Lecture4_Poverty_Presentation_8.8.22.pptx

Lecture Outline
 Measures of poverty
 Poverty and other welfare indicators
 Targeting poverty programs

Introduction
The most visible feature of developing countries is wide-spread poverty.
But what is poverty?
To know what helps to alleviate poverty, what works and what does not,
what changes over time, poverty has to be defined, measured, and studied.
Poverty refers to an inability of individuals to have access to a minimum
level of acceptable standard of living in a society.
Possible indicators -- levels of income and consumption, social indicators,
and now increasingly indicators of vulnerability to risks and of socio/political
access.

Introduction
The most commonly used way to measure poverty is based on income or
consumption levels.
i. A person is considered poor if his or her consumption or income level
falls below some minimum level necessary to meet basic needs. This
minimum level is usually called the "poverty line".
ii. Information on consumption and income -obtained through sample
surveys. This may be complemented by participatory methods, where
people are asked what their basic needs are and what poverty means for
them.

However, poverty lines vary in time and place, and each country uses lines
which are appropriate to its level of development, societal norms and
values.
Suppose we regard the poverty line to be an expenditure threshold that is
regarded as minimally necessary for “adequate” participation in
economic life. People below this threshold are said to be poor.
Other things the same, if people are closer to the poverty line, then, the
poverty measure must register a decrease. This principle can be stated
in formally as follows:
Monotonicity Principle: Other things the same, if the income of a poor
person is increased, then, poverty must go down.

i. Note: wrong to define poverty by, say, the % of population earning less
than half the average income of society. Why?
ii. Because such a measure confuses poverty with inequality.
iii. Also, “structural” or chronic poverty must be distinguished from
“temporary poverty”.

What is Poverty?
Basic concept of monetary
poverty
Multidimensional aspects
• Low incomes and the inability to
acquire the basic goods and
services necessary for survival with
dignity
• Low levels of health and education
• Poor access to clean water and
sanitation
• Inadequate physical security, lack
of voice
• Insufficient capacity and opportunity
to better one’s life

Progress against Extreme Poverty
0
20
40
60
80
100
1820 1840 1860 1880 1900 1920 1940 1960 1980 2000
ACH
ACN
BSM
France
Germany
Italy
PS
Scand.
UKI
USA
Poverty rate (% below about $1 a day)
World (including today's
developing countries)
Japan
Russia
UKI
USA
ACN: Australia-Canada-New-Zealand; ACH: Austria-Czechoslovakia-Hungary; BSM: Benelux-
Switzerland-Micro-European States; PS: Portugal-Spain; UKI: United Kingdom and Ireland
Source: Ravallion, Economics of Poverty,

How To Measure Poverty?
1. A welfare measure for individuals, used to derive a distribution of living
standards
2. A poverty line, threshold below which individuals are classified as poor
3. A poverty index, summary statistics of poverty in population
Non
poor
Poor
Threshold

Principle of Poverty Measure
12
 Understandable and easy to describe
 conform to a common sense notion of poverty
 Fit for the purpose for which it is being developed
 Technically solid
 Operationally viable
 Easily replicable

Poverty
Objective
Monetary
Relative
Absolute
Non-monetary
Basic Needs
Approach
Anthropometric
needs
Subjective Direct surveys
Poverty Measures
13

Headcount Index
 The most widely-used measure is the Headcount measure due to
its simplicity
 Let 𝑦𝑖 denote income (or expenditure) and subscripts 𝑖, 𝑗, … refer
to individuals. Let 𝑧 denote the poverty line.
Poor = 𝑦𝑖 < 𝑧
 Headcount Index, 𝑃0:
𝑃0 =
1
𝑁
𝑖=1
𝑁
𝐼(𝑦𝑖 < 𝑧 )
Where 𝐼 is indicator equals 1 if true (poor) and 0 otherwise.
 If 30 people are poor in survey that samples 300, then 𝑃0 = 10%.

Headcount Index
Problem? - fails to capture the extent to which individual income (or
expenditure) falls below the poverty line.
Eg people further below the poverty line are “poorer” than people closer to it,
the head count is insensitive to this.
Therefore the use of HC systematically biases policy in favour of individuals
who are very close to the poverty line.

