| 2015 | 3.36 | 1.86 | 1.04 | 0.27 | 0.19 |
| 2016 | 3.39 | 1.89 | 1.03 | 0.28 | 0.19 |
| 2017 | 3.37 | 1.86 | 1.06 | 0.27 | 0.18 |
| 2018 | 3.37 | 1.87 | 1.07 | 0.26 | 0.18 |
| 2019 | 3.42 | 1.94 | 1.04 | 0.25 | 0.19 |
| 2020 | 3.21 | 1.8 | 0.96 | 0.26 | 0.2 |
get_greenhouse_emissions
Greenhouse gases refer to the sum of seven gases that have direct effects on climate change:
- Carbon Dioxide (CO2)
- Methane (CH4)
- Nitrous Oxide (N2O)
- Chlorofluorocarbons (CFCs)
- Hydrofluorocarbons (HFCs)
- Perfluorocarbons (PFCs)
- Sulphur Hexafluoride (SF6)
- Nitrogen Trifluoride (NF3).
The data are expressed in CO2 equivalents and refer to gross direct emissions from human activities. CO2 refers to gross direct emissions from fuel combustion only and data are provided by the International Energy Agency. Other air emissions include emissions of sulphur oxides (SOx) and nitrogen oxides (NOx) given as quantities of SO2 and NO2, emissions of carbon monoxide (CO), and emissions of volatile organic compounds (VOC), excluding methane.
Air and greenhouse gas emissions are measured in tonnes per capita and kilogram per capita in which all metrics are converted to tonnes (1000kg) per capita.
See definition: https://data.oecd.org/air/air -and -ghg -emissions.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Environmental Tax.
As an example:
from financetoolkit import Economics
economics = Economics(start_date="2010-01-01", end_date="2020-01-01")
greenhouse_emissions = economics.get_greenhouse_emissions()
greenhouse_emissions.loc[:, 'United States']Which returns:
| Carbon Dioxide (CO2) | Carbon Monoxide (CO) | Greenhouse Gases (GHG) | Nitrogen Oxides (NOX) | Sulphur Oxides (SOX) | Volatile Organic Compounds (VOC) | |
|---|---|---|---|---|---|---|
| 2010 | 17.28 | 0.1719 | 22.818 | 0.0449 | 0.0203 | 0.0388 |
| 2011 | 16.44 | 0.1658 | 22.168 | 0.0423 | 0.0186 | 0.0389 |
| 2012 | 15.6 | 0.1538 | 21.252 | 0.0395 | 0.0147 | 0.0382 |
| 2013 | 15.93 | 0.1504 | 21.647 | 0.0368 | 0.0139 | 0.0364 |
| 2014 | 15.84 | 0.1443 | 21.667 | 0.0345 | 0.013 | 0.036 |
| 2015 | 15.36 | 0.1345 | 21.006 | 0.031 | 0.0097 | 0.0343 |
| 2016 | 14.97 | 0.1259 | 20.362 | 0.0278 | 0.0074 | 0.0332 |
| 2017 | 14.64 | 0.133 | 20.183 | 0.026 | 0.0067 | 0.0348 |
| 2018 | 15.02 | 0.133 | 20.667 | 0.0248 | 0.0064 | 0.0351 |
| 2019 | 14.44 | 0.1264 | 20.156 | 0.0236 | 0.0054 | 0.0332 |
| 2020 | 12.9 | 0.1172 | 18.178 | 0.0207 | 0.0047 | 0.0329 |
get_crude_oil_production
Crude oil production is defined as the quantities of oil extracted from the ground after the removal of inert matter or impurities. It includes crude oil, natural gas liquids (NGLs) and additives. This indicator is measured in thousand tonne of oil equivalent (toe).
Crude oil is a mineral oil consisting of a mixture of hydrocarbons of natural origin, yellow to black in colour, and of variable density and viscosity. NGLs are the liquid or liquefied hydrocarbons produced in the manufacture, purification and stabilisation of natural gas.
Additives are non -hydrocarbon substances added to or blended with a product to modify its properties, for example, to improve its combustion characteristics (e.g. MTBE and tetraethyl lead). Refinery production refers to the output of secondary oil products from an oil refinery.
This indicator is measured in thousand tonne of oil equivalent (toe).
