Understanding the Key Determinants of Early Childhood Education in Chad Ngarsaim Espoir Beram1 Summary Chad had the lowest Human Capital Index (HCI) in the world in 2018 (0.29)2, and little progress has been achieved since then (0.30 in 2020). This weak performance is largely driven by poor quality of education: the total expected years of schooling was estimated at 5 years but represented only 2.6 years of learning when adjusted for quality. Learning poverty3 was estimated at 98 percent. The primary school completion rate was only reached 45 percent in 2020.4 These poor learning outcomes could be attributed to the low level of development of Early Childhood Education (ECE). In rural areas, children have limited access to ECE because only 20 percent of available services are in rural areas and because they belong to the poorest households. Out-of-school children live largely in households whose head has no education (70 percent). When parents do send their children to ECE services, there is a strong tendency to start late, at 5 years instead of at age 3. Graph 1: Distribution of the GER by wealth Context index, HH education level and place of The Chad Interim Education Plan (PIET 2018- residence 2020) is the prevailing sectoral plan in the education sector. The targeted Gross Enrollment Rate (GER) in pre-primary was set to 4.2 percent, the baseline value being that of 2016 which was 2.8 percent. However, this ratio was reduced to 1.9 percent in 2018 (ECOSIT4).5 In addition to being very low, the GER is inequitably distributed across regions, the place of residence, the household head education level and wealth index, as shown in Graph 1. The highest rate was achieved in N’Djamena, the capital city (13.4 percent), whereas Hadjer-Lamis and Wadi-Fira regions lagged behind with a GER of 0.2 percent. Source: Author’s calculations based on ECOSIT4 data 1 This Policy Brief was prepared by the author as part of a capstone project for the Early Years Fellowship. The opinions are the author’s own and do not reflect the views of the World Bank Group. We thank Waly Wane (Practice Leader, HAWDR), Harisoa Danielle Rasolonjatovo Andriamihamina (Senior Education Specialist, HAWE3), Zacharie Ngueng (Education Specialist, HAWE2) and Haleinta Bara Traore (Education Specialist/Early Learning, UNICEF) for their respective inputs. The Early Learning Partnership (ELP) is a multi-donor trust fund, housed at the World Bank, which works with countries to build programs, policies and research to improve outcomes for young children around the world. ELP launched the Early Years Fellowship in 2016 to build capacity within countries and develop the next generation of leadership to scale-up investments in Early Childhood Development (ECD). 2 2018 World Bank Human Capital Project Report. A child born in Chad today will be 29 percent as productive when she grows up as she could be if she enjoyed full health and complete education. 3 Learning poverty means being unable to read and understand a simple text by age 10 (World Bank, 2019). 4 Education Statistics Yearbook, 2019-2020 5 Fourth Households Living Standards and Measurement Survey, ECOSIT4, 2019 Page 1 of 7 The GER reached 5.8 percent among children causes high schooling fees (even higher than belonging to the richest households (5th that in primary education) inaccessible to poor quintile) compared to only 0.5 percent for households. Moreover, these prices do not those living in the poorest ones (1st quintile), a necessarily reflect the quality of services difference of 5.3 percentage points. provided. There is neither a clear standard in Differences are even larger between children force in the pre-primary sector nor an effective living in households whose heads have higher supervision of preschools by the governing education levels and those whose household Ministry. heads have no education or achieve lower education level. In urban areas, the GER was The above-described conditions under which nearly ten times larger than that in rural areas. ECE services are supplied also determined the demand for these services. For example, poor The embryonic state of ECE in Chad is families would face financial constraints correlated with the weak institutional imposed by private providers and be in a environment and the lack of investment in this position of not sending their kids to preschools sub-sector. Indeed, pre-primary education or preferring to send them in primary schools. does not only evolve out of the formal education system but is also under the This brief focusses on the demand and supply management of the Ministry of Women, and factors of access to and quality of ECE in Chad Early Childhood Protection (MFPPE), not the using ECOSIT4 and Multiple Indicators Clusters Ministry of National Education and Civics Survey (MICS6, 2019). Promotion (MENPC) to which the pedagogic supervision of preschools is attributed. This situation led to an institutional confusion with Determinants of access to ECE in the two ministries that is hindering the Chad collaboration between them, especially on key The following variables are known to have an issues including the alignment of pre-primary impact on school attendance of a child and primary curricula, and data coverage regardless of the education level: child’s age within the Educational Management (Grootaert,1999) and gender (Canagarajah et Information System (EMIS). Coulombe, 1999; Grootaert, 1998); Also, the budget share of pre-primary households characteristics such as the head of education in the total government budget household’s gender (Durand, 2006) and devoted to education and training was the education level (Pilon, 1996), and the lowest in 2013 (0.5 percent).6 Further to the household wealth (Mingat, 2003); as well as successive shocks (the 2014 plunge in oil the community characteristics (place of prices, and the recent COVID-19 pandemic), residence, type of infrastructure, etc.). the funding of this sub-sector would certainly In this study, only children of pre-primary age, have deteriorated giving that in such situations that is 3-5 years, are considered. It is worth the Government used to cut social spending, noting that despite their young age, most of including education spending. Therefore, the them (75 percent) were attending primary public sector is underrepresented as less than school (ECOSIT4). Thus, instead of using a 10 percent of preschools are public. probit model, we use the ordered probit to The private sector is the dominant provider of capture this reality. The three modalities of the ECE services. Almost 7 in 10 preschools were independent variable (scol) included in the private (69.3 percent) in 2019, and community model are: 0 (being out of school), 1 (being preschools represented 25.2 percent of the enrolled in pre-primary, and 2 (being enrolled total. The predominance of the private sector 6 Rapport d’état sur le système éducatif national (RESEN), 2016 Page 2 of 7 in primary). The main database used for the Overall, the results are robust since the two estimation of the probability to attend graphs depict similar features. First, parents preschool is ECOSIT4 and consists of 5,530 tend to send their pre-primary age children to individuals. The robustness analysis of the primary schools instead of preschools. Second, results has been conducted using MICS6. The the positive impacts of the significant below graphs (Graph 2 & Graph 3) present the estimates on the attendance in both pre- margins of the estimates: primary and primary schools do not exceed 5 percent; they represent the chances for a child Graph 2: Margins of the significant factors to be enrolled in preschool with respect to (ECOSIT4) child’s age and as compared to reference modalities (“1st quintile� for wealth and “no education� for household head education level). Table 1 gives the magnitudes of margins of the covariates for the specific value of the dependent variable scol=1 (being enrolled in pre-primary). All the covariates have the expected sign after excluding place of residence from the equation (see Table 3 in Appendix). Its exclusion from the final equation is due to the fact that place of Source: Author’s calculations based on ECOSIT4 residence provides information that is already data capture in the variable wealth index: households living in urban areas are mostly Graph 3: Margins of the significant non-poor ones (from the 4th and 5th quintile) estimates (MICS6) whereas those living in rural areas are poor households (from 1st, 2nd and 3rd quintile). The head of household’s education level is the most impactful variable: a child living in a household whose head education level is university has 4.5 percent more chance to attend preschool as compared to the one who belongs to household whose head has no education. The positive impacts of higher education levels of household heads are related to the fact that the perception of the importance of education in general, and that of preschool in particular, become apparent as Source: Author’s calculations based on MICS6 data they move to higher level of education and are, thus, more willing to listen to and understand awareness messages. The proof is that 70 percent of pre-primary age out of school children live in household whose head has no education. Page 3 of 7 Table 1: Determinants of pre-schooling explanations to this: (i) richest parents