A2 Quality of in Sub-Saharan Africa: A multi-level analysis of success factors using PASEC2014 data

Hamidou Bocar Sall1 Cheikh Anta Diop (UCAD) and Office of Planning Dakar, Senegal Final report African Economic Research Consortium- AERC,NAIROBI

Abstract : In this study we investigate the link between academic achievement of students, their socio-economic status, and the level of resources available in school. Academic achievement is measured by reading and math scores among grade 6 pupils. And given the “nested" structure of education data, we employ a multi-level analysis procedure. The data used for this study were collected by PASEC (Programme d’Analyse des Systèmes Educatifs) as part of an international survey on the quality of education, carried out in 2014. Our results show strong academic inequality between schools in the education system, with a high intra-class correlation coefficient (ICC). Overall, it appears that grade repetition, age, sex, the use of French at home, school status (public vs. private), and the availability of school resources are the key factors contributing to the difference in pupil achievement in Senegal. Policy implication are emphasized. key-words : pupil achievement, multilevel model, sub-Saharan Africa, family background, school resources. Classification J.E.L : I21, C13, O55, I31

Résumé : Cette étude tente d’établir un lien entre la réussite scolaire des élèves de la sixième année du primaire, leurs statuts socio- économiques et le niveau de ressources disponibles à l’école, dans 10 pays d’Afrique subsaharienne francophone (Bénin, Burkina Faso, Burundi, Cameroun, Congo, Côte d’Ivoire, Niger, Sénégal, Tchad et Togo). La réussite académique étant mesurée par les compétences en lecture et en mathématique des élèves de la sixième année primaire. Pour tenir compte de la structure « hiérarchique » des données, nous adoptons une méthode d’analyse multiniveau (HLM). Les données utilisées ont été collectées par le PASEC dans le cadre d’une enquête internationale sur la qualité de l’éducation, réalisée en 2014. Les résultats montrent une forte inégalité d’apprentissage entre écoles dans les systèmes éducatifs des dix pays avec un coefficient de corrélation intra-classe (ICC) élevé. Aussi, il apparait que le redoublement, l’âge, le sexe, la pratique du français à la maison, le statut de l’école (public vs privé), la disponibilité de ressources pédagogiques sont les principaux facteurs explicatifs de la variation des scores des élèves. Les implications politiques sont discutées. Mots clés : qualité de l’éducation, modèle multiniveau, Afrique subsaharienne, statut socio-économique de la famille, ressources scolaires. Classification J.E.L : I21, C13, O55, I31

1 Author’s email adress : [email protected] ; Tel : 00221 77 485 47 89. 1

1. Introduction Recent contributions to economic research show that quality of education, measured according to international tests scores, is directly related to economic growth (Hanushek and Woessman, 2008). The implementation of effective education policies and an improvement in the levels of learning of pupils brings about subsantial economic and social gains. However, these studies say little about how to obtain this improvement in terms of quality. Based on this observation, it becomes important, from the policy point of view, to understand the factors that form the basis of the variations in the performance of education systems. The 1990s were decisive in terms of policy orientation for education in developing countries in general and for sub-Saharan African countries in particular. Indeed, the world conference on education for all (EFA) held in Thailand (Jomtien) declared that education is a “fundamental right” and thus invited States to actively commit to universal primary education by 2000. This would be through making primary education accessible to allow all children access to an . Through the Jomtien declaration, it is expected that achieving EFA goals requires a double objective: attain universal access and equity, all this through targeting higher standards of teaching and learning (UNESCO, 2000). Later, in 2000,The Dakar Framework for Action (Senegal) which takes into account the main objective of the global declaration of EFA, recommended in its goal 2, to « ensure that by 2015 all children, particularly girls, children who are in difficult circumstances and those belonging to ethnic minorities, have access to free and compulsory primary education of good quality the end » (UNESCO,2000). This objective has had strong policy implications on the education systems in developing countries. In poor sub-Saharan Africa countries, huge financial and human resources were devoted in order to achieve universal primary school education goal, in conformity with the Dakar commitments, and within the framework of achieving Millenium Development Goals (MDGs and SDGs). Indeed, the last two decades have seen the introduction of free primary education in several of these countries as a way of responding to the challenge posed by mass education (Dunga, 2013). Such initiatives are quite evident in countries like Malawi (1994), Uganda (1997), Tanzania and Lesotho (2000), Burundi, Rwanda, Ghana, Cameroon and Kenya (2003), and Senegal (2004), and have allowed the achievement of significant results in terms of expansion of education (Chimombo, 2005; Dunga, 2013 ; Abuya et al., 2015). For example, in Lesotho, enrolment in grade 1 of primary school increased by 75% in the first year; in Uganda the level of school enrolment increased by 68%, which brought the GER to 123% (Abuya et al.,2015); In Malawi, this policy implemented since 1994, allowed for an increase in enrolment from 1.9 million to 2.9 million children (Chimombo, 2005). However, increased access and an improvement in the rates of school enrolment were achieved at the expense of innumerable sacrifices in terms of quality. Indeed, it is theoretically accepted that the expansion of access to education should be accompanied by an improvement in the quality of education, the achievement of these both two objectives should perhaps be out of reach of developing countries (Chimombo, 2005 and Niang, 2014). Sub-Saharan Africa in particular, which has been particularly affected by rapid population growth and the scarcity of economic and human resources, turn to low quality educational systems. According to several observers and development partners such as the World Bank, we are experiencing a “learning crisis” (World Bank, 2018). This alert was given following a general observation that attending school is not synonym of learning (Pritchett, 2013) and that children learn very little in various education systems around the world. The survey undertaken in 2014 by PASEC focusing on the school performance in ten francophone west of Africa countries, shows that 71% of the student in grade two do not have a sufficient level in readind in French and are not capable of understanding clear instructions given to them orally, or understanding a series of written words. In mathematics, close to 60% of pupils in the fifth grade did not attain the threshold for sufficient competency, in other words, they could not identify a basic mathematical procedure needed to solve a problem (PASEC, 2015). Recent evaluations carried out in Ghana and in Malawi show that more than four out of five pupils finalising their second-grade primary school studies are not able to read a simple common word (Gove and Cvelich, 2001). More worrying still, this learning crisis widens inequalities and heavily handicaps young and minority people who have a greater need for this opportunity of access to education. In Senegal,for example, children belonging to the 40% of the most poor segment of the population represent only 22% of the pupils in the 6th grade who have acquired sufficient competencies both in reading and in mathematics (World Bank, 2016). Furthermore, despite the importance of the quality of education in the process of economic and social development, and all the attention that has since been devoted to policy documents, there is very little credible information on the main determinants of education quality. Despite the vast amount of literature and existence of advanced research techniques, to 2 date, there is little or no understanding of the impact of enrolment policies and enrolment incentives on the scores attained by pupils (Glewwe et al., 2013). Yet, it is quite important to bring to light factual data in order to make education beneficial to pupils (World Bank, 2018). Indeed, countries could use these data and other solid proof in the most optimal manner in order to determine which practices and innovations they must prioritise so as to improve the efficiency of their education systems. In sub-Saharan countries, very few studies have tried to examine how the individual charateristics of pupils and of a school affect examination results. Most of the studies on the subject are empirical and were undertaken more than 20 years ago (Chowa et al., 2015). Furthermore, library research on the main reasons for this problems allowed us to note that most of the studies undertaken on the subject focused more on anglophone sub-Saharan countries (member countries of SACMEQ) and less on the functioning and efficiency of the education systems of francophone countries. According to studies by Hanushek (1986), education is a multi-dimesional process which is guided by an education production function that transforms factors into inputs and outputs or enrolment results. These production factors (individual characteristics of pupils, socio-economic status of the family, scholarly environment, inputs, qualifications of teachers, etc.), interact at various levels (pupil level and school level), to contribute to the production of the final output which is academic success. In the past, such analyses on the determinants of quality of education in developing countries were limited by the availabiliy of comparable data (Michaelowa, 2001) and by the use of methodological approaches that were inappropriate for structured data (See Raudenbush and Bryk, 2002). Nevertheless, the deficit is in the process of being bridged thanks to efforts made in the collection and the provision of information on education systems through the Programme d’Analyse des Systèmes Educatifs (PASEC) platform of the Conference of the Ministers of Education in French-Speaking Countries (CONFEMEN), for sub-Saharan Francophone African countries and the consortium for Southern and East Africa, The Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ), which covers 14 southern African countries. This study fits within this framework and proposes a multi-level and multi-variate analysis of determinants of scholarly performance undertaken on pupils in ten francophone sub-Saharan African countries. More specifically, the study will: i) examine the performance gaps between the various education systems; ii) identify the factors that cause the performance variations; and iii) formulate policy on the determinants of quality of education. Success factors include the socio-economic status of pupils as well as the characteristics of the school. In order to do this, a two-level Hierarchical Linear Model (HLM2) (“pupil” level and “school” level) is proposed, following a multilevel procedure of analysis of the competencies of 6th grade primary school pupils in reading and in mathematics. The data used in this study is taken from almost 40000 pupils spread through more than 1800 schools (PASEC, 2017) in ten countries and was collected by the Programme d’Analyse des Systèmes Éducatifs of the Conference of Ministers of Education in French Speaking Countries (PASEC-CONFEMEN) under the framework of an international survey on quality of education undertaken in 2014. The rest of the study is structured as follows: in the section 2, we give an overview of theoretical and empirical studies on the determinants of quality of education;; the third section we will discuss the methodology and the data used and finally in the fourth and ultimate section, we will present and discuss the results of our regressions.

