# Connecting the Unconnected: Facebook Access and Female Political Representation in Sub-Saharan Africa

Sophie Hatte, Jordan Loper, Thomas Taylor

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Sophie Hatte, Jordan Loper, Thomas Taylor. Connecting the Unconnected: Facebook Access and Female Political Representation in Sub-Saharan Africa. 2025. hal-05056150v2

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Centre d'Études
et de Recherches sur le
Développement International

WORKING PAPER

No. 2025/1

Connecting the Unconnected:
Facebook Access and Female
Political Representation in
Sub-Saharan Africa

Sophie Hatte

Jordan Loper

Thomas Taylor
## Cite this working paper

Hatte, S., Loper, J., Taylor, T. "Connecting the Unconnected: Facebook Access and Female Political Representation in Sub-Saharan Africa", CERDI Working Papers, 2025/1, CERDI.

## The authors

### Sophie Hatte

Associate Professor of Economics,
ENS de Lyon, Center for Economic
Research on Governance, Inequality
and Conflict (CERGIC), 15 Parvis René
Descartes, 69007 Lyon, France.

Email: [sophie.hatte@ens-lyon.fr](mailto:sophie.hatte@ens-lyon.fr)

### Jordan Loper

Associate Professor of Economics,
Université Clermont Auvergne, CNRS,
IRD, CERDI, F-63000, Clermont-
Ferrand.

Email: [jordan.loper@uca.fr](mailto:jordan.loper@uca.fr)

### Thomas Taylor

Researcher,
European University Institute, Via dei
Roccettini 9, I-50014, San Domenico di
Fiesole (FI) Florence, Italy.

Email: [thomas.taylor@eui.eu](mailto:thomas.taylor@eui.eu)

## Acknowledgments

We thank Francesco Amodio, Julia Cagé, Joël Cariolle, Mathieu Couttenier, Thomas F. Crossley, Yarine Fawaz, Leopoldo Fergusson, Claudio Ferraz, Horacio Larreguy, Thomas Le Barbanchon, Benjamin Marx, Toussaint Nothias, Maria Petrova, Charles de Pierpont, Vincent Pons, Bryce Reeder, Eric Roca Fernandez, Felix Schaff, Carlo Schwarz, Alessandro Tarozzi, Mathias Thoenig, Morgan Ubeda, Leonard Wantchekon, Ekaterina Zhuravskaya, and participants of seminars at the University of Bath, Bocconi University, CERDI, CREST, DIAL, ENS de Lyon, European University Institute, King's College, and UCLouvain; workshop participants of the Arne Ryde Workshop on Culture, Institutions, and Development at Lund University, the CEPR Media Plurality webinar, the COSME workshop in Madrid, the ENS de Lyon workshop on Political Leaders, and the Online Political Influence and Misinformation FREE Network Workshop in Stockholm; as well as conference participants at ASREC, the Digital Economics Conference at TSE, ICDE and LAGV for helpful comments. We thank Clément Gorin and Etienne Madinier for invaluable help in creating Free Basics coverage data and Marion Dury, Mariette Francon, Lucile Laugerette, and Mickael Sixdenier for excellent research assistance.

<figure><img src="image_3.png" /></figure>

This work was supported by the French National Research Agency of the French Government through the following programs and grants: ANR JCJC grant number ANR-22-CE26-0006, the ANR grant number ANR-17-EURE-0020; the IDEXLYON, University of Lyon Programme Investissements d'Avenir (grant number ANR-16-IDEX-0005), Université Clermont Auvergne Programme "France 2030" (grant number ANR-16-IDEX-0001), as well as the grant number ANR-20-COV4-0005-01, CovidCo.

CERDI Working Papers are available online at: [https://tinyurl.com/2xwfzw8s](https://tinyurl.com/2xwfzw8s)

Director of Publication: Simone Bertoli

Editor: Catherine Araujo Bonjean

Publisher: Marie Dussol

ISSN: 2114 - 7957

## Disclaimer

Working papers are not subject to peer review; they constitute research in progress. Responsibility for the contents and opinions expressed in the working papers rests solely with the authors. Comments and suggestions are welcome and should be addressed to the authors.
Connecting the Unconnected: Facebook Access and Female
Political Representation in Sub-Saharan Africa *

Sophie Hatte† Jordan Loper‡ Thomas Taylor§

This version: September 5, 2025

**Abstract.** Can social media help promote female access to political positions? Using data from 8,814 parliamentary races across 17 sub-Saharan African countries, we study this question in a context of persistent underrepresentation of women and rising Facebook penetration. Leveraging the staggered introduction of Facebook's Free Basics, i.e., free access to the platform via partner mobile operators, we find that its introduction significantly increases the election of female candidates, though only after one electoral cycle. The delayed effect reflects a gradual process: initially, male candidates endorsed by parliamentary parties experience declining electoral margins, followed in subsequent elections by greater endorsement and electoral success of female challengers. These dynamics suggest strategic learning by political parties, particularly in supporting first-time female candidates. To uncover mechanisms, we analyze how Free Basics access and social media usage influences political attitudes and social norms. We find that access to Free Basics fosters more egalitarian gender norms, especially regarding women in politics, reflected more strongly in online than offline environments. These changes are not simply a byproduct of broader political dissatisfaction or a generalized demand for political renewal. Importantly, the effect is strongest in contexts where access to Facebook connects users to more diverse online networks with greater female leadership representation.

**JEL codes:** D72, J16, L86, L96

**Keywords:** Social media, Mobile internet, Gender norms, Elections, Candidate selection

<footnote>*We thank Francesco Amodio, Julia Cagé, Joël Cariolle, Mathieu Couttenier, Thomas F. Crossley, Yarine Fawaz, Leopoldo Fergusson, Claudio Ferraz, Victor Gay, Gunes Gokmen, Horacio Larreguy, Thomas Le Barbanchon, Benjamin Marx, Kristin Michelitch, Toussaint Nothias, Maria Petrova, Charles de Pierpont, Francisco Pino, Vincent Pons, Bryce Reeder, Éric Roca Fernández, Felix Schaff, Carlo Schwarz, Francesco Sobbrio, Alessandro Tarozzi, Andrea Tesei, Mathias Thoenig, Morgan Ubeda, Karine Van Der Straeten, Leonard Wantchekon, Ekaterina Zhuravskaya, and participants of seminars at the University of Bath, Bocconi University, CERDI, CREST, DIAL, ENS de Lyon, European University Institute, King's College, TSE, and UCLouvain; workshop participants of the Arne Ryde Workshop on Culture, Institutions, and Development at Lund University, the CEPR Media Plurality webinar, the COSME workshop in Madrid, the ENS de Lyon workshop on Political Leaders, the IAST-Sciences Po-Oxford Political Economy workshop at Nuffield College, and the Online Political Influence and Misinformation FREE Network Workshop in Stockholm; as well as conference participants at ASREC, the Digital Economics Conference at TSE, ICDE and LAGV for helpful comments. We thank Clément Gorin and Etienne Madinier for invaluable help in creating Free Basics coverage data and Marion Dury, Mariette Francon, Lucile Laugerette, and Mickael Sixdenier for excellent research assistance. This work was supported by the French National Research Agency of the French Government through the following programs and grants: ANR JCJC grant number ANR-22-CE26-0006; IDEXLYON, University of Lyon *Programme Investissements d'Avenir* (grant number ANR-16-IDEX-0005); Université Clermont Auvergne *Programme "France 2030"* (grant number ANR-16-IDEX-0001); as well as the grant number ANR-20-COV4-0005-01, CovidCo.</footnote>

<footnote>†Sophie Hatte: Ecole Normale Superieure de Lyon, CERGIC, CNRS and CEPR.</footnote>

<footnote>‡Jordan Loper: Université Clermont Auvergne, CNRS, IRD, CERDI, F-63000, Clermont-Ferrand.</footnote>

<footnote>§Thomas Taylor: European University Institute, Florence.</footnote>
# 1 Introduction

Can social media facilitate female access to political leadership in developing democracies? Over the past 20 years, the rapid expansion of mobile internet has deeply reshaped communication technologies and fueled the rise of social media,<fnref n="1" /> generating profound societal changes. By transforming the way people obtain information and interact, it has likely contributed to ongoing shifts in gender norms (Washington and Marcus, 2022). At the same time, this structural transformation has considerably lowered the cost of political mobilization and communication (Manacorda and Tesei, 2020), affecting both established actors and traditionally underrepresented groups (Guriev et al., 2021; Donati, 2023). Together, these changes have created significant potential for women's empowerment in the political sphere and for fostering more gender-equal representation.

Exposure to online content, in particular, may influence attitudes toward gender roles, leadership, and political participation. By connecting users to more diverse and progressive perspectives, social media platforms could provide an alternative to traditional information environments that often reinforce existing inequalities, thereby serving as an incremental step toward greater female political representation. Whether such effects occur in practice, however, is an open empirical question – one that is likely to be most consequential in contexts where women's political underrepresentation is most striking. Despite well-documented social and economic benefits of gender parity in leadership (Beaman et al., 2012), women comprised only 23% of government ministers and 26% of parliamentarians globally in 2022 (IPU, 2023). The disparity is even more pronounced in many fragile democracies, where women often account for less than 10% of members of parliament.

The extent to which social media can play this role, however, critically depends on access to and use of mobile internet, which remain highly unequal across regions. In sub-Saharan Africa, digital participation is particularly low, with only 22% of the population using mobile internet in 2021.<fnref n="2" /> While access has improved–coverage gaps have narrowed significantly since the mid-2010s–usage remains constrained by the high cost of mobile broadband. In response to these barriers, initiatives like Facebook’s Free Basics have sought to expand digital inclusion through zero-rating policies. Launched in many low-income countries, including several in sub-Saharan Africa, Free Basics allows mobile users with at least 2G coverage to access a limited, Facebook-centered version of the internet without incurring data charges, in partnership with local mobile operators (Nothias, 2020).<fnref n="3" />

These developments raise a critical question: Has the expansion of accessible internet and the widespread adoption of social media disrupted deeply rooted gender inequalities in political representation? In this paper, we investigate the impact of the connectivity shock resulting from Facebook's Free Basics initiative on the electoral prospects of female candidates in national parliamentary elections in sub-Saharan African countries with similar electoral rules (first-past-the-post). Specifically, we examine whether women are more likely to be elected and whether they receive higher vote shares following the introduction of Free Basics, and explore the role

<footnote n="1">¹In 2023, 4.7 billion people worldwide had a social media account, representing close to 90% of active internet users (We are Social, 2023).</footnote>

<footnote n="2">²By comparison, the global average for mobile internet usage was 55% in the same year (GSMA, 2022).</footnote>

<footnote n="3">³Other examples of zero-rating policies include Wikipedia Zero, which enabled users to browse Wikipedia without data costs, and Google Free Zone, a short-lived initiative offering free access to certain Google services before its discontinuation in 2014.</footnote>
played by candidate selection and party endorsement. To address this, we construct a novel
constituency-election-level panel dataset comprising 8,814 parliamentary races across 17 sub-
Saharan countries from 2001 to 2022. This dataset includes detailed information on the gender,
political affiliation, and candidacy history of parliamentary candidates, as well as geocoded in-
formation on the boundaries of constituencies. We measure constituencies' yearly Free Basics
population coverage by gathering data on individual agreements between Facebook and mobile
operators and integrating information on cellular antennas with population counts at a 1 km ×
1 km resolution.

To identify the impact of Facebook's Free Basics availability on the election of female members of parliament (MPs) in sub-Saharan Africa, we leverage its staggered rollout using a modern difference-in-differences framework. Specifically, we exploit two sources of variation: temporal variation in the timing of partnerships between Free Basics and individual mobile operators, and spatial variation in the local coverage of each partner operator. This approach enables us to control for both time-invariant constituency characteristics and time-varying factors at national levels. Crucially, the national or transnational nature of Free Basics partnerships provides strong support to the identifying assumption that the timing of Free Basics availability is uncorrelated with local political drivers. As such, our strategy provides credible causal estimates of the impact of the connectivity shock on female political representation.

Before investigating the effect of Facebook’s Free Basics on the election of female MPs, we first assess its impact on online behavior, particularly social media usage. We do so by leveraging Afrobarometer survey data and variations in Free Basics exposure, driven by the location of partner operator antennas and respondents. Comparing individuals within the same country and year, who share similar socio-demographic characteristics and reside in comparable locations, we find a significant positive effect of Free Basics on connectivity. This effect emerges in the short run, with social media usage for information increasing by 24% of the mean just two years after Free Basics’ introduction. Moreover, it persists and intensifies in the medium run, more than doubling social media usage after eight years of exposure. Importantly, this effect is specifically tied to Free Basics availability, as areas covered by mobile internet infrastructure but lacking the zero-rating policy exhibit lower connectivity–even when 3G or 4G is available. While somewhat noisy, the estimated coefficient for these areas is only 39% the magnitude of that for Free Basics. Crucially, Free Basics boosts connectivity across all segments of society, with particularly strong effects among groups traditionally less likely to use social media–namely, individuals with no primary education, older individuals, women, and those in rural or less developed areas.

Turning to the core analysis, our main finding is that the availability of Facebook’s Free Ba-
sics significantly increased female political representation in covered constituencies in the second
election after its introduction. Specifically, constituencies exposed to Free Basics were 10.2 per-
centage points more likely to elect a female MP.<fnref n="4" /> Consistently, we observe strong increases in
vote shares received by best-performing female candidates.

<footnote n="4">⁴Given the magnitude of the connectivity shock and the infrequent election of female MPs in the sample (only 1 in 10 constituencies), it translates into a persuasion rate of 11%. We build on the media economics literature, which introduced the concept of the persuasion rate to quantify the effect of a persuasion treatment on relevant outcomes (DellaVigna and Kaplan, 2007; DellaVigna and Gentzkow, 2010). In our setting, the persuasion rate answers the question: “What share of constituencies covered by Free Basics were *convinced* by its introduction to elect a woman?” The magnitude estimated in this paper is consistent with studies on the internet’s impact on political attitudes (Guriev et al., 2021).</footnote>
We show that these results depend on the presence of fair elections and are amplified in contexts where traditional press is censored. Our estimates remain largely unchanged across different levels of economic development, population density, and a key political system feature—the presence of reserved seats for women. These findings highlight the dual importance of free and fair elections for equitable representation and the role of social media as a catalyst for political change, particularly in settings with limited traditional media agency.

Exploring the role of electoral competition and candidate selection, we find that established parties are pivotal gatekeepers. The positive effect of Facebook's Free Basics on the election of female MPs is primarily driven by first-time female candidates endorsed by parties already represented in parliament, suggesting that digital exposure amplifies opportunities for new female entrants once parties lower barriers to entry. In contrast, male candidates from these same parties experience negative effects. Going beyond candidacy and winner identity, we track dynamics across the two elections following the platform's introduction and show that exposure to Free Basics reduced win margins against female opponents in the first election (-3.5 percentage points), with the effect intensifying in the second (-9.6 percentage points). Finally, focusing on races where a male parliamentary candidate was elected in the last two contests, we find that Free Basics has a larger impact in constituencies where incumbents had already faced tighter competition from female challengers in the first election after the platform's introduction. We interpret this pattern as evidence that digital access not only affects who enters and wins races, but also gradually erodes incumbency advantages by strengthening the competitiveness of female challengers.

Overall, our findings suggest that parties adapt to rising voter demand for female politicians by increasingly endorsing women in subsequent elections. These dynamics suggest that political parties adjust to rising voter demand for female representation by progressively increasing their endorsement of women in subsequent elections. We support this interpretation with evidence at both the constituency and party level: first, by showing the effect of Free Basics on female candidacy in treated constituencies, and second, through a complementary analysis of party endorsement strategies tracked across multiple election cycles.

An important question that follows is whether this shift in party endorsement strategies expands overall female representation or merely redistributes it across constituencies. To address this, we examine whether Facebook's Free Basics increases the total number of elected female politicians or instead reflects a reallocation, with parties compensating by endorsing more male candidates elsewhere. Analyzing regional variation in Free Basics coverage, we find that greater coverage in the second election leads to higher female representation. Results show a net increase in the election of female MPs, with a one-standard-deviation increase in Free Basics coverage at the regional level raising the share of elected female candidates by 3.9 percentage points (40% of the mean).

To shed light on the mechanisms behind these results, we test three complementary explanations by combining survey evidence on attitudes toward female leadership and gender norms, data on the structure of local online networks, and electoral outcomes. First, Free Basics may have improved public support for female political representation by shifting gender norms. We provide evidence suggesting that access to Free Basics and social media use are associated with more favorable attitudes toward female leadership and more egalitarian gender norms, even before observable effects on electoral outcomes. Social media users consistently express stronger
support for women in politics and gender equality across multiple domains, and access to Free
Basics appears to reinforce these patterns. Importantly, these effects are also present among
groups traditionally less supportive of women in politics, indicating that digital platforms may
foster broader attitudinal shifts that precede and enable changes in political representation.

Second, changes could have been driven by broader political disillusionment, as increased online exposure may heighten dissatisfaction with traditional elites and strengthen demands for political renewal. We examine whether the positive effect of Free Basics on attitudes toward female leadership reflects a broader demand for political renewal, as suggested by prior work linking internet access to political disillusionment in Africa. Consistent with the literature, we find that Free Basics users report lower trust in parliament and reduced voter turnout. However, when we split respondents by their level of political discontent, the positive effect of Free Basics and social media use on support for female leadership appears across both more and less disillusioned groups. This pattern suggests that the attitudinal shift in favor of female representation is not mechanically driven by voter disillusionment, but rather reflects changes in gender norms and exposure to supportive online networks.

Third, exposure to digital networks may have facilitated norm transmission, with users in treated areas more likely to encounter content and connections supportive of female leadership, especially when linked to regions with higher female representation. We find evidence that the attitudinal effects of Free Basics are shaped by the structure of online networks. Using Facebook's Social Connectedness Index, we construct new indices of online exposure to higher female political representation, measured as the average gender representation distance of one's Facebook feed. We show that users are more likely to be connected to regions with higher levels of female political representation than their own, thereby exposing them to a more gender-progressive information environment. Consistent with a mechanism of norm transmission, the effect of Free Basics and social media use on support for female leadership is significantly stronger among individuals whose online networks link them to more gender-equal regions. This pattern suggests that digital platforms can amplify shifts in gender norms by connecting users to discursive environments that are more supportive of female leadership than their offline contexts.

