Global poverty estimation using private and public sector big data sources.

Household surveys give a precise estimate of poverty; however, surveys are costly and are fielded infrequently. We demonstrate the importance of jointly using multiple public and private sector data sources to estimate levels and changes in wealth for a large set of countries. We train models using 63,854 survey cluster locations across 59 countries, relying on data from satellites, Facebook Marketing information, and OpenStreetMaps.

The evolution of the COVID-19 pandemic through the lens of google searches.

Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Using data from 221 countries and territories, we demonstrate the capacity of Google search data to anticipate reported COVID-19 cases and understand how containment policies are associated with changes in socioeconomic indicators. First, search interest in COVID-specific symptoms such as "loss of smell" strongly correlated with cases initially, but the association diminished as COVID-19 evolved; general terms such as "COVID symptoms" remained strongly associated with cases.

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development.

The impact of aid on health outcomes in Uganda.

The health sector has attracted significant foreign aid; however, evidence on the effectiveness of this support is mixed. This paper combines household panel data with geographically referenced subnational foreign aid data to investigate the contribution of health aid to health outcomes in Uganda. Using a difference-in-differences approach, we find that aid had a strong effect on reducing the productivity burden of disease indicated by days of productivity lost due to illness but was less effective in reducing disease prevalence.

Taking the health aid debate to the subnational level: the impact and allocation of foreign health aid in Malawi.

Objective: Cross-national studies provide inconclusive results as to the effectiveness of foreign health aid. We highlight a novel application of using subnational data to evaluate aid impacts, using Malawi as a case study.

Design: We employ two rounds of nationally representative household surveys (2004/2005 and 2010/2011) and geo-referenced foreign aid data.