Research objectives and questions The main objective of this study is to examine the determinants of academic success of pupils in the sixth grade of primary school. More specifically, it will respond to the following research questions :

i. What is the extent of the variation in the performance of education systems in Francophone African countries south of the Sahara? ii. How do the individual characteristics of pupils and their socio-economic status and the school resources explains explain academic success for primary school pupils (competency in reading and in maths) ? iii. What are the major policy implications arising from the multi-level analysis of determinants of quality education in sub-Saharan Africa?

2 2Hierarchial Linear Model (HLM). See Raudenbush and Bryk, 2002. 3

2. Literature on determinants of scholarly success : using the education production function (EPF) Why is it that some pupils perform better in assessment tests than others ? What are the characteristics of schools in the education system which promote good learning? These questions have for a long time been the central preoccupation of economists and educational policy managers. They still remain crucial in developing countries and despite considerable progress noted in the education of children,pupils still learn very little in schools. Basically, the focus of this debate is on the identification of the most promising interventions, likely to favour the acquisition of competencies in the school environment. There are several studies that try to identify and to estimate the causal relationships that underlie school results (teaching/learning) in developing countries with the aim of coming up with policy recommendation based on these estimations (see Glewwe and Kremmer, 2006; Hanushek, 2003, etc.). In order to evaluate this corpus of literature, a methodological framework is necessary that helps to clarify the different types of causal relationships so as to judge the credibility of the estimation methods used. It is the tradition, when examining the determinants of the quality of education, to adopt the approach through an education production function (Hanushek, 1986 ; Glewwe and kremer, 2006), which is a structural relationship that links a set of factors or inputs with education yields of pupils referred to as outputs Production inputs include individual characteristics (l) and family characteristics (F) of pupils, as well as factors linked to the school (R). Theoretically, education production technology can be summarised as follows :

푄 = 푓(퐹, 푅, 퐼), (1) Whereby 푄 represents a measurement of learning results, generally captured through the scores attained by pupils in international tests3 in reading, mathematics and sciences. 2.1. Socio-economic status and academic outcome of pupils The introduction of the socio-economic status of households (or “SES”) as well as the individual characteristics of learners in this theoretical context highlights the significance given to “extra-scholarly” factors in the determination of the yields of pupils (Coleman et al., 1966; Peaker, 1971; Fuller, 1987 ; Hanushek and Luque, 2003; Woessman, 2004; Hungui and Thuku, 2010). Initially, in the mid 1960s, the conclusions arrived at in the famous and influential Coleman report on the equity of education systems in the “Equality of Educational Opportunity” then suggested that scholarly factors have little effect on academic performance of pupils once the impact of family characteristics is taken into account (Coleman et al., 1966). Social relationships within the family- Family social capital- thus explains the basis of academic performance. This report as well as other similar studies (Peaker, 1971) thereafter opened up a vast field of studies and debates that have lasted over the decades on the contours of (Fuller, 1987 ; Zuze, 2008). Following Hanushek and Luque (2003) family variables such as the level of income and the level of education of parents, their job status, resources available to the household (number of books, computers, desks in the house, etc.) have a high impact on academic performance. These researchers point out that pupils coming from a poor background whereby the level of education of the parents is low, will have lower scores in assessment tests compared to those from other social categories. With the socio-economic status of the parents being positively related to the level of education attained and the quality of education, the fact of belonging to a rich household, that has easy access to material and human resources, and which provides the best nutrition for the children, favours success in the school environment (Ross and Zuze, 2004 ; Barro and Lee, 2015). Several studies undertaken in sub-Saharan Africa have shown at which point the individual characteristics of learners and those of their families were important for academic success (Michaelowa, 2001; Ross and Zuze, 2004 ; Chowa et al., 2015 ; Hungui and Thuku, 2010; etc.) and in explaining the differences in performance observed between rural and urban areas (Zhang, 2006). They also exert a huge influene on the probability of dropping out of school. Thus, using a Hierarchical Linear Model (HLM) (multi-level model) for the PASEC data, Michaelowa (2001) finds that the family characteristics such as the act of speaking French at home, having educated parents, owning textbooks, and also partaking of regular meals significantly impacts upon the quality of education. Also, it shows that pupils who have repeated a class prior to the sixth grade had a comparatively lower score. In an analysis undertaken in Ghana, Chowa et al., (2015) find that individual characteristics such as gender, age,and commitment of pupils to studies are significantly related to

3 These are mainly PISA (Program for International Students Assessment), PIRLS (Progress in International Reading ) and TIMSS (Trends in International Mathematics and Science Study). Besides these international tests, there are other evaluation platforms for learner competencies administered at the regional level, notably in sub-Saharan Africa: The Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) and the Programme d’Analyse des Systèmes Educatifs (PASEC) of the Conference of Ministers of Education in French-Speaking Countries (CONFEMEN) in regard to francophone sub-Saharan African countries. 4 educational outcomes. In the same vein, Lee et al., (2005), examine the effectiveness of education systems in 14 subSaharan member countries of SACMEQ, by using a multi-level model, they find that academic success is strongly related to the socio-economic background of pupils in all the countries under study. In an investigation on factors that influence the performance of primary school pupils carried out in Senegal, Diagne (2007) establishes that household characteristics (availability of books, age of admission into school, etc.) are significantlycorrelated to quality measures. The scores by pupils positively rely on the availability of textbooks (French and Maths), the level of education of their parents and the socio-economic situation of the households. Equally, the higher the level of education of the parents, the better the results of their children. Older children on average have a higher score and repeating classes is not beneficial to pupils. Finally, the researcher finds that there is no significant difference between the scores of boys and those of girls. In a recent investigation on the factors that influence the performance of primary school pupils carried out in Malawi, Mulera et al., (2017) use a multi-level approach on decentralised databases at the level of each district showing that pupils who have the lowest socio-economic status score also, in a significant manner, the lowest academic results.