Finally, we show that the impact of Free Basics on female candidacy and electoral success is amplified in constituencies whose online networks are more connected to regions with higher female political representation. In these areas, parties are significantly more likely to endorse first-time female candidates, and women endorsed by parliamentary parties have a greater chance of winning. A one-standard-deviation in Facebook feed distance in gender representation increases Free Basics' effect on candidacy by 13 percentage points (58% of the mean effect) and that on election by 2.2 percentage points (19% of the mean effect). This pattern supports a party learning process: established parties adapt their endorsement strategies most where online exposure to female leadership is strongest and voter attitudes shift most in response. In this way, digital access and online networks jointly shape not only gender norms but also the equilibrium outcomes of candidate selection and electoral representation.

**Literature review.** By showing how the introduction of Facebook’s Free Basics significantly improves the political representation of women in sub-Saharan Africa, our paper contributes to the literature in several ways.

First, we build on research examining the political effects of internet and social media, with
particular attention to how lowering the cost of access reshapes electoral competition and repre-
sentation. Existing studies show that digital expansion affects political attitudes and behaviors
differently across contexts: in mature democracies, it is associated with lower turnout (Falck
et al., 2014; Gavazza et al., 2019), increased communitarianism (Manacorda et al., 2025), and
support for populist parties (Campante et al., 2018; Guriev and Papaioannou, 2022; Guriev et al.,
2021); in fragile democracies, it tends to boost engagement, amplify government disapproval, and
mobilize citizens, though sometimes at the cost of greater conflict (Enikolopov et al., 2020; Fer-
gusson and Molina, 2019; Manacorda and Tesei, 2020; Guriev et al., 2021; Cariolle et al., 2024).<fnref n="5" />
In line with evidence that mobile internet can erode incumbency advantages by spurring political
renewal (Donati, 2023), we show that Free Basics alters the identity of electoral winners in sub-
Saharan Africa, with male politicians from established parties losing ground while new female
entrants, also endorsed by these parties, gain opportunities. Building on research demonstrating
that parties select candidates based on expected popularity (Casey et al., 2021; Gulzar et al.,
2021), we provide the first evidence that internet- and social media-driven shifts in political de-
mand not only reshape party endorsement strategies but also extend to gender representation. In
particular, we demonstrate that digital access strengthens voter demand for female politicians,
prompting established parties to adapt by endorsing more women.

Crucially, the literature on social media's impact on political attitudes and electoral behaviors
tends to emphasize two main perspectives. A widely held view is that online networks can exacer-
bate echo chambers (Pariser, 2011; Sunstein, 2018). Social media platforms may encourage the
consumption of one-sided information, as algorithmic curation reinforces individuals' self-selection
into ideologically aligned content. Empirical evidence supporting this idea is substantial.<fnref n="6" /> In
parallel, social media has also been shown to expose individuals to a broader range of political
viewpoints than traditional media or personal interactions (Barberá, 2014; Bakshy et al., 2015;
Nelson and Webster, 2017; Beam et al., 2020), further enriching the reporting of legacy media on
critical issues (Cagé et al., 2024; Hatte et al., 2025). As such, the overall slant of a platform can
have political consequences, even in the absence of ideological take-up or demand for content.
Fujiwara et al. (2024), for instance, show that the liberal nature of Twitter persuaded moderates
to vote against the Republican party in the 2016 and 2020 elections. These contrasting findings
highlight the complexity of social media's role in shaping political discourse, underscoring the
importance of examining its effects in diverse political and technological environments, such as
those in sub-Saharan Africa. Leveraging variations in the structure of local online networks, we
address these two perspectives and demonstrate the crucial role of online networks and content
exposure in shaping how Facebook's Free Basics influences attitudes towards female leadership
and the election of female MPs.

Secondly, by documenting the drivers behind established political parties’ decisions to en-
dorse women, our paper contributes to the literature documenting the strong impediments faced
by female politicians throughout their careers. Compared to their male counterparts, women in
politics are more likely to experience violence, both from the public (Daniele et al., 2023) and
fellow politicians (Krook, 2018), less likely to seek reelection (Baskaran and Hessami, 2022), and

<footnote n="5">⁵Studying Free Basics in Myanmar, Tähtinen (2024) finds it fueled ethnic conflict towards the Rohingyaas but did not have broader conflictual consequences.</footnote>

<footnote n="6">⁶See for instance Conover et al. (2011); Del Vicario et al. (2016); Halberstam and Knight (2016); Schmidt et al. (2017); Levy (2021a).</footnote>
often appointed to less prestigious offices (Krook and O’Brien, 2012; Goddard, 2019). These challenges, documented for women already in politics, are also crucial in the selection process for those running for office (Gulzar, 2021). For instance, political parties have been shown to amplify voter biases against women (Le Barbanchon and Sauvagnat, 2022) by endorsing them in less favorable districts (Fujiwara et al., 2024). Gender quotas, while increasingly common, have not effectively removed these barriers (Bagues and Campa, 2020, 2021), although they can trigger learning among parties over time (Lee and Zanella, 2024). In parallel, the positive policy impact of female leadership is well-documented, including more responsive public spending (Chattopadhyay and Duflo, 2004), better educational outcomes for girls (Beaman et al., 2012), improved health policies (Bhalotra and Clots-Figueras, 2014), lower corruption (Brollo and Troiano, 2016), and higher economic growth (Baskaran et al., 2024). Furthermore, higher female political representation can trigger virtuous cycles, enhancing competence among politicians (Besley et al., 2017) and fostering future female representation (Baskaran and Hessami, 2018). Our paper shows that online exposure to more progressive and gender-egalitarian views can trigger similar effects.

Our paper also contributes to the understanding of norm diffusion, particularly through media content. In particular, it builds on the rich literature documenting the importance of media representation as a catalyst for positive gender norms evolution. The depiction of relatable situations and role models in entertainment media has been shown to lower fertility (Jensen and Oster, 2009; La Ferrara et al., 2012; Kearney and Levine, 2015; Okuyama, 2021), increase divorce rates (Chong and La Ferrara, 2009) and educational performance (Riley, 2024), and lower tolerance for gendered violence (Jensen and Oster, 2009; Banerjee et al., 2019). In addition, evidence shows important political consequences, both in the short (Okuyama, 2021) and long term (Duquennois and Zeng, 2024). As such, entertainment and news media can act as stepping stone to norm transitions (Gulesci et al., 2025). Recent work on the #MeToo movement provide strong evidence for this by showing how social media exposure increased the perception of gravity of sexual misconduct (Gauthier, 2022; Levy and Mattsson, 2023). Crucially, this further highlights the importance of overcoming gender imbalances and stereotypical reporting in media coverage (Ash et al., 2021). Moreover, our work stands on the broader strand of literature studying gender norm diffusion through offline experiences.<fnref n="7" /> In particular, by documenting the learning process of political parties across electoral cycles, it confirms the importance of information provision, and the role of elections as information aggregator, in shifting gender norms (Bursztyn et al., 2020, 2024).

Finally, understanding the diffusion of norms in sub-Saharan Africa necessarily speaks to the abundant literature on the origins and persistence of deep-rooted gender norms in the region, which highlights the importance of cultural, historical and geographical factors (Alesina et al., 2013; Teso, 2019; Corno et al., 2021; Khalifa, 2022; Lowes, 2022; Becker, 2025; Loper, 2025).

The paper is organized as follows. Section 2 provides background on connectivity develop-

<footnote n="7">⁷Classroom interventions (Dhar et al., 2022) or meeting role models (Porter and Serra, 2020) have, for instance, been documented to be effective interventions on adolescents’ gender stereotypical attitudes and choices. Similarly, mixed peer groups plays an important role among adults in fostering gender equal views (Dahl et al., 2021). Rich evidence also shows the role of fathering daughters in shaping men’s behavior (Washington, 2008; Oswald and Powdthavee, 2010; Cronqvist and Yu, 2017; Ronchi and Smith, 2024). Such intergenerational exposure to norms has, in addition, important downstream effects, with children benefiting strongly from parental transmission (Fernández et al., 2004; Nollenberger et al., 2016).</footnote>
ments in sub-Saharan Africa and introduces the Free Basics initiative. Section 3 describes the data and presents descriptive statistics, while Section 4 documents the connectivity shock and examines the characteristics of the compliers. Section 5 outlines the empirical strategy for measuring the effect of Free Basics on the election of female MPs, and Section 6 reports the main results. Section 7 investigates the underlying mechanisms, and Section 8 concludes.

## 2 Background

**Connectivity and internet affordability in sub-Saharan Africa.** Over the past decade, sub-Saharan African countries have witnessed a quick increase in the deployment of communication technology infrastructures, allowing to reduce the share of population not covered by mobile internet technology from 50% in 2014 to 16% in 2022 (GSMA, 2022). Consequently, mobile internet usage doubled over the same period. Still, only 40% of the adult population uses mobile internet in 2022 in the region, an important reason for this being the very high cost of mobile broadband subscriptions. In sub-Saharan Africa, one gigabyte of mobile internet would cost on average 6.5% of monthly GDP per capita in 2016 (GSMA, 2022). While technological progress has permitted to drive this cost down to 3.4% in 2021,<fnref n="8" /> this still entails a significant barrier to generalized mobile connectivity, both at the intensive and extensive margins.

The size of the potential sub-Saharan market has driven major global tech companies to undertake large investments in the region in parallel of the government- and local operator-led network expansion, allowing to further drive down internet costs and connect remote regions. Led by Google, Microsoft and Facebook, these initiatives have taken the form, among others, of submarine cable and fiber infrastructure, the establishment of internet exchange points, and the deployment of innovative technologies, such as Google's WiFi-emitting balloons. In addition to such physical infrastructures, high-priced internet data created a market for "zero-rating policies" – i.e. programs allowing users to access certain internet services without incurring mobile data charges (Bates et al., 2017). At the forefront of these policies lies Facebook's Free Basics initiative, the focus of this paper.

**A brief history of the Free Basics partnerships.** Initiated by Facebook in 2013 as *Internet.org* and rebranded in 2015,<fnref n="9" /> Free Basics allows SIM card owners from partner mobile operators in areas with at least 2G coverage to access a Facebook-centered “light” version of the internet without the need to have an active internet plan nor incurring data costs. The first partnership was established in Zambia in 2014 with the Airtel operator and has since been extended to 65 countries worldwide, with 30 of them in Africa (Nothias, 2020).<fnref n="10" /> A strong emphasis is put on the actual Facebook app within Free Basics, both through the design of the app<fnref n="11" /> and the

<footnote n="8">⁸As a comparison point, one gigabyte of mobile broadband costs on average less then 1% of monthly GDP in Europe, North America and South Asia (GSMA, 2022).</footnote>

<footnote n="9">⁹The *Internet.org* name remained for the global project encompassing all of Facebook's connectivity initiative, *Free Basics* being it's flagship project.</footnote>

<footnote n="10">¹⁰The original Zambian partnership allowed users to browse 13 online services, which included “Facebook, Wikipedia, a Johnson & Johnson-sponsored maternal health site, the Zambian government’s app, a local job portal, and a women’s rights organization” (Nothias, 2020). Since then, the program has evolved, and any website administrator can apply to propose a Free Basics version of its service (Singh et al., 2017).</footnote>

<footnote n="11">¹¹Emphasis on Facebook is present from the name of the app–“Free Basics by Facebook”–on the Google Play Store platform. Within the app, Facebook is also listed first, prior to the main list of available services.</footnote>
operator advertising campaigns following Free Basics partnerships (Gebhart, 2016). This translates into a use of Free Basics mostly centered around the light-Facebook website.<fnref n="12" /> Eventually, this Facebook-centered connectivity shock helped the company gain millions of new users in the Global South.<fnref n="13" /> We present in Table A1 the complete list of partnerships established in our sample of countries.

**Drivers of Free Basics partnerships in sub-Saharan Africa.** Although partnership details are not widely disclosed, we analyze factors influencing their likelihood and timing. Appendix Table A2 presents coefficients from a country-operator-level regression, where a partnership indicator is regressed on country- and operator-level variables measured in 2014, before the first agreement. Results indicate that partnerships are established in strategic markets for Facebook. Countries with at least one partnership tend to have larger populations but lower GDP per capita. They also exhibit weaker internet infrastructure but higher mobile ownership rates. This suggests that Free Basics targets markets with a broad potential user base (high population and mobile penetration) and strong demand for zero-rated alternatives (low GDP per capita, weak internet performance). At the operator level, those with larger networks are more likely to participate. However, no significant correlation is found between electoral outcomes for women in an operator's coverage area and partnership likelihood. These patterns hold when analyzing partnership timing through a proportional hazard model (Cox, 1972) (Appendix Table A2).

**Facebook and the evolution of gender norms in politics.** Over the past two decades, the user-generated content nature of social media has provided a platform for a wide range of voices, sometimes challenging entrenched norms by circumventing traditional media gatekeepers. In sub-Saharan Africa, Facebook has been one such platform where civil-society organizations and global institutions have engaged in promoting gender-inclusive health practices and gender-equal norms.<fnref n="14" /> Feminist activists such as Stella Nyanzi (Uganda), Aisha Yesufu (Nigeria), and Esther Passaris (Kenya) have leveraged Facebook to advocate for women's rights, gaining visibility beyond social media.<fnref n="15" /> Their online presence has influenced public discourse, exemplified by media coverage of their activism.<fnref n="16" /> Political parties have increasingly adopted social media for campaigning, with initiatives aimed at enhancing female political representation. These range from online campaigns highlighting women candidates<fnref n="17" /> to establishing official “Women’s Wing” accounts.<fnref n="18" /> Crucially, the digital nature of social media enables these campaigns to reach global

<footnote n="12">¹²As described in the New York Times here: https://www.nytimes.com/2017/09/26/opinion/facebook-free-basics.html. See also Romanosky and Chetty (2018).</footnote>

<footnote n="13">¹³Internal documents revealed by The Wall Street Journal in 2022 suggest that, in the second semester of 2021, Free Basics added about 10 million users per month (https://www.wsj.com/articles/facebook-free-india-data-charges-11643035284). From 2016-Q3 to 2017-Q4, Facebook's earnings reports consistently highlighted Internet.org as a key driver of Monthly Active User growth (e.g., https://s21.q4cdn.com/399680738/files/doc_financials/2016/Q3/FB-Q316-Earnings-Transcript.pdf). All reports since 2016-Q1 are available here: https://investor.fb.com/investor-events/default.aspx.</footnote>

<footnote n="14">¹⁴See https://www.facebook.com/LeadingLadiesAfrica/ and https://www.facebook.com/unwomenafrica/.</footnote>

<footnote n="15">¹⁵See https://www.facebook.com/stella.nyanzi, https://www.facebook.com/aisha.yesufu.1/, and https://www.facebook.com/EstherMPassaris/.</footnote>

<footnote n="16">¹⁶See “Stella Nyanzi: The woman who used Facebook to take on Uganda’s president” in The Globe and Mail. https://www.theglobeandmail.com/news/world/ugandan-scholar-stella-nyanzi-the-woman-who-tickled-the-leopard/article35159152/.</footnote>

<footnote n="17">¹⁷See https://www.facebook.com/permalink.php/?story_fbid=1152610093095577&id=100050397913844.</footnote>

<footnote n="18">¹⁸See https://www.facebook.com/WomensWing20222024/.</footnote>
audiences, extending their impact beyond national borders. Facebook's granular Social Connectivity Index allows further investigation of these transnational networks, as examined in section 7 of this paper.

# 3 Data and Descriptive Statistics

In this section, we describe the main data sources used in this paper. In particular, we start by presenting the electoral and mobile connectivity data used in our main analysis of the effects of Free Basics on female political representation. We then describe the individual survey data which allow us to quantify both the size of the Free Basic connectivity shock as well as its impact on individual attitudes. Finally, we present data sources for constituency-level characteristics which we include as controls throughout our analysis.

**Parliamentary election data.** We collect and harmonize detailed data on electoral races at the constituency level for 64 parliamentary elections across 17 sub-Saharan countries over the period 2001-2022.<sup>19</sup> In particular, we focus on First-Past-The-Post (FPTP) races in single-member constituencies.<sup>20</sup> Since we are interested in candidate and winner identity, this restriction ensures that votes cast map directly onto support for a single individual (which is not trivial in party-list systems) and that these votes directly determine the winners (which is not the case with second-round run-offs). We further restrict the sample to races with available constituency boundary shapefiles,<sup>21</sup> yielding 8,814 constituency-level electoral races. Our dataset includes winner identity, ballot presence, and vote shares, as well as gender,<sup>22</sup> party endorsement, and candidacy history.<sup>23</sup>

Table 1 summarizes the electoral data. Women are markedly underrepresented, accounting for only 11% of elected MPs. No female candidate appears on the ballot in 54% of races. Among races where a female MP is elected, most victories (73%) are by candidates from parliamentary parties. Appendix Table B2 provides corresponding statistics for male candidates. On average, each race features six candidates: two from parliamentary parties, three from minor parties, and one independent. Voter turnout averages 63%, comparable to general and parliamentary elections in Western democracies over the same period.<sup>24</sup>

**Measuring Free Basics and mobile internet coverage.** A key component of our analysis, and a contribution in its own, is the construction of novel data on Free Basics availability across

<footnote n="19"><sup>19</sup>The complete list of countries is: Botswana, Gambia, Ghana, Guinea, Ivory Coast, Kenya, Lesotho, Liberia, Madagascar, Malawi, Nigeria, Seychelles, Sierra Leone, Tanzania, Uganda, Zambia, Zimbabwe. We obtain election data from the *Constituency-Level Election Archive* (CLEA), the Psephos electoral archive and substantially complement this data collection with information gathered from electoral commissions' websites. See Appendix Table B1 for the detailed list of sources used by national election.</footnote>

<footnote n="20"><sup>20</sup>Some countries in our sample have mixed electoral systems (Guinea, Lesotho, Madagascar, Seychelles and Zimbabwe), while others have a limited number of multi-member constituencies (Ivory Coast and Madagascar), in such cases we limit ourselves to the races whose rule satisfy FPTP and single member.</footnote>

<footnote n="21"><sup>21</sup>See Appendix Table B1 for shapefile sources, including Amodio et al. (2024), Bowles and Marx (2023) and Reeder and Seeberg (2018).</footnote>

<footnote n="22"><sup>22</sup>When gender is not directly available, we infer it using the Gender API (https://gender-api.com/) based on candidate names and countries.</footnote>

<footnote n="23"><sup>23</sup>A candidate is identified as previously running if the same first and last name appears in an earlier election in the same constituency.</footnote>

<footnote n="24"><sup>24</sup>Turnout data is not available for all elections in our sample.</footnote>
constituencies. To achieve this, we collect information on the timing and mobile operators involved
in Free Basics partnerships, covering 28 agreements since the first deal with Airtel in Zambia
(July 2014). These partnerships span 17 mobile operators across 12 countries in our sample.<fnref n="25" />
By integrating this data with geolocated cellular infrastructure records, we systematically map
the rollout and reach of Free Basics.

