Digital inequalities in North Africa: Examining employment and socioeconomic well-being in Morocco and Tunisia

Abstract

The term digital divide refers to disparities in digital access, infrastructure, and opportunities. However, it is important to recognize that information and communication technologies (ICTs) do not operate in isolation. They are influenced by social and structural factors. This study focuses on Tunisia and Morocco to examine access to and usage of digital technologies, factors shaping these patterns, and the impact of unequal access and usage on employment and socioeconomic well-being in the post-Arab Spring era. Using Afrobarometer household surveys from 2013 to 2022, encompassing 9595 respondents, we construct a digital inclusion index and disaggregate results to illustrate the dynamics of digital inequalities. We employ pooled logistic regression to explore the determinants of digital inclusion and examine how disparities shape well-being. Findings show improved digital inclusion in Morocco and Tunisia from 2013 to 2022, yet over 80% of their populations remain partially or entirely excluded. We confirm previous studies suggesting that digitalization mirrors or exacerbates preexisting inequalities, with gender, age, education, and socioeconomic status significantly influencing digital inclusion, indicating persistent inequalities and barriers. Our findings also have broader implications for the MENA region, emphasizing the need to address the complex interactions among sociodemographic factors, including gender, age, education, and socioeconomic disparities, in order to achieve equitable digital inclusion.

Keywords

Digital inclusion digital divide digital inequalities employment socioeconomic well-being MENA Morocco pooled logistic regression Tunisia

Introduction

The civil unrest and the protest movements against the dictatorships in the North African and Middle Eastern countries in 2011 culminated in regime changes in many countries, including Libya, Egypt, and Tunisia (Saideman, 2012). Information and communication technologies (ICTs) were considered at the heart of the civil uprising and hailed as a possible instrument of political change and social transformation (Hassan et al., 2020; Tufekci, 2017). Since then, several studies have focused on digital social movements, and the transformative potential of the technologies has been scrutinized (Benski et al., 2013; Hussain and Howard, 2013; Khondker, 2011). However, when a certain vegetable vendor named Mohamed Bouazizi from the Tunisian city of Sidi Bouzid set himself alight on the street outside the provincial government building, his reasons were far from being acknowledged on social media. His knee-jerk act of self-immolation was a response to political apathy, bribery, corruption, and inequalities that plagued the societal structures and rampant discrimination within the Tunisian working class (Ajami, 2012; Hamdy, 2012).

Since 2011 a number of studies by Kim and Lim (2019), Kim (2021), Comunello and Anzera (2012) and Van Niekerk et al. (2011) have underscored the importance of ICTs and their use. Triggered by the global drive towards digitalization, high penetration rates of mobile phone networks and the internet also seem to suggest a shrinking digital divide (Sparks, 2013). Nevertheless, it is imperative to question and investigate whether preexisting inequalities such as those in education, employment, gender, and demographics have also lessened along with the developments in digital infrastructures and access to the internet. ICTs do not evolve and operate in isolation. Their access and use are intertwined in the societal structures, indigenous cultures and technical capacities (Massimo Ragnedda and Muschert, 2013). The wide range of investments in mobile networks and broadband internet alone does not promise complete digital inclusion. The growing body of literature on digital inequalities argues for examining digital divides at three different levels, namely, access to the ICTs (first level), the choice and amount of individuals' use (second level) and converting their use to one's social and financial advantage (third level). All three levels of the digital divide consequently result in and may further reproduce other forms of digital inequalities (Ragnedda and Ruiu, 2017). As a result, there have been political and policy implications of a digital divide in the MENA region ranging from health, education, and employment sectors, as pointed out by Zhang (2022), Whaibeh (2022) and Cava et al. (2011). Therefore, it seems essential to deconstruct the notion of digital inclusion and investigate the inequalities that are the consequences of social, cultural and economic dispositions.

This paper examines the case of two countries in the MENA region, namely, Tunisia and Morocco, and explores how the prevailing digital divides affect employment and social well-being within these countries. Drawing on data from Afrobarometer spanning from 2013 to 2022, this paper investigates the digital inequalities in Tunisia and Morocco, considering variables such as gender, education level, and economic and employment status. This study addresses three main questions. The first question aims to investigate the access and use of ICTs in Tunisia and Morocco among different demographic and socioeconomic groups. Similarly, the second question aims to uncover the factors contributing to these disparities in digital inclusion. Finally, the study examines how employment and socioeconomic well-being are influenced by the prevailing digital divides.

By applying regression analysis to the data from Tunisia and Morocco, the findings suggest that large segments of the population in both countries remain digitally excluded. The rest of the paper is divided into three main sections. The first section reviews the contemporary perspectives on different levels of the digital divide. The second section presents the methodological approach adopted to examine the Afrobarometer data from 2013 to 2022. Based on the pooled logistic regression, this section also presents findings on digital inclusion in Morocco and Tunisia. The third part of this paper analyses the notion of digital inequalities from a comparative perspective. This section helps in understanding the digital inequalities that remain high in these two countries.

