An analytical framework to nowcast well-being using mobile phone data

An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory

Essays on Strategies for Increasing Repayment Rates of Digital Microloans

Access to credit can act as a highly effective tool for poverty reduction and economic growth. The ability to borrow increases the propensity of low-income people to start and maintain businesses, educate their children and withstand financial shocks. These factors, in turn, can help them to move out of poverty and lead to more sustainable economic development. However, traditional financial institutions have inherent limitations that have impeded their ability to serve the poor. Digital lenders are able to leverage the widespread adoption of mobile phones and mobile money to extend credit quickly and conveniently to more people, especially in developing countries. However, due to a lack of credit bureaus and available financial histories of borrowers, digital lenders frequently need to amass vast amounts of data in order to screen borrowers and experiment to find the appropriate loan amount by gradually increasing credit limits based on past repayment. This can lead to high user default rates and over-indebtedness. The lack of collateral during loan applications also means that digital lenders have limited mechanisms for enforcing repayment of loans. Both of these challenges threaten to limit further adoption of digital credit. Through three experimental studies conducted with an airtime lender, I explore theoretical and empirical mechanisms for reducing default rates of digital loans. In the first study, I demonstrate that limited mobile phone data contain enough signals for creating effective credit assessment methods that minimize privacy risks to borrowers. In the second study, I find that increasing credit limits negatively impacts repayments and future borrowing, and offer recommendations for increasing credit limits while minimizing the drawbacks. In the final study, I draw on theories from psychology and consumer behavior to develop vivid repayment reminders. This study found that vivid reminders had limited effectiveness for increasing loan repayment and reducing loan duration. Taken together, these three studies propose new avenues for digital lenders to reduce default rates. The hope of this dissertation is that these proposed methods would lead to a reduction in interest rates, that would ultimately benefit the borrowers

Annex 19 : predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka

National census information is time-consuming and expensive to collect. This research helps determine whether mobile phone data can provide a reliable, cheap proxy for census data within Sri Lanka, especially where post-conflict regions need more frequent data collection. Study findings suggest that socio-economic levels (SEL) can affect call detail records (CDR) data in a post-conflict, Sri Lankan setting. Analysis demonstrates the potential for telecom data to predict census features. The results correspond to assumptions about the population under study, which includes a high percentage of vulnerable, highly mobile groups displaced due to conflict

Mapping big data solutions for the sustainable development goals : draft

Annex I for IDL-56905This report aims to capture the applications of big data sources to measure sustainable development goals and targets by reviewing relevant literature and reports. It outlines current concerns with uses of big data (privacy, marginalization, competition, etc.) and provides a discussion of the interplay of these issues. Developing economies in particular have much lower levels of ‘datafication’ than developed economies, which means some of the most interesting and relevant data exists amongst the private sector. The state of the art in innovative development-focused applications of new data sources is still very much in its embryonic stages