# Educational Legacies of Christian Missions: Evidence from the Democratic Republic of the Congo

Pierre Donat-Bouillud, Coralie Hirschi, Etienne Le Rossignol

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Pierre Donat-Bouillud, Coralie Hirschi, Etienne Le Rossignol. Educational Legacies of Christian Missions: Evidence from the Democratic Republic of the Congo. 2025. halshs-05116796

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CES

Centre d'Économie de la Sorbonne
UMR 8174

Educational Legacies of Christian Missions:
Evidence From the Democratic Republic of the Congo

Pierre DONAT-BOUILLUD, Coralie HIRSCHI,
Etienne LE ROSSIGNOL

2025.13
# EDUCATIONAL LEGACIES OF CHRISTIAN MISSIONS: EVIDENCE FROM THE DEMOCRATIC REPUBLIC OF THE CONGO*

Pierre Donat-Bouillud†

Czech Technical University

Coralie Hirschi‡

Centre d'Économie de la Sorbonne

Etienne Le Rossignol§

Université de Namur

12 June 2025

Most Recent Version Here

## Abstract

This paper investigates the educational legacies of Christian missions established before in-dependence, in the Democratic Republic of the Congo, focusing on contemporary disparities in educational content, quality, access, and outcomes. Using georeferenced data from over 20,000 schools and two million students participating in the *Examen National d'État (Exetat)* between 2016 and 2019, as well as primary data from a phone survey of 624 school directors, we explore how proximity to Catholic and Protestant missions during the colonial period influences educational opportunities today. We find that schools near historical Christian missions offer a broader and higher-quality curriculum, with increased access to study tracks outside the basic Pedagogy section. Students attending these schools are more likely to pass the *Exetat* and achieve grades qualifying for university admission, although the magnitude of the effect is modest. Supply-side factors, such as better infrastructure and more conducive school environments, explain half of the missionary proximity premium, while demand-side mechanisms, including students' sorting and selection account for additional variation. Our findings reveal that the strong partnership between missions and the colonial state entrenched spatial inequalities in educational access and quality, inequalities that persist to this day.

Keywords: Christian Missions, Education, Democratic Republic of the Congo.

JEL Classification: Z1, Z10, N9, O1

<footnote>* This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101034383. This project also received funding from the Agence National de la Recherche under the framework of the *Investissements d'Avenir* grant programme reference ANR-17-EURE-001, the Centre d'Économie de la Sorbonne and the Banque Nationale de Belgique. We thank Bihter Erbaş and Manon Bellot for excellent research assistance. We are grateful to Pablo Álvarez-Aragon, Doux Baraka, Rémi Bazillier, Gabriel Brown, Jesse Bruhn, Cédric Chambru, Lisa Chauvet, David Cuberes, Abel François, Paula Gobbi, Catherine Guirkinger, Jean Lacroix, Bryce Steinberg, and seminar participants at: Université Libre de Bruxelles, Centre d'Économie de la Sorbonne, Economic History Society 2025 Annual Conference, and MyPeers workshop for their valuable comments and feedback. We thank Marakuja Kivu Research for the data collection. We also thank Rodrigue Kapayi Bebeka from the *Direction de l'Information pour la Gestion de l'Éducation* for sharing with us the 2018 census of schools of the Democratic Republic of the Congo. Jean-Pierre Cheruga Kusinza and Molakisi Leobanem provided valuable contextual information. Approval for this research was obtained from the Institutional Review Board at the Paris School of Economics (Application 2023-051 dated December 15, 2023).</footnote>

<footnote>† Czech Technical University in Prague, Northeastern University. Email: pierre.donat-bouillud@fit.cvut.cz. Website: www.pdonatbouillud.com.</footnote>

<footnote>‡ Université Paris 1 Panthéon-Sorbonne, CNRS, IRD, Centre d'Économie de la Sorbonne, Paris, France. Email: coralie.hirschi@univ-paris1.fr. Website: www.coraliehirschi.com.</footnote>

<footnote>§ Université de Namur. Email: etienne.lerossignol@unamur.be. Website: www.etiennelerossignol.com.</footnote>
# 1. Introduction

In sub-Saharan Africa, there are significant disparities in educational outcomes both across and within countries. These disparities manifest along gender, ethnic, and religious lines, as well as geographic location (Alesina, Hohmann, Michalopoulos and Papaioannou, 2021, 2023). As the primary providers of Western-style education in many former European colonies, Christian missionaries may play a central role in understanding the roots of location-based inequality in education. A rapidly growing literature in economics highlights a strong correlation between proximity to historical Christian missions and contemporary educational outcomes.<fnref n="1" /> While there is evidence of greater access to education near historical Christian missions, key questions remain: What are students actually learning in these areas? Is the quality of learning higher? Does the effect persist after controlling for historical exposure to colonial schools? And what are the mechanisms at work?

This paper seeks to estimate the legacies of historical Christian missions on the content of education and students' learning in the Democratic Republic of the Congo (hereafter DRC). Additionally, it explores the mechanisms linking past missionary presence to contemporary educational outcomes. Understanding how historical Christian missions have shaped the content, learning, and mechanisms perpetuating colonial-driven inequalities is crucial from a policy perspective, given the critical role of education in economic development and the development of democracy (Hanushek and Woessmann, 2010, Acemoglu, Johnson, Robinson and Yared, 2005).

Studying the consequences of exposure to historical Christian missions poses several challenges. The first arises from the non-random placement of missionary stations and the selection of their activities (Johnson, 1967, Fourie et al., 2015). Missionaries often settled in areas characterized by better sanitation, greater economic potential, and higher receptivity from the indigenous population, as well as in locations where industrial activities created a demand for skilled labor (Juif, 2019). Disentangling the specific effects of missionary presence from these contextual factors has remained an enduring challenge within the literature.

A second challenge involves reconstructing an accurate picture of missionary settlements within a given country during the colonial period (Jedwab, Meier zu Selhausen and Moradi, 2022). Typically, historical atlases depict only the locations of mission headquarters, despite the fact that missions commonly extended their influence through numerous outposts situated dozens or even hundreds of kilometers away from the main station.

Finally, most contributions to the literature rely on georeferenced survey data and measure exposure to missions by calculating a straight-line distance between survey clusters and the nearest mission headquarters. This approach introduces significant measurement errors, as the locations of surveyed respondents are deliberately displaced (e.g., DHS surveys) or aggregated

<footnote n="1">¹ In sub-Saharan Africa, see Gallego and Woodberry (2010), Nunn (2014), Cogneau and Moradi (2014), Fourie, Swanepoel et al. (2015), Wantchekon, Klašnja and Novta (2015), Okoye and Pongou (2023), Becker (2022), Becker and zu Selhausen (2023), Alvarez-Aragon, Guirkinger and Villar (2023), Bekkouche and Dupraz (2023); and elsewhere, see Bai and Kung (2015), Waldinger (2017), Castelló-Climent, Chaudhary and Mukhopadhyay (2018), Valencia Caicedo (2019), Calvi, Hoehn-Velasco and Mantovanelli (2022).</footnote>
from several villages into a single hypothetical equidistant locality (as in Afrobarometer and LSMS datasets).

In this paper, we address these challenges and improve our understanding of the educational legacies of historical Christian missions by adopting the granular identification strategy proposed by Wantchekon et al. (2015). Specifically, we leverage variation in mission exposure among students who sit for the Congolese national high school completion examination on the same day and within the same exam center, thereby holding constant contemporaneous contextual factors. By using the precise geographic coordinates of students' schools, rather than residential survey clusters, our approach mitigates measurement errors associated with location displacement and village aggregation.

In all specifications, we control for each school's geographic, epidemiological, and historical demographic environment. Specifically, we include controls for caloric suitability, maize suitability, cassava suitability, elevation, terrain ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. We also control for malaria and tsetse fly suitability. Finally, we account for historical population density using 1950s estimates from a colonial-era map published by the Royal Belgian Colonial Institute (Gourou, 1951).

Our case study focuses on the *Examen National d'État* (hereafter *Exetat*), the national high school completion examination, taken annually by approximately 500,000 students, theoretically aged 17. The *Exetat* serves as a prerequisite for university admission and is graded out of 100, with a minimum score of 50 required for passing. Approximately 70% of candidates successfully pass the exam each year. Using household survey data from Enquête 1-2-3 (2005), we document significant economic and human capital returns associated with obtaining the *Exetat*.

We have georeferenced all 20,000 schools participating in this exam over the 2016 - 2019 period using a complete census of secondary schools in the DRC obtained from the *Direction de l'Information et de la Gestion de l'Éducation* (DIGE). Crucially for our study, students from nearby schools converge at a designated examination center – selected based on their study track and geographical proximity – to take the test. For all students taking the exam, we have detailed information on their gender, study track, the location of their school, the location of their examination center, and their grade. Furthermore, the exams are conducted simultaneously across the country for all students, standardized by study track and year, and partially graded by computer in Kinshasa. This setup allows us to exploit variation in exposure to Christian missions among students taking the same test at the same examination center, based on the geographic locations of their schools. Therefore, in our main econometric specification, we are able to hold constant the location of the examination center.

Figure 1 provides a concrete example of our empirical analysis. Here, we focus on the year 2018 and examination center 55005, located in the Tshopo province. In 2018, this examination center hosted exams for three study tracks: Chemistry and Biology, Social, and General Pedagogy. Students from 20 schools in the region took the test in this center. Panel 1a displays the region where center 55005 is located. Panel 1b shows the locations of the 20 schools – across all study tracks – sending students to examination center 55005 to take the *Examen National d'État*. This
<figure><img src="image_2.png" /><figcaption>Figure 1: Empirical Setting</figcaption></figure>

Notes: This series of figures provides a concrete example of our empirical analysis. Data on historical Christian missions is from Mantnieks (1951)'s *Atlas Classique*.

panel also indicates the location of the examination center, along with two Catholic missions and two Protestant missions near the center. This example illustrates the variation in exposure to missions among schools sending students to the same examination center, a key aspect of our empirical strategy. Panel 1c is restricted to schools sending students in the General Pedagogy track to the examination center in 2018. Similarly, Panel 1d corresponds to a sample limited to schools sending students in the Chemistry and Biology track.

We supplement the *Exetat* data with primary data collected through a phone survey targeting 624 rural school directors from twelve provinces in Eastern DRC. The data was gathered by our local partner, the Marakuja Kivu Group, in December 2023 and September 2024. Using the
census of all schools in the DRC, which includes the contact information of school directors, we randomly selected 624 rural schools across these twelve provinces. Eastern DRC was chosen due to its proximity to Marakuja Kivu Research, which is based in Goma, Sud-Kivu. Our dataset includes basic school characteristics, the organization of the educational framework, the ethnic, religious, and wealth composition of students, and expectations for both girls and boys.

Throughout the paper, we measure the location of missions using data from the *Atlas Classique* published in Mantnieks (1951). Using our phone survey, we validate this data by asking school directors about the strength of historical missionary presence in the vicinity of their school. We find a strong positive correlation between a school's Euclidean distance to the nearest mission and the directors' assessments.

We begin by exploring the content of education offered by schools located closer to historical Christian missions. In the DRC, the secondary school system is divided into three sections: Pedagogy, General, and Technical. Each section is further subdivided into dozens of study tracks or specialization paths, with the choice of tracks offered being at the discretion of the school. The Pedagogy section is the least expensive to provide and serves as the baseline section. In contrast, the General and Technical sections require equipment, infrastructure, and skilled teachers, making them more costly to offer. For instance, the study track for Chemistry-Biology (section General) requires laboratory equipment such as microscopes, chemical reagents, and protective gear. In 2018, there were 45 possible study tracks, with the most popular being General Pedagogy (44.5%), Chemistry-Biology (11.9%), Commercial and Administration (7.1%), Social (6.4%), and Cutting and Sewing (4.3%).

In our analysis, the unit of observation is a school, and we investigate how proximity to historical Christian missions affects the number of study tracks offered. We then examine how proximity to missions influences the likelihood of offering study tracks within each of the three sections. Our findings indicate that schools near historical Christian missions offer more study tracks. The effect is small: on average, schools offer two study tracks, and those located one standard deviation closer to historical Catholic missions [22.3 km] offer approximately 2.5% of a standard deviation more study tracks (1% for proximity to Protestant missions [29.1 km]).

When examining the probability of offering study tracks within each section separately, we find that schools near historical Christian missions are more likely to offer study tracks in the General and Technical sections, but less likely to offer study tracks in the Pedagogy section. The magnitude of these effects is large. For instance, schools closer to historical Catholic missions are 11.8% of a standard deviation more likely to offer study tracks within the Technical section. Overall, these initial findings suggest that schools near historical Christian missions provide a more diverse educational content that is also more expensive to offer.

Next, we investigate how proximity to historical Christian missions affects the probability of succeeding at the *Exetat* and the quality of success. Our unit of observation becomes a student. Our specification is demanding, including fixed effects for study track, exam center, and year.

We find that students enrolled in schools located near historical Christian missions perform better on the *Examen National d'État* than their peers who take the same exam on the same day in the same examination center but are enrolled in schools located farther away from the missions.
The magnitude of the effect is moderate: a one standard deviation increase in exposure to historical Catholic missions increases the probability of success by 5 percentage points. Moreover, successful candidates enrolled in schools near historical Christian missions achieve higher grades. Specifically, students with greater exposure are less likely to receive low grades (typically below 55) and more likely to achieve high grades, particularly those qualifying for university admission (e.g., 60 or above).

While the existing literature suggests that the correlation between the historical presence of missionaries and education is stronger for Protestant missions than for Catholic ones, likely due to Protestant's emphasis on literacy – *sola scriptura* –, our results reveal that in the DRC, the correlation is stronger for Catholics.<fnref n="2" /> We attribute this difference between both denominations to the privileged position held by Catholic missionaries following the transition from the Congo Free State to Belgian Congo up until independence (Markowitz, 1970).

We next explore potential mechanisms that explain why, 65 years after the end of the colonial period in the DRC, schools located near historical Christian missions offer a greater number of study tracks, including those that are more costly to provide. Additionally, we investigate why, holding the content of education constant, schools in areas historically more exposed to Christian missions are more effective at ensuring their students validate the *Examen National d'État* compared to schools in less exposed areas. We first examine supply-side explanations, and then turn to demand-side mechanisms.

First, we study the supply of educational infrastructure in the DRC since independence. During the colonial period, the quasi universe of schools were managed by missionaries and hence located near mission stations. Existing literature suggests that post-colonial investments can either reduce historical inequalities in access to and quality of education (Salah, Chambru and Fourati, 2024) or, conversely, perpetuate them by reinforcing earlier patterns of colonial investment (Huillery, 2009). Using the 2018 georeferenced census of all schools in the DRC, which includes the opening year of each school, we examine whether the deployment of educational infrastructure has been skewed toward areas closer to historical Christian missions.

We find that, since independence, areas closer to historical Christian missions have experienced disproportionately more school openings. This trend is consistent across secondary schools, primary schools, and kindergartens. Consequently, in the post-independence period, spatial inequalities in access to schools have increased, with a greater concentration of schools emerging around historical Christian missions. This finding is consistent with our earlier results on the content of education, as a greater supply of schools leads to increased competition for students and may incentivizes product differentiation.

Next, we investigate systematic differences in schools' environments and quality between those located near and far from historical Christian missions. Christian missions may have historically triggered structural transformation, fostering conditions that make the areas surrounding them more conducive to educational activities today. Using the 2018 census of schools, which contains detailed information about each school's environment, we find that schools located closer to

<footnote n="2">² See Becker and Woessmann (2009) for an analysis of the crucial role that Bible reading historically played in Protestantism, contributing significantly to human capital formation.</footnote>
historical Christian missions operate in environments usually associated with higher returns to education. Specifically, these environments are more likely to be urban, better connected to the telephone network, and have greater access to the electrical grid.

We then explore whether these schools are of better quality. Using again the 2018 census, we focus on attributes that measure the inherent, essential qualities of educational institutions. Schools closer to historical Christian missions are older and more likely to have improved ratios, such as classroom-to-student, teacher-to-student, and teacher-to-classroom ratios. In addition, using data from our phone survey, we show that schools located closer to historical Christian missions, particularly Catholic missions, demonstrate higher quality across several additional dimensions: they are more likely to be connected to a kindergarten, organize holiday classes, allocate time for study, and have more class days per week. Furthermore, these schools tend to have separate latrines for boys and girls and exhibit higher aspirations for students, with Catholic mission schools notably having greater aspirations for girls' performance on the Exetat.

To assess the importance of differences in school environment and school quality in explaining our results, we conduct a mediating factor analysis. Specifically, we re-estimate the effect of exposure to historical Christian missions on the likelihood of success at the Exetat, this time controlling for measures of school environment and quality. The estimated coefficient decreases by 50%, indicating that these factors substantially explain the variation in students' success probabilities between schools near and far from missions. In other words, approximately half of the effect of exposure to historical Christian missions seems to operate through differences in school characteristics.

Motivated by these results, we further investigate the quality channel by leveraging a natural experiment: schools closure during the Covid-19 pandemic. During the pandemic, schools were closed from late March to October 2020 and again from December 2020 to February 2021. If the type of schools students have access to significantly influences educational outcomes, we would expect a reduction in the premium associated with exposure to Christian missions in the years following 2020, when schools were closed for most of the year.

To test this, we augment our sample to include the years 2020, 2021, and 2022, and re-estimate the effect of proximity to historical Christian missions for each year separately and plot the estimated coefficients year by year. Our results show that, indeed, during the period of school closures and after, the premium associated with greater exposure to historical Christian missions diminished.

Overall, we find that supply-side mechanisms explain about half of the missionary proximity premium. We next turn to examining demand-side mechanisms.

First, we explore student sorting using our primary data. The aforementioned results suggest that students may self-select into schools near historical Christian missions due to their better reputation and quality. To investigate this, we designed a survey instrument to measure the distribution of students based on ethnicity, religious affiliation, and parents' income. Our outcome variables are Herfindahl-Hirschman indices of heterogeneity.

Our results indicate that rural schools closer to historical Christian missions are more ethnically and religiously diverse. Furthermore, students in these schools are more heterogeneous with
respect to their parents' income. We also find that the proportion of students from very poor families is lower near historical Catholic missions, while the share of students from wealthier backgrounds is higher. Conversely, near historical Protestant missions, the proportion of students from very affluent families is smaller. However, the magnitude of the effects is small. For instance, schools located one standard deviation closer to a historical Catholic mission are only 6 to 7% of a standard deviation more heterogeneous. These results suggest that this mechanism has limited explanatory power.

Finally, we investigate who attends school near versus far from historical Christian missions. Our results suggest that there are more schools near missions, that these schools are of better quality, but that the learning effects are limited. This presents a puzzle. One hypothesis is that schools near historical Christian missions attract a larger share of the school-age population. Consequently, far from historical Christian missions, schools enroll only a small local elite, which, while privileged relative to their local community, is not necessarily wealthier in absolute terms compared to the affluent students attending schools near missions. In contrast, schools near missions attract both relatively wealthier students as well as children who otherwise might not attend school, resulting in greater socioeconomic diversity. To validate this hypothesis, we examine the share of girls enrolled in each section. Girls represent a traditionally marginalized group, often excluded from educational opportunities (Zozoungbo, 2024). Consistent with our hypothesis, we find that the proportion of girls in all three sections is significantly higher near historical Christian missions.

