Track Changes in Poverty Rates

Pro-poor organizations value knowledge of changes in the poverty level of their beneficiaries over time. Assessing changes in poverty levels can guide institutional policy to improve targeting, deepen poverty outreach, and determine appropriate interventions for different client segments.

In order to track changes in poverty levels over time, organizations must administer the PPI to either the same group of households or two equally representative samples of households at regular intervals. If an individual household’s poverty likelihood changes, the organization can infer that the household’s economic standing has changed.

For example, say an organization in Bolivia administers the PPI today to a particular household and determines that it has a 70% likelihood of living below the country’s National Poverty Line. Assume that the organization administers the PPI again to the same household a year later, and determines that the household now has a 50% likelihood of living below the same poverty line. The organization can then infer that the household’s poverty level has reduced over this period. Similarly, it can assess changes in poverty rates of a group of client households as well.

Please keep in mind that PPI data on its own does not help understand causality. Any measured reduction in poverty levels using the PPI cannot automatically be inferred to be a result of services offered by the organization. The PPI can be used in an impact evaluation, such as a randomized control trial, and is helping lead the way for innovative “lean data” techniques.

Related Content

  1. Poverty Movement Insights with Multi-year PPI Data - This report uses PPI data from two MFIs in the Philippines to explore which types of poverty movement analyses can be carried out with multiple years of PPI data and what implications can be drawn from such analyses.
  2. Grameen Koota - This case study describes how Grameen Koota, a leading socially-focused MFI in India, is using the PPI to measure and track changes in poverty levels of its clients over time.
  3. VisionFund - This blog describes how VisionFund, one of the largest users of the PPI, actively tracks changes over time with several MFIs in its portfolio. VisionFund has instituted rigorous quality standards around data collection, auditing, and analysis to facilitate ease and accuracy of tracking changes in poverty levels over time.