Perspectives on digital divide and digital inequalities

Digital inequalities are a complex phenomenon that necessitates understanding the intricate interplay between societal factors and digital technologies (Ragnedda, 2020). It represents the unequal distribution of digital resources, opportunities, and outcomes among individuals and communities, resulting from various socioeconomic, geographical, educational, and cultural factors (DiMaggio et al., 2004). Current scholarly discourse extensively explores the digital divide at three distinct levels (Lupač, 2018; M Ragnedda and Muschert, 2013; Van Dijk, 2020). However, early studies on the digital divide, conducted in the late 1990s and early 2000s, primarily focused on unequal access to ICTs, resulting in disparities among individuals excluded due to uneven infrastructural development (Norris, 2001; Van Dijk, 2006; Warschauer, 2003). Exploring the dynamics of the digital divide and studying the factors that influence it offers a valuable starting point for scientific inquiry into this phenomenon.

The first level of the digital divide is centred around access to ICTs (Ragnedda, 2019). It serves as a critical determinant of individuals’ inclusion in a digital society and their ability to benefit from digital technologies. Much of the research conducted about the first level of the digital divide focused on the material and physical resources such as infrastructure development, adoption rates and individuals' subscriptions to telephone and internet services (Van Dijk and Hacker, 2003). Country-specific or benchmarking studies conducted between 2000 and 2010 by multilateral institutions such as the United Nations, OECD, or International Telecommunication Union (ITU) also predominantly emphasized access-level indicators (International Telecommunication Union, 2005; OECD, 2001; UNCTAD, 2006). However, this approach has received a mixed response over the years due to its binary nature and macro-level understanding of the digital divide (Friemel, 2016; Heeks, 2022; Van Dijk, 2017).

To move beyond the binary view of the digital divide, Ragnedda and Muschert (2015) and Rogerson (2020) have highlighted the importance of incorporating socioeconomic and sociodemographic characteristics in investigating digital divides that explore the role of agency and human experience with the technologies. Factors such as income, education levels, motivation to use ICTs, and digital skills assume significant importance in determining individuals' access to and utilization of ICTs, forming the second level of the digital divide (SLDD). This level examines people's ability to navigate and utilize digital resources effectively. The third and most recent level of the digital divide (TLDD) stretches beyond mere access and usage of ICTs. TLDD explores individuals’ capacity to leverage ICTs to effectively attain tangible advantages and meaningful outcomes (Calderon Gomez, 2021; Van Deursen and Van Dijk, 2019). It diverges from FLDD and SLDD by shifting the focus towards understanding how individuals can transform digital resources into concrete benefits, such as improving job opportunities, increasing income, and enhancing the overall quality of life.

Ragnedda (2019) underscores the multi-dimensional and cross-sectoral character of digital inequality, which spans various tiers of societal development. Studying the digital divide at these three levels provides a comprehensive understanding of digital inequality's complexities and dynamics (Ragnedda and Ruiu, 2017; Van Dijk, 2013; Vassilakopoulou and Hustad, 2021). The digital divide framework offers more comprehensive and micro-level insights into different aspects of this gap, including access, skills, and outcomes. It enables a holistic analysis of digital inequality by systematically examining the interplay between socioeconomic factors, demographics, educational attainment, and digital literacy.

Societal factors play a crucial role in addressing and mitigating digital inequality. For instance, income levels determine access to and affordability of ICTs, while demographics such as age and gender contribute to digital access and usage disparities. Social and digital inequalities are also closely intertwined, often exacerbating each other (Ragnedda et al., 2022). This interplay creates a reinforcing cycle where existing social disparities contribute to digital disparities and vice versa. As Alhassan and Adam (2021) point out, a broader range of digital choices and competencies can enable individuals to make more effective use of ICTs, which can impact their income, education levels, and social well-being. Conversely, individuals with limited access to digital skills and resources may face challenges securing employment opportunities requiring advanced digital skills, further perpetuating socioeconomic disparities (Gray et al., 2017). Therefore, gaps in digital literacy and online skills can lead to digital inequality, even with ICT access (Frydenberg and Lorenz, 2020). Other predictors hindering digital equality include cultural barriers, linguistic issues, age and generational gaps, rural/urban divides, and demographic constellations (Mihelj et al., 2019; Rojas et al., 2004; Van Dijk, 2017).

Digital inequality is also closely intertwined with digital inclusion. Disparities and inequities in access to, usage of, and benefits from ICTs can impede full participation in the digital world (Heeks, 2022; Madon et al., 2009; Robinson et al., 2020). Societies and individuals who are unable to embrace ICTs may experience digital isolation. Alam and Imran (2015) argue that individuals who cannot leverage ICTs may also face social exclusion. Access to and use of ICTs, as well as gaining meaningful benefits from them, are critical factors in enabling digital inclusion (Van Deursen and Helsper, 2015). It is important to note that different levels of access to, participation in, and engagement with digital technologies and resources may manifest in different types of digital inclusion, including full inclusion, partial inclusion, or even exclusion from digital technology and resources. The categorization above seems crucial because it provides an opportunity to identify disparities in digital resources and opportunities, thus allowing for a comprehensive understanding of digital disparities within particular contexts (Alhassan and Adam, 2021; Reisdorf and Rhinesmith, 2020).

In this paper, we analyze digital inequalities in Tunisia and Morocco across all three levels of the digital divide. We also explore the drivers of the digital divide in both countries, utilizing geographic and socioeconomic data. Using this data, we examine individual experiences and the extent to which they are included or excluded in various contexts.