To further validate this mechanism, we examine the distribution of grades, focusing specifically on inequality in students' performance. The rationale behind this analysis is that if only the elite attends school in areas far from historical Christian missions, while all children attend school near these locations, grade inequality should be higher closer to missions. This is precisely what we find: closer to historical missions, both the standard deviation and the interquartile range of grades are wider.

This paper is part of the growing body of literature examining the legacies of European colonialism in sub-Saharan Africa (see Michalopoulos and Papaioannou (2020) for a comprehensive review). This literature underscores how contemporary local inequalities in economic development can, in part, be traced to colonial interventions (Acemoglu, Reed and Robinson, 2014; Archibong and Obikili, 2023), policies (Lowes and Montero, 2021b), and practices (Lowes and Montero, 2021a, Chiovelli, Le Rossignol, Michalopoulos and Papaioannou, 2023). Within this broader framework, the role of missionaries has garnered significant attention. Prior research has examined a diverse range of outcomes, including religious conversion (Nunn, 2010), democracy and political participation (Woodberry, 2012; Cagé and Rueda, 2016; Henn, Larreguy and Schmidt-Padilla, 2024), fertility (Okoye and Pongou, 2023), health (Cagé and Rueda, 2020; Brown, 2024), anti-gay attitudes (Ananyev and Poyker, 2021), tolerance (Mo, 2023), trust (Okoye, 2021), moral values (Bergeron, 2020), and of course education. In recent years, a growing body of literature has specifically explored the effects of proximity to missions in the Congo (Juif, 2019; Bergeron, 2020; Le Rossignol, Lowes and Nunn, 2022; Guirkinger, Villar et al., 2022; Alvarez-Aragon et al., 2023). Country case studies are particularly valuable, as they enable researchers to
hold the political and institutional environment constant, facilitating a more precise investigation
of underlying mechanisms. We contribute to this literature in three ways.

First, we show that colonial-era inequalities in access to, content of, and quality of education have persisted and remain unresolved since independence. Specifically, this paper advances the literature, which has largely focused on the quantity of education, by addressing the overlooked dimensions of quality and content. Our findings are particularly important given the strong correlation between differences in schooling quality and economic growth (Hanushek and Kimko, 2000, Hanushek and Woessmann, 2016).

Second, we show that context matters in understanding the legacies of missionaries. Most of the literature, with rare exceptions (Valencia Caicedo, 2019, Becker and zu Selhausen, 2023), suggests that Protestant missionaries had a more positive impact on education than Catholic ones. In our context, we find instead stronger and more persistent educational benefits linked to proximity to historical Catholic missions. This seemingly unexpected result can be explained by the unique colonial context of the Belgian Congo, where Catholic missions held privileged institutional status, enjoyed state subsidies, and dominated educational provision throughout most of the colonial period, in contrast to Protestant missions, which faced systematic discrimination. Consequently, our results indicate that state institutions and regulations are instrumental in shaping the long-run effects of missionary activities.

Third, we focus on secondary education, while much of the literature has concentrated on primary education and literacy. Focusing on secondary education is important because programs of free education have increased the number of children in school, many of whom have continued to secondary school (Duflo, Dupas and Kremer, 2021, Duflo, Dupas, Spelke and Walsh, 2024). Additionally, while education is often viewed as a monolith, our study examines how historical exposure to Christian missionaries influences students' and schools' specialization in different tracks, such as Technical, General, and abstract thinking.

We organize the paper as follows: section 2 discusses Christian missions in the Democratic Republic of the Congo and highlights their role in education. In this section, we also present the Congolese educational system. Section 3 introduces our data and provides descriptive statistics. We then explain our empirical strategy and deliver our econometric results in section 4. Sections 5 and 6 explore mechanisms, and section 7 concludes.

## 2. Background

During the colonial period in sub-Saharan Africa, Western-style education was both scarce and highly valued. The colonial state and large corporations needed Africans to acquire basic skills – such as reading, writing, arithmetic, and foundational agronomic, organizational, and management knowledge – to substitute European foremen and colonial administrators, who were considered too expensive. For Africans, accessing Western-style education was a *sine qua non* for achieving social mobility within the colonial regime.

There were two dominant paradigms for providing education: the British decentralized system and the highly centralized French system. In British colonies, education was outsourced almost
entirely to missionary orders. For missionaries, providing education was a key vehicle for conversion, as preaching the gospel alone often failed to instill the principles of a new religion. Within this system, missionary orders had the autonomy to define their own curricula, select teachers, teach religious content, and address their audience in vernacular languages. By contrast, in French colonies, the state was the primary provider of education, with the explicit aim of assimilating colonized populations. Instruction was conducted in French, and the curriculum was strictly defined by central authorities. Missionary activities in this system were tightly regulated and limited.

The provision of education in the Congo Free State [1885-1908] and Belgian Congo [1908-1960] lay somewhere between these two paradigms. It mirrored the British system in that education was almost entirely outsourced to missionaries, but, like in the French system, their activities were heavily restricted.

## 2.1. Christian Missions in the Congo Free State and Belgian Congo

African contact with Christianity in the Congo dates back approximately five hundred years. In the 16th century, Portuguese Catholic missionaries settled at the mouth of the Congo River in the Kingdom of Kongo (Markowitz, 1970, Reybrouck, 2014). Although this initial effort led to the conversion of some populations, including members of the local elite, it was ultimately short-lived. The mission faced significant challenges, including the prevalence of malaria and insufficient financial support from European governments (Van Straelen, 1898).<fnref n="3" />

The second phase of Christianization began in the 1880s when several Protestant missions, mainly from the UK, US, and Sweden, established themselves along the recently explored Congo and Kasaï rivers. The first mission in the Congo was the British Baptist Missionary Society (BMS), which arrived in 1878. Around the same time, the Catholic White Fathers entered eastern Congo from Zambia, aiming to combat the slave empire of Tippu Tip.

At the beginning of the colonial period, Leopold II, the King of the Belgians and owner of the Congo Free State, viewed the presence of Protestant missionaries favorably, as it helped him assert effective occupation over the vast territory he had acquired following the Berlin Conferences [1884-1885]. However, the collaboration between Leopold II and Protestant missionaries deteriorated after revelations of atrocities committed by colonial companies in the Congo Free State, which Protestant missionaries actively exposed. Leopold II's mistrust of Protestant missionaries persisted and was transferred to the Belgian administration, continuing until the 1950s.

Meanwhile, the position of Catholic missionaries was strengthened by the 1906 concordat between the Holy See and the Congo Free State. This agreement required the State to provide each Catholic mission with sufficient agricultural land free of charge to meet the mission's needs, in return for the construction of a school for Africans. This marked the beginning of a new era of collaboration between Catholic missionaries and the colonial State in the Congo (see De Jonghe (1933, p. 14-15)).

<footnote n="3">³ In a study of missionary expansion, Jedwab et al. (2022) document how malaria posed a geographical barrier to missionary activities in sub-Saharan Africa prior to the discovery of quinine.</footnote>
The third phase corresponds to the period of Belgian rule [1908–1960]. During this time, the Congo was governed by the *Colonial Trinity*, a term used to describe the collaboration between Christian (primarily Catholic) missionaries, the Belgian administration, and large colonial companies. Within this system of exploitation, missionaries acted as agents of the state, serving as surveyors, registrars, and linguists, while also providing skilled labor for colonial companies. They effectively substituted for the state by taking on the responsibility of education.

The privileged position of Catholic missionaries was further solidified in 1924, when a reform of the educational system stipulated that state subsidies could only be granted to *missions nationales*, which were *de facto* Catholic. Even the schools operated by large companies, such as Union Minière, Huileries du Congo Belge, and Forminière, were managed by Catholic missionaries.

In contrast, Protestant missions faced significant disadvantages. They were excluded from official education programs, denied state subsidies, and prohibited from issuing diplomas recognized by the colonial administration. Despite this unequal treatment, Catholic and Protestant institutions trained almost the same number of students by 1943: 295,000 and 282,000, respectively (Harris, 1946). This is particularly remarkable given that Catholic missions employed 3,873 missionaries at the time, compared to only 809 Protestant missionaries.

Catholic dominance persisted until the 1950s, when conditions for Protestant missions gradually improved. Protestants began receiving state subsidies around 1948, marking a shift toward more equitable treatment.

An important distinction between the Belgian Congo and British or French colonies lies in the evolution of cooperation between missions and the state. In most colonies, there was initially close collaboration, but as secular colonial administrations expanded, this cooperation gradually diminished. However, this trend did not occur in the Congo, where missions and the state maintained a strong partnership throughout the colonial period. Even after independence, as discussed in the next subsection, churches continued to play a prominent role in the provision of education, a role they maintain to this day.

The primary objective of missionaries was to convert people and save souls. In the long run, they were remarkably successful. According to the Pew Research Center: *Religion and Public Life* (2009), 79.6% of the population in the Congo identifies as Christian (12% Muslim, 3.3% Ancestral or Tribal, and 3.7% Unaffiliated). Additionally, 96.2% of Congolese believe in a single god, 88.2% believe that Jesus will return to Earth, and 96.8% regard the Bible as the word of God rather than a text written by men.

Education during the colonial era was closely aligned with systematic commercial and agricultural development, with a strong emphasis on vocational training. In the Belgian Congo, boys were primarily trained to meet labor demands, acquiring skills relevant to the colonial state and private firms. Girls, by contrast, were educated to become “good wives,” focusing on domestic skills and Christian household management (Masandi, 2004). As Coquery-Vidrovitch (2021) succinctly put it, women were “the unloved ones of the colonial school,” due to dual influences from their own culture and the one imposed by the colonizer. For example, while both boys and girls were taught agriculture in the 1930s, the content of this training differed significantly:
boys learned about yield calculations, agronomic knowledge, and advanced farming techniques, whereas girls were taught to grow crops for household consumption.

## 2.2. Congolese Educational System

In the Democratic Republic of Congo, most schools are autonomous government institutions. These schools receive state subsidies based on student enrollment, while the state defines the curriculum and administers final exams. However, due to the state's weak capacity, it exerts little to no control over teaching quality or the proper management of allocated funds, contrasting with contexts such as the UK or the US (Bloom, Lemos, Sadun and Van Reenen, 2015).

The Congolese educational landscape features a kindergarten for children aged 3 to 5, predominantly accessible in urban centers for one or two years. Following this, the primary education phase spans six years, catering to students from 5 to 11 years old. Primary schooling became compulsory and free starting in 2010 but the Congolese government only recently implemented this policy under Tshisekedi's first term. At the end of this phase, sixth graders take the *Examen National de fin d'Etudes Primaires* (ENAFEP) to progress to secondary education. Secondary education then takes students aged 12 to 17 through a foundational curriculum for the first two years, after which students choose one in three sections for the remaining four years. Available sections include General, Pedagogical, and Technical. Within each of these sections, students must select one study track – referred to as an *option* – which they will take to the *Examen National d'État*. Table B1 in Appendix B outlines all the available study tracks. The most sought-after study tracks include General Pedagogy, Chemistry and Biology, Commercial and Administration, Electricity, Construction, Latin and Mathematics, Latin and Philosophy, and Mechanics. Not every secondary school offers all these study tracks. General Pedagogy is the most popular choice as it is offered everywhere due to its relatively cheap cost of organisation. Importantly, not all schools provide every section or study track. Instead, the selection of study tracks offered is at the discretion of each school. Schools are usually vocal about the sections they provide, aiming to attract students and product differentiate. Figure C1 in Appendix C shows a series of photos illustrating how secondary schools advertise the study tracks they offer.

Students can take the *Examen National d'État* as they conclude the four-year cycle. This exam is the passport to entering University.<fnref n="4" /> To be deemed successful, a student must score above 50%. Students who score above 60% can pursue their studies at the university without having to take an additional test, while those who score between 50% and 59% must pass an additional test to gain university admission. Passing this exam is a major milestone in the lives of Congolese people, and the returns from validating the *Exetat* are substantial (see Hamadi (2014) and Appendix C.4 where we estimate the returns to the *Exetat*).

<footnote n="4">⁴ The *Examen National d'État* incurs a cost that is paid by students and varies arbitrarily between *provinces éducatielles* or educational provinces. The minimum price set by the ministry in Kinshasa was CF23,000 (approximately $25) in 2010, which included CF13,000 for the *épreuves hors-session* and 10,000 FC for the *session ordinaire*. In the DRC, the education system is structured into 60 administrative educational units called educational provinces. Importantly, aspects of educational administration and student evaluation, including part of the Exetat grading process, are managed at the educational province level, thereby decentralizing educational governance and allowing for region-specific approaches to assessment and resource allocation.</footnote>
## 3. Data

### 3.1. Missions, Education, Census of Schools

**Mission data:** Our mission data comes from *Atlas Classique Mantnieks* (1951). This published resource contains a series of maps at a 1:2,500,000 scale, representing various physical, administrative elements, the location of public goods and Christian missions operating in the Belgian Congo, in Rwanda and Urundi in the 1950s. We have corrected the map to address the inaccuracies inherent in constructing maps at this scale. The correction made aims to adjust the coordinates of the missions to the exact coordinates of the villages where they were located in the 1950s. This map, containing 330 Catholic missions and 195 Protestant missions in DRC, is used in Bergeron (2020) and Le Rossignol et al. (2022).

<figure><img src="image_3.png" /><figcaption>Figure 2: Catholic and Protestant Missions in the DRC</figcaption></figure>

**Notes:** This figure shows the location of 195 Protestant and 330 Catholic missions that operated in Belgian Congo around 1950 as recorded in Mantnieks (1951). The map displays the modern province boundaries. Figure C5 in Appendix C reproduces the original map.

**Education data:** We measure the content and quality of education using publicly available data from the *Examen National d'État*, the Congolese secondary school graduation test. We downloaded the raw data from the *Congolese Ministry of Education* website. This data comes as a PDF. We have digitized the PDFs for all sessions between 2016 and 2022. Appendix E.1 details the procedure and Figure E12 presents an extract of a page. For all study tracks, the examination schedule includes *épreuves hors-session* organized each year in May and *session ordinaire* held in June.
For each study track (i.e., Latin and Philosophy, Mathematics and Physics, Chemistry and Biology, Business and Administration, Mechanics, Agriculture, etc.), the exam consists of several tests.<fnref n="5" /> The content of the tests is specific to each study track and is the same for all students enrolled in a that study track across the national territory.

The grading system of the *Exetat* during our period of analysis is structured as follows: the *hors-session* test includes an essay (10% of the final grade), a French oral exam (2%), and an internship report (3%). The remaining 85% of the final grade is determined by study track-specific standardized tests administered during the *session ordinaire*. The *hors-session* tests are graded locally at the provincial education level, whereas the *session ordinaire* tests are graded centrally by computer in Kinshasa. This system has remained unchanged since 2008.<fnref n="6" /> The overall results of these assessments are published by the *Ministère de l'Enseignement Primaire, Secondaire et Technique* (EPST), and we obtained the data directly from their official website.

<figure><img src="image_4.png" /><figcaption>Figure 3: Questionnaire of the *Examen National d'État*</figcaption></figure>

Notes: This image shows a sample of the questionnaire taken by students during the 2020 *session ordinaire*. Courtesy of Jean-Pierre Cheruga Kusinza.

Students take the *session ordinaire* tests at dedicated test centers, which are usually large schools, on the same day across the country. To pass, candidates must achieve an overall score (sum of the *session ordinaire* and *hors-session*) above 50%. The comprehensive database we use provides detailed information about the schools – whose students participate in the test – and about the students taking the exam. For each school, we have access to the school's name, its educational province, and its identification number. Additionally, we know the number of male and female students taking the test for each school, stratified by study track, as well as the success rates for male and female students.

For each successful student, the database includes information on their gender, the study track they are enrolled in, and their final score. Unfortunately, the database does not provide any information on the candidates' ages. From this database, we retrieve several outcome variables:

<footnote n="5">⁵ Table B1 in Appendix B provides an overview of all study tracks by year.</footnote>

<footnote n="6">⁶ The Technical section underwent a grading reform in 2020. Until 2019, its grading scheme aligned with that of the General and Pedagogy sections. Since the 2020 session, the *hors-session* component for Technical study tracks now constitutes 65% of the final grade, broken down as follows: a French essay (5%), an internship report (10%), a practical integration situation (10%), and a written integration situation (40%). The remaining 35% is derived from the *session ordinaire* tests, which continue to be graded in Kinshasa.</footnote>
the study tracks offered by each school each year, students' success at the *Examen National d'État*,
successful students' overall scores at the *Exetat*.

For context, Table C2 in Appendix C.2 presents summary statistics. Across all years, the overall success rate is 70%. The most popular section is Pedagogy, followed by Technical and General. Success rates vary by section, with Pedagogy having the lowest rate (67%) and Technical the highest (73%). On average, successful candidates score 56, with a standard deviation of 5.5.<fnref n="7" />

**Census of Schools:** We obtained granular data on the location of all schools in the Democratic Republic of Congo thanks to the collaboration of the *Direction de l'Information et de la Gestion de l'Education* (DIGE). The school census was carried out with the financial support of the World Bank between 2018 and 2021. Figure C2 features a map of all 35,425 secondary schools in DRC.

This exhaustive census allows us to find the GPS coordinates of the schools presenting students to the *Exetat* via the school name and educational province. Overall, we successfully geocoded 80% of the schools – ranging from 15,000 to 20,000 schools annually – and an average of 82% of the students each year, amounting to over 2,000,000 students in total. <fnref n="8" />

In addition to latitudes and longitudes, this extensive database includes detailed information on schools' characteristics, such as the number of teachers, students, and administrative staff, the number of classrooms, the school's founding date, and the number of teachers paid by the state. It also provides details on the school's regime (public, private, Catholic, Protestant, Adventist, etc.), access to electricity, telephone network coverage, and overall accessibility.

Table C3 in Appendix C.2 shows that secondary schools, for which we have *Exetat* data, are situated an average of 24.51 kilometers away from the closest historical Catholic mission and 37.58 kilometers away from the nearest historical Protestant mission.

## 3.2. Primary Data

We supplemented this data with primary data that we collected through a phone survey conducted by our local partner, the Marakuja Kivu Research, in December 2023 and September 2024. The census of schools in the DRC includes the phone number of each school's director. To explore the mechanisms, we conducted a survey of approximately one hour with 624 school directors across 12 provinces in Eastern DRC. The choice of the Eastern provinces is due to the location of the Marakuja Kivu Research, which is based in Goma, Nord Kivu. In each province, we selected rural schools with a probability proportional to size. Appendix D describes the data collection.

