Policy Impact Evaluation Research Initiative (PIERI)

Providing training, support and mentoring for local researchers to conduct rigorous impact evaluations of social policies/programs on targeted populations in developing countries.

The resulting assessments provide scientifically-sound empirical evidence to inform program managers, donors, policymakers and civil society on program effectiveness, budget use and ways to improve the design of future interventions.

Based on either experimental or non-experimental approaches, PEP impact evaluation projects produce evidence that is either meant or can be used to:  

  • inform decision-makers, donors and taxpayers on the realization of expected benefits
  • help improve and assist in program design and implementation
  • foster accountability of implementation processes
  • generate political support for continuation or expansion of programs, both within and beyond national boundaries (public good value)

In addition, through the global infrastructure of PEP, the PIERI program also encourages comparative analyses between researchers from around the world, and especially in developing countries of Africa, Asia and Latin America, to compare methods, tools, findings and experience of impact evaluation projects.

Find out all you need to know about the PEP impact evaluation program and initiatives (including a detailed description of themes, methodology, policy impact, etc.) through this PEP-PIERI presentation document

NEW! interactive training material:

"Impact Evaluation Using Stata"

Two approaches:  Experimental & Non-experimental

In both cases, a good institutional knowledge of the program is required:
  • On eligibility rules and the target population
  • On the objectives of the intervention and its potential unintended effects
  • On outcomes that may potentially be affected by the program
  • On the intervention calendar and the timing of effects (short-term, medium-term, long-term)
Experimental approach - using randomized controlled trials (RCTs):
  • Identify eligible population (e.g. unemployed youth, school-age children, etc.) and randomly assign them to treatment & control groups BEFORE the intervention
  • Collect baseline data on characteristics and relevant behaviour of households/individuals in both groups (surveys of households, communities, schools, unemployment offices, health posts, etc.)
  • Conduct intervention for the treatment group ONLY
  • Follow-up: collect the (same) data again for both groups
  • Contrast changes in outcomes between treatment and control groups.
Non-experimental approach:
  • Usually helpful if the intervention has already started
  • Problem of the counterfactual: What would have happened to the population without the intervention?
  • Solution : Compare with situation of a similar population that has not experienced the intervention:
    • Individuals just outside of the eligibility cutoff - Regression discontinuity
    • Individuals with similar observed characteristics - Propensity score matching
  • If baseline (prior to the intervention) data is available, comparison of changes in outcomes between beneficiaries and counterfactual populations.

PIERI scientific support team

The PMMA program and related research/support activities are managed by the following team:

With support from a group of PEP resource persons, composed of leading international experts in the field of microeconomic analysis.

 

Partners

Funded by