How to make large-scale, voluntary COVID-19 testing work?

01 July 2021

New research from a behavioural economics perspective.

Using behavioural economics insights complemented with data from a novel survey in the US and a survey experiment in Luxembourg, new research paper examines behavioural factors associated with the individual willingness to get tested.

Testing is widely seen as one core element of a successful strategy to curtail the COVID-19 pandemic and many countries have increased their efforts to provide testing at large scale. As most democratic governments refrain from enacting mandatory testing, a key emerging challenge is to increase voluntary participation. 

Researchers from the Luxembourg Institute of Socio-Economic Research (LISER) have recently published an article entitled “How to make universal, voluntary testing for COVID-19 work? A behavioural economics perspective” in the journal Health Policy. In the paper, co-authored by Francesco Fallucchi, Joël Machado, Marc Suhrcke of LISER, Luise Görges of Leuphana University of Lüneburg, and Arne Pieters, an independent researcher, the authors lay out the challenges and potential solutions to encourage voluntary testing.

Testing heavily

In the early summer of 2020 (and again in this summer), European countries have been gradually reducing lockdown measures, upon having achieved significant reductions in the number of registered infections with SARS-CoV-2, while also having ramped up their health care and testing capacity and improved vaccine rollout. This allowed them to enter a new phase, trying to control the spread of the virus by a combination of looser measures and active monitoring. Such a strategy will need to involve some version of large-scale testing.

Most democratic governments hitherto prefer to rely on the voluntary participation of the population. Yet, both for accurately monitoring the virus spread and for successfully identifying infected individuals, it is crucial that a large share of people – and in particular those most likely to carry (and hence spread) the virus – are willing to participate. This does pose some challenges.

Will people go for testing if they don’t have to?

Will people come forward for testing if they do not have to? If not, why not? And do compliers, those taking the test, differ systematically from non-compliers, in ways that could seriously limit the usefulness of the entire strategy?

To assess this, policymakers need to understand people’s incentives for taking or avoiding a test. A good starting point is to assume that people act “rationally”, which means that people act as if they weigh (consciously or unconsciously) their personal (monetary and non-monetary) costs and benefits of testing and ultimately go for the option with the highest expected net benefits.

The personal expected benefits will derive from knowing one’s current COVID-19 status, which allows individuals to:

1. quickly eliminate uncertainty about their COVID-19-related health status and obtain a certificate in case of a negative result;
2. improve their health status and odds of recovery by seeking healthcare if needed;
3. prevent harming others in their immediate inter-personal environment, such as family and friends, as well as contacts in other relevant settings (work, school).

Critically now, the most important benefit from testing accrues at the societal level – getting tested has a positive externality for society, by helping the government to control the pandemic. While individuals also obtain benefits from getting tested, those benefits may not outweigh the personal costs of getting tested, leading to a misalignment of incentives. Paradoxically, this problem intensifies as strategies to manage and contain the spread of the virus become more and more successful: If the chances of contracting an infection are relatively small, so are the expected personal benefits from knowing one’s health status, seeking healthcare, protecting others. (It is important to note that, in the very current context (June/July 2021) – several months after the time of writing the article – the perks of showing certified, positive test results have significantly increased.)

Early evidence from Luxembourg, which embarked on a large-scale testing strategy (Luxembourg), pointed to what may appear as limited take-up of the test (in May 2020). For instance, take-up among final grade secondary students and teachers was around 40% according to some media sources; another report announced a take-up of less than 40% among students before the Government made a weekly self-test as a mandatory condition for school attendance.

These numbers illustrate the importance of aligning the societal with the individual benefits to raise the number of tests taken closer to the socially optimal level. To achieve this, policy makers may consider to

1. reduce personal costs and
2. increase personal (expected) benefits of test-taking, using both monetary and/or non-monetary incentives.  

Potential solutions

Meet LISER’s researchers

Francesco Fallucchi

Francesco Fallucchi is a researcher in the Behavioural and Experimental Economics platform. He joined LISER in 2017 after spending three years as a Research Fellow at the University of East Anglia and CBESS. He completed his Phd in Economics at the University of Nottingham in 2014.

Francesco uses experimental methods to explore individual behaviour in strategic settings as well as individual adaptation to social norms.

Joël Machado

Joël Machado completed his PhD in October 2014 at the Université catholique de Louvain under the supervision of Prof. Frédéric Docquier. From July 2015 to June 2017, he was a FNR AFR Postdoctoral researcher at CREA, University of Luxembourg.

His current research studies the impact of policies on immigration flows and immigrants’ behaviour.

Marc Suhrcke

Marc Suhrcke heads the cross-departmental Research Programme on ‘Health and Health Systems’ at LISER and is a Professor of Global Health Economics at the Centre for Health Economics (CHE) at the University of York, UK.

His research revolves around a wide range of health economic aspects, including the socio-economic determinants and consequences of health and health inequalities, as well as the evaluation of the impact of population- and system-level policies on health and related outcomes. Most of his work seeks to use observational data to uncover relevant, ideally causal relationships.

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