In November 2025, Nesta’s fairer start team launched the Parenting Support Commissioning Assistant for Local Areas (PASCAL), a free-to-use decision-making assistant intended to support local authority staff who commission parenting programmes. We’re sharing more about what PASCAL does, why we think it’s needed, and how it works.

PASCAL is intended to simplify the process of identifying suitable parenting programmes for local areas within England. This process can be fraught with difficulties, as shown in our report on the market for parenting support. Local authorities often don’t have the information needed about how effective various programmes are in improving outcomes for children, or about how appealing the programmes are to parents.

PASCAL takes a significant step towards simplifying this process by recommending portfolios of programmes to local authorities, based on a robust quantitative methodology. The portfolios are personalised to local authorities’ populations of families, and are chosen to maximise the expected impact on good level of development (GLD). The recommendations can also be tailored to the local authority’s preferences and budget. 

PASCAL brings together months of quantitative research on parenting programmes. We gathered consistent data on the features of 22 different parenting programmes, such as their modes of delivery, target group, cost per family, and much more. We also developed a new statistical model that can predict the likely impact of each programme on helping a child reach a good level of development. In August to September 2025, we ran a large-scale survey experiment that told us which sorts of programmes were most appealing to parents, including which features of the programmes are most appealing to different groups of parents. The data from this survey can be used to estimate the likely take-up of each type of parenting programme.

These bits of data and analysis form some key ingredients, which PASCAL combines to produce a consistent score for each intervention and family. These scores tell us the potential efficacy of parenting programmes for different families at the local population level. Having done this, PASCAL will then pick programmes and allocate them to families in the local population to maximise the score with the amount of money the local authority has to spend. 

In other words, PASCAL allocates interventions to families in a way that maximises the expected impact on GLD in the local population, given the available budget.

When we break down the varying needs of families and children across the country, and which programmes are best evidenced to support those specific needs, there are millions of possible combinations of programmes that could be offered in different locations. These need to be compared for their possible overall impact on how many children will reach a good level of development. The output from these models finds the portfolio of interventions that is likely to maximise the impact of a given area’s Best Start in Life funding on GLD, based on available information. PASCAL does this very quickly by using a ‘linear programming’ algorithm developed by computer scientists.

In order to do this, we must give each intervention a score - which we call ‘marginal social welfare’ - for each type of family in each area, identified using data from the National Pupil Database. This is done by calculating the likely impact on GLD at scale if the intervention is offered to each kind of family in the local population, and then giving more weight to interventions that match the commissioners’ preferences so that they are more likely to appear in the suggested portfolio.  

This calculation is the result of a mathematical formula that is applied in the same way to each intervention and group of people. To spell out the logic, we can split it into its three main components.

1. Predicted impact on GLD 

Nesta developed a statistical model to predict impact on GLD when direct impact on GLD has not been measured in a formal evaluation. It takes evidence from evaluations, like randomised-controlled trials (RCTs), and translates it into the most likely impact on the GLD measure. This is done by drawing on known relationships between the outcomes that have been measured in evaluations and GLD. 

Note: all modelling is based on predicted impact when the intervention is implemented with ‘high fidelity’ (to a high standard). 

2. The quality of evidence

Not all evidence is created equal. Some evaluation methods offer a much higher quality of evidence than others. However, our model’s estimates assume the effect seen in the evaluation is going to be replicated in the real world. This is very unlikely to be the case, because evaluations often occur under favourable conditions, and because some methods of evaluation (other than RCTs) can provide misleading results. 

For this reason, the quality of evidence underpinning the intervention’s evaluation - based on the Foundations Guidebook rating - is also a critical input to PASCAL. The Guidebook rating lets us build an estimate of the likelihood that an evaluation’s findings will replicate in the real world. 

We use academic research (‘meta-analyses’) to factor in programmes with less robust evaluations by estimating the likelihood that their findings will fail to replicate in the real world. We estimate that an intervention supported by a single RCT has a 60% chance of replicating its effects. In contrast, an intervention backed only by quasi-experimental evidence is 40% less likely to replicate than its RCT-backed counterpart.

3. The appeal to parents

No matter how effective a programme has been found to be, it won’t achieve impact unless parents sign up to it and participate. Therefore appeal to parents of each programme is critical as this affects the likelihood a parent will sign up. PASCAL accounts for this by using the findings from a rigorous survey experiment we ran with over 2,000 parents.

Each of these three elements - impact, real-world replication, and predicted sign-up - are critical to estimating impact. Fortunately, they can be combined using a neat mathematical formula that tells us the statistically expected impact on GLD among different sets of families for each intervention. This is the ‘marginal social welfare’ score for each intervention. 

We also know it’s important to account for local knowledge so finally, PASCAL adjusts the ‘marginal social welfare’ score to reflect the priorities selected by the user (in most cases a local authority commissioner). Interventions matching the user’s priorities will end up with a higher score after this adjustment is done, meaning they appear more often in your portfolio.

We developed PASCAL using an iterative, test-and-learn approach. We started developing PASCAL in July 2025, first conducting initial user testing with local authorities. We showed them an early version of PASCAL and received feedback on how we could develop the tool to be as helpful as possible.

Using this feedback, by the end of August 2025 we had created a working prototype of PASCAL for a subsequent round of user testing. We continued to refine the prototype tool throughout the autumn, involving users throughout the process.

We launched the final version of PASCAL in late November 2025, ahead of readiness checks for local authorities developing their Best Start in Life strategies. Next, through our supporting local areas to engage parents project we’ve turned our attention to how local authorities can improve the take-up of parenting programmes, and will be publishing a library of suggested approaches in the next couple of months.