The Costs of Exclusion
Economic Consequences of theDigital Gender Gap

Acknowledgements
This report was written by Ana María Rodríguez Pulgarín and Teddy
Woodhouse. Additional comments and suggestions were provided by
Catherine Adeya, Nathalia Foditsch, Carlos Iglesias, Onica Makwakwa,
Anju Mangal, Muthoni Muriithi, and Radhika Radhakrishnan. The model
was developed by Ana María Rodríguez Pulgarín and Carlos Iglesias.
This document is an output from a project funded by the InternetSociety Foundation. We are grateful for their support.
The authors also wish to thank the interviewees and focus group
participants from around the world who contributed their time andinsights to help inform the analysis and recommendations of thisreport. Fieldwork was conducted by Nila Achia, Amrita Choudhury,
Audrey Ehouman, Temitope Ogundipe, and Geeti Patwal. Any errors
remain the authors’ alone.

The Alliance for Affordable Internet(A4AI) is a global coalition working todrive down the cost of internet accessin low- and middle-income countries
through policy and regulatory reform.
We bring together businesses,
governments, and civil society actors
from across the globe to deliver thepolicies needed to reduce the cost toconnect and make universal, affordable
internet access a reality for all.

Suggested citation: Alliance for Affordable Internet (2021).
The Costs of Exclusion: Economic Consequences of the Digital
Gender Gap. Web Foundation.
This report is made available under a Creative Commons Attribution
4.0 International (CC BY 4.0) licence. For media or other inquiries:
press@webfoundation.org.

The World Wide Web Foundation
was established in 2009 by web
inventor Sir Tim Berners-Lee andRosemary Leith to advance the open
web as a public good and a basic right.
We are an independent, internationalorganisation fighting for digitalequality — a world where everyone
can access the web and use it toimprove their lives. The Foundation
holds the secretariat of the Alliance for
Affordable Internet.

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www.a4ai.org

Executive Summary
Governments are missing out on hundreds of billions of dollars
because of the digital gender gap. Closing this gap in the next five
years gives policy makers a $524 billion USD opportunity.
Across the world, millions of people are still
unable to access the internet and participate
online — and women are disproportionately
excluded. Men are 21% more likely to be online
than women globally, rising to 52% in Least
Developed Countries.
Various barriers prevent women and girls fromaccessing the internet and participating online,
including unaffordable devices and data tariffs,
inequalities in education and digital skills, social
norms that discourage women and girls frombeing online, and fears around privacy, safety,
and security.

While digital exclusion limits the opportunities
for those women and girls unable to connect, italso has broader societal and economic impacts
that affect everyone. With hundreds of millions
fewer women able to use the internet, the worldis missing out on untold social, cultural, andeconomic contributions that they could make ifthey were able to harness the internet’s benefits.
This report estimates the economic
impact of women’s digital exclusion.
Further, it underlines the economic opportunity
governments have to include women in a fully
inclusive digital economy.

Measuring the economic costof digital exclusion
This research focuses on low and lower-middle income countries (LLMICs), where thedigital gender gap is often greatest. To understand the economic impact of digital
exclusion, this report models the gender gaps in 32 LLMICs, covering over 70% of
the collective gross domestic product (GDP) of all LLMICs, and pairs it with existingmodels from the International Telecommunication Union (ITU) that calculate theeconomic effect of increasing mobile andfixed broadband penetration. This model
gives an estimate of the total effect of thedigital gender gap on the gross domestic
products (GDP) of these 32 countries andprojects the future impact if governments
do not act to address the problem.

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The Costs of Exclusion | Economic Consequences of the Digital Gender Gap

What we found1

 here is a substantial digital gender gap — and it’s not gettingTbetter. In the 32 countries we studied, just over a third of women
were connected to the internet compared to almost half of men.
Since 2011, the gender gap has only dropped half a percentage
point, from 30.9% to 30.4%.

INTERNET USERS (% WOMEN)INTERNET USERS (% MEN)GENDER GAP IN INTERNET USE

40%
30%
20%
10%
0%
2012

2014

2016

2018

Source: Alliance for Affordable Internet, 2021

2

 ountries have missed out on $1 trillion USD in GDP as a resultCof women’s exclusion from the digital world. In 2020, the loss to
GDP was $126 billion USD.

3 his economic hit means billions in lost taxes that could beTinvested to improve education, health, and housing. This lost
productivity translates to a missing $24 billion in tax revenues
annually for these governments, based on current tax-to-GDP ratios.

4

 overnments are not adopting the policies they need to bridgeGthe digital gender gap. Of all the policy areas covered by the Alliance
for Affordable Internet (A4AI) in its annual Affordability Drivers
Index, gender consistently receives the lowest scores. In the 2020
Affordability Report, over 40% of countries studied had no meaningfulpolicies or programs to expand women’s access to the internet.

