In spite of growing interest in socioeconomic differentials in health outcomes and access to health services, little has been written about methodologies for assessing the impact of equity-enhancing policies or programs. This paper describes three methodological challenges involved in designing a randomised trial with an equity outcome, and how these were met in a trial of alternative strategies to improving the uptake of benefits of a health insurance scheme among its poorest members. The Vimo SEWA trial is nested within a community-based insurance scheme in rural India. While conducting this trial, three methodological problems were encountered: (i) measuring poverty (or "wealth", or "socioeconomic status") (ii) assessing beneficiaries against an appropriate reference standard population and (iii) settling on an appropriate equity measure as an outcome indicator. These problems are likely to arise in any policy or program assessment that has an equity outcome. In the Vimo SEWA trial, the socioeconomic status of beneficiaries (claimants) is assessed relative to that of all scheme members living in same sub-district by applying a rapid assessment questionnaire--which reduces to an integrated index of socioeconomic status--to both a random sample of members in each sub-district, and to all claimants. The results are used to estimate the full distribution of socioeconomic status of members in each sub-district, with each member given a rank score between 0 and 100. Interpolation is used to estimate the rank scores of claimants relative to the membership base. The primary outcome measure for the trial is the mean socioeconomic rank score of claimants. In developing country settings, using an index of socioeconomic status is simpler than assessing household income or the value of household consumption. It is also relatively straightforward to compare the socioeconomic status of health program beneficiaries with a relevant reference population, although two independent surveys are required. Expressing relative wealth on a scale from zero to 100 is conceptually appealing, and the mean value of this rank score provides an equity-specific outcome measure readily integrated into the usual analytic framework for cluster-randomised trials.