Beyond baseline and follow-up: The case for more T in experiments

The vast majority of randomized experiments in economics rely on a single baseline and single follow-up survey. While such a design is suitable for study of highly autocorrelated and relatively precisely measured outcomes in the health and education domains, it is unlikely to be optimal for measuring noisy and relatively less autocorrelated outcomes such as business profits, and household incomes and expenditures. Taking multiple measurements of such outcomes at relatively short intervals allows one to average out noise, increasing power. When the outcomes have low autocorrelation and budget is limited, it can make sense to do no baseline at all. Moreover, I show how for such outcomes, more power can be achieved with multiple follow-ups than allocating the same total sample size over a single follow-up and baseline. I also highlight the large gains in power from ANCOVA analysis rather than difference-in-differences analysis when autocorrelations are low.