5

 olicymakers have a $500 billion+ economic opportunity. ClosingP
the digital gender gap in these countries would deliver an estimated$524 billion increase in economic activity by 2025.

2020

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www.a4ai.org

Bridge the digital gender gap
and grow digital economies
These findings show the magnitude of the digital gender gap and theopportunity that exists for governments willing to take action. As economies
have contracted in the face of the Covid-19 pandemic, governments arelooking to the digital world as a new, robust source of economic productivity
and growth. This growth must be inclusive and must invest in programmes,
policies, and infrastructure that enable more women to use the internet.
This policy approach will include investment in infrastructure to makesure stable, high-speed internet access is available and affordable toeveryone. But a strategy for an inclusive digital economy must go beyond
infrastructure to also address the economic, technical, and social barriersof digital exclusion.
The REACT framework, developed by the Web Foundation, defines fivecore pillars that give policymakers a holistic way to develop policy topromote women’s inclusion in technology through: Rights, Education,
Access, Content, and Targets. An effective broadband strategy must
include policies that guarantees the rights of women and girls; provide
skills and training for all; make internet access available and affordable;
promote relevant, local content; and include clear policy targets to create
accountability in the policy process.

A digital economy without the full participation of
women cannot scale to reach its potential. Digital
inclusion is not only good policy — it’s good economics.

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The Costs of Exclusion | Economic Consequences of the Digital Gender Gap

The digital divide is real —and it’s sexist
By the latest estimates from the International Telecommunication Union (ITU),
a majority of women in the world have never used the internet. It estimates
that, in 2019, 55% of men in the world had used the internet while only 48% of
women had. This gap, known as the digital gender gap, represents 303 million
people – almost the population size of the United States of America.1
The digital divide and its cousin, the digital gender gap,
have been near-permanent legacies of technologicalinnovation, and the adoption of the internet has beenno exception to this. As a global trend, women and girls
are less likely to use the internet than men and boys.
This has been the internet’s history: but it neither has
to be nor should be its future.
In several parts of the world, the number of men online
vastly outnumber the number of women online. The
ITU’s regional estimates for Africa put the gender ratio
at nearly three-to-two in favour of men over women.
According to the GSMA, around 234 million fewer
women in low- and middle-income countries use themobile internet than men. This divide is most stark in
sub-Saharan Africa and South Asia where the gender
gap persists over 55% more men than women.2

What are the digital divide
and the digital gender gap?
These terms are often used interchangeably bymany people.
The digital divide typically refers to a binary
division of people into the connected and theunconnected. It can also relate to the divisionof different user experiences (e.g., the featurephone-smartphone digital divide) or different
groups of people (e.g., the urban-rural digital
divide). It simply refers to the disparities oftechnological access and use.
The digital gender gap speaks to many of thesame aspects of the digital divide, as measured
by gender. However, a focus on the ‘gender gap’emphasises that this digital inequality is just one
aspect of a broader system of discriminationand disadvantages that limit women’s and girls’
potential to participate in society.
The digital divide is a technological problem:the digital gender gap is a human one.

1	This is calculated using the ITU’s gender gap projections for 2019 and the UNDESA’s estimates for world population by gender in the same year.
2	This is calculated using the Web Foundation’s women-centric approach. See more on Page ##.

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Several barriers limit women’s use of the internet.
There is no single reason women are less likely to use the internet than men. A combination ofindividual and societal factors accumulate into forms of intentional or incidental discriminationand disadvantages that discourage women from participating in the online world as equals. The
outcome of this is measured simply in the digital gender gap.

Affordability
The cost of connectivity keeps women offline.
Handset cost remains one of the most frequently
cited reasons among mobile phone users in low- andmiddle-income countries for not using the internet.
Beyond handsets, the cost of data tariffs negatively
limited how much 25% of respondents used theinternet in three low- and middle-income countries.

Wage gaps
Gender pay gaps make the problem of the cost ofconnectivity worse for women. In a 2021 survey ofdevice costs in 187 countries around the world, thecheapest new smartphone cost US$104 on average.
As a fraction of the average monthly income, thisis roughly one quarter. However, in the context ofthe global gender wage gap, where women globallyearn around 77 cents for each dollar a man earns,
these costs are, on average, higher for women as apercentage of their income. By that ratio, if a man
could pay for a smartphone on one month’s wages,
a woman would need to work an extra ten days toafford the same device.

Device gaps
In part because of cost, women globally have lower
rates of device ownership. By way of illustration,
women in low- and middle-income countries
are estimated to be 15% less likely to own asmartphone than men. This carries consequences
for the behaviours that mobile phone users have.
For example, in a GSMA consumer survey among
women who use mobile phones in Nigeria, 93%of smartphone users use the internet while only
12% with a basic or feature phone do. Lower ratesof smartphone ownership among women forms atechnical limitation to what women are able to do,even when they’re connected to the internet.
These lower rates of device ownership replicate into
lower rates of meaningful connectivity. The Alliance
for Affordable Internet (A4AI) introduced the conceptof meaningful connectivity in 2020 to start tracking
the depth of different online experiences between
simply being connected and having internet accessof sufficient quality to enable someone to work, live,
and participate in the online world. One of its fourtargets is smartphone ownership: where women’ssmartphone ownership generally lags across theworld. This represents part of the digital gender gap
that policymakers need to address.

