It might be helpful to really understand what a "difference-in-differences" analysis is - this article has a good explanation: Introduction to Difference-in-Differences Estimation
The DD model includes several pieces:
- A sudden exogenous source of variation, which we will refer to as the treatment. Treatment examples include changes in minimum wage, a new workplace non-discrimination policy, or a new CO2 emissions tax.
- A quantifiable and measurable outcome which is either the direct target of the variation or an indirect proxy.
- A treatment group which is subjected to the change.
- A control group which is similar in characteristic to the treatment group but is not subjected to the change.
Because the measurement occurs over time and you want it to be relative to companies that have not be subjected to the change, it is obvious that "surrounding" includes the event date. The "7 years surrounding the event date" have already been described by "2 years before and 5 years after the adoption of a leniency law in a country".
The control group are companies that did not experience "the adoption of a leniency law in a country" (the treatment) in that time period. If they had experienced an event during that time, they couldn't be used to measure what might have happened if the treatment had not occurred. It's important to analyze some time before the event in addition to time after the event, so that we can see how each group is related before the event we want to study happens.
An example chart looks like this:
The event is a single point that separates "pre-treatment" and "post-treatment".