summer school on multidimensional poverty analysis · poverty analysis 1 –13 august 2016 ......

Summer School on Multidimensional

Poverty Analysis

1–13 August 2016

Beijing, China

Changes over Time

Ana Vaz

OPHI

Outline

Descriptive Analysis using Repeated Cross-Sectional Data

Basic Concepts

Example: MPI Reduction in India

Analysis of Dynamic Subgroups using Panel Data

Descriptive Analysis using Repeated

Cross-Section Data

• Notation:

- 𝑡1 and 𝑡2 denote initial and final periods

- 𝑋𝑡1 and 𝑋𝑡2 are the achievement matrices for both periods

• The same set of parameters is used across the two periods

(deprivation cutoffs, weights, poverty cutoff)

• Expressions are equally applicable to:

- incidence (H),

- intensity (A),

- censored headcount ratios (ℎ𝑗 𝑘 ), and

- uncensored headcount ratios (ℎ𝑗).

Notation

• Absolute Rate of Change: is the difference in levels

between two periods.

• Relative Rate of Change: is the difference in levels across

two periods as a percentage of the initial period.

• Why use both absolute and relative?

Changes in M0, H and A

∆𝑀0 = 𝑀0 𝑋𝑡2 −𝑀0 𝑋𝑡1

𝛿𝑀0 =𝑀0 𝑋𝑡2 −𝑀0 𝑋𝑡1

𝑀0 𝑋𝑡1× 100

• Annualized Absolute Rate of Change: is the difference in

levels across two periods divided by the difference in the two

time periods.

• Relative Rate of Change: is the compound rate of reduction

per year between the initial and the final periods.

Annualized Changes

∆ 𝑀0 =𝑀0 𝑋𝑡2 −𝑀0 𝑋𝑡1

𝑡2 − 𝑡1

𝛿 𝑀0 =𝑀0 𝑋𝑡2

𝑀0 𝑋𝑡1

1𝑡2−𝑡1

− 1 × 100

Example

Year 1 Year 2

Statistical

Significance of

the Change

Annualized Change

Absolute Relative

Panel I: Multidimensional Poverty Index (MPIT)

Nepal 2006-2011 .350 (.013) .217 (.012) *** -.027 -9.1%

Peru 2005-2008 .085 (.007) .066 (.004) * -.006 -8.0%

Rwanda 2005-2010 .460 (.005) .330 (.006) *** -.026 -6.4%

Senegal 2005-2010/11 .440 (.019) .423 (.010) -.003 -0.7%

Panel II: Multidimensional Headcount Ratio (HT ,%)

Nepal 2006-2011 64.7 (2.0) 44.2 (2.0) *** -4.1 -7.4%

Peru 2005-2008 19.5 (1.5) 15.7 (.8) * -1.3 -6.9%

Rwanda 2005-2010 82.9 (.8) 66.1 (1.0) *** -3.4 -4.4%

Senegal 2005-2010/11 71.3 (2.4) 70.8 (1.5) -0.1 -0.1%

Panel III: Intensity of Poverty (AT ,%)

Nepal 2006-2011 54.0 (.6) 49.0 (.7) *** -1.0 -1.9%

Peru 2005-2008 43.6 (.5) 42.2 (.4) ** -0.5 -1.1%

Rwanda 2005-2010 55.5 (.3) 49.9 (.3) *** -1.1 -2.1%

Senegal 2005-2010/11 61.7 (1.0) 59.7 (.7) * -0.4 -0.6%

Example

Year 1 Year 2

Statistical

Significance of

the Change

Annualized Change

Absolute Relative

Panel I: Multidimensional Poverty Index (MPIT)

Nepal 2006-2011 .350 (.013) .217 (.012) *** -.027 -9.1%

Peru 2005-2008 .085 (.007) .066 (.003) * -.006 -8.0%

Rwanda 2005-2010 .460 (.005) .330 (.006) *** -.026 -6.4%

Senegal 2005-2010/11 .440 (.019) .423 (.010) -.003 -0.7%

Panel II: Multidimensional Headcount Ratio (HT ,%)

Nepal 2006-2011 64.7 (2.0) 44.2 (2.0) *** -4.1 -7.4%

Peru 2005-2008 19.5 (1.5) 15.7 (.8) * -1.3 -6.9%

Rwanda 2005-2010 82.9 (.8) 66.1 (1.0) *** -3.4 -4.4%

Senegal 2005-2010/11 71.3 (2.4) 70.8 (1.5) -0.1 -0.1%

Panel III: Intensity of Poverty (AT ,%)

Nepal 2006-2011 54.0 (.6) 49.0 (.7) *** -1.0 -1.9%

Peru 2005-2008 43.6 (.5) 42.2 (.4) ** -0.5 -1.1%

Rwanda 2005-2010 55.5 (.3) 49.9 (.3) *** -1.1 -2.1%

Senegal 2005-2010/11 61.7 (1.0) 59.7 (.7) * -0.4 -0.6%

Based on this information can we say that the number of

poor people is decreasing over time in Rwanda?

