The Impact of Teacher Knowledge on Student Achievement in 14 Sub-Saharan African Countries

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1 2014/ED/EFA/MRT/PI/10 Background paper prepared for the Education for All Global Monitoring Report 2013/4 Teaching and learning: Achieving quality for all The Impact of Teacher Knowledge on Student Achievement in 14 Sub-Saharan African Countries Nadir Altinok 2013 This paper was commissioned by the Education for All Global Monitoring Report as background information to assist in drafting the 2013/4 report. It has not been edited by the team. The views and opinions expressed in this paper are those of the author(s) and should not be attributed to the EFA Global Monitoring Report or to UNESCO. The papers can be cited with the following reference: Paper commissioned for the EFA Global Monitoring Report 2013/4, Teaching and learning: Achieving quality for all For further information, please contact

2 The Impact of Teacher Knowledge on Student Achievement in 14 Sub-Saharan African Countries Nadir Altinok 1 1 University of Lorraine (France) BETA (Bureau d Economie Théorique et Appliquée) IREDU (Institute for Research in Education, University of Bourgogne) CNRS (National Center for Scientific Research) Final Draft of the Background Paper to the Education for All Global Monitoring Report /06/2013 Abstract. While the most analyzed variable in education production function literature is teacher education and experience, very few research papers focus on teacher achievement. In this paper, we explore the potential effect of teacher knowledge in mathematics and reading on student achievement by using a unique international database which includes 14 Sub- Saharan countries. We show that teacher knowledge differs between countries, and between different clusters within countries. Moreover, while in some countries, teacher knowledge does not have a strong impact on pupil achievement, in others like Namibia and South Africa, its effect is very large and significant. In addition, in Tanzania and South Africa, more able teachers are unequally distributed within the country. Lastly, we show that teacher achievement is highly correlated with specific teacher characteristics, such as experience or education. Key words: achievement tests, SACMEQ, teacher quality 1 address: nadir.altinok@gmail.com. The author is very grateful to the SACMEQ team for having provided the data, Manos Antonisis (EFA GMR, UNESCO) and Kwame Akyeampong (EFA GMR, UNESCO). 2

3 Synthesis of results This paper focuses on the effect of teacher quality on pupil achievement in 15 African countries which took part to an international student achievement test, the SACMEQ (Southern and Eastern Africa Consortium for Monitoring Educational Quality). the third round of SACMEQ has been done in 2007 and included 15 countries. Among these countries, 14 accepted to test teachers in both reading and mathematics (Mauritius did not tested their teachers). Our analysis is structured in three main sections. Firstly, we explore the relationship between teacher and pupil achievement with descriptive statistics. Then, our paper focuses on regression techniques. Lastly, we analyze the potential explanatory variables which may explain teacher achievement. 1. Descriptive statistics The descriptive statistics results are presented in Tables 1-3 and Figures 1-4. The first interesting finding relates with top-performing countries in both pupils and teachers achievement tests. We find that Tanzania is ranked first in reading for pupils. Interestingly, teachers from Kenya over-perform all other teachers in mathematics (Tables 1.1 and 1.2). The distribution of teacher performance in each subject appears to be quite normal (Figure 2). The direct relationship between teacher and pupil achievement at a macro-level is shown in Figures 3.1. and 3.2. in reading and mathematics respectively. A positive correlation is found, where the effects is stronger in mathematics compared to reading (correlation equal to 0.69 in mathematics against 0.35 in reading). Then, we focus at the correlation within each SACMEQ country (Figures 4.1 and 4.2). Whereas a positive and significant correlation is found in 6 countries in both subjects, the coefficient of correlation is only higher than 0.3 in Namibia and South Africa. In other countries (such as Botswana, Kenya, Malawi, Mozambique and Tanzania), the correlation is positive but at a lower amplitude (below 0.2). The next step deals with performance of teachers between different groups within countries (Table 2). We control for four variables the teacher achievement (socio-economic level of pupils, school size, location of school and school resources level). Pupils with high socioeconomic level tend to be taught by more brilliant teachers in 3 countries in reading (Namibia, South Africa and Zanzibar). For instance, in South Africa, the difference is higher than 90 points between pupils with low socio-economic and high socio-economic level. Another interesting analysis relates with the difference of teacher reading achievement between rural and urban schools. In South Africa, teachers from urban schools tend to perform about 23 points higher in reading than their colleagues from rural areas. In Table 3.1., we show that teacher characteristics differ across the socio-economic level of pupils. For instance, female teachers are more present in high socio-economic level group of pupils in 8 countries (Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, Zanzibar and Zimbabwe). Teachers are more experienced when pupils have a high socio-economic level in 3