2.2.The impact of school resources on quality of education “the Heyneman and Loxley effect” revisited However, these strong conclusions on the determinant role of SES of households in explaining academic success and inherited from Coleman et al.,(1966), are subjected to a critical examination by two very influential researchers; these are Stephen Heyneman and William Loxley. In their seminal paper published in 1983, Heyneman and Loxley break with the pre-established opinion, and confirm that schoolarly resources, the quality of school and of teachers, remain the main sources of academic success of pupils. Basically, these authors compare the impact of family characteristics and in-school factors on the results of pupils in a sample of developing countries, middle-income countries and developed countries. They find that variables localised at school level explain the greater part of variances in academic performance in developing countries, compared to SES. This conclusion, better known under the name “the Heyneman-Loxley effect” or the “H-L effect” (See Baker et al., 2002), had significant implications in poor countries, because it has since then linked scholarly success of learners to the economic development of countries. The availability of pedagogic resources would be particularly beneficial to academic performance in developing countries, particularly in disadvantaged areas where they are scarce. Studies undertaken in these countries confirm the existence of the H-L effect (Fuller and Clarke, 1994 ; Michaelowa, 2001 ; Lee et al., 2005 ; Lee and Zuze, 2011 ; Dunga, 2013, etc.). For example, Lee and Zuze (2011) find that the relationship between the level of resources available to the school and academic performance of pupils is often strong and significant in four sub-Saharan African countries (Uganda, Botswana, Malawi and Namibia). According to Michaelowa (2001), the level of class equipment and the availability of textbooks are the most significant factors in regard to the academic performance of pupils.In the list of academic inputs likely to influence the performance of education systems, indicators such as the student/teacher ratio also referred to as the class size (Angrist and Lavy, 1999 ; Hanushek; 2006), the quality and the training of teachers (Hanushek and Rivkin, 2006) or also the availability of textbooks and library resources in schools (Michaelowa, 2001), have a direct relationship with the academic performance of pupils. Nevertheless, the impact of textbooks on the performance of pupils is still in dispute among researchers. For Glewwe et al. (2009) who use random evaluation in rural areas of Kenya, the availability of textbooks does not increase the mean of academic results contrary to previous studies. According to these researchers, these textbooks only have an impact on the score of the best pupils (those with the highest scores in the initial test) and have a marginal effect on the remaining learners. Equally, Kuecken and Valfort (2013) test the assumption that textbooks have a significant impact only for pupils coming from the most privileged backgrounds, in 11 sub-Saharan African countries. Their results do not show any sign of an average impact of textbooks on educational outcomes but identify a positive effect in one particular category of pupilsthose who are at the peak of the distribution of the socio-economic status of households. Nevertheless, it is important to indicate also that in literature, there are several researchers who register their disagreement in terms of there being an H-L effect (Baker et al.,2002; Hanushek and Luque, 2003). For example, Baker et al., (2002) reproduce the initial study of Heyneman-Loxley by using two estimation techniques, the Ordinary Least Squares and the Hierarchical Linear Models, on data from TIMSS covering 36 sampled countries. Their results suggest the absence of an H-L effect in all the countries where the study was undertaken, the socio-economic status of the households is demonstrated to be the main reason for academic success. This conclusion is thereafter confirmed by Hanushek and Luque (2003) who

5 do not find any empirical evidence to support the hypothesis that the impact of academic resources depends systematically on level of income or on economic development. Taking an opposing view to these results, Gameron and Long (2007) confirm on their part, that there exists a threshold above which, school resources would have a lower impact upon the results of the pupils and that the H-L effect is still applicable to developing countries

3. Data and Methodology This section presents the methodological approach chosen to determine the sources of academic success of pupils in the sixth grade of primary school in sub-Saharan African countries. It in the first place explains the data used, then, secondly, discusses the analytical approach used. 3.1. Data and Sampling issues : The PASEC 2014 survey This study is based upon recent data collected by PASEC, in 2014, during an evaluation of the quality of education in countries that are members of the Conference of Ministers of Education in French-Speaking Countries (CONFEMEN). This international evaluation, the first of its kind, covers six countries (Benin, Burkina Faso,Burundi,Cameroon, Congo, , Niger, Senegal, Chad and Congo) for a sampled almost 40 000 pupils spread out in almost 1 800 schools (PASEC, 2017). For this survey, emphasis was placed on compatibility of the results of the various national evaluations. The measurement on a common scale of competencies of pupils of different countries, at the beginning (2nd grade) and end (6th grade) of their primary schooling, allows to better examine and understand efficiency and equity of education systems, like other international programmes such as PISA,PIRL,TIMSS or SACMEQ. Equally, it allows us to examine the factors behind academic success following “value addition” models that take into account the cumulative effect of learning (PASEC, 2015). Two key competencies are evaluated for the two levels : reading/language and mathematics. The data from the PASEC evaluations was collected from a representative sample of the school population comprised of the total number of students enrolled in the 2nd and 6th grades of primary school regardless of the type of school (public, private, commmunity schools, etc.) in each country. A three-tier sampling plan was adopted: i) After having counted the schools that had at least one stream in 6th grade and spread these schools into different specific strata, 180 schools were selected according to a probability that was proportional to the number of pupils enrolled in the 6th grade. It is important to note that within each explicit stratum, a number of schools were chosen, ii) the second step consisted of selecting a stream in the 6th grade among the total number of classes at this level in the selected establishment. If the school only has one stream at this level,then that class is definitely selected; and iii) the third step involves the selection of 20 pupils within the 6th grade stream selected. The sampled 2nd grade only has half of the schools selected in the 6th grade sample. Equally, the data weight is guided by the three-tier sampling method. The final weight of a pupil comprises of: (i) the initial weight of a school, including the eventual weight adjustment for the non-response from schools in their respective strata; (ii) the weight of a class within the school; (iii) the initial weight of the pupil within the class including an eventual adjustment for a non-response by pupils in their respective classes. Using the background information gathered through a questionnaire collectively administered to the pupils, a questionnaire for the principals and teachers, data provided by PASEC allows for the examination of the existing relationship between the socio-economic status of learners (sex, age,repeating classes,parents occupation, etc.), the quality level of available resources in the school, and the final result provided by the system (competencies in reading comprehension, in maths, etc.).

3.2. Méthodology 3.2.1. Hierarchical linear model (HLM) approach Research in the science of education often tries to provide an answer to the question of the effects of various training systems on the individuals who are part of them (Raudenbush and Bryk, 1988). However, such an exercise is made to be rather difficult through the complexities of education data which is inherently “hierarchical” : pupils are located in classes, which themselves are to be found in schools. Modelling these type of phenomena leads to processing data which is derived from two different levels : some are related to the environment and are therefore overall and aggregated characteristics, whereas other are individual data and are located at a level that is lower than that of the environment in that it the latter covers several individuals (Bressoux, 2007). This poses the problem of choosing the unit of analysis. In this context, the weak consideration of the “nested” structure of these data or a poor processing of the latter, could lead to biased estimations (see Hox, 2010). This well known problem by researchers, has been the subject of several discussions but it is not until the development of multilevel models that a satisfactory solution can be found.

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For a long time, the classical technique consisted of using linear regression (Ordinary Least Squares) to estimate this relationship. Yet, the analysis of the data “structured” by a regression of the OLS type is inappropriate, since it makes some strong assumptions (Michaelowa, 2001; Bressoux, 2007; Raudenbush and Bryk, 2002; Hox, 2010). For Bressoux et al. (1998), one of the problems with the OLS model is that it is based on statistical assumptions which are often contradictory with research assumptions. Indeed, these models are based on the assumption of the independence of residuals. However, in the evaluation of class effects, it is assumed that the residuals may vary from one class to another and, therefore, that they are not independent of each other. In social sciences in general and particularly in education, the individuals or “subjects” selected for a study are not independent but are found more within organisational units ad they overlook these interrelations by aggregating the data leading to a precision bias (“ecological error” or aggregation bias ; Robinson, 1950). It is, for example, the case of pupils in a class ; beyond the characteristics that are specific to each one amongst them, these pupils benefit from common learning conditions, and thus could finally influence their educational outcomes. Thus, in the presence of an intra-class correlation (ICC) among the individuals studied within a group, the hypothesis of independence of violations is violated (Raudenbush and Bryk, 1988). The use of dummy variables could constitute an alternative to overcome this limitation of OLS models to effectively deal with this problem. In this case, it is a question of evaluating the class effects by introducing into the model n - 1 dummy variables each representing a class, the nth class serving as a reference. However, by treating the problem in this way, we consider that the class effects are fixed effects. However, it is difficult to support such an assertion in insofar as the distribution of performance in the classes remains a random variable, the effects of which are therefore random. This distinction between fixed effect and random effect is important. In fixed-effect models, we are interested in the mean, while in random-effect modeling, it is the variance. Multilevel models also known as Hierarchical Linear Models (HLM), or mixed models, have been developed to address the specific problems posed by data structured on several levels, typically in the case where individuals share a common environment that can affect the behavior in which one is interested (Givord and Guillerm, 2016). These models make it possible to aggregate the information collected at different levels (pupil level, class level, school level, etc.) to identify the structure of these data and of the error term to provide a robust estimate of the coefficients (Michaelowa, 2001). It thus circumvents the problem of choosing the unit of analysis and avoiding the dilemma of aggregation vs disaggregation (Bressoux et al., 1998). In addition, they give a simultaneous estimate of the composition of variance at the student level and at the school level, while maintaining an appropriate level of analysis with the structure of the data, allowing the effect to be tested at the within each level and the interrelationships that exist between them (Raudenbush and Bryk, 1994 and 2002; Hox, 2010). Mixed models contain a fixed part and a random part which fundamentally differentiates them from OLS models. The random part being made up of several residual terms, of which the variances and covariance become parameters to be estimated

