Poverty and Inequality 101

75-90 Minutes

Workshop 1: Measurement Frameworks & Core Metrics

Target Audience: Development economists, policy analysts, program managers, researchers, and practitioners working on poverty reduction and inequality

Prerequisites: Basic economics and statistics knowledge helpful but not required

Materials Needed: Calculators, household survey data excerpts, policy documents

Learning Objectives

By the end of this workshop, participants will be able to:

Calculate and interpret different poverty measures (headcount, poverty gap, severity)
Understand the difference between absolute and relative poverty concepts
Compute inequality indices (Gini coefficient, income ratios, Theil index)
Analyze multidimensional poverty using composite indices
Evaluate the policy implications of different measurement approaches

Part 1: Conceptualizing Poverty - Beyond Income

20 minutes

Three Families, Different Poverty Realities

Family A (Rajesh, Construction Worker, Delhi):

Income: ₹18,000/month (₹6,000 per capita for family of 3)
Assets: Motorcycle, smartphone, small savings
Challenges: Irregular income, no health insurance, living in slum with poor sanitation
Status: Above income poverty line but vulnerable

Family B (Sunita, Farmer, Odisha):

Income: ₹12,000/month (₹2,000 per capita for family of 6)
Assets: 2 acres land, cattle, traditional house
Access: Children in school, primary health center nearby
Status: Below income poverty line but some assets and access

Family C (Priya, Domestic Worker, Mumbai):

Income: ₹15,000/month (₹3,750 per capita for family of 4)
Constraints: No assets, children not in school, poor health access
Gender issues: Limited autonomy, domestic violence
Status: Multiple deprivations despite moderate income

Which family is "poorest"? The answer depends on how we define and measure poverty.

Dimensions of Poverty

Income/Consumption Poverty

Focus: Material living standards

Measures: Headcount, poverty gap, severity

Advantages: Clear, comparable, policy-relevant

Limitations: Ignores non-monetary deprivations

Capability Poverty

Focus: Functionings and capabilities (Sen)

Measures: Health, education, autonomy indicators

Advantages: Comprehensive, human development focus

Limitations: Complex, subjective weightings

Multidimensional Poverty

Focus: Multiple simultaneous deprivations

Measures: MPI, national MDI indices

Advantages: Captures complexity, actionable

Limitations: Methodological choices affect results

Subjective Poverty

Focus: Self-assessed well-being

Measures: Life satisfaction, perceived adequacy

Advantages: Captures lived experience

Limitations: Context-dependent, cultural variation

Absolute vs. Relative Poverty

Absolute Poverty

Concept: Fixed standard of basic needs

Examples:

$2.15/day (World Bank extreme poverty)
₹1,059/month rural, ₹1,286/month urban (India 2011-12)
Caloric requirements + essential non-food

Use: Global comparisons, basic needs assessment

Relative Poverty

Concept: Poverty relative to society's living standards

Examples:

50% of median income (EU standard)
Bottom quintile or decile
Distance from social average

Use: Inequality assessment, social exclusion

Part 2: Poverty Measurement - The Foster-Greer-Thorbecke (FGT) Family

25 minutes

The Three Core Poverty Measures

Measure	Formula	What it Captures	Policy Use
Headcount Ratio (P₀)	Number below poverty line ÷ Total population	Incidence: How many are poor?	Overall progress tracking
Poverty Gap (P₁)	Average poverty gap ÷ Poverty line	Depth: How poor are the poor?	Transfer amount calculation
Squared Poverty Gap (P₂)	Average squared poverty gaps ÷ (Poverty line)²	Severity: Distribution among poor	Targeting effectiveness

Problem Set: Calculating Poverty Measures (20 minutes)

Dataset: Household consumption data from 10 households in a village

Village Poverty Analysis

Poverty Line: ₹2,000 per capita per month

Household	Per Capita Consumption (₹)	Below Poverty Line?	Poverty Gap (₹)	Poverty Gap Ratio
1	₹1,200	Yes	₹800	0.40
2	₹1,800	Yes	₹200	0.10
3	₹2,500	No	₹0	0.00
4	₹900	Yes	₹1,100	0.55
5	₹3,200	No	₹0	0.00
6	₹1,500	Yes	₹500	0.25
7	₹2,800	No	₹0	0.00
8	₹1,100	Yes	₹900	0.45
9	₹4,000	No	₹0	0.00
10	₹1,600	Yes	₹400	0.20

Problem 1: Basic Poverty Measures (8 minutes)

Step-by-step calculations: P₀ (Headcount Ratio): • Number of poor households: _____ • Total households: 10 • P₀ = _____ ÷ 10 = _____% P₁ (Poverty Gap): • Sum of poverty gap ratios: 0.40 + 0.10 + 0.55 + 0.25 + 0.45 + 0.20 = _____ • P₁ = _____ ÷ 10 = _____ P₂ (Squared Poverty Gap): • Sum of squared gap ratios: (0.40)² + (0.10)² + ... = _____ • P₂ = _____ ÷ 10 = _____

Interpretation Questions:

What percentage of households are poor? _____%
On average, how far below the poverty line are poor households? _____%
Which household contributes most to P₂ (poverty severity)?
If you could transfer ₹100 to one household, which would you choose to maximize poverty reduction?

