Impact evaluation Difference-in-Differences (DiD) is a widely used statistical method designed to estimate the causal effect of a specific intervention, policy, or program by comparing changes in outcomes over time between a group that receives the intervention (the treatment group) and a group that does not (the control group).
As one of the most frequently used methods in impact evaluation studies, DiD provides a powerful way to isolate the impact of a program from other changes that might be occurring simultaneously.
How Does Difference-in-Differences Work?
The core idea behind DiD is its name: it calculates the "difference in differences." This involves two main comparisons:
- Before vs. After: It compares the outcome for each group (treatment and control) before and after the intervention.
- Treatment vs. Control: It compares the change over time experienced by the treatment group with the change over time experienced by the control group.
By combining these two comparisons, DiD effectively removes two potential sources of bias:
- Baseline differences: Any pre-existing differences between the treatment and control groups.
- Time trends: Any general changes over time that would have affected both groups regardless of the intervention.
The approach is based on a combination of before-after and treatment-control group comparisons. It assumes that in the absence of the intervention, the control group's outcome trend would have mirrored the treatment group's trend (this is known as the parallel trends assumption).
The Four Data Points Needed
Implementing a basic DiD analysis requires data on the outcome variable for both groups at two points in time:
| Before Intervention | After Intervention | |
|---|---|---|
| Treatment Group | Outcome (Treatment, Before) | Outcome (Treatment, After) |
| Control Group | Outcome (Control, Before) | Outcome (Control, After) |
Calculating the Impact
The estimated impact of the intervention is calculated as follows:
Impact = [(Outcome (Treatment, After) - Outcome (Treatment, Before))] - [(Outcome (Control, After) - Outcome (Control, Before))]
In simpler terms:
Impact = (Change in Treatment Group) - (Change in Control Group)
This calculation reveals the extent to which the outcome in the treatment group changed differently from the outcome in the control group, attributing this differential change to the intervention.
Applications of DiD
DiD has proven to be highly versatile and has been widely used in economics, public policy, health research, management and other fields. Examples include evaluating the impact of:
- Minimum wage laws on employment rates.
- Healthcare reforms on health outcomes.
- Educational programs on student performance.
- Marketing campaigns on sales.
Key Considerations and Assumptions
While powerful, DiD relies on critical assumptions, most notably the Parallel Trends assumption. If the control group's trend would have naturally diverged from the treatment group's trend even without the intervention, the DiD estimate can be biased. Researchers use various methods to test and address this assumption.
In essence, Impact Evaluation Difference-in-Differences leverages observational data to simulate a counterfactual scenario (what would have happened to the treatment group without the intervention) by observing the changes in a comparable control group over the same period.
