Teachers, Schools and Child Development in Benin

Marius K. Sossou∗ & Finagnon A. Dedewanou†

November 9, 2020

Abstract

Early childhood is the most and rapid period of development in a human life. The years from conception through birth to eight years of age are critical to the complete and healthy cognitive, emotional and physical growth of children. This paper explores the relative importance of teacher and school investments on cognitive development (mathematics and reading scores) of the second- year primary school pupils in Benin. We use data from the 2014 Program for the Analysis of Education Systems of CONFEMEN (PASEC) to ascertain how the two crucial environments children face during childhood (home and school) aﬀect the formation of children’s cognitive skills (maths and reading scores). Our empirical strategy is based on the inﬂuential model of skill formation estimated in Cunha et al.(2010).

JEL Codes: C38, I21, J13. Keywords: Child Development, Skill Formation, Teacher’s quality, School investment.

∗Department of Economics, Laval University. Email: [email protected] †Department of Economics, Laval University. Email: [email protected]

1 1 Introduction

Early childhood development is the key to a full and productive life for a child and to the progress of a nation (UNICEF, 2002). The wide dispersion of measured human capital in children and its strong relationship with later life outcomes has prompted a renewed interest in understanding the determinants of skill formation among children (Heckman and Mosso, 2014). Throughout their lives, children are influenced by many factors such as parents, neighborhoods, peers, teachers and schools. Quantifying the relative importance of these factors at various points in the development process is a central question. Empirical evidence of the strong impact of early investments by parents and others has motivated economic theories of skill formation in which early investments increase the productivity of later investments (Cunha et al., 2006). Children who are deprived of critical investments at young ages, because their parents lack either resources, parental skills or knowledge about parenting practices, may have negative consequences later in life (Heckman et al., 2006). Since the family choice of neighborhood and school depends on parents’ preferences and resources, pupils are nonrandomnly distributed across schools (Tiebout, 1956). Thus, besides parents’ characteristics, those of the schools and the teachers also play an important role in children’s cognitive and non-cognitive development. However, although there is a fairly broad consensus about the importance of investments in child development, and there is important research on the optimal timing of investments (early versus late childhood) and the types of skills in which to invest (cognitive versus non-cognitive), there is much less agreement about the specific types of interventions which are likely to be most effective. In this paper, we examine the relative importance of investments at home and school on pupils in grade 2 in Benin. We choose this group of pupils because at this grade children are in a step of their life that needs the transition to formal education and improved early primary school. In addition, early childhood development refers to the physical, cognitive, linguistic, and socio-emotional development of a child from the prenatal stage up to age eight. This development happens in a variety of settings (homes, schools, health facilities, community-based centers); and involves a wide range of activities from child care to formal education (Garcia and Neuman, 2010). Furthermore, according to the global Sustainable Development Goals, several Heads of State and Government and High Representatives have decided in 2015 to ensure by 2030, that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education. We synthesize two strands of largely separate and parallel research programs. First, the child development literature, which uses observational data on children’s cognitive skills to assess the

2 importance of parental investments on the development process (see Todd and Wolpin, 2007; Cunha and Heckman, 2007, 2008; Cunha et al., 2010, for a survey). This literature is largely silent on the role of schools, and how the distribution of school quality across pupils affects inequality in their skills. Second, the education production function literature, which mostly uses large administrative data from particular school systems to assess the importance of schools, classrooms, and teachers on test scores (see Rockoff, 2004; Rivkin et al., 2005; Aaronson et al., 2007; Jackson, 2012; Chetty et al., 2014; Flèche, 2017, for a survey). This research area is mostly silent on the importance of influences outside school. Using data from the 2014-PASEC (Program for the Analysis of Education Systems of CONFEMEN1), we perform several empirical analyses to ascertain the key features of the literature. More precisely, we analyze how the two crucial environments children face during childhood (home and school) affect the formation of children’s cognitive skills (mathematics and reading scores). Specifically, we tend to answer the following questions. First, how important are any differences in parents’ characteristics in the determination of pupils outcomes? Second, are any quality differences captured by observable characteristics of teachers and schools? The paper unfolds as follows. In the next section, we briefly present the childhood and primary education in Benin. Section3 describes our data and section4 discusses the skill development technology. The empirical framework of our analysis is presented in section5, while the expected policy analysis is underlined in section6.

