Organizing comprehensive humanitarian assistance is undoubtedly a challenging and often elusive objective for practitioners. The practitioner community also knows all too well that compiling evidence, metrics, and evaluations to apply usable learnings from experience remains imperfect in practice simply given the nature of the work.
Despite these difficulties, the need for learning in humanitarian assistance is enormous and growing, and achieving learning objectives will require a new look at how pre-crisis indicators are prioritized, collected, and utilized at the regional level.
We know that learning happens from data, and therefore the type of data matters. The data often reported to the public, through the media and others, include intuitively understood metrics such as the number of persons requiring humanitarian assistance, number of food insecure persons, metric tons of delivery assistance provided, and number of search and rescue operations. Such data inherently emphasizes macro-level activities and the context for what has occurred following a humanitarian crisis or the unfolding of a complex emergency. While these data are critical to understanding any humanitarian operation, they alone are insufficient to learn from given their ex-post nature; simply put, these indicators prioritize post-crisis activities.
Policy makers or anyone trying to improve humanitarian assistance performance first need to know that no matter what macro-level data might imply, humanitarian assistance, like emergency management, is not a simplistic and reactive exercise of providing relief and recovery. Rather, it is defined by a well-established, four-stage process that works to comprehensively manage disaster risks and address the catastrophic effects of slow or rapid onset humanitarian crises: Mitigation, Preparedness, Response, and Recovery. Most important among these four are mitigation and preparedness, without which, response and recovery will always suffer. Mitigation and preparedness are the foundation upon which responsible and effective response and recovery efforts are deployed, including monitoring, evaluation, research, and learning activities (MERL).
Policy makers and humanitarian leadership must recognize that mitigation and preparedness activities require looking beyond the macro-level data at the onset of humanitarian crises. Humanitarian practitioners require a robust, working knowledge of the intermediary data—data that guide the operations themselves and often dictate real-time outcomes of a crisis or emergency. Examples of these intermediary data, in contrast to the macro-level data, include: the measurable capacity of institutions to address the needs of at risk populations; availability, access, utilization, and distribution of food among specific populations; metric tons of supplies prepositioned for risk areas and the logistical capacity to move them in a non-permissive environment; or total surge personnel pre-staged to conduct search and rescue operations.
Simply put, if regional policy makers want to improve performance management in humanitarian operations then they need to look at the intermediate level data between countries.
There is no doubt that the complexity of intermediary data makes its collection and utilization elusive without a commonly agreed set of indicators that can be compared across countries, organizations and programs. We call this a common currency. The macro-level data presented in post-disaster scenarios already exist in this common currency form—number of persons in need of assistance, lives lost or saved, supplies delivered. Humanitarian assistance specialists easily exchange this ‘currency’ between countries in their fact sheets, donor reports, and public outreach. However, this exchange only reflects what practitioners were able to achieve in the post-disaster or emergency scenario and is hardly useful for other countries and local actors in their own efforts.
Such common currency for intermediary monitoring and preparedness activity indicators must be holistically adopted by regional humanitarian advisors. Some datasets already exist for these indicators and range from specific subject matter databases such as FEWSNET, to enormous clearinghouses for humanitarian datasets such as the Humanitarian Data Exchange (HDE). Therefore, the MERL problem of regional compatibility is not entirely related to data availability. The data, however, is difficult to aggregate because of missing information for specific countries or lack of consistent indicators over time. The data must be capable of being aggregated, adjusted, and applied in such a way that end users, namely local institutions and organizations, can understand their capacity over time, the role of external humanitarian intervention, and how to tailor mitigation and preparedness to their country’s needs.
While the common currency approach suggests that data should be understood across borders, there is little point in comparison contests. First and foremost, success is relative to where a community has been before. At the same time, local organizations can learn from other experiences, even if that means that data never highlights which country it came from e.g.: country X did [XX] which resulted in [XX]; [XX] is what you can do in your own local community to learn from this example. Additionally, these data can be exchanged regionally and wielded in such a way that the collective capacity of countries, and more importantly, local communities, are increased. The data must be commonly traded, exchanged, and stored at the regional level for each country’s humanitarian actors to learn about their communities over time as well as from each other’s mitigation and planning activities. Unless these exchanges occur, MERL efforts remain disconnected across pre- and post-crisis learning, and local humanitarian practitioners miss opportunities to maximize their capability to save lives.