4 3 countries (Kenya, Mozambique and Namibia). Contrasting results are found regarding to teacher training. Whereas teachers are more trained for the wealthiest group of pupils in countries like Botswana and Lesotho, the opposite effect is found in Namibia and Zanzibar (in reading). Similar analysis with type of location of school is presented in Table 3.2. Teacher characteristics are quite different between schools in urban and rural areas. For instance, Female teachers are more present in urban areas in most countries. In Tanzania, while only 21% of teachers are women in rural areas, the proportion increases to 52% in urban areas. Experience of teachers is often higher in urban areas, which would explain why pupils from urban areas perform better than pupils from rural areas. In South Africa, teachers from urban areas have approximately 3 years of experience higher than teachers from rural areas. However, education of teachers and the proportion of trained teachers are not factors which explain the difference of performance between urban and rural areas. It may be possible than while younger teachers are deployed in rural areas, they may have a higher education level than teachers from urban areas. 2. Results for regression analysis We begin by regressing teacher knowledge and student knowledge without any control variable (Table 4). Contrasting results are found. It appears that significant and positive effects are found for 7 countries in reading and 8 countries in mathematics. The teacher quality effect is quite high in Botswana, Kenya, Mozambique, Namibia, South Africa and Tanzania. For instance, an increase of one standard deviation of teacher knowledge induces an increase of about 0.38 standard deviation (SD) in South Africa in reading. When we include all controls (student, teacher and school levels), significant and positive teacher quality effect in both skills are found in 6 countries. These countries are Botswana, Kenya, Mozambique, Namibia, South Africa and Tanzania. Compared to baseline results, the amplitude of the teacher quality effect (TQE) is often reduced. For instance, while the TQE was equal to 0.38 in regression without controls, it is divided by two when we introduce all controls in South Africa (0.17). While the amplitude of the TQE decreased when we included control variables, the effect seems to be still overestimated. By combining both subjects into a single estimation, estimation bias would be reduced. For doing this, we perform a multivariate multilevel regression analysis (Table 5). TQE appears to be quite similar compared to multilevel estimation. All controls are included in each regression. However, as it was expected, its amplitude decreases. For instance, whereas the effect was equal to 0.08 SD in reading for Botswana, its amplitude is reduced to 0.03 with multivariate multilevel estimation technique. In 5 countries, TQE is still significant, but its amplitude is lower compared to standard multilevel estimation. While most able teachers would be allocated in urban areas, the variation of teacher knowledge may be higher in urban areas, compared the within-variation of teacher knowledge 4

5 in rural areas. Hence, it would explain why we find significant and positive TQE in urban areas, and not in rural areas. The most able teachers are allocated in urban areas in Botswana, Namibia, South Africa and Tanzania. Hence, for these countries, there may be an initial inequality of distribution of teachers between rural and urban areas, which may explain why pupils in rural areas perform lower than pupils from urban areas. This implies that there is a serious issue of distribution of teachers between socio-economic level of pupils in the following countries (Botswana, Namibia and South Africa). In the meantime, since teacher knowledge has a strong and positive impact on pupil performance, one policy intervention would be to allocate the most able teachers in either rural areas or to low socio-economic level groups (or both when it is possible). Another group deals with countries where there is a positive and significant effect of teacher knowledge on student achievement, but a lack of strong inequality between poor and rich people (or rural and urban areas). Countries included inside this group are Malawi, Mozambique, Swaziland, Uganda and Zanzibar. In these countries, teacher knowledge should be increased for the whole population in order to improve student achievement in primary education. 3. Results for teacher performance analysis While teacher quality effect is present in about half of countries which took part to SACMEQ, it could be useful to look at factors explaining the differences of teacher knowledge within countries. The main conclusion that can be made is that there is no strong correlation with a teacher characteristics and its level of knowledge in each skill. Moreover, teachers with a university education do not have a higher knowledge in basic skills compared to other teachers (except in Tanzania and Zimbabwe for reading, and South Africa, Zanzibar and Zimbabwe for mathematics). 4. Policy implications In Table 8, we summarized main findings of our paper. Three main results can be obtained. Firstly, teacher knowledge in reading and mathematics does not have a systematic and positive effect on pupil achievement in all countries. Hence, it is possible to divide countries into two groups. The first group includes countries where the TQE is significant and positive (Botswana, Kenya, Mozambique, Namibia, South Africa and Tanzania). In these countries, the first policy implication is to increase teacher knowledge in order to boost student performance. In other countries, it may exist some factors which do not enable teacher knowledge to be linked with student achievement. The second main result deals with the potential inequality of distribution of teachers within each country. If there is both a positive and significant TQE and teachers not allocated equally inside each country, then the policy implication would be to better allocate teachers within countries in order to reduce student achievement differences. Three countries are in this case, 5

6 namely Botswana, Namibia and South Africa. In these countries, we find a strong and positive TQE. Moreover, we found that teachers with better knowledge in mathematics and reading were mostly allocated in urban areas and schools with students with high socio-economic level. By choosing a more adequate distribution of teachers, these countries may both increase the students performance in reading and mathematics, and reduce inequality between students. The third important policy implication is that there is no specific characteristic which may explain teacher knowledge. However, we found some specific effects for a number of countries. For instance, the level of school resources explained teacher performance in 4 countries (Botswana, Kenya, South Africa, Zanzibar) where we initially found a significant and positive TQE. Of course, teacher performance can be seen as a black box which has to be more deeply explored in future. 6