Headcount Index- example
Assuming that 𝑧 = 125
Expenditure for each individual in community 𝑃0
Expenditure Community A 100 100 150 150 50%
Expenditure Community B 124 124 150 150 50%

As a welfare function, the headcount index is unsatisfactory- it violates the
transfer principle (Dalton,1920) .
Transfer principle states that transfers from a richer to a poorer person should
improve the measure of welfare.
With the headcount index, if a somewhat poor household were to give to a very
poor household, the index would be unchanged, even though it is reasonable to
suppose that poverty overall has lessened.
Second, the headcount index does not indicate how poor the poor are, and it
does not change if people below the poverty line become poorer.
The easiest way to reduce the headcount index is to target benefits to people just
below the poverty line, because they are the ones who are cheapest to move across
the line.

Third, the poverty estimates should be calculated for individuals, not households.
Eg- If 20 percent of households are poor, it may depend on hh size (i.e it
underestimates poverty in large hhs and overestimates poverty in small hhs)
Since survey data are collected at the household-level, we make a critical
assumption that all members of a given household enjoy the same level of well-
being.
This assumption may not hold in many situations. For example, some elderly
members of a household, or girls, may be much poorer than other members of the
same household. In reality, consumption is not always evenly shared across
household members.

One way to offset this bias, is to look at the average shortfall of income below the poverty line.
Poverty gap measures are designed to measure the depth of poverty.
It tells us by how much income or consumption of poor falls short of poverty line.
It gives an idea about the amount of resources required to lift poor people out of poverty.
Two types of poverty gap measures are used: poverty gap ratio and income gap ratio.

Poverty gap ratio (PGR)
Poverty gap ratio (PGR) measures the amount of resources (income or expenditure) required to
bring all poor to the poverty line as a percentage of average income
Poverty Gap Ratio (PGR): is the ratio of the average of income (or extra consumption)
needed to get all poor people to the poverty line divided by the mean income (or consumption)
of the society.
Let m be the average income in a society. Then
Numerator- the total amount of extra income required to bring all poor to the poverty line.
Denominator is the total income of the society.
nm
y
p
PGR
p
y
i
i




)
(

Poverty gap ratio (PGR)
In a sense, the PGR is not a measure of poverty but rather a measure of resources required to
eradicate it.
Problem? Dividing by average economy-wide income may give a misleading impression of
poverty in highly unequal (but overall wealthy) societies with a large number of poor people.
PGR also violates the Dalton principle. See eg below:
For both of these countries, the poverty gap rate is 0.10, but most people would argue that
country B has more serious poverty because it has an extremely poor member

Income gap ratio (IGR)
Income gap ratio (IGR): captures more directly the acuteness of poverty because it measures
it relative to the total income needed to make poverty go away.
It is exactly the same measure as the shortfall but we divide the shortfall by the total income
required to bring all poor people to the poverty line.
pHC
y
p
IGR
p
y
i
i




)
(

Foster-Greer-Thorbecke (FGT) Index
HCR and PGR ignore the relative depravity or inequality among poor.
FGT index is developed to take account of inequality among poor. It is given by
𝑃𝛼=
1
𝑛 𝑦𝑖<𝑝
𝑝−𝑦𝑖
𝑝
α
Note that when α= 0, P0 = HCR.
When α = 1, P1 simply becomes a variant of poverty gap measure.
For rises in α, FGT index increasingly becomes sensitive to poverty gaps.
For α > 1, this index satisfies weak transfers principle.

Example: FGT Index
Suppose that the headcount poverty rate in the urban areas, where 40 percent of the population
lives, is 8 percent, and that the rural poverty rate is 35 percent.
Then the national poverty rate may be obtained as the weighted average of these subnational
poverty rates, as
P0 = P0,urban(Nurban/N) + P0,rural(Nrural/N) = .08(0.4) + 0.35(0.6) = 0.242, or
24.2 percent.
the FGT measure provides an elegant unifying framework for measures of poverty, it leaves
unanswered the question of the best value of α.

Multidimensional Poverty
 Sabina Alkire and James Foster (2011) proposed Multidimensional
Poverty Index, MPI
 The Formula:
𝑀𝑃𝐼 = 𝐻 𝑋 𝐴
where
 𝐻 is the percent of people identified as poor, it shows the incidence of
multidimensional poverty.
 𝐴 is the average proportion of deprivations people suffer at the same time; it shows
the intensity of people’s poverty.

Multidimensional Poverty
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)
Deprived if no
household member
has completed five
years of schooling Dimensions are equally weighted, and each
indicator within a dimension is equally weighted

Poverty principles
Weak transfers principle states that a transfer of income from any person below
poverty line to anyone less poor, while keeping the set of poor unchanged, must raise
poverty.
FGT index has another desirable property. It can be decomposed to study poverty
among various subgroups – men-women, rural-urban etc.
A poverty measure satisfies the population principle under which a cloning of the
entire population does not change the change the poverty measure.
Useful for comparing poverty level across different economies, with different number
of people in it.

