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A Lean Data How-To Guide Understanding Gender Impact

Closing the gender gap, whether through the work of corporations, impact investors, social enterprises, governments, donors or not-for-profits, simply cannot happen without useful, practical, and accurate tools for measuring gender-specific impacts.

This report grew out of a multiyear partnership between Unilever and Acumen (back before 60 Decibels spun out) focused on developing entrepreneurial solutions to social challenges that each organization is tackling. In developing this report, we’ve also had the input of additional experienced advisors along the way, including the International Center for Research on Women (ICRW) and UN Women. This project builds on work done by Acumen and ICRW in 2015, shared in the report titled, Women and Social Enterprises: How Gender Integration Can Boost Entrepreneurial Solutions to Poverty. The report underscored the social and economic value intrinsic in incorporating a gender lens as a social enterprise and investor.

Read the Phase 1 report here.

This document reflects our first phase of work and was released in conjunction with the Gender Smart Investing Summit in November 2018. It is a preview of the second report (below). This report details the approach and methodology for the Gender Lean Data Tool and provides preliminary insights from two out of five projects that have used the tool: Inclusive Distribution Entrepreneurship Model “Shakti” in Colombia, a Unilever program, and LabourNet, an Acumen investee. The rest of the analysis follows in the next report.

Read the Phase 2 report here.

This document captures eight months of work measuring gender impacts across five projects in four countries. The subsequent sections of this report detail the approach, surveys and methodology for the Lean Data Gender Toolkit and provide both project-level analysis and consolidated insights across all five projects. This work represents the start of what we hope becomes a broader conversation around actionable gender impact measurement and lays bare some of the challenges and lessons learned from our experience testing different question modules and data collection methods.

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