Measuring and Fostering the Progress of Societies: Poverty and Exclusion

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)l 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, UK SoS 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. 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.

7. Panel data and understanding impact on poverty • 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.

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, particularly panel data?

9. 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”

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. 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.

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)

13. 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.

14. Statistics and global progress: Following the money • Urgent need for improved resource tracking- poor 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

15. Statistics and global progress: Social Protection • Global 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

16. Statistics and global progress:how much is enough? • Being more selective about what we need data for: • More access, less interpretation • How much is enough

17. 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

18. DATA AND EMPOWERMENT Latte £1.89 Ethiopian Farmer: 3p An outdated equation of poverty and exploitation (Get Cape, Wear Cape, Fly)

19. 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

20. Who is empowered by the data? • Who holds and has access to the data

21. 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

22. ENDS

23. G8 are largest donors – keeping to their resource commitments is vital for achieving MDGs

24. But aid has been virtually static since 2004. In 2006 G8 aid declined

25. Headline increases are not delivering on real resources for the poorest

26. Non-G8 donors have led the way on aid as a % of GNI. But EU G8 donors have set timetables for 0.7% target

27. Aid was more than 0.3% of GNI from 1975 to 1994 and has just returned to this level – mainly thanks to debt relief

28. Without debt relief (which doesn’t produce much in the way of new resources), aid to SSA has stagnated

29. Priority to Africa has increased amongst G8 donors.

30. Increased aid to reduce poverty is affordable in the light of other G8 spending priorities

31. Some grounds for optimism and need for real leadership • 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

32. 4% of the increase in G8 countries’ income would pay for the $25 billion promised to Africa