# 4. Empirical Strategy and Econometric Results

<footnote n="7">⁷ Mpey-Nka, Enguta and Kakenza Kitumba (2022) offer several detailed monographs on success in the *Examen National d'État* as well as descriptive statistics by province.</footnote>

<footnote n="8">⁸ E.2 details the matching procedure and offers a simple comparison between the sample that we can georeference and thus use in our analysis and the sample that we cannot georeference. Schools that we successfully geocoded have, on average, more participants than those we were unable to geocode. Furthermore, they tend to have a higher success rate. However, the average score of their students is lower, and the proportion of female candidates is also lower.</footnote>
## 4.1. Preliminary Analysis

Prior to conducting our main empirical analysis, we undertake two preliminary exercises. First, we validate our measure of historical missionary presence through a zero-stage analysis. Second, we estimate the economic and human capital returns to the *Examen National d'État*.

**Zero-stage:** In the phone survey, we asked school directors to report their perception of the strength of historical missionary presence in the vicinity of their school. We use this as a preliminary validation of our mission data. The survey question asks: "From your perspective, during the colonial period, was the presence of Catholic (Protestant) missionaries in the vicinity of the school very strong, strong, weak, or very weak?"

Reassuringly, in Figure 4, we find that directors from schools located close to historical Catholic missions, as measured in Mantnieks (1951), report a stronger historical Catholic missionary presence. Similarly, directors near historical Protestant missions report a stronger presence of Protestant missionaries.

<figure><img src="image_5.png" /><figcaption>Figure 4: The Influence of Christian Missions on the Strength of [...] Missionary Presence</figcaption></figure>

*Notes:* The figure shows, how Catholic (Protestant) missionary perceived presence varies with distance to the nearest Catholic and Protestant missions. Standard errors are clustered at the school level. Each estimation controls for the latitude and longitude of the schools, demographic controls including respondent age, age squared, and a dummy equal to 1 if the respondent is male and 0 otherwise, population density as of 1950, geographic controls, and disease controls. Each estimation also includes an Educational Province fixed effect and controls for the school's latitude longitude. The figure reports OLS estimates of equation 4. See Table C4 in Appendix C.3 for the regression table.

**Returns to Examen National d'État:** We use data from the *Enquête 1-2-3*, the first national household survey conducted in 2004/05 in the DRC, to examine the returns to obtaining the *Exetat*. Focusing on a sample of 26,882 individuals who have completed their education, we identify whether respondents obtained the *Exetat* based on their highest diploma, creating a binary indicator for those holding the *Exetat* or higher qualifications. Results, reported in Table C5 in Appendix C.4, indicate that individuals with the *Exetat* possess significantly higher levels of human capital, as reflected in greater literacy rates in both Congolese languages and French. Economically, these individuals are more likely to secure formal employment, evidenced by the presence of contracts or payslips, and earn approximately 12% higher income underscoring the substantial economic benefits of obtaining the *Exetat*.
## 4.2. Christian Missions and the Content of Education

Our first empirical exercise examines how proximity to historical Christian missions affects the content of education. We estimate the following equation:

$$y_{spt} = \alpha + \beta_1 \text{Catholic}_s + \beta_2 \text{Protestant}_s + \mathbf{X}_s \Gamma + \nu_{p(s)} \times \eta_t + \epsilon_{spt} \quad (1)$$

Our unit of analysis is a school $s$ located in an educational province $p$ in year $t$. We explore how greater exposure to historical Christian missions affects the sections offered by schools. Specifically, we investigate two outcome variables: the number of study tracks, or options, offered by a school in a given year, and the probability that a specific section is offered. As detailed in Table B1, study tracks are nested within broader sections (General, Technical, and Pedagogy). Catholic$_s$ and Protestant$_s$ represent our indexes of exposure to historical Christian missions, where our preferred measure of exposure is the inverted logged distance between the school's location and the nearest mission. In our tables, Catholic$_s$ and Protestant$_s$ are labelled as Ln(Inv DISTANCE) Catholic Mission and Ln(Inv DISTANCE) Protestant Mission, respectively, representing the inverted logged distance from each school to the nearest Catholic (Protestant) mission. By using the inverted logged distance, higher values correspond to schools being closer to missions, while lower values indicate greater distance.

Since Christian missions were not randomly established – with missionaries tending to settle in favorable locations (e.g., areas with better climates, lower disease prevalence, and proximity to rivers) – this introduces endogeneity bias. In Appendix C.6, we analyze the correlates of mission locations in the Congo by dividing the national territory into 10-km by 10-km grid cells and measuring exposure as the share of a cell's area within 10 kilometers of a mission. As expected, areas near missions tend to exhibit more favorable characteristics, such as better climates, lower disease prevalence, and closer proximity to navigable rivers.

To address this potential source of bias, we control for these factors in the covariate matrix $X$. We also include historical population density, as measured by Gourou (1951). In addition, we include educational province times year fixed effects, denoted by $\nu_{p(s)} \times \eta_t$, to account for time-varying heterogeneity across educational provinces, an important consideration given that grading and sector management are partially decentralized at the province level. Finally, $\epsilon_{spt}$ is the error term.

In specification 1, the identifying assumption implies that conditional on our large set of control variables in $X$ and educational province-by-year fixed effects, distances to missions is orthogonal to $\epsilon_{spt}$.

Table 1 presents the estimated coefficients and standard errors for exposure to Catholic and Protestant missions. The unit of observation is a school-year. We report both robust and account for spatial auto-correlation within 20 kilometers of a school using Conley standard errors (Conley, 1999). In addition, the table provides standardized coefficients ($\beta$) which helps interpreting results in terms of standard deviations.

In the first column, we find that schools near historical Christian missions offer a greater number of study tracks. The effect is small: on average, schools offer two study tracks, and those located one standard deviation closer to historical Catholic missions (approximately 22.34
km) offer about 8.3% of a standard deviation more study tracks (4% of the mean). Similarly, schools near historical Protestant missions (approximately 29.15 km closer) offer 4% of a standard deviation more study tracks.

In columns two to four, we examine the types of study tracks offered. The estimates reveal that schools near historical Christian missions are more likely to offer study tracks in the General and Technical sections but less likely to offer tracks in the Pedagogy section. In the DRC, the Pedagogy section is the least expensive to provide and serves as the baseline section. In contrast, General and Technical sections require equipment, infrastructure, and skilled teachers, which are more costly to offer. For instance, the Technical study track for "Electricity" requires access to electricity - far from universal in the Congo - as well as resources such as lightbulbs, tools, or solar panels, which are not available nationwide.

The magnitude of these effects is particularly notable. Schools located one standard deviation closer to historical Catholic missions are 4.1 percentage point more likely to offer a study track in the General section (or 15% of the average probability), 7.2 percentage point more likely in the Technical section (or 14% of the average probability), and about 3.3 percentage point less likely to offer tracks in the Pedagogy section (or 4% of the average probability). Similar patterns emerge for proximity to historical Protestant missions, although the effects are considerably smaller.

These results suggest that the study tracks available to students differ significantly between schools located near and farther from missions. Specifically, schools near historical Christian missions appear to offer a more diverse educational content.

Despite the inclusion of a rich set of control variables and the granularity of our empirical setting, concerns about omitted variable bias remain. To speak to this, we apply the procedure proposed by Oster (2019), which evaluates how strong selection on unobservables would need to be, relative to observables, to entirely eliminate our estimated effects. In Table C22, we report the δ statistic, which reflects the required ratio of selection on unobservables to observables that would drive the treatment effect to zero.

Following common practice, we set $R^2_{max}$, the $R^2$ from a hypothetical regression including both observables and unobservables to $1.3 \tilde{R}^2$, where $\tilde{R}^2$ is the $R^2$ from our fully controlled specification in Table 1. The δ values for Catholic missions is consistently higher than 1, offering some reassurance regarding the stability of the estimated effect of Catholic missions. Across columns (1) to (4), the δ values range from 1.269 to 4.165, implying that selection on unobservables would have to be at least as strong, and in some cases two to four times stronger, than selection on observables to nullify our findings. Compared to Catholic missions, Protestant missions generally exhibit lower δ values across all educational outcomes. While Protestant missions show lower δ values in columns (1) and (2), it increases substantially in columns (3) and (4) reaching 1.105 and 7.195. Overall, the magnitude of the δ statistics suggest that our findings are unlikely to be entirely driven by omitted variable bias.
<table><caption>Table 1: The Influence of Christian Missions on School Curriculum and Study Sections</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent Variable:</th></tr><tr><th>Log. Number of<br>Study Tracks</th><th colspan="3">School Offers Section [...]</th></tr><tr><th>(1)</th><th>General<br>(2)</th><th>Technical<br>(3)</th><th>Pedagogy<br>(4)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>0.061<sup>***</sup><br>(0.002)<br>[0.004]</td><td>0.031<sup>***</sup><br>(0.002)<br>[0.003]</td><td>0.054<sup>***</sup><br>(0.002)<br>[0.004]</td><td>-0.024<sup>***</sup><br>(0.002)<br>[0.003]</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td>0.032<sup>***</sup><br>(0.002)<br>[0.006]</td><td>0.018<sup>***</sup><br>(0.002)<br>[0.004]</td><td>0.025<sup>***</sup><br>(0.002)<br>[0.004]</td><td>-0.011<sup>***</sup><br>(0.002)<br>[0.003]</td></tr><tr><td>β Catholic [std.]</td><td>0.053</td><td>0.092</td><td>0.145</td><td>-0.075</td></tr><tr><td>β Protestant [std.]</td><td>0.025</td><td>0.048</td><td>0.061</td><td>-0.030</td></tr><tr><td>Difference P-Value</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Outcome Mean</td><td>1.96</td><td>0.28</td><td>0.50</td><td>0.74</td></tr><tr><td>Outcome SD</td><td>1.57</td><td>0.45</td><td>0.50</td><td>0.44</td></tr><tr><td>Adjusted R2</td><td>0.357</td><td>0.231</td><td>0.167</td><td>0.067</td></tr><tr><td>Observations</td><td>62,606</td><td>62,606</td><td>62,606</td><td>62,606</td></tr><tr><td>Educational Province × Year FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

Notes: The table reports OLS estimates of equation 1. The unit of observation is a secondary school in a year. Robust standard errors in parentheses. Conley standard errors in brackets. *Number of Study Tracks* refers to the total number of different study tracks offered by a school in a given year. *General, Technical, and Pedagogy* are dummy variables, each taking the value one if a school in a given year offers study tracks in the General, Technical or Pedagogy sections, respectively, and zero otherwise. *Ln(Inv. Distance) Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. *Geography Controls* include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasai rivers, and river length within a 10 km buffer around each school. *Historical Pop. Density Control* controls for population density as of 1950. *Educational Province - Year FEs* refers to fixed effects for each combination of educational province and year, controlling for all unobserved factors specific to a given educational province in a given year. *Disease Controls* include malaria suitability and tsetse suitability. Each estimation controls for the school's latitude longitude. * p < 0.10, ** p < 0.05, *** p < 0.01.

### 4.3. Christian Missions and Education Quality

Our second empirical exercise examines how proximity to historical Christian missions influences students' performance on the *Examen National d'État*. To this end, we estimate the following equation:

$$y_{isoct} = \alpha + \beta_1 \text{Catholic}_s + \beta_2 \text{Protestant}_s + \mathbf{X}_s \Gamma + \theta \text{Female}_i + \eta_{oct} + \epsilon_{isoct} \quad (2)$$

where $y_{isoct}$ denotes our outcome variable for student $i$, enrolled in school $s$ and study track $o$, taking the *session ordinaire* test in exam center $c$ in year $t$. We investigate two primary outcomes: first, the probability of student success on the *Examen National d'État*, which is a binary variable taking the value of one if a student passes (scores above 50 out of 100), and zero otherwise. Second, we examine the score of successful candidates (students who scored above 50).

As in equation 1, *Catholic*$_s$ and *Protestant*$_s$ represent our indexes of exposure to historical Christian missions. $\mathbf{X}$ is a matrix of control variables that holds constant various correlates of historical Christian mission locations. However, missionaries' decisions to locate on one side of a river versus the other can have highly localized effects (see Wantchekon et al. (2015)). Therefore, to approximate the causal effect of exposure to Christian missionaries on educational quality today, we take advantage of the fact that students from different schools converge, on the same days, at common exam centers to take the same *session ordinaire* exams.
The content of the *session ordinaire* exam is specific to each study track *o*, which, as we demonstrated in the preceding section, is endogenous. By including fixed effects for exam center, study track, and year ($\eta_{oct}$), we exploit variation in exposure among students taking the same test, in the same location, and in the same year. Finally, $\theta_i$ represents a female fixed effect, the only demographic information at our disposal, and $\epsilon_{isoct}$ is the error term.

As before, the identifying assumption implies that conditional on matrix **X**, and exam center, study track, and year fixed effects, proximity to missions is orthogonal to $\epsilon_{isoct}$.

Table 2 investigates how proximity to historical Christian missions influences students' probability of success at the *Exetat*. We present the estimates from equation 2 incrementally. First, we examine the effect of Catholic missions in column one and Protestant missions in column two. In column three, we include both Catholic and Protestant missions in the same regression. Column four adds a vector of geographical and disease control variables. Given that missionary orders differed in their views on Africans' capacity to learn and develop (Yates, 1980), the last column reports the most demanding specification, which augments equation 2 to include a series of nearest-mission fixed effects to capture unobserved heterogeneity in the educational philosophies of different missions. The variation therefore stems from students attending schools located at varying distances from the same Christian mission, taking their exams at the same exam center in the same year.

We find that students attending schools located closer to historical Christian missions are more likely to succeed in the *Examen National d'État*. The effect is moderate. Specifically, a one standard deviation increase in proximity to the nearest Catholic mission increases a student's probability of success by 0.05 standard deviation, equivalent to a 2 percentage point increase in the probability of success or about 3% of the average passing probability. Notably, this effect is stronger and significant only for Catholic missions as compared to Protestant missions.

We then investigate the effect of exposure to historical Christian missions on the grades of successful candidates. We only observe the grades of those who passed. We estimate equation 2 separately for each grade from 50 to 80, in one-unit intervals.<fnref n="9" /> Figure 5 presents the estimated coefficients and standard errors. The left panel shows the results for Catholic missions, while the right panel reports the equivalent for Protestant missions. The specification corresponds to the model in column 4 of Table 2, which includes geography and disease controls.

The results suggest that students near historical Catholic missions have a lower probability of receiving low grades (between 50 and 55) and a higher probability of scoring between 57 and 74. For context, 50% of successful candidates have grades between 50 and 55, 22% score above 60, and 2.55% score above 70. Notably, students from schools near historical Catholic missions are more likely to score around 60, which is the cutoff for university admission without the need for an additional entrance test. In contrast, students near historical Protestant missions generally show no significant difference in the likelihood of scoring in any grade category, with rare exceptions (65, 67, 75).

<footnote n="9">⁹ Because very few candidates scored above 80, we binned all scores above 80 into the category 80.</footnote>
<table><caption>Table 2: The Influence of Christian Missions on Students' Success at Examen National d'État</caption><thead><tr><th></th><th colspan="5">Dependent Variable:</th></tr><tr><th></th><th colspan="5">Students' Success at Exetat</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>0.015<sup>***</sup><br>(0.001)</td><td></td><td>0.015<sup>***</sup><br>(0.001)</td><td>0.015<sup>***</sup><br>(0.001)</td><td>0.018<sup>***</sup><br>(0.001)</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td></td><td>0.007<sup>***</sup><br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.002<br>(0.001)</td><td>0.004<sup>***</sup><br>(0.002)</td></tr><tr><td>β Catholic [std.]</td><td>0.046</td><td></td><td>0.045</td><td>0.047</td><td>0.055</td></tr><tr><td>β Protestant [std.]</td><td></td><td>0.022</td><td>0.002</td><td>0.005</td><td>0.013</td></tr><tr><td>Difference P-Value</td><td></td><td></td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Outcome Mean</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.70</td></tr><tr><td>Outcome SD</td><td>0.46</td><td>0.46</td><td>0.46</td><td>0.46</td><td>0.46</td></tr><tr><td>Adjusted R2</td><td>0.295</td><td>0.295</td><td>0.295</td><td>0.295</td><td>0.297</td></tr><tr><td>Observations</td><td>2,128,302</td><td>2,128,302</td><td>2,128,302</td><td>2,128,302</td><td>2,128,302</td></tr><tr><td>Clusters</td><td>61,855</td><td>61,855</td><td>61,855</td><td>61,855</td><td>61,855</td></tr><tr><td>Study Track-Center-Year FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Nearest Mission FEs</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td></tr></tbody></table>

Notes: The table reports OLS estimates of equation 2. The unit of observation is a student taking the *Examen National d'État*. Robust standard errors clustered at the school-year level are in parentheses. The dependent variable *Students' Success at Exetat* is a dummy variable equal to one if a student successfully passed the *Exetat* and zero otherwise. *Ln(Inv. Distance) Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. *Study Track-Center-Year FEs* are fixed effects that account for the exam center where the student takes the *session ordinaire*, the study track and the year of the exam. *Geography Controls* include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. *Disease Controls* include malaria suitability and tsetse suitability. *Historical Pop. Density Control* controls for population density as of 1950. Each column controls for gender with a dummy variable equal to 1 if the student is female and 0 otherwise and for the school's latitude longitude. *Nearest Mission FEs* refers to fixed effects for the geographically closest historical Catholic and Protestant missions. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

## 4.4. Christian Missions, Educational Content, and Quality: Additional Econometric Results

We provide several additional results in Appendix C.

First, we re-estimate equation 2 for each section separately: General, Technical, and Pedagogy. These sections differ in both content and the share of the final score determined by the *session ordinaire* and *examens hors-session* (see discussion in Section 3). Furthermore, as shown in Table 1, schools closer to and farther from historical Christian missions differ in the content of education they provide to students. Therefore, we investigate whether the effect of exposure to historical Christian missions on student success rates is uniform across sections. Table C8 reports the estimated coefficients and standard errors. We find that students attending schools closer to historical Catholic missions achieve higher success rates across all sections and particularly in the General section (which includes Sciences, Latin and Mathematics, Latin and Greek, Mathematics and Physics, Latin and Philosophy, and Chemistry and Biology). For all sections, proximity to Protestant missions is not associated with more success.
<figure><img src="image_6.png" /><figcaption>Figure 5: The Influence of Christian Missions on Grades at *Examen National d'État*</figcaption></figure>

*Notes:* The figure presents the effect of proximity to *Catholic* and *Protestant* missions on the grades of students who passed the *Examen National d'État*. The unit of observation is a successful candidate. The figure reports OLS estimates of equation 2. Robust standard errors are clustered at the school-year level. The outcome variables are dummy variables for each grade between 50 and 80, with all scores above 80 binned into the 80 category. *Ln(Inv. Distance Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Each specification includes *Study Track-Center-Year FEs* are fixed effects that account for the exam center where the student takes the *session ordinaire*, the study track and the year of the exam. *Geography Controls* include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. *Disease Controls* include malaria suitability and tsetse fly suitability. Each model also controls for gender, with a dummy variable equal to 1 if the student is female, for the school's latitude longitude and population density as of 1950.