7 1. Introduction While there is a clear evidence that teacher quality is a key determinant of student learning, very few information is available about which specific observable characteristics of teachers can account for this impact (Rockoff, 2004 ; Rivkin, Hanushek, Kain, 2005 ; Aaronson, Barrow, Sander, 2007). In the education production function literature, the most analyzed variables are teacher education and experience. Moreover, the only attribute that has been shown to be the most significantly correlated with student achievement is teachers' academic skills measured by scores on achievement tests (Wayne, Youngs, 2003 ; Eide, Goldhaber, Brewer, 2004 ; Hanushek, Rivkin, 2006). In his early review of literature, Hanushek (1986, p.1164) noted that "the closest thing to a consistent finding among the studies is that 'smarter' teachers, ones who perform well on verbal ability tests, do better in the classroom". A decade later, Hanushek (1997, p.144) counts a total of 41 estimates of the effect of teacher scores and finds that "of all the explicit measures [of teachers and schools] that lend themselves to tabulation, stronger teacher test scores are more consistently related to higher student achievement". Similarly, Eide, Goldhaber, and Brewer (2004, p.233) suggest that, compared to more standard measures of teacher attributes, "a stronger case can be made for measures of teachers' academic performance or skill as predictors of teachers' effectiveness". Previous studies estimating the association of teacher test scores with student achievement gains in the United States includes Hanushek (1971; 1992), Summers and Wolfe (1977), Murname and Phillips (1981), Ehrenberg and Brewer (1995), Ferguson and Ladd (1996), Rowan, Chiang, and Miller (1997), Ferguson (1998), and Rockoff et al. (2008). Studies focused on developing countries include Harbison and Hanushek (1992) in rural Northeast Brazil, Tan, Lane, and Coustère (1997) in the Philippines, Bedi and Marshall (2002) in Honduras, Behrman, Ross, and Sabot (2008) in rural Pakistan, and Metzler and Woessmann (2012) in Peru. More deeply, very few papers explored this topic in sub-saharan Africa. For instance, Bonnet (2009) combined both teachers' knowledge and behaviour using SACMEQ II data. However, Bonnet (2009) explores the relationship by controlling only two variables, which may lead to bias estimates. The analysis does not use regression technique, but only two-way graphical analysis. Wechtler, Michaelowa and Fehrler (2007) provide results of the cost-effectiveness of inputs in primary education by using data from PASEC and SACMEQ. Authors combine several factors at three different levels (pupils, schools and country) and provide results for the whole SACMEQ countries. Although they allow specific constants for each country, the estimation model used assumed that effects of each variable are the same across countries. This hypothesis has not been tested in their paper and do not seem to be valid when we look to the literature. Moreover, authors do not take into account the multiple potential difficulties which arise in this kind of estimation. Finally, they do not include the teacher score variable in their estimations, replacing this variable by teacher academic qualification. As main research papers in the education literature, authors add dozens of variables inside the estimation, which would lead to strong autocorrelation of errors. Zuze (2010) explores the potential explanatory variables explaining the performance of pupils from Botswana. Zuze (2010) shows in particular that there is modest evidence to suggest that students attending well resourced schools are likely to perform better. The main result relates with the association between teacher content preparation and student achievement. When assessments 7

8 are made regularly, Zuze (2010) find a better performance of pupils and more equity. However, these results are only available for Botswana and do not take into account for possible bias. The most important results were obtained by Hungi and Thuku (2010a,b) who use a hierarchical regression method to assess to what extent the teacher score may impact on student achievement. Although their main topic is not teacher achievement effect on pupil performance, this is the only research paper which includes teacher achievement as explanatory variable in a regression with Sub-Saharan African countries. Authors find that teacher reading score has an effect on pupil reading achievement in only 2 countries (Kenya and Lesotho) among the 12 countries included in the regression. While they focus on a large number of potential explanatory variables (such as pupil gender or teacher qualifications), Hungi and Thuku (2010b) do not correct their specification for selection bias or measurement error. Authors note in particular that "interestingly, this variable [average teacher score] had significant effects on pupil achievement across all the 14 school systems when it was added into the models as the only variable but the variable lost its significance in some models when other variables were added" (Hungi and Thuku, 2010b, p. 86). This remark highlights the potential bias due to omitted variable, but also to potential selection bias. In the econometric literature, the well-known issues of omitted variables and non-random selection are very complex to address when estimating causal effects of teachers characteristics. The existing evidence suffer from bias due to unobserved student characteristics, omitted school and teacher variables, and non-random sorting and selection into classrooms and schools (Glewwe and Kremer, 2006). Clear examples where such bias occur include cases where better-motivated teachers incite more student learning but also accrue more subject knowledge. Moreover, another example can be found when parents with a high preference for educational achievement both choose schools or classrooms within schools with teachers of higher subject knowledge and also further their children's learning in other ways. Another problem concern measurement error, which is likely to attenuate estimated effects (Glewwe and Kremer, 2006). The measurement error occurs when the measure proxies only poorly for the concept of teachers' knowledge. In most studies, the tested teacher skills is evaluated regardless to the academic knowledge in the subject in which student achievement is examined. Often, the examined skill is not subject-specific. Another source of measurement arises when the evaluation of teacher knowledge is done with considerable noise. For instance, teacher knowledge is sometimes evaluated with one single math question (Rowan, Chiang and Millet, 1997). In this study, we propose to evaluate the effect of teacher knowledge on student achievement for 14 Sub-Saharan Countries which took part to SACMEQ III study. As highlighted above, we try to take into account for possible bias which occurs in this kind of study. It permits us to test for the robustness of regressions and therefore to evaluate to what extent the knowledge of teachers can have an impact on student achievement. 8

