We try to understand who experiences an effect and how underserved they are in relation to the outcome that it relates to.

Why does this dimension matter?

The demographic and geographic characteristics of people who experience impact, or the environmental characteristics of the planet, help us to understand what outcomes are important and how much the outcome is needed.
EXAMPLE: A farmer in Nigeria may develop skills or receive a salary that enables her to have financial security. With these skills, she is well-served in relation to the outcome of financial security. A high-income professional in rural Missouri may be underserved in relation to social connection.
When we consider an effect, we assess how well-served the person experiencing it is, based on whether they are already experiencing - or have the opportunity to experience - the outcome that the effect relates to.

How do you know who is affected?

We recognise that we are all actors in wider systems and that the effects that one actor experiences when they engage with an enterprise always generates effects - however material - for other actors with whom they have a relationship. We also recognise that the material effects of relationships between actors are often institutionalised in:

  • Organisational forms,

    EXAMPLE: A market institutionalises the relationship of actors buying and selling from one another
  • or, widespread social or cultural norms

    EXAMPLE: Families living together
  • or, government policy

    EXAMPLE: Individuals not stealing from one another because they will be punished by law if discovered.

To the extent possible, when we describe impact we describe any material effects on relevant systems, being specific about which actors and relationships in a system we are referring to.

What information do we need?

To understand who is affected by interaction with an enterprise, we collect geographic and demographic data directly from people affected, or from experts in the outcomes we seek for the planet (such as scientists).

In this section, we bring the dimensions to life through examples of how of a number of enterprises work to deliver employment outcomes for young people with different needs in different geographies. The examples are drawn from specific organisations but illustrate useful approaches for any enterprise or investor - big, small, for-profit or non-profit - managing impact across the five dimensions.

Meet Career Connect, one of ten providers selected in 2011 to deliver a programme to help young people aged 14-24 who were NEET (not in education, employment or training) or at risk of becoming so, as part of the UK Government’s £30m Youth Unemployment Innovation Fund. The contract was structured on a 100% payment-by-results basis and financed with a social impact bond. It was the biggest of the 10 programmes commissioned by the Innovation Fund, targeting 4,000 beneficiaries. All programmes were commissioned to work with ‘risk-of-NEET’ young people, but the commissioner did not define which risk of NEET indicators to use so providers were free to develop their own.

To understand (what) is experienced, and (how much) of the effect is delivered,each effect achieved is verified and paid for against a rate card of multiple different effects, like improved attendance or qualifications achieved, where the price is relative to how strong the proxy is for whether the effect will lead to long-term, stable employment. The payments also rise incrementally in relation to the depth of the effect achieved. For example, starting full-time employment is the most valuable outcome. The programme ran from April 2012 to April 2015, with effects tracked to September 2015 through a text messaging and telephone follow-up service.

To understand (who) is experiencing the effects, Career Connect developed risk-of-NEET indicators to assess the level and type of need of each participant. The government commissioning department had stipulated a focus on three groups considered to have a higher-than-average propensity to become NEET, based on local government data (young offenders, those in or leaving care, and those with learning difficulties), but did not prescribe precise definitions. Career Connect Mental Toughness assessment – which highlighted the lack of resilience of the participants relative to the overall population – proved to be an additionally helpful way of assessing individual needs, helping to inform what kind of intervention was needed for each young person.

How does this help us to manage our impact?

We use this information to understand and improve how much of an effect is experienced by certain groups of people or the planet, or seek to understand whether the effects delivered are likely better or worse than what those people or the planet would experience anyway.

To ensure each young person received depth of effect, the programme was iterated and tailored to what was learned about what effects were delivered most effectively for which participants. Far fewer people gained employment than expected which was, in part, a reflection of the provider’s better-than-expected success in re-engaging the participants who were 16-years-old or younger with education. in other words, more of the young people than expected stayed in school, making it impossible for them to achieve employment within the time period of the contract.

To try to make sure that the important positive effects experienced were likely better than those that would otherwise have occurred, Career Connect closely tracked the risk-of-NEET indicators of its participants. This helped ensure it was avoiding ‘cherry-picking’, like deliberately relaxing eligibility criteria to make it easier to achieve the desired effects. Career Connect also tracked the destinations of participants following the end of the programme. Career Connect compared the effects of programme participants with a control group of young people with the same needs who did not receive the same targeted and intensive intervention from Career Connect, which showed very positive results. However, Career Connect did not claim this entire contribution, as it was hard to establish a fully accurate control group because risk-of-NEET indicators are not classified in a standard way nationally.

Resources

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See how the risk dimension has been helpful to Acumen and NPC to understand the risks they are willing to take, which risk factors matter most to them and how they manage to risk. If you are an investor or funder you may be interested to see how risk can be managed at a portfolio level

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