Incentive funds are an increasingly popular intervention in a range of sectors. Funds range from incentives designed to encourage devolved governments (e.g. states) to implement policy through to innovation funds designed to develop and prove successful new practices.

Incentives can be a powerful influence on behaviour. However, many funds find that their impact is lower than expected, their funding results in unintended consequences, or the interventions they fund prove either unsustainable or unable to scale.

We looked at successful and unsuccessful funds, the challenges they faced, and the strategies that were effective in overcoming them. We found that funds generally had one of four objectives, and that the challenges they faced, and things they needed to do to be successful, varied accordingly.

The four objectives were:

1. Drive behavioural change at scale. Examples include the ‘Race to the Top’ fund used drive change in state education in the United States, or Peru’s Incentives Plans for municipal governments and education authorities.
2. Target funding at areas where it is required most: Examples include ‘School Improvement Grants’ in the United States, which are targeted at schools with the highest level of need, or number of funds in Nigeria targeted at states with the lowest levels of performance.
3. Generate evidence about effective practice: Examples include most ‘Innovation Funds’ which aim to generate evidence about innovations and their potential impact.
4. Generate awareness about issues. Examples include the Global Teacher Prize, which aims to generate awareness about teaching, or the awards for health and education sponsored by Qatar’s WISH and WISE

The first two types of fund require relatively large amounts of funding to be effective, and aim to directly influence decisions. The second two require less funding, but influence decisions indirectly.

Different types of funds face different challenges. When funds try to drive change at scale, the biggest challenges tend to be around data quality and avoiding unintended consequences and incentives. For instance, funds which reward schools based on exam scores need to address a large number of issues around data and incentives. Conversely, where funds aim to generate evidence (about new innovations) or awareness, the main challenges are in ensuring that new ideas are taken to scale.

The challenges funds face, and the strategies to overcome them, vary based on their type:

01. Drive change

Challanges

Large funding requirement

• Risk of gaming and perverse incentives
• Ensuring data is rigorous
• Moving ‘hard-to-measure’ outcomes
• Ensuring capacity to implement

Strategies

• Demonstrating initial impact to gain long-term funding
• Heavy investment in monitoring
• Iteration in goals and monitoring to counter negative effects
• Processes to share effective practice and build capacity

02. Target resources

Challanges

Negative incentives (rewarding failure)

• Low beneficiary capacity
• Ensuring data is rigorous
• Potentially low results relative to investment

Strategies

• Detailed capacity assessments and strengthening plans prior to investment
• Distribution of funding in tranches based on performance
• Levers for intervention in no performing grants

03. Generate evidence

Challanges

Ensuring that interventions are scalable

• Generating interest and uptake of successful interventions
• Ensuring sufficient high quality applications

Strategies

• Building scalability into assessment criteria (e.g. realistic costs)
• Including participants from a range of context similar to those in target systems
• Wide publicity to generate the maximum number of applications

04. Generate awareness

Challanges

Translating increased awareness into improved implementation of policy

• Ensuring the integrity of successful examples

Strategies

• Rigorous due diligence of selected examples
• Broad outreach and awareness efforts
• Direct targeting of decision makers and involvement in the process

Successful funds correctly implemented the strategies applicable to their type. In all cases, strong routines, processes and data systems were integral to success – as always, good outcomes