Global Perspectives on Poverty and Inequality

Part 3, Chapter 7: Dimensions
Poverty and Inequality in the
World
Martin Ravallion
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Lecture notes to accompany Ravallion’s 
The Economics of Poverty.
There are large differences in living
conditions across the world today
2
 
These are averages. 
What about household income?
For GDP per capita by year see 
Our World in Data
.
  Penn’s parade of world incomes
4
Household income per person in $’s per day in 2008
Source: Lakner and Milanovic, 2013, “Global Income Distribution,” Policy Research
Working Paper 6719, World Bank.
  Penn’s parade of world incomes
5
Household income per person in $’s per day in 2008
Family living conditions across the world
6
Source
: Selected from 32 photos by 
Peter Menzel
. Life expectancy for 2015.
USA
Life expectancy at birth: 79 years
7
Brazil
China
Life expectancy: 75 years
Life expectancy: 76 years
8
Mali
India
Life expectancy: 58 years
Life expectancy: 68 years
Dollar Street
Imagine a street of 10 houses with the poorest on the
far left and richest on the far right. 
Where would you put
your family on this street?
The 
Dollarstreet
 project by Gapminder shows you how
people live across the world at different income levels.
Similar consumption at the same income.
Also see this TED talk by co-founder of Gapminder,
Anna Rosling Roonlund
.
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Learning about poverty from vignettes
Some surveys have described various families and
asked respondents to rank their “economic welfare” on
an ordinal scale.
“Imagine a 6-step ladder where on the bottom, the first step,
stand the poorest people, and the highest step, the sixth, stand
the rich. On which step are you today?”
The same respondent is then asked to rank her/his own
welfare on the same scale.
Let’s look at results for three countries.
10
Guatemala
11
Tajikistan
12
Tanzania
13
Access to basic services
14
“Poor”: consumption or income less than $1.25 a day
“Non-poor”: the rest.
Outline
Part 3,
Chapter 7
1. Poverty and inequality in America
2. Global inequality 
 
3. Global poverty
 
3.1 Absolute poverty globally
 
3.2 Taking 
social effects on welfare
 seriously
 
3.3 Measures of 
absolute
 poverty
 
3.4 Measures of 
relative
 poverty
 
3.5 Overall measures of global poverty
4. Urban and rural dimensions of poverty
5. Non-income dimensions of poverty
5.1 Child development and poverty
5.2 Demographics of poverty
5.3 Schooling and poverty
5.4 Gender dimensions of poverty
5.5 Nutrition and poverty
5.6 Violence and poverty
15
 
 
1. America
16
1.1 Inequality in America
17
Growth incidence curves for US
Pre- and post-tax 1967-2015
18
Source: Wimer et al. 2020 (*)
Two periods in America:
1946-1980 and 1980-now
19
Source: Saez and Zucman
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Gini index in US
20
Market
incomes
Disposable 
incomes
Source: Branko Milanovic
 
Redistribution
Rising inequality of incomes and wealth
in America since around 1980
The rise in income inequality
has been more marked than for
wealth.
But note how much higher the
Gini index is for wealth than
income.
Imagine 10 households with
wealths: (1,2,3,4,5,6,7,8,9,X).
What is X to get Gini=0.86?
X=750.
21
Source
: Kuhn et al. 2020
Gini=
0.58
Gini=
0. 86
Income share held by the richest one
percent of American households
22
Source: Alvaredo et al. (2014); author’s estimates of a nearest neighbor smoothed scatter plot.
Rising share of top 0.1% in US since late
1970s; returning to level of early C20th.
23
(Graphic from 
Economist
 magazine.)
Inequality of market incomes is rising
faster than disposable income, esp. US
24
 
Redist-
ribution
Note: Market income: income from all sources; Gross income: market income
less all transfers; Disposable income: Gross income net of taxes.
Source: OECD; for OECD as a whole see this 
paper
.
And the floor in US is sinking!
25
Stabilized in 2000s, mainly due to social policy (esp. SNAP)
Source: Joliffe et al. 2020.
Differing fortunes in US
Divergence is absolute and relative
26
 
Source: Jolliffe et al. (2020)
Declining absolute income mobility in US
27
Source: 
The Equality of Opportunity Project
.
Local inequality: Washington DC
DC has the highest income inequality of any major
metropolitan area of the US, with a Gini index of about
0.60
.
This reflects the special nature of DC and its history of
in-migration by both the rich and poor.
Also note that the large Washington area (incl. Arlington
and Alexandria (DC-VA-MD-WV) has average inequality
for the US.
However, some of the costs of high inequality (as we
discuss later) are likely to be localized.
28
Gini indices
within
countries
Source
: World Bank, 2006,
World Development Report
, 
Oxford University Press
20%
40%
60%
 
DC!
(Gini=60%)
Factors underlying the rise in US
inequality
30
Side-by-side with rising inter-personal
inequality in US we have seen:
Falling 
labor share 
in national income
Measured by ratio of worker compensation to national income
Falling in US since 1980; around 66% in 1980; <60% now.
Falling participation in 
labor unions
Down from 20% of workers in 1980 to about 10% now.
Rising industrial 
concentration
Larger and fewer firms; “superstars”; less competition.
Higher markups in pricing; higher profits => lower labor share.
Labor share has fallen faster in US industries that have become
more concentrated.
Rising returns to higher 
education =>
Higher costs of college education but also higher earnings gains.
31
Real wages by education over time in US
 
32
Source: David Autor
Redistributive effort has declined in US
33
Recent signs of a brake to the rise in
inequality in America (pre-COVID)
Federal Reserve’s 
Survey of Consumer Finances
indicate that m
edian household pre-tax income grew by
5% over 2016-19,
However, the mean fell by 3%, mainly due to decline in
incomes of the top 1%.
Are we seeing the end of rising inequality in the US? 
The
pandemic may well have increased inequality, as the
poor and middle-income groups are probably less able to
protect their living standards.
We await to see what new data tell us!
34
Identity and inequality
35
Identities matter to inequality and
injustice
Just as nationality may matter to assessments of
inequality, other aspects of identity have a salience that
is hidden by standard measures.
Ethnicity, race, religion and gender have been important
examples.
Even small between-group disparities have a large social
and political significance.
Standard decomposition methods do not attach any
extra weight on group identity.
Yes, there are inequalities between groups,
But they are given the same weight as inequalities within groups.
36
Stubborn and worrying inequalities by
race in the U.S.
Soon after the 50th anniversary of Martin Luther King
Jr.’s “I Have a Dream” speech, how are we doing in
reducing racial economic disparities?
The black unemployment rate has remained about twice
the white rate for 50 years
The gap in household income hasn’t narrowed in the last
50 years
Blacks are still far less likely to be health-insured than
whites.
37
Racial wealth gap in US
38
In 2016, non-Hispanic White families had a median net worth
that is 8-10 times higher than the other two main race groups.
Source
: 
Brad Plumer
, 
Washington Post
, August 28, 2013.
Other indicators of the racial wealth gap
Home ownership rates (2011):
White families: 73%
Black families: 45%
Hispanic families: 47%
Median wealth return to graduating college (2011):
White families: $60,000
Black families: $4,846
Hispanic families: $4,191
39
2.2 Poverty in the U.S.
40
Absolute poverty line in
the U.S.
Unusually amongst rich countries the U.S. uses an
absolute line—adjusted only for inflation over time.
US official line developed in 1965 by Mollie Orshansky,
an economist working for Social Security Administration.
A 1955 survey to determine the nutritionally adequate but
socially acceptable food bundle.
Poverty defined as making less than three times the cost of this
diet; factor of three came from a 1955 study indicating that food
spending accounted for one-third of a typical family’s budget.
Currently the threshold is set at $24,069 for a family of four ($16
per person per day).
Adjusted over time for inflation nationally, but no adjustment for
geographic cost-of-living differences.
41
 