Problem 2: Policy Simulation (7 minutes)

Scenario A: Transfer ₹300 to Household 4 (poorest)

Scenario B: Transfer ₹300 to Household 2 (just below line)

Scenario	New P₀	New P₁	New P₂	Cost per point reduction in P₀
Baseline	60%	___	___	-
Transfer to Household 4	____% (still poor)	___	___	No change in P₀
Transfer to Household 2	____% (exits poverty)	___	___	₹300 ÷ 10pp = ₹30/pp

Policy Trade-offs:

Which scenario reduces P₀ more effectively?
Which scenario reduces P₁ (depth) more effectively?
What does this tell us about targeting strategies?
How might your choice change if you prioritize different poverty measures?

Problem 3: Real-World Application (5 minutes)

India Poverty Trends (2011-12 to 2022-23):

Rural P₀: 25.7% → 16.4% (9.3 pp decline)
Urban P₀: 13.7% → 8.8% (4.9 pp decline)
Rural P₁: 5.1% → 3.2% (1.9 pp decline)
Urban P₁: 2.5% → 1.7% (0.8 pp decline)

Analysis Questions:

Did poverty decline faster in rural or urban areas?
Was the decline more in incidence (P₀) or depth (P₁)?
What might explain the rural-urban differences?
Are there still significant poverty gaps that need addressing?

Part 3: Inequality Measurement

20 minutes

Core Inequality Indices

Measure	Range	Interpretation	Key Properties
Gini Coefficient	0 to 1 (or 0-100)	0 = Perfect equality, 1 = Perfect inequality	Most widely used, based on Lorenz curve
Income Ratios	1 to ∞	Ratio of top to bottom groups	Simple, intuitive, focus on extremes
Theil Index	0 to ln(n)	0 = Perfect equality, higher = more inequality	Decomposable by groups
Atkinson Index	0 to 1	Incorporates social welfare function	Normative, sensitive to inequality aversion

Gini Coefficient Interpretation Guidelines:

< 0.30: Relatively equal (Denmark, Sweden)
0.30-0.40: Moderate inequality (Germany, Canada)
0.40-0.50: High inequality (USA, China)
> 0.50: Very high inequality (South Africa, Brazil)
India (2019-20): Gini ≈ 0.47 (consumption), 0.82 (wealth)

Problem Set: Inequality Analysis (15 minutes)

Dataset: Income distribution across 5 quintiles in two hypothetical states

Income Distribution Analysis State A (Population: 10 million): Quintile 1 (Bottom 20%): 8% of total income Quintile 2: 12% of total income Quintile 3: 16% of total income Quintile 4: 22% of total income Quintile 5 (Top 20%): 42% of total income State B (Population: 10 million): Quintile 1: 4% of total income Quintile 2: 8% of total income Quintile 3: 12% of total income Quintile 4: 20% of total income Quintile 5: 56% of total income

Problem 1: Basic Inequality Ratios (5 minutes)

Inequality Measure	State A	State B	Which is more unequal?
Quintile Ratio (Q5/Q1)	42% ÷ 8% = _____	56% ÷ 4% = _____	State _____
90/10 Ratio*	Assume 2.5×	Assume 3.2×	State _____
Top 20% share	_____%	_____%	State _____
Bottom 40% share	_____%	_____%	State _____ (lower = more unequal)

*90/10 ratio compares 90th percentile to 10th percentile income

Problem 2: Gini Coefficient Calculation (10 minutes)

Simplified Gini calculation using quintile data: Step 1: Calculate cumulative income shares State A: 8%, 20%, 36%, 58%, 100% State B: 4%, 12%, 24%, 44%, 100% Step 2: Calculate Gini approximation Gini ≈ 1 - Σ(cumulative_share × 0.2) State A Gini ≈ 1 - [(8×0.2) + (20×0.2) + (36×0.2) + (58×0.2) + (100×0.2)] ÷ 100 State A Gini ≈ 1 - [_____ + _____ + _____ + _____ + _____] ÷ 100 = _____ State B Gini ≈ 1 - [_____ calculation] = _____

Interpretation Questions:

Which state has higher inequality according to the Gini coefficient?
Do all inequality measures give the same ranking?
If you were designing a redistributive tax, which state would need more aggressive policies?
What additional information would help interpret these inequality levels?

