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Big Data

How big data helped us estimate the impact of an intervention with 0.8% take-up

Claudia Ruiz's picture

When asked if he would like to have dinner at a highly-regarded restaurant, Yogi Berra famously replied “Nobody goes there anymore. It’s too crowded”. This contradictory situation of very low take-up combined with large overall use is common with some financial products – for example, the response rate to direct mail credit card solicitations had fallen to 0.6 percent by 2012, yet lots of people have credit cards.

It is also a situation we recently found ourselves in when working on a financial education experiment in Mexico with the bank BBVA Bancomer. They worked with over 100,000 of their credit card clients, inviting the treatment group to attend their financial education program Adelante con tu futuro (Go ahead with your future). Over 1.2 million participants have taken this program between 2008 and 2016, yet only 0.8 percent of the clients in the treatment group attended the workshop. A second experiment which tested personalized financial coaching also had low take-up, with 6.8 percent of the treatment group actually receiving coaching.

In a new working paper (joint with Gabriel Lara Ibarra), we discuss how the richness of financial data on clients allows us to combine experimental and non-experimental methods to still estimate the impact of this program for those clients who do take up the program.

Traveling with ease, carrying disease? Using mobile phone data to reduce malaria: Guest post by Sveta Milusheva

This is the eighth in our series of job market posts this year
The Global Fund has disbursed nearly $28.4 billion in the last decade to reduce the disease burden from malaria, TB and HIV (Global Fund 2016). However, travelers can reverse the progress from campaigns that have decreased infectious disease prevalence (Cohen 2012 et al, Lu et al 2014), or can rapidly spread emerging diseases such as Ebola and Zika (Tam et al 2016, Bogoch et al 2016). While policymakers have largely targeted environmental drivers of malaria, this research provides evidence that human movement can play an important role in spreading disease in areas where incidence has been reduced.  Given that migration has numerous economic and social benefits, policymakers face important trade-offs in designing policies to reduce travel-linked malaria cases.  This paper provides a useful framework for identifying high-risk populations in order to reduce malaria incidence with minimal interference to movement patterns.

Blog links November 7: Impact Evaluation Existential Angst, Our Innate Grasp of Probability, big data, and More…

David McKenzie's picture

Big data, causal inference and ‘good data mining’?

Emanuela Galasso's picture
Last week I attended the International Development Conference at the Kennedy School of Government, joining a session on social protection. The conference is organized by KSG students (kudos to the students for their hard work in making it happen and interesting!), and has a format with no presentations and informal panel discussions with invited speakers.