To this aim, we leverage cellular tower-level data from the OpenCellID project,<fnref n="26" /> extracting
each tower's geographic coordinates, internet technology (GSM, UMTS, LTE), operator, and first
recorded signal. Coverage areas are estimated using 12 km buffers for 2G towers and 4 km buffers
for 3G/4G towers,<fnref n="27" /> enabling us to delineate both overall mobile internet coverage and Free Basics
availability. Given that Free Basics operates on 2G networks also (Sen et al., 2016), we classify
all 2G-, and 3G/4G-covered areas as Free Basics-enabled. To distinguish between legacy and
high-speed internet, we separately map 2G and 3G/4G coverage.<fnref n="28" /> Combining these data with
high-resolution (30 × 30 arc-second) population estimates from WorldPop (Tatem, 2017), we
compute constituency-level yearly coverage rates for (i) Free Basics mobile internet and (ii) 2G
and 3G/4G separately.

Descriptive statistics (Table 1) indicate that 49% of constituency-election-year observations have some mobile internet coverage, while 26% have Free Basics. Notably, Free Basics operators provide extensive coverage where they operate, particularly at election times. In constituencies experiencing Free Basics for the first election post-introduction, 70.6% of the population is covered on average (83.8% in the median constituency). By the second election, coverage rises to 84.7% on average and 99.9% in the median constituency. Given this near-universal expansion, our baseline analysis considers the electoral impact of transitioning from “no Free Basics” to “some coverage,” treating it as a binary intervention. The full distribution of treatment intensities is provided in Appendix Figure A2.<fnref n="29" />

**Online behaviors and attitudes.** We use geocoded individual-level data from the Afrobarometer survey to estimate the impact of Free Basics on online behaviors and attitudes toward women, particularly with respect to political leadership. Afrobarometer is a nationally representative survey conducted in 35 African countries since 1999, measuring public attitudes on social and political topics. To construct our variables, we leverage survey rounds 6 to 9 (2014-2022), which include questions on social media and internet use, as well as on political and gender-related attitudes.

Crucially, Afrobarometer provides geographic coordinates of respondents’ places of residence
(enumeration area), allowing us to match them with Free Basics coverage maps to measure indi-
vidual access. It also reports detailed individual characteristics, such as age, gender, education,
and religion, which we control for in our analysis. Our sample includes 85,117 respondents across
2,402 constituencies in the 17 countries covered in our parliamentary election data. Summary
statistics in Table 2 reveal important insights. First, mobile broadband coverage at the individual
level aligns with constituency-level election-year measures: 35% of individuals are covered by Free

<footnote n="25">²⁵See Appendix Table A1 for the full list of partnerships.</footnote>

<footnote n="26">²⁶https://www.opencellid.org/</footnote>

<footnote n="27">²⁷We follow the methodology in Bartholomew Collins (2021). We check the robustness of our results for larger buffers.</footnote>

<footnote n="28">²⁸Literature highlights the distinct social impacts of fast mobile internet (3G) compared to 2G. See Guriev et al. (2021); Manacorda et al. (2025).</footnote>

<footnote n="29">²⁹We demonstrate the robustness of our results to different measures of treatment intensity in subsection 6.1.</footnote>
<table><caption>Table 1: Summary statistics for constituency × election-year-level variables</caption><thead><tr><th></th><th>Observations</th><th>Mean</th><th>Standard deviation</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td><strong>Mobile broadband technology available</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Free Basics</td><td>8814</td><td>0.26</td><td>0.44</td><td>0</td><td>1</td></tr><tr><td>3G/4G</td><td>8814</td><td>0.36</td><td>0.48</td><td>0</td><td>1</td></tr><tr><td>2G only</td><td>8814</td><td>0.13</td><td>0.34</td><td>0</td><td>1</td></tr><tr><td><strong>Electoral Outcomes</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Winner is a woman</td><td>8814</td><td>0.11</td><td>0.31</td><td>0</td><td>1</td></tr><tr><td>... from a parliamentary party</td><td>8814</td><td>0.08</td><td>0.27</td><td>0</td><td>1</td></tr><tr><td>... from a minor party</td><td>8814</td><td>0.02</td><td>0.15</td><td>0</td><td>1</td></tr><tr><td>At least one woman on the ballot</td><td>8814</td><td>0.46</td><td>0.50</td><td>0</td><td>1</td></tr><tr><td>... from a parliamentary party</td><td>8814</td><td>0.19</td><td>0.39</td><td>0</td><td>1</td></tr><tr><td>... from a minor party</td><td>8814</td><td>0.29</td><td>0.45</td><td>0</td><td>1</td></tr><tr><td>Number of women on the ballot</td><td>8814</td><td>0.78</td><td>1.24</td><td>0</td><td>18</td></tr><tr><td>... from a parliamentary party</td><td>8814</td><td>0.20</td><td>0.44</td><td>0</td><td>3</td></tr><tr><td>... from a minor party</td><td>8814</td><td>0.47</td><td>1.06</td><td>0</td><td>18</td></tr><tr><td><strong>Population density and local development</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Log of population density</td><td>8814</td><td>5.09</td><td>1.73</td><td>0.16</td><td>11.16</td></tr><tr><td>Mean nighttime light, weighted by pop. density</td><td>8814</td><td>5.73</td><td>3.58</td><td>0.00</td><td>15.29</td></tr></tbody></table>

Notes: This table reports summary statistics for the main constituency × election-year variables used in the analysis. Summary statistics for additional electoral outcomes are provided in Appendix Table B2.

Basics, similar to the 26% of constituency-election dyads. Second, while most individuals own a mobile phone (78%) and have heard of social media (70%), only 30% use social media, and 27% use the internet regularly. Third, almost one-third of citizens remain unconvinced that women should have equal opportunities in politics. However, most respondents believe social media informs the public (63%) and shapes political processes (55%), reinforcing the idea that it can contribute to women's political representation. Finally, attitudes toward gender equality show room for improvement: 40% believe men should be prioritized in the labor market, 21% oppose equal land ownership rights, 52% think women should primarily handle household responsibilities, and 23% justify domestic violence. These summary statistics highlight persistent gender biases alongside the potential for digital connectivity to influence social and political attitudes.

**Constituency-level characteristics.** In order to support the causal interpretation of our estimation, we control in our main specifications for two important time-varying constituency characteristics: population density and nighttime light. We compute the former for each constituency-year by simply averaging yearly population density estimates at a 30 × 30 arc-second resolution (approximately 1 × 1 km at the Equator) provided by the *Worldpop* dataset (Tatem, 2017) within constituency boundaries. Following the literature (Chen and Nordhaus, 2011; Henderson et al., 2012; Michalopoulos and Papaioannou, 2014), we chose to proxy economic development using measures of nighttime light. In particular, we use satellite imagery data at the 30 arc-second resolution from Li et al. (2020) in order to compute the population-density-weighted mean nighttime light for each constituency-year observation. That is, when computing the mean nighttime light of a constituency-year, we weigh each pixel by its population density, using the Worldpop data described previously. Table 1 provides descriptive statistics of the population and nighttime light measures used throughout this analysis.
<table><caption>Table 2: Summary statistics for individual-level variables</caption><thead><tr><th></th><th>Observations</th><th>Mean</th><th>Standard deviation</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td><strong>Mobile broadband technology</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Free Basics</td><td>85117</td><td>0.35</td><td>0.48</td><td>0</td><td>1</td></tr><tr><td>3G/4G</td><td>85117</td><td>0.17</td><td>0.38</td><td>0</td><td>1</td></tr><tr><td>2G</td><td>85117</td><td>0.13</td><td>0.33</td><td>0</td><td>1</td></tr><tr><td><strong>Connectivity</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Get news from social media</td><td>85117</td><td>0.30</td><td>0.46</td><td>0</td><td>1</td></tr><tr><td>Get news from internet</td><td>85117</td><td>0.27</td><td>0.45</td><td>0</td><td>1</td></tr><tr><td>Uses internet frequently</td><td>85117</td><td>0.27</td><td>0.45</td><td>0</td><td>1</td></tr><tr><td>Owns mobile phone</td><td>85117</td><td>0.78</td><td>0.41</td><td>0</td><td>1</td></tr><tr><td>Heard about social media</td><td>19177</td><td>0.70</td><td>0.46</td><td>0</td><td>1</td></tr><tr><td><strong>Gender norms</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Equal chance for women in politics</td><td>64854</td><td>0.72</td><td>0.45</td><td>0</td><td>1</td></tr><tr><td>Men should be prioritized in labor market</td><td>39153</td><td>0.40</td><td>0.49</td><td>0</td><td>1</td></tr><tr><td>Equal land rights for women</td><td>39153</td><td>0.79</td><td>0.41</td><td>0</td><td>1</td></tr><tr><td>Women should take care of HH</td><td>16323</td><td>0.52</td><td>0.50</td><td>0</td><td>1</td></tr><tr><td>Beating wife never justified</td><td>39153</td><td>0.77</td><td>0.42</td><td>0</td><td>1</td></tr></tbody></table>

Notes: This table provides summary statistics for the main individual-level variables used throughout the analysis.

**Exposure to Facebook content and female leadership.** The potential political impact of greater Facebook access depends crucially on the mix of content users encounter online. To study this, we draw on two complementary data sources to capture variation in exposure to norms around female leadership. First, we use the Facebook Social Connectedness Index (SCI) (Bailey et al., 2018), which measures the relative frequency of Facebook friendship links between nearly all regional pairs (ADM1) worldwide. This index describes the geographic composition of a region's average user network. We exploit it to evaluate how locally concentrated the Facebook networks of constituencies in our sample are. Second, we combine this with the Women Power Index (Vogelstein and Bro, 2021), which tracks women's political representation across UN countries. Merging the two allows us to compute the gender representation distance of an individual's Facebook feed—that is, the gap between the level of female political representation at home and that in the countries most represented in one's Facebook network. Details on the construction of these measures are provided in Appendix C.1. Table 3 reports summary statistics on constituency-level Facebook networks. On average, users' networks are heavily locally concentrated (40%), though this share varies widely (from 4% to 81%). Conversely, connections abroad are less common overall (11%) but display similarly large variation (1% to 51%). Importantly, our constructed measure indicates that the average user in our sample is exposed to higher levels of female political representation online than in their home country, with a positive mean of 0.05. Appendix Figure C1 shows the full distribution of this measure, highlighting substantial heterogeneity in online exposure to female leaders across constituencies.
<table><caption>Table 3: Summary statistics for constituency-level online networks variables</caption><thead><tr><th></th><th>Observations</th><th>Mean</th><th>Standard deviation</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Facebook gender representation distance</td><td>2565</td><td>0.05</td><td>0.07</td><td>-0.18</td><td>0.17</td></tr><tr><td>Share of friends on Facebook</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>... in same ADM1 region</td><td>2565</td><td>0.40</td><td>0.17</td><td>0.04</td><td>0.81</td></tr><tr><td>... abroad</td><td>2565</td><td>0.11</td><td>0.07</td><td>0.01</td><td>0.51</td></tr></tbody></table>

Notes: This table provides summary statistics for constituency-level variables used throughout the analysis, on the corresponding estimation sample.

# 4 The connectivity shock: Free Basics availability and online behaviors

In this section, we assess the magnitude of the connectivity shock triggered by the availability of Free Basics, estimating its impact on online behavior, and in particular social media usage.

## 4.1 Connectivity shock: empirical specification

We leverage the staggered roll-out of Free Basics, driven by partnerships with mobile companies operating in the region, to quantify the extent of the connectivity shock. We combine this measure of Free Basics availability with individual-level data on online behavior and connectivity from rounds 6 to 9 of the Afrobarometer surveys.<fnref n="30" /> Our first goal is to estimate the following relationship:

$$Online\ Behavior_{i,t} = \beta Free\ Basics_{i,t} + C_{i,t} \cdot \gamma + \eta_{c,t} + \varepsilon_{i,t} \quad (1)$$

where *i*, *t*, and *c* index individuals, years and countries, respectively. *Online Behavior*<sub>*i*,t*</sub>, captures various aspects of online behavior and connectivity for individual *i* interviewed in year *t*.<fnref n="31" /> Specifically, this variable consists of five indicator variables measuring: the use of (1) social media or (2) internet as a source of information, (3) frequent internet use, (4) mobile phone ownership, and (5) awareness of social media.<fnref n="32" /> Our primary regressor of interest is a dummy

<footnote n="30">³⁰Data limitations prevent the use of earlier Afrobarometer rounds, as questions related to social media were only introduced in Afrobarometer 6 (2014-2015), as detailed below.</footnote>

<footnote n="31">³¹Throughout this paper (unless otherwise stated), observations in individual-level regressions are weighted using Afrobarometer within-country survey weights. These weights “adjust the distribution of the sample based on individual selection probabilities (i.e. based on religion, gender, urban-rural distribution, and size of household and enumeration area)” (source: Afrobarometer codebook).</footnote>

<footnote n="32">³²We use the following questions from the Afrobarometer surveys and apply the coding rule described here. (1) “How often do you get news from the following sources: Social media such as Facebook or Twitter?” (variable Q12E in Afro. 6 and 7, Q55E in Afro. 8, and Q74E in Afro. 9), we set the dummy “Get news from social media” to 1 for social media use at least a few times a month. (2) “How often do you get news from the following sources: Internet?” (variable Q12D in Afro. 6 and 7, Q55D in Afro. 8, and Q74D in Afro. 9), we set the dummy “Get news from internet” to 1 for internet use at least a few times a month. (3) “How often do you use: The Internet?” (variable Q92B in Afro. 6, Q91B in Afro. 7, Q92I in Afro. 8, and Q90I in Afro. 9), we set the dummy “Frequent use of internet” to 1 for internet use at least a few times a week. (4) “Which of these things do you or anyone in your household own: Mobile phone?” (variable Q91D in Afro. 6, Q89F in Afro. 7, Q92F in Afro. 8, and Q90F in Afro. 9), we set the dummy “Owns mobile phone” to 1 if respondent has answered yes to this question. (5) “Have you heard about social media, for example, Facebook, Twitter, and WhatsApp?” (variable Q59A in Afro. 8, not asked in other survey rounds), we set the dummy “Heard about social media” to 1 if respondent has answered yes to this question.</footnote>
variable indicating whether the respondent was within the coverage area of a mobile phone oper-
ator antenna that provided access to Free Basics at the time of the survey (*Free Basics*<sub>*i,t*</sub>). The
control set includes country-year fixed effects (η<sub>c,t</sub>), along with a comprehensive set of individual-
level predictors of online behavior (C<sub>i,t</sub>), such as the respondent's age, gender, primary education
completion, rural location, and religious affiliation. We adjust the standard errors to account
for spatial and temporal correlation. As the treatment stems from partnerships signed at the
country-year level, affecting individuals differently across regions, we employ two-way clustering
of standard errors at the country-year and enumeration area levels.<sup>33</sup>

To further account for key potential confounding factors linked to variations in socio-economic conditions, we include the population-density-weighted mean nighttime light, measured at the constituency-year level, as well as a set of enumeration area-level indicator variables for the presence of public facilities, including post office, police station, health clinic, market stalls, bank or money services, and paid transport options (e.g., bus, taxi, or moped).<sup>34</sup> Crucially, we also incorporate mutually exclusive individual-level indicator variables for 3G/4G and 2G coverage, which equal one if the respondent is covered by 3G/4G and not Free Basics, or by 2G only and not Free Basics, respectively. This approach allows us to capture the net effect of each mobile broadband technology and enables a comparison of the connectivity impact of Free Basics with that of mobile broadband internet in the absence of the zero-rated policy.

Given the inclusion of these controls, $\beta$ identifies the effect of Free Basics on online behaviors
by comparing the adoption of online technologies between individuals who are effectively covered
by both mobile broadband internet and Free Basics, and those from the same country and year,
with similar demographic and socio-economic characteristics, who live in areas with comparable
economic development and access to public facilities, but without Free Basics coverage (i.e., they
are covered by either 3G/4G, 2G, or no mobile internet).

## 4.2 Effect of Free Basics on connectivity

**Free Basics and online behaviors.** Table 4 presents the baseline estimation results, showing positive and significant effects of Free Basics availability across all five outcomes. Column 1 indicates that Free Basics increases the likelihood of using social media for news by 4.9 percentage points, equivalent to 16.6% of the sample mean. Similar patterns emerge in columns 2 and 3, where Free Basics coverage significantly increases both internet use as a monthly information source and weekly internet use in general. Notably, although 3G/4G availability also positively affects social media and internet usage, its impact is substantially smaller-only 39% that of Free Basics-and less precisely estimated.<fnref n="35" /> Overall, without Free Basics, 2G technology does not sufficiently support social media use, likely due to its limited data capacity.<fnref n="36" /> However, 2G is positively associated with mobile phone ownership and awareness of social media platforms, similar to patterns observed for 3G/4G and Free Basics.