See definition: https://data.oecd.org/energy/crude -oil -production.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Crude Oil Production.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2007-01-01')
crude_oil_production = economics.get_crude_oil_production()
crude_oil_production.loc[:, ['China', 'Saudi Arabia', 'Russia', 'Netherlands']]Which returns:
| China | Saudi Arabia | Russia | Netherlands | |
|---|---|---|---|---|
| 2007 | 186318 | 446438 | 470511 | 2109.1 |
| 2008 | 190440 | 467050 | 471814 | 1765.4 |
| 2009 | 189490 | 414458 | 479089 | 1338.07 |
| 2010 | 203014 | 413505 | 487106 | 1040.27 |
| 2011 | 202876 | 471515 | 494393 | 1102.48 |
| 2012 | 207478 | 495778 | 499908 | 1133.08 |
| 2013 | 209919 | 488039 | 499966 | 1144.3 |
| 2014 | 211429 | 491857 | 505603 | 1555.3 |
| 2015 | 214556 | 516157 | 512777 | 1423.99 |
| 2016 | 199685 | 531161 | 524319 | 975.589 |
| 2017 | 191506 | 504365 | 517105 | 970.892 |
| 2018 | 189324 | 522375 | 525934 | 918.789 |
| 2019 | 191014 | 496688 | 530219 | 761.583 |
| 2020 | 194769 | 467840 | 484621 | 751.952 |
| 2021 | 199264 | 463618 | 495677 | 763.855 |
get_crude_oil_prices
Crude oil import prices come from the IEA’s Crude Oil Import Register and are influenced not only by traditional movements of supply and demand, but also by other factors such as geopolitics.
Information is collected from national agencies according to the type of crude oil, by geographic origin and by quality of crude. Average prices are obtained by dividing value by volume as recorded by customs administrations for each tariff position.
Values are recorded at the time of import and include cost, insurance and freight, but exclude import duties. The nominal crude oil spot price from 2003 to 2011 is for Dubai and from 1970 to 2002 for Arabian Light. This indicator is measured in USD per barrel of oil.
The real price was calculated using the deflator for GDP at market prices and rebased with reference year 1970 = 100.
See definition: https://data.oecd.org/energy/crude -oil -import -prices.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Crude Oil Prices.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2015-01-01')
crude_oil_prices = economics.get_crude_oil_prices()
crude_oil_prices.loc[:, ['United Kingdom', 'Japan', 'Canada']]Which returns:
| United Kingdom | Japan | Canada | |
|---|---|---|---|
| 2015 | 53.81 | 54.2 | 53.48 |
| 2016 | 44.62 | 41.79 | 43.6 |
| 2017 | 54.69 | 54.42 | 54.3 |
| 2018 | 72.65 | 72.85 | 70.88 |
| 2019 | 65.58 | 66.78 | 63.12 |
| 2020 | 44.62 | 46.85 | 45.79 |
| 2021 | 71.2 | 70.25 | 69.44 |
| 2022 | 104.66 | 102.11 | 104.16 |
get_government_statistics
This function contains a variety of general government statistics. The following statistics are provided:
- General government deficit is defined as the balance of income and expenditure of government, including capital income and capital expenditures. “Net lending” means that government has a surplus, and is providing financial resources to other sectors, while “net borrowing” means that government has a deficit, and requires financial resources from other sectors.
- General government revenue is defined as the revenue required to finance the goods and services they provide to citizens and businesses, and to fulfil their redistributive role. Comparing levels of government revenues across countries provides an indication of the importance of the government sector in the economy in terms of available financial resources. The total amount of revenues collected by governments is determined by past and current political decisions.
- General government spending is defined as an indicator of the size of government across countries. The large variation in this indicator highlights the variety of countries’ approaches to delivering public goods and services and providing social protection, not necessarily differences in resources spent
- General government final consumption can be broken down into two distinct groups. The first reflects expenditures for collective consumption (defence, justice, etc.) that benefit the society as a whole, or large parts of society, and are often known as public goods and services. The second, referred to as “individual”, relates to expenditures for individual consumption (health care, housing, education, etc.), incurred by government for the benefit of individual households.
- General government debt -to -GDP ratio measures the gross debt of the general government as a percentage of GDP. It is a key indicator for the sustainability of government finance. Debt is calculated as the sum of the following liability categories (as applicable): currency and deposits; debt securities, loans; insurance, pensions and standardised guarantee schemes, and other accounts payable. Changes in government debt over time primarily reflect the impact of past government deficits.
