On June 12-13, the Aspen Network of Development Entrepreneurs (ANDE) hosted the 2018 "Metrics from the Ground Up" Conference in Washington, D.C. The event convened representatives from&nbsp;institutions supporting small and growing businesses in the developing world&nbsp;for two days of presentations and panels around emerging practices in measurement, practical strategies for adoption, and knowledge-sharing.&nbsp;<a href="/people/dean-karlan">Dean Karlan</a>, <a href="/people/elizabeth-koshy">Elizabeth Koshy</a>, and <a href="/people/rachel-wells">Rachel Wells</a> (PPI) joined these conversations and presented IPA's contribution to the measurement community in the following sessions:&nbsp;June 12&nbsp;<strong>Right-Fit Evidence for the Social Sector</strong>,&nbsp;<em>Dean Karlan, Innovations for Poverty Action&nbsp;</em>Social sector organizations and funders are paying more attention to the potential for data and evidence to support program management, learning, and improvement. But data collection that isn’t done well can waste money without improving decision-making. This discussion presented a set of principles organizations could use to identify the right time to engage in impact evaluation and build systems that provide information to support learning and improvement.&nbsp;&nbsp;<strong>Using Rigorous Evidence to Achieve Impact in SGB Development</strong>,&nbsp;<em>Elizabeth Koshy, Innovations for Poverty Action</em>&nbsp;Following a decade of fruitful collaborations between the worlds of research and practice, a promising body of rigorous evidence has emerged identifying effective solutions to some of the most pressing challenges small and growing businesses (SGBs) face. The sector is at a crucial juncture in which stronger partnerships between decision-makers and academics will be needed to build on the existing knowledge and enable the co-creation of a more intentional, cohesive, and actionable learning agenda for SGB development. Focusing on IPA’s extensive research in this sector, this session provided an overview of the state of the evidence and suggested new avenues for a consolidation research agenda.&nbsp;June 13&nbsp;<strong>Statistical Learning for Easy Custom Poverty Measurement,</strong>&nbsp;<em>Rachel Wells, Innovations for Poverty Action&nbsp;</em>The Poverty Probability Index (PPI) uses statistical learning to turn long consumption surveys into 10 simple questions to easily determine household poverty rates, but the methodology can also be adjusted or used to predict the outcomes of other variables that are complicated to measure. This session provided a high-level overview of the statistical learning model that the Poverty Probability Index (PPI) is built on, and how this methodology is being used to create customized tools to help organizations meet their specific poverty measurement needs beyond the typical PPI.<h2>Address</h2>Aspen Institute<h2>City</h2>Washington, D.C.<h2>Administrative Area </h2>District of Columbia<h2>Country</h2>United States