NEW FRONTIERS IN POVERTY MEASUREMENT James E. Foster George - - PowerPoint PPT Presentation

NEW FRONTIERS IN POVERTY MEASUREMENT

James E. Foster George Washington University and OPHI, Oxford

New Frontiers

Demand for new tools

Country Demand Policy Driven

Historical Emphasis

Aggregation Step Sen (1976) Emet

Frontier

Central Role of Identification of the Poor

New concepts of poverty New methods of identification

New Frontiers

Outline

Traditional Ultrapoverty Hybrid Poverty Lines Chronic Poverty Multidimensional Poverty

Methods MPI Country Applications WEAI Misunderstandings

Traditional Poverty Measurement

Variable – Single dimensional indicator Identification – Poverty line Aggregation – FGT (1984) Emet

Example Incomes y = (7,1,4,8) Poverty line z = 5

Deprivation vector g0 = (0,1,1,0)

Headcount ratio P0(y;z) = µ(g0) = 2/4 Normalized gap vector g1 = (0, 4/5, 1/5, 0) Poverty gap = P1(y;z) = µ(g1) = 5/20 Squared gap vector g2 = (0, 16/25, 1/25, 0) FGT Measure = P2(y;z) = µ(g2) = 17/100

Measuring Ultrapoverty

There are great differences among the poor

General idea behind construction of P1 and P2 Not P0! Greater depth matters

Who are the ultrapoor? How to measure ultrapoverty? Several possibilities

Deeply Deprived Persistently Deprived Multiply Deprived Spatially Concentrated Deprivation

Work in progress: Conferences at GW and Oxford

Measuring Ultrapoverty

Why not simply use P(x;zu) for a very low zu? Can give misleading picture

Although it identifies ultrapoor Aggregation at lower line can reduce measured poverty

Separate identification and aggregation

Use zu to identify and z to aggregate Resulting Pu(x;zu) satisfies all axioms (including focus)

Measuring Ultrapoverty

Example Incomes y = (7,1,4,8) Poverty line z = 5 and Ultrapoverty line zu = 3

Deprivation vector gu

0 = (0,1,0,0)

Headcount ratio Pu0(y;z) = µ(gu

0) = 1/4

Normalized gap vector gu

1 = (0, 4/5, 0, 0)

Poverty gap = Pu1(y;z) = µ(gu

1) = 4/20

Squared gap vector gu

2 = (0, 16/25, 0, 0)

FGT Measure = Pu2(y;z) = µ(gu

2) = 16/100

Contribution of ultrapoor to overall poverty?

Headcount: 1/4 out of 1/2; Poverty gap: 4/20 out of 5/20 FGT: 16/100 out of 17/100

Hybrid Poverty Lines

Absolute poverty line za such as $1.25/day

Unchanging over time and space

Hence 0 elasticity of poverty line wrt income

Useful for comparing countries at similar levels of development for a window of time However

make little sense for evaluating poverty across countries or regions at very different levels of development are fundamentally unsustainable over time ie have problems measuring poverty over time and space

Ex Is growth good for the poor? Foster and Szekely (2008) IER

Hybrid Poverty Lines

Relative poverty line zr such as 50% of mean income

Changes with the standard of living

Elasticity of poverty line wrt income is 1

However this seems too responsive Wind up measuring inequality, not poverty Empirical evidence: elasticity is between 0 and 1

Citro and Michael (1995) Measuring Poverty

Which poverty lines for measuring poverty over space and time?

Hybrid Poverty Lines

Hybrid poverty line z = zr

ρ za 1-ρ for 0 < ρ < 1

where

zr is a relative poverty line za is an absolute poverty line ρ is the elasticity of the poverty line with respect to income

Foster (1998) AER

The elasticity can be estimated for LAC by Foster and Szekely mimeo Or selected arbitrarily and subjected to robustness Madden (2000)

RevIncWealth for Ireland

Or seen as a normative decision “To what extent should the poor share

in growth?”

Hybrid Poverty Lines

Poverty has two components

Absolute – persons below absolute poverty line Relative or Hybrid – persons below hybrid line above abs.