2 Childhood and Primary Education in Benin

2.1 Childhood Education

Childhood education matters in Benin. Indeed, in October 2006, the Benin Government has adopted a Ten-Year Development Plan of the Education Sector. The main goals include universal primary education, the reduction of school disparities across gender and regions, a reduction of grade repetition down to 10% by 2015, and the improvement of education quality through a more adequate resource provision and a better management of primary education. In 2003, girls in rural areas were specifically targeted with the elimination of tuition fees and costs of school supplies in all public schools. In 2007, a National Policy for Girls’ Education was adopted by the Government to complement the initiative. It includes the Essential Educational Package (PEE) for accelerating girls’ education, defined as part of the Ten-Year. The PEE includes a public campaign for girls’ education “Toutes les filles à l’école”

1In French, Conférence des Ministres de l’Éducation des États et gouvernements de la Francophonie.

3 (All the Girls at School) with those words written upon huge billboards, on t-shirts, and in small leaﬂets to incite parents to register their daughters in school. The PEE program has contributed to some extent to a more equal participation of women (Garnier and Gbénou, 2011). In order to reduce schooling disparities and to promote universal primary education as targeted in the Millennium Development Goals, a key strategy adopted in Benin was to reduce costs borne by parents. In 2006, the Government announced the abolition of parent-paid tuition fees in all public schools at the primary and kindergarten levels. Subsidies to schools and increased resources to primary education support the decision. Government granted schools subsidies to cover tuition fees and textbook acquisition depending on their location.

2.2 Primary Education

The educational system in Benin was inherited from the French when the country achieved indepen- dence in August 1, 1960. It has since undergone many reforms to meet the needs of the country. The system is public and secular, and consists of two years of kindergarten education, six years of primary school, four years of junior secondary school, three years of senior secondary school, and a university. There are also three-year vocational or technical schools to attend in place of secondary schools. Primary education begins at six years and is free and compulsory. A national exam is given at the end of each level of schooling to determine eligibility for further education. Students graduating from primary (after 6 years of studies), junior secondary (4 additional years), and senior secondary schools (after 3 other years) receive the Certificate of Primary School (Certificat d’études primaires), Lower Secondary School Certificate (Brevet d’études du premier cycle) and Secondary School Certificate (Baccalauréat), respectively. Benin households tend to be heavily reliant on public schools for children’s education even though about 26.81% of schools are private (PASEC, 2016). Private schools are usually more expensive and attract more students from wealthier and more educated families. The central government is responsible for the development of educational programs and policies in Benin, including curriculum development, textbook selection, recruitment and appointment of civil servant teachers.

3 Data

Our analysis is based on the 2014 primary school survey data from the Program for the Analysis of Education Systems of CONFEMEN (PASEC). The PASEC–2014 survey was designed to assess

4 learning achievement and quality of primary education in French-speaking countries (Benin, Burkina Faso, Burundi, Cameroun, Congo, Côte d’Ivoire, Niger, Senegal, Chad and Togo) of Africa. It consisted of collecting data from a sample of pupils representative of the school population in each country, in the language of instruction and mathematics, at the beginning and the end of the primary cycle. The schools were chosen from the List of Francophone schools available at the National Ministries of Education (in French, “carte scolaire”). Coranic schools were not included since they follow a different curriculum and teaching generally takes place in Arabic. Other private schools were included if they were registered by the national authorities. In the case of Benin, around 180 schools were selected to evaluate pupils in 6th grade while 80 schools were selected for pupils in 2nd grade. Within the selected schools, one 2nd grade class and one 6th grade class were randomly selected. Within each class, around 10 pupils were randomly drawn. Data on Mathematics and French achievement are based on standardized tests with test items oriented at the typical curriculum of Francophone African primary education. Both tests were administered in French language. The Mathematics test contains a wide variety of items ranging from numeracy over problem solving (i.e. application to situations of daily life) to simple geometry. The French test covers general understanding and orthography as well as grammar skills. Tests were administrated in the classroom, item by item, following detailed instructions on the way to present each question and the time to be allocated to its response. Pupils attended the sampled class were tested at the beginning of the 2014 academic year and, using a similar test, again at the end of the academic year. Importantly for our analysis, the PASEC collects information on pupil’s family background, schools, classrooms, teachers and principals at the time of the post-test at the end of the academic year. For instance, survey staff was requested to fill in all questionnaires on the basis of individual interviews with each of the sampled pupils, their teachers and their principals, in order to provide explanations where necessary and to avoid unnecessary non-response. PASEC pupils data contain detailed information on the pupils themselves (age, gender, and support with homework), their families (whether their father and mother know reading), and the families’ endowment with goods that might be directly relevant for education (print and other media, books, dictionaries, black boards, etc.). Teachers and directors’ questionnaires contain detailed information on personal characteristics (age and gender), teaching quality (job experience, educational attainment, professional training, language skills, and indicators of personal job satisfaction and general attitudes). The PASEC data also contain rich information on school and classroom equipment, the location and structure of schools, the interaction of the different stakeholders within the school environment and