9 We propose to answer to the following questions: What relationship can we observe between teacher knowledge and school/regional characteristics? To what extent do teacher knowledge affect student performance, and how robust are findings? Is teacher performance effect stable and of similar amplitude between subpopulations (especially between low and high socio-economic group of pupils; male and female, and rural and urban areas)? Is there a relationship between teacher knowledge effect and the distribution of teachers among different socio-economic groups? What can explain differences of the teacher quality effect between countries? Which factors can explain teacher performance, and especially among teacher characteristics (experience, job satisfaction, education, gender )? The rest of the paper is structured as follows. First, we present the data and the methodology used in the paper. In section 3, we turn to the descriptive statistics analysis, where we correlate teacher quality factors and student achievement. We then control for potential explanatory variables such as socio-economic level of pupil or school resources index. In section 4, we proceed to the regression analysis. After having used the multilevel analysis, our paper propose to combine both subjects tested in SACMEQ by regressing our data with the multivariate multilevel regression method. In Section 5, we perform robustness test by dividing samples between several groups. In section 6, our paper focuses on potential explanatory variables of teacher quality. 2. Data and methodology 2.1. Data The Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) grew out of a very extensive national investigation into the quality of primary education in Zimbabwe in 1991, supported by the UNESCO International Institute for Educational Planning (IIEP) (Ross and Postlethwaite, 1991). Keen to follow up this successful initiative, several education ministers in southern and eastern African countries expressed an interest in the study and wished to take part in such an assessment. Planners from seven countries therefore met in Paris in July 2004 and established SACMEQ as a special group. The 15 SACMEQ-member education ministries are those of Botswana, Kenya, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, United Republic of Tanzania, United Republic of Tanzania (Zanzibar), Uganda, Zambia and Zimbabwe. Three different rounds of SACMEQ were done up today. However, only data for first two rounds are available for researchers. The SACMEQ study presents major advantages regarding to others existing assessments on pupils' achievement 9

10 It is a survey which focuses on developing economies, and especially African countries, while other assessments are based on developed countries (like PISA or TIMSS) The grade tested is appropriate to take into account the diversity of languages spoken inside each African country (Grade 6) Results from each round are directly comparable with results from other rounds. Hence, it is possible to track performance over time SACMEQ is the only international assessment which permits to distinguish pupils' performance between regions within each country and most importantly, SACMEQ survey permit to compare pupils and teachers' skills in reading and mathematics. Based on the points highlighted above, it becomes important to wonder to what extent the characteristics of teachers have an impact on pupil performance. While main student achievement tests collect only generic information on teachers - such as experience, gender or education -, SACMEQ study is the first and only assessment to test teachers in reading and mathematics. Our study will then focus on the effect of teacher performance on pupil performance, based on the SACMEQ study. Below, we present the different outputs which will be proposed during this study. The SACMEQ III data were collected using a stratified two-stage cluster sample design. At the first stage, schools were selected within provinces with probability proportional to the number of pupils in the defined target population. At the second stage, a simple random sample of 20 pupils was selected within each selected school. Variables used in this paper are divided in three parts (student, teacher and school levels). At the pupil level, we included pupil scores (on Rasch scales) in reading and mathematics tests at Grade 6. Rasch scores from all the 15 SACMEQ countries on this test were transformed so that the mean of the scores for the combined 15 countries was 500 with a standard deviation of 100. Apart from scores in reading and mathematics, we included potential predictors of academic achievement following previous research findings in the education production function literature. Concerning the pupil level, we included the following variables: gender, socio-economic index, language spoken at home, education of parents (percentage of father/mother who enrolled in university education), repetition rate, homework in mathematics and reading. We use several composite variables, such as the socio-economic index. For obtaining this index, the SACMEQ team combined parental education, home possessions (e.g. car, bicycle and electrical appliances), quality of the house (e.g. mud walls versus stone walls) and source of lighting (e.g. kerosene lamp versus electricity) (Dolata, 2005). Since the tests were provided in English, we constructed a dummy variable which is equal to 1 if the pupil speaks English often in home. Education of parents was divided in two dummies: a first dummy which indicates if the father has a university education level, and another variable for the mother. Since a large number of pupils never repeated, an indicator variable separating pupils who 10