Part 4: Multidimensional Poverty

20 minutes

The Multidimensional Poverty Index (MPI) Framework

Dimension	Indicators	Weight	Deprivation Threshold
Health (1/3)	Nutrition	1/6	Any household member undernourished
Health (1/3)	Child Mortality	1/6	Any child died in last 5 years
Education (1/3)	Years of Schooling	1/6	No member completed 6 years schooling
Education (1/3)	School Attendance	1/6	Any school-age child not attending
Living Standards (1/3)	Cooking Fuel	1/18	Uses dung, wood, charcoal, or coal
	Sanitation	1/18	Unimproved or shared sanitation
	Drinking Water	1/18	Unsafe water or 30+ min round trip
	Electricity	1/18	No electricity
	Housing	1/18	Poor floor, roof, or walls
	Assets	1/18	No radio, TV, phone, bike, motorbike, or car

MPI Calculation Method:

Calculate deprivation score for each household (sum of weights for deprived indicators)
Identify multidimensionally poor: Households with deprivation score ≥ 1/3
Calculate MPI: MPI = Headcount ratio × Average intensity
Decompose by groups: MPI can be broken down by geography, demographics

Problem Set: Multidimensional Poverty Analysis (15 minutes)

Task: Calculate MPI for 6 sample households

Household Deprivation Matrix

Household	Nutrition	Child Mortality	Schooling	Attendance	Cooking Fuel	Sanitation	Water	Electricity	Housing	Assets
Weight	1/6	1/6	1/6	1/6	1/18	1/18	1/18	1/18	1/18	1/18
H1	1	0	1	0	1	1	0	1	1	1
H2	0	0	0	0	0	0	0	0	0	0
H3	1	1	1	1	1	1	1	1	1	1
H4	0	0	1	0	1	0	0	0	0	1
H5	1	0	0	1	0	1	0	0	0	0
H6	0	0	0	0	1	1	1	0	0	0

1 = Deprived, 0 = Not deprived

Step 1: Calculate Deprivation Scores (8 minutes)

Household	Deprivation Score Calculation	Total Score	MPI Poor? (≥1/3)
H1	(1×1/6) + (0×1/6) + (1×1/6) + ... + (1×1/18)	_____	Y/N
H2	All zeros	0.00	N
H3	All dimensions deprived	1.00	Y
H4	_____	_____	Y/N
H5	_____	_____	Y/N
H6	_____	_____	Y/N

Step 2: Calculate MPI Components (7 minutes)

MPI Calculation: Headcount Ratio (H): • Number of MPI poor households: _____ • Total households: 6 • H = _____ ÷ 6 = _____% Average Intensity (A): • Sum of deprivation scores for MPI poor only: _____ • Number of MPI poor: _____ • A = _____ ÷ _____ = _____ Multidimensional Poverty Index: • MPI = H × A = _____ × _____ = _____ Interpretation: • ____% of households are multidimensionally poor • On average, MPI poor households are deprived in ____% of dimensions • MPI value of _____ indicates [low/moderate/high] multidimensional poverty

Policy Analysis Questions:

Which household would benefit most from targeted interventions?
Which dimensions contribute most to overall MPI?
How might income poverty and MPI poverty differ for these households?
What interventions would most efficiently reduce MPI?

Reflection & Policy Applications

10 minutes

Measurement Choice Analysis

Scenario: You're advising a state government on poverty measurement for program targeting.

Consider these measurement approaches:

Income poverty line only: Simple, comparable, focuses on purchasing power
Multidimensional poverty: Comprehensive, identifies specific deprivations
Subjective poverty: Captures lived experience, cultural relevance
Asset-based targeting: Easier to verify, harder to manipulate

Your recommendation for different contexts:

Cash transfer targeting: Which measurement approach? Why?
Rural development program: Which approach? Why?
Urban slum intervention: Which approach? Why?
National policy planning: Which approach? Why?

Key Takeaway

Poverty and inequality measurement shapes policy understanding and responses. No single measure captures all dimensions of deprivation. The choice of measurement approach should align with policy objectives, implementation capacity, and available data.

Essential Resources

Foundational Texts:

"Measuring Poverty" - Ravallion
"On Economic Inequality" - Amartya Sen
"Poor Economics" - Banerjee & Duflo
"The Economics of Inequality" - Thomas Piketty

Indian Context:

"Poverty and Inequality in India" - Jean Drèze and others
"India's Poverty Profile" - World Bank reports
"Inequality and Human Development" - UNDP India

Data Sources and Tools:

World Bank PovcalNet: International poverty comparisons
OPHI: Multidimensional poverty data and tools
NITI Aayog: India SDG Index and dashboards
NSS/PLFS: Household consumption and income data

Next Steps in ImpactMojo:

Poverty and Inequality 102: Advanced econometric methods and decomposition
Development Economics 101: Growth, distribution, and policy linkages
Social Safety Nets 101: Targeted interventions and program design
Data Feminism 101: Critical approaches to poverty measurement

This handout is part of the ImpactMojo 101 Knowledge Series
Licensed under CC BY-NC-ND 4.0 • Free to use with attribution • www.impactmojo.in

For calculation templates, datasets, and measurement tools, visit the ImpactMojo platform.