5 various pedagogical tools and attitudes.

3.1 Sample

Our sample consists of 732 pupils who where enrolled in the 2nd grade during the 2014 academic year in Benin. Two features of focusing on pupils in the second year of primary school are central to our analysis: (i) at this age, thinking and problem-solving skills are taking off. Children tend to talk at a more adult level and start to show an interest in specific activities that interest them. Cognitively, most children at this age look for the reasons behind things, ask questions for more information, understand cause and effect and make more in-depth connections, have a longer attention span, etc.; (ii) language development typically continues at a steady pace these two years. Vocabulary grows and kids start trying out words they have read but not heard.

3.2 Descriptive statistics

Table1 presents detailed descriptive statistics of our sample. The sample comprises 732 pupils of 2nd grade in all 80 schools of the survey. In term of pupils individual characteristics, we can see that female population is over distributed, and that the mean age is about 7 years. This ﬁgure makes sense since in Benin, most of children begin the primary school at 6 years old. Following recent various awareness programs in Benin on the need for parents to enroll their children to kindergarten, nearly half (41%) of the children in our sample attended kindergarten. Although most of these pupils have mathematics (92%) and reading book (93%) in class, very few (49%) have books at home to train outside of school. In addition, only 2% of pupils speak always French at Home. This is not surprising since, respectively 21% and 37% of their mother and father know reading. Examining the pupils classrooms and schools, the average class size is about 55 pupils. The teachers have an average of 4.42 years of experience in teaching at primary school. Moreover, 21 percent of them have an university degree. Finally, about 80 percent of the schools in the sample are public schools. In the next sections, we show how these characteristics could inﬂuence the pupils’ score in mathematics and reading.

6 Table 1: Summary Statistics

Mean Std. Dev. Min Max A: Characteristics of Pupils Age 6.80 1.19 4 13 Female 0.52 0 1 Pupils was at kindergarten 0.41 0 1 Has books at home 0.49 0 1 Has reading book in class 0.93 0 1 Has maths book in class 0.92 0 1 Reads at home 0.54 0 1 Can bring reading book at home 0.85 0 1 Can bring maths book at home 0.85 0 1 Speaks always French at home 0.02 0 1 B: Characteristics of Teacher Female 0.37 0 1 Age 28.99 6.80 20 56 Has secondary degree 0.94 0 1 Has university degree 0.21 0 1 Year of experience 4.42 3.98 0 19 Class size 54.56 24.05 9 123 Number of Girl in the class 26.97 13.21 7 68 Number of Boy in the class 28.11 12.92 2 72 C: Characteristics of Schools Public school 0.80 0 1 Has multigrade class 0.12 0 1 Number of teachers 5.68 1.34 3 10 Urban 0.41 0 1 D: Characteristics of Parents Mother knows reading 0.21 0 1 Father knows reading 0.37 0 1 Observation 732