11 repeated or not was also added in the analysis. Moreover, as we analyze teacher quality effects, we added some variables which can be related to this topic, such as the proportion of pupils who have homework in mathematics and reading almost every day. Variables at the school level include type of location, school size, index of teacher absence, school mean SES level and school resources index. Type of location is evaluated by an indicator variable which is equal to 1 if the location of the school is rural and 0 otherwise. The school size is measured by the number of pupils inside the school. Similarly to the pupil socio-economic index, a school resources index was obtained by combining several variables. This was the sum of the existence of a library, school meeting hall, staff room, separate office for school head, first aid kit, drinking water, electricity, telephone, fax machine, typewriter, duplicator, tape recorder, overhead projector, TV set, video cassette recorder, photocopier, radio, computer, fence or hedge around school borders, school canteen and sports equipment (Saito, 2007). An interesting aspect of the SACMEQ III assessment was that Grade 6 teachers were also assessed in reading and mathematics (expect in Mauritius). The teacher and pupil tests used different sets of items but the two tests had some common items (20 and 13 common items for the reading test and the mathematics test respectively) in order to anchor the results. Hence, variables concerning the teacher level include teacher scores in each skill (mathematics, reading), gender, education level (proportion of teachers who have either tertiary education or Level-A education level), training (at least one year), experience (in years), frequency of tests (proportion of teachers using tests at least once a week) and resources available in the school (with the school resources index). Since for some schools, teachers differ between reading and mathematics, we presented both subjects separately Methodology Following the work of Hungi and Thuku (2010), we consider an education production function with a two level model (with pupils nested in schools) for each skill achievement (reading and mathematics): Level 1 model y kij = β 0kj + β 1kj Z 1kij + β 2kj Z 2kij + + ε kij (1) Level 2 model β 0kj = γ 0k1 + γ 0k2 T 0kj + γ 0k3 C 0kj + ε 0kj (2) β 1kj = γ 1k1 + ε 1kij β 2kj = γ 2k1 + ε 2kij... β 6kj = γ 6k1 + ε 6kij 11

12 where y kij is the achievement score for skill k of pupil i in school j; β 0kj is the mean score of school j; Z 1, Z 2,, Z 6, are the 6 pupil-level variables examined in the study; β 1, β 2,, β 6, are the regression coefficients associated with the pupil-level variables. We propose to estimate each skill separately, following the initial work of Hungi and Thuku (2010a). However, since the two component scores are expected to be correlated, possibly with different correlations at the student and schools levels, estimating a multivariate multilevel model can take into account this possible correlation. A multivariate multilevel model consists of combining a seemingly unrelated regression estimation with a multilevel model. Hence, we propose to combine both skills (noted as k above) in a single estimation. This is done by changing the data to long form, stacking the reading exam and mathematics exams into one variable. The data can then be thought of as three-level data with response variables (y rij and y mij ). To obtain different intercepts for the two response variables, we will use dummy variables y r and y m, where y r is one when skill = 1 and zero otherwise, whereas y m is one when skill = 2 and zero otherwise. Since the teacher effect is not only related with teacher skills evaluated by teacher performance in tests, it may be interesting to include other teacher characteristics which are not subject-specific in the estimation. Moreover, the question relative to the factors explaining the teacher quality will be assessed as a standard education production function adapted to teachers: T ki = β 0k + β 1k X k1i + β 2k X 2ki + β 3k X 3kj + ε kij where T kj represents teacher i score in subject k, explained by factors X 2ki which are teacherspecific and other factors which are school-specific X 3kj. Similarly to the previous estimation, we propose to estimate this equation with robust OLS technique. Data is clustered at the class level in order to correct the potential bias due to repeated measures at pupil level. 3. Descriptive statistics 3.1. Direct relationship We firstly present descriptive statistics for students and school levels (Table 1.1) and teacher level (Table 1.2). In Table 1.1, student scores and their standardized scores are highlighted in each skill. Top performer countries in reading are Tanzania, Seychelles, and Mauritius. In mathematics, top performing countries are Mauritius (623), followed by Kenya (557) and Tanzania (554). The proportion of girls is quite low in Mozambique (46%) while it represents half of pupils in main countries. Since the socio-economic index is presented in an absolute way in Table 1.1, it permits us to distinguish different economic level inequalities between countries. Hence, while the SES index appears to be quite high in Seychelles (11.78) and Mauritius (10.70), it is very low in countries as Malawi (4.99) and Uganda (5.07). In the 12

