We need to frame the way we gather and use data in the context of the post-2015 agenda on poverty eradication. Tony German and Judith Randel from Development Initiatives examine issues around disaggregating data for better informed choices about development spending.

1. Measuring and Fostering
the Progress of Societies:
Poverty and Exclusion
Judith Randel and Tony German
di@devinit.org +44 (0) 1749 831141

2. Progress, Poverty and Exclusion
► What do we mean by progress on poverty and
exclusion and how can statistics help?
 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

3. 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) requires universal access – but
others are milestones
► Achieving the milestones means including the ‘hard-to-
reach’ poor

4. 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, May 2004, UK Secretary of State for International Development)

5. 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

6. 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.

7. Panel data: understanding impact on poverty
► The Rwandan government has been encouraging farmers to make
increased use of fertiliser.
► Two cross section surveys show % of farmers using fertiliser
increased from 2000 to 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.
• Without panel data we do not know the answer, so we don’t know if
fertiliser use has contributed to poverty reduction.

8. 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, especially panel data?

9. Counting the Uncounted
Statistics often exclude the most vulnerable
► Household surveys and censuses don’t cover the
homeless
► Disabled people & unwanted relatives 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”

10. 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)

11. 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.

12. 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 2004)

13. Excluded or Exploited?
 Will very poor people be able to escape
poverty if they are fully included in the
process of development and growth – or
are they already included – just on
profoundly disadvantageous terms?
(Adversely incorporated).
 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.

14. Statistics and global progress:
following the money
► Urgent need for improved resource tracking
► Poor people, their representatives and civil society
do not have access, in a timely fashion, to data on
whether the rhetoric on increased aid is being
translated into resource flows that actually reach
the poorest.
► One of the many rights currently denied to the
poor is the right to transparency on the resources
spent in their name – this is a major obstacle to
the effective use of both money and statistics for
poverty elimination.

15. Statistics and global progress:
Social Protection
► Global
access to social protection can
be seen as major indicator of progress
 The fact that we don’t have good statistics
on chronic poverty means that it is more
difficult to identify appropriate policy
responses.
 We need to measure the benefits as well
as the costs of social protection schemes

16. Data, empowerment, change
How much data do we need to stimulate
change and how do we weigh priorities?
“Latte £1.89, Ethiopian Farmer: 3p
An outdated equation of poverty and exploitation”
(Get Cape, Wear Cape, Fly – Glastonbury 2007)
“We have to move towards measuring welfare, not just output”
(Angel Gurría, Secretary-General to OECD Ministerial Council Meeting, 2007)

17. Statistics and global progress:
► Theway society measures, and the
value society chooses to accord to social
and economic ‘goods’, is critical
► The ‘Measuring Progress’ process
implies the need for a better balance in
what is measured, to give greater
weight to social priorities alongside
economic indicators

18. Key points on better data
for poverty elimination:
 Improving data access is central – that means building statistical
capacity
 Much greater effort is needed to make data easy to use for a range
of users at all levels
 Data ownership needs to be revisited, including the rights of poor
people in relation to data based on their lives
 A much greater effort is needed to ensure the poorest are not
‘invisible’
 Alongside the fundamental needs incorporated in the MDGs to
halve the proportion of people in poverty by 2015, the post 2015
agenda for poverty elimination will need to include the realisation
of political rights, such as the right of the poorest to information.