Digital divide in the MENA region

Analyzing inequalities in the MENA region entails examining a heterogeneous, culturally diverse, dynamic socioeconomic region. The digital inclusion indicators and their adoption rates in this region are reflected in their population set as a whole. According to World Development Indicators (WDI), the MENA region has a total population of 470 million people, with women accounting for 48% (World Bank, 2022). Over the past decade, mobile phone subscriptions surpassed 100 subscribers (per 100 people) in 2012. However, internet usage was relatively low, with only 25% of the population using the internet in 2010. Since then, the use of the internet has steadily increased, with a 50% increase in the number of internet users over the last ten years, reaching 77.83% of the population in 2020. While mobile phone subscriptions have shown a higher penetration rate throughout the MENA region, it remains unclear how digitalization affects the social and economic lives of people.

Tunisia and Morocco are two countries in the MENA region that seem to demonstrate potential for digital adoption. Morocco, in particular, has achieved an internet usage rate of 84% of the population. Tunisia, on the other hand, has been at the forefront of significant political and social developments, especially in the context of the global debate on digital inclusion. Firstly, it was the World Summit on Information Society (WSIS) that brought Tunisia onto the global stage as it hosted the second in a series of such summits in 2005 (Take, 2012). At the end of WSIS 2003 in Geneva, Tunisia was instrumental in offering the consultative dialogue on prevailing digital gaps to be hosted in Tunis. The United Nations-supported summit in Tunisia advocated bridging the digital divide by improving internet accessibility. The outcome of the WSIS resulted in an annual multi-stakeholder dialogue on governance-related issues of internet access, growth and development (Shahan, 2005). As a result, Tunisia's technology sector gained momentum, and since 2005, it has seen various infrastructure development, broadband and digital capacity-building programmes, among others. Secondly, Tunisia made headlines again in 2011 as the wave of protests against the rising inequalities caused nationwide unrest that spread to several other MENA countries. Even though the internet in Tunisia was hailed as a powerful communication medium for political mobilization, Tunisia has a lower percentage of individuals using the internet compared to Morocco (72% of the population). Nonetheless, relative to internet adoption rates, Tunisia and Morocco have phenomenal mobile cellular subscriptions of over 100 (per 100 people).

Recent studies on Tunisia and Morocco by Taamallah et al., (2019) and ElMassah and Mohieldin (2020) suggest that digital transformation is affecting all sectors of social and economic life, presenting both opportunities and potential threats to the exacerbation of poverty. Similarly, El Alaoui (2022) investigates social justice, education and the effects of COVID-19 in Morocco, and warns of alarming digital divides due to lack of resources, network infrastructure and the required skillsets. On the one hand, these disparities mirror the social and spatial inequities present in both countries. On the other hand, they also reveal the regions that are marginalized in terms of technology access, where limited access is compounded by higher costs and unjust distribution.

Youssef (2021) argues that despite Tunisia being one of the early adopters of digitalization in the MENA region, the benefits of digital adoption have not been evenly distributed among the population. Sharp differences can be observed across all three levels of the digital divide. For example, the rapid spread of mobile phones and smartphones has facilitated access to ICT devices and the internet at the first level. Hence the access gap may be narrowed, but significant disparities remain between different socioeconomic groups, big cities like Tunis and Sousse and the peripheral areas or countryside. Also, internet bandwidth quality and coverage of 4G networks are all better in urban than rural centres. At the second level, despite the availability of numerous digital services in Tunisia, such as e-commerce, e-learning, and e-administration, the distribution of these services remains unequal. The COVID-19 pandemic and its consequent lockdown measures have significantly shifted social and economic activities to the online sphere. This shift has unveiled contrasting realities, highlighting the varying degrees of digital participation among individuals. While some individuals were able to engage in digital activities, others faced barriers. These disparities in digital engagement manifest as differential outcomes resulting from technology utilization (the third level of the digital divide).

Digital inequalities have implications for employment and well-being. As Busemeyer et al. (2022) have shown, structural changes caused by digitalization can lead to labour market polarization and an increase in social inequalities. However, studies on how digital inequalities shape employment-related outcomes in the MENA region, including Morocco and Tunisia, are scarce (Krueger et al., 2018). In Morocco, Tunisia, and the wider MENA region, there is a significant prevalence of the informal economy. On average, approximately 60% of the workforce operates in the informal sector, and two out of every three workers lack coverage under labour laws and social security provisions. Moreover, the region experiences a substantial proportion of unemployed youth (Rizk, 2020). Consequently, the impact of digitalization on the labour market, whether in terms of opportunities or challenges, holds immense significance. To gain a more comprehensive understanding of digital inclusion in both Morocco and Tunisia, it seems necessary to analyze connectivity and usage rates in relation to sociodemographic and socioeconomic factors, including gender, age, education level, employment status, and urban/rural distribution. Such a comparative analysis would prove valuable in developing a micro-level comprehension of individuals' varying degrees of inclusion, as well as instances of digital exclusion, within the contexts of Tunisia and Morocco.

Data and methodology

The primary aim of this paper is to investigate the access and use of ICTs in Tunisia and Morocco across different demographic and socioeconomic groups. We examine the following research questions:

RQ1

How does access to and use of ICTs vary across different demographic and socioeconomic groups in Tunisia and Morocco?

RQ2

What factors contribute to the disparities in digital inclusion in Tunisia and Morocco?

RQ3

How do prevailing digital divides impact employment and social well-being in Tunisia and Morocco?