7 3.3 Average test scores by some control variables

Table2 shows another patterns in our data. Panel A presents the scores of girls and boys in maths and reading tests. In Benin, girls score less than boys in both maths and reading, by 5.32 and 2.35 points respectively. However, these differences are not statistically significant and may be due to the fact that boys are more likely to come from higher socio-economic status households in which children’s school enrolment suffers fewer delays. Indeed, as shown in Table4 of the Appendix, 42% of boys in our sample attended kindergarten compare to 40% of girls. In addition, even if girls are more likely to read at home and to have more educated parents, they are more likely to be absent from school because of late registration and agriculture or domestic works. Thus, the lower girl performance in maths and reading observed in raw data is likely to underestimate the true extent of the gender gap, and should tend to increase as I control for family background characteristics. The remaining rows in the Table2 contain information on two of the explanatory variables used in the empirical analysis: teacher’s gender and school’s type. Panel B shows how pupils performance in maths and reading differ when they are taught by female teacher and male teacher. Results show that, on average, when pupils are taught by a female teacher they score significantly 24.4 points higher in maths and 22.1 points higher in reading than those pupils who are taught by a male teacher. This may suggest significant difference between the attitudes of pupils taught by female teachers and that of pupils taught by male teachers. This implies that teacher gender significantly influences pupil performance. Panel C investigates pupils average scores in maths and reading among private and public schools. It reveals that pupils from private schools score more highly in maths and reading test scores than those from public schools, by 56.7 and 62.3 points respectively. All of these differences are statistically significant. In the next sections, we explore in more details these results.

8 Table 2: Mean outcomes

A. Child

Boys Girls Diﬀerence in mean t-stat (1) (2) (3) (4)

Maths test score 457.411 452.091 5.32 0.627

Reading test score 459.55 457.2 2.35 0.386

No. of children 351 381 - -

B. Teacher

Male Female Diﬀerence in mean t-stat (5) (6) (7) (8)

Maths test score 446.191 470.633 -24.442∗∗ 2.122

Reading test score 450.654 472.828 -22.173∗∗ 2.228

No. of teachers 461 271 - -

C. School

Public Private Diﬀerence in mean t-stat (9) (10) (11) (12)

Maths test score 445.716 502.499 -56.783∗∗∗ -5.545

Reading test score 448.521 510.846 -62.325∗∗∗ -4.919

No. of schools 582 150 - - Notes: Authors’ calculations from the Benin 2014 PASEC data. Panel A displays average test score in maths and reading among children. Reported in columns (3) and (4) are the diﬀerence (boys-girls) in mean score and the t-statistic. Panel B investigates the diﬀerence in pupils’ average score among the gender of their teacher. Column (5) presents average test score in maths and reading for pupils whose teachers are males, while Column (6) belongs to pupils whose teachers are females. Panel C addresses pupils’ performances by the type of their schools (public vs. public). *** p<0.01, ** p<0.05

9 4 Skill Development Technology

In this section we present a stylized model of skill development estimated in Cunha et al.(2010) and Saharkhiz(2018). Child development takes place over a discrete and ﬁnite period, t = 0, 1,..., T, where t = 0 is the initial period (birth in Cunha et al., 2010) and t = T is the ﬁnal period of childhood (age 16 in Cunha et al., 2010). There is a population of children and each child in the population is indexed by i. For each period, each child is characterized by a stock of skills θi,t, with θi,t > 0 for all i and t. Skills include both cognitive and non-cognitive skills, and include other attributes of the child such as health or personality traits. For each child, the current stock of skills produce next period’s stock of skills according to the skill formation production technology:

θi,t+1 = ft θi,t, Hi,t, Si,t , (1)

where Hi,t is a vector of investments from home and Si,t is a vector of investments from the school the child attends. Equation (1) can be viewed as a dynamic state space model with θi,t the state variable for each child i. The production technology ft(.) is indexed with t to emphasize that the technology can vary over the child development period. In Cunha et al.(2010), the technology (1) is written in recursive form. Indeed, substituting in (1) for θi,t, . . ., θi,t−1,... repeatedly, one can rewrite the stock of skills at period t + 1, θi,t+1, as a function of all past investments:

θi,t+1 = gt θi,0, Ii,0,..., Ii,t , (2) where Ii,τ(τ = 0,..., t) stands for both home and school investments in child development, and θi,0 is the vector of initial skills of the child, say at birth. Home investment represents all child development activities outside of school, and results from interactions with parents (Suomi, 1999; Olds, 2002; Levitt, 2003; Cunha et al., 2010), while investment from school can be from any interaction during the school day, including from teachers and other schools staﬀ (Saharkhiz, 2018). In the child development literature (see for instance, Heckman and Masterov, 2007), equation (1) is labeled “child development technology”. In the education literature (e.g., Rivkin et al., 2005; Krueger, 1999), the model (1) is labeled “education production function”. In the former case, the skills include cognitive and noncognitive skills measured in survey data, and the investments from parents are the focus of the analysis. In the latter case, skills are typically reading or mathematics skills measured using standardized tests administered in schools, and the productivity of school inputs is the focus. Our speciﬁcation nests both

10 of these frameworks, and an important emphasis of our approach is to examine the relative importance of home, teacher and school factors on child development. We present our empirical speciﬁcation in the next section.