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The Costs of Exclusion | Economic Consequences of the Digital Gender Gap

Privacy/security

Cumulative effect

Women, generally, also hold higher fears
around online privacy and security. In nationally
representative surveys in Colombia, Ghana, andUganda, women more frequently reported being
afraid about personal data privacy at the same timeas they reported lower rates of creating content
online. In our focus groups in Côte d’Ivoire, Nigeria,
India, and Bangladesh, women remarked on the fearof being manipulated or targeted because of what
they posted on social media. In their own eyes, theinternet is not a safe place for women.

The accumulation of these individual barriers that
discourage women from using the internet – each asmall manifestation of the digital gender gap — hasa cumulative impact on the content that women see
and the experiences they have online. At the sametime, women report facing greater family and socialpressures against internet use.

Literacy and skills
Educational gaps by gender also keep women offline.
Along with handset cost, literacy and skills are oneof the two most common barriers to mobile internet
use. As the literacy gap between men and womenin the world persists (90% of adult men, compared
to only 83% of adult women as of 2019), this gap
replicates itself into the digital world. Differencesin mean years of schooling also replicate into lower
access to digital skill-building in an educational
context. Together, the educational disadvantages
against girls become digital divides for women.

Together, the financial, technical, safety, andeducational gaps faced by women on an individual
basis accumulate into a social norm that reinforces
the myth that “access to technology and theinternet by women is ... immoral, inappropriate, or
unnecessary.” This myth discourages women andgirls from participating in the online world, defers
the potential benefits to their own education, health,or wellbeing, and hinders the potential cultural andeconomic benefits of the greater digital inclusion of
women and girls throughout the world.

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www.a4ai.org

Continued failure to act
on gender inequality has
cost countries billions
over the past decade
Governments throughout the world have failed women and girls in rising to
the challenge to close the digital gender gap. However, this is not just a failingfor women and girls: the costs of this exclusion are shared throughout society.
There is an untold wealth of cultural, social, and scientific knowledge lost
because of the exclusion of women’s and girls’ voices from the online world.

Gender-responsive broadband policy has
so far been an exception to the rule.
Broadband policy to date has failed to respondto the digital gender gap. Since 2017, A4AI has
tracked gender-responsive policy making in thetelecommunications sector for all countries
within the annual Affordability Drivers Index.
Of all the policy areas studied by A4AI, genderconsistently receives the lowest average scores.
In the 2020 Affordability Report, just over 40%of the countries in the study had no meaningfulpolicies or programs for women’s access to theinternet.

This failure comes with a huge economic cost.
Beyond the social cost, excluding women fromthe digital economy takes a significant economic
toll on low and lower-middle income countries(LLMICs). A4AI estimates that, over thelast decade, LLMICs have lost a total of $1
trillion USD in gross domestic product (GDP)to the gender gap in internet use. Based on
current tax-to-GDP ratios in these countries,
this loss represents an estimated $24.7 billion
in lost tax revenue in 2020.

In 2020, within our sample of 32 countries(covering 72.2% of LLMICs’ total GDP) just overa third of women were connected to the internet,
compared to almost half of men. This same
year, A4AI estimates that the gender gap costthe 32 countries in the sample $126 billion USD.
Unless the gap significantly narrows, LLMICs will
likely continue to lose billions more of economic
activity (USD) each year if women continue tobe excluded from the digital world.

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Source: Alliance for Affordable
Internet, 2021

ANNUAL COST OF EXCLUSION

Estimated GDP Lost to Digital Gender Gap(in Billions USD)

Figure A. Cumulative costof women’s exclusionfrom internet use in 32
countries, 2011–2020

The Costs of Exclusion | Economic Consequences of the Digital Gender Gap

CUMULATIVE COST OF EXCLUSION

1,000

750

500

250

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

The past decade failed to close the digital gender gap.
​​In the early 2000s, the overwhelming majorityof new internet users were male. This began
to change about a decade later as the internet
became more ubiquitous throughout the world.
Between 2011 and 2020, the share of women
connected to the internet rose from 12.2% to34.3%. Despite this increase, it wasn’t substantial
enough for women to catch up with their malecounterparts.
Despite gains in women’s access, men
continue to connect to the internet at afaster pace. In the last decade, the gender
gap in internet use in LLMICs appears
stable despite steady increases in the

Figure B. Percentage
internet users, 32 model
countries, by gender andgender gap, 2011–2020

number of women online. In 2011, thegap was 30.9% and by 2020, it dropped
only half a percentage point to 30.4%.
The gender gap oscillated from year to year.
In some years the gap narrowed; in others, it
widened. Overall, the contractions in the gap
were only marginally larger than the expansions.
This is how the gap appears to have changed
so little during this period.