As described above, coverage measures for Free Basics, 3G/4G, and 2G are constructed using

<footnote n="33"><sup>33</sup>We also demonstrate the robustness of our results when accounting for arbitrary spatial correlation (Conley, 1999; Colella et al., 2023), using thresholds of 50, 100, and 250 km.</footnote>

<footnote n="34"><sup>34</sup>Enumeration area is the primary sampling unit in Afrobarometer surveys. Note that we use enumeration area GPS coordinates to define individual-level measures of mobile internet coverage, including Facebook's Free Basics.</footnote>

<footnote n="35"><sup>35</sup>The difference between Free Basics and 3G/4G is statistically significant; in column 1, the p-value is 0.096.</footnote>

<footnote n="36"><sup>36</sup>The effects of Free Basics and 2G are significantly different (in column 1, the p-value for this test is 0.000).</footnote>
buffers around the GPS coordinates of cell towers. Our choice of buffer sizes aligns with the methodology used in the Bartholomew Collins (2021) dataset, specifically 12 km for 2G towers and 4 km for 3G/4G towers. To test the robustness of our results, we compute coverage measures using larger buffer sizes. This exercise assesses the robustness of our findings against potential measurement errors from two sources: the GPS coordinates of OpenCellID mobile phone towers and those of Afrobarometer enumeration areas. Furthermore, even in the absence of misgeolocation, this approach helps assess the robustness of our estimates with regard to the potential for individuals to commute and access social media during their travels. Results reported in Table D1 support our initial findings: expanding the buffers by 2 km (Panel A) or 4 km (Panel B) yields very similar results. As anticipated, increasing the buffer size to 8 km-representing a 200% increase for 3G/4G buffers and a 66.7% increase for 2G buffers-results in smaller estimated effects (Panel C).

In addition, the significance of our main effect remains robust under alternative assumptions about the variance-covariance matrix. Given the spatial nature of the data, we account for spatial correlation in the error term using thresholds of 50, 100, and 250 km, as reported in Table D2. These results convey that Free Basics constituted a significant and sizeable connectivity shock.

<table><caption>Table 4: Free Basics availability and connectivity</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="2">Gets news from social media</th><th>Uses internet frequently</th><th>Owns mobile phone</th><th>Heard about social media</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Free Basics</td><td>0.049***<br>(0.008)</td><td>0.044***<br>(0.009)</td><td>0.040***<br>(0.009)</td><td>0.038***<br>(0.010)</td><td>0.057**<br>(0.020)</td></tr><tr><td>3G/4G</td><td>0.019<br>(0.015)</td><td>0.022<br>(0.013)</td><td>0.022<br>(0.016)</td><td>0.031***<br>(0.011)</td><td>0.078**<br>(0.035)</td></tr><tr><td>2G</td><td>0.000<br>(0.007)</td><td>0.002<br>(0.006)</td><td>-0.007<br>(0.007)</td><td>0.021**<br>(0.009)</td><td>0.043*<br>(0.021)</td></tr><tr><td>Individual controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Constituency controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-year fixed effects</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>85117</td><td>85117</td><td>85117</td><td>85117</td><td>19177</td></tr><tr><td>Mean dep. var.</td><td>0.296</td><td>0.275</td><td>0.273</td><td>0.783</td><td>0.698</td></tr><tr><td>R<sup>2</sup></td><td>0.296</td><td>0.274</td><td>0.277</td><td>0.242</td><td>0.237</td></tr></tbody></table>

Notes: This table presents OLS estimates of the effect of Free Basics availability on online behaviors. The unit of observation is a survey respondent. Columns 1 to 4 include respondents surveyed between 2014 and 2022 (Afrobarometer rounds 6-9), while Column 5 uses only those surveyed between 2019 and 2021 (round 8). The dependent variables are binary indicators set to 1 if the respondent reports adopting the specified online behavior. “Free Basics” is a dummy variable set to 1 if the respondent is covered by a mobile operator providing access to Free Basics. “3G/4G” and “2G” are dummies set to 1 if covered by an operator providing access to 3G/4G but not Free Basics, and 2G but not Free Basics, respectively. Individual controls include age, gender, primary education completion, rural location, and religious affiliation, along with enumeration area-level dummies indicating the presence of facilities (post office, police station, health clinic, market stalls, bank/money services, and paid transport such as bus, taxi, or moped). Constituency controls consist of the log-transformed constituency-year-level population-density-weighted mean nighttime light. Country-year fixed effects are included. Observations are weighted using Afrobarometer within-country survey weights. Standard errors are two-way clustered at the country-year and enumeration area levels and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

**Length of exposure to Free Basics.** We investigate the dynamic effects of the Free Basics connectivity shock. To do this, we estimate a version of Equation 1 in which we interact our Free Basics coverage variable, as well as the 3G/4G and 2G dummy variables, with an array of
exposure-length dummies.<fnref n="37" /> The estimated coefficients are reported in Figure 1. We observe a clear linear dynamic over the duration of exposure, with a significant increase in social media consumption as early as two years after exposure to Free Basics. Notably, while only 14.3% of individuals not covered by any mobile broadband technology report accessing news via social media on a monthly basis, we estimate a substantial connectivity shock of 3.4 percentage points (or +24% relative to the mean of uncovered individuals) after just two years of exposure. Furthermore, social media usage more than doubled (+16.4 percentage points or +115% relative to the mean of uncovered individuals) after height years of exposure. These results suggest that Free Basics constituted a significant connectivity shock right from its launch, that persisted and strengthened over time.

<figure><img src="image_4.png" /><figcaption>Figure 1: Free Basics availability and social media use, over years of exposure</figcaption></figure>

Notes: This figure presents OLS estimates of the effect of Free Basics availability on social media consumption, differentiated by years since treatment. The unit of observation is a survey respondent. Respondents surveyed between 2014 and 2022 (Afrobarometer rounds 6-9) are included in this analysis. The dependent variable is binary indicator set to 1 if the respondent reports consuming social media as a source of information. Individual controls include age, gender, primary education completion, rural location, and religious affiliation, along with enumeration area-level dummies indicating the presence of facilities (post office, police station, health clinic, market stalls, bank/money services, and paid transport such as bus, taxi, or moped). Constituency controls consist of the log-transformed constituency-year-level population-density-weighted mean nighttime light. Country-year fixed effects are included. Observations are weighted using Afrobarometer within-country survey weights. Standard errors are two-way clustered at the country-year and enumeration area levels. Bars represent 95% confidence intervals.

**Compliers** We examine whether certain population segments responded more to the connectivity shock. Specifically, we analyze differences in reactions to Free Basics availability based on education, age, gender, urban versus rural residence, local development, and religion. To assess these differences, we estimate Equation 1 on subsamples split along these dimensions.<fnref n="38" /> The estimates, reported in Appendix Table D3, show that Facebook’s Free Basics successfully connected all segments of the population, including those that were less likely to use social media, notably due to data affordability issues–specifically non-educated, female, and rural individuals–thereby

<footnote n="37">³⁷Length of exposure is measured in years since the first coverage of a respondent's place of residence by Free Basics, 3G/4G, or 2G at the time of the survey.</footnote>

<footnote n="38">³⁸Afrobarometer sampling weights are not applied when splitting the sample.</footnote>
also affecting the composition of social media users in the treated areas.<fnref n="39" />

# 5 Empirical Strategy

## 5.1 Facebook's Free Basics and Female MP Elections: Empirical Strategy

Does the connectivity boost, driven by Facebook through Free Basics, shape female representation in national parliaments within sub-Saharan African developing democracies? To address this question, we compare electoral outcomes before and after the introduction of Free Basics in constituencies where it is available, to those in similar constituencies without Free Basics. A naive correlation may suffer from severe endogeneity concerns, as constituencies gaining access to Free Basics could differ systematically from those without it in ways that are related to electoral politics. To mitigate this issue, we leverage the staggered rollout of Free Basics across countries and constituencies, and exploit two crucial variations: that of the timing of partnerships between Free Basics and individual mobile operators, and spatial variation in local coverage by each partner mobile operator. Given that our difference-in-differences framework involves staggered treatment adoption, two-way fixed-effects (TWFE) estimators may yield inconsistent estimates in the presence of heterogeneous dynamic treatment effects (Borusyak et al., 2024; Callaway and Sant'Anna, 2021; de Chaisemartin and D'Haultfoeuille, 2020; Goodman-Bacon, 2021; Sun and Abraham, 2021).

To produce causal estimates, we employ the local projection approach to difference-in-differences (LP-DiD) developed by Dube et al. (2025). LP-DiD leverages local projections to estimate dynamic effects while incorporating a flexible clean control condition, in the spirit of the stacked difference-in-differences literature (Cengiz et al., 2019). This regression-based formulation accommodates our unbalanced panel of constituencies, where electoral outcomes are observed only in election years, and parliamentary elections occur at different times across countries. Additionally, the regression framework allows for a rich set of controls, facilitates the empirical investigation of heterogeneous effects, and accommodates alternative assumptions on the variance-covariance matrix of the estimator.

We estimate the effect of Facebook's Free Basics on the election outcomes, candidacy, and vote share of female politicians using the following equation:

$$Y_{j,t+k} - Y_{j,t-1} = \beta^k \Delta Free\ Basics_{j,t} + \mathbf{X}_{j,t} \cdot \delta^k + \eta_{c,t}^k + \varepsilon_{j,t}^k \quad (2)$$

where $Y_{j,t+k} - Y_{j,t-1}$ represents primarily the change in election outcomes, candidacy, or vote share of female politicians in constituency j of country c between two elections: election t+k and

<footnote n="39">³⁹Interestingly, we do observe some heterogeneities. First, the effect of Free Basics is larger in relative terms for individuals who did not complete primary education. Specifically, the probability of using social media increased by 25% for those without primary education, compared to a 14% increase for those with higher education. Second, we find that women were more responsive to Free Basics than men, with connectivity shocks of +18% for women compared to +13% for men. Third, the effect was stronger in rural areas compared to urban areas, with increases of +21% and +14%, respectively. This rural-urban divide is similar when comparing individuals in constituencies with high versus low levels of nighttime light (i.e., above or below the second tertile of the sample). Interestingly, we find no significant age-related heterogeneity, with individuals above and below the median age of 34 responding similarly to the connectivity shock (+13% and +16% in relative terms, respectively). We show in Appendix Table D4 that the same holds true for religion. Individuals from all major religious groups in SSA responded similarly to the introduction of Free Basics, with increases in social media consumption ranging from +13% for Catholics to +20% for non-Catholic Christians.</footnote>
the reference election *t*-1. Our panel of races allows for estimating this equation for up to two elections after the introduction of Free Basics, hence *k* ∈ {0, 1}.

$\Delta \textit{Free Basics}_{j,t}$ is an indicator variable denoting whether Free Basics became available in constituency $j$ between the reference election $t-1$ and election $t$.<fnref n="40" /> Thus, we estimate the effect of a change in Free Basics status on the change in outcomes between the first election with Free Basics ($k=0$) and the reference election, and separately, the second election with Free Basics ($k=1$) and the reference election. All regressions include $\eta_{c,t}$, a set of country-election fixed effects, and $\mathbf{X}_{j,t}$, a vector of controls observed at the race level (i.e. constituency $\times$ election-year level). A crucial aspect of LP-DiD is the exclusion of unclean observations from the regression sample–those whose outcome dynamics may still be influenced by a prior change in treatment status. As a result, each regression includes only newly treated constituencies (for which $\Delta \textit{Free Basics}_{j,t} = 1$) and the clean control (i.e. satisfying: $\textit{Free Basics}_{j,t+k} = 0$). We estimate Equation 2 using OLS and correct the standard errors for two-way clustering, accounting for the two key levels of treatment: election level (i.e. country $\times$ year) and constituency level.

The LP-DiD estimation framework presented in Equation 2 addresses several concerns that could otherwise compromise the causal interpretation of our results. First, the differencing process accounts for both observed and unobserved constituency characteristics, alleviating concerns that slow-moving factors–such as cultural norms, ethnic and religious composition, or long-term disparities in urbanization and economic development–might simultaneously influence the availability of Free Basics and the electoral prospects of female politicians. A within-constituency analysis of the staggered roll-out of Free Basics is particularly relevant in this context. Notably, the decomposition of the variance of the dummy variable indicating whether a female MP is elected reveals that within-constituency variation accounts for 53% of the total variance. Additionally, $\mathbf{X}_{j,t}$ includes time-varying controls to account for differences in constituencies’ short-term dynamics, specifically log population density and average nighttime light intensity, weighted by cell-level population density. In a robustness exercise, we control for local internet infrastructure to account for potential differences in pre-existing development factors that might correlate with electoral outcomes. Furthermore, we adjust our computation of the standard errors to account for spatial correlation, ensuring that statistical inference is robust to potential geographic clustering in treatment effects.

The primary identification assumption underlying our analysis is that the timing of Free Basics’ introduction is exogenous to local political dynamics. This assumption is supported by evidence that Free Basics partnerships are generally negotiated at the national or regional level with large telecommunications companies, such as Airtel, which operate across multiple countries. To validate the parallel trends assumption, we also estimate a placebo version of Equation 3 for two earlier elections: *t*-2 and *t*-3. In this case, we estimate the effect of a future introduction of Free Basics on the election preceding the reference election (*t*-2) and the election before (*t*-3).

<footnote n="40">⁴⁰Specifically, we set the dummy variable *Free Basics*<sub>j,t</sub> to 1 if any population in constituency *j* is covered by Free Basics at time *t*. Section 3 describes the variation in population coverage across treated constituencies. Population covered in the median treated constituency is 83.8% in the first election with Free Basics and reaches 99.9% in the second. However, due to occasional incongruencies between mobile internet markets and political boundaries, some constituencies are only very marginally covered. To avoid misclassifying these as treated, we define any constituency with coverage below 1% of its population as untreated in the baseline (affecting just 0.8% of the sample in the first election with Free Basics and 0.2% in the second election.). We also test the robustness of our results using alternative coding rules that classify constituencies as treated only if coverage exceeds a given threshold and explore potential treatment heterogeneities based on treatment intensity.</footnote>
These placebo estimations test whether a future introduction of Free Basics has any observable
effect on outcomes in elections preceding the reference election (i.e. in t-2 and t-3). We include
the same set of fixed effects and controls as in the main analysis. In the two specifications,
the sample includes the treated constituencies (i.e. satisfying: $\Delta \textit{Free Basics}_{j,t} = 1$) for which
we observe an election in the relative year of interest (respectively, elections t-2 and t-3), and
the clean control group, defined as the constituencies that are not yet treated in election t (i.e.
satisfying: $\textit{Free Basics}_{j,t} = 0$).

# 6 Results

This section presents our main findings on the impact of Facebook’s Free Basics availability on
parliamentary election outcomes for female candidates, analyzing 8,814 races conducted between
2001 and 2022 across 17 sub-Saharan African countries. We start by examining the influence of
Free Basics on the election of female MPs. This analysis is then complemented by an exploration
of the roles played by electoral competition and candidate selection. Importantly, we delve into
the pivotal role of political parties in shaping these outcomes as well as the dynamics of the
effects.

## 6.1 Free Basics and the election of female MPs

**Baseline results.** Figure 2 displays the estimates from Equation 2, with the dependent vari-
able indicating whether the election winner is a woman. Appendix Table E1 provides detailed
features of each regression included in the analysis. All regressions incorporate the full set of
controls described in section 5.1. This rich set of covariates allows us to isolate the effect of the
connectivity shock induced by Facebook’s Free Basics by comparing treated constituencies to
similar constituencies with comparable levels of urbanization and local economic development.

We highlight three key findings. First, the likelihood of electing a female MP increases sig-
nificantly in the second election following Free Basics’ introduction.<fnref n="41" /> This effect is both sizeable
and statistically significant at the 5 percent level, with treated constituencies experiencing a 10.2
percentage point higher likelihood of electing a woman–equivalent to one-third of a standard
deviation.<fnref n="42" /> Second, we detect no significant effect on the first election after its introduction.
The point estimate is -0.021 (p-value = 0.36), suggesting that the connectivity shock does not
immediately impact female candidates’ electoral prospects. On average, this first election occurs
2.7 years after Free Basics’ introduction, though timing varies from less than a year to five years.
Third, the estimates from the two placebo elections–held two and three electoral cycles before
Free Basics–are close to zero, supporting the parallel trends assumption and indicating no pre-

<footnote n="41">⁴¹In the regression presented in Figure 2 for election +1 (the second election with Free Basics, also reported in column 4 of Appendix Table E1), we analyze 4,640 races, as the outcome is expressed in differences. This second election occurs, on average, six years after the introduction of Facebook’s Free Basics, with a range of five to eight years.</footnote>

<footnote n="42">⁴²Building on the persuasion rate concept in media economics (DellaVigna and Kaplan, 2007; DellaVigna and Gentzkow, 2010), we can compute the share of constituencies “convinced” to elect a female MP through Free Basics. Rescaling our point estimate (0.102) by the share at risk, i.e. constituencies electing a male MP in the previous election (.895), yields a persuasion rate of $\frac{0.102}{0.895} = 11.39\%$. This falls within the typical range of persuasion effects found in studies of communication technologies and political behavior. Using a more conservative measure of exposure–the constituency population covered by Free Basics (97% on average in the second election)–gives a similar persuasion rate of 11.75%.</footnote>
existing differences in trends between treated and control units.

<figure><img src="image_5.png" /><figcaption>Figure 2: Effects of Free Basics availability on female MP election</figcaption></figure>

Notes: This figure presents the LP-DiD estimates of the effect of Free Basics availability on the likelihood of electing a female MP. The unit of observation is an electoral race, i.e. a constituency × election year. The dependent variable is a binary indicator set to 1 if a woman wins the election. All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Standard errors are two-way clustered at constituency and country-year level. Bars represent 95% confidence intervals. *** p<0.01, ** p<0.05, * p<0.1.

**Composition effects and alternative estimators.** One potential concern is that, as with any difference-in-differences analysis, composition effects might influence the observed differential effect between the first and subsequent elections following the introduction of Free Basics. To address this issue, we present the estimates in Panel B of Appendix Table E1, where we fix the set of countries across all four regressions. The results remain qualitatively consistent: we observe no differential trend in placebo elections between constituencies that will later adopt Free Basics and their unaffected counterparts. Moreover, we still find that Free Basics increases the likelihood of a female MP being elected, but this effect emerges only in the second election after its introduction.

