Data-driven policymaking has long been a goal for governments and public service agencies. But creating a reliable model for predicting the impact of policy options across a spread of changing variables can be elusive. Until now, that is. For the first time, we’re seeing enabling technology, data expertise and practical applications come together to turn aspiration into reality.

It’s a development whose moment has come. In these exceptional times, policymakers are having to make hard choices as they allocate resources to achieve different policy goals, to cushion the impact from the pandemic and support economic and social recovery. In our previous blog, we examined how hard it is to frame those choices and particularly so in the context of COVID-19. What we call policy agility requires human ingenuity and technology working together.

A key enabler of policy agility is the ability to harness data and present it in an intuitive way that helps public leaders and policymakers exercise judgment and reasoning. By using the power of machines to help them model scenarios across the range of policy levers, they will gain deep insight into understanding how calibrating the variables will influence outcomes. What’s more – as no policy is made in a vacuum – the approach can model the impact decisions will have on other policy areas and priorities.

Seeing a bigger picture

For example, policymakers design a targeted social policy initiative to reduce the incidence of families with young children living below the poverty line. At the same time, the intent may be to not increase the overall spend on a group of social programs. Using a data-driven approach, they would be able to see the impacts of moving resources from one program (eg pensions, housing allowance or a family tax credit). That enables them to model how, for instance, a small and perhaps reasonable reduction in benefits for some pensioner cohorts could achieve the desired optimal impact for achieving the target reduction for lifting families out of poverty.

We have partnered with the Australian National University, Centre for Social Research and Methods, building on their foundational work on Optimal Policy Modelling (OPM). OPM is designed to support policymakers with advanced data analytics, parametric evaluation, and visualisation tools to augment their judgement and reasoning as they make policy decisions.

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Policy agility in volatile times
A new approach to modelling social policy impact.
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A clearer picture of outcomes

This new approach allows for a range of scenarios to be modelled, with the impacts of changes targeting one area shown clearly on others. It can enable policymakers to see a clearer picture of ‘winners’ and ‘losers’ arising from even the smallest adjustments.

The approach is designed with a view to augment a policymaker’s judgment and experience, as they would know what to look out for in terms of risk factors or negative effects as a result of certain policy choices. While OPM can identify the optimal allocation, it offers a range of levers that can be tweaked to help discover the optimal policy settings for  a fairer rebalancing of resource allocation across the population. And because the focus is on modelling outcomes using various sources rather than over relying on  administrative data, policymakers can work with an evidence base for policy choices with less risk from inadvertent bias arising from, for example, how decisions have been made in the past.

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It can enable policymakers to see a clearer picture of ‘winners’ and ‘losers’ arising from even the smallest adjustments.

 

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From welfare to anywhere

While the initial use case for using this approach focused on the Australian welfare system, our research showed the approach and underlying methods can be adjusted to model different policy domains (eg tax, education, employment), and in different parts of the world. That could empower policymakers at a time where there is continuing uncertainty around the economy and labour market.

Despite the welcome news that vaccines for COVID-19 are now being rolled out, the stark reality for most governments is the economic pain of the pandemic is still early in the cycle.  Most commentators predict rising pressure on public finances for years to come even as economic indicators begin to improve.

Data-driven policy modelling will have a crucial role to play in helping identify and model the outcomes that policies are trying to achieve. Whether that’s the use of stimulus and recovery funds, such as the NextGenerationEU, to enhance citizens’ employability through training and skills, or countless other vital programs, an evidence-based and data-driven approach offers a new ally in addressing the challenge.

To find out more about OPM and Accenture’s work with the Australian National University, Centre for Social Research and Methods, read the paper here. If you are interested to learn more about the use cases, please reach out to us directly.

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Brian Lee-Archer

Managing Director – Consulting, Health & Public Service, ANZ


Gaurav Gujral

Managing Director – Strategy and Consulting, Public Service, Europe

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