A Topic Modeling Approach to Classifying Open Street Map Health Clinics and Schools in Sub-Saharan Africa

In the wake of the COVID-19 pandemic, the World Bank's 2020 Global Economic Prospects forecasts a baseline global GDP contraction of 5.2 percent, making it the deepest global recession in decades. Between 71 to 100 million people are expected to be pushed into extreme poverty, almost half of them in South Asia and more than a third in Sub-Saharan Africa. As a result, since March 2020 over 215 countries and territories have implemented 1,414 social protection measures to respond to the pandemic and ensuing economic crisis. Social assistance programs account for 62 percent of all social protection response measures, half of them being cash-based transfers of some sort. This major shock has revealed the many challenges governments face when attempting to quickly respond to crises in order to protect the poor and vulnerable. Providing timely assistance and support to those households most in need can increase their resilience and reduce the negative impacts of the shock on their short and medium-term well-being. Nonetheless, the lack of readily available and up-to-date socioeconomic data necessary to prioritize shock-responsive social protection measures is an important binding constraint for many governments in developing countries. This paper presents a portion of our work on a larger project with the World Bank to identify the most vulnerable populations in these countries. Having timely access to such information, particularly in data-deprived contexts, can improve the capacity of governments to design and operationalize better and more shock-responsive social protection measures.