For contributor entries \(c_{od,j}\) from calc_trades_content(..., breakdown=True), the method computes:

\[a_{rod} = \left|\sum_{j\in r}c_{od,j}\right|, \qquad s_{rod} = \frac{a_{rod}}{\sum_q a_{qod}}, \qquad \mathrm{HHI}_{od} = \sum_r s_{rod}^{2}.\]

Contributors are summed within each contributor Region r before taking the absolute value, so positive and negative entries within that Region can cancel. A zero denominator produces zero shares and HHI zero. Otherwise HHI lies between \(1/R\) and 1, where R is the number of contributor Regions with nonzero magnitudes. Higher values mean more concentrated content.

All selectors follow calc_trades_content. The result is a Region-by-Region DataFrame of dimensionless HHI values, or a dictionary of DataFrames when multiple scenarios are requested.

Database.calc_trades_concentration(

indicator,

item=None,

scenario='baseline',

method='total',

clusters=None,

clusters_direction='both',

intermediate=True,

final=True,

aggregate=True,

)

Calculate contributor-region concentration of embodied trade content.

This method returns the Herfindahl-Hirschman index computed on the absolute contributor-region shares behind calc_trades_content(..., breakdown=True).