• In order to reduce the absolute number of poor people,

the rate of reduction in the headcount ratio needs to be

faster than the population growth.

• So, don’t forget to also check if the number of poor

people is decreasing over time!

Change in Number of Poor

Population MPI Poor

Year 1 Year 2 Annual

Growth

Year 1 Year 2 Absolute

Reduction

(in thousands) (in thousands)

Nepal 2006-2011 25,634 27,156 1.2% 16,585 12,003 -4,582

Peru 2005-2008 27,723 28,626 0.6% 5,406 4,494 -912

Rwanda 2005-2010 9,429 10,837 2.8% 7,817 7,163 -654

Senegal 2005-2010/11 11,271 13,141 3.1% 8,036 9,304 1,268

Interpreting Dimensional Changes

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

.0

An

nu

al

ab

solu

te c

han

ge (

p.p

.)

Nepal 2006 - 2011

Raw Headcount (Shaded) Censored Headcount

What indicator had the biggest contribution to

poverty reduction?

• The (annualized) absolute rate of change in 𝑀0 can be

expressed as the weighted average of the (annualized)

absolute rates of change in censored headcount ratios.

• When different indicators have different weights, the

effects of their changes on the change in 𝑀0 reflect these

weights.

Dimensional Changes

∆ 𝑀0 = 𝑤𝑗∆ ℎ𝑗 𝑘

𝑑

𝑗=1

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

.0

An

nu

al

ab

solu

te c

han

ge (

p.p

.)

Nepal 2006 - 2011


Dimensional Changes

What indicator had the biggest contribution to

poverty reduction?

• Interpreting the real on-the-ground contribution of each

indicator to the change in 𝑀0 is not so mechanical.

• A reduction in censored headcount of j may reflect two

different situations:

Interpreting Dimensional Changes

- A poor person became non-deprived in indicator j

- A poor person who has been deprived in j became non-

poor due to reduction in other indicators, even though she

is still deprived in j.

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

.0

An

nu

al

ab

solu

te c

han

ge (

p.p

.)

Nepal 2006 - 2011


Dimensional Changes

Why the censored headcount reduced more

than the uncensored one?

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

.0

An

nu

al

ab

solu

te c

han

ge (

p.p

.)

Nepal 2006 - 2011


Dimensional Changes

It seems some people became non-poor but

remain deprived in fuel & flooring.

• Compare changes in censored and uncensored headcounts

to analyse the relation between the dimensional changes

among the poor and the society-wide changes in

deprivations.

• In repeated cross-sectional data, this comparison will also

be affected by migration and demographic shifts, as well as

changes in the deprivation profiles of the non-poor.

Dimensional Changes

Subgroup Decompositions

Nepal

-0.055

-0.045

-0.035

-0.025

-0.015

-0.005

0.005

-0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65

An

nu

al

Ab

solu

te C

han

ge i

n M

PI T

Multidimensional Poverty Index (MPIT) at initial year

Reduction

in MPIT

Size of bubble is proportional

to the number of poor in first

year of the comparison.

Subgroup Decompositions

Nepal

Eastern Mountain

Central Mountain

Western Mountain

Eastern Hill

Central Hill

Western Hill Mid-Western Hill

Far-Western Hill

Eastern Terai Central Terai

Western Terai

Mid-Western Terai

Far-Western Terai

-0.055

-0.045

-0.035

-0.025

-0.015

-0.005

0.005

-0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65

An

nu

al

Ab

solu

te C

han

ge i

n M

PI T

Multidimensional Poverty Index (MPIT) at initial year

Reduction

in MPIT

Size of bubble is proportional

to the number of poor in first

year of the comparison.

Population Shifts

• The interpretation of changes in regional poverty

estimates can be hugely influenced by populations shifts.

- Different rates of population growth

- Rural-urban migration

- Internal and International Migration

Example:

MPI Reduction in India

Alkire & Seth World Development 2015

22

• Data: National Family Health Surveys (NFHS)

- NFHS-2 (1998-99)

- NFHS-3 (2005-06)

• Indicators are strictly harmonised

Inter-temporal Multidimensional

Poverty in India

23

Uncensored Deprivations (raw)

Significant reduction in all deprivations. Highest reductions in housing,

sanitation, water and electricity deprivations.