Common currency is critical in regional humanitarian assistance programs where the focus is on building local capacity. New efforts to build upon existing capacity within a cluster of countries are commonly carried out through Regional Learning Advisors (RLA) who apply a regional approach to sustainable development, what USAID calls the Journey to Self-Reliance, in humanitarian assistance. RLAs serve as regional representative resource for their peers, collaborating to maximize data collection, and help their colleagues analyze and use it. When RLAs use a common currency of intermediary indicators they possess greater control over shared learning experiences for maximizing local humanitarian mitigation and preparedness capacity.
There are several examples of pre-crisis common currency indicators that humanitarian organizations can establish as this common currency, but let’s use humanitarian food logistics as a specific case.
Generally, the two most important pre-crisis, common currency indicators for this case are (1) the four pillars of food security and (2) a quantified local logistics capacity.
First, we need to understand the food security environment, specifically the four pillars: availability, access, distribution, and utilization of food for each country. These data are already made available by FEWSNET. From this information we can understand each population’s risks to food insecurity if a crisis takes place. This data is comparable across countries and otherwise complex landscapes, and clearly details populations’ food security or insecurity. Even threats to each of the four pillars of food security are common currency and are available in various databases: environmental changes, wage rates, livestock and staple market prices, disease, drought, the presence of conflict, availability of agricultural inputs, consistency of imports/exports, inflation, and many others. However, these threats are often not collected uniformly over time and therefore cannot be compared across countries. New indicators should be developed to supplement these gaps.
Next, we need the second common currency indicator, the local logistics capacity. This capacity is generally captured in the UN Logistics Cluster’s Country Logistics Capacity Assessments (LCA). Each assessment collects the specific data on a country’s strategic logistics capacity and reflect logistics mitigation and preparedness realities should a humanitarian event take place. Some examples of the specific LCA indicators are a country’s telecommunications availability and quantified reliability, cubic meters of preexisting food storage warehouses, average fuel costs by locality, local food basket procurement lists, and transportation availability. Again, despite great strides in data availability in the LCAs, some indicators are still missing for specific countries, and the data are difficult to measure over time in their current form.
The LCA indicators, combined with the FEWSNET data and new indicators to fill gaps in availability, can be exchanged as this common currency among humanitarian practitioners to establish baseline logistics capacity requirements to effectively mitigate and respond to humanitarian risks. Countries that have experienced humanitarian crises can compare their common currency indicators at regional levels to determine gaps in mitigation and preparedness that could have resulted in improved response and recovery efforts.
Wielding such mitigation and preparedness common currency indicators, RLAs and local humanitarian organizations are much more likely to succeed in regional learning exercises. Practically speaking, RLAs should develop common currency indicators from preexisting datasets and create new indicators that may lack certain country or time variables to compare communities against themselves over time. Countries can then measure their progress against humanitarian interventions and tailor further assistance according to local needs. This method helps countries climb up against a historical measure of their own readiness to practically find solutions. Furthermore, with common currency, both RLAs and local humanitarian organizations possess the working knowledge to appropriately address some of the most common challenges that face mitigation and preparedness efforts, and collectively function to mitigate risks and prepare their local communities.
Overcoming the challenges of learning in the humanitarian context requires the development and application of preexisting, pre-crisis, common currency indicators. Development should involve creating new indicators to address country and time gaps in preexisting datasets and ensure comprehensive coverage for comparability across countries. Empowering local learning and exchange through the application of the common currency approach within sound MERL practice can maximize regional collaboration and coordination to improve local capacity—and save lives.
Joel Taylor is a Program Analyst at Dexis Consulting Group with experience in cold chain logistics, food security, and the application of data in food market research. The author wishes to thank David Douglass and Mihir Desai for their inputs.
Photo by Maciej Moskwa/NurPhoto
