This dissertation shows how mobile phone data can be used in order to study the consequences of short term population movement. All three chapters use mobile phone data from Senegal for around 9.5 million unique SIM cards in 2013. This type of new data allows us to track population movement at a detailed spatial and temporal scale for an entire country. Chapter 1 studies the negative externality of human movement through its contribution to the spread of infectious diseases. Using the 15 billion mobile phone records, it quantifies the relationship between travel and spread of malaria and shows its implications for targeting policies in Senegal. Estimates indicate that an expected infected traveler entering a health facility's area causes reporting of 1.6 additional cases. Applying the results to policy simulations, the chapter shows that at the same cost, strategic targeting of travelers would result in over twice as many cases averted as compared to the next best strategy. Chapter 2 shows how even in the absence of this type of data, it can be possible to use other data sources to make predictions of short term movement. Putting together data available for the majority of low income countries and using Senegal as a case study, the chapter predicts short term movement within the country on a monthly basis. Focusing on two main drivers of movement, economic and social, almost 70% of the variation in short term movement can be explained. Studying the impact of population movement on spread of malaria in Senegal using the actual movement calculated from the mobile phone data and using the predicted short term movement demonstrates that the analyses provide comparable estimates. Chapter 3 explores potential bias in the use of these data arising from non-random error in the measurement of movement. The calls and text messages received from accounts that make calls en masse, which are exogenous in timing to population movement, are used to study whether bias exists from the likelihood of detecting an individual in a particular location. How the bias can be mitigated and its effect on several policy analyses are explored.
Milusheva, Svetoslava,
"Studying the Causes and Consequences of Population Mobility Using Mobile Phone Data"
(2017).
Economics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.7301/Z0707ZW4