Example 1: Let n=4, poverty line p = 2. Consider the following income
distributions A=(1,1,3,8) and B=(3,3,9,24).
Note that the distribution B is obtained from A by all incomes above 3. Thus,
if the relative income principle were to apply, poverty should remain the
same under both distributions.
However, given that the poverty line is at p=2, under the second distribution
B, everyone is over this poverty line while under A, two are below this line.
Therefore, A has higher poverty levels than in B.

However, consider a slight modification of example 1.
Assume now that the poverty line under A is p=2 while the poverty line in B is p=6.
In this example, compared to A, all incomes in B have gone up three times.
Moreover, the poverty line has also gone up three times. It might be reasonable to say
that poverty is the same in both the situations.
Consider two situations.
In situation 1, the income distribution is A and the poverty line p
while in the second situation, income distribution is B where B=kA, (B is obtained
from A by multiplying all incomes in A by k) and also the poverty line is kp.
A poverty measure is said to satisfy the Modified Relative Income Principle if
poverty associated with situations 1 and 2 are the same.

Poverty: Empirical Observations
 Demographic features: Poor households on average have larger size
compared to non-poor households
 Rural and Urban Poverty: In general, incidence of poverty is much
higher in rural areas compared to urban areas.
 Assets: Poor households are characterized by lack of assets.
 Nutritional status: In general, the incidence of malnutrition is much
higher among poor.

Some Characteristics of the Poor
 Large Family Size- poor households tend to be large relative to the
average family size of the economy.
 Many Children- high ratio of dependents. Larger families (especially
those with large numbers of children) have lower per capita income.
 More interestingly, a situation of poverty can indeed perpetuate itself by creating
incentives to have a large number of children.

Poverty and Non-cognitive abilities
 Studies across a number of countries have revealed sizable
differences in Peabody test results in early childhood between rich
and poor families.
 Across five Latin American countries, the differences in Peabody test scores
between children 3-6 years of age in the poorest 10% and the richest 10% of
families according to a wealth index range from 1.0-1.6 standard deviations, and
are statistically significant.
 Three-quarters or more of the variance in test scores is accountable to
wealth differences.
 poverty in childhood has negative effects on productivity and (hence)
incomes.

Schooling and Poverty
 School completion rates across
countries from the DHS for the
richest and poorest quintiles
based on the DHS wealth
index.
 Amongst countries where 50%
of those aged 15-19 had
completed grade 6, the mean
completion rate was 76% for
the richest quintile of families
but only 24% amongst the
poorest.
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Overall mean
Poorest quintile
Richest quintile
Proportion
of
15-19
year
olds
who
have
completed
grade
6
Proportion of 15-19 year olds who have completed grade 6
0.76
0.24
0.5
Source: Deon Filmer’s “Education Attainment” database
at World Bank.

Feminization of Poverty
 Four factors in the feminization of poverty:
1. Poor women typically work longer hours than do men, notably when
account is taken of domestic labor (within the household).
 The pressure of poverty to increase female labor force participation does not
typically come with reduced work at home.
2. Poor women typically face fewer opportunities for independently
escaping poverty.
 Their domestic commitments and cultural taboos often prevent them from
taking up new opportunities as readily as can men.
3. In some cultures widows face effective barriers against employment or
remarriage, and are treated as second-class citizens within the home,
leading to high risks of poverty.
4. Poor women are more vulnerable to risk

Nutrition and Poverty
 In measuring the nutritional status of children, two widely used
measures are weight-for-height (“wasting”) and height-for-age
(“stunting”).
 Wasting is usually indicated by a child being two standard deviations
below the median weight given height of a reference population.
 Stunting is indicated by a child being two standard deviations below the
median height for age of the reference population.
 Reference population is typically healthy well-nourished children in the
U.S. in ‘70s.

Nutrition and Poverty
Note: Stunting rate = % height-for-age z score two st. dev. below mean
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80
Headcount index (% living below $1.90 a day, 2011 PPP)
Stunting
rate
(%)
Burundi
Child stunting and household poverty across Africa

Motivation
 Economic growth is a necessary but insufficient condition for the
alleviation of poverty.
 Implementation of the agenda for reducing poverty requires
methods for reaching the poor
 Developing countries do not have enough resources to implement
universal poverty program

BENEFIT of Targeted Social Protection
 Objective: the desire to maximize the reduction in poverty or,
more generally, the increase in social welfare
 Budget constraint: a limited poverty alleviation budget
 Opportunity cost: the trade-off between the number of
beneficiaries covered by the intervention and the level of
transfers.