5 Empirical model

Children’s inputs strongly aﬀect individual development and future opportunities. Our empirical model aims to capture the impact of home and school investments on child development. We build upon the empirical model considered in Saharkhiz(2018) by including teacher’s gender as an additional explanatory variable for child achievement. However, since we do not observe child outcomes over the year in our data set, we consider a static version of the model (1). Indeed, even if several papers that focus on child development process consider a dynamic model of skill development, some studies such that Dearden et al.(2011), Schady et al.(2014), and Lopez-Boo and Creamer(2019) consider a static one due to the lack of data. Moreover, we consider a linear production function of equation (1) to deal with the identiﬁcation problem mentioned in Agostinelli and Wiswall(2016). Finally, our empirical model is presented as follows:

0 0 0 θi = Hi β + Siλ + Xiψ + εi, (3) where Hi and Si denote respectively the vector of investments from home and school in the child i’s cognitive skill θi. As mentioned in above sections, we consider two measures of child’s cognitive skills: maths and reading test scores. The vector Xi includes the observable characteristics of the child, his/her parents, his/her teacher, as well as his/her school’s characteristics. εi is an error term. Table3 details the variables that will be included in our empirical speciﬁcation (3). We rely on a baseline strategy that uses least square regressions when estimating equation (3). We estimate heteroskedasticity robust standard errors to deal with the potential clustering of observations at the school level.

11 Table 3: Measurements of investments and control variables

Child Skill Investments Control variables - Reading score From home: Pupil: - Maths score - Dummy variable indicating whether the - Age pupil attended kindergarten - Dummy variable indicating whether - Gender pupil reads at home - Dummy variable indicating whether the pupil has books at home Parents: - Dummy variable indicating whether mother knows reading

From school: Teacher: - Class size - Gender - Pedagogical resources index - Education - Teacher’s professional grad - Year of tenure

Other: - Dummy variable for public schools - Dummy variable for rural schools

6 Policy Analysis

We will explore several policy analyses in order to select appropriate policy recommendations. For this purpose, our analysis will be based on our findings when estimating the equation (3). Let’s assume we get the pedagogical resources index as a factor that is relevant to explain changes in maths or reading test score. We will therefore consider a policy which considerably increases the pedagogical resources index. We will compare the distribution of each predicted test score with the concerned in the data. Thus, if the difference between the two distributions is statistically significant, we will retain the policy that aimed at improving the pedagogical resources of schools to be more effective to increase children’s cognitive skills.

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14 7 Appendix

Table 4: Mean outcomes for boys and girls

Variable Girl Mean Boy Mean Difference in mean (girl-boy) t-stat Child’s characteristics Age 6.756 6.859 -0.103 -0.942 Attended Kindergarten (yes/no) 0.407 0.424 -0.017 -0.415 Pupil access to schooling inputs Has maths book in class (yes/no) 0.88 0.875 0.005 0.11 Has reading book in class (yes/no) 0.881 0.885 -0.004 -0.08 Pupil reason for absence from school Financial difficulties 0.527 0.578 -0.051 -0.931 Late registration 0.194 0.17 0.024 1.067 Agriculture/domestic work (yes/no) 0.29 0.235 0.055 1.15 School/classroom characteristics Public school (yes/no) 0.854 0.832 0.023 0.72 Rural School 0.582 0.538 0.045 0.862 Female teacher (yes/no) 0.361 0.333 0.028 0.656 Class equipment index 56.238 55.602 0.636 0.795 Family environment Mother is literate (yes/no) 0.266 0.251 0.046 1.286 Father is literate (yes/no) 0.457 0.408 0.039 0.763 Both parents are literate (yes/no) 0a.226 0.194 0.032 1.173 Pupil has books at home (yes/no) 0.505 0.497 0.008 0.172 Pupil reads at home (yes/no) 0.611 0.578 0.032 0.652 No. of children 732 Notes: Authors’ calculations based on the 2014 PASEC data. The t-tests reported in the last column are for the size and significance of the gender gap (girls-boys).