BPCE - 2020 Universal Registration Document

NON-FINANCIAL PERFORMANCE STATEMENT

PUTTING OUR COOPERATIVE IDENTITY TO WORK FOR REGIONAL DEVELOPMENT

2.1.2

Contributing to regional development

SOCIO-ECONOMIC FOOTPRINT OF THE BANQUE POPULAIRE AND CAISSE D’EPARGNE NETWORKS In 2019, Groupe BPCE commissioned a review of the socio-economicfootprint of its two retail networks, the Banques Populaires and the Caisses d’Epargne. The review was conducted using a LocalFootprint@ certified method (see Chapter 2.5, Methodology) based on data for 2018. It calculated the impact of the two Banque Populaire and Caisse d’Epargne networks’ activity (financing and operations) in terms of jobs supported and their contribution to GDP. Given the stability of the parameters applied to carry out the study, it was not updated in 2020. The study using the LocalFootprint@ method is still based on n-1 data. The update would have been based on 2019 figures and databases. The LocalFootprint@ method is based on the operating expenses of banking institutions (purchases from suppliers, payroll expenses and tax expenses) as well as the financing granted to customers (medium and long-term loans, leases, and microloans). The volumes committed by Groupe BPCE changed little between 2018 and 2019.

The LocalFootprint@ model consists of five types of parameters: the macro-economic parameters of a country or territory • (national accounts, imports, exports, etc.); the technical coefficients or “production function” of the • various sectors (breakdown of expenditure by sector and distribution of value added according to stakeholders); sector statistics (Production/Employment sector ratios); • data on the local economic fabric (INSEE open source data); • the local calibration algorithm (transitioningfrom a national to a • departmental model). These five parameters are relatively unresponsive to changes from one year to the next, due to relative macroeconomic stability, technical coefficients considered stable over a five-year period, a stable economic fabric (although the closure or relocation of some large companies may have an impact), and industry statistics updated every 2-3 years. While the input data in the model are stable, the reuse of data from year n-1 to present an impact in year n is entirely valid, with a limited margin of error (<5%). Thus, if the nature of the amounts analyzed – loans disbursed by market, type of customer, purpose of loans – with the LocalFootprint@ model change little over the year, the results will remain stable.

52

UNIVERSAL REGISTRATION DOCUMENT 2020 | GROUPE BPCE

www.groupebpce.com