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Back in the day, digital folks would say that ICTs were sure to empower the poor: that they’d be instruments of social transformation, enablers of equality.
Now we know things are much more complicated. Sure, there are ways in which new technologies create new opportunities. They can enable those without to gain more than they had (in terms of power and resources). This is a powerful positive agenda.
But inequality has also grown in the emerging age of digital technology. I’ve written in the past about two key factors here:
that those who have advantages to start with are better placed to buy, to use and to gain benefits from digital technologies than those who start with disadvantages;
and (therefore) that new technologies can both empower and disempower simultaneously – giving the disadvantaged opportunities they didn’t previously have, but widening the gap in opportunities and outcomes between them and those who’ve started out with more.
The key point here is the relationship between pre-existing social and economic inequality and the digital inequality that’s often called a digital divide.
I’m returning to this theme today thanks to a new analysis of the causes and consequences of digital inequality by Ellen Helsper, Professor of Digital Inequalities at the London School of Economics. Her focus lies in that relationship, and on the way poor understanding of it has led to policies that are inadequate and can be counterproductive.
She talks about this in The Digital Disconnect. The case she makes is thorough and sophisticated, and should be widely read. I’ll draw on it to revisit six themes that could improve policymakers’ understanding. The following’s inspired by her arguments, though not necessarily consistent with them.
Poverty and inequality
First, it’s important to distinguish poverty from inequality and disadvantage. It’s possible for people to move out of poverty, as quantitatively measured, and to do so with the help of ICTs, while their society gets more unequal, and the gap widens between the options, opportunities and outcomes they enjoy and those of people higher up the ladder of prosperity or social opportunity.
This is obviously true of individuals, and can also work through categories – the landless, say; those with limited educational experience; women and girls in societies that heavily constrain their opportunities.
Digital and other inequalities
Second, digital inequalities arise from (and tend to follow) pre-existing inequalities within society. The digital world differs for people, Helsper says, "based on [their] starting point", and she prefers therefore to use the term "socio-digital inequality" to describe the mix.
It’s not simply that the rich get richer because they can take more advantage of new things. It’s that they can leverage that digital advantage through their other networks and resources – economic, social, educational – and their established capabilities – to invest, to write, to publish, to engage with and be heard and listened to by others.
Digital inequalities don’t just arise from pre-existing inequalities, therefore; they also tend to reinforce them. New technologies are designed with the more prosperous in mind because that’s where the most profitable markets lie. There’s a feedback loop here, if you like, between digital and other inequalities, and it means two things:
digital inequality can’t reduce socio-economic inequality in the way that early optimists had hoped;
nor can digital inequality be tackled without also tackling the underlying inequalities within society.
Inequality is complex and intersectional
But inequalities are not (just) about individuals; they’re structural, they’re systemic, they’re intersectional.
To start with, in the ‘90s, policy on digital engagement was very simple: “there is a digital divide; it should be bridged.” This reached back to the Maitland Commission, which revealed the telecoms divide in 1984.
Over time, as the variety of digital divides became more clear, more disaggegation’s taken hold. Researchers and policymakers have spent more time looking at the differences due to geography, and exploring the experience of different demographic groups.
A lot of attention has been paid to gender. There’s been a lot of focus on engaging youth (in practice the most digitally active age group), much less focus on the elderly (the least digitally active). Significant attention has been paid to those with disabilities. Some analysis has addressed educational differences, less perhaps differences of language, race or class; much less, issues of caste.
So the diversity of inequality within society has been more recognised, but it’s still seen in rather simple terms, often in siloed groups. Helsper points to two things that are often missing here:
too little’s done to understand the differences within those demographic categories.
and too little’s done to understand that disadvantage is not siloed but intersectional: disadvantage is derived and exacerbated by the multiplicity of factors affecting individuals and social groups.
Inequality is complex and multifaceted
Inequalities arise in different contexts and in different ways – within the digital environment and in its impacts.
In the early days of thinking about digital divides, it was access that was emphasised: infrastructure, connectivity, cheap and accessible devices, services and places that gave public access. The focus shifted around the turn of the century when terminology like ‘real access’ became fashionable, recognising that capabilities and content also mattered. More recently, more attention has been paid to the quality of access, particularly broadband.