Another potential concern is that our findings may be influenced by the specific difference-in-differences estimator used (Dube et al., 2025) among the various methods recently proposed in the literature. Figure 3 addresses this concern by presenting estimates and 95% confidence intervals derived from alternative event-study estimators (Borusyak et al., 2024; Callaway and Sant'Anna, 2021; Sun and Abraham, 2021; de Chaisemartin and D'Haultfoeuille, 2020). The results are consistent across estimators and align with the parallel trends assumption. Regardless of the estimator employed, the connectivity shock initiated by Facebook is consistently associated with an increased probability of female MP elections in the second election following the introduction of Free Basics. Furthermore, the magnitude of the effect is remarkably similar across the different estimators, with point estimates ranging from 0.097 to 0.107.

**Local internet infrastructure.** Mechanically, a constituency can only be covered by Free Basics if at least one mobile operator is active locally. This raises two important questions. First,
<figure><img src="image_6.png" /><figcaption>Figure 3: Effects of Free Basics availability on female MP election: alternative difference-in-differences strategies</figcaption></figure>

*Notes:* This figure presents event-study plots from five estimators: Borusyak et al. (2024) (diamond markers), Callaway and Sant'Anna (2021) (cross markers), Sun and Abraham (2021) (triangle markers), de Chaisemartin and D'Haultfoeuille (2020) (square markers), and Dube et al. (2025) (red circle markers). We reproduce the Dube et al. (2025) estimates from Figure 2 for direct comparison. The unit of observation is an electoral race (constituency × election year), and the dependent variable is a binary indicator set to 1 if a woman wins the race. The time variable is the election year, and the treatment group (election 0 relative to Free Basics) corresponds to the first election in which Free Basics became available in a constituency. All specifications include country × election-year fixed effects and control for the logarithm of population density and nighttime light. Additionally, Borusyak et al. (2024), Sun and Abraham (2021), and de Chaisemartin and D'Haultfoeuille (2020) control for changes in these variables relative to the reference election. Standard errors are clustered at the constituency level for Borusyak et al. (2024) and de Chaisemartin and D'Haultfoeuille (2020) (as two-way clustering is not allowed), and two-way clustered at the country-year and constituency levels for Callaway and Sant'Anna (2021), Dube et al. (2025) (our baseline), and Sun and Abraham (2021). Bars represent 95% confidence intervals.

since Free Basics partnerships are established with mobile operators already serving the targeted country, it is possible that the observed effects reflect the broader impact of longstanding mobile network coverage and prolonged access to mobile internet, rather than the contribution of Free Basics. This distinction is particularly relevant, as the seminal work of Guriev et al. (2021) demonstrate significant effects of 3G exposure on political attitudes worldwide, although they do not specifically examine its impact on female political representation. To address this concern, we analyze the effects of the staggered roll-out of 3G infrastructure instead of Free Basics. The results of this analysis, presented in Appendix Figure E1, reveal no positive effect of 3G availability on the election of female MPs, even in constituencies exposed to 3G for a second or third election. In fact, the effect of 3G availability is negative in the second and third elections, though the estimates are imprecise. We interpret this absence of a positive effect of mobile internet, combined with our evidence of a stronger connectivity shock generated by Free Basics (discussed in section 4), as further evidence that in sub-Saharan Africa, where the cost of internet data is prohibitively high, granting free access to Facebook plays a pivotal role in shaping political outcomes.

Second, this analysis underscores the importance of investigating the robustness of the effect
of Free Basics, to the inclusion of controls for local internet availability, particularly 3G coverage. In our sample, internet is available locally for 48% of the races, and Free Basics is available in only 51% of them. Controlling for the availability of mobile internet allows us to account for unobserved factors that might drive internet access, thus isolating the effect of the connectivity boost provided by Free Basics partnerships at the local level. We thus estimate a version of Equation 2 that includes separate dummies for the presence of 2G and 3G. Our identifying variation comes from the fact that, within a given election, Free Basics becomes available only in constituencies served by a partner mobile operator, among those already covered by mobile broadband internet. As shown in Appendix Figure E2, our baseline result remains robust after this control. In the second election, the point estimate is 0.127, and the positive effect is significant at the 5% level.

**Correlates of urbanization.** Because the impact of Free Basics depends on technological adoption, urbanization and economic development are likely to matter. To examine this, we estimate two versions of Equation 2, interacting the treatment variable with constituency-level indicators of nighttime light intensity and population density (above vs. below the sample median). The results, reported in Figure E4, show a stronger effect in more urbanized areas (proxied by nighttime lights and population density), while a positive but imprecisely estimated effect remains in less urbanized regions.

**Length of exposure to Free Basics.** The dynamics observed across the two electoral cycles may mask a more gradual effect of Free Basics that begins to materialize with increasing exposure over time. To examine this possibility, we estimate an alternative version of Equation 2 that accounts for constituency-level treatment intensity. Specifically, we incorporate the number of years voters in a constituency had access to Free Basics, based on (i) the set of mobile operators active in the constituency and (ii) the timing of their partnership agreements with Facebook. This approach allows us to test whether the effect builds up progressively with longer exposure rather than appearing abruptly between electoral cycles. This leads to the following specification:

$$Y_{j,t+k} - Y_{j,t-1} = \sum_{l=L}^{x} [\Delta \text{Free Basics}_{j,t} \times \text{Years}_{l(j,t+k)}] \cdot \beta^{k,l} + \mathbf{X}_{j,t} \cdot \delta^{k} + \eta_{c,t}^{k} + \varepsilon_{j,t}^{k} \quad (3)$$

where Years$_{l(j,t+k)}$ is a set of dummy variables indicating whether the earliest partnership affecting constituency j occurred l years prior to the election t+k.<fnref n="43" />

Figure 4 presents the estimates of $\beta^{k,l}$ from Equation 3 for the first and second elections following the introduction of Free Basics. In the first election, we find that constituencies exposed to Free Basics do not differ in their likelihood of electing a female MP compared to their control counterparts, regardless of their length of exposure. In the second election after the introduction of Facebook's Free Basics, the length of exposure does not appear to drive systematic differences in outcomes. Constituencies with six to eight years of coverage show consistently positive and statistically significant coefficients, with no meaningful variation across exposure length. The only exception is constituencies with just five years of access, where no effect is detected. These areas also experienced their first election with Free Basics almost immediately after its rollout,

<footnote n="43">⁴³In other words, when a constituency is covered by more than one mobile operator partnering with Free Basics at the time of election, we retain the earliest partnership to calculate the length of exposure.</footnote>
leaving little time for voters or political actors to engage with the technology.

Overall, these findings suggest that the heterogeneity in the impact of Free Basics unfolds primarily within electoral cycles. This pattern is consistent with a learning process on the part of political actors (a hypothesis we investigate in subsection 6.3), whereby they adjust their strategies in response to evolving voter attitudes and preferences revealed through the electoral process. We explore this mechanism in greater detail in section 7.

<figure><img src="image_7.png" /><figcaption>Figure 4: Effects of Free Basics availability on female MP election over years of exposure</figcaption></figure>

Notes: This figure presents the LP-DID estimates of the effect of Free Basics availability on the likelihood of electing a female MP, over years of exposure to Free Basics. The unit of observation is an electoral race, i.e. a constituency × election year. The dependent variable is a binary indicator set to 1 if a woman wins the election. All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Standard errors are clustered two-way at constituency and country-year level. Bars represent 95% confidence intervals.

**Treatment intensity and robustness checks.** We examine treatment heterogeneity by exploiting variations in the treatment intensity, defined as the estimated share of the population covered by Facebook's Free Basics. As detailed in section 3, most of the population in constituencies served by a partner operator was covered during both elections. While the distribution of coverage is highly skewed (see Appendix Figure A2), we analyze treatment effects at varying coverage levels using 20-percentage-point bins. A limitation of this approach is the small sample size for coverage bins below 80% (175-213 treated units in the first election and 29-74 in the second).<sup>44</sup> This is why we prefer the binary treatment version in the baseline analysis. Appendix Figure E3 presents results from a model where treatment is divided into these five coverage bins. In the second election, effects are positive across all bins, but statistically significant only for the highest two: 60-80% (5% significance) and 80-100% (10% significance).<sup>45</sup>

We also show in Panel A of Appendix Table E5 that the positive effect of Free Basics on female MP election in the second election holds when using alternative coverage coding, employing

<footnote n="44"><sup>44</sup>Bins below 80% of coverage contain fewer treated units, particularly in the second election.</footnote>

<footnote n="45"><sup>45</sup>In the first election, coefficients are mostly negative or near zero and insignificant, except for coverage levels of 20-40% and 60-80%, which yield statistically significant negative effects at the 10% and 5% levels, respectively.</footnote>
alternative treatment definitions (rather than splitting the treatment into intensity categories as presented here), and accounting for spatial autocorrelation.

**Electoral rules.** While we focus on first-past-the-post (FPTP) races in single-member constituencies, some countries in our sample allocate a subset of parliamentary seats through alternative systems, including reserved seats for women.<fnref n="46" /> The impact of reserved seats on female candidacy in standard races is ambiguous, as they can create both crowding-out and ripple effects (Dahlerup and Freidenvall, 2011; Esteve-Volart and Bagues, 2012). However, by splitting the Free Basics effect between countries with and without reserved seats, Figure E4 shows similar estimates across both systems, suggesting that Free Basics' impact is independent of electoral reservation rules.

**Quality of Democracy: fair elections and freedom of the press.** The impact of Free Basics on women's electoral success depends on the strength of democratic institutions. Using data from the Varieties of Democracy (V-Dem) project,<fnref n="47" /> we assess heterogeneities in two areas: election fairness and press freedom.

First, we interact the treatment variable in Equation 2 with indicators of fair versus unfair elections, effectively splitting the effect across these categories. Figure E4 shows that Free Basics' impact on female representation is concentrated in fair elections. This pattern is consistent with two reinforcing mechanisms. Under fair elections, voters are freer to express their true preferences at the polls. At the same time, political actors can more readily enter, compete, or adjust their strategies in response to shifts in the voting environment. By contrast, under unfair elections, dominant actors face weaker incentives to adapt, which helps explain the smaller but still positive impact of Free Basics in those contexts.

Next, we examine press freedom by interacting our treatment variable with V-Dem's press freedom index, splitting elections into those above and below the sample median. As shown in Figure E4, Free Basics' effect is almost entirely driven by elections characterized by low press freedom. This aligns with the idea that social media serves as an alternative information source where traditional media is restricted, underscoring its role in sustaining media pluralism and fostering political change.

## 6.2 Effects of Free Basics on electoral competition and candidate selection

In this subsection, we examine how electoral competition shapes the dynamic effect of Facebook's Free Basics on the election of female MPs, focusing on the number of candidates and the distribution of votes. To capture vote share distribution, we use two measures. The fragmentation index (Laakso and Taagepera, 1979) calculates the effective number of candidates as: $\frac{1}{\sum_{i}^{N} v_i^2}$, where $v_i$ is the vote share obtained by candidate $i$. The fractionalization index, following Montalvo and Reynal-Querol (2005), is defined as: $\sum_{i}^{N} v_i(1 - v_i)$.

Table E2 shows no significant effect of Free Basics on electoral competition in either of the two subsequent elections: effect sizes are small and estimates imprecise (columns 1-6). Aggregate measures may mask heterogeneity, as social media can differentially affect mainstream and

<footnote n="46">⁴⁶Kenya, Tanzania, Uganda, and Zimbabwe have reserved seats. In Tanzania and Zimbabwe, they are assigned through proportional representation; in Kenya and Uganda, they follow FPTP but in larger constituencies, with citizens casting two votes.</footnote>

<footnote n="47">⁴⁷Data available at: v-dem.net</footnote>
marginalized candidates (Campante et al., 2018; Guriev et al., 2021; Manacorda et al., 2025). To test this, we separately examine candidates from parliamentary parties, non-parliamentary parties, and independents, but again find no meaningful effects (Appendix Table E3). Finally, we detect a negative and statistically significant impact on voter turnout in the second election, consistent with prior evidence that internet and social media can distract citizens or discourage participation.

We examine the role of candidate selection in explaining the positive effect of Facebook's Free Basics on female MP elections. Results are presented in Table 5. Columns 1 and 2 show that Free Basics reduced female candidacy by 10 percentage points in the first election, but had a positive, though imprecise, effect of 16 points in the second. Columns 5-6 indicate that the top female candidate's vote share was unaffected in the first election, but rose significantly by 7.8 percentage points in the second, mirroring the patterns in winning probabilities (columns 3-4).

<table><caption>Table 5: Effects of Free Basics availability on female candidacy, election and vote share</caption><thead><tr><th rowspan="3">Dependent variable<br>Election after Free Basics entry</th><th colspan="6">Woman</th></tr><tr><th colspan="2">Run</th><th colspan="2">Win</th><th colspan="2">Vote share</th></tr><tr><th>First<br>election<br>(1)</th><th>Second<br>election<br>(2)</th><th>First<br>election<br>(3)</th><th>Second<br>election<br>(4)</th><th>First<br>election<br>(5)</th><th>Second<br>election<br>(6)</th></tr></thead><tbody><tr><td>Free Basics</td><td>-0.101*<br>(0.059)</td><td>0.162<br>(0.128)</td><td>-0.021<br>(0.022)</td><td>0.102**<br>(0.049)</td><td>0.001<br>(0.014)</td><td>0.078***<br>(0.028)</td></tr><tr><td>Mean dep. var.</td><td>0.492</td><td>0.492</td><td>0.104</td><td>0.110</td><td>0.105</td><td>0.107</td></tr><tr><td>Observations</td><td>5547</td><td>2592</td><td>5547</td><td>2592</td><td>4867</td><td>1964</td></tr><tr><td>Country × election-year FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Notes: This table presents the LP-DiD estimates of the effect of Free Basics availability on female candidacy, election and vote share. The unit of observation is an electoral race, i.e. a constituency × election year. The dependent variable is respectively a binary indicator set to 1 if a woman wins the election (columns 3-4) and the vote share received by the best-ranked woman (columns 5-6). All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Standard errors are clustered two-way at constituency and country-year level. The mean dependent variable is computed in the reference election-year. *** p<0.01, ** p<0.05, * p<0.1.

## 6.3 Political parties as gatekeepers: candidate selection and learning over electoral cycles

Given the evidence that the availability of Facebook's Free Basics does not significantly affect overall electoral competition but is associated with an increased likelihood of female candidates appearing on the ballot, we investigate how political parties influence both the selection of female candidates and their subsequent electoral performance. Specifically, we examine how these effects evolve across the two electoral cycles following the platform's introduction, comparing outcomes for male and female candidates.

**Decomposition of the effects on candidate selection by party affiliation.** We first investigate the effects of Facebook's Free Basics on candidacies by party affiliation. We do so by
estimating a version of Equation 2 where the dependent variable is now alternatively the likelihood of at least one candidate of a given gender and party affiliation appearing on the ballot, and the total number of such candidates. The results are presented in Figure 5.

<figure><img src="image_8.png" /><figcaption>Figure 5: Effects of Free Basics availability on candidate selection</figcaption></figure>

Notes: This figure presents the LP-DID estimates of the effect of Free Basics availability on measures of candidate selection. The unit of observation is an electoral race, i.e. a constituency × election year. The dependent variable is respectively the number of candidates of a given type, i.e. a party affiliation-gender pair, running (Panel A) or a binary indicator set to 1 if at least one candidate of that given type runs (Panel B). All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Standard errors are clustered two-way at constituency and country-year level. Bars represent 95% confidence intervals.

The decline in female candidacy in the election following the introduction of Free Basics is driven by non-parliamentary party candidates. This pattern is evident in both their presence and overall numbers. In contrast, female candidates from parliamentary parties or running as independents remain unaffected, with coefficients near zero and statistically insignificant.

By the second election with Free Basics, the positive effect on female entry is concentrated among women affiliated with parliamentary parties. The 0.23 coefficient on candidate numbers represents a 153% increase relative to the low in-sample mean (0.15). Free Basics also increases the likelihood of at least one female candidate by 18 percentage points, resulting in a 20.11% persuasion rate. These effects are significant at the 1% and 5% levels (see columns 2 of Panels
A and C in Appendix Table E4). Panel C of Appendix Table E5 shows robustness when using alternative coverage coding, treatment definitions, and accounting for spatial autocorrelation.

The effects on male candidates mirror those observed for females. In the second election, the number of male candidates from parliamentary parties declines by 31% of its mean (1.16), a reduction significant at the 1% level (see columns 2 of Panel B in Appendix Table E4).

**Decomposition of the effects on electoral performance by party affiliation.** We analyze the role of established parties in Facebook's Free Basics' impact on female MP elections. Table 6 presents estimates where the dependent variable is either a dummy variable indicating victory or the vote share of the top-performing candidate, categorized by gender and party affiliation.

Panel A reveals that, in the second election, Free Basics significantly increases the winning probability of female politicians running with a parliamentary party by 11 percentage points (a 163% increase relative to the mean). This effect is statistically significant at the 5% level.<fnref n="48" /> Si-multaneously, the winning probability of male politicians from these established parties decreases by 8 percentage points (a 15% decline relative to the mean), as shown in Panel B.

Similar patterns are observed in the vote shares of the first-ranked female and male candidates affiliated with parliamentary parties. In the first election, the vote share of the highest-ranked male candidate from a parliamentary party decreases by 4 percentage points (a 10% decline relative to the mean), with this effect intensifying in the second election to a reduction of 13 percentage points (a 33% decline relative to the mean). Conversely, the vote share of the first-ranked female candidate from a parliamentary party increased significantly in the second election by 11 percentage points (a 164% increase relative to the mean).

**Bounds on the effects of Free Basics on party candidates, conditional on running.** We examine whether Free Basics affects candidates' chances of winning and vote shares, conditional on participation. Our focus is on the two groups where significant effects on candidacy were detected: male parliamentary candidates in the first election with Free Basics and female parliamentary candidates in the second. A direct comparison of electoral outcomes between constituencies with and without Free Basics is inappropriate, as our earlier analysis shows systematic differences in candidate composition. To address this, we apply the bounding methodology of Lee (2009) adapted to an electoral setting (see e.g. Cantoni and Pons (2022)). Appendix subsection E.3 details the computation of bounds on Free Basics' effects, with standard errors obtained via bootstrapping (10,000 replications). Results are presented in Table 7.