 
Debates on US poverty: Is the official
line too low or too high?
The U.S. line is the
average line for
countries with only
about one third of the
mean consumption
level of the US.
Also low relative to the
social subjective
poverty line.
But the income
concept excludes
taxes/transfers!
42
Note: Official line in 2005 is $13 is
$13 per day (family of 4 with 2 kids).
In 2013 it is about $16 per day
Official measures show little long-term
progress against poverty
Little progress against poverty: 1980-2015: the official
poverty rate rose by 0.5% points.
Growth rates in household incomes tended to be higher
for poorer groups, undoing some of the rise in inequality
that we have been seeing in the US for many years.
Among those who are below the official line, a rising
share are living on less than half that line.
43
 
Recent progress threatened by the 2020
pandemic
Good news in 2015-19: the US poverty rate fell and
median incomes rose. See the 
official report
.
Official poverty rate in 2019 is 10.5%. 3-year average for
2017-19 is 11.5%.
Early evidence suggests that the US poverty rate has
risen during the 2020 pandemic.
This has been counteracted to some extent by enhanced
spending on social protection.
44
Three measures of poverty in the US
Using the official poverty line.
Three measures in the Foster-Greer-Thorbecke class.
45
Source: Calculations from successive rounds of the 
Annual Social and Economic (ASEC) 
Supplement to the Current Population Survey (CPS)
.
Alternative measures of poverty for US
Official measures use 
pre-tax money income
.
This leaves out the effects of some pro-poor policies.
New Census Bureau “supplementary poverty line” is
more generous but uses a better income concept.
Similar overall poverty rate, but no series over time.
Regional COL differences (e.g., Texas vs California).
Consumption poverty measures have become available
and show much more progress against poverty.
Consumption poverty rate has fallen over time, despite the lack
of progress indicated by the official poverty measures.
1980-2015: consumption poverty rate down by 9.4% points.
See Meyer and Sullivan, Figure 1 
here
)
46
Poverty  in the US by race/ethnicity
47
Note: Non-Hispanic
whites have a
lower poverty rate
(% of this group)
but around half of
the poor are white.
Washington DC stands out again!
48
Update for 2017 is 
here
.
Poverty rates across counties of the US
3,000 counties.
Overall, the poverty
rate is 15%.
But wide variation.
Ranging from 2.6%
(Douglas County,
CO) to 54% (Oglala
Lakota County,
SD).
Strong correlation
with average
household income.
Elasticity = -1.5.
49
The changing geography of poverty in
the US
Historically, poverty
measures have been
higher in both large inner-
city areas and more remote
or rural communities.
This is changing, with
poverty shifting to suburbs.
Suburbs account for half
of the increase in number
of poor 2000-15.
50
Growth and poverty in the U.S.
From the 1970s, America’s economic growth started to
by-passed the country’s poor. (Recall that inequality has
been rising.)
By a Rawlsian assessment this was unjust because it did
not come with gains to the poorest stratum.
51
Source: Sheldon Danziger (2007) (as
cited by Jared Bernstein, 2014).
Absolute poverty rates in the US stopped 
falling from early 1970s
Why the stagnation in progress against
pre-tax income poverty in the U.S.?
Rising income inequality meant that the bulk of the gains
from growth were captured by the rich.
Later we will try to better understand the sources of
rising inequality in the US.
53
1. Inequality in the world
54
A broad-brush description of global
inequality
What is “global inequality”?
Global inequality is traditionally defined as the relative
inequality of incomes among all peoples of the world
ignoring where they live.
This treats everyone the same way no matter where they
live., i.e., nationality does not matter independently of
one’s own income.
That may be debatable:
People may care more about fellow citizens than
foreigners, though should they?
Living in a richer country brings external benefits not
reflected in own-income (better public services and
governance, cleaner air). (We return to this point.)
55
Looking back over 200 years
Period 1: 1820-1990
Global inequality was on a 
rising trend 
from 1820 to
about 1990.
This long period of rising inequality was driven in the
main by the 
divergent growth processes
, with today’s
rich world taking off economically from the early C19
th
(though with some late starters such as Japan)
Average inequality 
within
 countries was stagnant or even
falling over much of this period, most notably over the
middle half century of the C20
th
—known as the 
Great
Levelling
 in the rich world.
56
57
Bewteen vs. within country inequality
Between-country ineqality has become more important
Source
: Bourguignon and Morrisson
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                                         Based on Bourguignon-Morrisson (2002) and Milanovic (2005)
Looking back over 200 years
Period 2: 1990-now
This pattern changed dramatically toward the end of the
C20
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: new pattern of falling inequality between countries
alongside rising average inequality within countries.
59
Density functions for global income
distribution
60
 
Rich world
pulls away
 
Poor world
starts to
catch up
1820
1970
2000
Alternative measures and concepts
of global inequality
61
62
It’s a matter of weights: people weights
Inter-country inequality
International inequality
Global (interpersonal) inequality
Different concepts for different questions
When we ask how much inequality there is among the people
of the world we need the concept of “global inequality.”
When we ask: “is a typical family in America poorer than (say)
India” we are asking about inter-country inequality.
When we ask how much does inter-country inequality
contribute to global inequality we need the concept of
“international inequality.”
This is the measure of global (interpersonal) inequality that we
would obtain if everyone within a given country had the same
income, given by the mean for that country.
Recall: decomposable inequality indices (like MLD) can be
written as: Total inequality = Between-group inequality +
Within-group inequality.
63
 
64
Source: Branko Milanovic: The “adjusted” series includes 
and allowance for under-reporting by the richest 10%.
(“inter-country”)
Exchange rates also matter
Market exchange rates for different currencies are defensible
for internationally traded goods.
However, many goods are not internationally traded., esp.
services 
(classic example: a haircut).
These goods tend to be cheaper in poorer countries where
wages are lower (labor being the main input in production of
most services).
Then we need 
Purchasing Power Parity 
(PPP) exchange rates,
based on the actual prices faced in each country.
These data are collected by the International Comparison
Program (based at World Bank but involving the major
regional development Banks).
65
66
Different exchange rates 
=> different income weights
=> different global inequality measures
World Gini index (%)
Source
: Branko Milanovic
67
A less unequal world?
The weight of China and India
68
A less unequal world?
Absolute vs relative inequality
 
A Growth Incidence Curve:
The elephant graph
69
Source
: Lakner and Milanovic
Interpreting the elephant graph?
 
Rich world’s lower-
middle class has seen
little or no gain from
globalization.
 
The poor and middle-
class of the developing
world have seen
substantial gains.
 
Large gains
for the rich
 
Little gain for
the poorest
70
An ambiguous change in global
inequality
The Lorenz curves
intersect internally =>
Gini index fell slightly
(from 72% to 71%)
But a marked inward
shift of the LC
between the 30
th
 and
80
th
 percentiles +
outward shift among
the top decile +
declining share for the
poorest 5%.
71
Absolute incidence of the income gains
72
 
?
Under-reporting or
non-compliance by
the rich leaves doubt
about high-end.
What status for the “nation”?
Global inequality is typically measured by pooling all
incomes, ignoring where people live. No intrinsic role for
nationality.
Alternatively, we may want to put lower weight on
foreigners. But is that ethically acceptable?
Philosophers, such as Peter Singer, argue instead that national
borders are not morally relevant to the case for helping
disadvantaged people whom we can help.
By this view, on normative grounds, one 
should
 care about
everyone, no matter where they live.
This is called the “
cosmopolitan approach
”.
This begs the question: 
Inequality of what?
If people only care about 
relative
 income in their country
then global inequality = average inequality within countries.
Inequality within countries
74
Nationality matters
In the global measures so far, “countries” are just arbitrary
groupings of people.
The underlying concept of individual welfare:
 
      
 
     
U = U(own consumption)
Yet, it is clear that people care a lot more about 
inequality
within their country 
of citizenship or residence (or maybe
even their neighborhood, or some other reference group)
than globally.
Perceptions of 
relative deprivation
 are more local.
If people only care about 
income relative to the national mean
then “global inequality” is just average within-country inequality.
However, there may also be 
positive external effects 
of living
in a richer country.
75
Gini indices
within
countries
Source
: World Bank, 2006,
World Development Report
, 
Oxford University Press
20%
40%
60%
Top income shares across the world
77
Source: 
World Inequality Database
Rising inequality in
many developing
countries
Overall decline in inequality
in the developing world, but
not 
withi
n countries
Inequality increases about
half the time during spells of
growth.
Rising inequality on average
(more so in some regions
than others).
This is attenuating gains to
the poor from growth.
78
..and in many (but not all) rich countries
Rising inequality in
the US since around
1970. And in UK
since 1980.
Recall that attention
to inequality in the
literature started to
rise from about
1960.
But the momentum
may well reflect the
rising level of
inequality.
79
Source: Branko Milanovic
Most of the rich
nations have
seen sizeable
increases in the
Gini index since
1970.
France is one of
the exceptions.
Not much
change for Italy
or Spain.
Source
: Morelli, Smeeding
and Thompson (2014)
80
Falling then rising share for top 1%
81
Source: 
World Inequality Database
Ethnic/racial
inequalities
In many countries, there
are ethnic minorities
who fare worse on
income and non-income
dimensions of welfare
than the majority.
Of course, there are
also income inequalities
within
 ethnic groups.
82
http://www.economist.com/news/asia/21654124-myanmars-
muslim-minority-have-been-attacked-impunity-stripped-
vote-and-driven
The most persecuted people on Earth? 
The Rohingyas of Myanmar
However, these identity-based inequalities are not properly
captured by standard measures.
The injustice often has more than narrowly-defined
economic dimensions.
3. Global poverty
83
3.1 Absolute poverty in the world
84
Absolute poverty in the developing world
Recall that the US and many developing countries have
favored absolute poverty lines that aim to have the same
real value at different dates and places.
These are typically anchored to nutritional requirements
for good health and normal activities.
However, also recall that there are infinitely many
commodity bundles that can attain any given set of
nutritional requirements.
A (very) long run perspective
86
Good news! However, some important caveats:
This is absolute poverty; aims to hold real line constant.
Problems of price indices over longer-term. New goods.
Definitions of “poverty” will change over such a long period.
“Poor” by whose standard?
 
In assessing poverty in a given country, and how best to
reduce poverty, one naturally focuses on a poverty line
that is considered appropriate for that country.
The bulk of the World Bank’s poverty analysis is at
national
 level.
 
But how do we talk meaningfully about “
globa
l poverty”?
Poverty lines across countries have a strong 
economic
gradient
, such that richer countries tend to adopt higher
standards of living in defining poverty =>
87
Recall that we see higher lines in richer
countries, but with a lower bound
88
$1.25
Malawi, Mali, Ethiopia, Sierra Leone, Niger, Uganda, 
Gambia, Rwanda, Guinea-Bissau, Tanzania, Tajikistan, 
Mozambique, Chad, Nepal, Ghana
2005 PPP
“$1.25 a day” global poverty measures
To measure poverty in the world as a whole, the “$1.25 a
day” measures apply a common standard, anchored to 
what
“poverty” means in the world’s poorest countries.
2015 update: $1.90 at 2011 PPP.
Two people with the same purchasing power over
commodities are treated the same way—both are either poor
or not poor—even if they live in different countries.
By focusing on the standards of the 
poorest countries
, the
$1.25 a day line gives the global poverty line a 
salience in
focusing on the world’s poorest
.
It is a 
conservative
 definition; it focuses on the 
world’s
poorest stratum
.
One can defend higher lines.
89
Strongly relative poverty
 
Recall that t
he common practice in most OECD countries
(US is an exception) and Eurostat has been to set the
poverty line as a constant proportion—typically around
50%—of the (date and country-specific) mean or median
income:
This a 
strongly relative poverty line
Mean
Poverty line
Welfarist interpretation:
Disutility of relative deprivation
 
By this view, a person’s welfare evaluation of their own
consumption depends on its value relative to society’s
mean consumption.
The poverty line is then the level of income at which
some fixed reference utility is reached.
However, as we saw in Part 2, this implies strongly
relative poverty lines if (and only if) 
people care 
only
about relative income.
That is surely implausible except (possibly) in very rich
countries.
(Review EOP Box 4.8)
91
Non-welfarist interpretation:
Capabilities and the cost of social inclusion
We can think of poverty as having both absolute and
relative aspects:
The former is a failure to attain 
basic survival needs
: capabilities
of being adequately nourished and clothed for meeting the
physical needs of survival and normal activities.
On top of this, a person must also satisfy 
social needs
, which
depend on prevailing living standards in the place of residence.
To be non-poor one needs to be 
neither absolutely poor
(“survival” capabilities) 
nor relatively poor
 (social inclusion
capabilities).
It can be agreed that certain forms of
consumption serve an important 
social
 role
Famously, Adam Smith pointed to the social-inclusion
role of a linen shirt in eighteenth century Europe.
Anthropologists have often noted the social roles played
by festivals, celebrations, communal feasts, clothing.
However, the social role of consumption
does 
not
 imply strongly relative poverty lines
The key assumption of strongly relative measures: the
cost of inclusion is a constant proportion of the mean
.
That is hardly plausible. The social-inclusion needs of
very poor people may well be low, but it is difficult to see
why they would go to zero in the limit.
A socially acceptable linen shirt would not have cost any less for
the poorest person as for someone living at the poverty line.
Very poor people are highly constrained in spending on things
that facilitate their social inclusion, but that does not mean that
their inclusion needs are negligible!
 
    
 
            => 
“weakly relative lines”
Weakly vs. strongly relative lines
(EOP, Box 4.9)
 
Mean
95
Weakly vs. strongly relative lines
(EOP, Box 4.9)
 
Mean
96
Neither absolutely poor 
nor relatively poor
3.2 The “elephant in the room:”
Social effects on welfare
97
Stepping back: Why do we see higher
(real) poverty lines in richer countries?
98
Two possible reasons for the relativist
gradient 
(EOP: pp. 342-3)
1.
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99
 
Can we say which is right?
Social norms call for absolute lines; social
effects call for relative lines
100
Social norms:
Higher standard of 
living needed to not 
be considered “poor”
Social effects:
Higher cost of attaining 
the same standard of 
living
Absolute lines
(constant real value)
Higher poverty
lines in richer
countries
Relative lines
(higher real value 
in richer countries)
The big uncertainty about global poverty!
The problem is that we do not know which of these two
interpretations—differing social norms or social effects—
is right.
And we may never resolve the matter from conventional
empirical evidence. 
 
There have been many claims about the existence of various
social effects on subjective welfare responses, though problems
remain in credibly identifying such effects.
This uncertainty makes it compelling to consider 
both
approaches
 when measuring global poverty.
101
Bounds to global poverty
Absolute poverty measures can be interpreted as the
lower bound
 to the true welfare-consistent measure.
The lower bound assumes that the relativist gradient only
reflects differing social norms.
A weakly relative measure of poverty provides its 
upper
bound
, allowing for social effects on welfare.
The upper bound assumes that the relatavist gradient stems
solely from social effects on welfare—extra spending needed to
attain the same level of welfare in richer countries.
102
3.3 The lower bound: Global
absolute
 poverty measures
(EOP, Section 7.2)
103
Steps in measuring global absolute
poverty
1.
The international line is converted to local currencies at
Purchasing Power Parity in the base year—the International
Comparison Program benchmark year.
2.
It is then converted to the prices prevailing at the time of the
relevant household survey using the best available price
index for that country.
3.
Then the poverty rate is calculated from that survey using
the micro data or specially commissioned tabulations.
4.
Interpolation/extrapolation methods using national accounts
data are used to line up the survey-based estimates with
these reference years.
104
 
Huge expansion in survey coverage
since 1980s
22 countries in the original “$1 a day” measures for 1985 with
one survey per country
Today: 150 countries; over 1500 surveys; 10 per country
Latest surveys: Sample of 2.1 million households
Consumption preferred to income
Comprehensive consumption aggregate
But not complete welfare metric: Need to supplement with
other measures to capture 
non-market goods
 and 
intra-
household inequality
.
105
But many data challenges remain
Lags and uneven coverage
90% for developing world as a whole (94% East Asia)
But only 50% for Middle-East and North Africa
Declining coverage back in time
Comparability over time and across countries
Differences in questionnaire design and definitions
(consumption or income aggregates)
Under-reporting and selective compliance
But not valid to replace survey means by national accounts
aggregates, holding inequality (Lorenz curve) constant
The problems are unlikely to be distribution neutral
No allowance for intra-household inequality
Problems in the price indices (PPP rates and CPI)
Urban bias in price surveys; no adjustment for cost-of-living
differences within countries.
106
What has been happening to poverty
in the aggregate?
(EOP, Section 7.2)
107
Progress against absolute poverty
There is no denying that
we have seen huge
progress against extreme
absolute poverty in the
world over the last 200
years.
108
This progress should be acknowledged, because not
doing so risks undermining further progress.
It’s actually dangerous that people are focusing on the bad
news and not seeing the progress we’ve made. It means
they don’t look at the best practices, it makes them less
generous
.” (Bill Gates in an interview with Betty Liu on
Bloomberg Television, 21 January 2014.)
New trajectory in the 2
nd
 half of the C20
th
109
 
1.5 billion more
living under
$1.25 a day
(2005 prices)!
1950 saw a turning point,
with much faster progress
against extreme poverty
Income distribution in today’s developing
world over 30 years
110
 
Bulging “middle” in
the developing world
Source: Our World of Data
111
What has been happening to poverty
in the aggregate in recent times?
With aggregate economic growth in the developing world
since 1980 we have seen a trend decline in the
incidence of poverty by the lower bound (absolute) line.
Falling absolute numbers of extreme poor (<$1.25 a day)
but rising numbers living between $1.25 and $2, and less
progress in reducing the number living under $2.
Note
: 
$1.25 
is based on 2005 prices; 
$1.90 
is based on
2011 prices (using different rounds of the price surveys
used to set PPPs). 
On PPPs see Box 7.1 EOP; on the
$1.26 line see Box 7.2.
Progress for the poorest in the aggregate
 
Over 1981-2008, the % of the developing world’s population
living below $1.25 a day was halved, from 52% to 22%.
 
Number of poor
fell by nearly 700
million, from 1.9
billion to 1.3
billion.
 Aggregate
poverty rate fell in
all years.
112
Millennium Development Goal 1?
MDG1: To halve the 1990 “extreme poverty” rate by
2015.
Using $1.25 a day as the line, the 1990 rate was 43.1%.
Estimates for 2010 (representing 80% of population):
21% living below $1.25.
So MDG1 was attained by 2010—5 years ahead of the
target date—despite the crises.
But we did not attain MDG1 for developing world outside
China.
On track for Sustainable Development Goal 1? More
later.
113
Robust to poverty line?
The claim that poverty fell between either 1981, 1990 or 1999
and 2008 is robust. The claim that poverty fell over time from
1981 to 1990 to 1999 is only robust up to about $5 a day.
114
US poverty line
(family of 4; 2005)
115
Falling numbers under $1 and $1.25 
Rising numbers just above $1.25 a day
 
Less progress in getting over $2 a day
116
Millions of poor
And uneven progress across regions
The World Bank’s update with detailed results by
region is found 
here
.
Not just about success in China!
Since 2000 we
have seen a
marked
acceleration in
poverty reduction
outside China.
Ratification of
MDGs at
Millennium
Summit 
of 2000?
Maybe, but very
hard to say.
117
0.4% point per year
1.0% point per year
MDGs?
Revolving Fortunes for Poor People
Poverty incidence in three regions
118
119
Sub-Saharan Africa
$1.25 a day poverty rate for Africa has shown no sustained
downward trend over the whole period 1981-2005; starting
and ending the period at 50%. Falling to 47% in 2008.
The number of poor has almost doubled in Africa over 1981-
2005, from 200 million to 380 million. But falling number of
poor since 2005.
Share of poor in SSA has risen from 11% to 27%.
 
Greater 
depth of poverty
 in Africa. 
The mean consumption of
the poor is lower than any region, at around 70 cents per day
in 2005 (using the $1.25 line).
Depth of poverty implies that even higher growth will be
needed in Africa to bring its rate of poverty reduction into line
with other regions.
And it will be important that the growth does not come with
rising inequality.
Global poverty gaps
The aggregate PG for 2005 is 7.6% for the $1.25 line
and 18.6% for the $2 line.
The GDP per capita of the developing world was $11.30
per day in 2005 (at 2005 PPP).
The aggregate poverty gap for the $1.25 line is 0.84% of
GDP, whereas it is 3.29% for the $2 line.
120
Global poverty gap cont.,
World (including the
OECD countries)
GDP per capita was
$24.58 per day,
implying that the
global aggregate PG
was 0.33% of global
GDP using the $1.25
line and 1.28% using
$2.
121
$670bn; $166bn for $1.25 a day
On track for SDG1?
The first of the UN’s new “Sustainable Development
Goals” is to eliminate extreme poverty by 2030.
Are we on track? “Yes” if the present rate of progress
continues.
However, the last few % could well be much harder to
reach.
The fact that the floor is no rising much gives a very
different perspective!
122
A linear projection suggests
that SDG1 will be achieved
123
 
Reaches
zero in
2025 =>
 
Rate of decline of about 
1% point per year
Poverty rate (%)
Source: 
PovcalNet
 (World Bank)
Linear projection of global poverty rates
124
Focusing on the floor gives a very
different picture
125
Recall the Rawlsian approach
(Parts 1 and 2.2; EOP, pp. 87-91 and pp. 238-40)
Moral philosophy and social policy often emphasize the
need to “leave none behind.”
This approach typically focuses on the 
consumption
floor
—the lowest expected level of living.
If the poorest person sees a gain (loss) then (by
definition) the consumption floor must rise (fall).
However, falling poverty measures can happen without a
rise in the floor.
126
Recall: Same reduction in the poverty count
but different implications for the poorest
127
 
Floor stays put
 
Rising floor
Estimated mean floor = 
$1.00 a day
(EOP: pp. 329-330)
This is the estimated lower bound to the distribution of
consumption in the world, over the last 30 years.
Slow growth in the floor—at 0.3% per annum
And 
unresponsive to growth 
in the overall mean
consumption.
Using consumption surveys only, the floor is $1.03 a
day.
128
 
Much less progress in raising the
consumption floor
129
 
No sign that the new
Millennium raised the floor
 
$1.00 on
average
Absolute gains by percentile 1981-2011
130
Comparison to the biological floor
 
Recall that the consumption floor is not defined as the
biological minimum for survival, but rather the lower
bound to permanent consumptions.
The estimated mean floor is remarkably close to
Lindgren’s (independent) estimate of the “physical
minimum line,” which aims to measure the cost of a
“barebones basket” of food items that assure at least
2100 calories per person per day.
The present estimates can thus be interpreted as telling
us that the consumption floor in the developing world has
not yet risen above the biological floor.
131
Are we on track
for SDG1?
“Eradicating extreme poverty” requires that the poorest
person should have $1.90 a day or more.
As we saw, the floor in 2015 is almost exactly $1.00 a
day, up from $0.87 a day in 1981.
There is a (statistically significant) positive slope to how
the floor has evolved. But the slope is very small.
At this rate, extreme poverty will not be eliminated for
another 
250 years!
 We are way off target.
The developing world is not making enough progress in
reaching the poorest—well below the $1.90 line.
132
?
3.4 The upper bound: Global
relative
 poverty measures
(EOP, Section 7.3)
133
Global (weakly) relative poverty lines
 
Excellent fit with data on national lines
134
Weakly relative lines calibrated to
national lines
For the upper bound, we use the line that is expected for
each country/date according to its level of mean
consumption.
So to be not judged “poor” globally a person must be
neither poor according to the fixed international line nor
poor according to the line expected in the country of
residence.
135
Absolute and relative poverty in the
developing world
136
Rising proportion of relatively poor:
80% of the relatively poor in 1981
were absolutely poor, but by 2008 the
proportion had fallen to under half.
Numbers of absolutely and relatively poor
137
Absolutely poor
Relatively poor but not absolutely poor
Two-thirds of the increase in the number of people who are
relatively poor but not absolutely poor is accountable to the
decrease in the number of absolutely poor.
3.5 A global  perspective on
poverty
(EOP, Section 7.4)
138
Truly global poverty
Weakly relative lines applied to 
all
 
countries
.
i.e., a person is deemed to be poor if they are either poor by the
World Bank’s absolute standard 
or
 poor by a standard typical of
the country they live in.
World Bank data base (Chen-Ravallion) for developing
countries + Luxembourg Income Study for High-Income
Countries (HICs)
Virtually no extreme absolute poverty in HICs.
Relative poverty in HICs, but rising relative poverty in
middle-income countries.
139
Truly global poverty rates and the
differences between rich and poor countries
140
141
Observations 1
Global poverty rate has been falling steadily from 50% in
1990 to 44% in 2008.
But underlying this, we see sharply falling absolute
poverty rates for the developing world, and rising relative
poverty rates in both worlds, though less steeply for
HICs.
There are also clear signs of 
convergence
 in the overall
poverty rates between the two worlds; in 1990, the
overall poverty rate (absolute plus relative) was three
times higher in the developing world, but this had fallen
to double by 2008.
142
Observations 2
Possibly the most striking finding is that 
relative
 poverty
is now overwhelmingly a problem of the developing
world
.
The proportion of the population who are relatively poor
is about the same at 24% in both sets of countries in
2008.
In terms of the poverty counts, 
9 out of 10 
people who
are poor by the typical standards of the country one lives
in but not absolutely poor are now found in developing
countries.
The developing world contained 92% of the poor, and
86% of the purely relatively poor.
143
Summary of the story so far
 
Overall progress in
reducing the incidence
and numbers for extreme
absolute poverty
.
Less progress for 
relative
poverty
, and in assuring
that 
none are left behind
.
 
144
 
Relative inequality
 is falling globally, but rising within
many countries.
Absolute inequality
 is rising globally. Rising in growing
economies.
 
How are we doing globally?
4. Urban and rural dimensions of
poverty
(EOP section 7.3)
145
Similar poverty rates between urban and
rural areas of 
rich countries
In the U.S, the official poverty rate in 2019 is 15%
outside metropolitan areas as compared to 11% within
those areas, though rising to 15% in inner city areas.
(Lower poverty rate of 9% in suburbs.)
Source: 
US Census Bureau
146
Urban-rural differences tend to be much
larger in developing countries
Population urbanization figures prominently in the
questions asked about poverty by policy makers and the
development community at large.
How much of poverty is found in rural versus urban
areas?  How quickly is the problem of poverty shifting to
urban areas?
Not so long ago, good data for addressing such
questions were scarce. That has changed.
Many data problems remain, e.g. “
urban bias
” in PPPs
(
EOP Box 7.3
).
But there has been an undeniable advance in our
knowledge about poverty in the world.
147
Higher cost of living in urban areas
On average, the urban poverty line is about 
30%
 higher
than the rural line to reflect 
higher cost of living
.
There is also a tendency for poorer countries to have
higher ratios of the urban line to the rural line.
This is to be expected given that transport infrastructure
and internal market integration tend to improve as
countries become less poor.
148
Higher absolute poverty incidence in
rural areas
Even allowing for the higher COL facing the poor in
urban areas, one finds that the “$1.25 a day” rural
poverty rate in 2008 of 31% is more than double the
urban rate.
About 
three-quarters
 of the developing world’s poor by
an absolute standard still live in rural areas.
149
150
EOP Table 7.1
The urbanization of poverty
Poverty is becoming more urban over time.
The share of the $1.25 a day poor living in urban areas rose from
18% in 1990 to 25% in 2008
while the urban share of the population as a whole rose from
37% to 46% over the same period.
But even so, it will be many decades before a majority of
the developing world’s poor live in urban areas.
While current UN population forecasts imply that 60% of the
developing world’s population will live in urban areas by 2030,
It is forecast that this will be true of less than 40% of the poor by
2030.
151
The poor are urbanizing faster than the
population as a whole
This reflects a lower-than-average pace of urban poverty
reduction (
EOP: Table 4.1
).
One’s concern about this finding must be relieved by the
fact that there has been more rapid progress against
rural poverty.
Over 1990-2008, the count of $1.25 a day poor in urban
areas fell by only 
10 million
, from 320 to 310 million.
However, the number of rural poor fell by over 
500
million
 (from 1464 million in 1990 to 926 million in 2008).
152
Urbanization of poverty: simple example
Consider the distribution (1, 2, 3, 
4
) with z=2. H=50%.
The poor are initially in rural areas. Household 4 is in
urban, marked 
red
.
As the economy develops both the poor move to the city,
and one escapes poverty => (
1.5, 2.5, 
3, 
4
). H=25%.
Rural and national poverty fall, but urban poverty rises.
153
Marked regional differences
Almost half of Latin America’s poor live in urban areas,
though this is still lower than the urban sector’s share of
the total population.
By contrast, less than 20% of East Asia’s poor live in
urban areas.
There are also exceptions at the regional level to the
overall pattern of poverty’s urbanization; indeed, there
are signs of a 
ruralization of poverty 
in Eastern Europe
and Central Asia, although the poverty rates for that
region are low.
Heterogeneity
 within both urban and rural areas;
examples for China and India =>
154
Example: Rural poverty rates in India
155
 
<5%
5%-10%
10%-15%
15%-20%
 
>=30%
20%-30%
Rural Yunnan: 
 
   County poverty incidence
N=126
 
<5%
5%-10%
10%-15%
15%-20%
 
>=30%
20%-30%
  Township poverty incidence
N=1571
Poverty mapping 1
Recall that the aggregate poverty measure is the
population-weighted sum of the measures for all the
geographic areas by place of residence.
The county poverty map for Yunnan was based on a
large household sample survey.
But even so, the sample size was not adequate to go
below the county level.
By using statistical “small-area estimation” methods one
can obtain amore detailed map at the township level.
158
Poverty mapping 2
The trick is to build a statistical model of poverty as a
function of variables that are observed in the sample
survey as well as in the Population Census.
One can then use this model to predict the poverty rate
at a finer level than is possible from the sample survey.
The main problem is the existence of latent geographic
factors—”
bad places
.”
However, tests suggest that quite reliable maps can still
be constructed.
These can guide 
poor-area targeting 
(which we return to
in 
EOP Ch. 10
).
159
Is relative poverty also higher in rural
areas?
We have seen that absolute poverty comparisons (adjusting
only for differences in price levels, as best can be determined)
show that poverty measures are higher in rural areas of
developing countries, though with a tendency for poverty to
urbanize over time.
Relative comparisons cloud this picture: 
Migrants from rural
to urban areas (or previously rural areas that become urban)
may well be (absolutely) better off, but 
relatively worse off
.
Higher inequality in urban areas suggests that 
strongly
relative
 poverty measures (such as % below half the mean) will
tend to be higher in urban areas.
Much less clear for 
weakly relative 
measures.
160
Example of China 1
Huge success against absolute extreme poverty, as judged by
the World Bank’s international poverty line.
Much higher poverty rate in rural areas.
This type of poverty has nearly vanished in urban China.
161
 
Example of China 2
Possibly more than any country, the urban-rural gap in
absolute living standards has historically been large in China.
What about relative poverty? Using the same weakly relative
measures we used globally this is what we find:
162
 
Next, we turn to the “non-income”
dimensions of poverty
163
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  1. Georgetown University ECON 156: Poverty and Inequality Lecture notes to accompany Ravallion sThe Economics of Poverty. Part 3, Chapter 7: Dimensions Poverty and Inequality in the World Martin Ravallion

  2. There are large differences in living conditions across the world today 2

  3. For GDP per capita by year see Our World in Data. These are averages. What about household income?

  4. Penns parade of world incomes Household income per person in $ s per day in 2008 Mean income in $/day by ventile, 2008 100 80 60 40 20 0 2 4 6 8 10 12 14 16 18 20 4 Source: Lakner and Milanovic, 2013, Global Income Distribution, Policy Research Working Paper 6719, World Bank.