INTERNET USERS (% WOMEN)INTERNET USERS (% MEN)GENDER GAP IN INTERNET USE

40%
30%
20%
10%

Source: Alliance for Affordable
Internet, 2021

0%
2012

2014

2016

2018

2020

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www.a4ai.org

How we measure gender gaps
There are different ways to calculate the digital
gender gap, depending on the particular lensthrough which each person sees the world
and which group you choose as the reference.
We always calculate the gap as the difference
between how many men and how many womenare online, as a proportion of how many womenare online. The lower the percentage of women
online, the larger the digital gender gap willbe. We use women as the reference groupin order to put the focus on the disparity anddisadvantages faced by women.
More specifically, our approach explains how
many more women need to come online inorder to reach gender parity using the formulaon the right.

% of men online — % of women online% of women online

Gaps will remain constant as long as therelationship between the two groups remains
in proportion to each other. This is how, despite
the fact that more women are coming online
every year, the gender gap has not closed overthe past decade.

The year-to-year fluctuations may be due torandom variation. The long term trend runsin parallel to the absence of a sustained andconcerted effort on the part of policymakers toremove barriers that keep women unconnectedor only marginally connected. Extrapolatingfrom these results, it stands to reason that if
policymakers do not prioritise reducing thedigital gender gap, they run the risk of allowing
any future progress made in one year to be
washed away by a reversal in a subsequent year.

Figure C. Closing thegender gap in internet
use, 2011–2020

INTERNET USERS (% WOMEN)Over the last decade, the share of women online
has increased at a rate of 12.2% a year. For
the gap to have closed by 2020, women would
have had to have connected at a rate of 15.6%.
It means an additional 150.9 million women
would have had to have connected.

INTERNET USERS (% MEN)GENDER GAP IN INTERNET USE

40%
30%
20%
10%

Source: Alliance for Affordable
Internet, 2021

0%
2012

2014

2016

2018

2020

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The Costs of Exclusion | Economic Consequences of the Digital Gender Gap

This is our past, but it should not be our future.
Positive policy examples do exist for ways to close thedigital gender gap. Leaders in this area include Botswana,
Costa Rica, and Senegal: all of which include clear targets
for women’s inclusion within their national broadband
plans. Botswana’s national broadband strategy includes
gendered targets for smartphone access, digital literacy,
and ICT graduates, and the Digital Senegal plan includes
a high-level commitment to mainstream gender inall broadband policy decisions. In Costa Rica, thenational broadband plan includes women’s access asa core component and sets targets for women’s online
entrepreneurship. These policies are a crucial starting
point for governments looking to change history andneed to be followed up with implementation plans torevisit and evaluate progress.

At a programmatic level, countries have implemented
projects that have a clear gender dimension to help
address the digital gender gap. In the Philippines, theTechnology for Economic Development (Tech4ED) project— with over 2,200 public access centres built across thecountry since 2015 — have had women as a majority of
their users. In Rwanda, half of the positions within thegovernment-backed Digital Ambassadors program arereserved for women to enable them to be advocateswithin their own communities and networks to encourageother women and girls to come online. These examples
demonstrate what can be done in words and in action
to reduce the digital gender gap.

As governments look to the digital economy as
an engine for growth in the post-Covid recovery,
they must keep women’s access in mind.
For much of the past decade of the internet’s growth,
men have been coming online in greater numbers than
women across the 32 low and lower-middle income
countries included within our research. At the sametime, broadband policy has been underwhelming insetting targets and enacting policies to close the digital
gender gap.

Closing the digital gender gap offers a $524 billion
increase in economic activity over the next five years.
As the Covid-19 pandemic has had a devastating toll
on low and lower-middle income countries (LLMICs)
and as their economies have contracted, the needfor new, robust sources of economic productivity has
rarely been greater. At the same time, the pandemic
and subsequent lockdowns throughout the world
have shown how internet access is an essential lifeline
for millions and enabled economic activity in helping
suppliers reach new markets, enabling consumers topurchase essentials in the context of the pandemic,
and facilitating digital payments. Since the pandemic
has pushed more of the world online, the economic
returns associated with connectivity have likely risen.
We estimate that closing the gender gap offers billions

in economic activity, but further analysis of the economic
impact of Covid-19 on the digital world will offer a full
picture of the consequences of not being connectedduring and after the pandemic.
A4AI estimates that if countries are able to breakwith the past and significantly close the gender gap
in internet connectivity in the next few years, LLMICs
will be able to generate over half a trillion USD in
additional gross domestic product (GDP) between 2021
and 2025. Conversely, if little changes during thistime, the total loss of GDP between 2011 and 2025
among LLMICs due to the gender gap will surpass$1.5 trillion USD.