With different policy prescriptions

As in previous discussions of ultrapoverty

Other approaches

Atkinson and Bourguignon (2000) use a max function Ravallion and Chen (2011) REStat alter A&B to avoid range where elasticity is 1

Applications

Brazil (recent communication with de Barros)

Chronic Poverty

Note

Previous exercises altered identification The next two alter the variable and identification

Chronic Poverty (across many time periods) Multidimensional Poverty (across many dimensions

• f wellbeing)

Closely linked

Must have data linked across time or dimensions Must decide how to value different periods’ income or different dimensions Identification becomes more difficult Foster (2009) and Alkire-Foster (2011) JPubE are related

Chronic Poverty

First Approach: Components Jalan-Ravallion (2000) JDevS

Identify as chronically poor those whose incomes are on average below a poverty line z Aggregate using FGT applied to distribution of average incomes Assumes

Equal weights across periods – hence perfect substitutes First aggregate across periods, then see if chronically poor

Foster-Santos mimeo

Use method of averaging across periods that allows for imperfect substitutability

Chronic Poverty

Second Approach: Spells Foster (2009) Poverty Dynamics

Identify as chronically poor those whose incomes are frequently below the poverty line (eg 2 out of 4 periods) Aggregate using FGT applied to matrices in which the nonpoor spells have been censored out Assumes

No substitution of incomes across periods Indeed incomes are not aggregated Instead check how many periods deprived Aggregate spells across periods Each spell has the same value

Multidimensional Poverty

Everyone agrees poverty is multidimensional Real question is what to do about it. How to measure poverty when there are many variables or dimensions of wellbeing?

Multidimensional Poverty

Suppose many variables or dimensions Question

How to evaluate poverty?

Answer 1 If variables can be meaningfully aggregated into some

• verall resource or achievement variable, traditional

methods can be used

Multidimensional Poverty

Examples Welfare aggregation Construct each person’s welfare function

Set cutoff and apply traditional poverty index However Many assumptions needed Alkire and Foster (2010) mimeo “Designing the Inequality-Adjusted

Human Development Index”

Ordinal variables problematic

Multidimensional Poverty

Examples Price aggregation Construct each person’s expenditure level

Set cutoff and apply traditional poverty index However Many assumptions needed Ordinal and nonmarket variables problematic Link to welfare tenuous (local and unidirectional)

Foster, Majumdar, Mitra (1990) “Inequality and Welfare in Market Economies” JPubE

Multidimensional Poverty

Note Even if an aggregate exists, it may not be the right approach Idea

Aggregate resource approach signals what could be

The budget constraint

Does not indicate what is

The actual bundle purchased

Ex

Consumption poverty is falling rapidly in India Yet 45% of kids malnourished

Question Aggregating may hide policy relevant information can’t retrieve

Multidimensional Poverty

Suppose many variables or dimensions Question

How to evaluate poverty?

Answer 2 If variables cannot be meaningfully aggregated into some overall resource or achievement variable, new methods must be used

Multidimensional Poverty

Some go to great lengths to avoid this fact: Blinders approach

Limit consideration to a subset that can be aggregated, and use traditional methods.

Key dimensions ignored Marginal methods

Apply traditional methods separately to each variable

Ignores joint distribution

Where did identification go? Alkire, Foster, Santos (2011) JEI

Alkire-Foster Methodology: Overview

Identification – Dual cutoffs

Deprivation cutoffs - each deprivation counts Poverty cutoff - in terms of aggregate deprivation values

Aggregation – Adjusted FGT

Reduces to FGT in single variable case

Background papers

Alkire and Foster (2011) “Counting and Multidimensional Poverty Measurement” Journal of Public Economics Alkire and Foster (2010) “Understandings and Misunderstanding of Multidimensional Poverty” Journal of Economic Inequality Alkire, Foster, and Santos (2011) “Where Did Identification Go?” Journal of Economic Inequality

Adjusted Headcount Ratio

Concept - Poverty as multiple deprivations

Mirrors identification used by NGOs – BRAC Depends on joint distribution

Ordinal data

Dirt floors vs covered floors Qualitative data into quantitative data

Transparent

Defined by variables, deprivation cutoffs, deprivation values, poverty cutoff Can be replicated and tested for robustness