3.2.2. Models and Estimation steps Following the analytical approach initially proposed by Raudenbush and Bryk (2002) and adopted by Zhang (2006), Hungui and Thuku (2010) and Lee and Zuze (2011), we consider a two-level education production function (with the pupils within the schools). This multilevel model is suitable for nested data. Given the differences in the historical and organisational background of education systems in the ten countries under study, the option chosen consisted of undertaking seperate econometric regressions for each country by using the same analytical framework. In a formal manner, the theoretical models are presented as follows :

Level 1 model:

푌푖푗 = 훽0푗 + 훽1푗푋1푖푗 + 훽2푗푋2푖푗 + ⋯ + 훽푞푗푋푞푖푗 + 푟푖푗 (2) Level 2 model:

훽0푗 = 훾00 + 훾01푊1푗 + 훾02푊2푗 + ⋯ + 훾0푠푊푠푗 + 휇0푗 (3)

훽1푗 = 훾10

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훽2푗 = 훾20 ….

훽푞푗 = 훾푞0,

Whereby 푌푖푗 is the mean score in reading/writing of pupil i in grade j 훽0푗 represents the average score of school j; the vector 푋푞 (q=1……Q) contains explanatory variables at the level of “student” retained in this study and ; ; 훽1푗 − 훽푞푗 represents the estimation coefficients of the relationship between the reading competencies and the variables at the level of pupil; 푟푖푗 is the error term or the score deviation of a pupil i in relation to the mean of the school j (the “school effect”). At the second level,훾00 represents the mean average score of the sample; 푊푠 (s=1……S) is a vector that contains the explanatory variables and the “school” level; 훾01 − 훾0푠 are the coefficients of the estimation of the relationship between the average score in reading of the school and a set of explanatory variables at the “school” level; and

휇0푗is an error term which refers to the deviation in the mean score of school j in relation to the overal mean of the sample

(“the school effect”). Furthermore, it is assumed that the errorterms 푟푖푗 and 휇0푗 are normally distributed with a mean of zero 2 for the variances of 𝜎 et 휏00. We consider i=1, 2,..., 푁푗 pupils in j=1,2,..., J schools. It is usual to proceed step by step in a multi-level modelling. In order to do so, we begin by estimating an empty model. This model is said to be “empty” or “null” because it does not contain any explanatory variable (Bressoux, 2007). It is a simple decomposition of the variance on the one hand, and the inter-class variance on the other hand, following the ANOVA procedure. At this stage, an inter-class correlation coeffecient (ICC) is calculated, 𝜌 : 휏00 𝜌 = 2 (4) 휏00+ 휎

The second step of our investigation consists of estimating the model at the “pupil” level, by integrating the variables of the characteristics of the pupil and of their family without adding those at the “school” level. The third and last step of the multi-level analysis will consist of an estimation of the model at level 2 by using the variables on the characteristics of the school. The results of the multi-level analysis are taken from different regressions undertaken in each of the ten countries in our sample with a recourse to the normalisation of continous variables (transformation of the z-score). Nevertheless, in the descriptive table of the education systems of these countries, measures were replicated, just like was the case with PASEC. 3.2.3. Description and measurement of variables The PASEC survey, through the use of questionnaires collected data on the academic performance of pupils in the 6th grade, and also on the learning conditons as well as the environmental background of the pupils. This analysis examines these relationships through following two types of analyses : (1) “Pupil” level and (2) “School” level. a. outcome variables Academic success which in this case is our variable of results, is measured by the scores in language/reading obtained by the pupils in the sixth grade. These scores are standardised using an international average fixed at 500 and a standard deviation of 100 with an identical contribution for all countries. b. Explanatory variables Learning is a complex and multidimensional process in which the socio-economic environment, education context, cognitive capacities, and scholarly resources, etc. interact at different level. These inputs impact directly or indirectly on the performance of pupils and on the equality of the education systems. 4 In order to explain the variations in performance between different education systems, we use different explanatory variables located in two levels (Raudenbush and Bryk, 2002). These explanatory factors have been identified from literature on education and in regard to the informaton available on the PASEC database. Level 1 : Pupil-level variables In the first place, the pupils are used as a unit of analysis and the explanatory variables retained are the socio -demographic characteristics of pupils: sex (coded 1 if it is a boy and 0 if not) and the age (measured in years); the use of the test language at home (coded 1 if the child speaks sometimes or always the test language and 0 if not); the socio-economic status of

4 Reference framework of the PASEC background questionnaires 8 pupils5, continous variable); the level of parents éducation (coded 1 if at least one of the parents can read or 0 if not); the availability of textbooks at home (coded 1 if the pupil had enough books to fill a bookshelf; two bookshelves or a library, and 0 if not); extracurricular work (coded 1 if the pupil always, often, or sometimes does extracurricular work and 0 if not); and the school background of the pupil; attending nursery (coded 1 if the pupil went to nursery school, , or pre- school and 0 if not) and repeating classes (coded 1 if the pupil repeated at least one class and 0 if not). Level 2 : school/class-class variables At the “school” level (level 2), variables used could be regrouped in four main categories namely the composition of the school, the level of human and educational resources, the structures and social organisation of the school. Such a level of detail is introduced to take into account the objective of this analysis. The composition of the school is captured by the level of the mean aggregate SES of the school and the concentration of girls in the class. The level of pedagogic resources of the school is measured through the composite index of pedagogic resources of the school, calculated in PASEC2014. The infrastructure index of the school is also used. The quality of human resources of the school (teachers) is captured by three variables : the level of education attained by the teacher coded 1 if the person has attained a higher level of studies and 0 if not, the professional training with a code of 1 if the teacher had undergone professional training and 0 if not as well as their experience which is a continuous variable measured in years. Furthermore, the use of mother tongue by the teachers (code 1 if the teacher still uses the language spoken by most of the learners and 0 if not) allows us for the understanding of how the use of mother tongue affects the academic performance of the pupils. In relation to the structure of the school, four measures are used. The first measure addresses the student/ teacher ratio through the variable of the average size of classes. The influence of institutional factoes is examined using the status of t he establishment (coded 1 if the school is public and 0 if not) whereas physical accessibility is approximated using the geographic location of the school (code 1 if the school is found in an urban area and 0 if not). The introduction of the private mission school variable is an attempt to investigate the impact of religion in teaching/learning. Finally, in relation to the organisation and the functioning of the school, the variables retained are relative to the sociodemographic profile of the teacher (code 1 if the teacher is a man and 0 if not) and by the involvement of local communities in school managment (the director receives support from the local community for the construction, rehabilitation, maintenance of infrastructure (code 1 if it yes, 0 if not). The variables linked to the behaviour and to the incentives offered to teachers were also introduced. These are: (i) Absenteeism by pupils and teachers (number of days of absence by the teacher apart from public holidays in the course of the past 4 weeks and the average number of pupils absent per month in the class surveyed; (ii) the status of the teacher which is a dichotomous variable coded 1 if the teacher is a public servant/ permanently employed by the government and 0 if not; and (iii) The inspections done on the teacher (code 1 if since the beginning of the year the teacher has been visited by an inspector or Education officer and 0 if not). Finally, the social climate that is prevalent within the schools related specifically to problems of behaviour at the workplace of the teacher is introduced through the variable of harassment (if the teacher is a victim of moral or physical harassment at the school [1 if yes and 0 if not]. 4. Résults and discussion 4.1. Characteristics of 6th grade primary school pupils of Francophone sub-Saharan African countries Table 1 below presents the social characteristics and the educational environment of primary pupils who are members of PASEC. In this table, some variables are expressed in denominations (for example the scores of pupils, the size of classes, etc,) others, however, are displayed in percentages (gender of the pupil, repeating classes, etc.). Also, in order to be more representative, the statistics presented her were weighted by using the final weight of the pupil as is recommended in PASEC (2017). The scores of 6th grade pupils vary from one country to another. Indeed, Burundi (596.6), Senegal (546.4), Burkina Faso (539.5) and Togo (520) recorded average scores in mathematis that are higher than the PASEC international average fixed at 500. The six remaining countries are below the international average, with Chad at the bottom of the classification. In regard to reading, with the exception of Niger (403.5), Chad (432.5) and Togo (497.3) which registered scores average

