Позитивные изменения. Том 2, № 2 (2022). Positive changes. Volume 2, Issue 2 (2022)

The microfinance scheme is launched, and a year later rice yields have increased to 1,100 kg per hectare. (Point A in Figure 2). If you try to evaluate impact using a before-and-after comparison, you have to use the pre-intervention outcome as a counterfactual. Applying the basic impact evaluation formula, you would conclude that the scheme had increased rice yields by 100 kg per hectare. (A-B)

However, imagine that rainfall was normal during the year before the scheme was launched, but a drought occurred in the year the program started. Because of the drought, the average yield without the microloan scheme would have been lower than В: say, at level D. In that case, as the before-and-after comparison assumes, the true impact of the program would have been A-D, which is larger than 100 kg.

Rainfall was one of many external factors that could have influenced the outcome of interest (rice yield) of the scheme over time. Similarly, many of the outcomes that development programs aim to improve, such as income, productivity, health or education, are affected by multiple factors over time. For this reason, the preintervention outcome is almost never a good estimate of the counterfactual.

Comparing those who chose to enroll to those who chose not to enroll ("with-and-without") constitutes another risky approach to impact evaluation. The comparison group, which independently chose the program, will provide another «counterfeit» counterfactual estimate. The choice occurs when participation in the program is based on the preferences or decisions of each participant. This preference is a separate factor on which the outcome of participation may depend. It is impossible to talk about the comparability of those who enrolled with those who did not enroll under such conditions.

The HISP pilot evaluation consultants, in their attempts to mathematically understand the results, made both the first and the second mistake in evaluating the counterfactual, but the program organizers, realizing the risk of bias, decided to find methods for a more accurate evaluation.

RANDOMIZED ASSIGNMENT METHOD

This method is similar to running a lottery that decides who is enrolled in the program at a given time and who is not. The method is also known as randomized controlled trials (RCTs). Not only does it give the project team fair and transparent rules for assigning limited resources to equally eligible population clusters, but it also provides a reliable method for evaluating program impact.

"Randomness" applies to a large population cluster having a homogeneous set of qualities. In order to decide who will be given access to the program and who will not, we can also generate a basis for a reliable counterfactual evaluation.

In a randomized allocation, each eligible unit (e.g., individual, household, business, school, hospital, or community) has the same probability of being selected for the program. When there is excess demand for the program, randomized assignment is considered transparent and fair for all participants in the process.

Insert 1 provides examples of the use of randomized distribution in practice.