ICTs bring benefits to those who have them and not to those who don’t. They therefore increase inequality. Right? Well . . . let’s see.
First question: what do you mean by “those who don’t have ICTs”?
We need something a bit more nuanced than a simple, binary digital divide, and can use instead a digital divide stack of four categories (see figure below):
– Non-Users: those who have no access to either ICTs or ICT-based information and services.
– Indirect Users: those who do not get hands-on themselves, but gain access to digital information and services via those who are direct users.
– Shared Users: those who do not own the technology, but who directly use ICT owned by someone else (a friend, workplace, ICT business, community, etc).
– Owner-Users: those who own and use the technology
Of course we would need to make transverse slices through the figure; potentially, one slice for each different type of ICT, but particularly noting many in developing countries would be in a different category level for mobiles compared to the Internet.
Second question: what’s the evidence on inequality?
It is relatively limited and often bad at differentiating which digital divide categories it’s talking about. However, we can find three types of evidence.
The Rich Get Richer; The Poor Get Poorer: situations in which some category of user gains a benefit from ICT while non-users suffer a disbenefit. For example, micro-producers of cloth in Nigeria who owned or had use of a mobile phone found they were gaining orders and income; micro-producers without mobile phone access found they were losing orders and income (to those who had phones). (See also work on growing costs of network exclusion.)
Development vs. Stasis: situations in which some category of user gains a benefit from ICT while non-users do not gain that benefit. For example, farmers in rural Peru who used a local telecentre were able to introduce improved agricultural practices and new crops, which increased their incomes. Those who did not use the telecentre just continued farming in the same way as previously.
Spillover Benefits: situations in which some category of user gains a benefit from ICT while non-users also gain a (lesser) benefit. One rather less-publicised outcome from the case of Keralan fishermen using mobile phones to check market prices is an example. Those fishermen without mobile phones saw their profit rise by an average Rs.97 (c.US$2) per day as a result of the general improvements in market efficiency and reduced wastage which phones introduced. This was about half the profit increase seen by phone owners and meant, even allowing for the additional costs, that returns to phone ownership were greater than those for non-ownership. However, it was a spillover benefit to non-ICT-users.
ICT4D research on spillovers to non-users specifically has been rare, with the main interests in non-users being to understand why they are non-users; and most spillover work being done between sectors or enterprises and/or focusing on the spillover of encouraging ICT adoption rather than more immediate benefits.
This does seem to be changing, perhaps because of the growth of mobile and related to earlier work on the externalities to non-users of arrival of rural telecommunications. Rob Jensen’s Kerala study found a second digital spillover: while fishermen’s revenues rose, the price per kg fell due to the increase in supply arising from less waste. Fish consumers (many likely non-users) now paid less than previously thanks to the mobile-induced efficiency gains. More directly, a study of M-PESA’s community effects in Kenya found its use providing positive financial, employment, security and capital accumulation externalities that affected both users and non-users within the community.
We also have a little evidence of spillover benefits from owner-users to indirect users:
– Follow-up work with Keralan fishermen found fish workers who will only get into a boat with a mobile phone-owner due to safety concerns, with these indirect users able to benefit from the owner should the boat get into difficulties. That paper’s author (personal email) also gives the example of an indirect user citing as a benefit being informed of – and able to curtail – his daughter’s illicit elopement via his boat owner’s phone.
– Research on farmers in Northern Ghana[1] found those who did not themselves own or use mobiles benefitting from information passed on from phone owners, including more frequent meetings with agricultural extension officers; meetings that were coordinated by phone owners.
In all these cases, owner-users are benefitting more than the lower-category users to whom benefits spill over. That means – if you’ll forgive the pun – that in these cases ICTs are causing all boats to rise but the ICT-using boats to rise somewhat faster. Inequality may still grow; perhaps absolutely but not relatively.
I look forward to what appears to be forthcoming work by the Global Impact Study on non-user spillovers. However, this remains a poorly-understood and little-researched issue; one that needs a greater focus since it is central to understanding the digital divide and digital inequalities. It also has implications for practice; suggesting ICT4D projects should promote non-user spillovers as much as they promote ICT usage. As ever, your pointers to spillover research and practice are welcome.
Follow @CDIManchester[1] Smith, M. (2010) A Technology of Poverty Reduction for Non-Commercial Farmers? Mobile Phones in Rural North Ghana, BA dissertation, unpublished, University of Oxford, UK
ICTs for Development 