In the first election, Free Basics is found to reduce the probability of male candidates winning, conditional on their candidacy. Similarly, Free Basics availability lowers their vote share. Their conditional likelihood of election drops by 7.1 to 8.2 percentage points, with both the lower and upper bounds being statistically significant. In the second election, Free Basics increases female candidates' probability of winning, conditional on running, by 15.1 to 42.2 percentage points, and generates similar effects on their vote shares. While the estimation of the upper bounds are significant, the lower bounds are not.

These findings suggest that Free Basics' negative impact on male candidates in the first election results from both lower entry rates and reduced voter support, while its positive effect

<footnote n="48">⁴⁸Panel B of Appendix Table E5 shows robustness when using alternative coverage coding, treatment definitions, and accounting for spatial autocorrelation.</footnote>
<table><caption>Table 6: Effects of Free Basics availability on gender representation and vote shares, by candidacy type</caption><thead><tr><th rowspan="3">Election after Free Basics entry</th><th colspan="6">Electoral outcome of candidates</th></tr><tr><th colspan="6">endorsed by:</th></tr><tr><th colspan="2">a parliamentary party</th><th colspan="2">a non-parliamentary party</th><th colspan="2">running as independent</th></tr><tr><th>First election</th><th>Second election</th><th>First election</th><th>Second election</th><th>First election</th><th>Second election</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><th colspan="6">Panel A: Winner is a woman</th></tr><tr><td>Free Basics</td><td>-0.008<br>(0.030)</td><td>0.119**<br>(0.053)</td><td>-0.009<br>(0.011)</td><td>-0.017<br>(0.010)</td><td>-0.003<br>(0.005)</td><td>0.001<br>(0.001)</td></tr><tr><td>Mean dep. var.</td><td>0.069</td><td>0.073</td><td>0.030</td><td>0.032</td><td>0.007</td><td>0.004</td></tr><tr><td>Observations</td><td>5547</td><td>2592</td><td>5547</td><td>2592</td><td>5547</td><td>2592</td></tr><tr><th colspan="6">Panel B: Winner is a man</th></tr><tr><td>Free Basics</td><td>-0.081*<br>(0.044)</td><td>-0.170<br>(0.148)</td><td>0.084***<br>(0.029)</td><td>0.008<br>(0.095)</td><td>0.017<br>(0.019)</td><td>0.024<br>(0.022)</td></tr><tr><td>Mean dep. var.</td><td>0.555</td><td>0.542</td><td>0.262</td><td>0.282</td><td>0.043</td><td>0.026</td></tr><tr><td>Observations</td><td>5547</td><td>2592</td><td>5547</td><td>2592</td><td>5547</td><td>2592</td></tr><tr><th colspan="6">Panel C: Vote share of the first-ranked female candidate</th></tr><tr><td>Free Basics</td><td>0.008<br>(0.015)</td><td>0.107***<br>(0.014)</td><td>-0.008<br>(0.008)</td><td>-0.016<br>(0.011)</td><td>-0.004<br>(0.005)</td><td>-0.013<br>(0.012)</td></tr><tr><td>Mean dep. var.</td><td>0.060</td><td>0.065</td><td>0.034</td><td>0.041</td><td>0.011</td><td>0.008</td></tr><tr><td>Observations</td><td>4867</td><td>1964</td><td>4867</td><td>1964</td><td>4867</td><td>1964</td></tr><tr><th colspan="6">Panel D: Vote share of the first-ranked male candidate</th></tr><tr><td>Free Basics</td><td>-0.041*<br>(0.023)</td><td>-0.125*<br>(0.063)</td><td>0.024<br>(0.025)</td><td>0.008<br>(0.029)</td><td>-0.001<br>(0.009)</td><td>0.026**<br>(0.011)</td></tr><tr><td>Mean dep. var.</td><td>0.400</td><td>0.383</td><td>0.227</td><td>0.265</td><td>0.063</td><td>0.049</td></tr><tr><td>Observations</td><td>4867</td><td>1964</td><td>4867</td><td>1964</td><td>4867</td><td>1964</td></tr><tr><th colspan="7">All panels</th></tr><tr><td>Country × election-year FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Notes: This table presents the LP-DiD estimates of the effect of Free Basics availability on the gender of elected MPs and vote shares, by candidacy type. The unit of observation is an electoral race, i.e. a constituency × election year. For each type of candidacy (endorsed by a parliamentary party, by a non-parliamentary party and running as independent), the dependent variable is respectively a binary indicator set to 1 if a woman of that type wins the election (Panel A), a binary indicator set to 1 if a man of that type wins the election (Panel B), the vote share received by the first-ranked female candidate of that type (Panel C) and the vote share received by the first-ranked male candidate of that type (Panel D). All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Standard errors are clustered two-way at constituency and country-year level. The mean dependent variable is computed in the reference election-year. *** p<0.01, ** p<0.05, * p<0.1.

on female candidates in the second election is driven by higher entry rates and increased vote shares.

**Party-level analysis.** Motivated by the concentration of Free Basics' effects among candidates
<table><caption>Table 7: Bounds on the effects of Free Basics availability on electoral success, conditional on running</caption><thead><tr><th rowspan="3">Dependent variable</th><th colspan="2">Male candidate running with a parliamentary party</th><th colspan="2">Female candidate running with a parliamentary party</th></tr><tr><th>Win</th><th>Vote share</th><th>Win</th><th>Vote share</th></tr><tr><th>First election</th><th>First election</th><th>Second election</th><th>Second election</th></tr><tr><th>Election after Free Basics entry</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Lower bound</td><td>-0.082**<br>(0.038)</td><td>-0.056***<br>(0.018)</td><td>0.151<br>(0.217)</td><td>0.109<br>(0.110)</td></tr><tr><td>Upper bound</td><td>-0.071*<br>(0.040)</td><td>-0.044**<br>(0.019)</td><td>0.422*<br>(0.251)</td><td>0.380*<br>(0.210)</td></tr><tr><td>Bootstrap replications</td><td>10000</td><td>10000</td><td>10000</td><td>10000</td></tr><tr><td>Mean dep. var., absence of Free Basics</td><td>.692</td><td>.478</td><td>.402</td><td>.320</td></tr></tbody></table>

Notes: This table presents bounds to the LP-DID estimates of the conditional effect of Free Basics availability on the electoral performance of men and women running for a parliamentary party. Corresponding unconditional effects are presented in Table 6. The dependent variable is respectively a binary indicator set to 1 if the winner is a man from a parliamentary party (column 1), the vote share of the first-ranked man from a parliamentary party (column 2), a binary indicator set to 1 if the winner is a woman from a parliamentary party (column 3) and the vote share of the first-ranked woman from a parliamentary party (column 4) Standard errors are computed by bootstrap using 10,000 replication. *** p<0.01, ** p<0.05, * p<0.1.

endorsed by major parties, we now adopt an alternative empirical strategy that tracks the gender of the candidates of the main parties over time and across constituencies.<fnref n="49" /> This shifts the unit of observation to party × race, allowing us to take into account party-level dynamics at the national level, and exploit variations in the endorsement and electoral outcomes of the party candidates, at the local level. The specification, adapted from Equation 2, replaces country-year fixed effects with party-year fixed effects, and includes otherwise the same controls. The results, presented in Appendix Table F5, reinforce our earlier findings. Panel A confirms that, in the second election with Free Basics, female candidates endorsed by major parties are more likely to run (although imprecisely estimated) and more likely to win, with corresponding increases in vote shares for the top-ranked female candidate. Panels B and C refine this analysis by distinguishing between first-time (Panel B) and experienced (Panel C) female candidates, showing that the positive effect in the second election is driven entirely by new entrants.

**Aggregate effects.** We now examine whether Facebook's Free Basics increases the total number of elected female politicians or merely redistributes them by shifting party endorsement strategies. If parties compensate by favoring male candidates elsewhere, this would imply a reallocation rather than an expansion of female representation, raising concerns about spillover effects and potential violations of the Stable Unit Treatment Value Assumption (SUTVA) (Rubin, 1977).

This key identification challenge can arise if parties strategically endorse female candidates in Free Basics constituencies to maximize electoral gains, potentially affecting candidate selection

<footnote n="49">⁴⁹For each country included in our sample, and for the relevant time period, we track the main parties election-by-election. Appendix Table F1, Table F2 and Table F3 list the party × election-year we are able to track over time. Appendix Table F4 presents the descriptive statistics of the dependent variables used in this analysis.</footnote>
in non-treated areas. This is particularly relevant if female politicians are scarce or voter demand for female leaders is lower in untreated regions. To address this, we analyze variation at the regional (ADM1) level, estimating Free Basics' impact on female electoral outcomes using two specifications of Equation 2: one at the party × region × election-year level and another at the region × election-year level. In both, the dependent variable is the share of elected female politicians, while the treatment variable is the share of constituencies covered by Free Basics in the region-year. Results (presented in Appendix Table G1 and Table G2) show that in the second election with Free Basics, greater regional coverage leads to higher female representation. A one-standard-deviation increase in Free Basics coverage raises the share of elected female candidates by 0.5 percentage points at the party × region level (43% of its mean) and by 3.9 percentage points at the region level (40% of its mean). These findings suggest that Free Basics drives a net increase in female political representation rather than merely redistributing female MPs across constituencies.

<table><caption>Table 8: Effects of Free Basics availability on win margins - the role of gender and parties</caption><thead><tr><th rowspan="3">Sample restriction</th><th colspan="6">Races with victories of male candidates running with a parliamentary party</th></tr><tr><th colspan="6">Win margin over</th></tr><tr><th colspan="2">runner-up</th><th colspan="2">second best-ranked male</th><th colspan="2">best-ranked female</th></tr><tr><th>Election after Free Basics entry</th><th>First election</th><th>Second election</th><th>First election</th><th>Second election</th><th>First election</th><th>Second election</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Free Basics</td><td>-0.052*<br>(0.030)</td><td>-0.045<br>(0.049)</td><td>-0.015<br>(0.035)</td><td>-0.015<br>(0.075)</td><td>-0.035**<br>(0.014)</td><td>-0.096***<br>(0.023)</td></tr><tr><td>Mean dep. var.</td><td>0.275</td><td>0.258</td><td>0.284</td><td>0.266</td><td>0.493</td><td>0.473</td></tr><tr><td>Observations</td><td>3273</td><td>1401</td><td>3273</td><td>1401</td><td>3273</td><td>1401</td></tr><tr><td>Country × election-year FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Notes: This table presents the LP-DiD estimates of the effect of Free Basics availability on win margins of men endorsed by a parliamentary party. The unit of observation is an electoral race, i.e. a constituency × election year. The sample is restricted to races where the winner is a male candidate running with a parliamentary party. The dependent variable is the win margin in vote shares over respectively the runner-up (columns 1-2), the second best-ranked man (columns 3-4), the best-ranked woman (column 5-6). All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Standard errors are clustered two-way at constituency and country-year level. The mean dependent variable is computed in the reference election-year. *** p<0.01, ** p<0.05, * p<0.1.

**Dynamics of the effects.** To better understand party learning in the first election with the platform, and its implications for the second, we move beyond candidate identity and overall vote shares to examine victory margins in constituencies that elected a male parliamentary party candidate in the first election. This allows us to capture whether, even when male candidates continued to win, they did so with narrower margins than in comparable races without Free Basics.
Table 8 presents estimates for an alternative specification of Equation 2 applied to this sub-sample, where the dependent variable is, alternatively, the win margin of the elected male over the runner-up (columns 1 and 2), the second-best male candidate (columns 3 and 4), and the best female candidate (columns 5 and 6). In the first election, the win margin of parliamentary party-affiliated male winners decreases significantly with Free Basics–by 5.2 percentage points, a 19% reduction relative to the mean. This decline is also observed when focusing on the win margin over the best female candidate (-3.5 percentage points, a 7% decrease relative to the mean), with statistical significance at the 5% level. In the second election, the negative effect on the win margin over the best female candidate persists and intensifies. Meanwhile, the win margin over the second-best male candidate also shows negative point estimates in both elections but remains imprecisely estimated.

As a complementary exercise, we examine how the impact of Free Basics on female victories varies with the strength of prior male incumbents’ electoral dominance, accounting for changes in their win margins over time and with the introduction of the platform. To this aim, we restrict the analysis to constituencies where male parliamentary party candidates had secured the two previous elections. Results are reported in Table 9. Re-estimating the impact of Facebook’s Free Basics on the election of female parliamentary party MPs shows an increase of 4 percentage points (+89% at the mean value, column 1).<sup>50</sup> The effect is 2.9 percentage points smaller in constituencies where the male incumbent won by a landslide (i.e. margin above the median of 22.7 percentage points), amounting to 54.7% of the effect observed where margins were below the median. A 10 percentage point decline in the male incumbent’s win margin over the best-ranked female candidate between the last two elections increases the probability of electing a female parliamentary party MP by 7.1 percentage points (+173% relative to the baseline effect, column 3). This effect becomes negligible and statistically insignificant when margins are computed relative to the best-ranked male candidate (column 4). Taken together, these results provide empirical evidence consistent with learning by established parties: disappointing outcomes for male candidates in the first election with the platform appear to fuel the subsequent election of female parliamentary party candidates.

# 7 Mechanisms

Facebook’s Free Basics significantly expanded internet connectivity and, by the second electoral cycle after its introduction, contributed to the election of more female MPs. The effect appears to operate mainly through established parties, which responded to disappointing performances of male incumbents in the first election with the platform by fielding more female candidates and supporting them with the networks and resources required for electoral success. These patterns raise the key question of the mechanisms through which Free Basics shaped this dynamic.

If political parties act as “delegated monitors”, entrusted by voters to select suitable candidates (Caillaud and Tirole, 2002), they are also likely to respond to shifts in public opinion (Casey et al., 2021; Gulzar et al., 2021). One plausible mechanism, therefore, is that parties adjust their candidate selection strategies in response to growing public support for female lead-

<footnote n="50"><sup>50</sup>The share of races in which a parliamentary party endorsed a female MP is lower in this subsample (5% versus 7.3% in the full sample).</footnote>
<table><caption>Table 9: Effect of Free Basics on female MP election, conditional on male-dominated electoral history</caption><thead><tr><th>Sample restriction</th><td colspan="4">Races with victories of male candidates running with a parliamentary party over the last two elections (t-1 and t-2)</td></tr><tr><th>Dependent variable</th><td colspan="4">Winner is a woman endorsed by a parliamentary party (t)</td></tr><tr><th>Election after Free Basics entry</th><th>Second election (1)</th><th>Second election (2)</th><th>Second election (3)</th><th>Second election (4)</th></tr></thead><tbody><tr><td>Free Basics</td><td>0.041***<br>(0.014)</td><td>0.053***<br>(0.014)</td><td>0.041**<br>(0.015)</td><td>0.041***<br>(0.014)</td></tr><tr><td>... × male incumbent won landslide (t-1)</td><td></td><td>-0.029**<br>(0.012)</td><td></td><td></td></tr><tr><td>... × change in win margin (t-1 vs t-2)</td><td></td><td></td><td></td><td></td></tr><tr><td>... over best-ranked female</td><td></td><td></td><td>-0.071*<br>(0.038)</td><td></td></tr><tr><td>... over best-ranked male</td><td></td><td></td><td></td><td>-0.005<br>(0.037)</td></tr><tr><td>Mean dep. var.</td><td>0.050</td><td>0.050</td><td>0.050</td><td>0.050</td></tr><tr><td>Observations</td><td>1069</td><td>1069</td><td>1069</td><td>1069</td></tr><tr><td>Country × election-year FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Notes: This table presents LP-DiD estimates of the effect of Free Basics availability on the election of a female candidate endorsed by a parliamentary party, using the subsample of races in constituencies where a male candidate endorsed by a parliamentary party was elected in both of the last two elections. The unit of observation is an electoral race, i.e. a constituency × election year. The sample is restricted to races where the winner was a male candidate running with a parliamentary party in the last two elections. The dependent variable is a dummy equal to one if a female candidate endorsed by a parliamentary party wins. The variable “Male incumbent won landslide” equals one if the incumbent’s win margin in the last election (t-1) exceeded the median margin (22.7 p.p.). The variable “change in win margin (t-1 vs t-2)” is defined as the difference in win margin between the last election (t-1) and the previous election (t-2), computed alternatively using the margin over the best-ranked female candidate and over the best-ranked male candidate. All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Standard errors are clustered two-way at constituency and country-year level. The mean dependent variable is computed in election t (as it is equal to zero by construction in the reference election-year). *** p<0.01, ** p<0.05, * p<0.1.

ership. This motivates our first testable hypothesis. Hypothesis 1 (Shift in Gender Norms): Free Basics improves public attitudes toward female political representation.

Building on seminal work examining the intersection of mobile internet access, social media use, and political behavior, we propose two additional mechanisms. Hypothesis 2 (Political Disillusionment Mechanism): the shift in gender norms is partly a byproduct of increased exposure to political information online, which heightens dissatisfaction with traditional elites and fuels
demand for political renewal. Hypothesis 3 (Online Network Norm Transmission): the change in gender attitudes is driven by exposure to online content that is more favorable to female leadership, particularly when digital networks link users to regions with higher levels of female political representation, in contrast to offline environments that may be less supportive.

The increased online presence of female politicians, and their ability to leverage social media to enhance visibility and engage with voters, may also have contributed to these outcomes. By strengthening their public profiles and fostering direct connections with constituents, female politicians likely improved both their electability and their prospects for party endorsement. This mechanism points to a distinct theoretical perspective: rather than focusing on how voters respond to new information environments, it emphasizes how political actors strategically adapt to them. Exploring how female politicians use social media to reshape visibility, campaigning, and intra-party competition could stand as a research agenda of its own. While our analysis focuses on the first set of explanations due to data limitations, this alternative pathway offers a promising direction for future research.

## 7.1 Shift in gender norms [Hypothesis 1]

We empirically examine how access to Free Basics shapes voter attitudes toward female leaders, using individual responses to Afrobarometer survey questions on gender attitudes, particularly those related to women's political leadership.

