For ESDS Conference Social Inequality: Using Evidence from Data Monday 20 th June 2005

1. Monitoring the Millennium Development Goals?: Measuring Poverty and Death, Health and HealthCare Consumption in Developing Countries For ESDS Conference Social Inequality: Using Evidence from Data Monday 20th June 2005 Roy Carr-Hill Universities of East London, London and York

2. O. Background • Over 40% of annual 11million child deaths worldwide (a tsunami a week) due to diarrhoeal diseases and malnutrition, i.e. mostly preventable • In general, lower prevalence of diarrhoea correlated with piped water, toilet facilities, parental (not just maternal) educational level and functioning radio BUT

3. Importance of Accurate Measurement Debates about Poverty • Ever more sophistication in concepts (absolute/relative) and analysis of poverty (e.g poverty mapping; • Whilst recognised as a problem there is still very little attention to quality of basic data Millenium Development Goals • How will we know where we are in 2015? • How far are we away now?

4. Organisation of Presentation • Population Data – Chris Murray! • Measuring Poverty • Monitoring Surveys do not in general include Poorest of Poor • Reliability of Self-Reports of ill-Health biased • Problem of Databanks: they do not Encompass the Reality of Poverty

5. I. Population Denominator Data in Developing Countries • only a few countries have functioning registration systems • current population estimates are based on Coale-Brass-Demeny population models • As Chris Murray showed, in several countries, the estimates are based on parameters from neighbouring countries NONE OF DATABASES COVER THIS

6. Monitoring Poverty Reduction Strategies • International recognition that quality of statistics has deteriorated (e.g. Can’t Count Progress, ODI Review) BUT • Majority of information systems are donor funded with minimal national involvement leading to proliferation of proposals for electronic systems with little ongoing support • Still little attention to assuring the quality of the basic data

7. 1. Deterioration of Statistical Systems • Lack of attention since independence to ‘boring’ issue of infra-structure of statistical systems; but • Current trend towards decentralisation usually means that district estimates are central to resource allocation

8. 2. Donor Funded Systems “The global statistical system is fragmented and characterised by poor inter-agency co-operation. Whilst more information is now available compared with previous years, this is usually through the medium of donor funded household surveys, which may by-pass domestic information systems and serve the needs of donors rather than developing countries themselves.” Can’t Count Progress

9. 3. Quality of Basic Data • Entrenched systems • Little or no inspection or quality assurance • Weak capacity – numbers and qualifications • No local use of data – so no incentive to verify (Musgrove – data has to be used within 5km to ensure reliability)

10. II. Measurement of Poverty • Lack of agreement over whether to use absolute or relative poverty • Conventional levels like US$1 or US$2 a day per person are used with little evidence • Lack of relation with other measures of well-being e.g. mortality, education • Usually based on household expenditure surveys, i.e. what is consumed in the market omitting barter, black markets and exchange

11. Asset Indices Difficulty of asking expenditure has led to development of ‘asset indices’ but • no information on quality and quantity of goods and services including the reliability of the asset • distinguishing between household ownership, household based assets and individual access • Routine Administrative Data • problems in generalising across different communities

12. Routine Administrative Dataincluded in Databanks • Population Censuses – 100% coverage but usually insufficient information • Collection of Data on use of Education and Health services, rarely includes socio-demographic data • Collection of data on receipt of income support/welfare also biased on both data collector and supplier sides

13. III. Monitoring the Poor: Household Surveys MOST SOCIAL DEVELOPMENT DATA BASED ON HOUSEHOLD SURVEYS • Demographic and Health Surveys in 45 countries (USAID) • Living Standard Measurement Surveys in 40 countries (World Bank) • Multiple Indicators Cluster Surveys in 40 countries (UNICEF)

14. Three Difficulties • household surveys do not include the poorest of the poor (see next slide); • consumption expenditure is a poor substitute for measuring standard of living; • the proxies used to measure poverty are almost impossible to compare over time because of changes in reach of formal economy) so that even within country trends are very difficult to assess

15. Household Surveys: Omissions • Those not in households because they are homeless • Those who are in institutions • Mobile, Nomadic or pastoralist populations • Many of those in fragile or disjointed or multiple occupancy households.

16. IV. Limits of Self-Reporting • Focus on household rather than community – or intra-household – poverty • Known associations between income and relative reporting e.g. • reported illness rate higher in households with piped water supply, with inside toilets with central heating, with TV; • reported illness increases, reported deaths decrease with mother’s educational level • Consumption and income poverty may not be the most salient (e.g. refugee camps, communication in Palestine)

17. Covering Poor Populations • Extending the Sample • Need to be confident about sampling frame • Attributing • Poverty mapping assumes initial relationship valid and relies on outside experts

18. Reality of Poverty: A Health Related Poverty Line (ILLUSTRATED WITH SWAZI DATA) • Based on data collected as baseline for homecare services in Northern Lumombo • Survey of 619 household and 3838 persons based on power calculation of how many they estimated were caring informally • Proportion of people providing informal care much higher than they expected

19. A total of 155 deaths in the last 12 months .. an astonishing mortality rate of 40.4 per thousand per year .. more than five times the 1997 Census rate, which was 7.6 per thousand… most of the excess mortality is caused by the advancing of the AIDS epidemic ... • Almost half were between 15 and 40 years old, the age group most severely affected by the AIDS epidemic, while in the 1997 census this age group counted for only 14% of the deaths.

20. Hypothesis • At high levels, HIV incidence is social class neutral • Death from AIDS depends on capacity to survuve and eb cared for • Gradient with income should inflect at ‘true’ poverty line

22. V.B HealthCare leading to Poverty illustrated in Palestine • Households spend about NIS 399 per month (about US$1,000 per year) on HC. • dentistry (NIS 108.1 or 28%), • medications and vitamins (NIS 91.4 or 24%), • consultations with GPs and specialists (NIS 43.7 or 11%), • spectacles and hearing aids (NIS 43.0 or 11%) and • transportation (NIS 31.7 or 8%). • GNP in Palestine is c. US$1,200 per head

24. Conclusions • Measurement of poverty a growth industry • In developed countries, well-known issue of low response rates but no easy solutions • In developing countries, samples exclude poorest of poor • Household surveys inappropriate for monitoring MDGs • We don’t know where we are