Second, since the distributions of the distance-to-historical-Christian-mission variables are skewed, our results could potentially be influenced by a few schools located very far from missions. To address this concern, we truncate the distance variables at the 95th percentile and re-estimate our specifications. Tables C9 and C10 show that this adjustment does not affect our results. Additionally, one might argue that a continuous measure of distance is not the most appropriate way to capture exposure. To investigate this, Table C11 presents alternative measures of exposure to historical Christian missions, using dummy variables that indicate whether a Catholic or Protestant mission is within 10, 20, or 30 km of a school. The results show that the effect of exposure diminishes for buffers beyond 10 kilometers, suggesting that the impact of historical Christian missions on educational outcomes is highly localized.

Third, in Tables C12 and C13, we re-estimate our specifications using only the sample of rural schools. We exclude urban schools because, conceptually, the influence of distance on the content and quality of education is more relevant in rural than in urban settings. The key message remains unchanged.
Fourth, in Table C14, we examine how proximity to both Catholic and Protestant missions affects our main estimates. While existing literature argues that competition between missions spurred efforts and improved public goods provision (see Woodberry (2012)), our findings offer a more nuanced perspective. Specifically, regions with greater historical competition between Christian denominations exhibit a lower missionary proximity premium. In areas where competition between Catholic and Protestant missions was historically intense, schools may prioritize attracting or retaining students – likely motivated by religious conversion or retention – potentially at the expense of educational quality.

Finally, we re-estimate our main specifications while controlling for proximity to schools built during the colonial period. Exposure to historical Christian missions constitutes a bundled treatment, encompassing not only access to a church and the gospel, but also the introduction of European farming practices and of course formal education. Using the 2018 georeferenced census of all schools in the DRC, we construct a measure of proximity to colonial-era schools as of 1960 and include it as a control in our regressions (see map C7 in Appendix Appendix C). We report the results in Tables C6 and C7. Although proximity to colonial schools exhibits effects similar to those of historical Christian missions, our main results remain robust to the inclusion of this key confounder. This suggests that the influence of historical Christian missions on educational outcomes operates through channels beyond mere exposure to colonial-era schooling infrastructure.

## 5. Mechanisms: Supply

Our results suggest that schools located near historical Christian missions offer a greater number of study tracks, including those that are more resource-intensive to provide. Holding constant the content of education, schools in areas historically more exposed to Christian missions are more effective at ensuring that their students pass the *Examen National d'État* compared to schools in less exposed areas. Furthermore, students attending schools near historical Christian missions, particularly Catholic missions, are more likely to score above the threshold required for university admission and less likely to fall into the lowest passing band (scores between 50 and 54). Importantly, these effects persist even after controlling for proximity to colonial-era schools, underscoring that the legacy of Christian missions extends beyond access to early formal education infrastructure.

We now turn to the mechanisms underlying these effects. Our goal is to understand why students are more likely to succeed in the *Examen National d'État* when enrolled in schools located near historical Christian missions. Given our results on the content of education, this section focuses on supply-side mechanisms. Later, in section 6 we explore demand-side factors.

We begin by taking a historical perspective, examining how historical Christian missions influenced the expansion of secondary schools in the Democratic Republic of Congo (DRC) from the end of the colonial period to the present. We then investigate systematic differences in school environments and the quality of education provided in schools located near historical Christian missions.
## 5.1. Christian Missions and the Supply of Secondary Schools

In the past two decades, low-income countries have experienced significant growth in educational provision, largely driven by the adoption of free education policies. These policies have spurred the development of new schools, often within loosely regulated markets, without adhering to an agenda for the optimal spatial distribution of schools. The Democratic Republic of the Congo is no exception. In Figure C4, we plot the cumulative number of schools operating in the country, using data from the 2018 national school census. This dataset provides specific information on the opening years of each school.<fnref n="10" /> The growth in school openings since the 2010s, coinciding with the adoption of free primary education, is particularly notable. In this section, we examine the spatial distribution of this expansion: where have these new schools been established?

Our analysis examines the extent to which Christian missions influenced the supply of education from 1950 to 2015. To this end, we use the latitude-longitude coordinates and construction dates of all secondary schools in the Democratic Republic of the Congo, as provided by the 2018 census of schools. Our unit of analysis is a 10 km by 10 km grid cell. We estimate the following equation:

$$y_{cp} = \alpha + \sum \delta_p(\mathbf{1}_{\{period_p\}} \times \text{Catholic}_c) + \sum \phi_p(\mathbf{1}_{\{period_p\}} \times \text{Protestant}_c) \\ + \gamma_p \sum_{k=0}^{p} y_{c,k} + \lambda_c + \mu_p + \epsilon_{cp} \quad (3)$$

The dependent variable $y_{cp}$ takes the value of one if a new school opens in cell $c$ during period $p$, where $p$ corresponds to consecutive five-year intervals (e.g., 1950-1954, 1955-1959, [...], 2010-2014). The period index $p$ is defined such that $p$ equals 1 represents the interval 1950-1954, $p$ equals 2 corresponds to 1955-1959, and so on. For the period prior to 1950, $p$ equals 0, capturing any school openings before this time frame. $\text{Catholic}_c$ represents the share of grid cell $c$'s area located within 10 kilometers of a historical Catholic mission, while $\text{Protestant}_c$ is the analogous measure for Protestant missions. $\lambda_c$ are grid cell fixed effects, which control for time-invariant characteristics across cells, and $\mu_p$ are period fixed effects that capture nationwide shocks. Each specification controls for the stock of schools in cell $c$ during the preceding period, $\sum_{p=1950}^{p-1} y_{c,p-1}$, which accounts for the pre-existing supply of schools in the area. The term $\epsilon_{cp}$ represents the error term.

Our primary interest lies in the differential effect of proximity to historical Christian missions on the probability of a new school opening in period $p$. Specifically, we compute the difference between the coefficients $\delta_p$ (or $\phi_p$), which indicates the additional impact of being near a Catholic or Protestant mission, respectively, and $\gamma_p$, the general period effect. This differential captures how much proximity to a historical mission influences the likelihood of new school openings in grid cell $c$, controlling for overall time trends and other fixed factors.

Figure 6 plots the differences in the probability of new school establishments for each period. The left panel presents the results for Catholic missions, and the right panel for Protestant missions. The results indicate that from 1955 to 1965, proximity to either a Catholic or Protestant

<footnote n="10">¹⁰ It is important to acknowledge a selection bias inherent in this dataset, as it only includes schools that were operational in 2018, thereby excluding data on schools that may have closed before this date.</footnote>
mission had no discernible differential effect on the likelihood of a new school being established in a given cell. However, a shift occurs post-1965. Specifically, the historical presence of a Catholic or Protestant mission began to exhibit a differential effect on the probability of secondary schools being opened, particularly in more recent decades. This effect is more pronounced for Catholic missions compared to Protestant ones. The only noticeable drop post-1965 occurs during the 1975-1980 period. This decline follows Mobutu's zaïrisation policy, which severely impacted Congo's economic performance (see Reybrouck (2014)).

<figure><img src="image_7.png" /><figcaption>Figure 6: The Influence of Christian Missions on the Deployment of Secondary Schools in DRC</figcaption></figure>

Notes: The figure presents the differential effect of proximity to historical Catholic and Protestant missions on the establishment of secondary schools in the Democratic Republic of the Congo (DRC) over time. Panel shows the results for Catholic missions, and panel 6b for Protestant missions. The dependent variable is a binary indicator of whether a new secondary school was established in a given 10 km by 10 km grid cell during each period. The blue shaded areas represent 95% confidence intervals around the estimated coefficients. Results are estimated from equation 3, controlling for time-invariant characteristics of the grid cells and including period fixed effects.

In Figure C8 in C.1, we extend the analysis by estimating equation 3 for kindergarten and primary schools. The Catholic premium becomes even more evident when examining the establishment of these other types of schools. Once again, the influence of Catholic missions surpasses that of Protestant missions, further reinforcing the notion of a stronger Catholic effect in promoting the foundation of educational institutions across different educational levels. Overall, these results suggest that, in the post-independence period, spatial inequalities in access to schools have increased, with a greater concentration of schools emerging around historical Christian missions. This concentration may help explain our findings on the content of education, as a greater supply of schools leads to increased competition for students and incentivizes product differentiation.

## 5.2. Christian Missions and School's Environment

Next, we examine the environment surrounding the schools. It is plausible that Christian missions may have historically triggered a process of structural transformation, making the areas around these missions more conducive to educational activities today. To explore this, we analyze four binary outcome variables from the 2018 census of schools. These variables indicate whether a
school is located in an urban or rural area, whether it is easily accessible, whether it has telephone network coverage, and whether it is connected to the electrical grid. We estimate the following specification:

$$y_{sp} = \alpha + \beta_1 \text{Catholic}_s + \beta_2 \text{Protestant}_s + \mathbf{X}_s \Gamma + \nu_p + \epsilon_{sp} \quad (4)$$

The unit of analysis is a school *s* located in an educational province *p*. As before, *Catholic*<sub>s</sub> and *Protestant*<sub>s</sub> represent our indexes of exposure to historical Christian missions, where our preferred measure of exposure for school *s* is the inverted logged distance between the school's location and the nearest mission. As before **X** is our matrix of geography and disease control variables. $\nu_p$ are educational province fixed effects and the $\epsilon_{sp}$ is the error term.

<table><caption>Table 3: The Influence of Christian Missions on School Environment</caption><thead><tr><th></th><th colspan="4">Dependent Variable:</th></tr><tr><th></th><th>Urban</th><th>Accessible</th><th>Coverage</th><th>Electricity</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>0.038***<br>(0.002)<br>[0.007]</td><td>0.070***<br>(0.002)<br>[0.007]</td><td>0.051***<br>(0.002)<br>[0.007]</td><td>0.028***<br>(0.001)<br>[0.003]</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td>0.023***<br>(0.003)<br>[0.009]</td><td>0.034***<br>(0.002)<br>[0.007]</td><td>0.025***<br>(0.002)<br>[0.006]</td><td>0.017***<br>(0.002)<br>[0.005]</td></tr><tr><td>β Catholic [std.]</td><td>0.144</td><td>0.211</td><td>0.167</td><td>0.108</td></tr><tr><td>β Protestant [std.]</td><td>0.075</td><td>0.094</td><td>0.076</td><td>0.060</td></tr><tr><td>Difference P-Value</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Outcome Mean</td><td>0.12</td><td>0.72</td><td>0.79</td><td>0.14</td></tr><tr><td>Outcome SD</td><td>0.33</td><td>0.45</td><td>0.41</td><td>0.35</td></tr><tr><td>Adjusted R2</td><td>0.136</td><td>0.164</td><td>0.307</td><td>0.443</td></tr><tr><td>Observations</td><td>26,797</td><td>35,425</td><td>35,425</td><td>35,425</td></tr><tr><td>Educational Province FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

Notes: The table reports OLS estimates of equation 4. The unit of observation is a secondary school. Robust standard errors in parentheses. Conley standard errors in brackets. *Urban*, *Accessible*, *Coverage*, and *Electricity* are dummy variables, each taking the value one if a school is in an urban area, easily accessible, has telephone network coverage, or is connected to the electrical grid, respectively, and zero otherwise. Ln(Inv. Distance) Catholic (Protestant) Mission represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Each specification includes fixed effects for the educational province where the school is located. Geography Controls include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. Disease Controls include malaria suitability and tsetse suitability. Historical Pop. Density Control controls for population density as of 1950. Each estimation contain educational province fixed effects and controls for the school's latitude longitude. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

The estimates are presented in Table 3. We find that schools located near historical Christian missions are more likely to be urban, easily accessible, covered by a telephone network, and connected to the electrical grid. In terms of magnitude, the $\beta$ coefficient associated with proximity
to historical Catholic missions is approximately 100% larger than that of historical Protestant missions. This echoes the differential effect observed in the main results section. Overall, these findings suggest that schools closer to historical Christian missions, especially Catholic, are situated in ecosystems typically associated with higher returns to education.

### 5.3. Christian Missions and Schools' Quality

Thus far, we have shown that the establishment of secondary schools has been more pronounced in areas closer to historical Christian missions and that these schools operate in environments typically associated with higher returns to education. We now turn to examining whether schools located near historical Christian missions are of better quality. We investigate several indicators of school quality.

We focus on attributes that measure the inherent, essential qualities of educational institutions. We examine several outcome variables, including the number of years a school has existed, and ratios such as classrooms-to-student, teachers-to-student, administrative staff-to-student, teachers-to-classroom, paid teachers-to-teachers, paid female teachers-to-female teachers, and paid admin staff-to-admin staff.<fnref n="11" /> Figure 7 reports the estimated coefficients and standard errors for exposure to Catholic (left panel) and Protestant (right panel) missions, based on equation 4.

<figure><img src="image_8.png" /><figcaption>Figure 7: Christian Missions and Education Quality</figcaption></figure>

Notes: The figure reports OLS estimates of equation 4. The unit of observation is a school. Standard errors are clustered at the school level. Exposure to Catholic Mission is the inverted logged distance from each school to the nearest Catholic mission. Exposure to Protestant Mission is the inverted logged distance from each school to the nearest Catholic mission. Each specification includes fixed effects for the educational province where the school is located. *Geography Controls* include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. *Disease Controls* include malaria suitability and tsetse fly suitability. *Historical Pop. Density Control* controls for population density as of 1950. Each estimation controls for the school's latitude longitude.

<footnote n="11">¹¹ In this context, paid teachers refer specifically to those whose salaries are funded by the central administration, rather than being exclusively financed by the school itself.</footnote>
We find that, on average, schools located near historical Christian missions have been in operation for longer periods. We observe this pattern for both Catholic and Protestant missions. This finding is consistent with historical context: Christian missionaries were the earliest and primary providers of Western-style education in the Belgian Congo, and many of the oldest schools remain situated near former mission sites. However, the magnitude of this relationship is likely attenuated in recent years due to the rapid expansion of school construction and the resulting over-concentration of new schools near historical mission areas (see Figure 6).

Interestingly, and somewhat contrary to earlier results, schools near historical Protestant missions appear to be of better quality. The estimated coefficients indicate higher ratios of classrooms, teachers, and administrative staff per student in schools near Protestant missions compared to those near Catholic missions. Moreover, these schools exhibit a greater proportion of paid teachers and paid female teachers. In the 2018 school census, "paid" teachers are understood to be those financed by the state (écoles mécanisées), suggesting that schools near Protestant missions are more formalized than their Catholic counterparts.

In contrast, schools near Catholic missions exhibit very low classroom-to-student and teacher-to-student ratios. This discrepancy may be attributed to their business model. As the final three estimates suggest, teachers in schools near Catholic missions are less likely to receive state funding, implying that their salaries are primarily derived from local tuition fees, which necessitates larger student enrollments. The only dimension on which schools near Catholic missions consistently outperform others is the ratio of teachers to classrooms. This finding aligns with earlier results on the greater number of study sections offered in these schools, which likely necessitates a larger and more specialized teaching staff.

## 5.4. Aftermath: Christian Missions and Education Quality

### 5.4.1. Controlling for the Environment and Schools' Quality

Our results suggest that schools near historical Christian missions operate in environments where the returns to education are usually higher – these areas are more urban, easier to access, have better telephone network coverage, and are more likely to be connected to the electrical grid. These schools also show improved quality indicators, such as larger sections portfolio and better classroom-to-student, teacher-to-student, and classroom-to-teacher ratios. These factors may help explain the premium associated with proximity to historical Christian missions that we estimate. To test this hypothesis, we conduct a mediating factor analysis by reestimating equation 2, controlling for each factor independently and then collectively. We present the estimated coefficients and standard errors in Figure 8. The left panel shows the results for exposure to historical Catholic missions, while the right panel displays the corresponding results for historical Protestant missions.

We find that the estimated coefficient for exposure to historical Catholic missions nearly halves when controlling for school quality, indicating that approximately 50% of the observed effect can be explained by differences in school characteristics. In contrast, the effect of proximity to
<figure><img src="image_9.png" /><figcaption>Figure 8: The Influence of Christian Missions on Students' Success at Examen National d'État: Mediating Factor Analysis</figcaption></figure>

Notes: The figure reports OLS estimates of equation 2. The unit of observation is a student taking the *Examen National d'État*. Robust standard errors clustered at the school-year level are in parentheses. The dependent variable *Students' Success at Exetat* is a dummy variable equal to one if a student successfully passed the *Exetat* and zero otherwise. *Ln(Inv. Distance) Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Each specification includes fixed effects that account for the study track chosen by the student, the exam center where the student takes the *session ordinaire*, and the year of the exam. Each specification includes caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school, malaria suitability and tsetse suitability. Each specification controls for gender with a dummy variable equal to one if the student is female and zero otherwise and for the school's latitude longitude. The vertical bars, from darkest to lightest, denote the 90, 95, and 99% confidence intervals, respectively. The red bars indicate our baseline specifications. The bottom panel indicates the combination of mediating factors included in each specification.

historical Protestant missions remains relatively stable and statistically indistinguishable from
zero when controlling for the schools' environment and quality.

5.4.2. *Natural Experiment using Schools' Closure*

Our results suggest that schools' quality accounts for about 50% of the estimated effect. We
now propose to investigate this mechanism further by taking advantage of a natural experiment:
school closures during the Covid-19 pandemic crisis. If the quality of schools explains part of our
estimated effect, then the lack of access to schools due to closures should reduce the magnitude
of the effect. For this, we augment our database to include the data on the *Examen National d'État*
for the Covid period [2020-2022]. In the Democratic Republic of Congo, the school year was
disrupted in 2020, 2021, and 2022. Schools were closed from late March to October 2020 and
from December 2020 to February 2021. During the closures, lessons were delivered online and
via Radio Okapi and television. The *Examen National d'État* was also disrupted. In 2018 and 2019, the regular session exams took place from June 18 to 22 and from June 24 to 27, respectively. In 2020, the exams were held from September 6 to 10, in 2021 from August 30 to September 2, and in 2022 from July 18 to 21. Hence, students took these exams with limited access to their schools, providing a compelling setting to examine whether access to quality schools explains the observed differences in outcomes between students.

We examine the stability of the coefficients associated with Catholic and Protestant missions across years. If the quality of schools explains part of the effect, we should observe a steady decline in the missions' proximity premium from 2020 onward. To do this, we re-estimate equation 2 for each year separately and report the estimated coefficients in Figure 9. The outcome variable is a binary indicator equal to one if a student successfully passes the test, and zero otherwise.

<figure><img src="image_10.png" /><figcaption>Figure 9: The Influence of Christian Missions on the Probability of Success: By Year</figcaption></figure>

*Notes:* The figure reports OLS estimates of equation 2. The unit of observation is a student taking the *Examen National d'État*. Robust standard errors clustered at the school level are in parentheses. The dependent variable *Students' Success at Exetat* is a dummy variable equal to one if a student successfully passed the *Exetat* and zero otherwise. *Ln(Inv. Distance) Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Each specification includes fixed effects that account for the study track chosen by the student and the exam center where the student takes the *session ordinaire*. Each specification includes caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school, malaria suitability and tsetse suitability. *Historical Pop. Density Control* controls for population density as of 1950. Each specification controls for gender with a dummy variable equal to one if the student is female and zero otherwise and for the school's latitude longitude.

