diff --git a/docs/src/implemented-proposals/ed_overview/ed_validation_client_economics/ed_vce_state_validation_protocol_based_rewards.md b/docs/src/implemented-proposals/ed_overview/ed_validation_client_economics/ed_vce_state_validation_protocol_based_rewards.md
index 9ee3e64ce5..8f3fb5c03a 100644
--- a/docs/src/implemented-proposals/ed_overview/ed_validation_client_economics/ed_vce_state_validation_protocol_based_rewards.md
+++ b/docs/src/implemented-proposals/ed_overview/ed_validation_client_economics/ed_vce_state_validation_protocol_based_rewards.md
@@ -39,7 +39,9 @@ From these simulated _Inflation Schedules_, we can also project ranges for token
 
 Finally we can estimate the _Staked Yield_ on staked SOL, if we introduce an additional parameter, previously discussed, _% of Staked SOL_:
 
-%~\text{SOL Staked} = \frac{\text{Total SOL Staked}}{\text{Total Current Supply}}
+$$
+\%~\text{SOL Staked} = \frac{\text{Total SOL Staked}}{\text{Total Current Supply}}
+$$
 
 In this case, because _% of Staked SOL_ is a parameter that must be estimated (unlike the _Inflation Schedule_ parameters), it is easier to use specific _Inflation Schedule_ parameters and explore a range of _% of Staked SOL_. For the below example, we’ve chosen the middle of the parameter ranges explored above: