Measuring and Fostering
the Progress of Societies:
Poverty and Exclusion
Judith Randel and Tony German
[email protected] +44 (0) 1749 831141
Progress, Poverty and Exclusion
► What
do we mean by progress on poverty and
exclusion and how can statistics help?
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The post 2015 agenda for the eradication of poverty
Dis-aggregation and panel data
Counting the uncounted
Excluded or exploited?
► Fostering
and Measuring progress of the global
society in the fight against poverty and exclusion
Progress: MDGs PLUS
The MDGs have been a major force for progress
But…
► Even if the MDGs are met in 2015 there will still be
hundreds of millions of people living in chronic
poverty
► Only one goal (education)l requires universal
access – but others are milestones
► Achieving
the milestones means including the
‘hard-to-reach’ poor
Attention to universal rights and post
2015 agenda for poverty eradication
when we all signed up to the
Millennium Declaration which
committed us to making the
right to development a reality
for everyone…we meant
everyone”
(Hilary Benn, UK SoS for International Development)
We need to frame the way we gather
and use data in the context of the
post-2015 agenda on poverty
eradication
► Data
relevant to rights, means capturing
multidimensionality, vulnerability and
structural issues
1.Disaggregating data on poverty –
need for panel data
► We
need to know who stays poor and who moves
into and out of poverty so we need to measure
what is happening to specific people over time
► Between 1992 and 1999 the national poverty rate
in Uganda fell from 56% to 34%
► The panel data shows that in the same period,
30% of people moved out of poverty, but 20% of
people stayed poor and 10% fell into poverty.
► In other words there was a lot of mobility of living
conditions over time.
Panel data and understanding impact on
poverty
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The Rwandan government has been encouraging farmers to
make increased use of fertiliser.
Two cross section surveys show the % of farmers using
fertiliser increased between 2000 and 2005. They also
show that the non-poor are more likely to use fertiliser than
the poor.
But we don’t know whether the non-poor who used fertiliser
in 2005 were poor in 2000.
It may be that many of them were poor in 2000 and use of
fertiliser helped them become non-poor;
OR it may be they were always non poor and the non-poor
are always more likely to use fertiliser.
With panel data we could distinguish these two cases, but
without panel data we do not know the answer so we don’t
know whether fertiliser use has contributed to poverty
reduction.
Issues on panel data
Limitations of panel data
► “age” over time - samples representative at the
beginning become less so over time
► Attrition: People drop out - they may be the most
revealing.
Very few panel data sets AND difficult to access
► Serious difficulties of researchers and others (including
sometimes government) getting access. Panel data seen
as a valuable private resource for individual researchers
or groups of researchers (often international). This is an
issue with privately funded and statistics offices’ data.
Do we need a code of good practice (or something
stronger) on access to data, particularly panel
data?
2. Counting the Uncounted
Statistics often exclude those who are most
vulnerable
► Household surveys and censuses don’t cover the
homeless
► Disabled people and unwanted relatives are often
missed
► Difficult to count people in war zones, or remote
areas
► Children are often undercounted
► “We also miss the rich – they don’t want to
participate in income and expenditure surveys”
Death and Invisibility bias
► And
the most extreme form of invisibility is
death – deaths due from poverty make the
statistics look better. “Holding everything
else constant, if a poor person dies, the first
MDG is closer to being attained” (Ravi
Kanbur)
3. Respecting the perspective of
poor people
Extremely poor people experience
multidimensional disadvantage, vulnerable to
major impacts from tiny shocks. Consequently…
– looking through development ‘sectors’ makes
little sense
- Classifying response according to donors’
management categories of ‘humanitarian’ and
‘development’ makes even less sense.
Dependent or productive?
► “Njuma
is 70, a widow, she depends on gifts
from neighbours and earns about US$0.03
an hour gleaning corn. Economic surveys
and the census would, if they recognised
her at all, class her as poor and not
working. The reality is that she is employed
in some of the lowest paid work in the
world” (David Hulme, Chronic Poverty
Report)
Excluded or Exploited?
► Will
very poor people be able to escape poverty is
they are fully included in the process of
development and growth – or are they already
included – just on profoundly disadvantageous
terms?
► What we measure will be very different according
to the hypothesis we choose. If we consider
exploitation, then the statistics need to reveal the
systemic conditions that entrench poverty.
Statistics and global progress:
Following the money
► Urgent
need for improved resource trackingpoor people and their representatives and
civil society do not have access, in a timely
fashion, to data on whether rhetoric is being
translated into resource flows. Major deficit
in fostering progress
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Statistics and global progress: Social
Protection
► Global
o
o
o
access to social protection major
indicator of progress
The fact that we don’t have good statistics
on chronic poverty means that it more
difficult to identify appropriate policy
responses.
role of social protection and decent work
Measuring the benefits as well as the costs
of social protection schemes
Statistics and global progress:how
much is enough?
► Being
o
o
more selective about what we need
data for:
More access, less interpretation
How much is enough
DATA AND EMPOWERMENT
► To
FOSTER progress you need to convey
SCALE truthfully
► But to MEASURE progress you need
accuracy.
► Problems with conflating the two – leading
to too much unnecessary data
DATA AND EMPOWERMENT
Latte £1.89
Ethiopian Farmer: 3p
An outdated equation of poverty and
exploitation
(Get Cape, Wear Cape, Fly)
Following the money
► Current
systems do not allow aid money to
be tracked adequately – governments can’t
plan, citizens can’t monitor and hold to
account
► Timeliness
► Actual transactions and transfers
Who is empowered by the data?
► Who
holds and has access to the data
Ensuring assumptions don’t create
bias against extreme poverty
► Income
is the most difficult things to
measure for the poorest who may survive
on combinations of gifts, begging,
scavenging
► ENDS
ODA in 2006 $ millions:
G8 countries provide most aid
0
United States
United Kingdom
Japan
France
Germ any
Netherlands
Sw eden
Spain
Canada
Italy
Norw ay
Denm ark
Australia
Belgium
Sw itzerland
Austria
Ireland
Finland
Portugal
Greece
Luxem bourg
New Zealand
5000
10000
15000
20000
25000
G8 are largest
donors –
keeping to
their resource
commitments
is vital for
achieving
MDGs
But aid has been virtually static since 2004.
In 2006 G8 aid declined
Percentage change in total ODA 2005 to 2006
- the first full year since Gleneagles G8
UK, 13.1
Non-G7
countr ie s , 6.1
Fr ance , 1.4
Ge r m any, 0.9
G7 countr ie s ,
-8.7
Canada, -9.2
Japan, -9.6
USA, -20.0
Italy, -30.0
-40.0
-20.0
0.0
20.0
Headline increases are not delivering
on real resources for the poorest
Allocation of G8 bilateral ODA in 2006
Nigeria debt relief
18%
ODA to Iraq
11%
ODA to SSA less
Nigeria debt
22%
Other bilateral
ODA
49%
Non-G8 donors
have led the way
on aid as a % of
GNI.
ODA as a percentage GNI 2006:
G8 countries are not as generous - other
countries give a greater share of income
0.00
Sw eden
Luxem bourg
Norw ay
Netherlands
Denm ark
Ireland
United Kingdom
Belgium
But EU G8
donors have set
timetables for
0.7% target
Austria
France
Finland
Sw itzerland
Germ any
Spain
Australia
Canada
New Zealand
Japan
Portugal
Italy
United States
Greece
0.20
0.40
0.60
0.80
1.00
1.20
Aid was more than 0.3% of GNI from 1975 to
1994 and has just returned to this level
– mainly thanks to debt relief
The long term trend in aid as a % GNI for the DAC. G8 donors
provide about 80% of ODA, so their performance is crucial
0.7
0.6
0.5
0.4
0.3
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
0.2
Without debt relief (which doesn’t produce much in
the way of new resources),
aid to SSA has stagnated
G8 bilateral ODA to sub Saharan Africa since 1989
$25,000
US$m constant prices
ODA for Nigeria debt from G8
$20,000
G8 bilateral aid without Nigeria
debt relief
$15,000
$10,000
$5,000
$0
19891990
19941995
2001
2002
2003
2004
2005
2006
Priority to Africa has increased amongst
G8 donors.
Share of bilateral ODA allocated to Africa in 2006
21000
14000
Other bilateral
ODA to SSA
ODA to Iraq
7000
U
SA
U
K
pa
n
Ja
It a
ly
an
y
er
m
G
Fr
an
ce
C
an
ad
a
0
Increased aid to reduce poverty is affordable
in the light of other G8 spending priorities
Share of GNI to ODA and Military
Spending G8 donors 2006
4.0%
ODA
3.0%
Military spending
2.0%
1.0%
0.0%
Canada France Germany Italy
Japan
UK
USA
Some grounds for optimism
and need for real leadership
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Some G8 donors making major efforts to meet pledges
Paris Declaration is resulting in (modest) progress on aid
quality and efforts to shift ownership to south
Public commitment is robust and sustained
Real progress is being made – for instance on education
(number of children in school, long term funding
commitments) and HIV (access to ART up tenfold)
Evidence of affordability (eg. on social protection) removes
one excuse for donors not doing more
With renewed action at Heiligendamm, the G8 can live up
to their moral responsibility and political commitments to
help meet MDG pledges to halve poverty – a key step to
the longer term goal (agreed at the 1995 Social Summit) of
poverty elimination
4% of the increase in G8 countries’ income would
pay for the $25 billion promised to Africa
Increase in income for G8 countries
compared to $25 billion in extra ODA to Africa
Increase in G8
GNI over
2004 to 2005,
$660 billion
Additional aid
promised for
Africa, $25
billion or 4% of
one years
increase in G8
income