would have employed caregivers to take care of their in Chad children at younger age before sending them to preschool, and (ii) the supply of “quality� ECE is very limited (limited number of places), especially in N’Djamena where parents keep trying to enroll their children each year. Elements of ECE quality in Chad The proportion of surveyed children enrolled in the last grade (grade 6) of primary school who have attended preschool increased from 19 percent in 2014 to 24.9 percent in 2019. This reveals a positive influence of preschool on the academic achievement.9 However, during the same period, the proportion of those enrolled in grade 2 of the primary school decreased by 6.5 percentage points (from 13.5 Source: Author’s calculations based on ECOSIT4 percent to 7 percent), which is consistent with and MICS6 data the downward trend observed in the GER in pre-primary.10 The household wealth index also causes discrimination in access to ECE. Children who On the school performance side, there are belong to the richest households have 2.4 significant differences in reading and percent more chance to be enrolled in pre- mathematics between children of grade 2 who primary than those who live in the poorest have attended preschool and their peers who households. This probability is as twice as high have not attended it. But these differences are the probability of children living in households not significant between children of grade 6, of the 4th quintile. This explains the low level of regardless of preschool attendance. This preschool attendance in rural areas where the learning outcomes are certainly related to the supply is also insufficient (only 20 percent of qualification of both preschool and primary preschools are in rural areas),7 making most of teachers. Indeed, the number of preschool parents send their pre-primary age children to teachers trained at the National Schools of primary schools.8 It is worth noting that school Health and Social Workers (ENASS), the fees are not significantly different between institution in in charge of pre-primary training, pre-primary and primary in rural areas. is not only insufficient, but most of them are not employed by the Government. In addition, Another determinant of access to ECE in Chad the governing Ministry, MFPPE, lacks resources is child’s age. The probability of preschool to supervise preschools, especially private attendance increases by 2.4 percent as ones. children grow up. In other words, children arrive at preschool late. There are two possible 10 7 Education Statistics Yearbook, 2019-2020 The GER in pre-primary was 3.2 percent in 2013 8 65 percent of children of age 3-5 who were attending (RESEN, 2016), 2.8 percent in 2016 (PIET 2018-2020), primary schools resided in rural areas (ECOSIT4). and only 1.9 percent in 2018 (ECOSIT4) 9 Programme d’Analyse des Systèmes Educatifs de la Confemen (PASEC) 2014 et 2019. Page 4 of 7 Discussion Based on the above results and the findings of Remedial classes can also be considered to the two PASEC surveys of 2014 and 2019, the facilitate the transition for children who enter challenges of access to and quality of ECE in late to primary schools without having Chad can be summarized as follows: attended preschool. The effective implementation of these recommendations 1) How to improve ECE quality and highly depends on the level of collaboration provide a safe and secure learning between MFPPE and MENPC. In addition, there environment? is a clear need for more investments from the 2) What can be done to expand access to Government to complement private supply ECE giving the current institutional and help reduce preschool fees. environment and budget constraints? 3) How to integrate ECE into the formal We find that there is an interrelationship education system and/or to ensure a between the education level of household successful transition of children under heads and the enrollment of their pre-primary 6 years of age to primary education? age children in preschools. It is thus important to create greater consistency within the whole The challenge related to the quality of ECE education system and develop a common requires to conduct a survey that will serve to communication plan to encourage parents to develop a new preschool curriculum11 based send their children to school in general and to on the identified shortcomings and to set out preschool in particular at the right age. It minimum standards. would be also beneficial to undertake an in- The expansion of access to ECE can be achieved depth study to well understand the functioning through the creation of preschool classes in of Quranic schools (considered as informal community primary