The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world’s most pressing challenges. They come with a set of 232 indicators against which countries should regularly monitor their progress, ensuring that everyone is represented in up-to-date data that can be used to make decisions to improve people’s lives. However, existing data sources to measure progress on the SDGs are often outdated or lacking appropriate disaggregation. We evaluate the value that anonymous, publicly accessible advertising data from Facebook can provide in mapping socio-economic development in two low and middle income countries, the Philippines and India. Concretely, we show that audience estimates of how many Facebook users in a given location use particular device types, such as Android vs. iOS devices, or particular connection types, such as 2G vs. 4G, provide strong signals for modeling regional variation in the Wealth Index (WI), derived from the Demographic and Health Survey (DHS). We further show that, surprisingly, the predictive power of these digital connectivity features is roughly equal at both the high and low ends of the WI spectrum. Finally we show how such data can be used to create gender-disaggregated predictions, but that these predictions only appear plausible in contexts with gender equal Facebook usage, such as the Philippines, but not in contexts with large gender Facebook gaps, such as India.

中文翻译：

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使用社交媒体广告数据绘制社会经济指标

联合国可持续发展目标（SDGs）是关于世界最紧迫挑战的全球共识。它们带有一套232项指标，各国应根据这些指标定期监测其进展情况，以确保每个人都有最新数据，这些数据可用于制定改善人民生活的决策。但是，用于衡量可持续发展目标进展情况的现有数据源通常已经过时或缺乏适当的分类。我们评估了来自Facebook的匿名，可公开访问的广告数据在映射两个中低收入国家（菲律宾和印度）的社会经济发展中所提供的价值。具体而言，我们表明，受众群体对给定位置中有多少Facebook用户使用特定设备类型（例如Android与iOS设备或特定连接类型）的估计，例如2G与4G之类的数据，为根据人口与健康调查（DHS）得出的财富指数（WI）区域差异建模提供了强有力的信号。我们进一步表明，令人惊讶的是，这些数字连接功能的预测能力在WI频谱的高端和低端大致相等。最后，我们展示了如何使用这些数据来创建按性别分类的预测，但是这些预测仅在性别平等的Facebook使用率相同的情况下（例如菲律宾）才是合理的，而在性别差异很大的Facebook缺口（例如印度）下则不可行。这些数字连接功能的预测能力在WI频谱的高端和低端大致相等。最后，我们展示了如何使用这些数据来创建按性别分类的预测，但是这些预测仅在性别平等的Facebook使用率相同的情况下（例如菲律宾）才是合理的，而在性别差异很大的Facebook缺口（例如印度）下则不可行。这些数字连接功能的预测能力在WI频谱的高端和低端大致相等。最后，我们展示了如何使用这些数据来创建按性别分类的预测，但是这些预测仅在性别平等的Facebook使用率相同的情况下（例如菲律宾）才是合理的，而在性别差异很大的Facebook缺口（例如印度）下则不可行。