But it is no longer sufficient. A majority of the global South’s population now has internet access and is included in, not excluded from, digital systems. Yet, as the figure below illustrates, that inclusion also brings inequalities – the small farmers in digital value chains losing out to large intermediaries; the gig workers whose value and data are captured by their platforms; the communities disempowered when they are digitally mapped.
Figure 1: From an Exclusion-Based to an Inclusion-Based Perspective on Digital Inequality
We need a new conceptualisation to explain this emerging pattern. I refer to this as “adverse digital incorporation”, defined as inclusion in a digital system that enables a more-advantaged group to extract disproportionate value from the work or resources of another, less-advantaged group.
As shown below, I have inductively built a model of adverse digital incorporation, based around three aspects:
Figure 2. Conceptual Model of Adverse Digital Incorporation
Future digital development research can apply this model deductively to cases of digital inequality, and can further investigate the digitality of adverse digital incorporation.
For digital development practitioners, the challenge will be to achieve “advantageous digital incorporation”: designing digital interventions that specifically and effectively reduce existing inequalities. This means going beyond digital equity to digital justice: addressing the underlying and contextual causes of inequality not just its surface manifestations.
Why are marginalised groups self-excluding from digital systems?
The digital exclusion problem used to be people outside the house unable to get in. For example, the digital divide preventing groups from accessing the benefits of digital systems.
Recently, a new digital exclusion issue is arising: people deciding they’d rather stay outside the house. Some examples . . .
“businesses such as schools and pharmacies in Kibera did not wish to be [digitally] mapped. They feared visibility to the state might lead to closure if their location became known and their informal status or activities (e.g. sales of stolen drugs) were then discovered …
… Particular settlements in Chennai refused to participate in data-gathering. They believed that drawing attention to their existence and informal status – being under the ‘gaze of the state’ – would increase likelihood of eviction”
2. Refugees
The recent Information Technology for Development paper “Identity at the Margins” finds self-exclusion among refugees in relation to registration on UNHCR digital ID systems:
“Some participants were so concerned about the potential consequences of data sharing that they avoided registering altogether. For example, a male Syrian refugee living with his family in a one-room apartment in Lebanon told us:
Everybody was registering with the UN, but we did not. We were suspicious and scared. We don’t know if the UN shares information with anyone, so that is why I did not share many things with them.”
3. Migrants
The chapter, “The Dilemma of Undocumented Migrants Invisible to Covid-19 Counting” in recent online book “Covid-19 from the Margins” outlines the dilemma of those undocumented migrants unwilling to register with health systems despite contracting Covid, for fear of this alerting other arms of government which would then deport them.
In one sense there is nothing new here. Individuals have for centuries sought to avoid being included in government censuses and other records: to avoid tax, to avoid being conscripted for war, etc.
The difference with digital is the ease with which data can be transmitted, leading particularly to a fear that it will find its way to the agencies of state security. This fear applies not just to data collection by other state agencies but also to NGOs (who were undertaking the community mappings in the first examples) and to international organisations like UNHCR.
Whereas incorporation into historical data systems such as the census offered no individual benefit, this is not true of the digital systems cited above. In all these cases, the marginalised are foregoing direct benefits of incorporation – better community decision-making, access to UN assistance, access to healthcare – because these benefits are outweighed by the fear of perceived harm arising from visibility to particular arms of the state.
All this in turn can be understood in terms of data justice models such as the one below from “Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework”. At a basic level, the perceived utility of exclusion from these digital systems outweighs the perceived benefits. But these perception are themselves shaped by the structural and historical context:
– A lack of credible, known data rights for those in marginalised groups
– A structural relation of perceived powerlessness vis-à-vis the state
– A lack of institutions and resources with which that powerlessness could be counteracted
Unless those wider, deeper causes can be addressed, the marginalised will continue to self-exclude from digital systems.