13 exception of Mozambique, Tanzania and Zanzibar, English is spoken by a few proportions of pupils. For instance, less than 8% of pupils speak English at home in Zambia or Swaziland. While the proportion of parents who have some tertiary education is quite low in all SACMEQ countries, it is the highest in Seychelles (59% for mother and 53% for father). In contrast, less than 10% of parents have some tertiary education in Mozambique. Repetition is present in all SACMEQ countries, but it is more prevalent in Mozambique and Malawi, where only one-third of pupils did not repeated any class since they attended Grade 6. School level variables are presented in the second part of the Table 1.1. Seychelles is the most urbanized country with less than one third of pupils in rural areas. On the contrary, in countries as Malawi or Zimbabwe, 70% of pupils are enrolled in rural schools. School size is high in countries like Mozambique and Malawi. Teacher absence seems to be problematic in Uganda and Zanzibar. Lastly the analysis of the school resources index shows large disparities between countries. While it is clearly the highest in Mauritius, the lowest resources in schools are present in Malawi and Tanzania. In Table 1.2, we present descriptive statistics for teachers in both reading and mathematics. Since, teacher affectation can change between each subject, results differ between reading and mathematics. Top performing country in teacher score is Seychelles in reading (831 points), while teachers from Kenya perform the highest in mathematics (906 points). As Mauritius did not take part to the teacher testing, we do not have scores for this country. Proportion of women differ between countries. Almost all reading teachers are women in Seychelles, where less than one-third are in this case in Malawi, Mozambique and Uganda. Results for mathematics are quite similar. Very few teachers from Malawi, Mozambique, Tanzania and Zanzibar have a higher education level. Moreover, more than two-thirds of teachers from Lesotho, Namibia, Seychelles, South Africa and Zimbabwe have some training. Contrasting with school resources index, teacher resources are quite similar between countries. However, it may be possible that these resources are not equally divided within countries. Before the regression analysis, we propose to present the distribution of teacher scores in each subject for SACMEQ III countries (Figure 2 & Table 1.3.). s appear to be very concentrated in countries as Botswana, Lesotho, Tanzania, and Seychelles, while a greater variation can be observed in countries like Namibia and South Africa. Teacher performance appears to have a normal distribution in all countries. A first analysis of the relationship between pupil score and teacher score is made by plotting both variables in a two-way graph (see Figures 3.1. and 3.2). Firstly, we regress both variables with a macro dimension. The mean scores for each country and each group are calculated and reproduced in a two-way figure. Results for reading can be found in Figure 3.1. The coefficient of correlation is equal to +0.35, which represents a positive correlation between student and teacher performance in reading. The more the teachers perform well in reading test, the more the pupils have a good score in reading. This correlation is however done in a macro level. It does not imply that this relationship is found within countries. A direct comparison can be made between Malawi and Seychelles. While in Malawi, teachers perform about 100 score points lower than teachers in Seychelles, pupil performance is higher of about 120 points in Seychelles, compared to Malawi. This would mean that, for these two countries, an increase of about 1 point in teacher 13

14 performance in reading is equal to an increase of 1.3 points for pupils (143/111). If we use all information, the effect is divided by two: the increase of teacher score by 1 points permits an increase of about 0.5 point in pupil reading score. Results for mathematics are presented in Figure 3.2. The relationship is more robust in mathematics (r = +0.69). If we compare the same countries, it can be found that the effect of an increase of about 1 point in teacher performance, permits an increase of pupil score of about 1.7 point (104/61). With all countries included in a simple regression, the effect is reduced to 0.5 point. These positive effects should not hide that teacher performance is not positively correlated between all countries. For instance in reading, while teacher performance is higher than 70 points between Kenya and Tanzania (792 versus 722 points respectively), pupils reading score is higher in Tanzania compared to Kenya (543 versus 578 points respectively). We now turn to the direct relationship within countries (Figures 4.1 & 4.2). We separated graphs for each country and added coefficient of correlation for each case. A strong correlation can be found in only two countries (Namibia and South Africa) in reading, while there is apparently no correlation in 6 countries (Lesotho, Seychelles, Swaziland, Zambia, Zanzibar and Zimbabwe). The remaining 6 countries have a low and positive correlation between pupil score and teacher score in reading. Results for mathematics can be found in Figure 4.2. Similarly to reading, a clear correlation is found in few countries, like South Africa (r = 0.42), while a positive correlation with less intensity is observed in 4 other countries (Botswana, Kenya, Tanzania, Zanzibar). In the remaining 7 countries, the correlation is quite absent or very low. These direct relationships may hide a correlation between teacher and pupil performance due to omitted variables Relationship with controls In the previous section, we compared teacher and student performance without any control. It appeared that a positive relationship was present in about half of countries and that this positive effect was the most significant in South Africa. Now, we control for specific variables which may be potential factors explaining student performance. Four variables are used: mean socio-economic level of schools, school size, location of school and school resources index. It should be noted that the controls used are adapted for each country. Hence, the socio-economic index has been divided in four levels, depending on the distribution of pupils within the country. Indeed, in each country, we have approximately one-fourth of pupils who have a low socio-economic status, whereas another one-fourth of pupils have a high socio-economic status. Making relative all variables permit to obtain enough pupils within each group. Moreover, it is more appropriate to divide populations within countries, instead of comparing the socio-economic level between pupils from different countries. Main results can be found in Table 2. Variable- and subject-specific results are presented in different figures. It should be noted that while we focus on teacher performance, our first level of analysis remains the pupil. Hence, following results are disaggregated at the pupil level. In first column of Table 2, we compare the teacher performance in mathematics/reading between the mean socio-economic level of pupils. Last column in each variable (for instance "Q4-14