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Source: Alliance for Affordable
Internet, 2021

PAST & PRESENT

Estimated GDP Lost to Digital
Gender Gap (in Billions USD)

Figure D. Cumulative
costs of exclusion inlost GDP, 2011–2025

www.a4ai.org

CURRENT TRAJECTORY

1500
1000
500
0

2012

In the next few years, thegender gap will narrow, but it
will remain — unless we act.
A4AI estimates that, in the next five years, the numberof women connected to the internet in LLMICs willrise from just over a third of all women to nearly a half.
This model predicts that the rate at which women who
connect to the internet for the first time will be greaterthan that of men. In part, this is because, as a smaller
share of women are connected today, the possibility
for greater internet penetration among them increases
while for men lowers. During this period, A4AI estimates
women will connect to the internet at a growth rate of
7.16%, compared to men who will connect at a rate of
5.5%. Based on these estimates in 2025, 46.8 million
women will gain an internet connection, compared to45.7 million men.
According to the estimates, the digital gender gap inthese countries will shrink from 30.4% to 20.6% by 2025.
This will mean that the GDP lost to the gender gap will
also be less in 2025 than what it is today. Despite this,by 2025 the cost of exclusion will remain high, at an
estimated $99 billion USD.
The UN Broadband Commission for Sustainable
Development has set various targets regarding
broadband infrastructure, access, and use. Based onthese estimates, it is clear that LLMICs will notreach gender equality in broadband-internet user
penetration and will consequently fail to reach
the Commission’s Targets 3 and 7.

2014

2016

2018

2020

2022

2024

Gender Equality Targets
from the UN Broadband
Commission

3
7

By 2025, countries shouldreach various levels ofinternet penetration basedon development classification,
each with gender equality.
By 2025, all other targets —including affordability, skills,
and use — shall be met withgender equality between men
and women.

Source: UN Broadband Commission, 2018

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Figure E. Current
forecast of percentage
internet users, 32
model countries, bygender and gender
gap, 2011–2025

The Costs of Exclusion | Economic Consequences of the Digital Gender Gap

INTERNET USERS (% WOMEN)INTERNET USERS (% MEN)GENDER GAP IN INTERNET USE

60%

40%

20%

Source: Alliance for Affordable
Internet, 2021

0%
2012

2014

2016

2018

2020

2022

The cost of exclusion is not just economic.
A4AI’s estimates regarding the cost of exclusion
are based exclusively on the gender gap ininternet use. However, as mentioned above, thedigital gender gap comprises many dimensions— financial, technological, educational, andsocial. Closing the digital divide of connected
and unconnected in use will not do away withthe entirety of this gender gap. A4AI has not
estimated what the economic gains would be ifevery dimension of the gender gap in internet use
was closed, including in meaningful connectivity.
However, in all likelihood, this number would behigher than the estimates here.
This stresses the urgency of, along with closing
the gender gap in internet use, closing the gender
gap in meaningful connectivity. Often, women
who connect to the internet only benefit froma very limited connection. In many instances,
women are only able to connect infrequently or
are limited by a slow connection with minimal
bandwidth. Where this is the case, users will not
be able to benefit from data-intensive online
activities (e.g., video calling, online education, andtelemedicine) and will unlikely be able to reap
all the economic benefits the internet can offer.

Everyone should have the same opportunity toaccess a high-speed internet connection and beable to use it regularly, without a prohibitively
limited bandwidth, and through an appropriate
device. Research conducted by A4AI in Indonesia,
Ghana, and Colombia shows that the gap inmeaningful connectivity is larger than the
gap in internet use. Even countries that haveclosed the gap in internet use, suffer from asignificant gender gap in meaningful connectivity.
In addition to helping women simply connect for
the first time, governments should also work toeliminate the barriers preventing women fromhaving a truly meaningful internet connection.
Women that only benefit from a marginal
connection are also unlikely to reach their fullpotential — economic or personal.

2024

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www.a4ai.org

Women, when given accessto the internet, achieve
extraordinary things
Research from S&P Global in 2021 estimatesthat only around a third of ICT professionals
around the world are women, with even fewer
occupying senior or management roles — andidentifies the ICT sector as one of the worst forgender parity. A 2019 report from the EQUALS

Coalition summarises the depth of the problem
in the gender disparities in ICT skills, educational
degrees, and professions around the world. This
is not a sector built by or for women: and yet,examples of resilience emerge.