Adjusted Headcount Ratio

Can be implemented at many levels

Cross country – MPI in the 2010 HDR Within country – Mexico*, Colombia, Bhutan, etc. Local village level – Participatory methods India, Bhutan,

etc

Evaluation – Impacts on poverty As a coordination tool – Ministries in Colombia

Adjusted Headcount Ratio

Constructing other measures

– Gross national happiness index (Bhutan) – Women’s Empowerment in Agriculture Index (USAID/IFPRI/OPHI) – Service delivery performance measure (Allwine and Foster, 2011: Allwine 2011) – Corruption Foster, Horowitz, Mendez, 2012 WBER

Intro to: Multidimensional Methods

Matrix of achievements for n persons in d equally important domains (easily generalized) Domains Persons z ( 13 12 3 1) Cutoffs These entries fall below cutoffs y = 13.1 14 4 1 15.2 7 5 12.5 10 1 20 11 3 1 " # $ $ $ $ $ % & ' ' ' ' '

Deprivation Matrix

Replace entries: 1 if deprived, 0 if not deprived Domains Persons g0 = 1 1 1 1 1 1 1 " # $ $ $ $ $ % & ' ' ' ' '

Identification – Dual Cutoff Approach

Q/ Who is poor? A/ Fix cutoff k, identify as poor if ci > k (Ex: k = 2) Domains c Persons Note Includes both union and intersection Especially useful when number of dimensions is large

Union becomes too large, intersection too small

Next step - aggregate into an overall measure of poverty g0 = 1 1 1 1 1 1 1 " # $ $ $ $ $ % & ' ' ' ' ' 2 4 1

Aggregation

Censor data of nonpoor Domains c(k) Persons g0(k) = 1 1 1 1 1 1 " # $ $ $ $ $ % & ' ' ' ' ' 2 4

Aggregation – Headcount Ratio

Domains c(k) Persons Two poor persons out of four: H = ½ ‘incidence’ Critiques g0(k) = 1 1 1 1 1 1 " # $ $ $ $ $ % & ' ' ' ' ' 2 4

Aggregation – Adjusted Headcount Ratio

Adjusted Headcount Ratio = M0 = HA = µ(g0(k)) = 6/16 = .375 Domains c(k) c(k)/d Persons A = average intensity among poor = 3/4 Note: if person 2 has an additional deprivation, M0 rises g0(k) = 1 1 1 1 1 1 " # $ $ $ $ $ % & ' ' ' ' ' 2 4 2 / 4 4 / 4

Aggregation – Adjusted FGT Family

Adjusted FGT is Mα = µ(gα(τ)) for α > 0 Domains Persons gα (k) = 0.42α 1α 0.04α 0.17α 0.67α 1α # $ % % % % % & ' ( ( ( ( (

Aggregation – Adjusted Headcount Ratio

Observations

M0 uses ordinal data Similar to traditional gap P1 = HI

HI = per capita poverty gap = headcount H times average income gap I among poor HA = per capita deprivation = headcount H times average intensity A among poor

Decomposable across dimensions after identification

M0 = ∑j Hj/d where Hj are “censored” headcount ratios

Extends easily to the case where deprivations have different values

¡ ¡ ¡Example ¡-­‑ ¡MPI ¡

3 Dimensions 10 Indicators

Years of Schooling (1/6) School Attendance (1/6) Education (1/3) Child Mortality (1/6) Nutrition (1/6) Health (1/3) Standard of Living (1/3) Cooking Fuel Sanitation Water Electricity Floor Asset Ownership (1/18 Each)

MPI Methodology

Identification: Any person experiencing 30% or more of the weighted deprivations is poor. Aggregation: The MPI formulae is: MPI = H x A Incidence x Intensity

¡ ¡ ¡ ¡MPI ¡2011 ¡– ¡109 ¡Countries ¡ ¡

National Methodologies

Motivations for a National MPI

Show progress quickly and directly (Monitoring/Evaluation) Inform planning and focus policy Target poor people and communities more effectively Reflect poor people’s own understandings of poverty

Cases of National MPIs

Mexico December 2009 Colombia August 2011 Others in progress

§

Slides drawn from government agencies

§

Available on agency websites

www.coneval.gob.mx

Multidimensional Poverty in Mexico Methodology & results

First released December, 2009

Territorial

What are the main features of the new methodology?

