School bullying is a common yet unfortunate occurrence associated with several hindering outcomes for children’s educational and psychological development. Using a nationally representative data sample of 26,467 students from three sub-Saharan African countries of Ghana, Botswana and South African, we investigate the prevalence of bullying and its ramification on students’ academic performance. The data reveals that more than 50% of the survey participants were bullied regularly in school. We employ a Propensity Score based matching technique to estimate the effect of bullying of their performance on standardized reading, mathematics and science tests. The results of our estimation reveal statistically significant decreases due to bullying in scores on all three types of standardized evaluations between 3% to 8% for all three countries

Anton-Erxleben, Katharina

Kibriya, Shahriar

Naureen, Fatema

Zhang, Yu

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
Pervasive bullying and its negative
consequence on standardized tests of
Reading, Science and Mathematics – A
comparative analysis of three countries in
Sub-Saharan Africa
Shahriar Kibriya and Yu Zhang and Katharina
Anton-Erxleben and Fatema Naureen
Center of Conflict and Development, Texas A&M University, US
Agency for International Development, McGill University, Montreal
Canada
30 December 2016
Online at https://mpra.ub.uni-muenchen.de/81117/
MPRA Paper No. 81117, posted 6 September 2017 15:57 UTC
Pervasive bullying and its negative consequence on standardized tests of Reading, Science 
and Mathematics – A comparative analysis of three countries in Sub-Saharan Africa 
 
Authors:   Shahriar Kibriya, Yu Zhang, Katharina Anton-Erxleben and Naureen Fatema 
 
Authors’ affiliation and full address: 
Shahriar Kibriya: Center on Conflict and Development, Texas A&M University, 600 John 
Kimbrough Blvd, College Station, TX 77843  
(Email: shahriar.kibriya@gmail.com. Tel: +1 979 204 7890).  
  
Yu Zhang(corresponding author): Institute of Economics and Finance, Nanjing Audit 
University. (Email:Zhangyu.John@gmail.com) 
 
Katharina Anton-Erxleben: Raising Voices, Kampala Uganda.  
 
Naureen Fatema: McGill University, Montreal Canada.  
 
Abstract: School bullying is a common yet unfortunate occurrence associated with several 
hindering outcomes for children’s educational and psychological development. Using a 
nationally representative data sample of 26,467 students from three sub-Saharan African 
countries of Ghana, Botswana and South African, we investigate the prevalence of bullying and 
its ramification on students’ academic performance. The data reveals that more than 50% of the 
survey participants were bullied regularly in school. We employ a Propensity Score based 
matching technique to estimate the effect of bullying of their performance on standardized 
reading, mathematics and science tests. The results of our estimation reveal statistically 
significant decreases due to bullying in scores on all three types of standardized evaluations 
between 3% to 8% for all three countries.   
 
Acknowledgements: This research was funded and commissioned by the United States Agency 
for International Development (USAID). Subsequently, a longer version of the study was 
submitted to USAID as a report. We are grateful to the Africa Education Office, USAID, 
especially to Julie Hanson Swanson and Michelle Jones for their valuable comments.  The views 
expressed in this article are solely of the authors and any limitations of the research are solely 
and equally shared by the authors. 
 
 
 
 
 
 
 
  
Pervasive bullying and its negative consequence on standardized tests of Reading, Science 
and Mathematics – A comparative analysis of three countries in Sub-Saharan Africa 
  
