Stop targeting; start performing
Vulnerability analysis: a compromise rather than a solution
So much of the data we collect in humanitarian action is about figuring out who is eligible for support. This is done because there is never enough money to actually support all people, families and communities that have been devastated by a conflict, natural disaster, or climate change. We figure out who is in most need, and then focus resources there.
To do this, organizations conduct ‘vulnerability analysis’ to try to decipher ever more complicated levels of personal devastation so that we can figure out who is most in need and what types of services they need. Generally and humanely, this tends to emphasize the needs of women (and especially mothers), girls, and other children. Their needs, especially in the worst humanitarian contexts, are undeniable. Vulnerability analysis also tends to focus on food security and increases in malnutrition, especially in children. Yet, again, vulnerability analysis is not done to figure out how best to support these people and their communities so that they can recover and improve (resilience capacities); it is borne from the fact that we don’t have enough resources to help everyone in dire need. It is a compromise rather than a solution.
Need to move away from formalist approaches and toward communities as systems
The problem is that the tools we use for targeting are in the formalist tradition, meaning that they distill diverse variables into a standard set of metrics that can then be set against thresholds. For instance, some tools might ask how many times in the last week you went to sleep hungry. If more than a certain number, then you may be eligible for support.
Even if the notion of going to sleep hungry rings human, this type of question is in the quasi-scientific formalist tradition: if x (number of hungry nights) is greater than y (2 nights a week) you will get z (conditional cash support). It is formulaic and reductive; not human.
This is why it doesn’t work. It does not pay attention to the powerful individual and societal dynamics associated with emergencies at such large scale. It is also callous to think that it is appropriate to go into these contexts like scientists in white lab coats with a set of perfect metrics. It is inhumane to go in with a dispassionate approach towards mothers who are trying desperately to care for their hungry children. How the hell would you answer such questions? There are various other problems with this approach.
First, people lie. It does not take long for people to figure out how to answer to get support and nearly everyone in major humanitarian crises need support. People also lie or simply don’t answer because of the caustic intrusion of such questions. Why should I admit to you the soul-wrenching pain I feel about not being able to provide for my children?
Second, people don’t know how to answer honestly. How hungry? How much food? What if we decided to miss some meals so that we could keep our kids in school rather than having to send them out to beg? Or, conversely, what if we are less hungry because the children are out begging? People facing trauma and bad decision after bad decision are hardly in a position to make some quasi-experimental assessment of whether they are hungry enough to receive support.
Third, and this is the doozy, what if some people are hungry because within their community they are the least able to make decisions that could thwart the worst aspects of a crisis and that, over years, that inability to figure things out compounds to a point where they are simply desperate.
The first two of these are actually not that difficult to address through well designed surveys and well-trained surveyors. The last points to a hard reality that direct assistance to the absolute most vulnerable in a community will not enable that person to move toward self-sufficiency/increased resilience capacities because they are far too lost. They need much more diverse, localized, and long-term levels of support.
This is hardly revelatory; it has been a subject of debate in social support systems globally for over 30 years.
The first step to a better way is to move toward a systems-based approach. Without going into a lot of agonizing theoretical detail, this means focusing on groups or communities (the system), rather than individuals, and then performing many different interventions in the community, measuring quickly and simply how and if they are delivering system-wide results. Any set of interventions should prompt positive results across the whole community, as illustrated by some aggregates and/or thorough community wide indicators. This mix of individual assistance integrated within a community-wide response is the best way to ensure recovery.  As a community or group of people recover, they will be better able to find ways to help the most vulnerable amongst them either from a strictly human, brother and sister, level of empathy or because they will become secure enough that direct support to the most vulnerable will not detract from their needs. Everyone will have a stake in ensuring that the assistance works.
Community/systems-based approaches require many smaller interventions and good data to test efficacy.