  5. Penns parade of world incomes Household income per person in $ s per day in 2008 200 Mean income by ventile ($/day) Mean for top 1% 160 120 80 40 0 2 4 6 8 10 12 14 16 18 20 5

  6. Family living conditions across the world USA Life expectancy at birth: 79 years Source: Selected from 32 photos by Peter Menzel. Life expectancy for 2015. 6

  7. Brazil China Life expectancy: 76 years Life expectancy: 75 years 7

  8. Mali India Life expectancy: 58 years Life expectancy: 68 years 8

  9. Dollar Street 7 8 9 10 1 2 3 4 5 6 Imagine a street of 10 houses with the poorest on the far left and richest on the far right. Where would you put your family on this street? The Dollarstreet project by Gapminder shows you how people live across the world at different income levels. Similar consumption at the same income. Also see this TED talk by co-founder of Gapminder, Anna Rosling Roonlund. 9

  10. Learning about poverty from vignettes Some surveys have described various families and asked respondents to rank their economic welfare on an ordinal scale. Imagine a 6-step ladder where on the bottom, the first step, stand the poorest people, and the highest step, the sixth, stand the rich. On which step are you today? The same respondent is then asked to rank her/his own welfare on the same scale. Let s look at results for three countries. 10

  11. Guatemala % of survey respondents who rated their own welfare at or below this family 32% Family Castillo lives in an adobe house with one room and no latrine. The house does not have electricity or running water. The family eats beans and tortillas, but is never able to afford meat, eggs. Family Gomez lives in an adobe house with two rooms and a latrine. The house has electricity but no running water. The family owns a bicycle and small battery-powered radio. They eat mainly beans, eggs, tortilla, rice and corn. 79% 11

  12. Tajikistan % of survey respondents who rated their own welfare at or below this family 14% Family A can only afford to eat meat on very special occasions. During the winter months, they are able to partially heat only one room of their home. They cannot afford for children to complete their secondary education because the children must work to help support the family. When the children are able to attend school, they must go in old clothing and worn shoes. There is not enough warm clothing for the family during cold months. The family does not own any farmland, only their household vegetable plot. Family B can afford to eat meat only once or twice a week. During winter months, they can heat several rooms, but not the entire house. They cannot afford for all their children to complete secondary education. Their clothing is sufficiently warm, but they own only simple garments. In addition to their household vegetable plot, they own a small plot of poor quality farmland that is distant from their home. 60% 12

  13. Tanzania % of survey respondents who rated their own welfare at or below this family 25% Joseph's/Josephine's family has 6 people 3 adults and 3 children living in a mud house with the river as the main source of water. One of the children is in primary school. None of the adults are literate. The family has no land and supports itself by engaging in casual agricultural labor for a large landowner. The have one small meal a day and very rarely eat matooke, meat or fish. The family has no furniture and sleeps on the floor. Edward s/Esther s family has 6 people 3 adults and 3 children living in a mud house with the river as the main source of water. One of the children is in primary school. None of the adults are literate. The family has a one acre banana plantation. The adult male does some casual labor in construction in town. The family eats two small meals a day, and is able to occasionally eat meat or dagaa. The family has three old mattresses, a bench for guests and a few chickens. 61% 13

  14. Access to basic services 100% Poor Non-Poor Percentage of population with access 87% 80% 60% 61% 56% 49% 40% 26% 20% 20% 0% Water Electricity Sanitation Poor : consumption or income less than $1.25 a day Non-poor : the rest. 14

  15. Outline Part 3, Chapter 7 1. Poverty and inequality in America 2. Global inequality 3. Global poverty 3.1 Absolute poverty globally 3.2 Taking social effects on welfare seriously 3.3 Measures of absolute poverty 3.4 Measures of relative poverty 3.5 Overall measures of global poverty 4. Urban and rural dimensions of poverty 5. Non-income dimensions of poverty 5.1 Child development and poverty 5.2 Demographics of poverty 5.3 Schooling and poverty 5.4 Gender dimensions of poverty 5.5 Nutrition and poverty 5.6 Violence and poverty 15

  16. 1. America 16

  17. 1.1 Inequality in America 17

  18. Growth incidence curves for US Pre- and post-tax 1967-2015 18 Source: Wimer et al. 2020 (*)

  19. Two periods in America: 1946-1980 and 1980-now Growth incidence curves: 19 Source: Saez and Zucman

  20. Gini index in US Market incomes Disposable incomes Redistribution Source: Branko Milanovic 20

  21. Rising inequality of incomes and wealth in America since around 1980 Gini= 0.58 The rise in income inequality has been more marked than for wealth. But note how much higher the Gini index is for wealth than income. Imagine 10 households with wealths: (1,2,3,4,5,6,7,8,9,X). What is X to get Gini=0.86? X=750. Gini= 0. 86 Source: Kuhn et al. 2020 21

  22. Income share held by the richest one percent of American households Share of household income in the U.S. held by top 1% 25 20 15 10 5 Share excluding capital gains Share including capital gains 0 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 Source: Alvaredo et al. (2014); author s estimates of a nearest neighbor smoothed scatter plot. 22

  23. Rising share of top 0.1% in US since late 1970s; returning to level of early C20th. (Graphic from Economist magazine.) 23

  24. Inequality of market incomes is rising faster than disposable income, esp. US Redist- ribution Source: OECD; for OECD as a whole see this paper. Note: Market income: income from all sources; Gross income: market income less all transfers; Disposable income: Gross income net of taxes. 24

  25. And the floor in US is sinking! Stabilized in 2000s, mainly due to social policy (esp. SNAP) 7 6 Floor in $ per person per day 5 4 3 2 1 0 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Note: 2010 prices; family of 4 (2 adults, 2 children) 25 Source: Joliffe et al. 2020.

