Digital Inequality Beyond the Digital Divide

How can we understand digital inequality in an era of digital inclusion?

As the open-access journal paper, “Digital Inequality Beyond the Digital Divide: Conceptualising Adverse Digital Incorporation in the Global South” explains, the digital divide has been an essential and powerful concept that links digital systems with inequality.

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.

For further details, please refer to the paper; “Digital Inequality Beyond the Digital Divide: Conceptualising Adverse Digital Incorporation in the Global South”.

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Climate change, birth weight and smartphone: handsome digital dividends

Gindo Tampubolon, University of Manchester

Climate change threatens the next generation as young activists around the world tell world leaders insistently. The unborn are not exempt. Secular temperature rises, covering pregnancy period, have led to babies born with low weight (less than 2.5 kilogram) in America while in India changing rainfalls have led to increased deaths among infants under two. Mitigating this are programmes such as government workfare and community health workers supporting vulnerable young families with incomes and healthcare.

Personal actions, however, can help mitigate the harm climate change visits on pregnant mothers. I look at the effects of temperatures and rainfall, daily, during pregnancy on weights of nearly 50,000 births in Indonesia in 2017 to 2019. Then I examine whether mothers’ use of smartphones modifies the effects of climate on the probability of giving birth to a baby with low weight.

Pregnancy and Smartphones

In developing countries like Indonesia, temperatures and rainfall affect pregnancy outcome through various paths, broadly forming physiological and economic routes that intersect. These are susceptible to modifications in the hands of pregnant mothers with smartphones. Extremes of heat and rainfall can reduce nutrients intake in pregnant mothers and thereby in developing foetuses. Not only babies were born with low weight, they also become more vulnerable to environmental shocks during the early months of their lives.

Although food availability may not be under threat such that wide varieties are available in the market, entitlement to food and other nutrients can still be compromised, especially in their early pregnancy, if commands over resources are unequal to the disadvantage of women (under certain social norms) or if there is limited knowledge of safe pregnancy.

Now if mothers are availed a convenient and sophisticated device like a smartphone, which facilitates social networking and information seeking, will the pregnancy outcome be affected even under a warming planet? If the effect is beneficial for mothers, they can speak of digital dividends.

Satellite data

With widely available earth observations collected by satellites it is possible to examine how climate affects birth weight of babies across the entire 1,300 inhabited islands. This can correct limited evidence on pregnant mothers’ experience from observations in half a dozen sites or islands. Much like evidence in America may not be generalisable to India, evidence from the main island Java may not be generalisable to hundreds of other islands.

So earth observations were fetched from NASA (MERRA2) for nearly 500 grid points measured four-by-five eighths degree latitude by longitude. Each observation consists of temperature and rainfall matched with the days of pregnancy for each birth to examine spells of extreme temperatures (33 °C) and rainfalls (190 cm).

I augment climate and birth information with personal and family attributes such as education and family incomes and residence over the last five years from national socio-economic surveys 2012 – 2019. I applied random effect probit model to predict the probability of giving birth to babies with low weight.

Results: digital dividend for pregnant mothers

First the associations between extreme rainfall with probability of normal birth weight are drawn in figure 1, after controlling for temperatures, personal and family attributes and residential locations. It shows that prolonged spell of extreme rainfall during pregnancy associates with lower probabilities of normal birth. Temperatures on the other hand are not significant.

The lower line traces the birth outcome for pregnant mothers exposed to such extreme rain; this lies 5 percentage point significantly below the normal rain line. Mothers exposed to extreme rain have lower probabilities of giving birth to babies with normal birth. The horizontal line, expressing consumption, helps to show that with higher economic status, the probabilities of normal birth also increases.

Figure 1. Normal birth (2.5 kilogram or more) by rainfalls, temperatures and personal, family and residential location attributes

Does this picture change when mother’s use of smartphone is considered? Figure 2 shows the change. The obvious one is this: the difference between the exposures narrows. Whether mothers were exposed to extreme rain or normal rain becomes statistically insignificant. The distance between the two lines narrows; what remaining separation there is in the plot is no difference from pure chance. Mothers’ use of smartphone yield healthy digital dividends in the next generation.

Figure 2. Normal birth (2.5 kilogram or more) by rainfalls, temperatures and personal, family and residential location attributes, as well as mother’s use of smartphones

How big is the digital dividend across all levels of economic status? This final plot shows the difference accruing to mothers with use of smartphones in terms of the probabilities of giving birth to babies of normal weight.

Figure 3. Normal birth (2.5 kilogram or more) by climate, with and without smartphones

Even under the warming planet which exposes all mothers to increasing frequencies of extreme rainfalls, mothers with use of smartphones are giving birth to babies of normal weight with higher probabilities instead of babies with low weight. But the experience of mothers without one is significantly different. They have lower probabilities of giving birth to babies of normal weight by a somewhat larger percentage point than the difference due to extreme rainfall (compare the first and last figures).

