India Livelihoods-Based Study Wins FII Data Challenge Grand Prize
InterMedia is pleased to announce the winners of the 2014 FII Open Data Analysis Challenge!
The winning submissions were chosen from among dozens sent in by participants in 13 countries across Africa, Asia, North America and Europe. Participants hailed from academia, global development, government and the private sector, but their common denominator was an evident passion for using data as a tool to advance financial inclusion.
We sincerely appreciate the great amount of effort that went into producing the Challenge submissions. InterMedia would like to thank Dr. Jonathan Morduch and Timothy Ogden of New York University’s Financial Access Initiative for judging this year’s Challenge.
Challenge Grand Prize Winners: Rossi Abi-Rafeh, Joshua Bernstein, Chandni Raja and Ella Spencer
This participant group, all graduates of the London School of Economics, analyzed the level of financial exclusion of Indians through the lens of livelihood groups, identified in this paper as: farmers, farm workers, manual laborers, shop/business owners, service workers and professionals. Their analysis focused on three identified dimensions of exclusion: the person’s level of literacy, under the assumption that illiterate Indians have a hard time accessing formal financial services; the type of financial institutions the person typically accesses (formal versus informal); and the cost the person faces in time and money to access financial services in relation to their income levels.
Using multiple correspondence analysis, the authors identified farm workers and manual laborers as the most financially excluded groups. The authors then investigated potential determinants of financial exclusion and potential policy responses to improve the chances of these groups joining the financially included. The potential determinants highlighted were: trust in financial institutions; access to mobile phones; ownership of TVs (by extension, access to advertising); and distance to a formal financial institution (i.e. a bank).
After considering current financial inclusion policies in India and areas where there may be gaps to fill in relation to what their analysis showed, the authors made a number of recommendations, including:
- Offer more financial products and services that are tailored to the needs of excluded groups, notably, through repayment plans that reflect the inconsistent or lumpy income streams of many members of these groups. For example, matching loan repayment schedules to the times of the year when the borrower typically would have a predictable income stream, such as at harvest times.
- Create deposit accounts to promote savings by allowing withdrawals only during times of unemployment, not during times of employment.
- Tap into data from mobile money transactions to better understand different groups’ financial habits to be able to design financial products which match their cash flow patterns.
Group member Chandni Raja said she and her co-authors would like to continue their exploration of financial inclusion dynamics, perhaps by integrating other data sources beyond FII to see how they compare and whether they show different trends.
They’re also hoping that mobile money usage increases in India in the near future so that there is more data to work with. “It’s a challenge with the small sample sizes we have now,” she said. The 2013 FII survey of India showed that less than 1 percent of India’s adults (15+) have ever used mobile money.
Challenge Runner-Up: Khyati Malik
Submission: “Mobile Money in Developing Countries: Common Factors”
In this paper, Khyati (another LSE grad) conducted a cross-analysis of all of the FII country datasets to try to identify the dynamics of mobile money adoption, in the context of the following key question:
Is mobile money being more widely adopted by segments of the population which do not have adequate banking services, or is it replacing conventional banking services as a better, cheaper and safer option?
While each country has specific market characteristics that are likely to influence mobile money adoption, Khyati combed the data to see if there were adoption variables which cut across countries. Using a logistic regression model, which indicates correlations between a number of variables and mobile money adoption, she concluded that literacy, numeracy, economic status and a person’s availability or willingness to take advantage of banking services “are non-country specific factors which influence the adoption of mobile money.”
The FII team is now gearing up for the 2015 Data Analysis Challenge, which is likely to be launched in mid-spring, after all the datasets from the 2014 FII national surveys are ready for public release. These will include all eight FII countries: Bangladesh, India, Indonesia, Pakistan, Kenya, Nigeria, Tanzania and Uganda. Watch for an announcement in early 2015.