Social Rights

Deprivations

Population Wellbeing

Income Current income per capita Six Social Rights:

• Education
• Health
• Social Security
• Housing
• Basic Services
• Food

3 2 1 4 5 6

Social Rights Deprivations

Poverty Identification EWL

With Deprivations

EXTREME Multidimensional Poverty 3 Moderate Multidimensional Poverty

Vulnerable by social deprivations

Vulnerable by income 5 2 4 1 6 Ideal Situation MWL

$1,921.7 U $1,202.8 R $874.6 U $613.8 R Without

D e p r i v a t i

• n

s

MULTIDIMENSIONALLY POOR

Economic wellbeing line Minimum wellbeing line

Income

MODERATE POVERTY

33.7% 36.0 millions 2.3 Deprivation

Social Rights Deprivations Wellbeing

Income

Vulnerable by income Vulnerable by social deprivations

Total Population 2008

18.3% 19.5 millions 33.0% 35.2 millions 2.0 Deprivation average

3 2 1 4 5 6

EXTREME POVERTY

average average

10.5% 11.2 millions 3.9 Deprivation 4.5% 4.8 millions

MODERATE POVERTY

36.5 % 2.5 millions 3.1 Deprivation

Social Rights Deprivations Wellbeing

Income

Vulnerable by income Vulnerable by social deprivations

Indigenous Population 2008

1.2% .1 millions 20.0 % 1.4 millions 2.8 Deprivation average

3 2 1 4 5 6

EXTREME POVERTY

average average

39.2 % 2.7 millions 4.2 Deprivation 3.1% 0.21 millions

• ­‑10.0
• ­‑8.0
• ­‑6.0
• ­‑4.0
• ­‑2.0

0.0 2.0 4.0 6.0

Fuente: estimaciones del CONEVAL con base en el MCS-ENIGH 2008 y 2010

Income Poverty

Food security Basic Services Housing Social Security Access to Health Care Education

Millions of people

Extreme poverty

Poverty

2008

44.5 % 48.8 million

2010

46.2 % 52.0 million

2008

10.6 % 11.7 million

2010

10.4% 11.7 million 6 4 2

• 2
• 4
• 6
• 8
• 10
• 9.0
• 2.9
• 2.5
• 2.3
• 0.8

4.1 3.5 3.2 0.0

Social Deprivations

4.8

Extreme income poverty

Multidimensional Poverty Index for Colombia and its applications (MPI-Colombia)

ROBERTO ANGULO YADIRA DÍAZ RENATA PARDO

National Planning Department Division of Social Promotion and Quality of Life September 2011

The MPI-Colombia:

n Proposed by the National Planning Department

based on the Alkire & Foster methodology

n Instrument for design and monitoring public policy n Complements the income poverty measure n Discussed with the Colombian academy and policy

makers

Dimensions, Variables and Weights MPI-Colombia

Educational Conditions Childhood & Youth

Work Health

Housing & Public Services

Schooling Illiteracy School Attendance At the right level Access to infant services No Child Labour Absence of long-term unemploy- ment Coverage Access to health care given a necessity

Improved Water

Flooring Overcrowding Sanitation Exterior Walls Formal work 0.1

0.2 0.2 0.2 0.2 0.2

0.05 0.1 0.1 0.04

Results released 2011:

Incidence (H) and Adjusted Headcount (M0) for k = 5/15 decreased 1997-2008

Fuente: DNP , DDS, SPSCV. 2011

MPI-Colombia Key goal for the Government’s National Development Plan 2010-2014 and for monitoring poverty reduction

49

Poverty committee Coordinating and monitoring poverty reduction

▪ Leaders

– Counselor for the Presidency – National Planning Department

▪ Permanent members

– Ministry of Health – Ministry of Labor – Ministry of Housing – Ministry of Agriculture – Ministry of Education – Ministry of Finance

MANDATORY PRESENCE The President of Colombia

Application: Women’s Empowerment in Agriculture Index (WEAI)

WEAI Purpose

n Design, develop, and test an index to measure the greater

inclusion of women in agricultural sector growth that has

• ccurred as a result of US Government intervention under

the Feed the Future Initiative

n What is “greater inclusion”? The concept of Inclusive

Agricultural Sector Growth is broad and multi-dimensional

n Feed the Future defines it as: “the empowerment of women

in their roles and engagement throughout the various areas

• f the agriculture sector, as it grows, in both quantity and

quality”

