Proposals for incorporating information on the quality of human, social, and environmental conditions in more authentic and comprehensive versions of the Gross National Product (GNP) or Gross Domestic Product (GDP) date back to the foundations of econometrics. Typically treated as external to markets, these domains have lately been objects of renewed interest. Calls for accountability and transparency have expanded to include the now topical but previously neglected economic implications of human, social, and natural capital. Clear advantages for the measurement and management of these forms of capital can be drawn from econometric criteria for identifiable models of structurally invariant relationships. The United Nation's Millennium Development Goals (MDG) provide an example application of a probabilistic model for measurement used to evaluate data quality, reduce data volume with no loss of information, estimate linear units of comparison with known uncertainties, express measures from different sets of indicators in a common metric, and frame a meaningful interpretive context. Data on 22 MDG indicators from 64 countries are scored and analyzed. Model fit was reasonable, the item hierarchy tells a meaningful story of structural invariance in economic development, and Cronbach's alpha was 0.93. The measures estimated in this study correlated over 0.90 with independently produced measures of per-capita GDP and life satisfaction. These results provide a positive demonstration of relevant methods applicable in the context of today's Sustainable Development Goals 2030 Agenda.