5 PASEC collects information on the socio-economic level of families from pupils at the top end of the primary school cycle through a series of questions relating to the availability of the resource materials in the households and the characteristics of the household : the number of booksin the house, possession of material goods, etc. The responses by the students are applied on an international scale with an average of 50 and a standard deviation of 10 so as to construct a socio-economic index (PASEC, 2015). 9 scores that were below the international average, the rest of the sampled countries had average scores that were above the average. These results also reflect the level of competencies acquired. If on average 60% of pupils in the sixth grade attain the “satisfactory” threshold in mathematics in the ten countries sampled, Burundi is notable in this overall tendency with almost 9 out of 10 who attained the “satisfactory” threshold (PASEC, 2015). It is followed by Senegal and Burkina Faso where the percentage of pupils who attain “satisfactory” competencies is higher than the average (almost six pupils out of 10). Chad, with less than 20% and Niger, with less than 10%, have the lowest levels of achievement. Moreover, the concentration of math and reading scores around the 45 ° diagonal indicates a strong relationship between the score obtained by students in the two disciplines (Figure 1). However, countries like Burundi and Ivory Coast show a dispersion of scores that deviates a little more from the 45° line, thus showing differences between the distribution of mathematics and reading scores in these countries. A nonparametric Wilcoxon test is applied to compare the two distributions (mathematical and reading) in each country (Table 1b). This test assumes as a null or H0 hypothesis that there are no significant differences between the score obtained in mathematics and in reading. Thus, the results reject the null hypothesis for all countries, except for Niger. As a result, math scores are significantly different from reading scores in these nine countries. However, in Niger, the distribution of the two scores is identical. In the ten countries under study, the family environment of pupils also differs as is evident from the variations in the indexes of the socio-economic status of the family which measures the wealth level of the households. This index is higher than 50, in other words, the PASEC international index, in six countries. In the rest of the sample, this index is below the average with Niger (45) registering the lowest index. Girls remain under -represented in the 6th Grade. With the exception of Senegal, Bénin and Burkina Faso, where the school enrolment for girls is higher than that of boys, the remaining countries present an education proifile that is unfavourable to girls. In terms of the education environment, table 1 shows that the average level of the socio-economic status of schools is higher for Cameroon, Congo, Senegal, and Togo. The index of education resources varies from one country to another, with the highest being in Senegal. Burkina Fasso hosts the most densely populated classrooms (68 pupils). With the exception of Benin, Senegal and Congo, most of the primary education structures are located in rural areas. Public schools are the majority in all PASEC countries. In regard to the community participation, this is relatively higher in the contruction and maintenance of infrastructure. 4.2. Résults et discussion The results of the analyses are indicates in tables 2,3,4,5 and 6 found in the annex to this document. Table 2 presents the spread of the difference in mathematics and reading. Tables 3 to 6 present the coefficients of the regression of the models of level 1 and 2 consecutively, undertaken seperatedly in the ten countries that comprise our sample. At the bottom of these tables there is also the spread of the difference in particular the information contained in the explained difference in other words the percentages in reduction of intra and inter school differences in the null model. Furthermore, with a view to making it easier to read the tables, only the coefficients that are significantly associated with the score in mathematics and in reading with a threshold of 5% (p < 0.05) in at least one country are displayed. 4.2.1. Variance decomposition The results of the equation on the breakdown of the difference which gives the psychometric properties of the mathematics and reading scores of sixth grade primary school pupils are presented in table 2. The coefficients of intra-class relations are found in Francophone sub-Saharan African countries remain high and disparate. Indeed, they are high for Cameroon (0.62 in mathematics and 0.63 in reading), Chad (0.63 in mathematics and 0.66 in reading) and for Togo (0.6 in mathematics and 0.58 in reading) and less for Burundi (0.3 in mathematics, and 0.44 in reading) and Ivory Coast (0.34 in mathematics and 0.43 in reading). Given that the quantity of differences available in schools is a measurement of the levels of equity in education systems, the extent of coefficients of intra-class coefficients found in the ten countries under study is evidence of the high levels of inequality that exist among the primary schools in the PASEC countries. Only two countries (Ivory Coast and Burundi) do not display this tendency. Furthermore, this high level of heterogeneity (inequality) at the “school” level suggest the existence of agglomeration effects in our data, which justifies the use of the Hierarchical Linear Model to explain this differences.

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4.2.2. Déterminants at the « student » level In regard to reading, eight (08) variables at the “pupil” level are significantly correlated to the score. These are age, gen der, classes, domestic labour, the speaking of french in the household, the availability of textbooks in the house hold, the level of education of the parents as well as the homework done. In mathematics, these variables are all equally significant. Repeating classes is highly related to academic performance in all the CONFEMEN member countries. The negative coefficient associated with this variable indicates that, all other things remaining equal, pupils who have never repeated a class have higher scores in reading and mathematics as compared to those who repeated at least one class. This result agrees with those arrived at in studies undertaken in sub-Saharan African on the negative impact of repeating classes on academic performance (Bernard et al., 2005 ; Hungui and Thuku, 2010 ; Michaelowa, 2001 ; Diagne, 2007 ; Lee and Zuze, 2011 ; PASEC, 2015). The advanced age of pupils effects negatively the quality of éducation received by pupils in nine out of ten countries in the study (with the exception of Chad). This result was expected to the extent that the older a child is, the higher they risk being exposed to child labour and the less time they would thus have to devote to school work (Diagne, 2007). In some cases, the advanced age of the pupil is strongly related to repeating classes such that its negative effect disappears one repeating classes is taken into account. Michaelowa (2001) indicates that in several studies, the impact of repeating classes was not well controlled which translates into a negative impact attributed to age. Equally, Chowa et al., (2015) note that in Ghana, most older pupils either repeated classes or experienced some interruption in their studies due to various reasons, including weak academic results. There is gender inequality in mathematics as well as reading, in all the 10 CONFEMEN countries. According to our résults, boys have better scores than girls. This observation holds true in all countries under study with the exception of Burundi which has an opposite result in terms of inequality in both subjects. The observation is widely noted in empirical studies, in both developing and developed countries (Fryer and Levitt, 2010 ; Fuchs and Woessman, 2004 ; Hungui and Thuku, 2010 ; Zuze and Reddy, 2014 ; Cobb-Clark and Moshion, 2017). These gender-specific tendencies appear very early (from primary school) and last throughout the school career (Zuze and Reddy, 2014). The disadvantage of girls in terms of competencies in mathematics limits their capacity to pursue studies in scientific disciplines at higher levels (Cobb-Clark and Moshion, 2015) or to develop careers in areas of technology-intensive fields such as and Information Communication Technology (Contini et al.,2017). This significant variation between sexes in terms of educational output originates from both biological and genetic factors as well as in socio-cultural idiosyncracies. Against all expectations, the practice of domestic chores has a positive and significant impact on the yields of pupils at the end of their primary school years. Generally, the impingement of extra-curricular work on the home study time of pupils should have a negative impact on their academic performance. A net difference in the acquisition of competencies in relation to the socio-economic status of families was also observed. Variables such as the level of education of parents, the use of French language to communicate in the household and the availability of textbooks are strongly correlated to academic success. All other things remaining equal, pupils who speak French outside school (at home) score more highly than their classmates who do not use the language outside school. Indeed, speaking French at home familiarises the child with the language that they will use at school and ensures continuity between that milieu and the home environment (Diagne, 2007). However, the very weak impact of this variable in Burundi (non-significant in reading and significant at a level of 0.05 in mathematics) could be related to the specific context of the country which uses another language to evaluate academic performance. Furthermore, out of the ten countries examined, Burundi and Niger have the lowest percentage in terms of the use of French in the household (Table 1). The availability of textbooks in the household is also strongly correlated to the performance of pupils in the two disciplines. Our results demonstrate the pupils who have more textbooks at home perform better in reading tests and in mathematics compared to others. Textbooks held pupils to practice reading at home as well as to do mathematics exercises. These results are in line with research studies (Coleman et al., 1996 ; Michaelowa, 2001 ; Hungui and Thuku, 2010 ; Barro and Lee, 2015) which suggest that the home environment is an important factor in the learning process. Indeed, the strong impact of family characteristics on the performance of pupils is an indication of the persistence of the element of intergenerational transmission of education within society (Barro and Lee, 2015). 4.2.3. Determinants at the “school” level