- The net financial worth of the general government sector is the total value of its financial assets minus the total value of its outstanding liabilities. The general government sector consists of central, state and local governments as well as social security funds.
- General government production costs are decisions about the amount and type of goods and services governments produce, as well as on how best to produce them. They are often political in nature and based on a country’s social and cultural context. Governments use a mix of their own employees, capital, and outside contractors (non -profit institutions or private sector entities) to produce goods and services. Government production costs include: compensation costs of general government employees; goods and services used and financed by general government (including intermediate consumption and social transfer in kind via market producers paid for by government); and other costs, including depreciation of capital and other taxes on production less other subsidies on production. The data include government employment and intermediate consumption for output produced by the government for its own use, such as roads and other capital investment projects built by government employees.
All of these metrics are reported as a percentage of GDP which makes comparison between countries relatively straight forward.
See definition: https://data.oecd.org/gga/general -government -deficit.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Government Statistics.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2012-01-01')
goverment_statistics = economics.get_government_statistics()
goverment_statistics.loc[:, 'Switzerland']Which returns:
| Deficit | Revenue | Spending | Debt | Financial Wealth | Production Costs | |
|---|---|---|---|---|---|---|
| 2012 | 0.0024 | 0.3292 | 0.3269 | 0.4373 | -0.0309 | 0.1559 |
| 2013 | -0.0043 | 0.332 | 0.3363 | 0.4299 | -0.0393 | 0.1579 |
| 2014 | -0.0025 | 0.3302 | 0.3327 | 0.4293 | 0.0158 | 0.1589 |
| 2015 | 0.0055 | 0.3416 | 0.3361 | 0.4287 | -0.0218 | 0.1607 |
| 2016 | 0.0024 | 0.3391 | 0.3367 | 0.4165 | 0.0056 | 0.1612 |
| 2017 | 0.0113 | 0.3484 | 0.3371 | 0.4246 | 0.1066 | 0.1618 |
| 2018 | 0.0129 | 0.3428 | 0.3299 | 0.4028 | 0.0927 | 0.1584 |
| 2019 | 0.0134 | 0.346 | 0.3326 | 0.4007 | 0.1769 | 0.1596 |
| 2020 | -0.0306 | 0.3527 | 0.3834 | 0.4379 | 0.1763 | 0.1687 |
| 2021 | -0.0031 | 0.3545 | 0.3589 | 0.4137 | 0.2029 | 0.1683 |
| 2022 | 0.012 | 0.3417 | nan | 0.3754 | 0.0436 | nan |
get_central_government_spending
Central government expenditure is defined as the central government budget expenditure as reported in the final central government accounts. Data are based on the System of National accounts (SNA), a set of internationally agreed concepts, definitions, classifications and rules for national accounting.
Central government spending by function is the breakdown of expenditures on the basis of the activities governments support. The classification system used to provide this breakdown on an internationally comparable basis is known as Classification of Functions of Government (COFOG).
COFOG expenditures are divided into in the following ten functions:
- General Public Services;
- Defence;
- Public Order and Safety;
- Economic Affairs;
- Environmental Protection;
- Housing and Community Amenities;
- Health;
- Recreation, Culture and Religion;
- Education;
- Social Protection.
Data on central government expenditures by function include transfers between the different levels of government. The general government sector consists of central, state and local governments, and the social security funds controlled by these units. The political authority of central government extends over the entire territory of the country; central government has the authority to impose taxes on all resident and non -resident units engaged in economic activities within the country.
The responsibility for the provision of public goods and services and redistribution of income is divided between different levels of government. Data on the distribution of government spending by both level and function can provide an indication of the extent to which key government activities are decentralised to sub -national governments. This indicator of central government spending by function is measured as a percentage of total expenditures.
