DID requires data from pre-/post-intervention, such as cohort or panel data (individual level data over time) or repeated cross-sectional data (individual or group level). Hence, Difference-in-difference is a useful technique to use when randomization on the individual level is not possible. DID relies on a less strict exchangeability assumption, i.e., in absence of treatment, the unobserved differences between treatment and control groups arethe same overtime. Difference-in-Difference estimation, graphical explanationĭID is used in observational settings where exchangeability cannot be assumed between the treatment and control groups. DID is typically used to estimate the effect of a specific intervention or treatment (such as a passage of law, enactment of policy, or large-scale program implementation) by comparing the changes in outcomes over time between a population that is enrolled in a program (the intervention group) and a population that is not (the control group).įigure 1. DescriptionĭID is a quasi-experimental design that makes use of longitudinal data from treatment and control groups to obtain an appropriate counterfactual to estimate a causal effect. The difference-in-difference (DID) technique originated in the field of econometrics, but the logic underlying the technique has been used as early as the 1850’s by John Snow and is called the ‘controlled before-and-after study’ in some social sciences.
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