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In the search for common factors at the school level that are responsible for the variance in scores in mathematics and in reading among the ten CONFEMEN countries, nineteen (19) variables were found (Table 5 and 6). Nevertheless, the impact was not uniform in all the countries, or in the two subjects considered for this study. Pupils who attend schools located in an urban area achieved better academic performance than their counterparts in rural schools. The inequalities related to place of residence are still quite high in the education systems of sub -Saharan African countries (Zhang, 2006 ; PASEC, 2015). Zhang (2006) observe that in most SACMEQ member countries, the rural-urban divide in terms of reading remains more pronounced than that observed between countries. Pupils in the rural area experience learning conditions that are less favourable and often come from disadvantaged households, with a low socioeconomic status. The type of school enrolled in is also a determinant of academic performance. All other things remaining equal, pupils who attend public schools score lower in average than those who attend other types of establishments in six countries out of ten. This result is in agreement with the overall observation on academic performance of public schools compared to private schools (Dronkers and Roberts, 2008, Furchs and Woessman, 2004 ; PASEC, 2016). In general, pupils who attend private schools are from more privileged socio-economic backgrounds than their counterparts in public schools (PASEC,2015). However, beyond this aspect, the result confirms the idea that the “institutional” characteristics of the education system (public schools vs private schools : centralisation vs autonomy in the management of curricula, budgets and personnel ; organisation of entry examinations, etc.) influence the performance of pupils (Fuchs and Woessman, 2004). In agreement with observations by Heyneman and Loxley (1983), and Lee and Zuze (2011), educational resources are significanlty related to the performance of pupils. These factors are all the more significant because in the education systems of sub-Saharan African countries, there is a deficit of material as well as infrastructure adapted for use by pupils. Thus, the characteristics of the teacher exert an influence on the academic performance of the pupils. Nevertheless, out of the three variables used to measure the quality of human resources (teachers), only one variable would be significantly associated to the score of the pupils in mathematics and in reading. Furthermore, the impact is only evident in two countries (Congo and Senegal). Thus, in these two countries, the pupils who are under the tutelage of a teachers who have higher levels of education have higher scores in mathematics and in reading than those whose teachers did not attain a university level of education. However, the fact that the quality of teachers does not seem to have an effect on the quality of learners is counterintuitive and out of tune with the results of several research studies undertaken in this area (Michaelowa 2001 ; Diagne, 2007 ; Hungui and Thuku, 2010 ; Lee and Zuze, 2011 ; PASEC, 2016). To this extent, the explanation could come from a measurement of these variables. In other words, could the level of education of the teacher, their professional training, or also their experience be synonymous with a good quality of human resources ? Indeed, Lee et al.,(2005) who established the positive relationship between the quality of the teacher and the scores of learners in Botswana, Seychelles, Namibia and Mozambique, include in addition to these indicators, the scores of the teacher in SACMEQ evaluations. In doing so, these researchers use a more reliable measurement of the quality of the teacher. In addition to the quality of human resources, the availability of teaching and infrastructure resources are also important for academic outcomes. Thus, pupils attending schools that have more material resources (equipped and functional library, computer room, computers, photocopying machines, etc.) score more highly than their counterparts in schoo ls where the level of resources is much lower. The use of mother tongue by pupils in class does not seem to improve the level of academic performance of pupils and in Certain situation lowers their education quality. According to our results, the coefficient on this variable is negative and significant for Ivory Coast, and is negative for Niger. This result suggests that the use of vernacular languages in schools lowers the learning of pupils. Furthermore, no effect could be established in Burundi, which unlike other CONFEMEN countries, uses a language other than French in its education system. Teaching at the private mission school level has a negative impact on the score of the pupils in three countries out of ten (Burkina Faso, Cameroon and Togo). Moreover, the status of teachers exerts an influence on the performance of learners. In Congo, the public servant status of the teacher has a negative impact on the performance of learners. Variance partitionning analysis gives us an indication about the quality of the models. The lower sections of tables 3 and 4 give the available differences and explained in the models at the pupil level whereas tables 5 and 6 provide the same information for the models at the school level. It seems that Burundi, Ivory Coast and Niger have more significant quantities of available differences. On the other hand, Chad and Togo and Cameroon are characterised by the lowest intra-school

12 differences, in mathematics scores. In regard to reading, the same tendency is noted. At the school level, Cameroon and Chad and Togo have the highest inter-class differences whereas Burundi and Ivory Coast have the lowest variables. In regard to the differences explained through the integrated variables in the final models, they vary between 26.4% (Ivory Coast) and 68,7% (Cameroon) in mathematics and 32% (Chad) to 73.5% (Congo) in regard to reading. Furthermore, the quantity of unexplained variance gives an indication of the other explanatory variables left out in this analysis. Thus, several of the key variables were left out in the explanation of the quality of education in Ivory Coast and Chad.

5. Conclusions and policy implication

Overall, the comparative analysis undertaken in these countries allowed us to identify a certain number of explanatory factors of the quality of education, in Francophone sub-Saharan African member countries of CONFEMEN. These results should have important policy implications with the aim of strengthening the quality of teaching in these countries and help in meeting the Millenium Development Goals that are related to education (MDG 4). The education policy should reinforce the fight against repeating classes. This educational practice is not unanimously approved by actors in the education system (teachers, school heads, etc.). For some, it is an intervention tool geared towards helping pupils in difficulty, and giving them a second chance. For others, however, repeating classes is a punitive method which contributes to increasing the rate of school drop outs. In actual fact, the analyses undertaken show that repeating classes does not improve performance of education systems. The education policy should also take into account the question of equity by reducing disparities between residential areas (rural-urban). The PASEC analysis established that significant differences were observed between the pupils in urban areas and those in rural areas. It should among other things optimise the allocation of human and material resources by taking into account the equity dimension at the national level. In line with our results which show that attending a public primary school disadvantages learning by pupils when compared to the private primary schools, serious measures should be undertaken by education authorities to improve the means of management/running of public schools and/or promote the development of private school networks. Nevertheless, this recommendation should be studied by taking into account the impact of disparities between the place of residence given that most private schools are located in urban areas. Also, there are still efforts to be made in the design of the PASEC survey so as to find a more reliable variable for the measurement of the quality of teaching. This recommendation could be implemented through giving a direct test to teachers, which would be like that of SACMEQ. In line with the conclusions arrived at by Heyneman and Loxley (1983), our results also suggest that the level of educational resources in schools remain an important aspect of the improvement of the quality of learners. In terms of policy, education officials should further strengthen the level of equipment in these establishments. Nevertheless, the availability of educational resources does not guarantee the best performance by pupils. It is important to ensure that resources are managed in an efficient and effective manner by pupils and teachers in order to obtain high scores.

