A basic income pilot program is severely limited.

Determining the impact of a basic income requires comparing outcomes, both with the behaviour and attitudes of people beforehand (before the ‘treatment’ began) and with ‘control groups’, otherwise similar, who do not receive the ‘treatment’ or come under the policy. This is to ensure that outcomes are the result of the basic income and not a change coming from the outside.

While randomised control trials (RCTs) are currently in vogue, they have severe limitations when it comes to evaluating basic income. The principle behind RCTs, as the name implies, is that those receiving the ‘treatment’ should be selected ‘randomly’ from a wider population, and the control group should also be selected ‘randomly’. This would typically mean listing households in an area and drawing a sample, so that one family (or individual within a family) would receive the basic income while the family next door would not.

Apart from the risk of generating inter-community and inter-familial tensions, one likely outcome would be some sharing of the basic income between households and between family members, on a non-random basis. This would negate the universality of the basic income, dilute its impact and make it hard to evaluate outcomes. In the Indian pilot in Madhya Pradesh, 18 similar villages were selected randomly from a wide area: in eight of these, everyone received the basic income and in the remaining 12 villages no one did. It was thus possible to explore a range of effects, both at micro-level and community level, including attitudinal and behavioural changes.