Why focus on women?

n Women are important in agriculture, account for 43% of

the agricultural labor force worldwide (SOFA 2011)

n Yet women consistently have less resources than men:

land, education, access to extension and credit, inputs-- resulting in yield gaps of between 20-25%

n Closing the gap in access to resources could increase

agricultural productivity—with benefits for families and the next generation

What is new about the WEAI?

n An aggregate index in two parts:

q Five domains of empowerment (5DE): assesses whether

women are empowered in the 5 domains of empowerment in agriculture

q Gender Parity Index (GPI): reflects the percentage of women

who are as empowered as the men in their households

n It is a survey-based index, not based on aggregate statistics or

secondary data, constructed using interviews of the primary male and primary female adults in the same household

The pilot

n Tested feasibility in a real-world setting before scale-up n New survey instrument was piloted in 3 countries

(Bangladesh, Guatemala, Uganda), with ~350 households/ 625 individuals each, focusing on the Feed the Future zones

• f influence

n Representative of the zone of influence (not nationally) n An innovation in the measurement and monitoring of

women’s empowerment in agriculture—not the final word on it!

Five domains of empowerment A woman’s empowerment score shows her own achievements

Who is empowered?

A woman who has achieved ‘adequacy’ in 80% or more of the weighted indicators is empowered

How is the Index constructed?

Five domains of empowerment (5DE)

A direct measure of women’s empowerment in 5 dimensions

Gender parity Index (GPI)

Women’s achievement’s relative to the primary male in hh

Women’s Empowerment in Agriculture Index (WEAI) WEAI is made up of two sub indices All range from zero to one;

higher values = greater empowerment

5DE = (1-M0) GPI = (1-P1)

5DE = He + HdAe = (1- HdA)

He is the percentage of empowered women Hd is the percentage of disempowered women A is the average absolute empowerment score among the disempowered

GPI = Hp+ HwRp = (1- HwI)

Hp is percentage of women with gender parity Hw is the percentage of women without gender parity Rp is the women’s relative parity score compared to men = (1-I)

He + Hd = 100% Hp + Hw = 100%

Formula

Understandings and Misunderstandings

Recent debates

WB Several conferences Blogs Duncan Green; World Bank Africa, CGD Online CGD Academic Journal of Economic Inequality

Benefited greatly from the discussion Points of contention/confusion

Understandings and Misunderstandings

Concept of Poverty: Multiple deprivations

Depends on joint distribution M0 = ¼ M0 = 0 Matrix 1 Matrix 2 ⎥ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎢ ⎣ ⎡ ⎥ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎢ ⎣ ⎡ = 4 1 1 1 1  g ⎥ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎢ ⎣ ⎡ ⎥ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎢ ⎣ ⎡ = 1 1 1 1 1 1 1 1  g

Understandings and Misunderstandings

Data Requirements: Single survey sourcing

Depends on joint distribution, need information on joint dist. Q: What if “best available data” are in different datasets? A: Not best available data Ex: Elasticity exercise with best available price data from one source and best available quantity data from another Ex: Unlinked expenditure surveys

Understandings and Misunderstandings

Adjusted Headcount Ratio vs. MPI vs. HDI

Adjusted headcount ratio M0 – general methodology MPI – a specific implementation for cross-country comparisons HDI – not a poverty measure

Understandings and Misunderstandings

Underpinnings: Poverty and Welfare

Firmly rooted in axiomatic poverty analysis Evaluate methods via axioms satisfied and violated MPI – a specific implementation Adjusted headcount ratio crude (like unidimensional headcount ratio) not directly linked to welfare (ditto) conveys tangible information transparent parameters

Understandings and Misunderstandings

Calibration: Who chooses the parameters?

Context dependent

Gonzalo Hernandez of Coneval can comment on this!

Robustness

Generally robust methodology Intersection is robust to changing deprivation values M0 becomes H in that case (since A = 1) But ignores conditions of those with d-1 or fewer deprivations

Summary

Intuitive Transparent Flexible

MPI – Acute poverty Country Specific Measures

Policy impact and good governance Targeting

Accounting structure for evaluating policies Participatory tool

Thank you