Data and study sample

This paper relies mainly on Afrobarometer data to examine how prevailing digital divides affect employment and socioeconomic well-being in Morocco and Tunisia (Afrobarometer, 2022). Afrobarometer is an African research network that conducts opinion-based surveys on governance, democracy, the economy, and other key aspects of society. Established in 1999, Afrobarometer now covers over 30 countries in Africa and has generally collected up to eight rounds of cross-sectional data. We utilize four data points from survey rounds 5 to 8 (2013–2022) because these datasets are available for both Morocco and Tunisia and contain information on crucial ICT indicators that enable us to examine digital inclusion over time in these two North African countries. A vital strength of the Afrobarometer data is that it is collected at the micro-level and yet nationally representative to a large extent. We complement this with macro-level data on key ICT indicators from the International Telecommunication Union's (ITU) digital development indicators from 2000 to 2020 (see Figure 2(b)) that enables us to examine the aggregate trend of digital development in the two countries vis-à-vis the Afrobarometer data. The Afrobarometer data used covers a total of 9595 respondents from both countries over the specified period (see descriptive statistics in Table A1). The survey data includes information on ownership and use of key ICT devices such as mobile phones, computers, radios, and television, as well as the frequency of internet use. It also includes data on the sociodemographic characteristics of the respondents along with measures of socioeconomic well-being, such as lack of food, water, medicine, fuel and income, aggregated as lived poverty.

Computing digital inclusion index

Using the survey data on access to mobile phones, computers and the internet, we construct an indicator to measure the digital inclusiveness or otherwise of the two countries. We exclude ownership and use of radio and television sets from the index because the advent of the internet and smartphone technologies make it possible to access radio and television services without the need to own dedicated devices for these purposes.

Therefore, using the ownership of radio and TV sets to measure people’s digital capacity may not provide an accurate reflection of the phenomenon of digitalization. In computing the digital inclusion index, we employ a simple method of equal weighting and aggregation. That is, we sum up the individual item scores for the variables measuring the ownership and use of mobile phones, computers, and the use of internet. The ‘unweighted’ sum implies that, in general, all the variables contribute equal information to the score.

Each of the variables in the scale measures a binary outcome of ownership, use, or otherwise of the items in question. Consequently, the resulting scale ranges from 0 to 1. We then decompose this into three categories: a score of 0 signifies total exclusiveness, 1 represents ‘full’ inclusiveness and a score between 0 and 1 represents partial inclusiveness. This means that digitally excluded respondents own neither a mobile phone nor a computer nor have they ever used these or the internet. On the other hand, those who are digitally included use the internet, and they also own and use a mobile phone and a computer. Ownership or usage of any of these three items, apart from the mentioned criteria, implies only partial inclusion. We utilize Cronbach’s alpha (α) coefficient to measure the internal consistency of the resulting scale, which has a mean of 1.3 and a standard deviation of 0.6.

We use the digital inclusion score to first graphically examine the dynamics of digital inequalities in both countries by disaggregating the score based on gender, rural-urban locality, age groupings, and education. Additionally, we examine the predictors of digital exclusion, partial inclusion and full inclusion using binary logistic regressions. We use pooled logistic regression models to examine the predictors of digital inclusion and how it affects employment and well-being. In each of these models, we pool the data from the four waves (2013, 2016, 2018, 2022) and control for a set of covariates, as well as year and country-fixed effects. More formally, we fit a pooled binary logit model as follows.

\Pr (Y_{i, t} > 0) = β_{0} + β_{1} X_{i, t} + γ_{c} + δ_{w} + + \forall_{c} + ϵ_{i, t}

where

Y_{i, t}

is the indicator of an individual

i^{'} s

outcome (e.g. digitally included or otherwise) measured at time

t

β_{0}

is the intercept,

X

is a matrix of the individuals' characteristics and

β_{1}

represents the coefficients,

γ_{c}

and

δ_{w}

represent a vector of country and time dummies respectively and

\forall_{c}

is a vector of additional controls such as interviewer effects. The country dummies enable us to control for time-invariant country-level characteristics correlated with macro-level conditions and the outcome variable, whereas the time dummies also help to control for year-specific effects associated with both the macroenvironment and the outcome. The error is captured by the last term,

ϵ_{i, t}

Results and discussion

Complete and partial digital inclusion in Morocco and Tunisia

As technology continues to evolve, Morocco and Tunisia have experienced notable progress in digital inclusion, although challenges and variations persist. Overall, digital inclusion has improved in both Morocco and Tunisia since 2013. The share of the population that is completely digitally excluded declined from 35% in 2013 to 5.1% in 2022, as shown in Figure 1. The decline is, however, more pronounced in Morocco (18% in 2013 to 1.4% in 2022) than in Tunisia (17% to 3.6% in 2022). Similarly, the share of partially digitally included people increased from 23% to 31% for both countries over the same period. However, complete digital inclusion has stalled overall since 2019, remaining at 19% in both 2019 and 2022.

Figure 1.

Digital inclusion in Morocco and Tunisia (by year).

Figure 2(a) and (b) show the trend of ICT access and use, shedding further light on digital inclusion in Morocco and Tunisia since 2013. Figure 2(a) shows that mobile phone coverage is approaching 100% of the population in both countries. However, a significant share remains offline. Although internet use has improved in both countries, there is still more room for improvement, particularly in Tunisia, where almost 40% of people remain offline. Interestingly, unlike mobile phone ownership, ownership of other digital devices, such as TV and radio sets as well as computers, has generally been on a declining trend since 2013. A potential reason for the declining trend in TV and radio set ownership could be attributed to the increasing functionality of mobile phone devices and the use of streaming services, which makes it possible to access radio and television content without necessarily requiring devices dedicated to them. This also partly explains the downward trend in computer ownership, alongside the possibility that people might think about desktop computers when asked about computers. The trend could perhaps have been different if specific references were made to laptops and notebooks, for instance, instead of computers.

