Using big data to analyse WFP’s digital cash programme in Lebanon

by Tobias Flaemig, Susanna Sandstrom, Oscar Maria Caccavale, Jean-Martin Bauer, Arif Husain, Arvid Halma, and Jorn Poldermans.

Humanitarian programmes have recently started to shift from delivering in-kind aid – such as food, clothes and tents – to cash-based assistance. According to the New York Times, before 2015, 99% of the world’s humanitarian assistance came in the form of goods. By last year, that number had decreased to 94%.

As of 2016, just over a quarter of the United Nations World Food Programme’s (WFP) assistance worldwide is cash-based (WFP, 2016a and 2016b). This includes both e-vouchers and cash transfers through financial service providers.

This article explores how the large quantities of data generated by transferring cash digitally could help transform humanitarian programme design, implementation and monitoring.

Both public and private sectors have used ‘big data’ analytics to great effect, for example, to detect fraud in the financial and insurance sectors. Big data has only seen limited use in the humanitarian sector however. To help change this, WFP and the Centre for Innovation (CFI) at Leiden University collaborated on researching how to support food security programming in Lebanon, using data derived from WFP’s cash-based transfer programme there.

The dataset: 6 million records from WFP’s e-card programme in Lebanon

Launched in 2012, WFP’s e-voucher programme in Lebanon is currently the agency’s largest cash-based transfer intervention; between US$18 and US$22 million is uploaded to e-cards every month.

As of early 2017, the programme was assisting 700,000 Syrian refugees, 54,000 Lebanese and 23,000 Palestinian refugees who can use e-cards to purchase food in some 500 shops located across the country. Each participating shop is equipped with at least one electronic payment device that clears transactions with the Banque Libano-Française.

Our study analysed the records of more than 6 million individual e-card transactions over an 18-month period from 2014 to 2015. Each transaction generated a record, which is geo-located, time-stamped and includes the monetary value of the transaction, as well as the reference of the device and shop where it was processed.

The raw dataset included some personally identifiable information so appropriate data protection precautions were taken. These included a non-disclosure agreement between WFP and CFI, removing personally identifiable information, ‘blurring’ locations by reducing the number of digits the GPS codes and secure data transfers.

Read the full report on ODI - HPN.