Issue 33 - Article 4

How dangerous are poor people's lives in Malawi?

April 18, 2006
Charles Rethman, Save the Children US

When we talk about vulnerability, we mean people’s inability to meet their basic human needs. In Malawi, there is a broad agreement that the basic need that people are most visibly lacking is food – both in quantity and in quality. Transient or short-term vulnerability, if acute enough, can easily lead to disaster if action is not taken in time. Less obvious are the high levels of ongoing constant or cyclical vulnerability to hunger that people in Malawi always face, regardless of weather conditions or natural disasters. This chronic or predictable vulnerability is usually at a lower, less acute level, and is not as easy to detect and understand. Nonetheless, we know that the poor in Malawi have very low incomes and small asset bases, and live close to or slightly below their minimum food energy requirements. Coping is very difficult, even under mild hazards.

In order to improve the lot of the hungry in Malawi, we need information. Principally, the kinds of information we need are:

  • Information about overall numbers, the symptoms of hunger and its causes.
  • Information that provides guidelines for targeting and actual implementation on the ground.

This article describes and proposes mechanisms to measure vulnerability in Malawi, for use by the Malawi Vulnerability Assessment Committee (MVAC). The MVAC was set up in early 2002, following the 2001–2002 hunger crisis. It comprises government ministries and institutions, UN agencies and NGOs, together with a small secretariat to provide technical and administrative support. The chair, the Ministry of Economic Planning and Development, has a broad cross-sectoral mandate, which enables it to tap into a wide range of skills and to facilitate technical collaboration and consensus on issues of vulnerability. The MVAC usually presents its findings and conclusions in reports and through presentations at national and regional levels. Over the last two years, the MVAC has produced timely information that has helped agencies to plan and deliver large quantities of food aid on time to vulnerable populations. More importantly, MVAC information has fed into the design of cash-based transfers to beneficiaries; in particular, the European Union (EU)-funded Public Works Programme has prioritised its activities among populations in need, and Oxfam has designed a direct cash transfer pilot. To evaluate this, a review of the MVAC’s predictive ability is being planned.

Current systems for measuring acute vulnerability in Malawi

Systems for measuring acute vulnerability in Malawi draw on the Household Economy Approach (HEA) developed by Save the Children UK. This recognises the two dimensions of vulnerability: people’s exposure to hazards or shocks, and their ability to cope (their resilience). It does this by analysing two types of information: baselines (or references), which describe how different groups of people normally survive and what they can do to maximise their consumption; and problem specifications, which define the nature of change – usually for the worse.

Baselines are derived by asking people how they normally obtain their food and income, how they spent their money and what they can do to maximise their access to food. To make these enquiries manageable at a national level, geographical areas are grouped into livelihood zones (areas where people have similar options for obtaining food or income). Within these zones, households are grouped according to their resource holdings (these are called wealth groups). Baselines are published by the MVAC as baseline profiles. Problem specifications are calculated by comparing production, consumption and price components for the period under analysis with the baseline. In the present system, baselines capture resilience, while problem specifications show exposure to hazard.

These two types of information are combined, and estimates are made of the shortfall in energy (or ‘calories’). Because income and expenditure are combined in the analysis, it is also possible to express shortfalls in cash terms: that is, how much money people would need to meet their minimum calorie requirements.

The present system for measuring acute vulnerability in Malawi has some recognised shortcomings. There is a lack of precision in measuring shortfalls, and there are calls for more in-depth detail, with population breakdowns by various characteristics (age and gender, dependency ratios, health status) and describers within livelihood zones. The fault is not with the analysis; rather, it results from a strategic decision to find, for the output required, the best balance between scale, cost, speed of analysis and resolution or detail.

It has been suggested that vulnerability analysis in Malawi should make more use of data collected using a statistically valid methodology. However, to provide information that can really guide policy and programming, very large samples would be necessary to keep the levels of aggregation small. A single statistic for the country, while perhaps useful for making global comparisons, is not much help in designing a response, especially when resources are limited.