Navigating without instruments:
limits of data availability

Figure F. ITU data availability disaggregatedby gender in 82 LLMICs, 2010 - 20192010

The lack of data disaggregated by gender, especially in LLMICs,
limits policy-making processes. For a country to set targets,and allocate the resources and investments required toclose the gender gap, accurate and up to date data is critical.
Unfortunately, this sort of data is rarely available for LLMICs.
Today, the main available sources for ICT data disaggregated bygender with a broad country coverage are the ITU, the Inclusive
Internet Index, the Digital Gender Gaps project, and GSMA.
Unfortunately, these data sets offer limited historical coverageof LLMICs. In 2019, out of the 82 LLMICs in the world only 10
had ITU indicators on internet use disaggregated by gender.
A lack of household surveys or other instruments regularlycollecting gender-disaggregated data in ICT is a worrisome
and persistent problem.
Moreover, few countries and organizations have committed
to dedicate additional resources to collect data disaggregatedby gender, especially in LLMICs. If countries are to close thegender gap, they should allocate more financial resources tocollecting data that could inform policy making.

2019

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The Costs of Exclusion | Economic Consequences of the Digital Gender Gap

Despite the barriers that exist, women are increasingly
using the internet to navigate a place for themselvesin the digital economy. In interviews and focus groups
commissioned by A4AI-Web Foundation in 2021,
women used the internet to further their economic
prospects across a wide range of sectors, acrossdifferent geographies and cultural backgrounds.
In India, one person started with a single plot of land
to develop her own floriculture business with nationalreach by expanding her potential customer basebeyond her immediate community. In Côte d’Ivoire, arestauranteur saved her business and moved to online
orders during the Covid-19 pandemic. A duo worked
in two cities in Bangladesh — a full seven hours’ driveaway from each other — to run a grocery and spice
shop. In Nigeria, a fashion designer finds inspirationand customers online. These are just samples of what
is happening, which is itself just a sample of what is
possible with an inclusive digital economy.
In particular, the digital economy has reduced upfront
capital costs and opened up the potential for newmarkets. For Idjatou Diallo, a restauranteur in Côte
d’Ivoire, she was able to set up her business withouta physical store, using the internet for sales that she
commissioned from her home. For Swati Lodh Kundu, acareer coach in India, the internet helped her extend herreach to find clients in other countries and continents
and in turn increase her income.
However, these advances are not without concerns.
Across different countries, women spoke about the fearof balancing a career, personal health, and family life —and the social expectations of that balance. In particular,
the freelancing approach to many online opportunities
comes with greater insecurity for a woman’s income,healthcare, and employment rights than may otherwise
be available to her. Many of these battles are not new,
but the economic relationships and responsibilities
are, as platforms take a new role in the marketplace.
Security and safety remain urgent problems for womenparticipating in the online economy. Across continents,
economic class, and skill level, women experienceharassment and other forms of online gender-based
violence. Women operating online businesses aretargeted by male customers to answer personalquestions, while women considering online shopping
for the first time worry about scams and losing out theirmoney in a world they cannot trust yet.

As an entrepreneur:“Recently there was this customer who texted
me and wanted to know product details.
Turns out, he started texting me almost every
day and instead of talking about the product,
he started asking me personal questions.
This was quite disturbing for me, yet I tried
my best to be polite and focused more ontalking about the products since he was mycustomer. This happened a couple of times.”— Most. Humayera Kabir Oshie, Bangladesh

As a consumer:“Often, I want to try [online shopping],
but I’m a bit afraid. And after
all, there are so many scams.”— Anonymous, suburban Côte d’Ivoire

While currently, many of these stories are exceptions
rather than expected, an inclusive digital economy
uses the foundations of equal access to the internet
to enable people to achieve their potential.
The best innovations of an inclusive digital
economy may not be societal transformations
but simply better ways of living and working foreveryone. Overall, and beyond the roles of content
creators and entrepreneurs, women look to technology
to make everyday life easier. In multiple countries,
women spoke of desires for technology to improve thespeed and convenience of everyday activities. The hopes
for the digital economy were accessing healthcare via
the internet rather than making an arduous trip to a
hospital, online shopping, and making administrative
tasks more efficient.
The failure to close the gap has enormous costs— not just for the people left behind, but for
the entire world in lost opportunities. It is time
for governments to close the digital gender gap andbuild the right foundations for a digital economy thatis inclusive and resilient in the face of a post-Covid
economic recovery.

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Governments urgently need
to use public policy to close thedigital gender gap
Adopted in 2016 by attendees of the inaugural Africa Summit for Women andGirls in Technology, the REACT framework still offers strong guiding principles
for closing the digital gender gap. As the name implies, it pulls together fivepillars for policy action: Rights, Education, Access, Content, and Targets.

Importantly, the framework emphasisesthat a broadband strategy for an inclusive
digital economy cannot simply be about
the infrastructure that grants access to theinternet. It must come with guarantees for therights of women and girls; skills and training forall; relevant, local content; and clear policy targets
to create accountability in the policy process.

left vulnerable to scams and abuse. Millions havebeen left behind without sufficient support toaccess the internet, know how it works, or realise
their full potential in using it. If governments
want to see a digital economy as a coreengine to a post-Covid recovery, they must
invest in gender-equitable foundations toinclude everyone.

