Projects

Mobile Exclusion Modelling

2018 - 2019
Prepared For

FinMark Trust

FinMark Trust’s (FMT) partner Insight2Impact (i2i) sought to assess if short message service (SMS) survey data can be used to collect financial inclusion data in developing countries. Mathematica developed a predictive model to estimate financial inclusion (access to banking systems) in eight countries in Africa and Asia.
These countries are: Tanzania, Uganda, Nigeria, Kenya, Pakistan, Bangladesh, India, and Indonesia. The focus of the study was to test if we could accurately estimate financial inclusion using data from mobile SMS surveys, which are much less expensive than more traditional face-to-face surveys but also systematically under-represent certain target groups, including the rural poor, women, and the elderly. In order to adjust our estimates to account for this non-representative sampling, we used a technique called multilevel regression with post-stratification (MRP, or “Mr. P”). MRP is widely used across the social sciences to adjust non-representative data, and has shown to provide more efficient estimates than traditional (frequentist) post-stratification in cases with many small post-stratification cells. We found MRP to perform well with the SMS data. We also explored novel approaches of combining data from multiple survey modalities, including computer-assisted telephone interview (CATI) data and face-to-face data, with the larger SMS survey data.

Related Staff

Sarah Hughes

Sarah Hughes

Senior Fellow

View Bio Page
Jonathan Gellar

Jonathan Gellar

Senior Statistician

View Bio Page

See Clearly. Act Quickly.

From local to global challenges in health, human services, and international development, we’re here to improve public well-being and make progress together. Learn more about becoming a Mathematica client or partner.

Work With Us