ASRA Module 7 Prioritizing Risk
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Transcript of ASRA Module 7 Prioritizing Risk
In previous sessions, we learned about how to iden4fy and assess risks and quan4fy their impacts. We learned various approaches on assessing the capacity of stakeholders’ to manage different risks. In this session, we’ll learn approaches to priori4zing risks. Again, why do we priori4ze? So that we can op4mize available resources to beDer manage those risks that are having the biggest adverse impacts on incomes and livelihoods, and sector growth.
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At this point we need to understand which risks are more likely to cause the most adverse shocks to specific stakeholder groups, target sub-‐sectors and the sector as a whole. The primary criteria used to priori4ze risk are: (1) probability of event (or frequency of occurrence), and (2) severity of impact. However, lack of clarity about the meaning of different terms might introduce undesired bias. Strategic Priori,es-‐ Certain stakeholders, objec4ves or regions may be priori4zed based on sub-‐sector context or client preferences. Regional varia,ons -‐ There may be a need to account for different regional or crop risk profiles given the varia4ons in climate, agro-‐ecological and socio-‐economic condi4ons. Recovery period -‐ The amount of 4me it takes stakeholders to recover could be used for priori4za4on. For example:
Short term. Single produc4on season/year. Medium term. Impact lasts a three to five seasons / years. Long term. Event permanently cripples an industry.
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Classifica4on is based on the occurrence of historical events that have records and their impact is known. But in some cases, historical records might not be available. In such cases, interviews with the stakeholders and key informants and their individual (subjec4ve) experiences about the frequency of occurrences can help classify the risk in an appropriate category.
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Historical occurrence of events is the best way to determine the probability of events occurring in the future. Frequency of occurrence of risk events can be es4mated from news, databases and stakeholder input. To reduce subjec4ve bias, define clear, measurable terms such as those in this table.
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The magnitude of losses from each risk reflects the severity of impacts. This in turn is a func4on of two different factors: 1) frequency of the risk event and 2) likelihood (or probability) of loss from a risk event. This o\en can be calculated by mul4plying the financial loss sustained by an actor by the frequency/spread of the event). Since the objec4ve of the assessment is to provide a na4onal snapshot, losses should be assessed at na4onal level, using disaggregated regional and district level data, where available.
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How to summarize (illustrate) priori4za4on factors
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There are different approaches to visualizing scope and frequency of vola4lity and risk events. The first step is mapping via a visual 4meline of loss events. This will provide insights into what are the principal causes of observed vola4lity and resul4ng losses. Time-‐series FAOSTA Produc4on indices (e.g., crops, good, livestock) can be useful or Agricultural GDP growth.
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As we saw earlier, iden4fying the specific sources of risks that may have contributed to observed shocks based on best available primary and secondary sources allows us to ascribe aDribu4on
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Another tool leverages the results of the crop loss analysis to compare scope and frequency of losses across commodi4es to see which are most impacted, in terms of frequency and cumula4ve financial losses This aids in highligh4ng which commodi4es are most suscep4ble to risk-‐induced losses and therefore might benefit from closer scru4ny during the solu4ons assessment. In the risk assessment of Paraiba, Brazil, sugar cane and fruit, especially grapefruit, due to their large share in the total agricultural output value of Paraiba, are most suscep4ble to losses, and so, might be given priority in the risk management strategy.
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Another way is assessing losses by risk event type. This bubble graph provides another way to illustrate severity of impact by illustra4ng es4mated losses vs. frequency of event, and thus, the priori4za4on of risks. Financial losses at level of produc4on are calculated by es4ma4ng the number of hectares of lost produc4on mul4plied by the value of that produc4on (per hectare). Frequency of event is calculated by the number of 4mes the event has occurred divided by the total number of risk events. In both cases, assump4ons are derived in part from stakeholder input and the data gathering process.
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Par4culalry when quan4ta4ve data is scarce, focus groups interviews can be useful to inform the development and comparison of how stakeholders perceive risks from one region to the next. These bar charts illustrate the risk priori4es for rice producers in different regions of Guyana. Flooding was the highest ranked priority in Region 5 whereas red rice was the highest ranked priority in Regions 3 and 6.
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To help inform the risk priori4za4on process, it is helpful for the Team to collec4vely iden4fy the leading risks for each target commodity to iden4fy similari4es and divergences. This list can then be more easily aggregated at sector level.
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Ranking risks by region is yet another lens that aids the risk priori4za4on process.
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This chart illustrates how observed risks can also be weighted by percep4ons of stakeholder vulnerability: 1) expected losses when a given risk event manifests and perceived levels of exis4ng stakeholder capacity to manage that risk. Risks in the top le\ quadrant are given the highest priority.
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This table illustrates the priori4za4on of risk to the cocoa supply chain in Ghana based on to two indicators: (1) probability of event, and (2) severity of impact. For the Y-‐axis, each risk is ranked from remote to highly probable…
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This table illustrates the priori4za4on of risk to the cocoa supply chain in Ghana according to two indicators: (1) probability of event, and (2) severity of impact.
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