One of the other problems with traditional vulnerability analysis is that it often fails to consider context-specific variables. The full CSI is an exception but it has also been reduced and re-configured so that it can be used as a standard tool across different contexts. This goes back to resources. Big organisations, like WFP, UNICEF, UNHCR, need tools they can use across global operations quickly so that they can go to donors with a ‘precise’ number of people they can serve as part of a humanitarian response. They aren’t focused on results and solutions; they are focused on getting funding for their services and approaches and so they need to identify as many people as possible who fall into one area of vulnerability or another.
A community/systems-based approach would only require basic demographic information and a relatively straight forward assessment of the needs across the community—not a household by household, person by person survey. Then a series of services/activities could be conducted, over a few days or weeks, with a very clear set of measures to see which of these are delivering results, like reductions in malnutrition, preservation of assets associated with income, stress migration, conflict, etc. As soon as the efficacy of any one or a set of interventions is established, it could be replicated and scaled-up. This will give us a much better understanding, through micro-interventions and their effects, of the localized factors that lead to immediate recovery and toward longer term resilience. This will cut across subjects and sectors and be emergent rather than determinist.
This is a much quicker way to respond. It does not depend on conducting a vulnerability assessment before acting. It forces an early response, with a range of interventions occurring very quickly, and then with careful analysis to focus on those that are producing clear results. The level of funding, interestingly, should remain largely the same but with a much greater focus on things that work as the crisis continues.
This also shifts the focus of questions from what should we do to how things are working for those most in need. We can ask the mother with suffering children how the support is helping her, rather than asking awkward and rude questions about what she needs when there is no assistance available.
This requires very good tools for measuring immediate and slightly longer results. Good tools are not really the issue. We have lots of tools. However, given the need for a lot of data produced quickly, the tools need to be simple. For instance, at MESH (www.mesh-somalia.net) conducted over 800 call center surveys a day during the 2017 famine threat in Somalia. These assessed direct cash support and focused on three basic indicators: did they receive the right amount of money; did they suffer from any diversion or fraud; and how did they use the money.
This enabled MESH to show how expenditure patterns shifted during the response, from upwards of 90% of the money used for food to lower than 70% as the response matured. This signaled that resources could be shifted from cash support to, for instance, water infrastructure for agriculture and herding, health care, and education, amongst other areas as the threat of famine was reduced. These other activities enabled people who were already desperate to stay in their communities, keep their children safe and fed, and to re-focus on longer-term recovery.
A system/community-based approach combined with rapid assessments allows humanitarians to meet the needs of the most vulnerable while also ensuring that they can recover from crisis.
The research and common-sense point to the effectiveness of this approach. It would require a major step-change in how humanitarian actors work. We need to re-focus resources on the monitoring of activities and learn how to position this with donors so that they understand the funding parameters and expected returns on investment—which should be massive. We also need to make sure that this is done in ways that uphold humanitarian principles and that, as we rush to launch a series of different interventions in a community, that those who are facing the worst aspects of the crisis are not ignored. That is an issue of balance and insight which can also be achieved if we are smart and focused in performance.
 A good recent review on the subject, albeit with a focus on urban, contexts, see: Patel, R.B., King, J., Phelps, L. and Sanderson, D. (2017). What practices are used to identify and prioritize vulnerable populations affected by urban humanitarian emergencies? A systematic review. Humanitarian Evidence Programme. Oxford: Oxfam GB.
 This is reductive, of course. Most tools have a series of subjects and questions that are used. They can also be adapted to specific contexts. A good example of this is the Coping Strategies Index. See: Daniel Maxwell and Richard Cladwell; “The Coping Strategies Index: Field Methods Manual.” Second Edition. January 2008.
 This is not a wishy-washy idea. It just hasn’t gained the traction it deserves. Maybe Esther Duflo’s recent Nobel prize in economics may create some momentum as this type of analysis lies at the heart of her work. See: Esther Duflo and Abhijit Vinayak Banerjee; “Handbook of Field Experiments, Vol.1 and 2.” North –Holland (an imprint of Elsevier); 2017.