**Empirical strategy.** We estimate the following equation:

$$Gender Attitude_{i,t} = \beta Free Basics_{i,t} + C_{i,t} \gamma + \eta_{c,t} + \varepsilon_{i,t} \quad (4)$$

where the dependent variable is an indicator for whether respondent $i$ at time $t$ agrees that women should have the same opportunities to be elected to political office as men. The key independent variable is access to Free Basics. The vector $C_{i,t}$ includes individual-level controls,<fnref n="51" /> $\eta_{c,t}$ captures country-time fixed effects, and $\varepsilon_{i,t}$ is the error term. We also estimate the same equation using social media use, rather than access to Free Basics, as the main explanatory variable. Although this approach does not permit a strong causal interpretation, it enables us to examine whether gender attitudes systematically differ between social media users and non-users, controlling for a broad set of socio-demographic characteristics.

**Results.** Table 10 presents the estimates. Access to Free Basics has a positive and statistically significant effect on attitudes toward female political leadership, increasing favorable views by 3.3% of the sample mean (column 1, Panel A). Notably, this effect emerges before any observable impact on actual female political representation. This is demonstrated in Appendix Table H1, which replicates the analysis using only the subsample of survey waves conducted prior to a second parliamentary election following the introduction of Free Basics. In this restricted sample,

<footnote n="51">⁵¹We use the same vector of individual controls as in Equation 1, namely: respondent age, gender, completion of primary education, rural residence, and religious affiliation, along with the level of economic development in the constituency and the local availability of public services. In addition, when exploring gender attitudes outcomes we control for interviewer gender to mitigate potential response biases (Alvarez-Aragon and Champeaux, 2025). Note that the gender composition of interviewers is balanced in our sample (i.e. column 1 in Table 10), with 50% of respondents being interviewed by female interviewers). Likewise, about 50% of female and about 50% of male respondents are interviewed by female interviewers.</footnote>
the estimated effect is similar in magnitude, amounting to 3.6% of the sample mean (column 1, Panel A). In addition, the results for social media use reveal strong and systematic differences in attitudes toward female political leadership. Social media users are 7.3% more likely to express favorable views (column 1, Table 10, Panel B), and this difference remains stable in the pre-second-election subsample (+7.1%, column 1, Table H1, Panel B). While this pattern is likely influenced, at least in part, by self-selection into social media use, it nonetheless highlights a meaningful mechanism: because social media content is largely user-generated, the more favorable attitudes of social media users toward female leadership are likely reflected in the content they produce and share. In the context of our sample of sub-Saharan African countries, connecting to a social media platform may therefore expose users to a discursive environment that is more supportive of female leadership than that encountered by non-users, potentially helping to normalize or amplify such views within the digital public sphere.

We further explore potential heterogeneities in the relationship between access to Free Basics or social media use and attitudes toward female leadership by splitting the sample according to key demographic and socio-economic characteristics, as reported in Appendix Table H2 and Appendix Table H3.<fnref n="52" /> We find that virtually all population subgroups exhibit more favorable attitudes toward female political leadership when having access to Free Basics (Panel A of both tables) or using social media (Panel B of both tables). Importantly, this positive association is observed even among segments of the population typically less supportive of women in politics, such as individuals without primary education, men, Muslims, rural residents, and those living in less developed constituencies (proxied by nighttime light intensity).

To complement this analysis, we examine the effects of both Free Basics access and social media use (separately) on gender attitudes beyond the political sphere, focusing on views related to women's rights to employment, land ownership and inheritance, domestic roles, and tolerance of domestic violence.<fnref n="53" /> While Afrobarometer surveys offer valuable insights, gender-related questions were included only in Round 7 (2016-2018) and Round 9 (2021-2022), limiting our analysis to two repeated cross-sections, both conducted after the initial roll-out of Free Basics partnerships.

Despite these data limitations, we find a consistent and statistically significant difference in gender attitudes between social media users and non-users across all domains: social media users express more egalitarian views regarding women's rights and roles in society (columns 2-4, Table 10, Panel B), and so prior to any observable effect of Free Basics on female electoral outcomes (columns 2-4, Table H1, Panel B). We observe similar patterns when estimating the causal effect of Free Basics access on these attitudes, although the estimates are somewhat noisier, likely due to reduced variation stemming from the limited temporal and geographic coverage of the relevant survey questions (see columns 2-4, Table 10, Panel A for the full sample, and columns 2-4, Table H1, Panel A for the pre-second-election sample).

<footnote n="52">⁵²Note that sampling weights from Afrobarometer are not applied in these subsample analyses.</footnote>

<footnote n="53">⁵³Specifically, we use the following questions: “When jobs are scarce, men should have more rights to a job than women?” (Q38D in Round 7 and Q21A in Round 9); “Women should have the same rights as men to own and inherit land?” (Q38E in Round 7 and Q21B in Round 9); “Is it better for a family if a woman has primary responsibility for the home and children rather than a man?” (Q38F in Round 7, not asked in later rounds); and “Can it ever be justified for a man to beat his wife?” (Q78B in Round 7 and Q52B in Round 9).</footnote>
<table><caption>Table 10: Free Basics availability, social media usage and gender attitudes</caption><thead><tr><th>Dependent variable</th><th>Equal chance<br>for women<br>in politics<br>(1)</th><th>Men should<br>be prioritized<br>in labor market<br>(2)</th><th>Equal<br>land rights<br>for women<br>(3)</th><th>Women<br>should take<br>care of HH<br>(4)</th><th>Beating<br>wife never<br>justified<br>(5)</th></tr></thead><tbody><tr><td colspan="6" style="text-align: center; font-weight: bold;">Panel A: Free Basics availability</td></tr><tr><td>Free Basics</td><td>0.024**<br>(0.010)</td><td>-0.015<br>(0.011)</td><td>0.003<br>(0.008)</td><td>-0.017<br>(0.014)</td><td>0.018<br>(0.013)</td></tr><tr><td colspan="6" style="text-align: center; font-weight: bold;">Panel B: Social media usage</td></tr><tr><td>Social media</td><td>0.053***<br>(0.007)</td><td>-0.067***<br>(0.011)</td><td>0.020***<br>(0.007)</td><td>-0.029**<br>(0.012)</td><td>0.012<br>(0.009)</td></tr><tr><td colspan="6" style="text-align: left; font-weight: bold;">All panels</td></tr><tr><td>Individual controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Constituency controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-year fixed effects</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>64854</td><td>39153</td><td>39153</td><td>16323</td><td>39153</td></tr><tr><td>Mean dep. var.</td><td>0.722</td><td>0.396</td><td>0.788</td><td>0.522</td><td>0.772</td></tr><tr><td>Sample period</td><td>2014-2022</td><td>2016-2022</td><td>2016-2022</td><td>2016-2018</td><td>2016-2022</td></tr></tbody></table>

Notes: This table presents OLS estimates of the effect of Free Basics availability (Panel A) and social media usage (Panel B) on gender attitudes. The unit of observation is a survey respondent. Column 1 includes respondents surveyed between 2014 and 2022 (Afrobarometer rounds 6, 7 and 9), Columns 2, 3 and 5 use only those surveyed between 2016 and 2022 (round 7 and 9), Column 4 includes respondents surveyed between 2016 and 2018 (Afrobarometer round 7). The dependent variables are binary indicators set to 1 if the respondent reports the specified gender attitude. “Free Basics” if a dummy variable set to 1 if the respondent is covered by a mobile phone operator providing access to Free Basics. “Social Media” is a dummy variable set to 1 if the respondent declares using social media. Individual controls include age, gender, primary education completion, rural location, religious affiliation as well as interviewer’s gender, along with enumeration area-level dummies indicating the presence of facilities (post office, police station, health clinic, market stalls, bank/money services, and paid transport such as bus, taxi, or moped). In Panel A, they also contain dummy variables controlling for the availability of 2G and 3G/4G internet but not Free Basics. Constituency controls consist of the log-transformed constituency-year-level population-density-weighted mean nighttime light. Country-year fixed effects are included. Observations are weighted using Afrobarometer within-country survey weights. Standard errors are two-way clustered at the country-year and enumeration area levels and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

## 7.2 Political disillusionment mechanism [Hypothesis 2]

Seminal works on the effects of mobile internet in the region have documented a substantial mobilization effect related to political discontent in Africa (Manacorda and Tesei, 2020), as well as a positive association between internet availability, reduced confidence in government, and increased voter disillusionment in fragile democracies (Guriev et al., 2021). Confirming these findings, we show in Appendix Table I2 that individuals exposed to Free Basics are more likely to consider social media as a platform for obtaining information with political relevance, and that they exhibit lower levels of trust in parliament and their representatives, which is reflected in reduced voter turnout.<fnref n="54" />

<footnote n="54">⁵⁴Specifically, we re-estimate variants of Equation 4, focusing on views related to the role of social media as an information source and its broader political impact, perceptions of corruption among MPs, trust in parliament, disapproval of one's own MP, and voter turnout (see summary statistics in Table I1). To do so, we use the following Afrobarometer questions “Regardless of whether you personally use social media, please tell me whether you agree or disagree that social media: Makes people more informed about current events?” (variable Q59B in Afro. 8, not asked in other survey rounds); “Regardless of whether you personally use social media, please tell me whether you agree or disagree that social media: Helps people have more impact on political processes?” (variable Q59D in Afro. 8, not asked in other survey rounds); “How many of the following people do you think are involved in corruption, or haven't you heard enough about them to say: [members of Parliament]?” (variable Q53B in Afro. 6, Q44B in Afro. 8).</footnote>
As such, we investigate whether the positive effect of access to Free Basics on more favorable attitudes toward female political leadership is, in fact, driven by a broader demand for political renewal. Although the link between reduced trust in national politics and politicians and more favorable views of female leaders is far from mechanical, one could argue that political parties may be encouraged to promote more female politicians *because* of growing voter disillusionment.

To directly explore the potential link between disapproval of national politics and politicians and the demand for female political representation, we re-estimate Equation 4 while splitting the sample of respondents across levels of political discontent. Strikingly, we find that the positive effect of Free Basics on attitudes toward female political leadership—as well as the positive association with social media use—emerges also (when not exclusively) among individuals who exhibit more trust in parliament, who disagree with the statement that MPs are corrupt, and who do not disapprove of their own MPs (Appendix Table I4).

In other words, we find no support for the hypothesis that Free Basics has a stronger effect on attitudes toward female leadership among—and is then mechanically driven by—those who exhibits larger political disillusionment.

## 7.3 Online network norm transmission [Hypothesis 3a]

A pivotal factor may underlie the relationship between access to Free Basics, social media use, and attitudes toward female leadership: exposure to more diverse and gender-equal content through online networks. This mechanism is particularly relevant in our sample of countries, where male political dominance remains the norm—indeed, 9 out of 10 constituencies elect a male MP.

Building on the literature documenting the key role played by features of online versus offline networks (Gentzkow and Shapiro, 2011; Levy, 2021b), we characterize female political representation in the online network of Facebook users. We draw on Facebook's Social Connectedness Index (SCI) (Bailey et al., 2018), which measures bilateral online connections between nearly all ADM1 regions worldwide. As described in section 3, the SCI allows us to calculate, for each origin region (ADM1), the share of an average Facebook user's online friends located in each destination region. We use this information to construct a measure of users' online exposure to regions with higher levels of female political representation.

**Facebook Feed Distance in Gender Representation.** We combine the SCI data with a measure of gender representation in politics to develop a region-level index of online exposure to female political representation. To this end, we use the *Women Power Index* (WPI), a country-level measure that aggregates several indicators of women's political representation.<fnref n="55" /> For each region in our sample (hereafter referred to as *home regions*), we compute: (i) the difference in

<footnote>Afro. 7, Q42B in Afro. 8, and Q38B in Afro. 9); “How much do you trust each of the following, or haven’t you heard enough about them to say: Parliament?” (variable Q52B in Afro. 6, Q43B in Afro. 7, Q41B in Afro. 8, and Q37B in Afro. 9); “Do you approve or disapprove of the way the following people have performed their jobs over the past 12 months, or haven’t you heard enough about them to say: your [member of Parliament]?:” (variable Q68B in Afro. 6, Q58B in Afro. 7, Q51B in Afro. 8, and Q47B in Afro. 9); “Understanding that some people were unable to vote in the most recent national election in [20xx], which of the following statements is true for you? (dummy = 1 if answer is “You voted in the elections”)” (variable Q21 in Afro. 6, Q22 in Afro. 7, and Q13 in Afro. 8 and Afro. 9). We further perform the same estimation on the subsample interviewed before the second election with Free Basics and find similar patterns (Appendix Table I3).</footnote>

<footnote n="55">⁵⁵The WPI includes the share of women in national cabinets, legislatures, electoral candidates, and heads of state. It is available annually since 2020 and measured as of January 1st each year. We use the initial 2020 version for observations up to 2020, and the 2021 and 2022 versions for the corresponding years.</footnote>
WPI between the country of the *home* region and that of each connected *friend* region, and (ii) the share of connections<sup>56</sup> that the average Facebook user has with every ADM1 region globally (*friend* regions). We then aggregate these WPI differences across all *friend* regions, weighting by the respective share of connections, to construct a region-level measure of the average user's gender representation distance on Facebook. Full computational details are provided in Appendix subsection C.1. Intuitively, higher positive values of this measure indicate greater online exposure to regions with higher female political representation relative to the user's local offline context.

Across our sample of Afrobarometer respondents, the average Facebook feed distance in gender representation is positive (mean = 0.05), indicating that users tend to be more connected online to regions with greater female political representation than their own. Nearly 70% of these individuals are exposed to a more gender-progressive information environment via Facebook, including users with access through Free Basics (see Panel A of Appendix Figure C1 for the distribution of the Facebook feed distance measure).

We then estimate variants of Equation 4, interacting access to Free Basics and social media use (separately) with the Facebook feed distance in gender representation, which we standardize across regions prior to inclusion in the estimations.

**Results.** Panel A of Table 11 shows that the effect of Free Basics on support for female political leadership increases with the distance in gender representation of a user's Facebook feed. This effect is both sizable and statistically significant: a one-standard-deviation increase in feed distance more than doubles the effect of Free Basics at the mean level of distance (column 1). To ensure that this effect is driven by exposure to more progressive regions, rather than simply by having a broader, more geographically dispersed network, we further control for the share of Facebook connections within a user's own region and its interaction with Free Basics. This is particularly relevant because exposure to distant, more progressive regions may be mechanically (and negatively) correlated with local network concentration. As shown in column 2, the results remain robust to this inclusion. We further show that the amplifying effect of online gender representation distance holds even when we additionally control for the interactions between Free Basics and both nighttime lights and rural location (column 3). This suggests that the structure of online networks plays a key role, and that the observed effects are not simply artifacts of local economic development or urbanization. Robustness checks in Appendix Table H4 and Table H5 confirm that these results hold even when accounting for spatial autocorrelation and applying different internet coverage measures.

Panel B of Table 11 presents results from analogous estimations in which we interact social media usage, rather than access to Free Basics, with our measure of Facebook’s feed distance in gender representation. Consistent with the previous results, we find that the association between social media usage and support for female political leadership is amplified by greater exposure to female leadership online. Specifically, a one-standard-deviation increase in representation distance strengthens this association by 1.4 percentage points (26% of its value at the mean distance, column 1). This effect is both strong and highly statistically significant, and remains robust to the inclusion of controls for local network concentration as well as proxies for local economic development and urbanization (columns 2 and 3).

We complement this analysis by showing in Appendix Table H6 that the impact of access

<footnote>56These connections correspond to *friends* on Facebook.</footnote>
to Free Basics on a broad set of gender norm measures, and their association with social media usage, is also shaped by the degree of online exposure to female leadership. In particular, we find a negative association between feed distance and agreement with the statement that men should be prioritized in the labor market, and a positive association with agreement that women should have equal rights to land as men. While these effects are smaller in magnitude or less precisely estimated, likely due to a weaker conceptual link between the type of exposure measured (female political leadership) and the specific beliefs, they nonetheless provide further support for the channel of norm transmission through online exposure.

A necessary condition for this mechanism to explain electoral change is that such shifts in gender norms precede the second election after Free Basics' introduction—the point at which its electoral impact becomes evident (see section 6). Appendix Table H7 confirms this timing by replicating the analysis while excluding respondents surveyed after the second election following Free Basics' rollout in their country. The results show that the observed effects on political gender norms do indeed precede the electoral impacts.

## 7.4 Online network norm transmission and female MP elections [Hypothesis 3b]

Having shown that online social network structure plays an important role in driving shifts in gender norms, we now perform similar exercises to examine how this ultimately translates into the candidacy and election of female MPs. In line with the strategy presented in subsection 7.4, we estimate versions of Equation 2 in which we interact the Free Basics treatment variable with our measure of Facebook feed distance in gender representation.<fnref n="57" /> Results are presented in Table 12.

We find that the positive impact of access to Free Basics on the likelihood of women being endorsed by—and winning with—a parliamentary party is magnified in constituencies with greater distance in gender representation online. In particular, a one-standard-deviation increase in representation distance raises the effect of Free Basics on female endorsement by parliamentary parties by 55 to 101% relative to its effect at the mean distance (columns 1 to 3). This large and highly significant effect provides strong support for the party learning mechanism described in section 6: parties adapt their endorsement strategies most in constituencies that are both more exposed online to female leadership and where acceptance of such female representation responded more strongly to the introduction of Free Basics (see subsection 7.4). Although less precisely estimated, we also observe that this heterogeneity extends to the likelihood of electing a woman endorsed by a parliamentary party (columns 4 to 6).

The results are robust to controlling for the local concentration of online networks, thereby accounting for variation in the breadth and diversity of online connections and their interaction with Free Basics (columns 2 and 5), as well as for the interaction of Free Basics with population density and economic development (columns 3 and 6). Additionally, we show in Panel D of Appendix Table E5 that the results hold when accounting for spatial autocorrelation, using alternative treatment definitions, and applying different internet coverage measures.

