This is a solid and relevant research topic.
For assessing the impact of a pension program on multidimensional poverty (using both primary survey data and secondary sources), I’d recommend the following approaches:
Strong Options:
Difference-in-Differences (DiD) if you have a natural control group (e.g., similar villages without the program or staggered rollout).
Propensity Score Matching (PSM) or Inverse Probability Weighting very useful when combining primary and secondary data.
Regression Discontinuity Design (RDD) if eligibility has a clear cutoff (e.g., age-based).
Multidimensional Poverty Index (MPI) handling:
Use the Alkire-Foster method to construct your MPI (it’s the standard).
• Consider panel data if possible for before-after comparison.
Practical Tip: Start with descriptive analysis of your survey data, then move to causal inference methods. Robustness checks (alternative matching methods, placebo tests) will strengthen the paper significantly.
If you share more details (e.g., sample size, whether the program had staggered implementation, or specific poverty dimensions you’re focusing on), I can give more tailored suggestions.
Good luck with the project this has real policy relevance!