1. Introduction:  
Bullying in and around educational settings is a global phenomenon. Bullying can be physical, 
verbal, or relational, which refers to children being systematically excluded from social activities 
by their peers (Olweus, 1993).  Most Current estimates by the non-governmental organization 
Plan International suggests that around 20% of the global student population is affected by 
bullying (Greene et al., 2013). A few recent studies from countries of Europe, South America 
and USA have shown that bullying can lead to school avoidance and poor attendance, inability to 
concentrate, negative attitudes, lack of academic engagement, depression and reduced self-
esteem, and even physical health problems (e.g. Ammermueller 2012; Brown & Taylor 2008; 
Eriksen et. al 2012; Ponzo 2013; Hazel, 2010; Ouellet-Morin et al., 2011). However, for African 
countries the only previous research on bullying and its negative consequence have been 
documented for a gender based study of Ghanaian 8th grade mathematics students’ (Kibriya et al. 
2016). In this research note, we extend on the previous knowledge and generate new evidence of 
pervasiveness of bullying and its negative consequences through an extensive comparative 
analysis of standardized Reading, Mathematics and Science tests administered on 4th and 8th/9th 
grade students in Ghana, Botswana and South Africa using a nationally representative data 
sample of 26,467 students.      
2. Data  
We construct a large data set from two international assessments, the Trends in Mathematics and 
Sciences Study (TIMSS) and the Progress in Reading and Literacy Study (PIRLS), conducted in 
2011 by the International Association for the Evaluation of Educational Achievement (IEA). 
These include assessments of students’ reading, math, and science skills and knowledge as well 
as school environment and demographic measures. 
 
 
   
2.1 Participants 
 
The PIRLS and TIMSS use nationally representative samples of students in the fourth and eighth 
grade in Botswana, Ghana, and South Africa. 3,108 participants are analyzed for reading 
performance, 7,807 for math, and 15,552 for science. (The same students from each grade are 
surveyed for math and science performance) Ninth grade students participated in the TIMSS in 
Botswana and South Africa. In Botswana and South Africa, the pre-PIRLS, an easier and shorter 
version of the PIRLS, was administered. All students and their associated schools were randomly 
chosen.   
2.2 Measures 
Student achievement in reading, math, and science is reported on a scale of 0 to 1000 with 
typical scores in the range of 300 to 700. Experiences of bullying were measured through the 
Student Questionnaire. The “Students Bullied at School” scale was constructed from students’ 
responses to the following six items:  
a) I was made fun of or called names  
b) I was left out of games or activities by other students  
c) Someone spread lies about me  
d) Something was stolen from me  
e) I was hit or hurt by other student(s) (e.g., shoving, hitting, kicking)  
f) I was made to do things I didn’t want to do by other students 
Response options were: “At Least Once a Week,” “Once or Twice a Month,” “A few times a 
year,” or “Never.” From these responses, three categories of bullying frequency were created: 
“About Weekly”, “About Monthly”, and “Almost Never.” Students bullied “Almost Never” 
reported never experiencing at least three of the six bullying behaviors and each of the other 
three behaviors “a few times a year,” on average. Students bullied “About Weekly” reported 
experiencing each of three of the six behaviors “once or twice a month” (bullied 3-6 times a 
month) and, in addition, each of the other three “a few times a year,” on average.   
In addition to the Student Questionnaire, the Home Questionnaire (completed by parents or legal 
guardians), Teacher Questionnaire, School Questionnaire (completed by the school’s principal), 
and Curriculum Questionnaire provide relevant information about other variables that may 
influence performance, including school resources, instructional approaches, teacher 
characteristics, student attitudes, and home support for learning.   
 
3. Data Analysis Method 
“Bullied weekly,” a binary variable, is used as the key explanatory variable for the analysis 
presented. To obtain un-confounded estimates, we control for school specific characteristics; 
students’ age, and sex; teachers’ age, sex, and experience; parents’ income and education, and 
facilities available to students at home.  The propensity score in this experimental setting is the 
probability of a unit (i.e., a student) being assigned to a treatment (i.e., being bullied weekly), 
given a set of observed covariates. This approach, proposed by Rosenbaum and Rubin (1983), 
solves the ‘curse of dimensions’ by combing all confounders into a single propensity score, and 
matches observations based on the score. Thus we are able to identify the influence of bullying 
by comparing the average difference in academic performance between the group of students 
who are bullied and the matched sample of students who had a similar chance of being bullied, 
based on their other observed covariates, but are not actually bullied (Abadie and Imbens, 2016; 
Rosenbaum and Rubin, 1983). We obtain the average treatment effect on the treated (ATT). To 
formally define the ATT, we define two potential outcomes. 𝑌0𝑖 is the academic performance (in 
this case, test score) for individual i if he or she is not treated (i.e. bullied) and 𝑌1𝑖 is the value of 
the outcome variable for individual i if he or she is treated. The ATT is given by 
E[(Y1i−Y0i)|T=1].    
 