Figure 2.

(a) ICT access and use based on Afrobarometer data and (b) ICT access and use based on ITU data.

Figure 2(b) uses data from the ITU to examine the trend of ICT access and use since 2000, juxtaposing it with the findings using the Afrobarometer data (Figure 2(a)). From Figure 2(b), mobile cellular subscriptions in both countries have surpassed 100 for every 100 people, suggesting that, on average, people own more than one mobile phone in these countries. Internet use has also been on an upward trajectory since 2000. Both Figure 2(a) and (b) suggest that internet use is higher in Morocco than in Tunisia. While secure internet servers per million people have also been on an upward trend since 2000, the same cannot be said for fixed-telephone subscription, like TV, radio and (desktop) computers in Figure 2(a). Again, both Figure 2(a) and (b) suggest that, while digital inclusion may have generally increased in the last decade, the type of digital technology is also important to consider.

Digital inclusion and demographic and socioeconomic characteristics

The analysis of digital inclusion and its relationship with demographic and socioeconomic characteristics reveals distinct patterns among different population segments, as indicated by Table A2 in the appendix. Table A2 suggests that the three distinct segments of the population – the totally excluded, partially included, and the completely included – display statistically significant differences on average. Complementing this analysis, Figures (A1)–(A4) in the appendix provide descriptive evidence elucidating the relationship between digital inclusion and the demographic and socioeconomic characteristics of these groups. Notably, the results indicate that the digitally excluded group is characterized by lower levels of education, relatively older age, predominantly female gender, rural residence, and unemployment.

To delve deeper into the influence of education on digital inclusion, Figure 3 focuses on Morocco and Tunisia, examining the distribution by educational attainment. The figure demonstrates a substantial dominance of individuals without formal schooling within the digitally excluded category, accounting for 59% of this group. However, those with no formal education comprise only 1.5% of the digitally included segment. Conversely, individuals with post-secondary education, despite constituting a mere 2% of the excluded group, represent the majority, or 54%, of the included category. Furthermore, age plays a significant role in digital inclusion. Among the digitally included population, 42% belong to the age group of 18–25, illustrating the higher digital inclusion among younger adults. In contrast, the combined share of digitally included individuals aged 46 or older amounts to 11.79% (7.5% + 3.4% + 0.89%), suggesting that ‘seniors’ are less likely to be digitally included compared to their younger counterparts (see Figure A1).

Figure 3.

Digital inclusion in Morocco and Tunisia (by education).

Geographical location also emerges as a crucial factor influencing digital inclusion. Among the digitally excluded, 60% reside in rural areas, underscoring the rural-urban divide in terms of access and adoption of digital technologies (see Figure A2). In contrast, among the digitally included, 78% are based in urban areas, indicating a higher level of digital inclusion in urban settings. Gender dynamics are another important consideration. Approximately 73% of the digitally excluded population comprises females, with a significant proportion (42%) concentrated in Tunisia (see Figure A3). This finding also highlights the need to address gender disparities in digital inclusion efforts. Moreover, the employment status of individuals matters for their digital inclusion. Strikingly, nearly all (86%) of the digitally excluded individuals are unemployed, emphasizing the pivotal role of economic activity in fostering digital inclusion (see Figure A4).

Determinants of digital inclusion in Morocco and Tunisia

The descriptive evidence presented in the previous section highlights the prevailing patterns of digital inclusion, which favour relatively younger individuals, those with higher education, predominantly male, urban dwellers, and those who are employed. This section delves into the statistical determinants of digital inclusion and exclusion as evidenced by Table 1, which presents logistic regressions analyzing exclusion, partial and complete inclusion in relation to a set of covariates. Models (1), (4), and (7) show the determinants while controlling for time and country-fixed effects. The results reveal distinct patterns for gender and rural residence. Females are more likely to be completely digitally excluded (Model 1) or partially included (Model 4) compared to males, but less likely to achieve full inclusion (Model 7). Similar dynamics are observed for individuals residing in rural areas compared to their urban counterparts. Furthermore, individuals with at least primary, secondary, or tertiary education exhibit a decreased likelihood of exclusion and an increased likelihood of inclusion in comparison to those without formal education.

Table 1.