Pregnant mothers with smartphones are more than compensating the risk put on them by extreme rainfall spells, thus reaping handsome digital dividends for safer pregnancies.

The Rise of Digital Self-Exclusion

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 . . .

1. Informal Settlement Residents

In researching for our paper, “Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework”, my co-author Satyarupa Shekhar identified this pattern among informal settlement residents:

“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.

4. LGBTQ People

The report, “Privacy, Anonymity, Visibility: Dilemmas in Tech Use by Marginalised Communities” explains how some LGBTQ people in Kenya have been unwilling to use digital systems designed to help them report discriminatory violence because of fears that their identities would become known.

Analysis

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.

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Measuring the Broadband Speed Divide using Crowdsourced Data

Digital applications and services increasingly require high-speed Internet connectivity. Yet a strong “broadband divide” exists between nations [1,2]. We try to understand how big data can be used to measure this divide. In particular, what new measurement opportunities can crowdsourced data offer?

The broadband divide has been widely measured using subscription rates. However, the broadband speed divide measured using observed speeds has been less explored due to the lack of data in the hands of regulators and statistical offices. This article focuses on measuring the fixed-network broadband speed divide between developed and developing countries, exploring the benefits and limitations of using new crowdsourced data.

To this aim we used measurements from the Speedtest Global Index, generated by Ookla using data volunteered by Internet users verifying the speed of their Internet connections [3]. These crowdsourced tests allow this firm to estimate monthly measurements of the average upload and download speeds at the country level.

The dataset used for this analysis comprised monthly data, from January to December 2018, for a total of 120 countries. Using the income and regional categorisations set by the World Bank we identified 64 developing countries and 54 developed countries in seven regions. Complete data for only two of the least developed countries were available so these were not included in the analysis.

The following table presents the download and upload speed averages on the fixed network, aggregated by region and level of development, and the totals for all the countries in our final sample (n=118), while the figure below shows the download and upload speeds aggregated by level of development.

Table 1. Average upload and download speed by region and development level, fixed network. January – December 2018 (Mbps)

Note: Unweighted averages
Source: Author calculations using data from Ookla’s Speedtest Global Index [3]

Figure 1. Average upload and download speed by level of development, fixed network. January – December 2018 (Mbps)

-Download speeds. We observe that the divide between developed and developing countries is pronounced with average download speeds for the latter being around one-third of the former. However, the divide is also evident within regions: in the developed world, countries in North America have speeds three-times higher than those in the Middle East. Within the developing countries those in Europe & Central Asia have the highest download speeds and those in the Middle East & North Africa have the lowest. Overall, download speeds are much lower in the developing world, thus creating an important impediment to the use of data-intensive digital applications and services.

-Upload speeds. We identify that overall there is an existing divide between developed and developing countries similar in magnitude to the one observed in download speeds. However, when looking at the group of developing countries we see that regional rankings are different compared to those identified using download speeds: the East Asia & Pacific region ranks first and North America ranks third – the latter with speeds that are two-thirds of their download speeds. Across regions, upload speeds are always slower in the developing world, and again the Middle East & North Africa region ranks at the bottom; but the divide between download and upload speeds is lower in the developing world. Considering that faster upload speeds are also required in a data-intensive era, the majority of the countries are far from the ideal of having faster networks with synchronous speeds.

Some benefits and limitations are identified when measuring the broadband speed divide using this type of crowdsourced data.

-Benefits. First, the availability of these types of data allows us to measure the broadband speed divide between developed and developing countries using observed instead of theoretical speeds. Second, these measurements are openly available on a website that can be accessed by the general public at no cost. Third, the divide can be measured and tracked over time more frequently than when using survey or administrative data. Finally, this site reports both download and upload speeds which are important to measure in a data-intensive era.

-Limitations. Even if there are data available for a good number of countries there are no complete data about the least developed countries, leaving behind this group. Also, there might be some bias in the production of data as crowdsourced measurements might be coming from ICT-literate individuals in certain countries [4]. Finally, from this source it is not possible to access complete datasets with additional data points such as the number of observations, medians, and latencies for each country.

These findings derive from a broader research project that, overall, is researching use of big data for measurement of the digital divide.  Readers are welcome to contact the author for details of that broader project: luis.riveraillingworth@manchester.ac.uk

References

[1] ITU (2018). Measuring the Information Society Report 2018. Geneva, Switzerland: International Telecommunication Union.

[2] Broadband Commission (2018). The State of the Broadband: Broadband catalyzing sustainable development. Geneva, Switzerland: Broadband Commission for Sustainable Development.

