Valuing The Global Food System Dashboard

Food Systems, Financial Risk & Reward, Equity

Valuing the global food system

Valuing the global food system is not easy due to a lack of data. You can read more about how we have approached the estimation process and how we derived our valuation estimate in the accompanying blog.

This dashboard allows you to calculate your own estimate of the aggregate revenues, equity value, and enterprise value for the global food system using the same approach that we have used in our blog but using your assumptions instead of ours.

There are two alternative models:

– Scenario 1 (S1) uses assumptions about the number of businesses in the global food system (smallholders and non-farm businesses) and their average revenues to determine the aggregate revenues for the unquoted companies in the system. These revenues are added to the USD 5.0 trillion revenues generated by the quoted food system companies in our database to produce an aggregate food system total. In this model the aggregate revenues are sensitive to changes in the estimated number of businesses in the global food system and their average earnings, but because we assume these businesses would be given a lower value by the financial markets due to their smaller size and greater risk, the overall valuation is less sensitive to these changes.

– Scenario 2 (S2) uses a protein price model combined with average per person protein consumption figures and a global population figure to generate aggregate revenues for the food retail and food service part of the system, and then an assumption for these revenues as a proportion of the whole, to calculate aggregate food system revenues. This model is much more sensitive to changing assumptions because changing the assumptions changes the total revenue figure.

The valuation section allows you to alter the discount that is applied to non-quoted companies to reflect their higher risk (and lower value) compared to quoted companies. You can apply a different discount to the larger unquoted companies (those we have analysed in our database) vs the smaller ones (those our database does not capture).

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