We have been using this index since 2013, thanks to the work of the Grameen Foundation. Our first endeavor was in collaboration with LeapFrog Labs to design a simple process for BIMA Mobile to gain insights about its mobile MicroInsurance clients. This methodology is especially important for programs that use low-touch models for distributing and transacting with clients, such as mobile phone platforms, because they often have limited contact with end-users and gather mostly transactional rather than demographic or socio-economic information (see an example of how CGAP used this data here). Since we began using PPI, a number of impact investors, including ACUMEN, have also adopted the measure. A new benefit of PPI, as a result, is that a company can benchmark its outreach to low-income clients vis-à-vis many others in the social impact space. It is a simple, affordable tool.
Reporting from Zambia this month, our Associate, Danielle Sobol interviewed users of mobile financial services, who were also surveyed using PPI as well as other data. One indicator used in Zambia’s PPI score is a household’s usage of electricity (suggesting higher financial means) vs. coal and other primitive fuel (suggesting more modest means) for cooking. During qualitative interviews, we learned that many of the end-users were switching from electricity to coal in recent weeks not because of their income but power shortages in the country. One small business man that sold coal in the markets told Danielle that his business had been “booming” ever since the shortages began. Through these qualitative interviews we realized that our PPI surveys may have been understating their incomes.
This brings up issues to consider when applying the PPI. A quantitative closed end measure of poverty and income can be extremely useful to gather quick insights from large groups of people about whether a product is reaching low-income households. But context matters and shouldn’t be forgotten. Talk to people, read the papers, and think through the results.