Determinants of digital inclusion in Morocco and Tunisia.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Variables	Excluded	Excluded	Excluded	Partially Included	Partially Included	Partially Included	Included	Included	Included
Female	0.689***	0.692***	0.624***	0.136**	0.138**	0.099*	−0.366***	−0.369***	−0.303***
Rural	0.592***	0.599***	0.589***	0.325***	0.326***	0.350***	−0.660***	−0.666***	−0.711***
Age group
26-35	−0.130	−0.127	−0.075	0.666***	0.667***	0.603***	−0.760***	−0.761***	−0.723***
36-45	0.718***	0.717***	0.730***	0.740***	0.747***	0.714***	−1.103***	−1.108***	−1.110***
46-55	0.776***	0.793***	0.795***	1.111***	1.111***	1.045***	−1.481***	−1.483***	−1.456***
56-65	1.045***	1.062***	1.029***	1.221***	1.223***	1.207***	−1.876***	−1.879***	−1.901***
Over 65	1.578***	1.586***	1.489***	1.236***	1.246***	1.231***	−2.635***	-2.676***	-2.744***
Education group
Primary	−0.947***	−0.950***	−0.953***	0.398***	0.403***	0.378***	1.256***	1.250***	1.270***
Secondary	−2.002***	−1.991***	−2.051***	−0.660***	-0.653***	-0.583***	2.610***	2.600***	2.530***
Post-secondary	−2.924***	−2.910***	−2.863***	−2.149***	−2.140***	−1.820***	4.081***	4.070***	3.715***
Economic status
Employed	−0.266**	−0.262**		0.066	0.065		0.118	0.116
Ever gone without income	0.222***	0.220***		0.078***	0.076***		−0.227***	−0.223***
Occupation^a
Never had a job			0.830			0.912***			−1.261***
Student			0.924			0.397**			−0.794***
Housewife/homemaker			0.775			1.042***			−1.413***
Agriculture/fishing/forestry			0.758			0.934***			−1.200***
Trader/hawker/vendor			−0.090			0.985***			−1.084***
Retail/Shop			0.133			0.817***			−0.934***
Unskilled manual worker			0.629			1.192***			−1.456***
Artisan or skilled manual worker			0.139			1.093***			−1.226***
Clerical or secretarial			0.364			0.695***			−0.820***
Security services			0.603			0.563**			−0.626**
Mid-level professional			-0.154			0.522***			−0.505***
Other job			0.259			0.795***			−0.784***
Country & year effects
Tunisia	0.402***	0.415***	0.465***	0.106**	0.115**	0.113**	−0.306***	−0.325***	−0.307***
2016	−0.608***	−0.623***	−0.444***	−0.904***	−0.904***	−0.898***	1.303***	1.306***	1.229***
2019	−0.848***	-0.856***	-0.691***	0.405***	0.407***	0.430***	-0.269***	-0.274***	-0.351***
2022	−2.678***	−2.668***	−2.471***	0.704***	0.713***	0.743***	−0.293***	−0.313***	−0.408***
Additional controls
Respondent at ease		0.094	0.071		0.055	0.056		−0.111	−0.117*
Respondent honest		0.036	0.072		0.010	0.010		−0.024	−0.041
Observations	9384	9369	9362	9404	9389	9487	9404	9389	9487

Note: Robust standard errors. *** p<.01, ** p<.05, * p<.1.

^aBase category: Supervisor/Foreman/Senior/Upper-level professional.

Likewise, employment status plays a role in digital inclusion, as the employed are less likely to experience digital exclusion and more likely to be fully included. Notably, the statistically significant positive coefficient of ‘Without cash income’ in Model (1) and its opposite in Model (7) suggest that digital exclusion or inclusion is also influenced by economic resources. In other words, cash income correlates with levels of inclusion and exclusion. The longer an individual goes without cash income, the higher the probability of digital exclusion, and vice versa. Even after controlling for additional characteristics, such as the respondent's honesty and ease (Models 2, 5, and 8), the findings in Models (1), (4), and (7) remain robust.

Furthermore, specific occupational activities are explored through Models (3), (6), and (9). Interestingly, all occupational activities demonstrate statistically significant associations with partial and complete digital inclusion but not total exclusion. In particular, when compared to upper-level professionals, supervisors, and seniors, those in medium to low-level professions and individuals engaged in elementary occupations (e.g., security services, clerical or secretarial services, skilled and unskilled manual work, hawking, retail activities, farming, fishing, and forestry) exhibit a lower likelihood of complete digital inclusion and a higher likelihood of partial digital inclusion. These results indicate that the respondent's skill level positively correlates with digital inclusion, suggesting a higher likelihood of inclusion among individuals with an advanced skill level.

The effect of digital inclusion on employment and socioeconomic well-being in Morocco and Tunisia

This section investigates the impact of digital inclusion on employment and socioeconomic well-being in Morocco and Tunisia. Table 2 presents Models 1-3, which examine employment status as the binary outcome, indicating whether the respondent is employed or not. The main predictors of interest are digital inclusion, specifically partial and complete inclusion, while controlling for various respondent characteristics, country-fixed effects, and year-fixed effects. It is worth noting that all nine models consider the two categories of digital inclusion, as shown in the Table 2: partial and complete inclusion, with complete exclusion as the reference category. The findings from these models indicate that individuals who are partially or fully digitally included are more likely to be employed compared to those who are completely excluded. Specifically, both partial and complete digital inclusion have a positive effect on employment status. This suggests that digital inclusion, in its partial and complete forms, plays a significant role in facilitating employment opportunities.

Table 2.