[3] Ookla. (2018). Speed Test Global Index [Online]. Available: http://www.speedtest.net/global-index/about [Accessed 01/03/2019]

[4] Bauer, S., Clark, D. D. & Lehr, W. (2010). Understanding broadband speed measurements. In,TPRC 2010. Available at SSRN: https://ssrn.com/abstract=1988332

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The Puzzle of Digital Financial Inclusion: A Generation Game?

If we thought that financial inclusion and its digital variant are tightly correlated, we may be in for a surprise, judging from the Global Findex 2017 microdata released by the World Bank last month. Owning a bank account (financial inclusion) and owning a mobile money account (its digital variant) throw a puzzling pattern. I plot the averages of bank account ownership and mobile money account ownership in 144 countries across groups of low to high incomes economies, showing a clear separating trend. The thought is borne by 25 low income countries with the two measures of financial inclusion strongly correlated at 0.7. But as income level steps up (to middle and high incomes level) bank account shares increase while mobile money shares decrease. The final panel is flat at the bottom right: most of the 44 high income countries have more than 80% bank account shares with less than 20% mobile money account shares. The correlation? –0.2. One explanation for this negative correlation can be discounted. The digital variant is not yet a substitute for a bank account: savers cannot yet use their mobile money account on its own or as a substitute to secure property or business investment. As countries move up the economic ladder, the puzzle of separation insists on an explanation.

 

Figure 1. The puzzle of bank account ownership vs mobile money account ownership (number of countries in parentheses) Source: calculated from Global Findex 2017 microdata

I explore an alternative here. In high income economies financial inclusion is nearly universal among adults. Not so in low and middle income economies; on the demand side lower average incomes as well as lack of trust in banks coupled with, on the supply side, weak financial infrastructures combine to leave many adults financially excluded. But the costs of financial services, such as sending and receiving money, have been pared down thanks to mobile technology, especially in low income economies. In Uganda, transfers can be made cheaply and directly from the south west to the north east without recourse to Kampala in the centre.

First in this exploration I show a map of the uneven financial inclusion around the world (https://globalfindex.worldbank.org/ accessed 31 October 2018). Map 1 shows that financial inclusion varies along levels of development. The high income economies of North America, Europe, Australia and New Zealand, are homes to adults with the majority of them having a bank account. Moreover a financial inclusion gradient is discernible with economies around the equator, where many lower and middle income economies are located, reporting lower percentages of account ownership. In particular, available data from African economies in the Global Findex and on the map show how financial inclusion is still a minority story on the continent.

 

Map 1 Financial inclusion around the world 2017, source: Global Findex 2017 report

But has mobile technology made any difference to financial inclusion? It is increasingly so. A map of ownership of mobile money accounts (those who own an account and use a mobile phone to access it) tells how things have improved (Map 2). Over the last three years, some economies in East Africa such as Uganda or Kenya have accumulated owners of mobile money accounts; West African economies are treading the same path. Although it remains the case that the majority of African economies are home to the majority of adults without a mobile money account (60% or more without one).

 

Map 2 Digital financial inclusion in Sub-Saharan Africa, source: Global Findex 2017 report

To explore further I build a non-linear multilevel model of accounts for each type of financial inclusion: in one the model explains owning a bank account, in the other owning a mobile money account. The model is non-linear because ownership is an indicator, as well as multilevel because 154,472 adults reside in 144 countries. The models account for country income groups, average national incomes, population, age, gender, education, employment, and personal incomes (quintiles). The most interesting findings relate to the associations with age and gender. I show marginal predictions of age and gender for financial inclusion below.

 

Figure 2. Marginal predictions of financial inclusion (own a bank account), calculated from the Global Findex 2017 microdata

Figure 2 shows the age gradient of financial inclusion that is consistent with the life cycle effects of incomes and wealth. With age comes accumulation of wealth from earnings that needs to be stored for investment and consumption. So for both genders higher age groups have higher odds of owning a bank account (compared to the youngest age group) in a step-wise manner. The youngest (hollow point ○) and the oldest (solid point ●) form bookends to the predictions; both for men (left) and for women (right). There is also a clear gender inequality, although by age 25 women (diamond ◊, right) already have higher odds than the youngest male group. Thus financial inclusion reflects the life cycle effects of earning and saving.

 

Figure 3. Marginal predictions of digital financial inclusion (own a mobile money account), calculated from the Global Findex 2017

 

 

But the marginal predictions for digital financial inclusion do not conform at all to the life cycle effect (figure 3). Digital financial inclusion does not move lock-step with age. In contrast with traditional financial inclusion, the two oldest age groups have lower odds of owning a mobile money account; instead the highest predicted marginals are attained by the mid-30s. The solid point (● oldest group) for instance is furthest below the hollow point (○ youngest group). Here the two oldest–youngest groups do not form bookends. The gender digital divide is also sharper. For similar levels of other characteristics, no female groups have higher odds of owning a mobile money account than the youngest male group.