15 Q1" for socio-economic level) represents the absolute difference between the high SES level and the Low SES level. For instance, it appears that teachers from South Africa who teach in a class with a high level of SES level (computed as a mean level), have 94 score points higher than teacher who teach in a low SES level in mathematics. In most countries, teachers present in classes with high SES level pupils tend to perform better than other teachers. Results for reading show a more important effect of SES level on teacher performance difference. This is especially the case of South Africa where the difference of performance is equal to 53 points between the two groups of teachers. However, in two countries (Zambia for mathematics and Swaziland for reading), teachers present in low SES level schools tend to perform better than other teachers. The reason of these differences may be either political (i.e. the Ministry tend to send high skilled teachers in poor areas) or random (i.e. teachers are chosen independently to their skills). More research on this difference is needed. Teacher performance may differ between small and large schools. In reading, the difference of teacher performance between the two groups is present in three countries (Malawi, South Africa and Zimbabwe). In South Africa, teachers from large schools perform about 23 points higher than their colleagues from small schools. In contrast, in Malawi and Zimbabwe, the difference of performance is on the advantage of teachers from small schools. Again, results in mathematics show a more significant effect of school size on teacher performance difference. In South Africa, the difference is approximately equal to 23 points in favor of teachers from large schools. In the third column of Table 2, teacher performance is compared between teachers from rural and urban areas. In reading, significant differences can be found in Kenya (+16 points) and South Africa (+73 points) in favor of urban areas. In mathematics, in Namibia results show a significant difference which is higher, compared to reading (+21 points against +13 points in mathematics). On the contrary in Seychelles and Zimbabwe, reading teachers from rural areas tend to perform better than teacher from urban areas. Lastly, we distinguish teachers regarding to the mean level of school resources where they work. Three different levels of school resources are defined within each country. It should be noted that here the level of school resources are not absolute (i.e. comparable between countries). Therefore, the performance of teachers from one country in high school resources may not be compared to teachers in another country from low school resources. The main objective here is to analyze the potential differences of teacher performance within countries, instead of between countries. Similarly to previous results, South Africa is shown as a country where teachers from high school resources level tend to perform better than other teachers, in both skills. For instance, we find a 118 points difference in reading between teachers from high level of school resources and teachers from low level of school resources. The difference in reduced to 82 points in reading, but remains very important. It should be interesting to focus on teacher characteristics among different socio-economic levels of pupils. In Table 3.1., we divide pupils into four groups according to their socioeconomic level. Moreover, we include four main teacher characteristics and test to what extent teacher attributes may differ among the mean socio-economic level of pupils in each 15

16 school. The four teacher characteristics chosen are gender of teacher (percentage of women among teachers), experience of teachers (in years), education of teachers (percentage among teachers who have some higher education level) and training of teachers (percentage who have at least one year of training). We highlighted significant results inside the Table Surprisingly, strong differences appear regarding the gender of teachers between socioeconomic levels of pupils. In most countries, female teachers tend to be more present in classes where high SES level of pupils are present. This is especially the case of 7 countries in reading (Malawi, Mozambique, Tanzania, Uganda, Zambia, Zanzibar and Zimbabwe). For instance, in Mozambique, while only 24 percent of teachers are women in the low SES level group, this proportion raises to 45 percent for the high SES level group. Results are quite similar for mathematics (and sometimes the same when teachers are not separated between skills inside each school). Experience of teachers differs slightly for 3 countries, in favor of high SES level pupils group. For example, in Mozambique, the pupils from high SES level are taught by teachers with approximately 4 years of experience more than pupils from low SES level. These differences are confirmed in mathematics. Education of teacher, and more especially the proportion of them who obtained some higher education level, presents some differences between the different socio-economic levels of pupils. More precisely, the case of Kenya is quite interesting, since it appears that about 20 percent of teachers have some higher education level for the high SES level pupils group, while this proportion increases to 34 percent for the low SES level pupils group. This may be explained by the fact that more experienced teachers have less education than young teachers. Since, the high SES level group of pupils are taught by experienced teachers, these one does not have some higher education level. Lastly, the variable related to teacher training shows contradictory results between countries. While in some countries, teachers from high SES level group tend to have more training compared to low SES level group (for instance Kenya and Mozambique), the opposite is found in other countries (for instance in Seychelles and Zimbabwe). Similar analysis with type of location of school is presented in Table 3.2. Teacher characteristics are quite different between schools in urban and rural areas. For instance, Female teachers are more present in urban areas in most countries. In Tanzania, while only 21% of teachers are women in rural areas, the proportion increases to 52% in urban areas. Experience of teachers is often higher in urban areas, which would explain why pupils from urban areas perform better than pupils from rural areas. In South Africa, teachers from urban areas have approximately 3 years of experience higher than teachers from rural areas. However, education of teachers and the proportion of trained teachers are not factors which explain the difference of performance between urban and rural areas. It may be possible than while younger teachers are deployed in rural areas, they may have a higher education level than teachers from urban areas. 16