4. Results and Discussion:   
Figure 1A shows the average test scores for each country and each discipline. Considering that 
that the international average is 500 points (source: PIRLS and TIMSS) and the low international 
benchmark is 400 points (source: PIRLS and TIMSS), we confirm that academic performance in 
the three countries is not up to international standards. Figure 1B shows the percentage of 
students who were bullied weekly, disaggregated by sex.  The figure shows that bullying is 
widespread in all three countries, with around 50% of the students reporting regular experiences 
of bullying compared to only 20% at a global level.  
 
 [Figure 1 here] 
 
Following, we estimate the possible negative impact of “bullied weekly” on standardized 
reading, mathematics and science tests. We implement three matching algorithms: nearest 
neighbor, radius, and kernel (Caliendo & Kopeining, 2008; Imbens, 2015).  Table 1 summarizes 
the results of bullying on 4th graders performance in pre-PIRLs (reading), mathematics and 
science tests for Botswana and pre-PIRLS (reading) test scores in South Africa with the three 
matching algorithms. In terms of the direction of effect, both countries show consistent and 
significant negative effects of bullying for the tested disciplines. The magnitude of this impact is 
highest for South African students’ reading scores with a decrease around 24.51 points on 
average, while lowest is for mathematics exams in Botswana with 12.37 points decrease on 
average. Reading and Science scores of Botswanan students’ exhibit an average decrease of 
13.49 and 13.67 respectively across the three matching methods. The negative impact for 4th 
grade students is almost equal for all subjects in Botswana implying that the effect is robust and 
consistent across all disciplines.  
 
[Table 1 here] 
 
Table 2 summarizes the results of bullying on 8/9th graders performances on math and 
science exams for Botswanan, Ghanaian and South African students. The results show 
statistically significant negative effect of bullying across all three countries and matching 
algorithms in both of the disciplines. However, unlike 4th grade students, 8th/9th grade bullied 
students are more sensitive towards science scores than mathematics scores. The average score 
decrease in science exams are 30.53, 24.06 and 24.06 for Botswana, Ghana and South Africa 
respectively. Math scores decreased an average of 15.65 and 13.49 for Botswanan and South 
African students. Botswanan students were the most affected by bullying at school and also had 
the highest discrepancy levels between mathematics and science scores.    
  
To summarize our results, at an average score of 400 points, the effects of bullying 
correspond to a 3% to 8% decrease in performance and are consistent across all countries, 
disciplines and grades. As robust check the same analysis was performed using the “bullied 
monthly” variable and showed similar results. The impact of “bullied monthly” is provided in the 
online appendix.   
 
 
[Table 2 here] 
 