These findings highlight that the positive effect of Free Basics on women's political prospects is amplified by online exposure to higher female political representation, providing further support

<footnote n="57">⁵⁷Panel B of Appendix Figure C1 shows the distribution of our measure of Facebook’s feed distance in gender representation in the sample of races, i.e. constituency × election-year.</footnote>
<table><caption>Table 11: Free Basics availability, social media usage and attitudes towards women in politics - the role of online content exposure</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="3">Equal chance for women in politics</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td colspan="4"><strong>Panel A: Free Basics availability</strong></td></tr><tr><td>Free Basics</td><td>0.024**<br>(0.011)</td><td>0.024**<br>(0.011)</td><td>0.031<br>(0.030)</td></tr><tr><td>... × Facebook gender representation distance</td><td>0.027**<br>(0.013)</td><td>0.026*<br>(0.013)</td><td>0.026*<br>(0.013)</td></tr><tr><td colspan="4"><strong>Panel B: Social media usage</strong></td></tr><tr><td>Social media</td><td>0.054***<br>(0.007)</td><td>0.051***<br>(0.007)</td><td>0.088***<br>(0.016)</td></tr><tr><td>... × Facebook gender representation distance</td><td>0.014**<br>(0.007)</td><td>0.018**<br>(0.007)</td><td>0.019***<br>(0.007)</td></tr><tr><td colspan="4"><strong>All panels</strong></td></tr><tr><td>Country × year FEs</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Individual controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Constituency controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Network structure controls</td><td></td><td>✓</td><td>✓</td></tr><tr><td>Interaction terms: nighttime light, rural</td><td></td><td></td><td>✓</td></tr><tr><td>Observations</td><td>62463</td><td>62463</td><td>62463</td></tr><tr><td>Mean dep. var</td><td>0.718</td><td>0.718</td><td>0.718</td></tr></tbody></table>

Notes: The table presents OLS estimates of the effect of Free Basics availability (Panel A), social media usage (Panel B) and online content exposure on attitudes towards women in politics. The unit of observation is a survey respondent. All columns include respondents surveyed between 2014 and 2022 (Afrobarometer rounds 6, 7 and 9). The dependent variable is a binary indicator set to 1 if the respondent reports agreeing with the specified statement. “Free Basics” if a dummy variable set to 1 if the respondent is covered by a mobile phone operator providing access to Free Basics. “Social Media” is a dummy variable set to 1 if the respondent reports using social media. “Facebook gender representation distance” denotes the Facebook-exposure-weighted average distance in WPI between respondent’s home region and all regions worldwide. Individual controls include age, gender, primary education completion, rural location, religious affiliation as well as interviewer’s gender, along with enumeration area-level dummies indicating the presence of facilities (post office, police station, health clinic, market stalls, bank/money services, and paid transport such as bus, taxi, or moped). In Panel A, they also contain dummy variables controlling for the availability of 2G and 3G/4G internet but not Free Basics. Constituency controls consist of the log-transformed constituency-year-level population-density-weighted mean nighttime light. Network structure controls denotes the share of the average user’s Facebook connections located in the same ADM1 region and its interaction with “Free Basics”/“Social Media”. Column 3 also include the interactions between “Free Basics”/“Social Media” and population-density-weighted mean nighttime light as well as rural residence. Country-year fixed effects are included. Observations are weighted using Afrobarometer within-country survey weights. Standard errors are two-way clustered at the country-year and enumeration area levels and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

for the role of political parties in responding to evolving demands for representation.

# 8 Conclusion

The findings of this paper demonstrate that broader access to social media can significantly increase female political representation in sub-Saharan Africa, as illustrated by the case of Facebook’s Free Basics. While the platform rapidly expanded social media use, particularly among
<table><caption>Table 12: Effects of Free Basics availability on the female candidacy and election in the second Free Basics election - the role of online content exposure</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="3">At least one female candidate<br>from a parliamentary party</th><th colspan="3">Winner is a woman<br>from a parliamentary party</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Free Basics</td><td>0.225***<br>(0.070)</td><td>0.241***<br>(0.068)</td><td>0.184***<br>(0.049)</td><td>0.116<br>(0.070)</td><td>0.122*<br>(0.071)</td><td>0.119**<br>(0.056)</td></tr><tr><td>... × Facebook gender representation distance</td><td>0.130**<br>(0.052)</td><td>0.170***<br>(0.052)</td><td>0.185***<br>(0.053)</td><td>0.022<br>(0.046)</td><td>0.038<br>(0.042)</td><td>0.052<br>(0.048)</td></tr><tr><td>Controls: nighttime light, population density</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-year FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Network structure controls</td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td></tr><tr><td>Interaction terms: nighttime light, rural</td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Observations</td><td>2565</td><td>2565</td><td>2565</td><td>2565</td><td>2565</td><td>2565</td></tr><tr><td>Mean dep. var.</td><td>0.139</td><td>0.139</td><td>0.139</td><td>0.069</td><td>0.069</td><td>0.069</td></tr></tbody></table>

Notes: This table presents the LP-DiD estimates of the effect of Free Basics availability and online content exposure on the likelihood of electing a female MP in the second election following Free Basics' introduction. The unit of observation is an electoral race, i.e. a constituency × election year. The dependent variable is a binary indicator set to 1 if a at least one woman endorsed by a parliamentary party is present on the ballot (columns 1-3) or if a woman endorsed by parliamentary wins the election (columns 4-6). "Facebook gender representation distance" denotes the Facebook-exposure-weighted average distance in WPI between a constituency's home region and all regions worldwide. All specifications include country × election-year fixed effects, and the following controls: logarithm of population density and nighttime light. Network structure controls denotes the share of the average user's Facebook connections located in the same ADM1 region and its interaction with "Free Basics". Columns 3 and 6 also include the interactions of "Free Basics" and the control variables. Standard errors are two-way clustered at constituency and country-year level. The mean dependent variable is computed in the reference election-year. *** p<0.01, ** p<0.05, * p<0.1.

populations traditionally less likely to be online, its political and electoral effects materialized gradually, one election at a time. Constituencies exposed to the technology became significantly more likely to elect female MPs, but only in the second election following its introduction. This shift was driven primarily by female candidates endorsed by established parties, following the disappointing electoral outcomes of male incumbents in the first election with the platform, and suggests that political institutions closely monitored the evolving electoral landscape and adjusted their strategies in response to changing voter preferences.

Our results further highlight the role of social media in reshaping gender norms and facilitating political change. The availability of the platform fostered more progressive attitudes toward female leadership—attitudes that are more prevalent among social media users than among non-users—and contributed to greater female representation in national politics. These effects were particularly pronounced in regions where online networks connected users to more gender-egalitarian societies and in contexts with otherwise limited press freedom. Moreover, the impact of Free Basics was amplified in the presence of fair elections.

More broadly, this study highlights the need for a deeper understanding of how digital media interacts with norm transitions across different dimensions of society. While our analysis focuses on gender norms and political representation, similar mechanisms may influence female labor force participation, particularly by reducing barriers to entry in traditionally male-dominated sectors. Likewise, online exposure to diverse perspectives may contribute to shifting entrenched social norms, shaping attitudes on issues such as harmful traditional practices, ethnic sentiment, and broader gender inequalities.

Looking ahead, the current evolution of the online sphere—characterized by artificial intelligence, personalized content algorithms, and little content moderation—raises new questions about
the role of technology in shaping political and social dynamics. Algorithmic and AI-driven content curation could either reinforce or challenge existing norms, depending on how these systems are designed and regulated. When applied to political campaigning, these may further influence voter perceptions and candidate strategies. As digital technologies continue to advance, understanding their implications for democratic participation and social transformation remains a critical area for future research.

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# Online Appendix

## Table of Contents

<table><tr><td>A</td><td>Information on Free Basics partnerships and coverage</td><td>47</td></tr><tr><td>A.1</td><td>List of Free Basics partnerships</td><td>47</td></tr><tr><td>A.2</td><td>Drivers of Free Basics partnerships</td><td>48</td></tr><tr><td>A.3</td><td>Population covered by Free Basics</td><td>49</td></tr><tr><td>B</td><td>Sources and description of electoral data</td><td>50</td></tr><tr><td>B.1</td><td>Data sources</td><td>50</td></tr><tr><td>B.2</td><td>Additional summary statistics</td><td>51</td></tr><tr><td>C</td><td>Description of measures of online exposure to gender representation</td><td>52</td></tr><tr><td>C.1</td><td>Social Connectedness Index and measures of online exposure</td><td>52</td></tr><tr><td>C.2</td><td>Online exposure to female politicians</td><td>53</td></tr><tr><td>D</td><td>Connectivity shock: robustness checks and additional results</td><td>54</td></tr><tr><td>D.1</td><td>Robustness checks to results on the connectivity shock</td><td>54</td></tr><tr><td>D.2</td><td>Analysis of the compliers to the connectivity shock</td><td>56</td></tr><tr><td>E</td><td>Electoral consequences of Free Basics: robustness checks and additional results</td><td>58</td></tr><tr><td>E.1</td><td>Validity and robustness checks to results on the electoral consequences of Free Basics</td><td>58</td></tr><tr><td>E.2</td><td>Additional results on the electoral consequences of Free Basics</td><td>58</td></tr><tr><td>E.3</td><td>Description of the methodology used to estimate the Free Basics effects on winning conditional on running: derivation of the bounds</td><td>65</td></tr><tr><td>F</td><td>Party-race analysis of electoral outcomes</td><td>67</td></tr><tr><td>F.1</td><td>Construction of the data used in the analysis at party-race level</td><td>67</td></tr><tr><td>F.2</td><td>Summary statistics at party-race level</td><td>70</td></tr><tr><td>F.3</td><td>Effects of Free Basics on electoral outcomes at party-race level</td><td>71</td></tr><tr><td>G</td><td>Aggregates effects of Free Basics on female MP election</td><td>72</td></tr><tr><td>H</td><td>Free Basics availability, social media usage and gender norms: additional results and robustness checks</td><td>74</td></tr><tr><td>H.1</td><td>Free Basics availability, social media usage and gender norms: pre-second election with Free Basics</td><td>74</td></tr><tr><td>H.2</td><td>Free Basics availability, social media usage and attitudes towards women in politics: heterogeneities and robustness checks</td><td>75</td></tr><tr><td>I</td><td>Free Basics availability, social media usage and political attitudes</td><td>81</td></tr><tr><td>I.1</td><td>Free Basics availability, social media usage and political attitudes</td><td>81</td></tr><tr><td>I.2</td><td>Heterogeneity in the association between Free Basics availability, social media usage and attitudes towards women in politics</td><td>84</td></tr></table>
# A Information on Free Basics partnerships and coverage

This section presents background information on the Free Basics partnerships and their drivers. Subsection A.1 presents the list of operators in our sample of countries who signed a partnership with Free Basics and the date at which it was signed. Subsection A.2 provides an analysis of the drivers behind the signing and timing of such partnerships. Subsection A.3 reports descriptive statistics on the population covered by Free Basics in sample.

## A.1 List of Free Basics partnerships

<table><caption>Table A1: In-sample Free Basics roll-out</caption><thead><tr><th>Country</th><th>Free Basics</th><th>Operators</th><th>Partnership Date</th></tr></thead><tbody><tr><td>Botswana</td><td>Yes</td><td>BTC</td><td>End 2017-Early 2018</td></tr><tr><td>Gambia</td><td>No</td><td></td><td></td></tr><tr><td>Ghana</td><td>Yes</td><td>Airtel</td><td>01/2015</td></tr><tr><td></td><td></td><td>Tigo</td><td>10/2014</td></tr><tr><td></td><td></td><td>MTN</td><td>02/2015</td></tr><tr><td></td><td></td><td>Vodafone</td><td>2017</td></tr><tr><td>Guinea</td><td>Yes</td><td>Cellcom</td><td>11/2015</td></tr><tr><td>Ivory Coast</td><td>Yes</td><td>MTN</td><td>08/2017</td></tr><tr><td></td><td></td><td>Moov</td><td>2019</td></tr><tr><td>Kenya</td><td>Yes</td><td>Airtel</td><td>11/2014</td></tr><tr><td></td><td></td><td>Safaricom</td><td>2016</td></tr><tr><td>Lesotho</td><td>No</td><td></td><td></td></tr><tr><td>Liberia</td><td>Yes</td><td>Cellcom<sup>1</sup></td><td>10/2015</td></tr><tr><td></td><td></td><td>Lonestar cell</td><td>06/2020</td></tr><tr><td></td><td></td><td>Orange</td><td>04/2016</td></tr><tr><td>Madagascar</td><td>Yes</td><td>BIP (Blueline)<sup>2</sup></td><td>06/2016</td></tr><tr><td></td><td></td><td>Orange</td><td>08/2016</td></tr><tr><td>Malawi</td><td>Yes</td><td>TNM</td><td>05/2015</td></tr><tr><td></td><td></td><td>Airtel</td><td>05/2015</td></tr><tr><td>Nigeria</td><td>Yes</td><td>Airtel</td><td>05/2016</td></tr><tr><td></td><td></td><td>Etisalat<sup>3</sup></td><td>02/2017</td></tr><tr><td></td><td></td><td>Tizeti<sup>4</sup></td><td>11/2017</td></tr><tr><td></td><td></td><td>Coolink<sup>4</sup></td><td>08/2016</td></tr><tr><td></td><td></td><td>9Mobil</td><td>07/2017</td></tr><tr><td>Seychelles</td><td>Yes</td><td>Airtel</td><td>11/2015</td></tr><tr><td>Sierra Leone</td><td>No</td><td></td><td></td></tr><tr><td>Tanzania</td><td>Yes</td><td>Tigo</td><td>10/2014</td></tr><tr><td></td><td></td><td>Airtel</td><td>06/2016</td></tr><tr><td></td><td></td><td>Vodacom</td><td>2016 - 2019</td></tr><tr><td>Uganda</td><td>No</td><td></td><td></td></tr><tr><td>Zambia</td><td>Yes</td><td>Airtel</td><td>07/2014</td></tr><tr><td></td><td></td><td>MTN</td><td>07/2017</td></tr><tr><td>Zimbabwe</td><td>No</td><td></td><td></td></tr></tbody></table>

Notes: Countries with electoral data in our sample. <sup>1</sup> Cellcom becomes Orange in 2016 in Liberia and is therefore not separately identified in *OpenCellID*. <sup>2</sup> BIP (Blueline) does not have towers recorded in *OpenCellID*. 31 Etisalat becomes 9Mobile in July 2017 in Nigeria, they are therefore considered as a single operator in our sample. <sup>4</sup> Tizeti and Coolink provide WiFi hotspots instead of cellular towers.
## A.2 Drivers of Free Basics partnerships

<table><caption>Table A2: Analysis of drivers of Free Basics partnerships</caption><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">Signed a partnership with Facebook</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>Country-level</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Log population</td><td>0.170*<br>(0.085)</td><td>0.088<br>(0.097)</td><td>0.270***<br>(0.099)</td><td>0.181*<br>(0.106)</td></tr><tr><td>Log GDP per capita</td><td>-0.043<br>(0.134)</td><td>-0.388*<br>(0.210)</td><td>-0.750***<br>(0.193)</td><td>-0.566***<br>(0.192)</td></tr><tr><td>Trade openness</td><td>0.007<br>(0.005)</td><td>0.009*<br>(0.005)</td><td>0.019***<br>(0.005)</td><td>0.013**<br>(0.005)</td></tr><tr><td>Literacy rate</td><td>-0.001<br>(0.004)</td><td>0.002<br>(0.005)</td><td>0.006<br>(0.006)</td><td>0.005<br>(0.006)</td></tr><tr><td>Population covered by internet (%)</td><td></td><td>0.217**<br>(0.089)</td><td>0.114<br>(0.139)</td><td>0.096<br>(0.140)</td></tr><tr><td>Internet infrastructure performance</td><td></td><td>-0.045***<br>(0.015)</td><td>-0.046***<br>(0.017)</td><td>-0.052***<br>(0.016)</td></tr><tr><td>Price of internet data</td><td></td><td>-0.001<br>(0.002)</td><td>-0.002<br>(0.001)</td><td>-0.001<br>(0.002)</td></tr><tr><td>Mobile ownership</td><td></td><td>0.044*<br>(0.024)</td><td>0.093***<br>(0.027)</td><td>0.082***<br>(0.024)</td></tr><tr><td><strong>Operator-level</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Population covered (%)</td><td></td><td></td><td>0.676*<br>(0.349)</td><td>0.639*<br>(0.376)</td></tr><tr><td>Nighttime light, weighted by pop. density</td><td></td><td></td><td>-0.000***<br>(0.000)</td><td>-0.000***<br>(0.000)</td></tr><tr><td>Log population density</td><td></td><td></td><td>0.101<br>(0.103)</td><td>0.141<br>(0.117)</td></tr><tr><td>Female MPs (%)</td><td></td><td></td><td>-1.729<br>(1.242)</td><td></td></tr><tr><td>Vote share of female candidates (MP)</td><td></td><td></td><td></td><td>-1.167<br>(0.754)</td></tr><tr><td>Observations</td><td>57</td><td>57</td><td>54</td><td>53</td></tr><tr><td>R<sup>2</sup></td><td>0.097</td><td>0.296</td><td>0.532</td><td>0.543</td></tr><tr><td>Mean dep. var.</td><td>0.39</td><td>0.39</td><td>0.39</td><td>0.39</td></tr></tbody></table>

Notes: This table presents OLS estimates of the drivers of Free Basics partnerships. The unit of observation is a mobile operator × country. The dependent variable is a binary indicator set to 1 if the mobile operator signed a Free Basics partnership. Heteroskedasticity-robust standard errors are reported. *** p<0.01, ** p<0.05, * p<0.1.

<figure><img src="image_9.png" /><figcaption>Figure A1: Survival analysis of drivers of Free Basics partnerships</figcaption></figure>

Notes: This figure presents Cox proportional-hazard estimates of drivers of Free Basics partnerships. The unit of analysis is a mobile operator × country. Failure corresponds to signing a Free Basics partnership. Duration of survival is defined as the difference between the year of partnership and 2014, i.e. Free Basics' launch. Operators not signing a partnership have maximum survival duration. Heteroskedasticity-robust standard errors are reported. Bars represent 95% confidence intervals.
A.3 Population covered by Free Basics

<figure><img src="image_10.png" /><figcaption>Figure A2: Distribution of Free Basics coverage in treated constituencies</figcaption></figure>

Notes: This figure presents the distribution of population covered by Free Basics in treated constituencies at the time of the first and second election following it's introduction. The width of each bin is equal to 5 percentage points.