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

Also known as: consumer sentiment, spending outlook.

Consumer Confidence Index in Python or via the MCP

Install the Finance Toolkit with:

pip install financetoolkit -U

Then call get_consumer_confidence_index as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2008-09-01', end_date='2009-03-01')

economics.get_consumer_confidence_index(countries=['Germany', 'France', 'Portugal'])

Or, after setting up the Finance Toolkit MCP, ask:

How did consumer confidence in Germany, France and Portugal develop each month from September 2008 to March 2009?

Which returns:

  Germany France Portugal
2008-09 98.3529 97.6684 98.0214
2008-10 98.228 97.6761 97.8728
2008-11 98.1059 97.7517 97.4371
2008-12 97.9237 97.7033 96.956
2009-01 97.8036 97.6175 96.6248
2009-02 97.7333 97.5525 96.6024
2009-03 97.7251 97.6748 96.9086

Parameters

get_consumer_confidence_index accepts the following parameters.

ParameterTypeDescriptionExample
countrieslist[str] | str | NoneThe countries to include in the data. Defaults to None.["United States", "Germany"]
rollingintThe rolling window size to use for smoothing the data (simple moving average). Defaults to None.4
trailingintThe trailing window size to use for summing the data over trailing periods (e.g. a trailing-4-quarter sum). Defaults to None.4
growthboolWhether to return the growth data or the actual data. Defaults to False.True
lagintThe number of periods to lag the data by. Defaults to 1.4
standardizeboolWhether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.True
roundingint | NoneThe number of decimals to round the results to. Defaults to None.2
Share