Free Education and Student Test Scores in Chad Gbetonmasse B. Somasse Worcester Polytechnic Institute (WPI) International Conference on Sustainable Development in Africa Dakar– November 26-27, 2015 G. Somasse, Economics 1

Context “Fundamental education is compulsory.“ (Primary and middle) for 6-14 years old since 2006 Key Issues Has Free education led to lower test scores? What are the determinants of teacher’s value-added? Relevance Understanding the determinants teacher’s value- added helps promote student’s learning The Problem Research Questions and Relevance

Background Primary Education in Chad education system  6 years in primary school  exit exam (CEP).  4 more years  middle school exam (BEPC)  3 other years  senior high school exam (BAC) Compulsory Primary Education  “Fundamental education is compulsory.“ (Primary and middle) schools for years old since 2006 Mostly Community Schools.  51% of primary schools are managed by communities (2012)  Community teachers represent 74% of the teaching personnel  48% of schools do not provide a full cycle of learning (In N'Djamena less than 8% of schools are incomplete v. nearly 90 % in Tibesti.)  Only 33% of enrolled children are taught by qualified teachers.

Significant differences in access, completion, and learning in primary schools across region and gender.  Gross enrollment ranges from 21% in the region of Ennedi to 124% in the capital region N'Djamena. PASEC results suggest that 2nd graders scores  39.1 in language (French), 2 percentage points lower than in 2004  40.3 or 2.5 percentage points less than in For fifth graders, the results are slightly better:  38 in French in 2010 against 32.1 in 2004  38.1 in math against 34 in 2004 Teacher’s absenteeism and strikes are common. Background Primary Education in Chad

Methodology A Difference-in-Difference Analysis of Achievement

Challenges in evaluating effectiveness of teachers and schools:  Self-selection: Schools are chosen by students and their parents  Classroom assignments are not random – based on prior test performance (Chetty etal, 2014). A solution: value-added (VA) of student achievement.  Pionnered by Hanushek (1971) and Murnane (1975).  Recently: Rockoff (2004), Rivkin, Hanushek, and Kain (2005)  Use a student’s prior scores and characteristics to predict test score.  Prediction residual as a measure of the contribution of school- teacher to student achievement. Methodology A Value-Added Model of Teacher’s Effectiveness

Methodology Stage 1: Student-Level Regression

Methodology Stage 2 – Classroom-Level Regression

Methodology Stage 3 – Determinants of Teacher’s Value-added

Cross-sectional data from PASEC 2004 and 2010 Stratified random sample of 2 nd and 5 th graders  3 types of schools: community, public, private  Learning language : French, Arabic, or bilingual For each year,  Pre- and post-test scores for literacy and mathematics  Student and family characteristics  Classroom conditions and characteristics of school principal  Teacher profile Restrict sample to students in grade 5 for whom both the pretest and posttest are available for both subjects (1237 students in 2004 and 1453 in 2010). Data PASEC Data

Results Summary Stats – Test Scores

Results Summary Stats – Student & Teacher’s Characteristics Change Female Student Student Age Student With Grade Repetition Father Is Literate Mother is Literate Fraction of Time Student Absence0.099 Student Speaks French at Home Bounded Class Size Class Size Students in Private Schools Students in Rural Areas Students in Community Schools Change Education Level of School Principal Teacher Has Middle School Degree Teacher Absence Student's Teacher Age Teacher Is Female Teacher Is Civil Servant Teacher Is on Short Term Contract Teacher Is Volunteer0.013 Student's Teacher Has High School Degree Teacher Has Primary School Degree

Dependent Variable: Standardized Score Posttest MathPosttest Literacy (1)(3) MoreTreated ** Post Policy Dummy1.506***.6886 MoreTreated x Post Policy Dummy-1.442*.5555 (.815)(.7805) Class Size Standardized Pretest Score in Math.2135***.3454*** Standardized Posttest Score Literacy.2387***.1963*** Female Student *** * N2690 R-SQ Results Decrease in Math Scores, Not in Literacy Scores

Student characteristics  Pretest scores affect positively posttest  Girls’ average score in math is lower than boys’  Grade repetition leads to poorer scores School characteristics  Class size matters for student scores both in math and literacy  Composition of the classroom (average pretest, gender, days absent, rural) Results Determinants of Student’s Test Scores

Standardized Pretest Math Standardized Posttest Literacy Standardized Pretest Literacy.2104***.3182*** Standardized Pretest Math.1972***.1757*** Female Student *** Student's Age Student Repeats at Least a Grade **-.1401*** Father Is Literate Mother Is Literate * Percent days student is absent Student Speaks French at Home N1111 R-sq Results VA Model – Student Level Analysis

Standardized Posttest Adjusted for Student's Characteristics MathLiterature Class Average Standardized Pretest Literacy.393***.4566*** Class Average Standardized Pretest Math.3631***.3536*** Class Proportion of Female Student.5471*** Class Average Student's Age Student Repeats at Least a Grade.3118***.542*** Class Proportion - Father Is Literate Class Proportion - Mother Is Literate *** Class Average Percent days student is absent-.5475***-.3346** Class % of Students Speaking French at Home ** Class Size *** *** Years of Schooling of Principal.0284* School In Private-.1592* School in Rural Area.1878*** Is Community School N1090 R-sq Results VA Model – Classroom Level Analysis

MathLiteracy Teacher's Absence Teacher's Age Female Teacher * Civil Servant Teacher.2419*.1894 Contractual Teacher Volunteer Teacher Teacher Holds BAC degree or higher N94 R-sq Results VA Model – Determinants of Value Added

MathLiteracy Rural Area Community Schools Public Schools Rural Area Community Schools Public Schools Teacher's Absence Teacher's Age-.0176* * Female Teacher * Civil Servant Teacher.3362* Contractual Teacher * Volunteer Teacher * Teacher Holds BAC degree or higher N R-sq Results VA Model – Determinants of Value Added

 Type of contract: Tenured civil servant has on average a value-added in math that is 0.24 higher than a community teacher.  Gender: female teacher has a lower value added that her male counterpart  Diploma: a teacher with a high school diploma or higher tends to be less effective than a teacher who holds a middle school degree. Results Determinants of Teacher’s Value-Added

Making schooling more affordable and available may  reduce education disparities with implications for growth and Inequality  lead to overcrowded classroom if more teachers are not recruited In Chad:  Pretest, grade repetition, and gender are associated with posttest scores  In Chad FPE lead to significant decrease in Math scores but not in literacy scores  Teacher’s type of contract is important for value added Mere fee elimination may not be enough  Complement with a set of measures to increase supply and inputs in order to match the increased demand of schooling Conclusion Summary

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