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Pritchett L. (2013). The rebirth of education: Schooling ain’t learning.Washington D.C: Center for Global Developpement Bryk, A.S., Raudenbush, S.W., 1988. Toward a more appropriate conceptualization of research on school effects: a three-level hierarchical linear model. Am. J. Educ. 97, 65–108 Raudenbush, S.W., Bryk, A.S., (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed. Sage Publications, Thousand Oaks, CA. Zuze T. L. et Reddy, V., (2014) “ School resources and the gender reading literacy gap in South African schools ”. International Journal of Educational Development 36 (2014) 100–107 Robinson, W. S. (1950), «Ecological correlations and the behavior of individuals», AmericanSociological Review, vol. 15, no 3, p. 351–357. UNESCO 2000. Cadre d’action de Dakar. Éducation pour tous : tenir nos engagements collectifs. Texte adopté au Forum mondial sur l’éducation, Dakar, Sénégal, 26-28 avril 2000. Paris, UNESCO. Zhang, Y. (2006). Urban-rural literacy gaps in Sub-Saharan Africa: The roles of socioeconomic status and school quality. Comparative Education Review, 50(4), 581-602.

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Table 1a : Descriptive statistics of PASEC 2014 data at the end of the Primary school cycle in the ten education systems surveyed. Bénin Burkina Faso Burundi Cameroun Congo Côte d'Ivoire Niger Sénégal Tchad Togo Average score in reading 523,4 531,6 525,4 517,5 503,4 517,0 403,5 548,4 432,5 497,3 (94,53) (83,87) (50,76) (101,20) (89,51) (92,14) (81,84) (100,65) (76,37) (89,17) Average score in mathmatique 496,9 539,5 593,6 489,5 481,4 475,7 405,8 546,6 450,9 520,2 (85,77) (89,11) (63,04) (92,87) (72,37) (68,25) (70,09) (96,44) (72,49) (97,83) Level 1 (pupil level variable) Socio-economique status of the 52,4 50,2 43,4 52,9 54 52,3 45 55,1 45,6 49 famille* (8,81) (7,53) (7,01) (9,72) (10,37) (8,36) (11,87) (9,08) (10,68) (9,29) Age of the student 12,7 13,3 14,5 11,9 12,6 12,2 12,9 12,4 13,4 12,6 (1,69) (1,41) (1,66) (1,58) (1,50) (1,52) (1,26) (1,08) (1,64) (1,67) % of girls 52,7 50,9 45,2 45,7 49,8 45,8 43,4 52,8 34,6 46,1 % of repeating 42,9 43,3 17,9 454,1 42,8 38,5 60,2 64,2 36,5 34,2 % using french at home 81,9 84,6 59,4 90 81,1 92,2 48,6 84,6 79,4 84,5 %domestic chores (extracurricular) 94 94 95,5 93,8 87,3 94,2 94,6 92 90 97,6 % of literate parents 74,9 63,4 83,4 87,8 95,4 75,2 56,1 78,9 71,5 76 % homework 0,98 0,96 0,95 0,96 0,91 0,94 0,73 0,96 0,94 0,98

Level 2 (school level variables)

School composition average socio-économique status 49,6 49,2 47,6 51,4 50,1 49,7 48,4 51,1 48,5 50 (4,25) (4,00) (4,86) (5,72) (4,74) (4,07) (5,22) (4,44) (10,68) (4,35) % of girls in classe 45,6 51,8 56 47,6 49,6 43,9 42,7 53,1 34,12 45,7

Human and educational resource Educational resource* 50,6 48,3 47,1 52,4 51,1 48,1 47 60 47,1 48,5 (10,28) (6,52) (8,72) (14,70) (12,66) (6,47) (6,73) (13,48) (5,57) (7,35) School infrastructure index* 54,8 52,5 45,8 50,2 54 51,9 41,2 58 44,9 46,7 (6,61) (6,83) (8,51) (12,10) (9,41) (7,9) (9,08 (7,32) (10,47) (10,40) % educated teacher (university) 23,2 60,4 24,6 27,8 34 51,8 29,6 47,2 58,1 28,5 % trained teacher (professional) 4,2 14,7 10,2 8,6 7,9 3,1 4,1 4,2 10,8 34,2 Teaching experience 19,6 11,8 11,5 11,7 12,1 12,8 13,1 11,8 9,12 12,5 (10,3) (6,13) (8,09) (7,85) (8,10) (8,06) (7,46) (5,88) (6,25) (7,62) % use of mother tongue 6,5 28,0 38,8 11,3 6,3 10,3 38,7 18,4 8,3 5,4

School structure Classe size 35,5 68,3 44,1 47,5 55,2 43,5 39,6 42,8 46,4 36,1

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(14,88) (28,67) (14,19) (25,20) (28,22 ) (24,81) (15,42) (18,73) (25,99) (14,61) % rurale school 49,6 54,7 82,2 64,4 42 51,5 79,3 44,1 54,6 60,8 % public schoolque 85,7 89,1 94,3 72,9 63,6 90,2 97,1 82,6 77,1 71,8 % private mission school 4,0 5,5 0 20,9 2,9 3,7 0 4,7 2,2 14,1

School organisation % female teacher 20 20,2 81,2 24,3 25,4 4,9 30,3 11,9 1,6 2,3 % civil servant teacher 58,4 28,9 96,0 9,3 33,1 86,1 57,7 48,76 43,7 45,6 % inspected classroom 88,8 74,2 87,6 94,8 90,7 83,2 85,6 49 89,9 81,3 % community help 13,9 38,9 62,2 17,8 6,3 61,1 77,1 39,3 39,6 22,7 Teacher absenteism 1,5 1,3 0,9 1,2 1,3 1 1,6 1,5 2,7 0,8 (2,27) (1,72) (1,44) (1,79) (1,88) (1,44) (2,65) (2,80) (3,46) (1,79) Pupil absenteism 8,23 4,55 15,1 11,2 21,9 9,5 8,4 7,2 15,9 9,9 (18,29) (6,90) (16,97) (13,65) (34,22) (14,82) (10,46) (5,79) (15,24) (11,94) % moral harassment 8,9 8,5 6,4 18,3 15,2 7 10,9 5,7 25 16,1 % physical harassment 1,6 0,5 2,4 6,9 4,9 2,8 2,9 2,6 6,1 2,9 Pupil sample size 3033 3416 3461 3817 2673 2972 3196 2905 2484 3256 Source : Author using information derived from PASEC2014 database Standard deviation in parentheses. *: are the composite indexes calculated from PASEC2014. These indexes have an international average of 50 and a standard deviation

Table 1b : Results of the signed Wilcoxon test in the ten educational systems surveyed. Bénin Burkina Faso Burundi Cameroun Congo z = -28.457 z = 8.192 z = 50.496 z = -38.678 z = -18.338 Prob > |z| = 0.0000 Prob > |z| = 0.0000 Prob > |z| = 0.0000 Prob > |z| = 0.0000 Prob > |z| = 0.0000

Cote d’ivoire Niger Sénégal Tchad Togo z = -35.612 z = 1.591 z = 3.637 z = 18.569 z = 24.213 Prob > |z| = 0.0000 Prob > |z| = 0.1117 Prob > |z| = 0.0003 Prob > |z| = 0.0000 Prob > |z| = 0.0000 Source : Author using information derived from PASEC2014 database

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Figure 1 : grade 6 math and reading score

BENIN BURKINA FASO BURUNDI CAMEROUN

800

600

400 200

CONGO COTE D'IVOIRE NIGER SENEGAL

800

600

400 200

score maths 200 400 600 800 200 400 600 800

TCHAD TOGO

800

600

400 200

200 400 600 800 200 400 600 800 score lecture score_maths line45°

Graphs by Nom du pays

Source : Author using information derived from PASEC2014 database

Table 2 : Psychometric properties (null model) of score in mathematics and in reading of sixth grade primary school

Bénin Burkina Faso Burundi Cameroun Congo Côte d'Ivoire Niger Sénégal Tchad Togo Average within schools sample size 18,4 18,8 19,2 14,3 16,3 21,4 18,2 18,2 15,8 17,2

