The pandemic is teaching us to use data in new ways


At the beginning of the coronavirus pandemic, our focus was on protecting those who were most vulnerable to Covid-19 – the elderly and people with comorbidities. As the months wore on and the virus lingered, our focus shifted from those with fragile health to the economically vulnerable.
In 2021, we will see the naming of at-risk populations and the demand that lawmakers construct policies to protect these groups. A vulnerable population is defined as one that is at greater risk of poor economic, educational, health and political status or outcomes. Predictably, the factors that cause a population to be exposed are rooted in institutional racism, systemic sexism and the often overlooked structural classism.Structural classism describes the wedge between those who own the means of production and wealth and the working-class people who do the labour.


It often colours the language that’s typically used to describe the poor. When you intersect structural classism with institutional racism and systemic sexism, the net result is the criminalisation of the poor, a devaluing of low-wage workers and a widening of economic inequality.
In 2021, we will see academics, activists and grassroots organisations unite to advocate for policies that provide workers with a living wage, paid leave and a pathway to wealth accumulation. These policies will have a central theme: the redistribution of wealth with the intent to reduce economic inequality and increase economic security.
Economic insecurity will take centre stage as we enter 2021 in a recession. Politicians will blame each other for not enacting policies that prioritise the safety of frontline workers, renew consumer confidence or stabilise disruptions to the supply chain. As the death toll increases from Covid-19, we will hear a familiar term: “disproportionately” – this is code for how institutional racism and anti-Black policies have impacted Black, Hispanic and Indigenous communities.
We will also hear a new term, “disaggregated data” – in other words, data that has been divided into detailed sub-categories – which will allow us to understand who exactly Covid-19 has killed, who was denied or benefited from relief packages and who was completely forgotten. Disaggregated data will also allow for the identification of the nuances in the characteristics and outcomes that define vulnerability. We will require that data be collected and reported with the purpose of being separated into its component parts. This will give us the accountability we need to measure progress in the wellbeing of vulnerable populations.


In 2021, policy-makers will learn what cancer researchers already know – that disaggregated data can inform policies to increase health and economic outcomes of vulnerable groups. While the discussion has focused on people, disaggregated data can also identify vulnerable industries and sectors. The beauty of the mechanics surrounding this process is that every person and business can see themselves in the data (and there will be plenty of data).
The lessons that policy-makers learn from Covid-19 will have an impact on the way they collect data, which new policies they enact, and what measures they take to protect populations most exposed to the virus. The pandemic will teach us that vulnerability is dynamic: think health versus economic.
But we will also learn that framing policy by centring on the vulnerable can unite. Many countries are experiencing deep divisions in their societies and global co-operation has reduced. The only hope for healing is to focus on those most at risk who have been under-served by social and economic policies for decades.


In 2021, we will see societies begin to address, in measurable ways, all of the “isms” and “phobias” that have debilitated our ability to respond adequately to crisis, preventing us from moving forward as a society that is equitable and inclusive.
Rhonda Vonshay Sharpe is founder and president of the Women’s Institute for Science, Equity and Race

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