schools and the training of education) which absorb about 10 percent of women in the communities and teachers of pre-primary age children. lower primary classes to teach preschool children.12 11 12 The current one dates from 1994 and is no longer A similar approach has been adopted in Pakistan in relevant since the context has changed since then. 2002 (Wendy and al., 2007). Page 5 of 7 References Confemen. (2014, 2019). Programme d’Analyse des Systèmes Educatifs de la Confemen (PASEC). Education Development Center, I. (. (2015). Radio Instruction to Strengthen Education (RISE) and Zanzibar Teacher Upgrading by Radio (ZTUR) Post-project Evalaution. Washington , DC. Education Development Center, I. (., & World Bank. (2015). Expamding Access to Early Childhood Development Using Interactive Audio Instruction: A toolkit and Guidelines for Program Design and Implementation. Washington, DC. Grootaert, C. (1998). Child Labor in Côte d'Ivoir: Incidence and Determinants. Policy Research Working Paper. INSEED. (2019). Fourth Households Living Standards and Measurement Survey (ECOSIT4). Klees, S. (2017). Will We Achieve Education for All and the Education Sustainable Development Goal? Comparative Education Review. Koçak, A., & Bekman, S. (2010). Mothers Speaking: A Study on the Experience of Mothers with Mother- Child Education Program (MOCEP). Istanbul: Bogaziçi University Faculty of Education. Martinez, S., Sophie, N., & Pareira, V. (2012). Promise of Preschool in Africa: Community-Based Preschools in Rural Mozambique. Washington: Save the Children and the World Bank. MENPC. (2016). Rapport d'Evaluation du Système Educatif National (RESEN). MENPC. (2019-2020). Education Statistics Yearbooks. Mingat, A. (2006). Disparités sociales en éducation en Afrique sub-saharienne: Genre, localisation géographique et revenu du ménage. Colloque International "Economie de l'Education: Principaux Apports et Perspectives". Ministère de l'Education Nationale et de la Promotion Civique. (2014). Rapport d’état sur le système éducatif national (RESEN). Pilon, M. (1995). Les déterminants de la scolarisation des enfants de 6 à 14 ans au Togo en 1981 : apports et limites des données censitaires. Institut of Research for Development. Wendy, R.-O., Jamshed, K., & Audrey, J. (2007). Early Childhood Education in Pakistan. Evaluation Report of USAID's Supported Program. World Bank. (2017). Promising Approaches in Eraly Childhood Development. World Bank Group. (2018). Learning to Realize Education Promise. World Development Report. World Bank Group. (2018, 2020). Human Capital Index Report. Page 6 of 7 Appendix Table 2: Descriptive statistics of the variables included in the estimation model ECOSIT4 MICS6 Independent variables Mean Num. Obs.\ Stand. Dev. Num. Obs. Mean Stand. Dev. Out of school 5,332 0.95 0.22 11,424 0.94 0.24 Preschool 5,332 0.01 0.12 11,424 0.02 0.14 Primary school 5,332 0.04 0.19 11,424 0.04 0.20 Child's age 5,369 3.97 0.83 14,257 4.00 0.82 Child's gender=male 5,369 0.52 0.50 14,257 0.50 0.50 Place of residence=rural 5,367 0.79 0.41 14,257 0.84 0.37 hhgender=male 5,367 0.87 0.34 14,257 0.83 0.37 1st quintile 5,367 0.24 0.43 14,257 0.22 0.41 2nd quintile 5,367 0.21 0.40 14,257 0.21 0.41 3rd quintile 5,367 0.20 0.40 14,257 0.20 0.40 4th quintile 5,367 0.19 0.39 14,257 0.20 0.40 5th quintile 5,367 0.16 0.37 14,257 0.17 0.49 hheduc=no education 5,367 0.67 0.47 14,257 0.60 0.40 hheduc=primary 5,367 0.16 0.37 14,257 0.20 0.38 hheduc=secondary 5,367 0.14 0.34 14,257 0.17 0.17 hheduc=university 5,367 0.03 0.17 14,257 0.03 0.12 Source: Author’s calculations based on ECOSIT4 and MICS6 data Table 3: Results of the estimation of the ordered probit model Dependent variable scol (I) (II) (III) Independent variables Child's age 0.65 *** 0.65 *** 0.65 *** (0.04) (0.04) (0.05) Child's gender=Male 0.05 0.05 0.02 (0.06) (0.06) (0.05) Place of residence=Rural -0.44 *** (0.06) hhgender=Male -0.03 -0.05 -0.19 * (0.08) (0.07) (0.08) 2nd quintile -0.06 0.01 0.05 (0.12) (0.11) (0.10) 3rd quintile 0.02 0.12 0.1 (0.11) (0.11) (0.10) 4th quintile 0.14 0.29 ** 0.25 * (0.11) (0.10) (0.10) 5th quintile 0.31 ** 0.54 *** 0.70 *** (0.10) (0.10) (0.09) hheduc=Primary 0.26 ** 0.26 ** 0.43 *** (0.08) (0.08) (0.07) hheduc=Secondary 0.18 * 0.24 ** 0.72 *** (0.08) (0.08) (0.07) hheduc=University 0.70 *** 0.83 *** 1.07 *** (0.10) (0.10) (0.10) / cut1 4.29 *** 4.62 *** 4.88 *** (0.23) (0.22) (0.24) cut2 4.58 *** 4.91 *** 5.11 *** (0.23) (0.22) (0.23) Number of observations 5330 5330 11424 Pseudo-R2 0.16 0.14 0.16 Wald Chi2 514.01 466.78 579.02 Notes : The standard errors are in brakets. Estimations (I) and (II) are based on ECOSIT4 data and estimation (III) is based on MICS6 data. Page 7 of 7