Targeting the poor is an integral part of social program design in low-income countries. Geographical targeting gives priority to areas with high concentrations of poverty. However, traditional data sources, such as household surveys often lack the spatial resolution to estimate poverty at a highly disaggregated level and are costly to collect on a regular basis. We leverage the proliferation of big data obtained from mobile devices and satellites to generate poverty measures at a highly disaggregated spatial level in a new country context. Previous applications rely on computationally intensive methods to extract information from raw cellphone transaction data. We show how similar levels of prediction accuracy can be achieved by using key performance indicators (KPIs) that Mobile Network Operators produce regularly as part of their operations, lowering the financial and data access barriers to estimate and update prediction models. This can help to facilitate the use of these data for policy in low- and middle-income contexts.

It has become common for governments and practitioners to measure mobility using data from smartphones, especially during the COVID-19 pandemic. Yet in countries where few people have smartphones, or use mobile internet, the movement of smartphones may not be a good indicator of the movement of the population. This paper develops a framework for approaching potential bias that can arise when measuring mobility with smartphones. Using mobile phone operator records in Uganda, we compare the mobility of smartphones and the basic and feature phones that are more common. Smartphones have different travel patterns, and decrease mobility substantially more in response to a COVID-19 lockdown. This suggests caution when interpreting smartphone mobility estimates in contexts with low adoption.

No abstract available.

UPDATED---June 1, 2020. Road traffic crashes (RTCs) are the primary cause of death among children and young adults. Yet data on RTCs is incomplete, hindering effective road safety policymaking in many developing countries where mortality is purportedly highest. We web-scrape 850,000 tweets to create crash data and develop a machine learning algorithm to geolocate RTCs. Our algorithm is nearly twice as precise as a standard geoparsing algorithm in identifying the set of locations that include the crash location. Above and beyond, it identifies the unique location of a crash from the set of possible locations in a majority of cases. We dispatch a set of motorcycle drivers to the site of the presumed crash in real time to verify the validity of the crowdsourced data and document the performance of the algorithm. The study can be used as a proof of concept for countries interested to improve RTC data at low cost through a machine learning approach and substantially increase the data available to analyze RTCs and prioritize road safety policies.