5. Conclusion:  
The determinants of in-class performance depend on a myriad of quantitative and qualitative 
factors. While we remain cautious to claim causality, our efforts at least establish consistent, 
strong, negative consequences of bullying on academic performance in various disciplines across 
sub-Saharan Africa with a relatively large dataset. We also provide evidence of an alarming 
percentage of students being bullied and harassed in selected schools. We recommend stronger 
and specific programs targeted to the reduction of bullying in African schools and expect such 
programs would go a long way in increasing student performance to international levels.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
REFERENCES  
Kibriya, Shahriar, Zhicheng Phil Xu, and Yu Zhang. "The negative consequences of school 
bullying on academic performance and mitigation through female teacher participation: evidence 
from Ghana." Applied Economics 49.25 (2017): 2480-2490. 
Abadie, A., & Imbens, G. W. (2016). Matching on the estimated propensity 
score. Econometrica, 84(2), 781-807.  
Ammermueller, A. (2012). Violence in European schools: A widespread phenomenon that 
matters for educational production. Labour Economics, 19(6), 908-922. 
Brown, S., & Taylor, K. (2008). Bullying, education and earnings: evidence from the National 
Child Development Study. Economics of Education Review, 27(4), 387-401. 
Eriksen, T. L. M., Nielsen, H. S., & Simonsen, M. (2012). The Effects of Bullying in Elementary 
School (No. 6718). The Institute for the Study of Labor.Greene, M., Robles, O., Stout, K. and 
Suvilaakso, T., 2013. A girl’s right to learn without fear: Working to end gender-based violence 
at school. Woking: Plan International. 
Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton, B., & Scheidt, P. (2001). 
Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. 
Journal of the American Medical Association, 285(16), 2094-2100. 
Olweus, D. (1993), Bullying at School: What We Know and What We Can Do. Blackwell 
Publishing. 
Ouellet-Morin, I., Odgers, C. L., Danese, A., Bowes, L., Shakoor, S., Papadopoulos, A. S., & 
Arseneault, L. (2011). Blunted cortisol responses to stress signal social and behavioral problems 
among maltreated/bullied 12-year-old children. Biological psychiatry, 70(11), 1016-1023. 
Ponzo, M. (2013). Does bullying reduce educational achievement? An evaluation using matching 
estimators. Journal of Policy Modeling, 35(6), 1057-1078. 
 Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in 
observational studies for causal effects. Biometrika, 70, 41-55. 
 
 
 
 
 
 
 Table 1: Impact on weekly bullying on 4th grade academic performances 
 
Matching methods 4th-grade reading 
(perPIRLS) scores 
Botswana  
4th-grade math 
scores Botswana 
4th-grade science 
scores Botswana 
4th-grade reading 
scores (pre-PIRLS) 
South Africa 
Nearest neighbor -16.22*** 
(5.60) 
-14.09** 
(6.32) 
-15.04* 
(9.01) 
-24.52*** 
(9.01) 
Number of treated 862 844 844 575 
Number of controls 951 954 954 720 
     
Radius/caliper -12.48*** 
(4.64) 
-11.11*** 
(3.37) 
-11.90* 
(6.46) 
-25.52*** 
(6.28) 
Number of treated 834 827 827 562 
Number of controls 947 948 948 680 
     
Kernel 
(Epanechnikov) 
-11.79*** 
(3.90) 
-11.93*** 
(3.46) 
-14.13*** 
(5.04) 
-23.52*** 
(4.34) 
Number of treated 862 844 844 575 
Number of controls 951 954 954 720 
*,**,*** denotes significance levels at 10%, 5% and 1% respectively. Standard errors are in parenthesis   
 
 
 
 
 
 
 
 
 
 
 
0
10
20
30
40
50
60
70
80
P
e
rc
e
n
t 
B
u
lli
e
d
Female
Male
Figure 1B: Percentage of 
students reporting that they have 
been bullied weekly. 
0
50
100
150
200
250
300
350
400
450
500
550
Botswana Ghana South
Africa
T
e
s
t 
S
c
o
re Pre-PIRLS
TIMSS 4th Math
TIMSS 4th Science
TIMSS 8th Math
Average
International 
Average 
Figure 1A: Average test scores by assessment and country. 
 Table 2: Impact on weekly bullying on 8th grade academic performances 
 
Matching 
methods 
9th-grade 
math scores 
Botswana 
9th-grade 
science scores 
Botswana 
8th-grade 
science scores 
Ghana 
9th-grade 
math scores 
South Africa  
9th-grade 
science scores 
South Africa  
 
Nearest neighbor -18.84*** 
(4.35) 
-32.30*** 
(4.50) 
-22.72*** 
(4.07) 
-12.62*** 
(2.84) 
-23.06*** 
(3.83) 
 