One solution to this problem is to zoom in on specific ‘vulnerable areas’ and to carry out detailed studies using statistical data in place of key informants and semi-structured interviews. A methodological approach has been formulated by John Seaman and Celia Petty (the Individual Household Method or IHM). This gives highly detailed and very accurate information, even if the geographical areas where it has been applied are quite small (typically one to three districts). This approach has the advantage that MVAC member organisations that only work in certain districts can focus on their areas of concern and use the data directly in their project designs. However, resource contributions to the MVAC are voluntary, and participating members will need to be enthusiastic about the work if they are to commit the necessary time and staff to the assessments. Extra support would still have to be provided by the MVAC for enumerators, travel, analysis seminars, write-ups and printing. A second solution may be to combine existing national data sets with existing HEA baselines. This would avoid adding new surveys (saving both costs and time), although much work would still need to be done to improve the accuracy of the different data sets; they may also need to be redefined. This approach is discussed below.

A new idea for measuring chronic vulnerability on a national scale

One suggestion for measuring resilience in an area is to start with thinking of livelihood zones as containing only descriptors of how people go about their economic activities. By descriptors, I mean thinking of the baselines for each of the livelihood zones as describing a series of activities and options for obtaining food and income, rather than as containing generalisations about how many people have what assets, and how many people there are in each household unit.

These latter data on household numbers are instead contained in a series of extra problem specifications for each livelihood zone. This is not much different from the approach used to measure transient vulnerability, except that ‘problem specifications’ are not temporal events affecting the whole population, but more permanent ‘states’ that affect different sections of the population. The baselines then become a kind of ‘normal household’ in a wealth group – reflecting the average or typical conditions in a ‘normal situation’.

How could such an analysis be carried out? For a start, baseline descriptions and generalisations would need some fairly systematic testing. This would require careful mapping of all relevant entities (livelihood zones, administrative areas and enumeration areas for surveys), the output of which would be a table or system of tables relating each spatial entity to every other entity, and placing a defined proportion of each entity within each livelihood zone. This has already been done in Malawi. There may be a need to disaggregate some data down to smaller geographical units; although difficult, this can sometimes be done, as in the poverty mapping in Malawi’s Social Atlas, for example. The premise would be to identify the most important livelihood-defining characteristics of each sub-unit and compare them with its neighbours, testing to see whether similarities within and across zones are greater than differences.


The next step is to define the spatial ‘problem specifications’. These can be derived from demographic data (for example, dependency ratios), health data (the obvious ‘problem’ that springs to mind is HIV/AIDS prevalence, but others may be included), social data (marginalised groups) or households with significant variations in their economic situation (debt burdens, very low asset holdings). Data sets that can be drawn on include:

  • The National Census from the National Statistical Office (the last one was in 1998).
  • The Integrated Household Survey (IHS) from the Ministry of Economic Planning and Development (1998 and 2003).
  • The Demographic Health Survey (DHS) from the Ministry of Health (2000 and 2004)
  • The Malawi Nutrition Survey from the Ministry of Health and UNICEF (2005).
  • The Malawi Baseline Profiles and the important livelihood zoning reports.

The derivations of the problem specifications above will need to be made individually and in combinations. For example, some households may only suffer from one ‘problem’, such as a chronically ill member, while others may experience more than one problem, such as a debt burden and a chronically ill member. It is important, if we want to get the overall scale correct, to be able to say how many households in each economic category have each type of problem.

The actual analysis of all of this will be complex, but it can be automated. Sufficiently sophisticated software is under development by the MVAC and the government of Malawi.

Hazards should also be included in the analysis of predictable or chronic vulnerability, provided that these hazards are predictable as well. For example, regular destructive flooding or dry spells during the farming season could be a greater cause of vulnerability among people dependent on rain-fed cropping than poverty or low resilience. A measure of the frequency and intensity of shocks can be extracted from existing longitudinal data sets.

This analysis would not affect the early-warning analysis that may be carried out in the event of some acute hazard (such as the crop failure in 2005). In fact, it would greatly improve its accuracy, especially with regard to population numbers.

Finally, if enough in-depth IHM studies (even if only on a small scale) are also carried out, it may be possible to use them as controls to check the validity of the results obtained from the analysis using HEA and the national datasets. This will depend on having compatible surveys, which in turn will mean good coordination and goodwill among participating members and partners. It is hoped that the information generated in this way will provide a sound evidence base for designing wider social protection measures that are predictable in the medium and even long term, and which can go beyond meeting immediate humanitarian needs, and address chronic vulnerability.

Charles Rethman works for Save the Children US. He is currently seconded to the Ministry of Economic Planning and Development in Malawi as a Vulnerability Assessment and Analysis Advisor to the Malawi Vulnerability Assessment Committee (MVAC).


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