The underwhelming reaction from policymakers
to the digital gender gap has created an economic
shortfall estimated to be around one hundredbillion USD each year. The gender gap is more
stark — and the problem more urgent — incertain parts of the world. However, the digital
gender gap is not an individualistic concern. It
has consequences for us all.

Pathways exist to move the policy debate
forward and to close the digital gender gap.
The REACT framework — engrossed by theattendees of the inaugural Africa Summit for
Women & Girls in Technology — offers a setof guiding principles for policy action on thisissue. Policy change designed by womenand for women must occur to close thedigital gender gap — or we will all suffer
the costs of exclusion.

Governments in 32 LLMICs have already lost out
an estimated $1 trillion USD in gross domestic
product over the past decade because of thedigital gender gap. Without action, this number
will grow to $1.5 trillion USD by 2025. Equitable
access to the internet is an economic and moral
priority.
Women already achieve incredible things in thedigital economy. However, their experiences ascontent creators and entrepreneurs are more an
exception than part of everyday life. Those whohave been early leaders in their fields have been

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Policy strategies for an inclusive digital economy

RIGHTS
Protect and enhance
everyone’s rights online.

• Challenge gender norms that would curba woman’s or a girl’s right to own a device,
use the internet, and express herself.
• Adopt adequate data protection laws that
ensure users’ privacy is respected.
• Update consumer protection laws to build
confidence in the online marketplace.

EDUCATION
Use education to equip
everyone – especially women– with the skills they need toaccess and use the web.

ACCESS
Deliver affordable — or free —access to an open web.

• Close the educational gap and support the schooling of
all children with free primary and secondary education.
• Include digital skills within the curriculum
to introduce new technologies.
• Attract and retain women as teachers and professors
in STEM fields, especially computer science.
• Reduce the cost of connectivity through policy
strategies such as the A4AI Good Practices.
• Adopt, regularly review, and update the National
Broadband Plan and the Universal Access Strategy/
Policy, including gender as part of its mandate.
• Include gender and inclusivity as an evaluation
criterion in public access projects and the operationof the Universal Service & Access Fund.

CONTENT
Ensure relevant andempowering content for
women is available and used.

• Support the creation of locally-relevant
content, including through institutions suchas the Universal Service & Access Fund.
• Prioritise the development of content in local languages
and audiovisual content that reduces the need forliteracy to participate in the digital economy.
• Provide fair and free information to womenand girls on topics important to them,including sexual and reproductive health,legal rights, and digital financial services.

TARGETSSet and measure concrete
gender-equity targets.

• Set clear targets, including for indicators on meaningfulconnectivity, with gender-disaggregation within policies.
• Regularly collect gender-disaggregated data throughstandard statistical practices to track progressand monitor any other emerging gender gaps.
• Make targets and data publicly available for otherstakeholders to engage and create accountability.

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Appendix 1:
Costs of Exclusion Model
Methodology
Research design
In our analysis, we used panel data (also referred toas time-series–cross-sectional data) to (1) estimate theshare of women and men connected to the internet;(2) calculate the gender gap in internet connectivity;and (3) estimate the cost of the gap today and in thenext five years.
A number of organizations collect data on internet
use disaggregated by gender. Examples include theUniversity of Oxford in collaboration with the Qatar
Computing Research Institute, GSMA, ITU, the Economist
Intelligence Unit, and Equals, among others. Despite
the collection efforts carried out by these organizations,time-series-cross sectional data for most LLMICs iseither scarce or nonexistent. Where data has beencollected by more than one source, commonly there
are large discrepancies between the sources.
Our research design required data produced by credible
and reliable organisations, with a good historical
coverage in terms of time periods as well as countries,
with indicators disaggregated by gender. Initially, we
anticipated using Digital Gender Gaps data, however,
we encounter some limitations with this data as it is
not based on household surveys, which did not allow
us to estimate the exact number of women and men
connected to the internet, and had a limited coveragein terms of time-series, only covering from 2018-2021.
In light of these limitations, A4AI selected only the mostreliable and consistently collected data points includedin the available sources and used this data as the basisof its analysis. We opted to only use data from ITU andthe Inclusive Internet Index, even with its limitations,such as the limited availability of data points for LLMICs,
these two sources offered the most complete data tobase our estimations. We built a time-series-cross-sectional dataset using these two sources, several data
points were missing, making it necessary to use an
imputation method to fill the missing data points.
The first step of the methodology required assemblinga dataset to perform the imputation, given the limited
available data. We began this process by compiling adataset that included the Gross Domestic Product (GDP),
Human Development Index (HDI), the Unemployment
Gender Ratio (female to male), the total share of internet
users and the share of internet users disaggregatedby gender. These variables were selected based on
the work by Kashyap et al. (2020) and on correlation
calculations performed by A4AI. Kashyap et al. (2020)
uses the online population of Facebook and Google tomonitor the gender gaps around the world, as part ofthis research they explore the correlations between thegender gaps and several offline variables. This work gave
us substantial guidance on which offline variables toinclude on the dataset to help the model produce thebest estimations of the missing values. Using this data
and multiple imputation, we created a new dataset for
32 LLMICs consisting of 2 variables – the share of female
and male internet users. The data was imputed using
an expectation-maximization with bootstrapping (EMB)
technique. For this process to have been successful, thevariables included in the underlying dataset need to
be normally distributed and the missing data needed
to be missing at random. We were able to confirm thatboth these requirements were met.