Consistent with our previous results, we find that exposure to Catholic missionaries has a positive and significant effect on students' success during the period 2016–2019, while the effect of exposure to Protestant missions remains null. However, during the Covid-19 period, when access to schools became limited, the coefficient associated with exposure to Catholic missionaries began to converge toward zero, indicating a reduction in the proximity premium. Interestingly, in 2020, the missionary premium between Catholic and Protestant missions appears similar. While this result should be interpreted cautiously, as other factors – such as differential selection into
dropout rates near and far from missions – may also play a role, it suggests that limited access to schools during the Covid-19 crisis reduced the gap in students' probability of success between those enrolled near and far from historical Christian missions.

## 5.5. Additional Results

In Appendix C.5, we explore the quality channel using alternative outcome variables. Our specification is that in equation 4. In Table C15, using data from the phone survey, we show that schools located closer to historical Catholic missions are more likely to be connected to a kindergarten. Table C16 shows that schools closer to historical Christian missions are slightly more likely to have separate latrines for boys and girls and slightly less likely to have mixed latrines. In Table C17, we show that schools located closer to historical Christian missions, particularly Catholic missions, are more likely to organize holiday classes and purposefully allocate time for study. Additionally, these schools tend to have more class days per week. Collectively, these findings further support the argument that schools near historical Christian missions, especially Catholic missions, exhibit higher quality.

Finally, using data from the phone survey we investigate school directors' aspirations for students in terms of grades. Here, our outcome variable is a score [50-100]. The exact survey question asks "In your opinion, what is a good grade at the *Examen National d'État?*". We also asked that question separately for boys and girls. On average school directors thought a good grade to be 63.64, which is approximately 9 points higher than the sample mean. Expectations vary by gender with a good grade for boys of 63,11 and 61,79 for girls. Table C20 in Appendix C reports the estimated coefficients and standard errors from estimating equation 4. We find that schools near historical Catholic missions have higher aspirations for their students than schools located further away. In contrast, schools located near historical Protestant missions tend to have lower aspirations. For schools located near historical Catholic missions, their exceptionalism lies in their greater aspiration for girls specifically.<fnref n="12" />

## 6. Mechanisms: Demand

Thus far, we have focused primarily on supply-side mechanisms, finding that these explain approximately half of the estimated effect of proximity to Catholic missions. We now turn to examining demand-side channels. First, we investigate student sorting. Next we propose an analysis of schools' gender composition to explore patterns of student selection. Finally, we examine learning inequality.

<footnote n="12">¹² Table C18 examines whether proximity to historical Christian missions influences the share of students attending university after completing secondary education. According to our phone survey, 26% of boys and 16% of girls continue their studies at the university level. We find positive, but statistically insignificant, effects of proximity to both Catholic and Protestant missions on university attendance for boys and girls.</footnote>
## 6.1. *Students' Sorting*

In the DRC, Christian missionaries laid the foundation for the contemporary education system. Missionary schools trained the first generations of post-independence political leaders and the initial cohorts of state administrators and business leaders. As a result, schools located near historical Christian missions may have a better reputation, attracting students from a wider geographical radius compared to schools farther away, which tend to serve more localized populations. Assuming that students traveling from distant areas to attend schools near missions contribute to greater diversity, schools near historical missions are likely to exhibit greater heterogeneity in their student body. This assumption is reasonable, given that the DRC is home to over 80 ethnic groups, and students who move to attend these schools are likely to come from various backgrounds, thereby increasing the diversity of the student population.

To test this mechanism, we use primary data from a phone survey conducted in twelve provinces in Eastern DRC to investigate whether schools located closer to historical Christian missions are indeed more heterogeneous. In Section 5.2, we showed that schools located closer to historical Christian missions tend to be more urban. Urban areas often host more heterogeneous populations. Importantly, our phone survey targeted only rural schools. Therefore, any potential differences in heterogeneity are not merely a reflection of the rural-urban divide.

We assess the student populations in our sample of schools along three dimensions: parents' income, students' ethnicity, and religion. School directors were asked to classify their students into different categories. For example, for income, the exact wording was: "In our study, families earning more than 20,000 Congolese francs per day (approximately $7.7 as of December 2023) are classified as 'very rich'; families earning between 10,000 and 20,000 francs are considered 'rich'; those earning between 5,000 and 10,000 francs are classified as 'poor'; and families earning less than 5,000 francs are categorized as 'very poor.' With this scale in mind, could you estimate the percentage of students in your school from families that fall into each category."

For ethnicity and religion, we first asked respondents to identify the number of ethnic (or religious) groups in the school and then to estimate the percentage of students belonging to the three largest groups. If there were more than three groups, the remaining groups were combined into a fourth category.

For each of these dimensions, we compute the Herfindahl-Hirschman Index (HHI) as a measure of concentration, where zero indicates maximum heterogeneity and one indicates complete homogeneity.<fnref n="13" /> Our specification mirrors the one outlined in equation 4.

Table 4 presents the OLS estimates. On average, we find that schools closer to historical Catholic missions are more heterogeneous along income and ethnic dimensions. The magnitude of the effects is moderate: schools located one standard deviation closer to a historical Catholic mission are 7% of a standard deviation more heterogeneous. This indicates that students do sort into schools near historical Christian missions. These results suggest that the population

<footnote n="13">¹³ The Herfindahl-Hirschman Index (HHI) is calculated as $HHI = \sum_{i=1}^{N} s_i^2$, where $s_i$ is the proportion in the $i$th group.</footnote>
<table><caption>Table 4: Distance to Mission and Heterogeneity</caption><thead><tr><th rowspan="3"></th><th colspan="9">Dependent Variable:</th></tr><tr><th colspan="9">School's Heterogeneity</th></tr><tr><th colspan="3">HHI Income</th><th colspan="3">HHI Ethnicity</th><th colspan="3">HHI Religion</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>-0.013*<br>(0.007)<br>[0.006]</td><td>-0.013*<br>(0.008)<br>[0.007]</td><td>-0.016**<br>(0.007)<br>[0.009]</td><td>-0.016**<br>(0.008)<br>[0.009]</td><td>-0.013*<br>(0.008)<br>[0.009]</td><td>-0.000<br>(0.004)<br>[0.004]</td><td>-0.000<br>(0.005)<br>[0.004]</td><td>-0.001<br>(0.005)<br>[0.004]</td><td>-0.001<br>(0.005)<br>[0.004]</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td></td><td>-0.007<br>(0.008)<br>[0.008]</td><td>-0.002<br>(0.008)<br>[0.009]</td><td>-0.012<br>(0.008)<br>[0.009]</td><td>-0.007<br>(0.008)<br>[0.009]</td><td>-0.004<br>(0.005)<br>[0.005]</td><td>-0.004<br>(0.005)<br>[0.005]</td><td>-0.004<br>(0.005)<br>[0.005]</td><td>-0.004<br>(0.005)<br>[0.005]</td></tr><tr><td>β Catholic [std.]</td><td>-0.071</td><td></td><td>-0.068</td><td>-0.084</td><td></td><td>-0.073</td><td>-0.002</td><td></td><td>0.008</td></tr><tr><td>β Protestant [std.]</td><td></td><td>-0.030</td><td>-0.011</td><td></td><td>-0.057</td><td>-0.035</td><td></td><td>-0.031</td><td>-0.033</td></tr><tr><td>Difference P-Value</td><td></td><td></td><td>0.404</td><td></td><td></td><td>0.640</td><td></td><td></td><td>0.556</td></tr><tr><td>Outcome Mean</td><td>0.54</td><td>0.54</td><td>0.54</td><td>0.55</td><td>0.55</td><td>0.55</td><td>0.40</td><td>0.40</td><td>0.40</td></tr><tr><td>Outcome SD</td><td>0.28</td><td>0.28</td><td>0.28</td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.16</td><td>0.16</td><td>0.16</td></tr><tr><td>Adjusted R2</td><td>0.408</td><td>0.405</td><td>0.407</td><td>0.349</td><td>0.346</td><td>0.349</td><td>0.204</td><td>0.204</td><td>0.203</td></tr><tr><td>Observations</td><td>621</td><td>621</td><td>621</td><td>610</td><td>610</td><td>610</td><td>604</td><td>604</td><td>604</td></tr><tr><td>Educational Province FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Respondent Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geographic Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

of students taking the test near historical Christian missions may differ significantly from those farther away.

We further investigate the sorting of students in Table C19, using data from our phone survey to examine the effect of proximity to historical Christian missions on the share of students from different income categories: very poor, poor, rich, and very rich. Our results show that schools near historical Catholic missions are less likely to enroll students from very poor backgrounds and more likely to enroll students from rich and very rich backgrounds. Conversely, schools near historical Protestant missions are more likely to enroll students from poor and rich backgrounds and less likely to enroll students from the very poor or very rich categories. These findings suggest that schools near historical Catholic missions tend to attract students from higher parts of the income distribution, while schools near historical Protestant missions tend to enroll students more evenly distributed across the middle of the income spectrum, with fewer from the extremes.<fnref n="14" />

<footnote>Notes: The table reports OLS estimates of equation 4. The unit of observation is a secondary school. Robust standard errors in parentheses. Conley standard errors in brackets. HHI Income, HHI Ethnicity, and HHI Religion are Herfindahl-Hirschman Indices measuring the concentration of income, ethnic groups, and religious affiliations within each school. Ln(Inv. Distance) Catholic (Protestant) Mission represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Each estimations include Educational Province fixed effects. Respondents controls include the respondent age, age squared and a dummy equal to 1 if the respondent is a male and 0 otherwise. Geography Controls include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasai rivers, and river length within a 10 km buffer around each school. Disease Controls include malaria suitability and tsetse suitability. Historical Pop. Density Control controls for population density as of 1950. Each estimation also controls for the school's latitude longitude. * p < 0.10, ** p < 0.05, *** p < 0.01.</footnote>

<footnote n="14">¹⁴ In addition, Table C21 analyzes how proximity to historical Christian missions relates to school entry requirements. It shows that schools located closer to historical missions are neither more nor less likely to require students to pass an entrance exam or to have completed the primary school completion exam. Among schools that do require the primary completion exam, those nearer historical Catholic missions tend to request higher scores, although the difference is not statistically significant. This pattern does not appear for schools close to Protestant missions. Overall, these findings suggest minimal barriers to entry near historical Catholic or Protestant missions, consistent with our hypothesis that these schools attract a broader and more diverse segment of the school-age population.</footnote>
## 6.2. Gender Composition

The magnitude of our results on the probability of success is moderate, which is puzzling given the documented differences in school environments and quality. Schools near historical Christian missions appear to be more heterogeneous. This suggests that the population of students may differ significantly between areas close to and far from missions. In other words, near historical Christian missions, the entire distribution of school-aged children may attend school, whereas in areas farther from missions, access to education might be limited to an elite minority.

To test this hypothesis, we estimate the following equation:

$$y_{sopt} = \alpha + \beta_1 \text{Catholic}_s + \beta_2 \text{Protestant}_s + \mathbf{X}_s \Gamma + \mu_o + \nu_{p(s)} \times \eta_t + \epsilon_{spt} \quad (5)$$

where $y$ is the share of girls in school $s'$ section $o$ in educational province $p$ at time $t$. $\text{Catholic}_s$, $\text{Protestant}_s$ and $\mathbf{X}$ are set as before. $\mu_o$, and $\nu_{p(s)} \times \eta_t$ are section, and fixed effects for each combination of educational province and year, controlling for all unobserved factors specific to a given province in a given year, respectively.

Table 5 reports the OLS estimates of equation 5. The outcome variable in the first column is the share of female students taking the *Examen National d'État* in a school. In columns two to four, we present the estimates for each section separately. The results suggest that girls represent a larger proportion of the student population across all sections in schools located closer to historical Christian missions, particularly Catholic ones.

## 6.3. Aftermath: Learning Inequality

Our results indicate that schools located near historical Christian missions exhibit distinct characteristics. First, there has been a continuous opening of new schools in these locations, suggesting greater competition for students. Second, we find evidence of student sorting, with schools attracting individuals from diverse ethnic and income backgrounds. Third, there, girls represent a larger share of the student cohorts. Together, these findings suggest that schools near historical Christian missions are more inclusive, attracting a diverse pool of students from different walks of life.

Furthermore, our results demonstrate that students taking the *Examen National d'État* in schools near historical Christian missions are more likely to succeed and achieve higher grades, often securing grades that allow them to enter university without additional testing. However, the magnitude of these effects is modest. One potential explanation is that, while schools near historical Christian missions are open to all students, including students with poor backgrounds, and that only the elite may have access to education in schools farther from these missions.

To speak to this, we investigate the effect of proximity to historical Christian missions on learning inequality. Specifically, we explore two outcome variables: (i) the standard deviation and (ii) the interquartile range of grades among successful candidates measured at the study-track-year level. Our specification follows equation 5 where subscript $o$ now represents a study track and not a section. If schools near historical Christian missions are indeed more inclusive and open to all students, we would expect to observe greater inequality in grades in these schools, reflecting a broader range of student performance.
<table><caption>Table 5: The Influence of Christian Missions on the Share of Female Participant at the Examen National d'État</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent Variable:</th></tr><tr><th colspan="4">Share of Female in [...] Sections (%)</th></tr><tr><th>All<br>(1)</th><th>General<br>(2)</th><th>Technical<br>(3)</th><th>Pedagogy<br>(4)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>1.033***<br>(0.083)<br>[0.113]</td><td>0.864***<br>(0.223)<br>[0.229]</td><td>0.863***<br>(0.159)<br>[0.147]</td><td>0.236***<br>(0.082)<br>[0.083]</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td>0.641***<br>(0.087)<br>[0.134]</td><td>0.159<br>(0.235)<br>[0.304]</td><td>0.148<br>(0.168)<br>[0.136]</td><td>0.450***<br>(0.093)<br>[0.106]</td></tr><tr><td>β Catholic [std.]</td><td>0.063</td><td>0.041</td><td>0.040</td><td>0.014</td></tr><tr><td>β Protestant [std.]</td><td>0.037</td><td>0.008</td><td>0.007</td><td>0.026</td></tr><tr><td>Difference P-Value</td><td>0.003</td><td>0.056</td><td>0.005</td><td>0.118</td></tr><tr><td>Outcome Mean</td><td>44.65</td><td>47.38</td><td>37.44</td><td>50.84</td></tr><tr><td>Outcome SD</td><td>22.18</td><td>27.87</td><td>29.32</td><td>22.04</td></tr><tr><td>Adjusted R2</td><td>0.073</td><td>0.417</td><td>0.546</td><td>0.645</td></tr><tr><td>Observations</td><td>94,465</td><td>39,864</td><td>59,414</td><td>75,661</td></tr><tr><td>Clusters</td><td>62,064</td><td>6,222</td><td>11,265</td><td>15,651</td></tr><tr><td>Educational Province × Year FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

Notes: The table reports OLS estimates of equation 5. The unit of observation is a secondary school in a year. Robust standard errors clustered at the school level are in parentheses. Share of Female in [...] Sections refers to the share of female students in the different study sections (General, Technical, or Pedagogy) offered by a school in a given year. Ln(Inv. Distance) Catholic (Protestant) Mission represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Column (2) to (4) include controls for the overall female share. Educational Province - Year FEs refers to fixed effects for each combination of educational province and year, controlling for all unobserved factors specific to a given educational province in a given year. Geography Controls include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. Disease Controls include malaria suitability and tsetse suitability. Historical Pop. Density Control controls for population density as of 1950. Each estimation controls for the school's latitude longitude. * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 6 presents the OLS estimates. As expected, we find that schools located closer to historical Christian missions exhibit greater inequality in grades, measured both by the standard deviation and the interquartile range. These results suggest that such schools attract a larger share of school-aged children, increasing student diversity and leading to greater variability in academic performance.
<table><caption>Table 6: The Influence of Christian Missions and Learning Inequality</caption><thead><tr><th rowspan="3"></th><th colspan="6">Dependent Variable:</th></tr><tr><th colspan="3">Standard Deviation of Grades</th><th colspan="3">Inter-Quartile Range of Grades</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>Ln(Inv. Distance) Catholic Mission</td><td>0.037***<br>(0.005)<br>[0.010]</td><td>0.033***<br>(0.005)<br>[0.010]</td><td>0.047***<br>(0.009)<br>[0.015]</td><td></td><td>0.042***<br>(0.009)<br>[0.014]</td><td></td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td></td><td>0.026***<br>(0.006)<br>[0.009]</td><td>0.017***<br>(0.006)<br>[0.008]</td><td></td><td>0.032***<br>(0.009)<br>[0.015]</td><td>0.021**<br>(0.010)<br>[0.014]</td></tr><tr><td>β Catholic [std.]</td><td>0.034</td><td></td><td>0.030</td><td>0.025</td><td></td><td>0.022</td></tr><tr><td>β Protestant [std.]</td><td></td><td>0.000</td><td>0.015</td><td></td><td>0.000</td><td>0.011</td></tr><tr><td>Difference P-Value</td><td></td><td></td><td>0.078</td><td></td><td></td><td>0.141</td></tr><tr><td>Outcome Mean</td><td>2.78</td><td>2.78</td><td>2.78</td><td>3.57</td><td>3.57</td><td>3.57</td></tr><tr><td>Outcome SD</td><td>1.47</td><td>1.47</td><td>1.47</td><td>2.52</td><td>2.52</td><td>2.52</td></tr><tr><td>Adjusted R2</td><td>0.191</td><td>0.191</td><td>0.191</td><td>0.140</td><td>0.140</td><td>0.140</td></tr><tr><td>Observations</td><td>98,129</td><td>98,129</td><td>98,129</td><td>106,386</td><td>106,386</td><td>106,386</td></tr><tr><td>Clusters</td><td>19,196</td><td>19,196</td><td>19,196</td><td>19,867</td><td>19,867</td><td>19,867</td></tr><tr><td>Educational Province × Year FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

Notes: The table reports OLS estimates of equation 5. The unit of observation is a study track in a secondary school in a year. Robust standard errors clustered at the school level in parentheses. Each estimation controls for the number of student and the number of student squared. *Standard Deviation of Grades* refers to the standard deviation of student grades within each study track in each schools and each year. *Inter-Quartile Range of Grades* represents the inter-quartile range of student grades within each study track in each schools and each year. *Ln(Inv. Distance) Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. *Educational Province - Year FEs* refers to fixed effects for each combination of educational province and year, controlling for all unobserved factors specific to a given educational province in a given year. *Geography Controls* include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. *Disease Controls* include malaria suitability and tsetse suitability. *Historical Pop. Density Control* controls for population density as of 1950. Each estimation controls for the school's latitude longitude. *p* < 0.10, **p* < 0.05, ***p* < 0.01.

## 7. Conclusion

We study the influence of proximity to historical Christian missions on education today in the Democratic Republic of the Congo. During the colonial period, Christian missionaries were the primary providers of Western-style education in Sub-Saharan Africa, particularly in the Belgian Congo.

Our analysis combines original data from the *Examen National d'État*, the 2018 census of all schools in the DRC, and a phone survey conducted with 624 school directors in Eastern DRC. We identify the locations of historical missions using the 1951 *Atlas Classique* (Mantnieks, 1951).

