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Mainstreaming institutional resilience and systems strengthening in donor policies and programming

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By Huma Haider

1. Summary

Institutional resilience is the ability of a social system (society, community, organisation) to absorb and recover from external shocks, while positively adapting and transforming to address long-term changes and uncertainty (Anderson and Tollenaere, 2020; Juncos & Joseph, 2020; Aligicia & Tarko, 2014). Investing in strong, well-functioning and adaptable social systems, such as health, education and social protection systems, can build resilience, as these help to cushion the negative economic and social effects of crises (Strupat & Marschall, 2020).

While development actors have established guidance on how institutions can be made more effective, inclusive and accountable, there is much less literature on institutional resilience and how development actors can help to foster it (Anderson & Tollenaere, 2020). Much of the literature notes a lack of systematic evidence on applying the concept of resilience. These gaps extend to a dearth of guidance on how development actors can mainstream institutional resilience and systems strengthening into their policies and programmes. This rapid review thus draws on common factors discussed in the literature that are considered important to the strengthening of resilience and particular systems. These may in turn provide an indication of ways in which to mainstream institutional resilience and systems strengthening into development policy and programming. They include:

Risk assessment and analysis: Effective interventions for fostering resilience require well-designed programming based on a comprehensive multi-hazard, multi-sector assessment of all the contextual factors that affect the system(s) under study. This informs the theory of change (Frankenberger et al., 2014). The OECD’s Resilience Systems Analysis tool, for example, aims to build a shared understanding of key risks in a given context and existing capacities within those societies to cope with such risks (OECD and Sida, 2016).

Systemic thinking: A systems-level theory of change and approach explores what intervention or set of interventions will tip a conflict system to a non-violent system that is improving over time (see Juncos & Joseph, 2020). A systemic view also requires consideration of how to adapt and absorb repeated shocks and to understand how these shocks affect different sectors (Gilson et al. 2017; cited in Hanefeld et al., 2018). Data analysis in resilience-oriented evaluation should be concerned with interactions, pathways and trajectories (Constas et al., 2020). In order to promote institutional resilience, cross-sector programming, across the humanitarian-development-peace nexus, should be the norm (Carey et al., 2020). Policy makers should incentivise a ‘whole-of-government’ framework for addressing global systemic risks, allow for longer-term horizons, and remove unnecessary barriers to collaboration (Carey et al., 2020; Al-Ahmadi & de Silva, 2018).

Local knowledge and sources of resilience – and scaling up: To strengthen institutional resilience, development actors are encouraged to identify, support and build on local knowledge, experience and sources of resilience, rather than create new structures (Anderson & Tollenaere, 2020, 191). Local structures and systems that have survived during protracted conflict need to be proactively rebuilt through improving capacities, incentives, ownership, and participation of the communities (Roach & Al-Saidi, 2021). Repeated exposure to crises can also generate new sources of endogenous resilience (Anderson & Tollenaere, 2020). In Liberia, the resilient community networks that were critical to survival during the civil conflict also enabled the country to mount an effective, community-led response during the Ebola outbreak. Development actors subsequently designed Liberia’s response around these systems (Anderson & Tollenaere, 2020). Institutional resilience can be further strengthened by expanding and replicating local-level successes (Anderson & Tollenaere, 2020). Scaling up should be considered from the beginning of planning and implementing an intervention, rather than asking ‘what next’ at the end of a project (Begovic et al., 2017).

Social capital and social cohesion: Internal capacities of societies, such as social capital, networks and leadership, are often highlighted in the literature as key to fostering community resilience and enabling institutions to adapt and innovate (Juncos & Joseph, 2020; Lee, 2020; Barma et al., 2014; Frankenberger et al., 2014). Institutions that build relations with citizens and gain citizens’ trust are also more resilient (Anderson & Tollenaere, 2020). Although institutional resilience is often attributed to charismatic leadership, various case studies highlight that leadership is not exercised by a single individual, but rather a network of core group of senior technical staff and managers (Barma et al., 2014). In other instances, self-help groups or women’s groups in rural communities have been integral to knowledge sharing on adaptation and coping mechanisms, providing loans during crises, and linking women to formal institutions that they could rarely access individually (Liru & Heinecken, 2021). Donors can support local networks by expanding their ability to connect and learn from each other (Barma et al., 2014).

Complexity, flexibility and iteration: Complexity, a key feature of a resilience approach, forces development actors to think about how to address problems that cannot be fully resolved or that may have no endpoint (Joseph and Juncos, 2019). Policies and programmes aimed at fostering institutional resilience and systems strengthening need to adopt flexible and adaptable processes that allow the system to adjust to changes and new pieces of information quickly – and that allow for experimentation (Shakya et al., 2018; Aligica & Tarko, 2014). Financing strategies and mechanisms to support resilience initiatives also require flexibility, linked to multidimensional analyses and long-term horizons (Carey et al., 2020).

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