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Figure G. Countries included within the Costs of Exclusion model

Algeria

AngolaBangladesh

BurundiCambodia

Cameroon

Côte d’Ivoire

DR Congo

Egypt

El Salvador

Ghana

GuineaHonduras

India

Kenya

Mongolia

MoroccoMozambique

MyanmarNicaragua

Niger

Nigeria

Pakistan

Philippines

SenegalSudan

Tanzania

Tunisia

UkraineUzbekistan

ZambiaZimbabwe

Once our dataset was fully assembled, we estimated theshare of women and men connected to the internet ineach of the 32 countries for every year between 2000
and 2020. Based on these estimates, we then calculated
a yearly average of both of these variables. We thenforecasted the share of male and women internet users
for every year between 2021 and 2025. Based on these
forecasted values we calculated the gaps.

We converted the gender gaps (total woman broadband
penetration) for each year into the corresponding
percentage increase for the total population (womenand men) in broadband penetration.
To calculate the gender gap in internet access, wefollowed the World Wide Web Foundation’s womencentred method.

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To forecast the share of women and men that will
be connected to the internet by 2025, we used an
Autoregressive Integrated Moving Average (ARIMA)
model. For the model selection and fitting, we took
a number of steps to ensure the specific model weused would be appropriate for our purposes. As
part of this process, we check for stationarity andplotted Autocorrelation Function (ACF) and Partial
Autocorrelation Function (PACF) graphs. We thencalculated both the Akaike’s Information Criterion(AIC) and Schwartz Bayesian Information Criterion (BIC)
and preselected the models with the lowest values of
each. Finally, we checked the residuals and successfully
ensured there was no autocorrelation that could bias
the results.
We used forecasts from the Economic Research Serviceof the U.S. Department of Agriculture to find estimates
for the GDP of the 32 countries in our sample from2021 – 2025.

To calculate the total broadband penetration, we reliedon two models developed by ITU that calculated theimpact of an increase in mobile and fixed broadband
penetration on GDP. We run an Autoregressive
Integrated Moving Average (ARIMA) model, using datafrom ITU, to calculate the portion of fixed broadband
penetration of LLMICs in the next 5 years. Knowing
the total broadband penetration figures, we used thefixed broadband penetration estimates to calculate
the mobile broadband penetration.
With all the elements in place (total GDP per year, total
increase in fixed broadband penetration, total increasein mobile broadband penetration, ITU’s fixed impact
coefficient, and ITU’s mobile impact coefficient), we
calculated the total cost of exclusion.

Figure H. Data sources of Costs of Exclusion model.
DATA POINT

SOURCE

Percentage of females
using the internet

International Telecommunication Union & Inclusive Internet Index

Percentage of males
using the internet

International Telecommunication Union & Inclusive Internet Index

GDP, World Bank Indicators

World Bank Indicators

Human Development Index

United Nations

Unemployment Ratio

World Bank Indicators

Total percentage of the
population using the internet

International Telecommunication Union

GDP projections, by country

US Department of Agriculture

Fixed broadband connections

International Telecommunication Union

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Limitations
To build the most complete dataset possible, A4AI combined data from the ITU
and the Inclusive Internet Index on the shares of internet users broken down bygender. Although A4AI carefully reviewed the data, there is no method that can fully
guarantee the data between the two sources is fully comparable. This could create
inaccuracies in the final results.
In the absence of a gender-specific econometric model that could quantify the
economic loss of women that are not connected to the internet. A4AI estimatedthat the contribution to the economy of both genders is 50-50. A model that could
estimate the impact of connecting women would be a better fit and a more precise
estimation of the cost of excluding women from the digital economy.
We estimated the share of women and men connected to the internet in each of the
32 countries for every year between 2000 and 2020. Based on these estimates, wethen calculated a yearly average of both of these variables. We then forecasted theshare of male and women internet users for every year between 2021 and 2025. Basedon these forecasted values we calculated the gaps. A possible different approach
would have been to have instead forecasted the gaps for every country by runningthe model separately for each country and then used these forecasted numbers
to find the average gap into the future. However, due to limited data availability inseveral LLMICs, the present design was chosen to reduce the potential uncertainty
in a per-country approach and to improve accuracy.

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