Our findings indicate that schools located near historical Christian missions are of higher quality. These schools offer a broader range of study tracks, particularly those outside the General Pedagogy section, which serves as the baseline and least expensive option to provide. In terms of educational outcomes, we find that students taking the *Exetat* near historical Catholic missions are more likely to succeed and achieve “good” grades – grades that permit university entry – and less likely to achieve “low” grades. The magnitude of the missionary proximity premium
is modest, with a one standard deviation increase in proximity to historical Catholic missions raising a student's probability of success by only two percentage points. In contrast, greater proximity to historical Protestant missions shows virtually no effect. Controlling for the quality of schools near historical Catholic missions reduces the estimated effect by half.

To explain the missionary proximity premium we investigate supply and demand sides mech-
anisms. We find a greater concentration of schools around historical Christian missions during
the post independence period. This concentration may help explain our findings on the content
of education, as a greater supply of schools leads to increased competition for students and
incentivizes product differentiation. Additionally, these schools operate in environments that
are more conducive to higher returns on education, characterized by greater competition among
schools for students. While this pattern holds for both Catholic and Protestant missions, the
effects are more pronounced for exposure to Catholic missions.

Exploring demand-side mechanisms, we find that schools near historical Christian missions enroll a more heterogeneous student population, with a higher proportion of girls. This greater diversity contributes to greater inequality in academic outcomes.

Overall, our results suggest that post-colonial education policies in the DRC have failed to address colonial-era inequalities in both access to and quality of education.
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Web Appendix for

EDUCATIONAL LEGACIES OF CHRISTIAN MISSIONS: EVIDENCE
FROM THE DEMOCRATIC REPUBLIC OF THE CONGO

(Not for Publication)

12 June 2025

Most Recent Version Here

PIERRE DONAT BOUILLUD

Czech Technical University

CORALIE HIRSCHI

Centre d'Économie de la Sorbonne

ETIENNE LE ROSSIGNOL

Université de Namur
# Contents

1 Introduction 1

2 Background 8

2.1 Christian Missions in the Congo Free State and Belgian Congo 9

2.2 Congolese Educational System 11

3 Data 12

3.1 Missions, Education, Census of Schools 12

3.2 Primary Data 14

4 Empirical Strategy and Econometric Results 14

4.1 Preliminary Analysis 15

4.2 Christian Missions and the Content of Education 16

4.3 Christian Missions and Education Quality 18

4.4 Christian Missions, Educational Content, and Quality: Additional Econometric Results 20

5 Mechanisms: Supply 22

5.1 Christian Missions and the Supply of Secondary Schools 23

5.2 Christian Missions and School's Environment 24

5.3 Christian Missions and Schools' Quality 26

5.4 Aftermath: Christian Missions and Education Quality 27

5.4.1 Controlling for the Environment and Schools' Quality 27

5.4.2 Natural Experiment using Schools' Closure 28

5.5 Additional Results 30

6 Mechanisms: Demand 30

6.1 Students' Sorting 31

6.2 Gender Composition 33

6.3 Aftermath: Learning Inequality 33

7 Conclusion 35

References 37

Appendix A Data Sources and Variable Definitions A4

Appendix B Summary Statistics B5

Appendix C Additional Figures and Tables C6

C.1 Additional Figures C6

C.2 Summary Statistics C13

C.3 Zero-Stage: Exposure to Historical Christian Missions and Knowledge Exposition C14

C.4 Returns to the *Examen National d'État* C15

C.5 Additional Tables: Supply C15

C.6 Correlates of Historical Christian Missions' Location C31

C.7 Robustness Test C32

Appendix D Phone Survey D33

D.1 Sampling Procedure D33
<table><tr><td>D.2 Data Collection</td><td>D33</td></tr><tr><td>D.3 Phone Survey: Summary Statistics</td><td>D35</td></tr><tr><td>D.4 Phone Survey: Exetat Results</td><td>D36</td></tr><tr><td><strong>Appendix E Digitization and Geocoding Procedures</strong></td><td><strong>E40</strong></td></tr><tr><td>E.1 Digitization of Examen National d'État Results</td><td>E40</td></tr><tr><td>E.2 Geocoding Schools from the Examen National d'État</td><td>E43</td></tr></table>

List of Tables

<table><tr><td>1</td><td>The Influence of Christian Missions on School Curriculum and Study Sections</td><td>18</td></tr><tr><td>2</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État</td><td>20</td></tr><tr><td>3</td><td>The Influence of Christian Missions on School Environment</td><td>25</td></tr><tr><td>4</td><td>Distance to Mission and Heterogeneity</td><td>32</td></tr><tr><td>5</td><td>The Influence of Christian Missions on the Share of Female Participant at the Examen National d'État</td><td>34</td></tr><tr><td>6</td><td>The Influence of Christian Missions and Learning Inequality</td><td>35</td></tr><tr><td>B1</td><td>Annual Participant Distribution by Study Track and Section at the Examen National d'État</td><td>B5</td></tr><tr><td>C2</td><td>Summary Statistics from Examen National d'État Data</td><td>C13</td></tr><tr><td>C3</td><td>Distance of Secondary Schools from Historical Christian Missions</td><td>C13</td></tr><tr><td>C4</td><td>Distance to Mission and Missionary Presence</td><td>C14</td></tr><tr><td>C5</td><td>Returns to Examen National d'État</td><td>C16</td></tr><tr><td>C6</td><td>The Influence of Christian Missions on School Curriculum and Study Sections</td><td>C17</td></tr><tr><td>C7</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État</td><td>C18</td></tr><tr><td>C8</td><td>The Influence of Christian Missions on the Probability of Success</td><td>C19</td></tr><tr><td>C9</td><td>The Influence of Christian Missions on School Curriculum and Study Sections: Winsorized Distances</td><td>C20</td></tr><tr><td>C10</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État: Winsorized Distances</td><td>C21</td></tr><tr><td>C11</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État: Alternative Exposure Variables</td><td>C22</td></tr><tr><td>C12</td><td>The Influence of Christian Missions on School Curriculum and Study Sections: Rural Sample Only</td><td>C23</td></tr><tr><td>C13</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État: Rural Sample Only</td><td>C24</td></tr><tr><td>C14</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État</td><td>C25</td></tr><tr><td>C15</td><td>Distance to Mission and Schools Characteristics</td><td>C26</td></tr><tr><td>C16</td><td>Distance to Mission and Latrines</td><td>C27</td></tr><tr><td>C17</td><td>Distance to Mission and Study Support</td><td>C28</td></tr><tr><td>C18</td><td>Distance to Mission and Share of Students Accessing to University</td><td>C29</td></tr><tr><td>C19</td><td>Distance to Mission and Poverty</td><td>C29</td></tr><tr><td>C20</td><td>Distance to Mission and Expectations</td><td>C30</td></tr><tr><td>C21</td><td>Distance to Mission and Student Criterium for Secondary Entry</td><td>C30</td></tr><tr><td>C22</td><td>Oster (2019)'s δ Statistics: Selection on Unobservables</td><td>C32</td></tr><tr><td>D23</td><td>Statistics Survey Variables</td><td>D35</td></tr><tr><td>D24</td><td>The Influence of Christian Missions on School Curriculum and Study Sections</td><td>D37</td></tr><tr><td>D25</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État</td><td>D38</td></tr><tr><td>E26</td><td>Matching Results Between the 2018 School Census and Examen National d'État Schools</td><td>E44</td></tr><tr><td>E27</td><td>Balance Test between Matched and Not Matched Exetat Students and Schools</td><td>E45</td></tr></table>
<table><tr><td>E28</td><td>Statistics Matching Sources</td><td>E46</td></tr></table>

List of Figures

<table><tr><td>1</td><td>Empirical Setting</td><td>3</td></tr><tr><td>2</td><td>Catholic and Protestant Missions in the DRC</td><td>12</td></tr><tr><td>3</td><td>Questionnaire of the Examen National d'État</td><td>13</td></tr><tr><td>4</td><td>The Influence of Christian Missions on the Strength of [...] Missionary Presence</td><td>15</td></tr><tr><td>5</td><td>The Influence of Christian Missions on Grades at Examen National d'État</td><td>21</td></tr><tr><td>6</td><td>The Influence of Christian Missions on the Deployment of Secondary Schools in DRC</td><td>24</td></tr><tr><td>7</td><td>Christian Missions and Education Quality</td><td>26</td></tr><tr><td>8</td><td>The Influence of Christian Missions on Students' Success at Examen National d'État: Mediating Factor Analysis</td><td>28</td></tr><tr><td>9</td><td>The Influence of Christian Missions on the Probability of Success: By Year</td><td>29</td></tr><tr><td>C1</td><td>Secondary Schools Advertising the Study Tracks They Offer</td><td>C6</td></tr><tr><td>C2</td><td>Location of Secondary Schools whose Students Participated in the Examen National d'État between 2016 and 2022</td><td>C7</td></tr><tr><td>C3</td><td>Partitioning DRC in 10 × 10-kilometers grid cells</td><td>C7</td></tr><tr><td>C4</td><td>Schools Construction [1950-2020]</td><td>C8</td></tr><tr><td>C5</td><td>Historical Christian Missions (Mantnieks, 1951)</td><td>C8</td></tr><tr><td>C6</td><td>Historical Population Density (Gourou, 1951)</td><td>C9</td></tr><tr><td>C7</td><td>Colonial Schools</td><td>C10</td></tr><tr><td>C8</td><td>The Influence of Christian Missions on the Deployment of Kindergarten and Primary Schools in DRC</td><td>C11</td></tr><tr><td>C9</td><td>The influence of Christian Missions on the Probability of Success: By Year and Section</td><td>C12</td></tr><tr><td>C10</td><td>Correlates of Historical Christian Missions' Location</td><td>C32</td></tr><tr><td>D11</td><td>Location of Surveyed Respondents</td><td>D34</td></tr><tr><td>E12</td><td>Examen National d'État: Raw Data from 2019</td><td>E42</td></tr></table>
# Appendix A. Data Sources and Variable Definitions

## Main Data:

* Missions: Data comes from the (Mantnieks, 1951)'s Atlas Classique.

* Examen National d'État: Data comes from the Congolese Ministry of Education website. We have digitized the PDFs for all sessions between 2016 and 2022 using the function detailed in section Appendix E.1.

* Census of schools: We obtained access to the 2018 complete census of schools in the Democratic Republic of the Congo through the assistance of Rodrigue Kapayi Bebeka from the Direction de l'Information pour la Gestion de l'Éducation. The data was collected between 2018 and 2022 and covered all schools, including primary, secondary, and kindergarten. In the final dataset, one row corresponds to a school, and the dataset provides detailed information on management type (private or public, including religious denomination), year of construction, various quality indicators, and the number of teachers, classrooms, and students by grade. It also includes the phone number of each school's director.

* Enquête 1-2-3 [2004-2005]: Enquête 1-2-3 (2005)

## Geographic Data:

* Precipitation: Global Agro-Ecological Zones (GAEZ) data developed by the Food and Agriculture Organization of the United Nations (FAO) https://gaez.fao.org Fick and Hijmans (2017)

* Temperature: Global Agro-Ecological Zones (GAEZ) data developed by the Food and Agriculture Organization of the United Nations (FAO) https://gaez.fao.org Fick and Hijmans (2017)

* Caloric Suitability: Data is sources from Galor and Özak (2016)

* Ruggedness: Data is sources from Nunn and Puga (2012)

* Elevation: Elevation of the terrain is calculated using the third version of the Global Digital Elevation Model (GDEM) released by the Ministry of Economy, Trade and Industry (METI) of Japan and the United States National Aeronautics and Space Administration (NASA) https://www.earthdata.nasa.gov NOAA National Geophysical Data Center (2009)

## Disease Data:

* Tsetse fly suitability: Data is sources from Alsan (2015)

* Malaria suitability: Data is sources from Kiszewski, Mellinger, Spielman, Malaney, Sachs, Sachs et al. (2004)

## Population Density Data:

* Population density as of 1950: Data is sources from Gourou (1951)
Appendix B. Summary Statistics

