Measuring Poverty and Vulnerability
Teaching material for a development economics course introducing students to poverty measurement concepts and hands-on data analysis in R. Designed as an early-semester module that builds foundational skills for subsequent topics.
Overview
- How are poverty measurements used in policy making?
- Why does the choice of poverty indicator matter?
- Household survey structure and data sources
- R/Stata: weighting, merging, summary statistics
- Foundational building blocks for further data analytics
Learning Goals
- Understand the implications of choosing a poverty line or indicator for policy — including how preferences, values, costs/benefits, and politics shape those choices.
- Gain exposure to sources of poverty measurement such as household surveys.
- Learn basic techniques for working with household surveys: weights, merging datasets, and computing summary statistics by group.
Case Studies & Activities
Are social safety nets targeted correctly? Discussion activity based on Meyer and Sullivan (2012). Students examine demographic differences across poverty groups and debate which measures should be used and why.
Discussion ActivityHands-on coding exercise using the Malawi LSMS+ Survey from the World Bank. Students apply weighting, merging, and grouped summary statistics to compute a basic poverty rate.
Coding ExerciseReadings
De Janvry, A., & Sadoulet, E. (2022). Development Economics: Theory and Practice. 2nd Edition. Routledge.
Chapter 5 covers various measures of poverty and vulnerability and discusses issues of bias. Good background reading for students.
Meyer, B. D., & Sullivan, J. X. (2012). Identifying the Disadvantaged: Official Poverty, Consumption Poverty, and the New Supplemental Poverty Measure. Journal of Economic Perspectives, 26(3), 111–136.
Paper used for Case Study I.
Assignment
This assignment replicates the in-class exercise with a few modifications. Students practice the same R commands and make adjustments to improve the poverty rate estimates — moving from a household-level to a per-capita measure. Homework is included in the slide data package below.
Download Materials
Instructor Notes: Lecture 1 Outline
- 1a — Bigger picture questions concerning poverty that economists try to understand.
- 1b — Anchoring slide for today's class agenda.
- 2 — How are poverty measurements used in policy making? When discussing tracking, ask students what they can find about their own region and compare with others. Why can two students find different poverty numbers online? When discussing targeting, have examples from various countries as well as a localized example.
- 3 — Importance of paying attention to definition of indicators. Politicians use indicators all the time to argue.
- 4a — Do different ways of calculating poverty matter? Consumption vs. income measurements.
- 4b — Explain the three poverty measurements from Meyer and Sullivan.
- 4c — Case Study & Discussion I: Have students spend 5 minutes looking at the tables and note what demographic differences they see between the two poverty groups. Then discuss as a class.
- 4d — Group Discussion: Which measures should we choose and why?
- 5 — Summary slides.
Instructor Notes: Lecture 2 Outline
- 1a — Recap of what was discussed in the previous lecture on poverty measurements.
- 1b — Anchoring slide for today's class agenda.
- 2a — What are household surveys? Show a video from the Cambodia LSMS+ Survey.
- 2b — Show topics covered in the LSMS+ survey. Spend 5 minutes asking students what questions interest them and why.
- 3 — Case Study & Discussion II: Open the R-script and data (Case Study II Malawi Simple Poverty Rates.R). Give students time to set up. Walk through the code with the following slides.
- 4 — Go over the three commands: summarize, merge, grouped_stats, and weights.
- 5 — Discuss why the poverty rate from the code seems too low. What adjustment can one make? Homework will try a small adjustment (per capita instead of household).