The strong age reversion effect (inclusion does not move in lock-step with age but reverts after age 40) suggests a generation effect. This is also consistent with the fact that many of the low income economies are still young while many of the high income economies are already ageing.

The puzzle that digital financial inclusion parts ways with financial inclusion may be driven by the generation effect. But there is no reason to expect that the life cycle effect should disappear soon. Thus the need for financial accounts around the world is likely to grow as adults age, leading to some reconciliation in paths of financial inclusion.

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Measuring the Big Data Knowledge Divide Using Wikipedia

Big data is of increasing importance; yet – like all digital technologies – it is affected by a digital divide of multiple dimensions. We set out to understand one dimension: the big data ‘knowledge divide’; meaning the way in which different groups have different levels of knowledge about big data [1,2].

To do this, we analysed Wikipedia – as a global repository of knowledge – and asked: how does people’s knowledge of big data differ by language?

An exploratory analysis of Wikipedia to understand the knowledge divide looked at differences across ten languages in production and consumption of the specific Wikipedia article entitled ‘Big Data’ in each of the languages. The figure below shows initial results:

• The Knowledge-Awareness Indicator (KAI) measures the total number of views of the ‘Big Data’ article divided by total number of views of all articles for each language (multiplied by 100,000 to produce an easier-to-grasp number). This relates specifically to the time period 1 February – 30 April 2018.
• ‘Total Articles’ measures the overall number of articles on all topics that were available for each language at the end of April 2018, to give a sense of the volume of language-specific material available on Wikipedia.

‘Big Data’ article knowledge-awareness, top-ten languages*

ko=Korean; zh=Chinese; fr=French; pt=Portuguese; es=Spanish; de=German; it=Italian; ru=Russian; en=English; ja=Japanese.
Note: Data analysed for 46 languages, 1 February to 30 April 2018.
* Figure shows the top-ten languages with the most views of the ‘Big Data’ article in this period.
Source: Author using data from the Wikimedia Toolforge team [3]

 

–Production. Considering that Wikipedia is built as a collaborative project, the production of content and its evolution can be used as a proxy for knowledge. A divide relating to the creation of content for the ‘Big Data’ article can be measured using two indicators. First, article size in bytes: longer articles would tend to represent the curation of more knowledge. Second, number of edits: seen as representing the pace at which knowledge is changing. Larger article size and higher number of edits may allow readers to have greater and more current knowledge about big data. On this basis, we see English far ahead of other languages: articles are significantly longer and significantly more edited.

–Consumption. The KAI provides a measure of the level of relative interest in accessing the ‘Big Data’ article which will also relate to level of awareness of big data. Where English was the production outlier, Korean and to a lesser extent Chinese are the consumption outliers: there appears to be significantly more relative accessing of the article on ‘Big Data’ in those languages than in others. This suggests a greater interest in and awareness of big data among readers using those languages. Assuming that accessed articles are read and understood, the KAI might also be a proxy for the readers’ level of knowledge about big data.

We can draw two types of conclusion from this work.

First, and addressing the specific research question, we see important differences between language groups; reflecting an important knowledge divide around big data. On the production side, much more is being written and updated in English about big data than in other languages; potentially hampering non-English speakers from engaging with big data; at least in relative terms. This suggests value in encouraging not just more non-English Wikipedia writing on big data, but also non-English research (and/or translation of English research) given research feeds Wikipedia writing. This value may be especially notable in relation to East Asian languages given that, on the consumption side, we found much greater relative interest and awareness of big data among Wikipedia readers.

Second, and methodologically, we can see the value of using Wikipedia to analyse knowledge divide questions. It provides a reliable source of openly-accessible, large-scale data that can be used to generate indicators that are replicable and stable over time.

This research project will continue exploring the use of Wikipedia at the country level to measure and understand the digital divide in the production and consumption of knowledge, focusing specifically on materials in Spanish.

References

[1] Andrejevic, M. (2014). ‘Big Data, Big Questions |The Big Data Divide.’ International Journal of Communication, 8.

[2] Michael, M., & Lupton, D. (2015). ‘Toward a Manifesto for the “Public Understanding of Big Data”.’ Public Understanding of Science, 25(1), 104–116. doi: 10.1177/0963662515609005

[3] Wikimedia Toolforge (2018). Available at: https://tools.wmflabs.org/

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From Digital Divide to Digital Provide: Spillover Benefits to ICT4D Non-Users

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.

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[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