Opinion

What the Eye Does Not See…: Fighting Inequality with Better Data

The privations of Covid-19 have shifted the issue of inequalities – there are many forms – to center stage in political arenas around the world. One reason is that the pandemic ruthlessly revealed the depth of our ignorance about inequalities and the brutal consequences. A right-wing lawmaker from the party Alternative for Germany, for example, claimed migrants and Muslims were catching the virus more frequently because they were “a group that refuses to follow rules.” His Bundestag peers were outraged about his accusation against ethnic and religious minorities – but they had no hard data with which to smother his incendiary claim.

A correlation between lower social status and Covid-19  

Studies in the UK and the US have found a correlation between lower social status and the chance of catching Covid-19 and usually put this down to disadvantageous living conditions, non-socially distanced jobs, and poor health – not ethnicity or religion. But the comparable database in Germany is thin at best, so in Europe’s largest economy, risk factors other than age and physical conditions remain unclear. Consequently, migrants and other vulnerable groups cannot be sufficiently protected neither from dangerous populist accusations and myths nor from Covid-19. Alarmingly, problems caused by lack of good data about inequalities go far beyond Germany and the pandemic.

The United Nations High Level Political Forum on Sustainable Development in July discussed progress towards the Sustainable Development Goals (SDGs), including SDG 10, which aims to “reduce inequality within and among countries.” While its targets are ambitious and broad, covering social, political, and economic inequalities, its indicators largely fall back onto economic parameters often presented as national averages. These measures of progress do not depict the complexity of inequalities and are insufficiently granular. To realize its aim “to leave no one behind,” the UN needs additional data disaggregated by age, gender, and so on.

The data gaps can conceal societal challenges

Take target 10.2, the “social, economic and political inclusion of all.” How can it be monitored in all of its complexity simply by looking at the “proportion of people living below 50 percent of median income”, even if here, at least, the data is disaggregated “by age, sex and persons with disabilities”?  What about social and political indicators? What about other groups? Regardless of whether it is German lawmakers or high-level UN representatives, the lack of meaningful indicators, the data gaps and the often listless use of existing information can create dangerous myths or conceal societal challenges, and thwart effective policy making. What groups should be prioritized for Covid-19 vaccination? Where do privileges threaten equal democratic participation? Sometimes we know, but all too often we could know even better. 

Governments, multilateral organizations, research institutions and civil society groups have a huge role to play in understanding inequalities. The World Bank, Oxfam and the Commitment to Equity Institute last year introduced an SDG 10 indicator that monitors the redistributive effect of fiscal and social policy by comparing pre-tax and post-tax income inequality. The Leave No One Behind partnership of twelve international civil society organizations, supported by the Robert Bosch Stiftung, gathers disaggregated data on progress made on SDG targets, e.g. among groups like sex workers and street vendors, and collects feedback on related programs, like cash or food assistance. The data does not treat these as homogenous groups but allows intersectional analyses, for example, about ethnicity or HIV/AIDS or both. 

New methods for collecting data can empower citizens and communities and even provide insights in real-time. The Leave No One Behind Partnership collects community-driven or citizen-generated data and makes a point of giving survey groups a role in the entire process of data generation, from defining needs to interpreting results. For example, persons with disabilities in Bangladesh have designed scorecards for health services, now fill them in regularly and collaborate with local and national authorities about how the data is used.

Data that “accurately describe all populations”   

New possibilities of access to existing data also need to be used to the full. Many national statistics offices have already become more transparent about the data they have and provide it in a workable format. The UK Office for National Statistics, for example, has adopted an Inclusive Data Charter and pledges to collect data that “accurately describe all populations” and to make them available “to all.” Of course, accessibility also depends on users’ data-handling competence. Ironically, data about this is scarce, but a 2019 study by Qlik and Accenture said 74% of surveyed professionals felt overwhelmed when working with data. Civil society has a huge interest in changing this: high data quality and data literacy are key to holding governments accountable. 

And, crucially, politicians in general. The rightwing German lawmaker might have remained silent had he known what researchers brought to light a few weeks after his remarks – a higher correlation between Covid-19 infections and votes for his party in the 2017 general election than between virus infections and German residents of Muslim faith. Data are a powerful tool in public debate and political decision-making. If we are serious about realizing the SDGs and fighting the threat of inequalities to social cohesion, economic development, and peace, we need to work with more granular, more complex data. So, let’s start collecting it.