24

Change in MPII Nationally for Different

Poverty Cut-offs

Poverty Cutoff (k) 1999 2006 Change

Union (>0) M0 0.366 0.320 -0.046 ***

H 92.9% 88.9% -4.0 ***

A 39.4% 36.0% -3.4 ***

One-fifth (0.2) M0 0.343 0.293 -0.050 ***

H 73.8% 65.5% -8.2 ***

A 46.5% 44.7% -1.7 ***

One-third (0.33) M0 0.300 0.251 -0.050 ***

H 56.8% 48.5% -8.3 ***

A 52.9% 51.7% -1.2 ***

Half (0.5) M0 0.197 0.156 -0.041 ***

H 30.6% 24.4% -6.2 ***

A 64.5% 64.1% -0.4 *

Where and How?

• Where poverty has been reduced?

- Across geographic regions, social groups and household

characteristics

• How poverty has been reduced?

- By reducing incidence or intensity?

- By improving which indicators?

25

Where and How?



characteristics




26

Absolute Reduction in Poverty across

Large States

27

Green = faster.

Stronger

reductions in

southern states

-0.110 -0.090 -0.070 -0.050 -0.030 -0.010

Andhra Pradesh (***) [0.299]

Kerala (***) [0.136]

Tamil Nadu (***) [0.195]

Karnataka (***) [0.255]

Jammu (***) [0.226]

Gujarat (***) [0.248]

Orissa (***) [0.381]

Maharashtra (***) [0.226]

West Bengal (***) [0.339]

Himachal Pradesh (***) [0.154]

Eastern States (***) [0.315]

Madhya Pradesh (***) [0.368]

Haryana (**) [0.19]

Uttar Pradesh (***) [0.348]

Rajasthan (**) [0.341]

Punjab (***) [0.117]

Bihar (**) [0.442]

Absolute Change (99-06) in MPI-I

Sta

tes

(Sig

nif

ican

ce)

[MP

I-I

in 1

99

9]

Absolute Reduction in Poverty across

Large States

28

We combined Bihar and Jharkhand, Madhya Pradesh and Chhattishgarh,

and Uttar Pradesh and Uttarakhand

Significant

reduction in all

states

Strongest Reductions

Absolute Reduction in Poverty Across

Sub-Groups

29

-0.110 -0.090 -0.070 -0.050 -0.030 -0.010

Urban (***) [0.116]

Rural (***) [0.368]

General (***) [0.229]

OBC (***) [0.301]

SC (***) [0.378]

ST (***) [0.458]

Sikh (***) [0.115]

Christian (***) [0.196]

Hindu (***) [0.306]

Muslim (*) [0.32]


Su

b-G

rou

ps

(Sig

nif

ican

ce)

[MP

I-I

in 1

99

9]

Significant

reduction for all

sub-groups

But slowest for

the poorest

Absolute Reduction in Acute Poverty Across

Household Characteristics

-0.110 -0.090 -0.070 -0.050 -0.030 -0.010

Female () [0.275]

Male (*) [0.302]

12 Years or More (*) [0.055]

11–12 Years (*) [0.114]

6–10 Years (*) [0.188]

1–5 Years (*) [0.31]

No Education (*) [0.448]

1–3 (*) [0.248]

4–5 (*) [0.265]

6–7 (*) [0.321]

10 or More (*) [0.332]

8–9 (*) [0.34]


Sta

tes

(Sig

nif

ican

ce)

[MP

I-I

in 1

999]

30

HH

Siz

e

Head

’s E

du

c.

Head

’s

Gen

der

Slower progress

for female headed

households and

larger households

Where and How?



characteristics




31

Improvement in Poverty: H &/or A?

32

Andhra Pradesh

Arunachal Pradesh

Assam Bihar

Goa Gujarat

Haryana Himachal Pradesh

Jammu & Kashmir

Karnataka

Kerala

Madhya Pradesh

Maharashtra Manipur

Meghalaya

Mizoram

Nagaland

Orissa

Punjab

Rajasthan

Tamil Nadu

Tripura

Uttar Pradesh

West Bengal

-1.0%

-0.8%

-0.6%

-0.4%

-0.2%

0.0%

0.2%

0.4%

-3.4% -2.9% -2.4% -1.9% -1.4% -0.9% -0.4% 0.1% 0.6%

An

nu

al

Ab

solu

te V

ari

ati

on

in

In

ten

sity

(A

)

Annual Absolute Variation in Headcount Ratio (H)

Reduction in

Intensity of

Poverty (A)

Bad/Good

Bad/Bad Reduction in Incidence of Poverty (H)

Good /Good

Good/ Bad

Performance

consistently strongest in

Kerala, TN, & AP.