<table><caption>Table B1: Annual Participant Distribution by Study Track and Section at the Examen National d'État</caption><thead><tr><th rowspan="2">Section</th><th rowspan="2">Original Label in French</th><th rowspan="2">English Translation</th><th colspan="12">Total Number of Participants in [...] and Share of Total Participants in [...]</th></tr><tr><th colspan="3">2016</th><th colspan="3">2017</th><th colspan="3">2018</th><th colspan="3">2019</th><th colspan="3">2020</th><th colspan="3">2021</th><th colspan="3">2022</th></tr><tr><th></th><th></th><th></th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th></tr></thead><tbody><tr><td>General</td><td>Sciences</td><td>Sciences</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>3306</td><td>0.49</td><td>14986</td><td>2.32</td><td>0</td><td>0.00</td></tr><tr><td>General</td><td>Latin mathematique</td><td>Latin mathematics</td><td>33</td><td>0.01</td><td>11</td><td>0.01</td><td>17</td><td>0.01</td><td>26</td><td>0.01</td><td>36</td><td>0.01</td><td>32</td><td>0.01</td><td>36</td><td>0.01</td><td>32</td><td>0.00</td><td>36</td><td>0.01</td><td>36</td><td>0.01</td></tr><tr><td>General</td><td>Latin grec</td><td>Latin grec</td><td>47</td><td>0.01</td><td>49</td><td>0.00</td><td>46</td><td>0.00</td><td>37</td><td>0.00</td><td>39</td><td>0.01</td><td>52</td><td>0.01</td><td>73</td><td>0.01</td><td>52</td><td>0.00</td><td>73</td><td>0.01</td><td>73</td><td>0.01</td></tr><tr><td>General</td><td>Mathematique physique</td><td>Mathematics physics</td><td>8558</td><td>1.77</td><td>7862</td><td>1.57</td><td>7983</td><td>1.44</td><td>7085</td><td>1.27</td><td>7362</td><td>1.09</td><td>6243</td><td>0.92</td><td>4047</td><td>0.63</td><td>6243</td><td>0.92</td><td>4047</td><td>0.63</td><td>6243</td><td>0.92</td></tr><tr><td>General</td><td>Latin philosophie</td><td>Latin philosophie</td><td>23664</td><td>4.90</td><td>25046</td><td>5.01</td><td>26985</td><td>4.88</td><td>26466</td><td>4.74</td><td>28596</td><td>4.24</td><td>26221</td><td>3.88</td><td>25023</td><td>3.87</td><td>26221</td><td>3.88</td><td>25023</td><td>3.87</td><td>26221</td><td>3.88</td></tr><tr><td>General</td><td>Chimie biologique</td><td>Chemistry biology</td><td>6305</td><td>12.69</td><td>61919</td><td>12.31</td><td>65987</td><td>11.94</td><td>62180</td><td>11.14</td><td>69234</td><td>10.27</td><td>6172</td><td>9.49</td><td>51664</td><td>9.78</td><td>6172</td><td>9.49</td><td>51664</td><td>9.78</td><td>6172</td><td>9.49</td></tr><tr><td>Pedagogy</td><td>Education physique</td><td>Physical training</td><td>46</td><td>0.01</td><td>61</td><td>0.01</td><td>80</td><td>0.01</td><td>45</td><td>0.01</td><td>65</td><td>0.01</td><td>68</td><td>0.01</td><td>62</td><td>0.01</td><td>68</td><td>0.02</td><td>62</td><td>0.01</td><td>62</td><td>0.01</td></tr><tr><td>Pedagogy</td><td>Pedagogie maternelle</td><td>Kindergarten pedagogy</td><td>99</td><td>0.02</td><td>71</td><td>0.01</td><td>83</td><td>0.02</td><td>94</td><td>0.02</td><td>72</td><td>0.01</td><td>140</td><td>0.01</td><td>175</td><td>0.02</td><td>140</td><td>0.02</td><td>175</td><td>0.01</td><td>175</td><td>0.02</td></tr><tr><td>Pedagogy</td><td>Normale</td><td>Normal</td><td>2396</td><td>0.50</td><td>2629</td><td>0.53</td><td>2691</td><td>0.49</td><td>2716</td><td>0.49</td><td>3344</td><td>0.50</td><td>3025</td><td>0.45</td><td>2547</td><td>0.39</td><td>3025</td><td>0.45</td><td>2547</td><td>0.39</td><td>3025</td><td>0.45</td></tr><tr><td>Pedagogy</td><td>Pedagogie generale</td><td>General pedagogy</td><td>234088</td><td>48.50</td><td>240389</td><td>48.09</td><td>246111</td><td>44.53</td><td>243551</td><td>43.62</td><td>310138</td><td>45.99</td><td>312154</td><td>46.18</td><td>275984</td><td>42.64</td><td>312154</td><td>46.18</td><td>275984</td><td>42.64</td><td>312154</td><td>46.18</td></tr><tr><td>Technical</td><td>Arts dramatiques</td><td>Dramatic arts</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>5</td><td>0.00</td><td>4</td><td>0.00</td><td>4</td><td>0.00</td><td>6</td><td>0.00</td><td>3</td><td>0.00</td><td>6</td><td>0.00</td><td>3</td><td>0.00</td><td>6</td><td>0.00</td></tr><tr><td>Technical</td><td>Coiffure</td><td>Hairdressing</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>6</td><td>0.00</td><td>6</td><td>0.00</td><td>12</td><td>0.00</td><td>11</td><td>0.00</td><td>11</td><td>0.00</td><td>11</td><td>0.00</td><td>11</td><td>0.00</td><td>11</td><td>0.00</td></tr><tr><td>Technical</td><td>Engins agricoles</td><td>Agricultural machines</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>1</td><td>0.00</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Technical</td><td>Installation sanitaire</td><td>Sanitary installation</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>3</td><td>0.00</td><td>29</td><td>0.00</td><td>34</td><td>0.01</td><td>33</td><td>0.01</td><td>34</td><td>0.00</td><td>33</td><td>0.01</td><td>33</td><td>0.01</td></tr><tr><td>Technical</td><td>Production des produits agricoles</td><td>Production of agricultural products</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>13</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Esthetique</td><td>Esthetics</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>4</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Hydropneumatique</td><td>Hydropneumatic</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>1</td><td>0.00</td><td>1</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Musique</td><td>Music</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>22</td><td>0.00</td><td>32</td><td>0.01</td><td>21</td><td>0.00</td><td>17</td><td>0.00</td><td>16</td><td>0.00</td><td>17</td><td>0.00</td><td>16</td><td>0.00</td><td>16</td><td>0.00</td></tr><tr><td>Technical</td><td>Tolery</td><td>Tolery</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>3</td><td>0.00</td><td>23</td><td>0.00</td><td>22</td><td>0.00</td><td>18</td><td>0.00</td><td>22</td><td>0.00</td><td>18</td><td>0.00</td><td>18</td><td>0.00</td></tr><tr><td>Technical</td><td>Mines &amp; carrières</td><td>Mines &amp; carrires</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>14</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Transformation des produits ag</td><td>Transformation des produits ag</td><td>1</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Esthetique &amp; coiffure</td><td>Esthetics &amp; hairdressing</td><td>0</td><td>0.00</td><td>5</td><td>0.00</td><td>12</td><td>0.00</td><td>2</td><td>0.00</td><td>10</td><td>0.00</td><td>11</td><td>0.00</td><td>18</td><td>0.00</td><td>11</td><td>0.00</td><td>18</td><td>0.00</td><td>18</td><td>0.00</td></tr><tr><td>Technical</td><td>Agro forestiere</td><td>Agro forestry</td><td>2</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>7</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Froid &amp; climatisation</td><td>Froid &amp; climatisation</td><td>4</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Electricite auto</td><td>Electricity auto</td><td>5</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Horticulture &amp; productions ho</td><td>Horticulture &amp; productions ho</td><td>5</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Carrosserie</td><td>Carrosserie</td><td>7</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Informatique de gestion</td><td>Computer science de gestion</td><td>8</td><td>0.00</td><td>11</td><td>0.00</td><td>45</td><td>0.01</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Ajustage &amp; soudure</td><td>Adjustment &amp; welding</td><td>8</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>2</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Peinture / vitrebire</td><td>Painting / glazery</td><td>8</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>5</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Plomberie (installation sanitaire)</td><td>Plumbing (sanitary installation)</td><td>8</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>29</td><td>0.01</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Commutation</td><td>Electronics</td><td>9</td><td>0.00</td><td>11</td><td>0.00</td><td>47</td><td>0.01</td><td>42</td><td>0.01</td><td>53</td><td>0.01</td><td>54</td><td>0.01</td><td>54</td><td>0.00</td><td>54</td><td>0.00</td><td>54</td><td>0.00</td><td>54</td><td>0.00</td></tr><tr><td>Technical</td><td>Aviation civile</td><td>Civil aviation</td><td>9</td><td>0.00</td><td>2</td><td>0.00</td><td>3</td><td>0.00</td><td>2</td><td>0.00</td><td>7</td><td>0.00</td><td>43</td><td>0.01</td><td>32</td><td>0.00</td><td>43</td><td>0.00</td><td>32</td><td>0.00</td><td>32</td><td>0.00</td></tr><tr><td>Technical</td><td>Radio transmission</td><td>Radio transmission</td><td>13</td><td>0.00</td><td>10</td><td>0.00</td><td>26</td><td>0.00</td><td>36</td><td>0.01</td><td>28</td><td>0.00</td><td>17</td><td>0.00</td><td>11</td><td>0.00</td><td>17</td><td>0.00</td><td>11</td><td>0.00</td><td>11</td><td>0.00</td></tr><tr><td>Technical</td><td>Construction metallique</td><td>Steel construction</td><td>17</td><td>0.00</td><td>0</td><td>0.00</td><td>20</td><td>0.00</td><td>15</td><td>0.00</td><td>47</td><td>0.01</td><td>53</td><td>0.01</td><td>23</td><td>0.00</td><td>23</td><td>0.00</td><td>23</td><td>0.00</td><td>23</td><td>0.00</td></tr><tr><td>Technical</td><td>Mine &amp; geologie</td><td>Mine &amp; geology</td><td>19</td><td>0.00</td><td>24</td><td>0.00</td><td>60</td><td>0.01</td><td>59</td><td>0.01</td><td>60</td><td>0.01</td><td>82</td><td>0.01</td><td>104</td><td>0.02</td><td>104</td><td>0.02</td><td>104</td><td>0.02</td><td>104</td><td>0.02</td></tr><tr><td>Technical</td><td>Tourisme</td><td>Tourism</td><td>29</td><td>0.01</td><td>25</td><td>0.01</td><td>49</td><td>0.01</td><td>73</td><td>0.01</td><td>46</td><td>0.01</td><td>53</td><td>0.01</td><td>106</td><td>0.02</td><td>106</td><td>0.02</td><td>106</td><td>0.02</td><td>106</td><td>0.02</td></tr><tr><td>Technical</td><td>Metallurgie</td><td>Metallurgy</td><td>35</td><td>0.01</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Technical</td><td>Mecanique d'entretien</td><td>Maintenance mechanics</td><td>36</td><td>0.01</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>27</td><td>0.00</td><td>6</td><td>0.00</td><td>11</td><td>0.00</td><td>5</td><td>0.00</td><td>5</td><td>0.00</td><td>5</td><td>0.00</td><td>5</td><td>0.00</td></tr><tr><td>Technical</td><td>Arts plastiques</td><td>Plastic arts</td><td>41</td><td>0.01</td><td>74</td><td>0.01</td><td>213</td><td>0.04</td><td>345</td><td>0.06</td><td>322</td><td>0.05</td><td>346</td><td>0.05</td><td>353</td><td>0.05</td><td>353</td><td>0.05</td><td>353</td><td>0.05</td><td>353</td><td>0.05</td></tr><tr><td>Technical</td><td>Peche &amp; navigation</td><td>Fishing &amp; navigation</td><td>42</td><td>0.01</td><td>58</td><td>0.01</td><td>90</td><td>0.02</td><td>94</td><td>0.02</td><td>77</td><td>0.01</td><td>99</td><td>0.01</td><td>57</td><td>0.01</td><td>57</td><td>0.01</td><td>57</td><td>0.01</td><td>57</td><td>0.01</td></tr><tr><td>Technical</td><td>Foresterie</td><td>Forestry</td><td>46</td><td>0.01</td><td>113</td><td>0.02</td><td>123</td><td>0.02</td><td>174</td><td>0.03</td><td>177</td><td>0.03</td><td>176</td><td>0.03</td><td>168</td><td>0.03</td><td>168</td><td>0.03</td><td>168</td><td>0.03</td><td>168</td><td>0.03</td></tr><tr><td>Technical</td><td>Imprimerie</td><td>Printing</td><td>65</td><td>0.01</td><td>87</td><td>0.02</td><td>90</td><td>0.02</td><td>68</td><td>0.01</td><td>98</td><td>0.01</td><td>145</td><td>0.02</td><td>146</td><td>0.02</td><td>146</td><td>0.02</td><td>146</td><td>0.02</td><td>146</td><td>0.02</td></tr><tr><td>Technical</td><td>Chimie industrielle</td><td>Industrial chemistry</td><td>93</td><td>0.02</td><td>116</td><td>0.02</td><td>141</td><td>0.03</td><td>116</td><td>0.02</td><td>152</td><td>0.02</td><td>174</td><td>0.03</td><td>186</td><td>0.03</td><td>186</td><td>0.03</td><td>186</td><td>0.03</td><td>186</td><td>0.03</td></tr><tr><td>Technical</td><td>Petro chimie</td><td>Petro chemistry</td><td>147</td><td>0.03</td><td>166</td><td>0.03</td><td>190</td><td>0.03</td><td>145</td><td>0.03</td><td>97</td><td>0.01</td><td>79</td><td>0.01</td><td>58</td><td>0.01</td><td>58</td><td>0.01</td><td>58</td><td>0.01</td><td>58</td><td>0.01</td></tr><tr><td>Technical</td><td>Industries agricoles</td><td>Agricultural industries</td><td>209</td><td>0.04</td><td>283</td><td>0.06</td><td>506</td><td>0.09</td><td>550</td><td>0.10</td><td>575</td><td>0.09</td><td>714</td><td>0.11</td><td>837</td><td>0.13</td><td>837</td><td>0.11</td><td>837</td><td>0.13</td><td>837</td><td>0.13</td></tr><tr><td>Technical</td><td>Hotellerie &amp; restauration</td><td>Hotellerie &amp; restauration</td><td>334</td><td>0.07</td><td>463</td><td>0.09</td><td>596</td><td>0.11</td><td>702</td><td>0.13</td><td>707</td><td>0.10</td><td>689</td><td>0.10</td><td>602</td><td>0.09</td><td>602</td><td>0.09</td><td>602</td><td>0.09</td><td>602</td><td>0.09</td></tr><tr><td>Technical</td><td>Mecanique machines outils</td><td>Mechanics machine tools</td><td>345</td><td>0.07</td><td>342</td><td>0.07</td><td>367</td><td>0.07</td><td>339</td><td>0.06</td><td>362</td><td>0.05</td><td>471</td><td>0.07</td><td>555</td><td>0.09</td><td>555</td><td>0.09</td><td>555</td><td>0.09</td><td>555</td><td>0.09</td></tr><tr><td>Technical</td><td>Secretariat administration</td><td>Secretariat administration</td><td>390</td><td>0.08</td><td>358</td><td>0.07</td><td>454</td><td>0.08</td><td>2126</td><td>0.38</td><td>2305</td><td>0.34</td><td>2701</td><td>0.40</td><td>3208</td><td>0.50</td><td>3208</td><td>0.50</td><td>3208</td><td>0.50</td><td>3208</td><td>0.50</td></tr><tr><td>Technical</td><td>Hotesse d'accueil</td><td>Receptionist</td><td>426</td><td>0.09</td><td>534</td><td>0.11</td><td>623</td><td>0.11</td><td>615</td><td>0.11</td><td>541</td><td>0.08</td><td>484</td><td>0.07</td><td>445</td><td>0.07</td><td>445</td><td>0.07</td><td>445</td><td>0.07</td><td>445</td><td>0.07</td></tr><tr><td>Technical</td><td>Maconnerie</td><td>Masonry</td><td>563</td><td>0.12</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>694</td><td>0.12</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Secretariat informatique</td><td>Secretariat computer science</td><td>687</td><td>0.14</td><td>985</td><td>0.20</td><td>1397</td><td>0.25</td><td>17</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Metallurgie/mecanique auto</td><td>Metallurgy/auto mechanics</td><td>1136</td><td>0.24</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>1186</td><td>0.21</td><td>25</td><td>0.00</td><td>75</td><td>0.01</td><td>59</td><td>0.01</td><td>59</td><td>0.01</td><td>59</td><td>0.01</td><td>59</td><td>0.01</td></tr><tr><td>Technical</td><td>Menüserie ebeniste</td><td>Carpentry and joinery</td><td>1556</td><td>0.32</td><td>915</td><td>0.18</td><td>1271</td><td>0.23</td><td>2383</td><td>0.43</td><td>2640</td><td>0.39</td><td>3646</td><td>0.54</td><td>3642</td><td>0.56</td><td>3642</td><td>0.54</td><td>3642</td><td>0.56</td><td>3642</td><td>0.56</td></tr><tr><td>Technical</td><td>Electronique</td><td>Electronics</td><td>2164</td><td>0.45</td><td>2118</td><td>0.42</td><td>2554</td><td>0.46</td><td>2599</td><td>0.47</td><td>2576</td><td>0.38</td><td>2831</td><td>0.42</td><td>2726</td><td>0.42</td><td>2726</td><td>0.42</td><td>2726</td><td>0.42</td><td>2726</td><td>0.42</td></tr><tr><td>Technical</td><td>Nutrition</td><td>Nutrition</td><td>2299</td><td>0.48</td><td>3695</td><td>0.74</td><td>6138</td><td>1.11</td><td>7232</td><td>1.30</td><td>9235</td><td>1.37</td><td>10029</td><td>1.48</td><td>11129</td><td>1.72</td><td>1129</td><td>1.48</td><td>11129</td><td>1.72</td><td>1129</td><td>1.72</td></tr><tr><td>Technical</td><td>Mecanique automobile</td><td>Automobile mechanics</td><td>3229</td><td>0.67</td><td>4340</td><td>0.87</td><td>5774</td><td>1.04</td><td>6415</td><td>1.15</td><td>8022</td><td>1.19</td><td>8993</td><td>1.33</td><td>10721</td><td>1.66</td><td>10721</td><td>1.33</td><td>10721</td><td>1.66</td><td>10721</td><td>1.66</td></tr><tr><td>Technical</td><td>Veterinaire</td><td>Veterinary</td><td>3338</td><td>0.69</td><td>3646</td><td>0.73</td><td>5648</td><td>1.02</td><td>5965</td><td>1.07</td><td>7907</td><td>1.17</td><td>6305</td><td>1.23</td><td>8815</td><td>1.36</td><td>8815</td><td>1.23</td><td>8815</td><td>1.36</td><td>8815</td><td>1.36</td></tr><tr><td>Technical</td><td>Agriculture generale</td><td>General agriculture</td><td>6476</td><td>1.34</td><td>7665</td><td>1.53</td><td>10447</td><td>1.89</td><td>10815</td><td>1.94</td><td>12934</td><td>1.92</td><td>12488</td><td>1.85</td><td>11128</td><td>1.72</td><td>1128</td><td>1.85</td><td>11128</td><td>1.72</td><td>1128</td><td>1.72</td></tr><tr><td>Technical</td><td>Construction</td><td>Construction</td><td>8809</td><td>1.83</td><td>10328</td><td>2.07</td><td>13770</td><td>2.49</td><td>15210</td><td>2.72</td><td>20365</td><td>3.02</td><td>23522</td><td>3.48</td><td>25988</td><td>4.01</td><td>25988</td><td>3.48</td><td>25988</td><td>4.01</td><td>25988</td><td>3.48</td></tr><tr><td>Technical</td><td>Commerciale informatique</td><td>Commercial computer science</td><td>9857</td><td>2.04</td><td>13683</td><td>2.74</td><td>18762</td><td>3.39</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td><td>0</td><td>0.00</td></tr><tr><td>Technical</td><td>Mecanique generale</td><td>General mechanics</td><td>11419</td><td>2.37</td><td>11622</td><td>2.32</td><td>14537</td><td>2.63</td><td>15718</td><td>2.82</td><td>17388</td><td>2.58</td><td>15994</td><td>2.37</td><td>17788</td><td>2.75</td><td>17788</td><td>2.37</td><td>17788</td><td>2.75</td><td>17788</td><td>2.75</td></tr><tr><td>Technical</td><td>Coupe &amp; couture</td><td>Cutting &amp; Sewing</td><td>15301</td><td>3.17</td><td>18184</td><td>3.64</td><td>23925</td><td>4.33</td><td>26483</td><td>4.74</td><td>34449</td><td>5.11</td><td>37317</td><td>5.52</td><td>41902</td><td>6.47</td><td>41902</td><td>5.52</td><td>41902</td><td>6.47</td><td>41902</td><td>5.52</td></tr><tr><td>Technical</td><td>Electricite</td><td>Electricity</td><td>15310</td><td>3.17</td><td>16288</td><td>3.26</td><td>19871</td><td>3.60</td><td>22939</td><td>4.11</td><td>26427</td><td>3.92</td><td>27778</td><td>4.11</td><td>31021</td><td>4.79</td><td>31021</td><td>4.11</td><td>31021</td><td>4.79</td><td>31021</td><td>4.79</td></tr><tr><td>Technical</td><td>Sociale</td><td>Social</td><td>27465</td><td>5.69</td><td>28901</td><td>5.78</td><td>35504</td><td>6.42</td><td>35276</td><td>6.32</td><td>48512</td><td>7.19</td><td>46488</td><td>6.61</td><td>42685</td><td>6.59</td><td>42685</td><td>6.61</td><td>42685</td><td>6.59</td><td>42685</td><td>6.59</td></tr><tr><td>Technical</td><td>Commerciale &amp; administrative</td><td>Commercial &amp; administrative</td><td>40661</td><td>8.42</td><td>37198</td><td>7.44</td><td>39415</td><td>7.13</td><td>57560</td><td>10.31</td><td>59179</td><td>8.78</td><td>58432</td><td>8.64</td><td>58060</td><td>8.97</td><td>58060</td><td>8.64</td><td>58060</td><td>8.97</td><td>58060</td><td>8.97</td></tr><tr><td></td><td></td><td>Total</td><td>482668</td><td>100.00</td><td>499887</td><td>100.00</td><td>552684</td><td>100.00</td><td>558345</td><td>100.00</td><td>674305</td><td>100.00</td><td>676004</td><td>100.00</td><td>647301</td><td>100.00</td><td>647301</td><td>100.00</td><td>647301</td><td>100.00</td><td>647301</td><td>100.00</td></tr></tbody></table>

<footnote>Notes: This table details the number of participants per study track and section in the Examen National d'État for each year from 2016 to 2022, along with the percentage each study track represents of the total annual participants. The first column categories each study track by section, the second column lists the study track labels as they appear in the data sourced from the Ministry of Education's website, and the third column provides the English translations of these labels. Please note that not all study tracks are available every year and that labels may vary over time.</footnote>
# Appendix C. Additional Figures and Tables

## C.1. Additional Figures

<figure><img src="image_11.png" /><figcaption>Figure C1: Secondary Schools Advertising the Study Tracks They Offer</figcaption></figure>

Notes: This series of photos, taken in North-West DRC in 2023, illustrates how secondary schools advertise the sections they offer. All the photos were taken in Gemena, the capital of Sud-Ubangi province. For example, *Complexe Scolaire Les Aiglons* (Figure C1a) offers Veterinary, Nutrition, General Mechanics, Commercial, and Agriculture. In contrast, *I.T.C.A. Duale* (Figure C1d) offers Commercial and Administration, as well as General Pedagogy. Photos are courtesy of Ignace Zikifa y Sena.
<figure><img src="image_12.png" /><figcaption>Figure C2: Location of Secondary Schools whose Students Participated in the Examen National d'État between 2016 and 2022</figcaption></figure>

Notes: This figure shows the location of the 25,501 secondary schools who sent at least one student to take the Examen National d'État between 2016 and 2022. Schools were georeferenced using the 2018 census of all schools in the DRC. The map displays the modern province boundaries.

<figure><img src="image_13.png" /><figcaption>Figure C3: Partitioning DRC in 10 × 10-kilometers grid cells</figcaption></figure>

Notes: This figure displays our partition of the Democratic Republic of the Congo into 10-by-10-kilometer grid cells. Panel C3a highlights the cells exposed to Catholic missions, while Panel C3b shows the corresponding cells for Protestant missions. The data on missions is sourced from the *Atlas Classique* (Mantnieks, 1951).
<figure><img src="image_14.png" /><figcaption>Figure C4: Schools Construction [1950-2020]</figcaption></figure>

Source: Complete Census of Secondary Schools in DRC [2018]

*Notes*: This figure shows the cumulative number of schools operating in the Democratic Republic of the Congo since 1950, stratified by type of school. Data is sourced from the 2018 complete census of all schools in the DRC.

