Presentation by : Tendayi Kureya Development Data, tendayi@developmentdata.co.zw

1. Piloting the Household Vulnerability Index to Improve Resilience of Vulnerable Rural Households in Lesotho, Swaziland and Zimbabwe Presentation by : Tendayi Kureya Development Data, tendayi@developmentdata.co.zw FANRPAN Partners meeting, Pretoria 23 June 2009

2. Context • Policies backed by evidence are required to transform the lives of the poorest in a regional context of: • Increasing food prices • Limited food production or access • Declining global food availability • Climate change, need for bio-fuels, • HIV and AIDS, • dynamic communities (gender , power, politics)

3. About the pilot project In February 2008, WVI in partnership with FANRPAN agreed on a 2-year project to assess household vulnerability and improve resilience using the Household Vulnerability Index (HVI) in three pilots of WVI’s development programmes. • The goal of the project is to: apply the HVI to improve development responses in three pilot Area Development Programmes (ADPs) in Lesotho, Swaziland and Zimbabwe.

4. Results expected • Database and index that is community owned and regularly updated to: • Improve targeting • Facilitate integration of interventions and actors • Provide evidence base • Paradigm shift/change of mindsets • Evidence based community participation in development, focusing on ownership, collaboration and sustainability • Govt, Civil society and academia integration in development work • Policy options • Prioritizing limited resources • Assessment of Impact

5. A brief note about the HVI • It is a powerful statistical index for measuring vulnerability. • It categorizes a household by assessing “external” vulnerability that is introduced by shocks and “internal” vulnerability or inability of such a household to withstand shocks, then classifies the household as coping, acute, or in an emergency situation, depending on the household’s ability to prevail. • It was developed between 2004-7 using thorough statistical research methods on data from seven (plus three) countries. • It uses Fuzzy logic on 15 variable classes or dimensions to explore the relationships between vulnerability and households’ access to and use of 5 capital assets. (In English: It assesses a combination of truths about a household's behaviour on the capital assets to conclude on its degree of vulnerability or resilience).

6. Households Each with different Natural, Physical, Human, Social and Financial Capita assets Coping- able to adjust and prevail Acute- able to meet minimum requirements with some help Shock such as HIV and AIDS Emergency- unable to meet requirements External vulnerability Internal vulnerability Resultant impacts X = The theoretical HVI model:

7. Database • Developed as an advanced standalone software capable of storing, retrieving and searching million of records • Available as self-installing software on CD, and soon to be on FANRPAN website • User-friendly menu system employed, with ongoing tweaking to increase usability. • Data analysis done using most common statistical applications (SPSS, Epi Info, SAS etc)

9. Swaziland Example • Dynamic database with 3212 Households’ data and >18,000 occupants. • Data collected using enumerators form target community, with significant support from the Central Statistical Office, local authority, NERCHA and CANGO. • High level of support from local politicians, community leaders, and community members (8/3212 households refused to be interviewed- 5 because head was away and left instruction not to talk to strangers). • Data entry and analysis nearing completion, and some results are ready for sharing

10. Selected Results from Swaziland Swaziland context • > 60% rural is into subsistence farming, • cattle are status symbols • land area of 17364 sq.km but only 11% is arable • 69% of the population lives in poverty: on less than US$1 a day. • Overgrazing, soil depletion, drought and floods are problems • Life expectancy dropped to 33 years down from 49 years in 1975 • 52% have access to clean water and sanitation • below-five infant mortality rate is 156 per 1000 births. • 16 doctors for every 100.000 people • world’s highest HIV prevalence rate- 33.4% of 15 and 49s.

11. 1. New data has allowed us to correct flawed planning data available • Population for Mpolonjeni was estimated at 24, 000. It actually is 18,947 • 73.6 percent of the population was said to be females and 26.4 percent males. Actually, 51.2% are females, and 48.9% are males. • 3,230 households. (3212 from the census) • 33.7 percent households headed by women (32.4% from census)

12. 2. New Data has helped magnify the size of the development challenge • Literacy levels are low (26% are illiterate, 34% have some primary education). 2% have some university or college education. • Only 7% fully rely on own production of staple foods, 60% purchase, 24% rely on donations. 85% indicate they have no reliable secondary source of food. • 2506/3212 (78%) have received food aid, of which 46% were within the last month • 30% of households have a salaried household member • As many as 88% of individuals indicate they have no reliable source of income (this includes half of those with a salaried household member) Example Question: how is food aid assisting or stifling own production or other sustainable efforts?

13. Main Income sources

15. 3. Development Responses have not always been logical Example: • More than 90% of households have a reliable water source, yet 33% have no toilets! • To solve the urgent sanitation problem means constructing 1000 pit latrines (with community input) for US$ 300,000 cost which is the same cost as 50 boreholes or 1000 tones of food aid (US$300/t) (enough to feed this community for 5 and half months!)

18. 4. Development responses have not necessarily been responsive to expressed needs • 80% of parents express need for support with school fees, but do not always get this support. • Parents (48%) and Government (35%) are paying most fees. • Result? Literacy levels are low (26% are illiterate, 34% have some primary education). Only 2% have some university or college education.

20. Who is sponsoring school fees? World Vision’s primary focus is under five mortality

21. 5. Using the HVI, we can get even more detailed insights… • Viable/Coping level Households: HVI<47 Total: 41.3% • Acute level Households: 47<HVI<63.1 Total:54.2% • Emergency level Households: HVI>63.1 Total 4.5%

23. HVI categories based on poverty as the shock (generic model): • Acute level Households: 47<HVI<63.1 • Total:54.2% • 72% • 33% Coping level Households: HVI<47 a) Total: 41.3% b) 60% are cultivating a proportion of their land c) 25% headed by women or children Emergency level Households: HVI>63.1 a) Total 4.5% b) 85% are cultivating only a proportion of their land c)45% headed by women or children

24. Conclusions • Now we are able to pinpoint vulnerable households with accuracy • There is overwhelming evidence in support for a paradigm shift regarding what we believe communities need, how to integrate programmes, and on choices given limited available resources, • We can then plan in advance, and implement objectively • The possibilities for further data analysis are limitless • Over time, we are able to assess impact

25. Selected Lessons • Project pace unavoidably determined by levels of stakeholder engagement- significant resources ($, time etc required for mobilisation) • Resistance and fear of data require champions • Communication/visioning of the HVI approach is different for different stakeholder groups-messages needed to be carefully developed • Clients (WVI) have conflicting priorities given the macro environment. (flexibility is key) • MDGs reporting requires this level of detailed analysis (at least)

26. Gaps • Resources not adequate- financial, equipment, human capacity • Pace not entirely determined by FANRPAN • Different components (University input, communication etc still need to be better coordinated)

27. Thank you