Mobile Internet Market and Connectivity Enablers
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Emerging Technologies
- Sustainable Growth and Tech Trends
Publication | Update: Oct 2021
By the end of 2020, 51% of the world’s population – just over 4 billion people – were using mobile internet, an increase of 225 million since the end of 2019. The coverage gap – those living in areas without mobile broadband coverage – stands at 450 million people, or 6% of the world’s population. The biggest increases in coverage have occurred in Sub-Saharan Africa and the Pacific Islands. Mobile data traffic reached record highs in 2020, with global data per user reaching more than 6 GB per month – double the data usage for 2018. Both the private and public sectors responded to the surge in traffic, increasing network capacity and delivering better quality networks for consumers. At the end of 2020, download speeds were on average higher than the year before.
Owning a smartphone is important for adopting and using mobile internet and benefiting from the life enhancing services the internet can offer. Recent research has found that smartphone owners are much more likely than owners of feature phones or basic phones to progress to regular mobile internet use. India has been at the forefront of this growth, with smartphone adoption among adults increasing from 22% in 2017 to 51% in 2020. The gender gap in smartphone ownership in low and middle-income countries has also reduced – for the first time since 2017, again driven by South Asia. Women are 15% less likely to own a smartphone than men, down from 20% in 2019. Overall, despite the most vulnerable groups being most affected by the COVID-19 pandemic across most socioeconomic indicators in 2020,12 it is encouraging that it does not appear to have led to an overall decline in mobile internet use among women across rural populations in many of the countries surveyed.
There are five overarching barriers to mobile internet adoption and use. Awareness of mobile internet and its benefits are a critical step in the journey to mobile internet use. Yet, not all those aware of mobile internet go on to use it, suggesting other reasons are preventing them from going online. Analysis from the 2020 GSMA Consumer Survey shows that for mobile users who are aware of mobile internet but do not use it, the main barriers are literacy and digital skills, and affordability, especially of internet enabled handsets. These barriers are broadly unchanged since 2018 and disproportionately affect certain segments of the population more than others due to structural inequalities and underlying social norms. Affordable internet-enabled handsets and data are critical to increase demand for mobile internet services and enable the digital inclusion of underserved populations.
Analyzing mobile internet activities helps to understand what people are doing on their phones and get an indication of data consumption. However, it does not provide an understanding of the different needs being met with mobile internet, as activities are often undertaken to meet more than one need. Across the surveyed countries, both rural and urban mobile internet users reported using mobile internet for a wider range of activities and more frequently. However, there is still a rural-urban gap. With a few exceptions, urban mobile internet users were more likely to use a broader range of activities than their rural counterparts.
Operators also increased capacity by continuing to invest in network upgrades and expanding 3G, 4G and (in some markets) 5G coverage, particularly in the second half of the year. In Sub-Saharan Africa, operators in several countries extended their 3G and 4G network coverage, increasing from 76% to 81% and from 41% to 51%, respectively, between 2019 and 2020. In 2020 alone, industry investments in connectivity, new services, start-up programs and other activities generated $ 4.4 trillion of economic value added (5.1% of GDP) globally. Over the next five years, mobile operators are expected to invest 0 billion in capex.
Despite the significance of the usage gap, efforts to advance digital inclusion have tended to focus on increasing coverage. While enabling infrastructure investment should remain a priority to realize better mobile internet experiences, this alone will not be sufficient to achieve truly inclusive digital growth.
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.