<figure><img src="image_15.png" /><figcaption>Figure C5: Historical Christian Missions (Mantnieks, 1951)</figcaption></figure>

*Notes*: This map from (Mantnieks, 1951) provides the geographic distribution of Christian missions in Belgian Congo in the 1950s. It serves as the basis for the mission data used in the study.
<figure><img src="image_16.png" /><figcaption>Figure C6: Historical Population Density (Gourou, 1951)</figcaption></figure>

Notes: This map from (Gourou, 1951) provides the population density in Belgian Congo in the 1950s.
<figure><img src="image_17.png" /><figcaption>Figure C7: Colonial Schools</figcaption></figure>

Notes: This map shows the schools built during the colonial period. Data from the 2018 Census of schools in the DRC.
<figure><img src="image_18.png" /><figcaption>Figure C8: The Influence of Christian Missions on the Deployment of Kindergarten and Primary Schools in DRC</figcaption></figure>

Notes: The figure reports OLS estimates of equation 3. The figure presents the effect of historical exposure to *Catholic* and *Protestant* missions on the deployment of kindergarten and primary schools in the Democratic Republic of Congo each five years between 1955 and 2015.
<figure><img src="image_19.png" /><figcaption>Figure C9: The influence of Christian Missions on the Probability of Success: By Year and Section</figcaption></figure>

Notes: The figure reports OLS estimates of equation 2. The unit of observation is a student taking the *Examen National d'État*. Robust standard errors clustered at the school level are in parentheses. The dependent variable *Students' Success at Exetat* is a dummy variable equal to one if a student successfully passed the *Exetat* and zero otherwise. *Ln(Inv. Distance) Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Each specification includes fixed effects that account for the study track chosen by the student and the exam center where the student takes the *session ordinaire*. Each specification includes caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school, malaria suitability, tsetse suitability and population density as of 1950. Each specification controls for gender with a dummy variable equal to one if the student is female and zero otherwise.
## C.2. Summary Statistics

<table><caption>Table C2: Summary Statistics from *Examen National d'État* Data</caption><thead><tr><th></th><th>Count</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td><strong>Probability of Success:</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Student is successful</td><td>4,166,120</td><td>0.703</td><td>0.457</td><td>0</td><td>1</td></tr><tr><td>Student is successful: section General</td><td>692,257</td><td>0.709</td><td>0.454</td><td>0</td><td>1</td></tr><tr><td>Student is successful: section Pedagogy</td><td>1,936,783</td><td>0.676</td><td>0.468</td><td>0</td><td>1</td></tr><tr><td>Student is successful: section Technical</td><td>1,537,080</td><td>0.734</td><td>0.442</td><td>0</td><td>1</td></tr><tr><td><strong>Grades > 49:</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grades</td><td>2,927,434</td><td>56.73</td><td>5.565</td><td>50</td><td>96</td></tr><tr><td>Grades: section General</td><td>490,799</td><td>57.96</td><td>6.471</td><td>50</td><td>94</td></tr><tr><td>Grades: section Pedagogy</td><td>1,308,670</td><td>56.28</td><td>5.184</td><td>50</td><td>91</td></tr><tr><td>Grades: section Technical</td><td>1,127,965</td><td>56.73</td><td>5.484</td><td>50</td><td>96</td></tr></tbody></table>

Notes: Summary statistics on *Examen National d'État* data.

<table><caption>Table C3: Distance of Secondary Schools from Historical Christian Missions</caption><thead><tr><th></th><th>Count</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Distance Catholic Mission</td><td>25,501</td><td>24.51</td><td>22.34</td><td>0.00504</td><td>141.4</td></tr><tr><td>Distance Protestant Mission</td><td>25,501</td><td>37.58</td><td>29.15</td><td>0.0281</td><td>257.8</td></tr></tbody></table>

Notes: Summary statistics on distance between secondary schools for which we have the results of the *Examen National d'État* and the closest Catholic mission in the first line, and the closest Protestant mission in the second.
### C.3. Zero-Stage: Exposure to Historical Christian Missions and Knowledge Exposition

<table><caption>Table C4: Distance to Mission and Missionary Presence</caption><thead><tr><th colspan="7" style="text-align: center;">Dependent Variable:</th></tr><tr><th colspan="7" style="text-align: center;">Strength of [...] Missionary Presence:</th></tr><tr><th></th><th colspan="3" style="text-align: center;">Catholic</th><th colspan="3" style="text-align: center;">Protestant</th></tr><tr><th></th><th style="text-align: center;">(1)</th><th style="text-align: center;">(2)</th><th style="text-align: center;">(3)</th><th style="text-align: center;">(4)</th><th style="text-align: center;">(5)</th><th style="text-align: center;">(6)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>0.083***<br>(0.027)</td><td></td><td>0.098***<br>(0.028)</td><td>0.033<br>(0.028)</td><td></td><td>0.011<br>(0.030)</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td></td><td>-0.026<br>(0.030)</td><td>-0.058*<br>(0.032)</td><td></td><td>0.090***<br>(0.028)</td><td>0.087***<br>(0.030)</td></tr><tr><td>Outcome Mean</td><td>1.90</td><td>1.90</td><td>1.90</td><td>1.90</td><td>1.90</td><td>1.90</td></tr><tr><td>Outcome SD</td><td>0.94</td><td>0.94</td><td>0.94</td><td>0.94</td><td>0.94</td><td>0.94</td></tr><tr><td>Adjusted R2</td><td>0.092</td><td>0.080</td><td>0.095</td><td>0.074</td><td>0.086</td><td>0.085</td></tr><tr><td>Observations</td><td>620</td><td>620</td><td>620</td><td>616</td><td>616</td><td>616</td></tr><tr><td>Educational Province FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Respondent Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geographic Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

*Notes:* The table reports OLS estimates of equation 4. Standard errors clustered at the school level. Each estimation controls for the latitude and the longitude of the schools. Demographic controls include the respondent age, age squared and a dummy equal to 1 if the respondent is a male and 0 otherwise. *Geographic Controls* include caloric suitability, ruggedness, maize and cassava suitability, average precipitation and average temperature. *Disease Controls* include malaria and tse-tse fly suitability. *Historical Pop. Density Control* controls for population density as of 1950. Each estimation also includes an Educational Province fixed effect and controls for the school's latitude and longitude. *p* < 0.10, **p* < 0.05, ***p* < 0.01.
### C.4. Returns to the Examen National d'État

This section documents the returns to obtaining the *Exetat* using data from the *Enquête 1-2-3* (Enquête 1-2-3, 2005). The *Enquête 1-2-3* is the first national household survey conducted in the DRC, focusing on three phases: Employment (Phase 1), the Informal Sector (Phase 2), and Household Expenditures (Phase 3). The survey was implemented in two stages: an initial phase in Kinshasa in 2004, followed by an expansion to other provinces in 2005, covering a sample of 13,688 households. The survey was conducted by the Congolese National Institute of Statistics with financial and technical support from the World Bank, the African Development Bank, and other partners. The full sample includes 72,685 individuals.

To document the human capital and economic correlates of obtaining the *Exetat*, we focus on a sample of 26,882 individuals who have completed their education. We determine whether a respondent has successfully passed the *Exetat* based on the question, "Highest diploma obtained?" The possible responses include: 'None', 'Primary school certificate', 'Middle school certificate', 'D4, A3', 'PP5', 'D6, A2', 'Bachelor's degree', 'Master's degree', 'PhD', and 'Other'. We create a dummy variable equal to one if a respondent reports having obtained 'D6, A2', 'Bachelor's degree', 'Master's degree', or 'PhD', and zero otherwise. The 'Other' category is classified as missing. Out of 26,796 individuals, 15.93% have at least obtained the *Exetat*. We investigate several outcome variables. First, we examine the correlation between having obtained the *Exetat* and literacy in Congolese languages and French. Next, we analyze the effects on labor market participation and income. To do so, we estimate the following specification:

$$y_{il} = \alpha + \beta_1 \text{Exetat}_i + \mathbf{X}_i\Gamma + \eta_l + \epsilon_{il} \quad (A1)$$

where $y_{il}$ stands for human capital and economic outcomes for individual $i$ residing in locality $l$. *Exetat* is a dummy variable equal to one if a respondent reports having obtained the *Examen National d'État* or above and zero otherwise. $\mathbf{X}_i$ is a matrix of demographic control variables: age, age squared, and gender, $\eta$, a set of locality fixed effects and $\epsilon$ the error term.

Table C5 presents the OLS estimates. The results in Panel A indicate that individuals who have obtained the *Exetat* are 10 percentage points more likely to be literate in Lingala or another language spoken in the DRC. The effect is four times stronger for French, which is the language of instruction. These findings suggest that *Exetat* holders possess a higher level of human capital. We then examine the economic implications of obtaining the *Exetat*. In Panel B, we find that individuals with the *Exetat* are more likely to have worked at least one hour in the week preceding the interview. Additionally, conditional on working, they are more likely to hold a formal position, as measured by having a contract or a payslip. The magnitude of the effect is large. Finally, degree holders earn approximately 12% higher income.

### C.5. Additional Tables: Supply
<table><caption>Table C5: Returns to Examen National d'État</caption><thead><tr><th colspan="5" style="text-align: center;">Dependent Variable:</th></tr><tr><th colspan="5" style="text-align: center;">Literate in:</th></tr><tr><th colspan="5" style="text-align: center;">National Languages:</th></tr><tr><th></th><th style="text-align: center;">Lingala<br>(1)</th><th style="text-align: center;">Other<br>(2)</th><th style="text-align: center;">Any<br>(3)</th><th style="text-align: center;">French<br>(4)</th></tr></thead><tbody><tr><td>Exetat</td><td>0.109***<br>(0.007)</td><td>0.212***<br>(0.006)</td><td>0.106***<br>(0.007)</td><td>0.392***<br>(0.008)</td></tr><tr><td>Outcome Mean</td><td>0.78</td><td>0.14</td><td>0.79</td><td>0.49</td></tr><tr><td>Outcome SD</td><td>0.41</td><td>0.35</td><td>0.41</td><td>0.50</td></tr><tr><td>Adjusted R2</td><td>0.221</td><td>0.293</td><td>0.206</td><td>0.347</td></tr><tr><td>Observations</td><td>25,899</td><td>25,898</td><td>25,899</td><td>25,899</td></tr><tr><th colspan="5" style="text-align: center;">Panel B:</th></tr><tr><th></th><th style="text-align: center;">Worked > One<br>Hour Last Week</th><th style="text-align: center;">Conditional on working:<br>Contract</th><th style="text-align: center;">Payslip</th><th style="text-align: center;">Monthly<br>Income ln(.)</th></tr><tr><td>Exetat</td><td>0.053***<br>(0.007)</td><td>0.259***<br>(0.012)</td><td>0.253***<br>(0.012)</td><td>0.125***<br>(0.036)</td></tr><tr><td>Outcome Mean</td><td>0.69</td><td>0.35</td><td>0.29</td><td>8.53</td></tr><tr><td>Outcome SD</td><td>0.46</td><td>0.48</td><td>0.45</td><td>1.96</td></tr><tr><td>Adjusted R2</td><td>0.306</td><td>0.428</td><td>0.394</td><td>0.342</td></tr><tr><td>Observations</td><td>25,894</td><td>6,922</td><td>6,918</td><td>15,956</td></tr><tr><td>Demographic Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Locality FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

Notes: Robust standard errors in (). All columns include demographic controls: age, age squared, gender. Every specification controls for locality fixed effects, an indicator variable equal to one if the respondent answered for themselves, and zero if someone else answered on their behalf, and an indicator variable ranging from 1 to 3 that reflects the enumerator's perception of the response quality. * p < 0.10, ** p < 0.05, *** p < 0.01.
<table><caption>Table C6: The Influence of Christian Missions on School Curriculum and Study Sections</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent Variable:</th></tr><tr><th>Log. Number of</th><th colspan="3">School Offers Section [...]</th></tr><tr><th>Study Tracks<br>(1)</th><th>General<br>(2)</th><th>Technical<br>(3)</th><th>Pedagogy<br>(4)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>0.048***<br>(0.002)<br>[0.005]</td><td>0.011***<br>(0.002)<br>[0.003]</td><td>0.045***<br>(0.002)<br>[0.004]</td><td>-0.019***<br>(0.002)<br>[0.003]</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td>0.029***<br>(0.002)<br>[0.006]</td><td>0.010***<br>(0.002)<br>[0.004]</td><td>0.022***<br>(0.002)<br>[0.004]</td><td>-0.008***<br>(0.002)<br>[0.004]</td></tr><tr><td>Ln(Inv. Distance) Colonial School</td><td>0.021***<br>(0.001)<br>[0.004]</td><td>0.010***<br>(0.001)<br>[0.002]</td><td>0.018***<br>(0.001)<br>[0.002]</td><td>-0.013***<br>(0.001)<br>[0.002]</td></tr><tr><td>β Catholic [std.]</td><td>0.041</td><td>0.034</td><td>0.120</td><td>-0.057</td></tr><tr><td>β Protestant [std.]</td><td>0.022</td><td>0.028</td><td>0.054</td><td>-0.021</td></tr><tr><td>Difference P-Value</td><td>0.000</td><td>0.712</td><td>0.000</td><td>0.000</td></tr><tr><td>Outcome Mean</td><td>1.95</td><td>0.28</td><td>0.50</td><td>0.74</td></tr><tr><td>Outcome SD</td><td>1.57</td><td>0.45</td><td>0.50</td><td>0.44</td></tr><tr><td>Adjusted R2</td><td>0.364</td><td>0.287</td><td>0.173</td><td>0.070</td></tr><tr><td>Observations</td><td>60,947</td><td>60,947</td><td>60,947</td><td>60,947</td></tr><tr><td>Educational Province × Year FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

<footnote>Notes: The table reports OLS estimates of equation 1. The unit of observation is a secondary school in a year. Robust standard errors in parentheses. Conley standard errors in brackets. *Number of Study Tracks* refers to the total number of different study tracks offered by a school in a given year. *General, Technical, and Pedagogy* are dummy variables, each taking the value one if a school in a given year offers study tracks in the General, Technical or Pedagogy sections, respectively, and zero otherwise. *Ln(Inv. Distance) Catholic (Protestant) Mission* represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. *Geography Controls* include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasai rivers, and river length within a 10 km buffer around each school. *Historical Pop. Density Control* controls for population density as of 1950. *Educational Province - Year FEs* refers to fixed effects for each combination of educational province and year, controlling for all unobserved factors specific to a given educational province in a given year. *Disease Controls* include malaria suitability and tsetse suitability. *Historical Pop. Density Control* controls for population density as of 1950. Each estimation controls for the school's latitude longitude. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.</footnote>
<table><caption>Table C7: The Influence of Christian Missions on Students' Success at Examen National d'État</caption><thead><tr><th></th><th colspan="5">Dependent Variable:</th></tr><tr><th></th><th colspan="5">Students' Success at Exetat</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>0.007***<br>(0.001)</td><td></td><td>0.007***<br>(0.001)</td><td>0.008***<br>(0.001)</td><td>0.010***<br>(0.001)</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td></td><td>0.003**<br>(0.001)</td><td>0.000<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.004**<br>(0.002)</td></tr><tr><td>Ln(Inv. Distance) Colonial School</td><td>0.008***<br>(0.001)</td><td>0.009***<br>(0.001)</td><td>0.008***<br>(0.001)</td><td>0.008***<br>(0.001)</td><td>0.008***<br>(0.001)</td></tr><tr><td>β Catholic [std.]</td><td>0.022</td><td></td><td>0.021</td><td>0.023</td><td>0.030</td></tr><tr><td>β Protestant [std.]</td><td></td><td>0.009</td><td>0.001</td><td>0.004</td><td>0.012</td></tr><tr><td>Difference P-Value</td><td></td><td></td><td>0.002</td><td>0.004</td><td>0.018</td></tr><tr><td>Outcome Mean</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.70</td></tr><tr><td>Outcome SD</td><td>0.46</td><td>0.46</td><td>0.46</td><td>0.46</td><td>0.46</td></tr><tr><td>Adjusted R2</td><td>0.294</td><td>0.294</td><td>0.294</td><td>0.295</td><td>0.296</td></tr><tr><td>Observations</td><td>2,030,173</td><td>2,030,173</td><td>2,030,173</td><td>2,030,173</td><td>2,030,173</td></tr><tr><td>Clusters</td><td>60,209</td><td>60,209</td><td>60,209</td><td>60,209</td><td>60,209</td></tr><tr><td>Study Track-Center-Year FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Nearest Mission FEs</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td></tr></tbody></table>

Notes: The table reports OLS estimates of equation 2. The unit of observation is a student taking the Examen National d'État. Robust standard errors clustered at the school-year level are in parentheses. The dependent variable *Students' Success at Exetat* is a dummy variable equal to one if a student successfully passed the Exetat and zero otherwise. Ln(Inv. Distance) Catholic (Protestant) Mission represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Study Track-Center-Year FEs are fixed effects that account for the exam center where the student takes the session ordinaire, the study track and the year of the exam. Geography Controls include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. Disease Controls include malaria suitability and tsetse suitability. Historical Pop. Density Control controls for population density as of 1950. Each column controls for gender with a dummy variable equal to 1 if the student is female and 0 otherwise. Nearest Mission FEs refers to fixed effects for the geographically closest historical Catholic and Protestant missions. Each estimation controls for the school's latitude longitude. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.
<table><caption>Table C8: The Influence of Christian Missions on the Probability of Success</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent Variable:</th></tr><tr><th colspan="4">Students' Success at Exetat</th></tr><tr><th>Baseline<br>(1)</th><th>General<br>(2)</th><th>Technical<br>(3)</th><th>Pedagogy<br>(4)</th></tr></thead><tbody><tr><td>Ln(Inv. Distance) Catholic Mission</td><td>0.015***<br>(0.001)</td><td>0.028***<br>(0.004)</td><td>0.012***<br>(0.002)</td><td>0.014***<br>(0.001)</td></tr><tr><td>Ln(Inv. Distance) Protestant Mission</td><td>0.002<br>(0.001)</td><td>-0.005<br>(0.005)</td><td>0.003<br>(0.003)</td><td>0.002<br>(0.002)</td></tr><tr><td>β Catholic [std.]</td><td>0.046</td><td>0.077</td><td>0.037</td><td>0.043</td></tr><tr><td>β Protestant [std.]</td><td>0.005</td><td>-0.015</td><td>0.008</td><td>0.006</td></tr><tr><td>Difference P-Value</td><td>0.000</td><td>0.000</td><td>0.019</td><td>0.000</td></tr><tr><td>Outcome Mean</td><td>0.70</td><td>0.69</td><td>0.76</td><td>0.67</td></tr><tr><td>Outcome SD</td><td>0.46</td><td>0.46</td><td>0.43</td><td>0.47</td></tr><tr><td>Adjusted R2</td><td>0.295</td><td>0.363</td><td>0.321</td><td>0.250</td></tr><tr><td>Observations</td><td>2,128,302</td><td>387,752</td><td>738,686</td><td>1,001,864</td></tr><tr><td>Clusters</td><td>61,855</td><td>17,310</td><td>30,587</td><td>46,063</td></tr><tr><td>Study track-Center-Year FEs</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Geography Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Disease Controls</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Historical Pop. Density Control</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>

Notes: The table reports OLS estimates of equation 2. The unit of observation is a student taking the Examen National d'État. Robust standard errors clustered at the school level are in parentheses. The dependent variables Students' Success at Exetat are dummies variables equal to one if a student successfully passed the General Technical or Pedagogy Exetat and zero otherwise. Ln(Inv. Distance) Catholic (Protestant) Mission represents the inverted logged distance from each school to the nearest Catholic (Protestant) mission. Study Track-Center-Year FEs are fixed effects that account for the study track chosen by the student, the exam center where the student takes the session ordinaire, and the year of the exam. Geography Controls include caloric suitability, maize suitability, cassava suitability, elevation, ruggedness, precipitation, temperature, distance to the Congo and Kasaï rivers, and river length within a 10 km buffer around each school. Disease Controls include malaria suitability and tsetse suitability. Historical Pop. Density Control controls for population density as of 1950. Each column controls for gender with a dummy variable equal to 1 if the student is female and 0 otherwise and for the school's latitude longitude. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.