See definition: https://data.oecd.org/gga/central -government -spending.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Central Government Spending.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2012-01-01')
goverment_spending = economics.get_central_government_spending()
goverment_spending.loc[:, 'OECD - Total']Which returns:
| General Public Services | Defence | Public Order and Safety | Economic Affairs | Environmental Protection | Housing and Community Amenities | Health | Recreation, Culture and Religion | Education | Social Protection | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2012 | 0.2408 | 0.1005 | 0.0298 | 0.0932 | 0.0054 | 0.0182 | 0.1478 | 0.0085 | 0.0731 | 0.2827 |
| 2013 | 0.2415 | 0.0957 | 0.0302 | 0.0912 | 0.0059 | 0.0159 | 0.1517 | 0.0083 | 0.074 | 0.2856 |
| 2014 | 0.2371 | 0.0924 | 0.0302 | 0.087 | 0.0055 | 0.016 | 0.1615 | 0.0084 | 0.0748 | 0.287 |
| 2015 | 0.2297 | 0.0911 | 0.0306 | 0.0877 | 0.0055 | 0.0152 | 0.1677 | 0.0084 | 0.0748 | 0.2893 |
| 2016 | 0.2263 | 0.0901 | 0.031 | 0.0862 | 0.0056 | 0.014 | 0.1719 | 0.0092 | 0.0735 | 0.2923 |
| 2017 | 0.2236 | 0.0903 | 0.0308 | 0.0892 | 0.0053 | 0.0147 | 0.1726 | 0.0094 | 0.0736 | 0.2906 |
| 2018 | 0.2247 | 0.0922 | 0.0311 | 0.0883 | 0.0051 | 0.0142 | 0.1753 | 0.0093 | 0.074 | 0.286 |
| 2019 | 0.2205 | 0.0945 | 0.0309 | 0.0888 | 0.005 | 0.014 | 0.1774 | 0.0092 | 0.0729 | 0.2866 |
get_trust_in_government
Trust in government refers to the share of people who report having confidence in the national government. The data shown reflect the share of respondents answering “yes” (the other response categories being “no”, and “dont know”) to the survey question: “In this country, do you have confidence in… national government?
Due to small sample sizes, country averages for horizontal inequalities (by age, gender and education) are pooled between 2010 -18 to improve the accuracy of the estimates.
The sample is ex ante designed to be nationally representative of the population aged 15 and over. This indicator is measured as a percentage of all survey respondents.
See definition: https://data.oecd.org/gga/trust -in -government.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Trust in Government.
As an example:
from financetoolkit import Economics
economics = Economics()
trust_in_government = economics.get_trust_in_government()
trust_in_government.loc[:, ['United States', 'Greece', 'Japan']]Which returns:
| United States | Greece | Japan | |
|---|---|---|---|
| 2006 | 0.558 | 0.4875 | 0.3503 |
| 2007 | 0.3932 | 0.3814 | 0.24 |
| 2008 | 0.3792 | nan | 0.2212 |
| 2009 | 0.503 | 0.3162 | 0.2518 |
| 2010 | 0.4183 | 0.2365 | 0.2703 |
| 2011 | 0.3825 | 0.1752 | 0.2311 |
| 2012 | 0.3489 | 0.1262 | 0.1692 |
| 2013 | 0.2886 | 0.1436 | 0.3581 |
| 2014 | 0.3487 | 0.1883 | 0.3795 |
| 2015 | 0.3469 | 0.4373 | 0.3529 |
| 2016 | 0.2972 | 0.1325 | 0.3622 |
| 2017 | 0.3865 | 0.1399 | 0.4125 |
| 2018 | 0.3138 | 0.157 | 0.3849 |
| 2019 | 0.3628 | 0.3964 | 0.4112 |
| 2020 | 0.4649 | 0.3975 | 0.4234 |
| 2021 | 0.4046 | 0.4017 | 0.2908 |
| 2022 | 0.3102 | 0.2563 | 0.4315 |
get_unemployment_rate
The unemployed are people of working age who are without work, are available for work, and have taken specific steps to find work. The uniform application of this definition results in estimates of unemployment rates that are more internationally comparable than estimates based on national definitions of unemployment.
This indicator is measured in numbers of unemployed people as a percentage of the labour force and it is seasonally adjusted. The labour force is defined as the total number of unemployed people plus those in employment. Data are based on labour force surveys (LFS).
For European Union countries where monthly LFS information is not available, the monthly unemployed figures are estimated by Eurostat.
See definition: https://data.oecd.org/unemp/unemployment -rate.htm
Args:
- period (str | None, optional): Whether to return the monthly, quarterly or the annual data.