COST of Targeted Social Protection
 Administrative Costs: collecting huge information
 Private Costs: Households also incur private costs involved in
taking up transfers.
 Incentive Costs: the presence of eligibility criteria may induce
households to change their behaviour in an attempt to become
beneficiaries
 Social Costs: social stigma, conflict, and social unrest
 Political Costs: Excluding the non-targeted classes may remove
broad-based support

Targeting Methods
 Individual Assessment
 Simple Mean Tests
 Assessment based on qualitative measures (Jamaica’s food stamp program in 1980;
Unconditional Cash Transfer in Indonesia in 2005, etc)
 Proxy Mean Tests
 Generates a score for applicant households based on several indicators (Indonesia Poverty
programs implemented in 2008 and afterward)
 Categorical Assessment
 Community Based Targeting
 Geographical Targeting
 Self Targeting

Targeting performance
Poverty Status
Total
Poor Non-poor
Beneficiaries status of
program j
Beneficiary
Correct inclusion
(C1)
Error of Inclusion
(E1)
B
Non-
beneficiary Error of Exclusion
(E2)
Correct Exclusion
(C2)
NB
P NP T
𝑈𝑛𝑑𝑒𝑟𝑐𝑜𝑣𝑒𝑟𝑎𝑔𝑒 = 𝑒𝑒 =
𝐸2𝑗
𝑃
𝐿𝑒𝑎𝑘𝑒𝑔𝑒 = 𝑖𝑒 =
𝐸1𝑗
𝐵𝑗

Poverty Targeting in Practice: Indonesia

The Evolution of Indonesia’s Targeting
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
Percentage
of
Decile
Covered
Consumption Decile
National Urban Rural Perfect Targeting
Percentage of Receiving 2008 Cash Transfer
Using PSE08
 BPS (Statistics Indonesia) created the PMT scores
using indicators found in Susenas and Podes. 39
indicators from Susenas and 12 indicators from
Podes.
 The PSE05 list was updated. Remove households
who had left
 All updated households were surveyed using
PPLS08 including 18 HHD and 6 individual
indicators in the questionnaire.
Source: Susenas, author calculation

Methodology and targeting of welfare programs
 The GoI has made some improvements in both
methodology and implementation of the poverty
programs
 The GoI issued the Social Security Card (Kartu
Perlindungan Sosial) for 25% of the poorest or 15.5 million
HHDs
 The card was sent directly to the HHDs via post service
together with Information about the amount of benefits
received by KPS holders
 It can be used to access all household targeted poverty
alleviation programs, such as BLT, Raskin, BSM, and
Jamkesmas. (Unified Targeting System)

Developing the Unified Database
 In terms of the methodology and targeting (TNP2K 2015):
 More variables used in the PMT of PPLS11 (Poverty Census)
 PMT conducted based on 471 district-specific models
 PPLS11 had greater coverage of households, reaching 40 percent of the population.
 Using two-stage targeting in the data collection process
 Develop National Targeting System called the Unified Database (UDB)
Beneficiaries data from
previous program
Consultation with the poor
Population census 2010
Initial
List
Data
Collection
Data analysis
and
Development
Unified
Database
Involved
120.000
enumerators

Proxy Mean Tests
 Given Indonesian heterogeneity, models are made for each of 500 districts
 Employing information within the PPLS, an index denoting the household
consumption level can be calculated:
Index = f (household & regional characteristics)
 The household characteristics includes housing conditions and status of ownership,
assets, number of household members, level of education, working status, etc.
 Households can then be ranked according to the index
 The formula leading to the index is specific for each Kabupaten/Kota

4
3
2
1
10
9
8
7
6
5
Poor
Near - Poor
Vulnerable
11.37%
25%
40%
60%
Consumption
Decile
Indonesia’s Major Poverty Programs and their target
The health insurance for the poor (Jamkesmas) coverage
targeting 24.7 million households or 96.4 million people is
the same coverage with the UDB
Unconditional Cash Transfer (BLSM), and Rice for the Poor (Raskin)
coverage which targets 15.5 million households or 65.6 million people
The number of poor in 2013 was 5.7 million households or 28.59 million
people
 Jamkesmas (for Jaminan Kesehatan Masyarakat or Health Insurance for the Poor) provides health
insurance.
 Raskin (for Beras untuk Masyarakat Miskin or Rice for the Poor) provides rice for the poor households
about 15 kg/household/ month (Subsidy received about 100 thousand IDR or 10 AUD/household/month)
 BLT/BLSM (for Bantuan Langsung Sementara Masyarakat or Unconditional Cash Transfer) provides
150 thousand IDR/ household/month (about 15 AUD)
Under the UDB targeting mechanism (since 2011), the
eligibility of the households for each program is selected
based on their Score of Proxy Mean Test (PMT) measured
using 471 district-specific models