The effect of digital inclusion on employment and socioeconomic well-being.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Variables	Employed	Employed	Employed	Without food	Without water	Without medicine	Without fuel	Without income	Lived poverty
Partially digitally included	0.818***	0.390***	0.384***	−0.378***	−0.347***	−0.336***	−0.318***	−0.416***	−0.334***
Digitally included	1.066***	0.428***	0.420***	−0.624***	−0.438***	−0.784***	−0.441***	−0.871***	−0.717***
Controls
Female		−2.428***	−2.427***	0.099	−0.108*	−0.074	−0.008	−0.099*	−0.083
Rural		−0.197***	−0.199***	0.234***	0.830***	0.544***	0.541***	0.435***	0.613***
26-35		1.682***	1.680***	-0.010	0.067	0.073	0.026	−0.034	−0.065
36-45		1.833***	1.828***	−0.105	-0.013	0.078	−0.069	−0.104	−0.112
46-55		1.774***	1.773***	−0.199*	−0.114	−0.052	−0.134	−0.260***	−0.200**
56-65		1.744***	1.737***	−0.153	-0.135	−0.060	−0.213*	−0.609***	−0.386***
Over 65		1.957***	1.964***	−0.482***	−0.333**	−0.164	−0.471***	−0.920***	−0.519***
Primary		0.730***	0.728***	−0.166**	−0.228***	−0.222***	−0.232***	−0.201***	−0.271***
Secondary		0.689***	0.690***	−0.491***	-0.325***	−0.412***	−0.413***	−0.361***	−0.381***
Post-secondary		1.263***	1.260***	−0.777***	−0.518***	−0.550***	−0.668***	−0.469***	−0.636***
Never had a job				0.922***	0.560***	1.112***	0.751***	1.404***	0.956***
Student				0.567**	0.387**	0.894***	0.473**	1.084***	0.646***
Housewife/homemaker				0.767***	0.507***	0.894***	0.543***	1.106***	0.696***
Agriculture/fishing/forestry				0.641***	0.550***	0.842***	0.474**	1.277***	0.780***
Trader/hawker/vendor				0.852***	0.308	1.121***	0.423*	1.390***	0.971***
Retail/Shop				0.155	0.083	0.417**	0.231	0.810***	0.364**
Unskilled manual worker				1.332***	0.653***	1.306***	0.942***	1.723***	1.229***
Artisan or skilled manual worker				0.731***	0.417**	0.756***	0.528***	1.154***	0.824***
Clerical or secretarial				0.659**	0.410*	0.721***	0.632***	0.962***	0.666***
Security services				0.301	0.354	0.493**	0.591**	0.485**	0.292
Mid-level professional				0.178	0.265	0.319*	0.329*	0.417**	0.317**
Other job				0.362	0.280	0.464*	0.224	0.797***	0.395*
Tunisia	0.533***	0.450***	0.446***	0.145**	0.523***	−0.500***	0.407***	−0.378***	−0.003
2016	−1.497***	−2.527***	−2.525***	0.128	0.098	0.172**	−0.017	0.494***	0.421***
2019	−1.438***	−2.464***	−2.463***	−0.272***	0.378***	0.020	−0.398***	0.449***	0.447***
2022	−1.442***	−2.531***	−2.536***	0.069	0.431***	0.147*	−0.392***	0.623***	0.593***
Additional controls	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes
Observations	9485			9474	9475	9474	9472	9433	9485

Note: Robust standard errors in parentheses, *** p<.01, ** p<.05, * p<.1, Base category of occupational variables: Supervisor/Foreman/Senior/Upper-level professional.

Furthermore, the analysis reveals that digital inclusion and exclusion are also predictive of socioeconomic well-being. Models 4-8 examine different measures of well-being while incorporating the digital inclusion indicators and controlling for various covariates. Model 9, in particular, presents an aggregate outcome that encompasses multiple indicators of well-being, including the lack of access to food, water, medicine, fuel, and income. The results consistently demonstrate that both partial and complete digital inclusion contribute to a reduced likelihood of experiencing shortages in essential resources and ultimately living in poverty, compared to complete digital exclusion. This implies that digital inclusion, whether partial or complete, serves as a statistically significant predictor of lived or experienced poverty.

Overall, the outcomes presented in Table 2 establish a strong positive association between digital inclusion and both employment status and socioeconomic well-being. Individuals who are partially or completely digitally included exhibit higher odds of being employed and lower odds of facing deprivation in terms of food, water, medicine, fuel, and income. Consequently, digital inclusion plays a critical role in improving employment opportunities and overall socioeconomic well-being. These findings underscore the importance for policymakers in Morocco and Tunisia, as well as the wider MENA region, to prioritize digital inclusion as a key component of their strategies to alleviate unemployment, poverty, and socioeconomic disparities. This can be achieved through initiatives such as expanding digital infrastructure, ensuring increased access to affordable internet services, and providing comprehensive digital skills training programs.

Addressing preexisting inequalities in digital age

The analysis of Afrobarometer data reveals notable progress in digital inclusion indicators in both Morocco and Tunisia from 2013 to 2022. Following the civil uprisings of 2011, Morocco witnessed a more substantial increase in digital adoption trends compared to Tunisia. The data highlights that neither country has achieved complete digital inclusion over the past decade, as more than 80% of their populations remain partially or entirely excluded. This prevailing trend toward partial digital inclusion aligns with our earlier assertion regarding the interconnectedness of ICTs with a nation's socioeconomic and cultural fabric.

El Alaoui (2022) in her comprehensive study on education in Morocco identifies numerous factors contributing to the partial nature of digital inclusion. She emphasizes that unequal access to education plays a crucial role in perpetuating social inequalities, which subsequently manifest as disparities in digital participation. Our analysis confirms this relationship, as individuals with higher levels of education are more likely to be digitally included. This suggests that improving access to quality education for all could help bridge the digital divide in Morocco and Tunisia. In addition to education, our results further highlight several determinants of digital inclusion in Morocco and Tunisia, including gender, location, age, economic status, and occupation. These findings align with existing literature that has explored the reasons behind the partial nature of digital inclusion in these countries. For instance, Yerkes and Ben Yahmed (2019) argue that gaps in basic infrastructure availability have directly impacted digital transformation not only in Tunisia but also in Morocco. Our study corroborates this argument, revealing that rural residents, who are more likely to face infrastructural challenges, are also more likely to be digitally excluded. This highlights the need for infrastructure investments, particularly in rural areas, to foster digital inclusion.