Mathmatiques Total Variance within schools (𝜎2) 3094,6 4015,2 2773,6 3186,3 2223,9 2869,2 3045,9 4226,0 1968,0 3805,4 19

Total Variance between schools (휏00) 4337,0 4429,8 1190,2 5289,6 3156,9 1816,9 2874,7 5062,1 3397,3 5791,8 Intraclass corrélation (ICC)a 0,58 0,52 0,30 0,62 0,58 0,38 0,48 0,54 0,63 0,60

Reading Total Variance within schools (𝜎2) 4096,9 3384,0 1431,3 3752,6 3208,3 4867,9 2928,0 4448,7 2051,3 3309,6

Total Variance between schools (휏00) 4881,1 4119,2 1130,9 6394,6 5112,2 3693,3 3702,5 5671,8 3973,6 4562,2 Intraclass corrélation (ICC)a 0,54 0,55 0,44 0,63 0,61 0,43 0,56 0,56 0,66 0,58 2 a. ICC=휏00⁄(휏00 + 𝜎 )

Table 3 : Level 1 HLM results in mathematics for primary school, sixth grade

Bénin Burkina Faso Burundi Cameroun Congo Cote d'ivoire Niger Sénégal Tchad Togo Girls 18.92*** -14.86*** 33.16*** -10.41*** -21.70*** -17.95*** -12.52*** -19.50*** -21.84*** -16.04***

Repeating -28.20*** -14.58*** -4.990* -22.55*** -8.508** -14.71*** - -42.67*** -11.67*** -24.54***

French 23.36*** 34.66*** 4.517* 16.74*** 16.11*** 17.93*** 14.51*** 25.77*** 10.41** 19.76***

Books - 9.795** - 16.81*** 13.12*** - 17.19*** 11.88*** - -

homework 19.35* - 14.84** 28.04*** - 11.72* 18.99*** - 11.62* 25.48*

age -9.264*** -3.465* -10.42*** -13.42*** -16.88*** -3.451** -4.689* -10.67*** 3.127* -5.647***

Literate parents ------7.821** - 6.468** -

Domestic chores 18.99*** 17.93*** - 21.56*** 11.07** - 12.68* - 15.35*** 22.05**

Within school Variance Modèle nul (𝜎2, % available 42% 48% 70% 38% 41% 61% 51% 45% 37% 40% variance) Modèle final (𝜎2, % explained 14% 5% 13% 9% 14% 7% 6% 16% 15% 8% variancea) *p < 0.05, **p < 0.01, ***p < 0.001 a: percentage of reduction of the intra-school variance (sigma^2) in the unconditional model (null model) -: non-significant variable at p<0.05

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Table 4 : Level 1 HLM results in reading for primary school sixth grade Burkina Bénin Burundi Cameroun Congo Cote d'ivoire Niger Sénégal Tchad Togo Faso Filles 8.007** -5.489* 12.41*** - -5.685* - -12.55*** -5.878* -18.29*** -7.364***

Repeating -39.71*** -13.45*** -7.345*** -25.36*** -14.02*** -28.28*** -10.45*** -40.67*** -8.465*** -26.72***

French 22.08*** 26.60*** - 26.37*** 23.24*** 28.48*** 16.87*** 29.71*** 11.38** 16.04***

Books 7.794* 10.42*** - 15.41*** 14.34*** - 15.32*** 8.733* 11.51*** -

Socioéconomic status 9.384*** 7.479*** - 6.949*** 6.794** 9.169*** 5.627** - 4.235* 5.303**

homework 34.40*** - - 22.30*** 20.48*** 19.71** 25.06*** 24.58*** - -

age -10.39*** -7.277*** -9.733*** -15.72*** -19.95*** -4.312* - -14.20*** - -8.704***

Literate parents - - - 13.62*** - 8.553** 9.606*** - - 7.598**

Domestic chores 12.67* 15.77*** - 15.48** 9.622* 9.889* 15.25** - 12.92** 18.05**

Within school Variance Null model (𝜎2, % 45,6% 45,1% 55,9% 37,0% 38,6% 56,9% 44,2% 44,0% 34,0% 42,0% available variance Final model (𝜎2, % 13,7% 4,2% 8,0% 12,8% 12,2% 6,2% 6,7% 17,4% 13,1% 9,7% explained variancea) *p < 0.05, **p < 0.01, ***p < 0.001 a: percentage of reduction of the intra-school variance (sigma^2) in the unconditional model (null model) -: non-significant variable at p<0.05

Table 5 : Level 2 HLM results in mathematics for primary school, sixth grade Bénin Burkina Faso Burundi Cameroun Congo Cote d'ivoire Niger Sénégal Tchad Togo Socioéconomic status - 15.20* - 12.14** ------

Girls in class - - -6.229* - 10.04* - - - - - Educational resource 14.21** - 10.71** - - - - - 22.25* - - - - - 17.88* - - 42.69*** - -

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Mother tongue ------52.32* - - - Infrastructural resource 26.34*** - - 10.37* 11.20* 11.95* - - - - Urban school 20.88* - - 28.16* - 34.09*** - 47.99** - 27.05* Public school - - - - -29.32* -69.39* - -66.83** - -84.71*** Class size - - -10.99** - - -8.845* - - - 23.46** Private mission school - -78.58*** - -39.21* ------40.12* supp_comm_constr_rehab - - - - - 15.47* - - - - supp_comm_canteen_meal - - - - 23.91* - - - - - Pupil Absenteeism ------14.92** - - - Teacher absenteeism - - -7.875* - - -11.27* -7.347* -12.87** - - Moral harassement ------35.84** - - - Physic harassement -79.67* ------102.4** - -69.83** Civil servant teacher - - - - -28.79** - - - - - constant 464.0*** 520.3*** 619.6*** 471.6*** 476.0*** 449.5*** 450.8*** 520.5*** 462.8*** 539.2*** Between schoolVariation Null model (휏 , % available 00 58,4% 52,5% 30,0% 62,4% 58,7% 38,8% 48,6% 54,5% 63,3% 60,3% variance Final model (휏 , % explained 00 64,8% 42,4% 45,8% 68,7% 62,1% 26,4% 41,8% 52,6% 29,6% 59,0% variancea) *p < 0.05, **p < 0.01, ***p < 0.001 a: percentage of reduction of the intra-school variance (sigma^2) in the unconditional model (null model) -: non-significant variable at p<0.05.

Table 6 : Level 2 HLM results in reading for primary school, sixth grade Bénin Burkina Faso Burundi Cameroun Congo Cote d'ivoire Niger Sénégal Tchad Togo Socioéconomic status - 12.35* - 15.52*** - - - - - 10.58** Girls in school - - -7.210** - 9.529* - - - 11.60* - Educational resource 15.49*** - 10.31** ------Teacher education ------39.26*** - -

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Mother tongue ------19.42* - - - - Infrastructure resources 18.88** - - - 16.04** - - 18.33* - - Urban school 22.47* 30.43** - 41.76** - 49.30*** 27.17* 50.64** - 41.20*** Public school -29.88* - - - -37.06* -84.29* -64.88** -69.43** - -74.88*** Class size - - -9.103** ------Private mission school - -68.39*** ------42.32** support_comm_canteen_meal - - - - 24.00* - - - - - Pupil Absenteeism ------14.12* - - - Teacher Absenteeism - - -7.304** - - -12.94* -7.957** -11.10** - - Moral harassment ------35.50** - - - Physical harassement -61.14* ------98.33** - -40.92* Inspected class ------23.61* - -48.58** - constant 484.4*** 515.2*** 562.2*** 502.8*** 482.0*** 482.2*** 452.0*** 517.1*** 454.0*** 506.4*** Between school variance Null model (휏 , % davailable 00 54,4% 54,9% 44,1% 63,0% 61,4% 43,1% 55,8% 56,0% 66,0% 58,0% variance Final model (휏 , % explained 00 72,9% 51,2% 60,6% 73,2% 73,5% 48,4% 53,2% 59,3% 32,1% 69,0% variancea) *p < 0.05, **p < 0.01, ***p < 0.001 a: percentage of reduction of the intra-school variance (sigma^2) in the unconditional model (null model) -: non-significant variable at p<0.05

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