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Unemployment Rate.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2021-03-01', end_date='2023-01-01')
unemployment_rate = economics.get_unemployment_rate(period='quarterly')
unemployment_rate.loc[:, ['Germany', 'United States', 'Japan']]Which returns:
| Germany | United States | Japan | |
|---|---|---|---|
| 2021Q1 | 0.039 | 0.062 | 0.0283 |
| 2021Q2 | 0.037 | 0.0593 | 0.029 |
| 2021Q3 | 0.0343 | 0.0513 | 0.0277 |
| 2021Q4 | 0.0323 | 0.042 | 0.0273 |
| 2022Q1 | 0.031 | 0.038 | 0.0267 |
| 2022Q2 | 0.03 | 0.036 | 0.026 |
| 2022Q3 | 0.0307 | 0.0357 | 0.0257 |
| 2022Q4 | 0.0303 | 0.036 | 0.0253 |
| 2023Q1 | 0.0293 | 0.035 | 0.026 |
get_labour_productivity
GDP per hour worked is a measure of labour productivity. It measures how efficiently labour input is combined with other factors of production and used in the production process. Labour input is defined as total hours worked of all persons engaged in production. Labour productivity only partially reflects the productivity of labour in terms of the personal capacities of workers or the intensity of their effort.
The ratio between the output measure and the labour input depends to a large degree on the presence and/or use of other inputs (e.g. capital, intermediate inputs, technical, organisational and efficiency change, economies of scale).
This uses 2015 as the base year (= 100)
See definition: https://data.oecd.org/lprdty/gdp -per -hour -worked.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Exchange Rates.
As an example:
from financetoolkit import Economics
economics = Economics()
labour_productivity = economics.get_exchange_rates()
labour_productivity.loc[:, ['Bulgaria', 'Croatia', 'Spain']]Which returns:
| Bulgaria | Croatia | Spain | |
|---|---|---|---|
| 2013 | 1.4736 | 0.7572 | 0.7529 |
| 2014 | 1.4742 | 0.7629 | 0.7527 |
| 2015 | 1.7644 | 0.9103 | 0.9013 |
| 2016 | 1.768 | 0.9033 | 0.9034 |
| 2017 | 1.7355 | 0.8791 | 0.8852 |
| 2018 | 1.657 | 0.8334 | 0.8468 |
| 2019 | 1.747 | 0.879 | 0.8933 |
| 2020 | 1.7163 | 0.8778 | 0.8755 |
| 2021 | 1.6538 | 0.8441 | 0.8455 |
| 2022 | 1.8601 | 0.9503 | 0.9496 |
get_income_inequality
Income is defined as household disposable income in a particular year. It consists of earnings, self -employment and capital income and public cash transfers; income taxes and social security contributions paid by households are deducted. The income of the household is attributed to each of its members, with an adjustment to reflect differences in needs for households of different sizes. Income inequality among individuals is measured here by five indicators.
- The Gini coefficient is based on the comparison of cumulative proportions of the population against cumulative proportions of income they receive, and it ranges between 0 in the case of perfect equality and 1 in the case of perfect inequality.
- P90/P10 is the ratio of the upper bound value of the ninth decile (i.e. the 10% of people with highest income) to that of the first decile;
- P90/P50 of the upper bound value of the ninth decile to the median income;
- and P50/P10 of median income to the upper bound value of the first decile.
- The Palma ratio is the share of all income received by the 10% people with highest disposable income divided by the share of all income received by the 40% people with the lowest disposable income.
- S80/S20 is the ratio of the average income of the 20% richest to the 20% poorest;
See definition: https://data.oecd.org/inequality/income -inequality.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Population Statistics.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2013-01-01')
income_inequality = economics.get_income_inequality()
income_inequality.loc[:, 'United States']Which returns:
| Gini Coefficient | P90/P10 | P90/P50 | P50/P10 | Palma Ratio | S80/S20 | |
|---|---|---|---|---|---|---|
| 2013 | 0.396 | 6.4 | 2.3 | 2.7 | 1.82 | 8.6 |
| 2014 | 0.394 | 6.4 | 2.3 | 2.7 | 1.79 | 8.7 |
| 2015 | 0.39 | 6.1 | 2.3 | 2.7 | 1.75 | 8.3 |
| 2016 | 0.391 | 6.3 | 2.3 | 2.7 | 1.77 | 8.5 |
| 2017 | 0.39 | 6.2 | 2.3 | 2.7 | 1.76 | 8.4 |
| 2018 | 0.393 | 6.3 | 2.3 | 2.8 | 1.79 | 8.4 |
| 2019 | 0.395 | 6.3 | 2.3 | 2.7 | 1.81 | 8.4 |
| 2020 | 0.377 | 5.8 | 2.2 | 2.6 | 1.64 | 7.5 |
| 2021 | 0.375 | 5.4 | 2.2 | 2.4 | 1.63 | 7.1 |
get_population_statistics
Population is defined as all nationals present in, or temporarily absent from a country, and aliens permanently settled in a country. This indicator shows the number of people that usually live in an area. Growth rates are the annual changes in population resulting from births, deaths and net migration during the year.