The role of economic status and income disparities in shaping digital inclusion is also evident in our findings. Nasri (2022) and Jouini et al. (2018) discuss the level of poverty in Tunisia and the existence of subsidies and cash distribution programs, which they argue contribute to greater inequalities. They note that these financial instruments often benefit non-poor populations more, exacerbating existing inequalities. Similarly, Clementi et al. (2019) report that in 2013, approximately 50% of the Moroccan lower middle-class population considered themselves either poor or very poor compared to their peers. Our analysis confirms the significance of economic status, with individuals experiencing income disparities being more likely to be digitally excluded.

Furthermore, our study indicates that occupational skills level is a relevant factor in digital inclusion, and vice versa. Our results show that not only are the unemployed less likely to be digitally included, but low-level workers and mid-level professionals are also less likely to be digitally included compared to upper-level professionals. This suggests that high-skilled workers are better positioned to benefit from digitalization (Krutova et al., 2021). The observed negative relationship between low-skilled employment and digital inclusion is concerning, as it exacerbates social and economic inequality. For instance, high-skilled workers may earn higher wages than their counterparts, widening the wealth inequality gap and leading to job polarization (Acemoglu and Autor, 2011). Undoubtedly, the educational systems in both Morocco and Tunisia will need to play a crucial role in preparing the labour force for the future of work. Nevertheless, as Frydenberg and Lorenz (2020) indicate, digital literacy and lifelong learning are essential.

Moreover, our results demonstrate that digital inclusion, whether partial or complete, promotes overall well-being. This finding supports the claim that digitalization contributes to increased welfare (Youssef, 2021). This presents a reason for optimism about the potential benefits of digitalization; however, without significant improvements in digital equality, the most substantial advantages will accrue to those who are already privileged or in a better position to benefit from digitalization, thereby widening and deepening existing inequalities.

Conclusion

Barely a decade ago, a series of civil uprisings swept through the Middle East and North Africa region, leading to various regime changes. Central to these uprisings was the rapid diffusion of information communication technologies (ICTs) that facilitated information sharing and social mobilization. Today, many of the underlying factors that fuelled these protests, including economic hardships, income disparities, structural inequalities, and high rates of youth unemployment, persist and may have even worsened in the aftermath of the COVID-19 pandemic and its economic consequences.

In this study, we examined digital inclusion and its impact on social and economic well-being. Using Afrobarometer data from 2013 to 2022, supplemented by ITU data on key ICT indicators from 2000 to 2020, we provided insights into the dynamics of digital inequalities in two MENA countries affected by the civil uprisings: Morocco and Tunisia. We examined the predictors of digital exclusion, partial inclusion, and full inclusion, focussing on indicators of access to and usage of digital technologies such as mobile phones, computers, and the internet. This analysis encompasses the first and second levels of the digital divide spectrum. We further investigated how digital inclusion affects employment and well-being. We found that about two-thirds of the population in both countries is only partially included. Given that our definition of inclusion covers only the first two levels of the divide, an even greater share will be excluded when the scope is expanded. We also found evidence that digitalization mirrors preexisting demographic and socioeconomic inequalities. For instance, the digital inclusion statistics show a favouring of males over females, the young over the aged, urban over rural dwellers, the employed over the unemployed and high-skilled jobs over low-skilled jobs. Turning to well-being, we found that digital inclusion statistically significantly predicts exposure to lived poverty.

Our findings have significant implications. Firstly, they suggest that digital inclusion levels are low in both countries, particularly when we expand the scope of measuring digitalization. Apart from fundamental infrastructural challenges, continuous investments in digital infrastructure are imperative for Morocco and Tunisia's socioeconomic development. Secondly, our study affirms the notion in the technology literature in developing country contexts that digitalization mirrors and, perhaps, even exacerbates preexisting inequalities. Thirdly, we have demonstrated a positive association between digital inclusion and well-being. Additionally, the significant positive correlation between high-skilled jobs and inclusion implies that the theory of skilled-biased technological change holds true for both countries. In summary, efforts to facilitate digital inclusion in both countries, and possibly in the MENA region as a whole, such as providing ICT skills or literacy training and enhancing ICT infrastructure, could have a positive impact on social life, economic well-being, and overall progress. However, inclusion efforts or policy initiatives need to be carefully targeted to avoid further exacerbating preexisting inequalities and economic hardships, which are already significant challenges in the region.

Overall, our key findings align with and build upon the insights provided by the current literature. Addressing the digital divide in Morocco and Tunisia will require targeted policies and interventions that consider the complex interplay of factors influencing digital inclusion. Strategies should focus on improving access to quality education that integrates ICT skills literacy at an early age, investing in both traditionla (e.g., electricity) and ICT infrastructure, and developing more equitable economic policies to help bridge the gap between the digitally included and excluded.

Supplemental Material

Supplemental Material - Digital inequalities in North Africa: Examining employment and socioeconomic well-being in Morocco and Tunisia

Supplemental Material for Digital inequalities in North Africa: Examining employment and socioeconomic well-being in Morocco and Tunisia by Hasnain Bokhari and Evans T Awuni in Convergence

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Hasnain Bokhari

Evans T Awuni

Supplementary Material

Supplementary Material for this article is available online.

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