33

Scheduled Castes

Scheduled Tribes

Other Backward Classes

General

-0.4%

-0.3%

-0.3%

-0.2%

-0.2%

-0.1%

-0.1%

0.0%

0.0%

-2.5% -2.0% -1.5% -1.0% -0.5% 0.0%

An

nu

al A

bso

lute

Vari

ati

on

in %

In

ten

sity

(A

)

Annual Absolute Variation in % Headcount Ratio (H)

Reduction in Intensity of Poverty (A)

Bad/Good

Bad/BadReduction in Incidenceof Poverty (H)

Good /Good

Good/ Bad

Caste

34

Hindu

MuslimChristian

Sikh

-0.3%

-0.3%

-0.2%

-0.2%

-0.1%

-0.1%

0.0%

0.1%

0.1%

0.2%

-2.0% -1.5% -1.0% -0.5% 0.0%

An

nu

al A

bso

lute

Va

ria

tio

n in

% I

nte

nsi

ty (

A)

Annual Absolute Variation in % Headcount Ratio (H)

Reduction in Intensity of Poverty (A)

Bad/Good

Bad/BadReduction in Incidenceof Poverty (H)

Good /Good

Good/ Bad

Religion


Where and How?



characteristics




35

Changes among people who are poor and

deprived in each indicator

36

Where and How?



characteristics > Population subgroup decomposability


- By reducing incidence or intensity? > H-A breakdown

- By improving which indicators? > Dimensional breakdown

37

Analysis of Dynamic Subgroups

using Panel Data

With Panel data we can identify 4

type of poor people

1. Chronic poor – across time periods

2. Churning (in and out)

3. Falling into poverty

4. Moving out of poverty.

1. How do the four groups differ – either demographically or in

the structure of their poverty?

2. Poverty traps? Are any dimensions in which chonic poor are

always deprived?

3. Does the composition of poverty for chronic poor change?

Does chronic poverty decrease over time?

4. How does poverty evolve across different ages? For different

social groups and household types?

Panel data enables new analyses:

• Did some people exit poverty?

• Did some exit poverty, and others become newly poor?

• Did some go in and out of poverty various times?

• Were the people that exited poverty among the poorest, or

the less poor in the previous period(s)?

How did poverty change?

• Assuming two period panel data with n individuals.

• Consider 4 mutually exclusive groups:

N: 𝑛𝑁 people who are non-poor in both periods

O: 𝑛𝑂 people who are poor in both periods (ongoing)

E_: 𝑛𝐸−

people who are poor in 𝑡1 but exit poverty

E+: 𝑛𝐸+

people who are non-poor in 𝑡1 but enter poverty

Dynamic Subgroups – Panel Data

• Change in 𝑀0 can be decomposed as follows:

∆𝑀0 =

=𝑛𝑂

𝑛𝑀0 𝑋𝑡2

𝑂 −𝑀0 𝑋𝑡1𝑂

−𝑛𝐸−

𝑛𝑀0 𝑋𝑡1

𝐸−

+𝑛𝐸+

𝑛𝑀0 𝑋𝑡1

𝐸+

Change in 𝑴𝟎

• It is impossible to decompose ∆𝑀0 with the empirical

precision as when using panel data.

• Theory-based approaches to decomposing ∆𝑀 between

incidence (∆𝐻) and intensity (∆𝐴)

- Based on assumptions regarding the intensity of those who

exited or remained poor…

• However, these give precise estimates that might be very

innacurate.

Dynamic Subgroups –

Repeated Cross-Sectional Data

References

• Alkire, S. et al. (2015). Multidimensional Poverty Measurement

and Analysis, Oxford: Oxford University Press, ch. 9.

• Alkire, S., Roche, J. M., and Vaz, A. (2015). “Changes Over

Time in Multidimensional Poverty: Methodology and Results

for 34 Countries.” OPHI Working Papers 76, University of

Oxford.

• Alkire, S. and Seth, S. (2015). “Multidimensional Poverty

Reduction in India between 1999 and 2006: Where and How?”,

World Development, 72, 93-108.

Thank you

summer school on multidimensional poverty analysis · poverty analysis 1 –13 august 2016 ......

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