Number of treated 1481 1617 2378 2424 2445  
Number of 
controls 
1467 1640 2112 3522 3562  
       
Radius/caliper -14.06*** 
(2.928) 
-29.24*** 
(3.295) 
-24.47*** 
(2.815) 
-16.14*** 
(1.647) 
-24.43*** 
(2.214) 
 
Number of treated 1475 
 
 
1611 2359 2425 2437  
Number of 
controls 
1436 1607 2042 3562 3530  
       
Kernel 
(Epanechnikov) 
-14.06*** 
(2.649) 
-30.16*** 
(3.285) 
-25.00*** 
(2.762) 
-17.02*** 
(1.458) 
-24.71*** 
(2.020) 
 
Number of treated 1481 1617 2378 2424 2445  
Number of 
controls 
1467 1640 2112 3522 3562  
        
*,**,*** denotes significance levels at 10%, 5% and 1% respectively. Standard errors are in parenthesis   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix  
 
 
 
 
Table 1: Impact on monthly bullying on 4th grade academic performances 
 
Matching methods 4th-grade reading 
(perPIRLS) scores 
Botswana  
4th-grade math 
scores Botswana 
4th-grade science 
scores Botswana 
4th-grade reading 
scores (pre-PIRLS) 
South Africa 
Nearest neighbor -17.08*** 
(8.498) 
-14.87*** 
(8.235) 
-26.36*** 
(10.74) 
-34.47*** 
(11.49) 
Number of treated 1275 1218 1218 922 
Number of controls 209 203 203 217 
     
Radius/caliper -15.135*** 
(5.97) 
-20.260*** 
(6.42) 
-32.196*** 
(11.50) 
-39.23*** 
(8.67) 
Number of treated 1268 1205 1205 861 
Number of controls 205 203 203 213 
     
Kernel 
(Epanechnikov) 
-16.34*** 
(6.003) 
  -20.42*** 
(6.268) 
-32.89*** 
(7.378) 
-31.14*** 
(6.306) 
Number of treated 1275 1218 1218 922 
Number of controls 209 203 203 217 
     
*,**,*** denotes significance levels at 10%, 5% and 1% respectively. Standard errors are in 
parenthesis   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Table 2: Impact on monthly bullying on 8th /9th grade academic performance 
Matching 
methods 
9th-grade 
math scores 
Botswana 
9th-grade 
science scores 
Botswana 
8th-grade 
science 
scores 
Ghana 
9th-grade 
math scores 
South 
Africa  
9th-grade 
science 
scores South 
Africa  
 
Nearest neighbor -17.89*** 
( 4.14) 
-38.01*** 
(4.91) 
-27.69*** 
(4.66) 
-9.15*** 
(3.08) 
-20.89** 
(3.93) 
 
Number of 
treated 
2370 2611 3556 4283 4279  
Number of 
controls 
536 593 954 1656 1681  
       
Radius/caliper -21.58*** 
(3.399) 
-35.48*** 
(  4.217) 
-25.96*** 
(3.733) 
-10.44*** 
(1.782) 
-23.19*** 
(2.50) 
 
Number of 
treated 
2359 2603 3543 4282 4279  
Number of 
controls 
525 582 953 1650 1667  
       
Kernel 
(Epanechnikov) 
-21.40*** 
(3.42) 
-35.327*** 
(3.36) 
-25.89*** 
(3.46) 
-12.06*** 
(1.85) 
-25.05*** 
(2.33) 
 
Number of 
treated 
2370 2611 3556 4283 4279  
Number of 
controls 
536 593 954 1656 1681  
       
*,**,*** denotes significance levels at 10%, 5% and 1% respectively. Standard errors are in 
parenthesis   
 
 
 
 


Pervasive bullying and its negative consequence on standardized tests of Reading, Science and Mathematics – A comparative analysis of three countries in Sub-Saharan Africa

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Pervasive bullying and its negative consequence on standardized tests of Reading, Science and Mathematics – A comparative analysis of three countries in Sub-Saharan Africa

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