17 4. Regression results 4.1. The effect of teacher knowledge on student performance General estimation: Multilevel and Multivariate multilevel estimations The regression results are presented in Tables 4-5. We proceed in several steps. For space convenience, only results concerning the teacher knowledge coefficient are presented in different tables. In Table 4, we firstly use a standard multilevel regression technique. By adding several controls, we test for the stability of coefficients and potential omitted variables bias. Secondly, in order to reduce the bias relative to the amplitude of the effect, we perform a multivariate multilevel estimation technique (Table 5). Lastly, our analysis tries to evaluate to what extent sub-populations may have distinct teacher quality effects. For testing this, we separate the sample for each gender (Table 6.1), between type of location (Table 6.2) and each socio-economic group (Table 6.3). Basic results are provided in Table 4. We begin by regressing teacher knowledge and student knowledge without any control variable. Contrasting results are found. It appears that significant and positive effects are found for 7 countries in reading and 8 countries in mathematics. The teacher quality effect is quite high in Botswana, Kenya, Mozambique, Namibia, South Africa and Tanzania. For instance, an increase of one standard deviation of teacher knowledge induces an increase of about 0.38 standard deviation (SD) in South Africa in reading. However, these effects may be lower in amplitude. By adding student controls (Column 2), school controls (Column 3) and teacher controls (Column 4), our strategy consists of reducing the omitted variables bias. Hence, most important results are presented in the last column, where we include all controls (student, teacher and school levels). The most important results are highlighted in blue. In six countries, we find a significant and positive teacher quality effect in both skills. These countries are Botswana, Mozambique, Namibia, South Africa, Tanzania and Uganda. Compared to baseline results, the amplitude of the teacher quality effect (TQE) is often reduced. For instance, while the TQE was equal to 0.38 SD in regression without controls, it is divided by two when we introduce all controls (0.17 SD). In Namibia, the TQE diminishes from 0.21 to 0.07 SD in reading. Similar changes can be observed in mathematics. Hence, for these countries, teacher knowledge has a strong and positive impact on student performance. In Lesotho, a negative and significant effect is observed in mathematics, while the effect is not significant in reading. This is a different result compared to the findings of Hungi and Thuku (2010b). While the amplitude of the TQE decreased when we included control variables, the effect seems to be still overestimated. By combining both subjects into a single estimation, estimation bias would be reduced. For doing this, we perform a multivariate multilevel regression (Table 5). TQE appears to be quite similar compared to multilevel estimation. However, as it was expected, its amplitude decreases. For instance, whereas the effect was equal to 0.08 SD in reading for Botswana, its amplitude is reduced to 0.03 SD with multivariate multilevel estimation technique (with all controls). In 5 countries, TQE is still significant, but its amplitude is lower compared to standard multilevel estimation. In Malawi, we find a significant effect for multivariate multilevel estimation, but not for standard 17

18 multilevel estimation. Countries where there is a strong TQE remain the same: Botswana, Mozambique, Namibia, South Africa and Tanzania. Similarly, there is no significant TQE in Kenya, Seychelles, Zambia, Zanzibar and Zimbabwe. In the remaining countries, results are contrasting (Kenya, Lesotho, Uganda) Robustness tests We then test for stability of TQE among subpopulations. In Table 6.1., we show that TQE is stronger in Male population compared to Female population, when the effect is significant. For instance, in Namibia, TQE is equal to 0.14 SD in mathematics for Male population, opposed to 0.12 SD difference in Female population. In Swaziland, the TQE is only significant and positive for Male population in reading. The comparison between rural and urban population shows that the TQE is most important in urban areas (Table 6.2). For 4 countries, the TQE is only present in urban areas and not significant in rural areas (Botswana, Kenya, Mozambique, Swaziland). Based on these results, we can make three interpretations. Firstly, more able teachers may be distributed more in urban areas while rural areas may receive less able teachers. Since less able teachers may have lower TQE on pupil achievement, the lack of effect in rural areas may be mainly explained by the difference of distribution of teachers between rural and urban areas. Another important conclusion can be obtained. While most able teachers would be allocated in urban areas, the variation of teacher knowledge may be higher in urban areas, compared the within-variation of teacher knowledge in rural areas. Hence, it would explain why we find significant and positive TQE in urban areas, and not in rural areas. We can look at these hypotheses by comparing results in Table 2, Table 4 and Table 5.2. It can be seen in Table 2 that most able teachers are allocated in urban areas in Botswana, Namibia, South Africa and Tanzania. Hence, for these 4 countries, there may be an initial inequality of distribution of teachers between rural and urban areas, which may explain why pupils in rural areas perform lower than pupils from urban areas. It is possible that pupils from poor families are mostly located in rural areas. We can test for this hypothesis by comparing the TQE between different subpopulations divided by their socio-economic level. Since the socioeconomic status is not very precise in the case of SACMEQ study, we preferred to divide the whole population in only two subgroups (Table 6.3). Among the 5 countries where a significant effect is found, we can see that the TQE is higher for 4 countries in high SES group in both reading and mathematics (Botswana, Mozambique, Namibia, South Africa). This implies that there is a serious issue of distribution of teachers between socio-economic level of pupils for the following countries (Botswana, Namibia and South Africa). In the meantime, since teacher knowledge has a strong and positive impact on pupil performance, one policy intervention would be to allocate most able teachers in either rural areas or to low socio-economic level groups (or both when it is possible). A third interpretation deals with countries where there is a positive and significant effect of teacher knowledge on student achievement, but a lack of strong inequality between poor and rich people (or rural and urban areas). Countries included inside this group are Malawi, 18

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