  26. Differing fortunes in US Divergence is absolute and relative 500 quantile 99% Income in $ per day, 2010 prices 400 300 quantile 95% 200 quantile 90% 100 mean median floor 0 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 26 Source: Jolliffe et al. (2020)

  27. Declining absolute income mobility in US Source: The Equality of Opportunity Project. 27

  28. Local inequality: Washington DC DC has the highest income inequality of any major metropolitan area of the US, with a Gini index of about 0.60. This reflects the special nature of DC and its history of in-migration by both the rich and poor. Also note that the large Washington area (incl. Arlington and Alexandria (DC-VA-MD-WV) has average inequality for the US. However, some of the costs of high inequality (as we discuss later) are likely to be localized. 28

  29. 20% 40% 60% Gini indices within countries DC! (Gini=60%) Source: World Bank, 2006, World Development Report, Oxford University Press

  30. Factors underlying the rise in US inequality 30

  31. Side-by-side with rising inter-personal inequality in US we have seen: Falling labor share in national income Measured by ratio of worker compensation to national income Falling in US since 1980; around 66% in 1980; <60% now. Falling participation in labor unions Down from 20% of workers in 1980 to about 10% now. Rising industrial concentration Larger and fewer firms; superstars ; less competition. Higher markups in pricing; higher profits => lower labor share. Labor share has fallen faster in US industries that have become more concentrated. Rising returns to higher education => Higher costs of college education but also higher earnings gains. 31

  32. Real wages by education over time in US Source: David Autor 32

  33. Redistributive effort has declined in US 33

  34. Recent signs of a brake to the rise in inequality in America (pre-COVID) Federal Reserve s Survey of Consumer Finances indicate that median household pre-tax income grew by 5% over 2016-19, However, the mean fell by 3%, mainly due to decline in incomes of the top 1%. Are we seeing the end of rising inequality in the US? The pandemic may well have increased inequality, as the poor and middle-income groups are probably less able to protect their living standards. We await to see what new data tell us! 34

  35. Identity and inequality 35

  36. Identities matter to inequality and injustice Just as nationality may matter to assessments of inequality, other aspects of identity have a salience that is hidden by standard measures. Ethnicity, race, religion and gender have been important examples. Even small between-group disparities have a large social and political significance. Standard decomposition methods do not attach any extra weight on group identity. Yes, there are inequalities between groups, But they are given the same weight as inequalities within groups. 36

  37. Stubborn and worrying inequalities by race in the U.S. Soon after the 50th anniversary of Martin Luther King Jr. s I Have a Dream speech, how are we doing in reducing racial economic disparities? The black unemployment rate has remained about twice the white rate for 50 years The gap in household income hasn t narrowed in the last 50 years Blacks are still far less likely to be health-insured than whites. 37

  38. Racial wealth gap in US Source: Brad Plumer, Washington Post, August 28, 2013. In 2016, non-Hispanic White families had a median net worth that is 8-10 times higher than the other two main race groups. 38

  39. Other indicators of the racial wealth gap Home ownership rates (2011): White families: 73% Black families: 45% Hispanic families: 47% Median wealth return to graduating college (2011): White families: $60,000 Black families: $4,846 Hispanic families: $4,191 39

  40. 2.2 Poverty in the U.S. 40

  41. Absolute poverty line in the U.S. Unusually amongst rich countries the U.S. uses an absolute line adjusted only for inflation over time. US official line developed in 1965 by Mollie Orshansky, an economist working for Social Security Administration. A 1955 survey to determine the nutritionally adequate but socially acceptable food bundle. Poverty defined as making less than three times the cost of this diet; factor of three came from a 1955 study indicating that food spending accounted for one-third of a typical family s budget. Currently the threshold is set at $24,069 for a family of four ($16 per person per day). Adjusted over time for inflation nationally, but no adjustment for geographic cost-of-living differences. 41

  42. Debates on US poverty: Is the official line too low or too high? The U.S. line is the average line for countries with only about one third of the mean consumption level of the US. Also low relative to the social subjective poverty line. But the income concept excludes taxes/transfers! 50 National poverty line ($PPP per day per person) Luxembourg 40 30 20 USA 10 0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Log private consumption per capita ($PPP per day) Note: Official line in 2005 is $13 is $13 per day (family of 4 with 2 kids). In 2013 it is about $16 per day 42

  43. Official measures show little long-term progress against poverty Little progress against poverty: 1980-2015: the official poverty rate rose by 0.5% points. Growth rates in household incomes tended to be higher for poorer groups, undoing some of the rise in inequality that we have been seeing in the US for many years. Among those who are below the official line, a rising share are living on less than half that line. 43

  44. Recent progress threatened by the 2020 pandemic Good news in 2015-19: the US poverty rate fell and median incomes rose. See the official report. Official poverty rate in 2019 is 10.5%. 3-year average for 2017-19 is 11.5%. Early evidence suggests that the US poverty rate has risen during the 2020 pandemic. This has been counteracted to some extent by enhanced spending on social protection. 44

  45. Three measures of poverty in the US Using the official poverty line. Three measures in the Foster-Greer-Thorbecke class. .16 .14 .12 Poverty measure Poverty measure Headcount index Poverty gap index Squared poverty gap index .10 .08 .06 .04 .02 1988 1992 1996 2000 2004 2008 2012 2016 Source: Calculations from successive rounds of the Annual Social and Economic (ASEC) Supplement to the Current Population Survey (CPS). 45

  46. Alternative measures of poverty for US Official measures use pre-tax money income. This leaves out the effects of some pro-poor policies. New Census Bureau supplementary poverty line is more generous but uses a better income concept. Similar overall poverty rate, but no series over time. Regional COL differences (e.g., Texas vs California). Consumption poverty measures have become available and show much more progress against poverty. Consumption poverty rate has fallen over time, despite the lack of progress indicated by the official poverty measures. 1980-2015: consumption poverty rate down by 9.4% points. See Meyer and Sullivan, Figure 1 here) 46

  47. Poverty in the US by race/ethnicity Note: Non-Hispanic whites have a lower poverty rate (% of this group) but around half of the poor are white. 47

  48. Washington DC stands out again! Headcount index 2013 25.0 20.0 15.0 10.0 5.0 0.0 Hawaii New Jersey New York Alabama Utah Virginia Maine Montana Vermont Iowa Alaska Ohio California Nevada Colorado Connecticut Wyoming Oregon South Dakota Washington Missouri Delaware South Carolina West Virginia North Carolina North Dakota Indiana Illinois Rhode Island Arkansas Tennessee Minnesota Michigan Kansas Nebraska Idaho Florida Georgia Texas Massachusetts Kentucky Maryland Louisiana Arizona Wisconsin Mississippi Pennsylvania Oklahoma District of Columbia New Mexico New Hampshire US as a whole 14.5% 6.3% Washington DC 21.3% 11.5% Poverty rate (100% official line) Poverty rate (50% official line) Update for 2017 is here. 48

  49. Poverty rates across counties of the US 3,000 counties. Overall, the poverty rate is 15%. But wide variation. Ranging from 2.6% (Douglas County, CO) to 54% (Oglala Lakota County, SD). Strong correlation with average household income. Elasticity = -1.5. 4.5 Poverty rate (official line; log scale) Poverty rate (official line; log scale) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4 11.6 11.8 12.0 Median household income (log scale) Median household income (log scale) 49

  50. The changing geography of poverty in the US Historically, poverty measures have been higher in both large inner- city areas and more remote or rural communities. This is changing, with poverty shifting to suburbs. Suburbs account for half of the increase in number of poor 2000-15. 50

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