But inequality’s experienced – and affects experience – in different layers along a kind of value chain: in access, skills and usage, in the opportunities that arise from these, and above all in the outcomes/impacts felt by users and non-users.
There’s a crucial difference here between what technology can offer – its potential, its opportunity – and the extent to which it’s used in practice by different individuals and social groups. A crucial difference too between opportunity and outcome, and the different extents to which the former turns into the latter.
"What [people] get out of digital engagement depends on the offline resources that [they] have and the online resources [that they] use," says Helsper. Some individuals will gain from this, and others will lose out.
Where impact is concerned, she suggests five areas of focus.
Most attention has been paid to economic opportunities and outcomes, partly because that fits within prevailing government and business models, partly (I suspect) because they’re easier to measure and claim credit for. Less, though significant, attention has been paid to educational outcomes and civil and political engagement, which are of special interest to rights communities. Least has been paid to qualitative aspects of the digital experience: to what economists in government have often seen as trivial pursuits (like entertainment) and to the glues that hold societies together.
What does this mean for policies on inequality: digital, social and socio-digital? Essentially that they have been too unsophisticated, less effective than they might have been, and at times have been counterproductive.
Digital technologists, businesses and policymakers have held powerful aspirations for their tech, right from the start and as it has evolved. They have believed and have declared that it will deliver better lives for all, including more prosperity and less inequality/disadvantage.
More tech – more and better infrastructure, more and cheaper access, bridging ‘digital divides’ in connectivity – has therefore been the heart of their response not just to digital inequality but inequality all round. Coupled, more recently, with an emphasis on building skills, especially the skills required to work in digital economies.
More tech and skills are necessary aspects of enabling digital equality, but Helsper powerfully shows that they are not sufficient. Infrastructure, access and lessons in digital literacy don’t necessarily turn into use; use does not necessarily turn into opportunity; and opportunity does not necessarily turn into favourable outcomes.
Other factors are important here, such as social norms that constrain behaviour, lack of access to financial capital and social networks, lack of confidence, the sense that technology and the opportunities it offers aren’t for “the likes of us” (often because those opportunities are, in fact, much more available to “them”).
Many policies designed to address digital inequalities miss their targets because they’re driven by supply-side assumptions – that better infrastructure will be of equal benefit to all, for instance, or that teaching skills in schools will reach all children equally – rather than real lives lived on the demand side of the tracks. Why so? Partly, at least, because too few policymakers have spent sufficient time asking the targets of their policies about their lives and what might make them better.
Politics, policy and scholarship
Some final thoughts about the relationship between policy and scholarship – for work like Helsper’s is found largely in academic books and journals.
Politicians, technologists and business leaders, I’d suggest, tend to have powerful goals they hope to gain. Politicians are keen to find ‘solutions’ that will be popular. Technologists are eager to promote new tech they have developed. Businesses are keen to promote new services they’ve brought to market and maximise their profit margins.
All are keen to move fast, achieve quick gains, and fix things later if they don’t work out quite as they had assumed.
The long-term interests of society, however, require more than this. Short-term gains don’t necessarily bring long-term value. What works well for the majority might well work badly for minorities. Including the marginalised is difficult and requires deep understanding of the way that public policies are interacting with the causes and the consequences of digital and social inequalities.
That sophisticated understanding has to come from research and analysis, from multiple disciplines, from academia and the more scrupulous research institutes and think tanks.
Scholarship’s inevitably slower off the mark, requiring evidence to be gathered that will test assumptions; in-depth analysis of what has happened rather than what might, what’s been unanticipated as well as what had been expected; investigating impacts not just on target populations but on groups within those groups, and other groups who’ve also been affected.
The language of politics (and of enthusiasm)’s often populist; that of scholarship nuanced and specialist.
Good policymaking needs to tread a line between the urgency of acting now and the need to understand the implications, enabling innovation while mitigating risks. To achieve it, governments and businesses need to fund the necessary research, understand the complexity of the societies they serve and govern, learn from experience and from analysis, and be prepared to change their policies and practices when what they have been doing has proved ineffective. If they do that, they have more chance of achieving ‘digital connect’ than ‘disconnect’.
Image: Photo by Biao Xie on Unsplash.