Total population includes the following:
- national armed forces stationed abroad; merchant seamen at sea;
- diplomatic personnel located abroad;
- civilian aliens resident in the country;
- displaced persons resident in the country.
However, it excludes the following:
- foreign armed forces stationed in the country;
- foreign diplomatic personnel located in the country;
- civilian aliens temporarily in the country.
Population projections are a common demographic tool. They provide a basis for other statistical projections, helping governments in their decision making. This indicator is measured in terms of thousands of people.
Furthermore the following statistics are provided:
- The youth population is defined as those people aged less than 15 as a percentage of the total population.
- The working age population is defined as those aged 15 to 64 as a percentage of the total population.
- The elderly population is defined as those aged 65 and over as a percentage of the total population.
- The total fertility rate in a specific year is defined as the total number of children that would be born to each woman if she were to live to the end of her child -bearing years and give birth to children in alignment with the prevailing age -specific fertility rates.
- The old -age to working -age demographic ratio is defined as the number of individuals aged 65 and over per 100 people of working age defined as those at ages 20 to 64.
See definition: https://data.oecd.org/pop/population.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Population Statistics.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2010-01-01', end_date='2019-01-01')
population_statistics = economics.get_population_statistics()
population_statistics.loc[:, 'Japan']Which returns:
| Population | Young Population | Working Age Population | Elderly Population | |
|---|---|---|---|---|
| 2010 | 128.057 | 0.1315 | 0.6383 | 0.2302 |
| 2011 | 127.834 | 0.1307 | 0.6365 | 0.2328 |
| 2012 | 127.593 | 0.1298 | 0.6288 | 0.2415 |
| 2013 | 127.414 | 0.1288 | 0.6207 | 0.2506 |
| 2014 | 127.237 | 0.1277 | 0.6126 | 0.2597 |
| 2015 | 127.095 | 0.1255 | 0.6081 | 0.2665 |
| 2016 | 127.042 | 0.1244 | 0.6035 | 0.272 |
| 2017 | 126.918 | 0.1232 | 0.6003 | 0.2765 |
| 2018 | 126.749 | 0.1221 | 0.598 | 0.2799 |
| 2019 | 126.555 | 0.1206 | 0.5969 | 0.2825 |
get_poverty_rate
The poverty rate is the ratio of the number of people (in a given age group) whose income falls below the poverty line; taken as half the median household income of the total population.
It is also available by broad age group:
- child poverty (0 to 17 year -olds);
- working -age poverty (18 to 65 year -olds);
- and elderly poverty (66 year -olds or more).
However, two countries with the same poverty rates may differ in terms of the relative income -level of the poor.
See definition: https://data.oecd.org/inequality/poverty -rate.htm
Args:
- growth (bool, optional): Whether to return the growth data or the actual data.
- lag (int, optional): The number of periods to lag the data by.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Returns: pd.DataFrame: A DataFrame containing the Poverty Rates.
As an example:
from financetoolkit import Economics
economics = Economics(start_date='2012-01-01', end_date='2020-01-01')
poverty_rate = economics.get_poverty_rate()
poverty_rate.loc[:, 'Portugal']Which returns:
| Total | 0-17 Year | 18-65 Year | 66 or More | |
|---|---|---|---|---|
| 2012 | 0.13 | 0.178 | 0.129 | 0.082 |
| 2013 | 0.135 | 0.183 | 0.133 | 0.097 |
| 2014 | 0.135 | 0.182 | 0.133 | 0.097 |
| 2015 | 0.125 | 0.155 | 0.123 | 0.108 |
| 2016 | 0.125 | 0.155 | 0.126 | 0.095 |
| 2017 | 0.107 | 0.122 | 0.105 | 0.101 |
| 2018 | 0.104 | 0.122 | 0.103 | 0.09 |
| 2019 | 0.106 | 0.131 | 0.098 | 0.107 |
| 2020 | 0.128 | 0.152 | 0.118 | 0.138 |