In Chad, the price of staying connected is not distributed evenly between men and women. A new economic analysis of household spending finds that men spend more on communications and telecommunications than women, exposing an often-overlooked dimension of the digital gender divide. The study, led by Themoi Demsou of the University of N’Djamena, examines data from three household income and consumption surveys conducted in Chad in 2018. Its central finding is straightforward but consequential: the average difference in communications and telecommunications expenditure is positive in favor of men. That pattern does not simply measure who owns a phone or uses the internet. It captures how much individuals spend on the systems that allow them to make calls, send messages, access online services and participate in increasingly digital markets. In a country where communication infrastructure can shape access to jobs, education, finance and social networks, unequal spending may reflect deeper inequalities in income and opportunity.

The study focuses on a category of household consumption that is easy to treat as a minor budget item but has become an essential part of economic life. Telecommunications spending can include payments associated with mobile services, internet access and other communication tools. Such expenses are both consumption and, in many circumstances, an investment in economic participation. A phone connection can help a trader locate buyers, allow a worker to search for employment, enable a farmer to obtain market information or give a family access to mobile financial services. The ability to spend on these services therefore depends not only on personal preferences but also on resources and constraints. If one group has less disposable income, less control over household finances or fewer opportunities to earn, its lower communication spending may be an outcome of restricted choice rather than lower demand. The Chadian analysis places this question at the center of its investigation, asking why men and women do not spend the same amount on connectivity.

To separate the possible sources of the difference, Demsou applied the Blinder–Oaxaca decomposition model, a statistical method widely used to analyze gaps between two groups. The technique begins with an observed difference—in this case, the difference between men’s and women’s average communications and telecommunications spending—and divides it into components associated with measurable characteristics and an unexplained remainder. The explained component can reflect differences in variables such as income, education, employment or other observed individual attributes included in the analysis. The unexplained component is not automatically proof of discrimination: it may include the influence of unmeasured factors, different responses to the same characteristics, model assumptions or unequal treatment. In economic research, this distinction matters because an overall gap can arise through several mechanisms at once. Decomposition does not erase uncertainty, but it provides a framework for asking which measurable conditions account for the difference and which part remains unaccounted for after those conditions are considered.

The results identify individual income as a particularly important factor. Income has a significant and positive effect on an individual’s communications and telecommunications spending, meaning that people with greater income tend to allocate more money to these services. Income also has a significant positive effect on the spending difference between men and women. This result links the telecommunications gap to the broader distribution of economic resources. When men have greater access to income, the resulting difference in purchasing power can translate into a difference in connectivity-related expenditure. More money may support larger data plans, more frequent calls, wider use of digital services or the ability to maintain multiple forms of communication. The study does not claim that income is the only cause of the gender difference, nor does it provide a single behavioral explanation for every individual. Instead, it shows that the capacity to pay is central to understanding why communications spending is higher among men on average.

That conclusion gives the findings significance beyond the telecommunications market itself. Digital access is often measured through indicators such as phone ownership, network coverage or internet use, but spending provides another window into digital inclusion. Two people may technically have access to a mobile network while experiencing very different levels of practical connectivity. One may be able to purchase data regularly, replace a damaged handset or use communication services for work and education. Another may ration airtime, rely on shared devices or limit use to urgent needs. Expenditure can therefore reflect the intensity, reliability and purpose of access, although it cannot reveal every aspect of digital behavior. In Chad, where income constraints may be substantial, the amount an individual can spend may help determine whether telecommunications serve as a basic social link or as a platform for economic advancement. The gender gap in spending consequently signals a possible gap in the benefits people can obtain from communication technologies.

The pattern also illustrates why digital inequality cannot be reduced to infrastructure alone. Building networks and extending coverage are necessary steps, but a connection that remains unaffordable cannot provide equal opportunity. The study’s findings suggest that policies addressing women’s incomes could reduce the difference between male and female spending. Demsou points toward employment and education policies for women as part of that response. These interventions operate through different channels. Education can strengthen skills and improve access to information, while employment can provide independent income and greater control over spending decisions. Higher earnings may increase the ability to pay for communication services, but they could also expand the economic uses of those services by connecting women to employers, customers, suppliers and financial tools. The relationship can become cumulative: income supports connectivity, connectivity can improve access to opportunities, and those opportunities may in turn support higher income. The study identifies this possibility without claiming that telecommunications alone can overcome structural gender inequality.

Affordability is another lever highlighted by the research. The study argues that lower communication and telecommunications prices could reduce users’ expenditures while also narrowing the average gap between men and women. At first glance, lower prices might appear likely to reduce spending rather than increase access. Economically, however, cheaper services can lower the financial barrier to participation, allowing people who currently use telecommunications sparingly to connect more often or adopt services previously beyond their budgets. A reduction in the price of calls, messages, data or related services could make the same level of communication possible with less income, or permit greater use without increasing the share of household resources devoted to connectivity. The effect would depend on how providers price services, how coverage is distributed and whether women can control the resources needed to purchase them. Price policy is therefore not a substitute for income and employment reforms, but it could complement them by making existing resources go further.

The analysis also carries an important methodological caution. The surveys describe conditions in Chad in 2018, so the results provide a snapshot of that period rather than a real-time measure of current spending. The study reports no newly generated or analyzed dataset beyond the survey information used in the research, and the source material does not provide a sample size or detailed list of all variables included in the models. Nor can the decomposition by itself establish that discrimination caused every portion of the observed difference. The unexplained part of a Blinder–Oaxaca analysis is best understood as a residual associated with characteristics and returns not fully captured by the model. It may be consistent with unequal treatment, but it can also reflect missing information or differences in preferences and circumstances. These limitations do not undermine the core result that men’s spending was higher on average and that income was strongly associated with the gap. They define what the evidence can—and cannot—show.

The broader message is that closing the digital gender divide requires more than distributing devices or expanding networks. Connectivity is embedded in economic life, and unequal economic power can determine who is able to use it fully. For Chad, the study points toward a coordinated strategy combining women’s education, employment and income growth with efforts to make communication services more affordable. Such policies could have effects that extend beyond monthly telecommunications budgets. Better access to communication may improve the flow of market information, strengthen social and professional networks and support participation in services that increasingly depend on mobile technology. The research does not present a technological quick fix, but it identifies a measurable pathway through which gender inequality is reproduced: differences in income become differences in spending, and differences in spending can become differences in practical access. In that sense, the cost of a phone call or data bundle is also a measure of who can fully take part in the connected economy.