Tohari A, et al (2019).
 Propose a new evaluation method of poverty targeting under
complementarities.
 Measures the impact of program complementarities on poverty
reduction.
 Contributes to the current debate on the best response (cash vs.
in-kind) to accelerate poverty reduction

Data
 Poverty Censuses (PPLS2011) which covers 40 percent of the poorest or 25,5 million
households surveyed by the Indonesian Central Bureau of Statistics (BPS)
 National Social Economic Survey (Survei Sosial Ekonomi Nasional – Susenas) 2005-2014
conducted by the BPS which provides
 information on household and individual characteristics
 Social Protection Survey (Survei Perlindungan Social – SPS) which were conducted in the
4th quarter of 2013 and the 1st of 2014 by TNP2K and BPS as a Susenas complement
which provides
 information about social protection programs, just after the implementation of KPS.
 Village Potential Statistics (Potensi Desa - Podes) 2011 and 2014 were conducted by BPS
which provide
 Information about access, local village government, and other information related
where the location of HHDs

| 52
Source: Susenas & Social Security Survey, author’s calculation
47.34
66.17
48.42
48.51
71.75
27.13
38.13
78.83
54.46
43.11
29.12
88.25
63.98
28.81
60.25
38.37
Exclusion Error Inclusion Error Exclusion Error Inclusion Error Exclusion Error Inclusion Error
Raskin BLT/BLSM Jamkesmas
2005
2009
2014
Inclusion & Exclusion Errors

| 53
Results

Notes on table
• compares the joint and marginal probabilities of participating in the poverty programs
comparing poor households that received the KPS (KPS holders) to those that did not
(Non-KPS holders).
• From columns (5) and (9), the joint probability of participating in all three programs
for KPS holders is significantly higher than for non-KPS holders (56.64 percent as
opposed to 3.78 percent).
• i.e, the joint probability of not receiving any of the three programs for a KPS holder
is significantly lower than for a non-KPS holder (0.45 percent compared to 30.83
percent).
• The marginal probabilities are also much higher for KPS holders. For example, the
probability of receiving BLT is 96.25 percent for KPS holders, while it is only 11.45
percent for non-KPS holders.

| 55
Complementing Probabilities (1)
Targeting Methods → PPLS08 The UDB
Probabilities →
Joint
Marginal
Joint
Marginal
Programs ↓ BLT Raskin Jamkes BLT Raskin Jamkes
BLT only 4.76 4.76 3.96 3.96
Raskin only 23.74 23.74 19.66 19.66
Jamkesmas only 1.55 1.55 4.49 4.49
BLT and Raskin only 24.78 24.78 24.78 9.42 9.42 9.42
BLT and Jamkesmas only 1.61 1.61 1.61 8.21 8.21 8.21
Raskin and Jamkesmas only 3.53 3.53 3.53 9.20 9.20 9.20
BLT, Raskin and Jamkesmas 12.67 12.67 12.67 12.67 27.51 27.51 27.51 27.51
None 27.36 17.55
Total 100 43.82 64.72 19.36 100 49.10 65.79 49.41
Observed joint and marginal probabilities

| 56
Observed conditional and unconditional probabilities
Targeting Methods → PPLS08 the UDB
Probabilities → BLT Raskin Jamkesmas BLT Raskin Jamkesmas
Programs ↓
P(.) 43.82 64.72 19.36 49.10 65.79 49.41
P(. |BLT = 1) 100.00 85.46 32.59 100.00 75.21 72.75
P(. |Raskin = 1) 57.86 100.00 25.03 56.13 100.00 55.80
P(. |Jamkesmas = 1) 73.76 83.68 100.00 72.29 74.30 100.00
P(. |Raskin = 1, Jamkesmas = 1) 78.21 100.00 100.00 74.94 100.00 100.00
P(. |BLT = 1, Jamkesmas = 1) 100.00 88.73 100.00 100.00 77.02 100.00
P(. |BLT = 1, Raskin = 1) 100.00 100.00 33.83 100.00 100.00 74.49
Complementing Probabilities (1)

Summing up
 Using the proposed method, the implementation of Unified Targeting System
improves program complementarities.
 Program complementarities significantly improve the welfare of poor households
by about 30 percent on average.
 Type of program does not matter, program complementarities are the key for
accelerating the poverty reduction.

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