Difference-in-difference (DID) designs measure the impact of a policy by the difference in the before-after change in the outcomes. The impact is estimated by computing a double difference: one over time (before-after) and one across subjects (between beneficiaries and non-beneficiaries). Simply observing the before and after change in the treatment group is not sufficient as there may be other factors likely to influence the outcome over time.