4.1 SDGs and COVID-19–how can data and
statistics help building back better?
My name is Natasa Todorovic. I work with the Red Cross, in Serbia on issues of older people. Today I am representing the Civil society mechanism, the RCEM. Of the UNECE, and its constituency of older people. There has been tremendous progress in data collection techniques as well as in technologies to process and distribute data. We have the means and opportunity to base decision making on evidence and put in place public policy based on knowledge and verifiable information. Our success or failure to achieve the sustainable development goals rests on us being able to ’ treasure what we measure’.
However, the data revolution is still unfolding and it is not developing evenly and fairly. even with the adoption of Agenda 2030 and commitments from member states, international financial institutions, and the private sector. There are massive data gaps reflecting who we treasure most and who we accept to be excluded. It is therefore essential that we understand who and what we are missing and how we can move ahead to build a world where no one is being left behind. Data can tell us a powerful story about priority setting. For example UN Women data today highlighted the hardest hit sectors by Corona to be health and social care, education and domestic services, predominantly occupied by female workers – all the things we need and rely on. But the EU Recovery plan focuses on male dominated sectors – construction, energy, agriculture, transport and Information and Communication. How did this happen? Where is the social investment?
Having all this data at our disposal is a privilege like no one had in human history, but it also acts as a challenge knowing we have the resources but not yet the political will to make sure we act on the information and have the right policies to include and make a difference to the most disadvantaged. Simply put, and speaking from the point of view of someone working in ageing for decades, we have large quantities of data today, but we lack political will and are still information and policy-poor. One of the main issues is how assumption and prejudice is behind the idea that a one size fits all approach is fine. This is true for all population groups. In my field of ageing it is very clear. It is not acceptable that the data for older persons is bundled in an oversized category where everybody over the age of 65 is treated the same, without gender and disability analysis, and that there be statistical age cut offs – 74 for example – when we know in this region people live to very old ages.. Risks to Health , poverty and abuse according to ethnicity, gender, disability, location and sexual orientation intersect differently for persons who are 65, 70, 75, 80, 85 and so on. .older women and men, in rural and urban areas, older persons with and without education, they all face very different challenges, in regular times and during emergencies. This is why the UN recommends data disaggregation in 5 to 10 year cohorts after 60 years. The need to disaggregate the data by gender, age – all age cohorts – disability, socioeconomic status, geographical position, education level and other categories goes hand in hand with the need to dismantle and unpick data silos, where large quantities of data are collected by one institution or agency and there is no easy way to share it with others or use it for anything except its original purpose. For example across the EU data on intimate partner violence is limited to age cohorts between 15-49, through health surveys designed for reproductive health services – nothing to do with violence. In some cases data on violence against women is collected up to 74 – even though we know that violence against women happens in very old age.
This is a serious human rights issue as it effectively both makes invisible and silences the older victim. Intersectional analysis and systems that do not develop in parallel and do not talk to each other are needed. When data is not collected for by age, gender, persons with disability, migrants, homeless, ethic minorities they are not included in policy analysis and programmes, as they are invisible.Disaggregated data is central to Agenda 2030 success because it is the strategy for inclusive human rights based policy making.
The COVID-19 crisis showed so clearly how important is to be able to document and to share and cross-examine multiple data about people affected, with data about healthcare, social welfare, social protection coverage, housing, types of work and access to child support and pensions. In future emergencies we will also need to use the information on levels and impact of climate change, localised weather changes, air pollution, localised food or drug emergencies etc. While statistics gathered through public services and census is important so too is making sure we diversify data sources. We citizens will often be able to provide data that is more precise or more relevant, especially for marginalised populations and which will be complementary to the data collected by the state. This collaboration between public sector, civil sector and citizens will make decision making better and more just. Of course, in all this we must never forget privacy concerns and build in privacy mindset in all our systems.
We need better use of the existing data and smarter ways to collect it, classify it and share it but we must also always be mindful not to fall in the digital trap and believe we know everything there is to know just because computers and algorithms now work for us. Covid has made us operate digitally and has shown us that digital exclusion is real and is an extension of inequalities and social divides in the analogue space. Those persons who lack education, access to hardware, support with using it will not only miss out on potentially important news and information.
They will also be locked out of an increasing number of public and private services, which will directly affect their lives, preventing them from exercising their human rights, but they will also be erased from the future data landscape, falling through the cracks in the statistics. So, ironically, persons who may need support the most will be the ones least likely to get it.
In other words, digital tools privilege those already privileged. We have the responsibility to be aware of this and pledge to develop and evolve these tools so they serve those most in need. We must never forget that we don’t collect data for collection’s sake, but to ensure our interventions, services and support programmes are adequate and match the human rights and needs of different groups in the population and in different regions. This is what leaving no-one behind means.