









































Abstract
This study examined the impact of rural and urban schools on pupils’ competencies in the English language 

and Mathematics tests. The sample comprised 16,481 Primary 3 and 14,495 Primary 6 pupils from 448 and 
426 schools respectively. The schools were selected using the stratified random sampling technique and the 
data analysed using a multilevel modelling technique. The study found statistically significant differences in 
achievement between rural and urban school pupils at both the national and regional levels. Compared with 
urban school children, rural school children on average scored 2 and 4 marks less in primary 3 and 6 English 
language tests respectively. Similarly, rural school children on average earned 1 mark less in primary 3 and 6 
mathematics tests. Rural school children in the Eastern Region were the most disadvantaged by scoring 6 and 
8 marks less in the primary 3 and 6 English language tests respectively when compared with their urban peers. 
The only exception was the Northern Region where the average achievements of rural school children were 
higher than their urban peers. The findings suggest that it significantly mattered which part of the country a 
child attends school. This runs counter to the nation’s educational policies and the realisation of the United 
Nation’s Sustainable Development Goal 4. Hence, to provide quality and inclusive education for all pupils, 
resources for schools and communities should be equitably distributed and effectively utilised.
Keywords: School locality, Socioeconomic, Competency, Regions    

Nyatsikor, M. W,, Abroampa, W. K. & Esia-Donkoh, K.  
Global Journal of Transformative Education (2020) Vol 2  
DOI 10.14434/gjte.v2i1.31174

Open Access

Published by the Global Insitutute of Transformative Education (http://www.gite.education)
© Nyatsikor, M. K.. Abroampa, W. K., & Esia-Donkoh, K. 2020. Open Access This journal is distributed under the terms of the 
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provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,

The Impact of School Locale on Pupils’ Competencies in Selected Subjects: 
Does It Matter More for Specific Regions in Ghana?

Maxwell Kwesi Nyatsikor,1 Winston Kwame Abroampa,2 & Kweku Esia-Donkoh3

Full listing of authors and contacts can be 
found at the end of this article.

Introduction
Numerous studies on school and pupils’ achieve-

ment across different contexts have found significant 
differences between schools serving the more-advan-
taged urban and the less-advantaged rural localities 
(Çiftçi & Cin, 2018; Bashir, Lockheed, Ninan & Tan, 
2018; Ministry of Education [MOE], 2018; United 
Nations Educational, Scientific and Cultural Organisa-
tion [UNESCO], 2018). Between countries, evidence 
from international examinations such as Trends in 
International Mathematics and Science Study [TIMSS] 
and Programme for International Student Assessment 
[PISA] show significant differences in the achievement 
of children from low- to middle-income countries and 
high-income countries (Mullis, Martin & Loveless, 2016; 
Martin, Mullis, Foy & Hooper, 2016; Meyer & Benavot, 
2013). These achievement gaps are linked to the exist-
ing disparities in the social, economic, and educational 
resources in the nations and schools (Bashir, et al.,

2018; Martin et al., 2016; Meyer & Benavot, 2013; MOE, 
2016b).  Many studies suggest that the effects of the 
rural-urban dichotomy on academic achievement are 
evident from the onset of formal schooling (MOE, 2018; 
UNESCO, 2018; World Development Report, 2018; 
Hanson et al., 2011). The sustained interest in the 
achievement outcomes at the primary level of educa-
tion is reinforced by the fact that the quality of learning 
at this stage is crucial to later learning proficiencies and 
life’s opportunities (Fredriksen & Kagia, 2013; UNESCO, 
2018; Bashir et al., 2018; United Nations [UN], 2015). 
In the Ghanaian context, evidence from the assess-
ment of early and primary grade-levels in national 
assessments shows statistically significant differences 
in children from rural and urban schools (MOE, 2014; 
MOE, 2016a). Consistently, preschool and primary-level 
children from rural schools underachieve in nation-
al examinations when compared with children from 
urban schools (MOE, 2016b; MOE, 2018). This study 
examines the unique impact of the location of schools 
in rural and urban areas on the achievement levels of 
Ghanaian primary school children in the national



Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  73 

education assessment tests in Mathematics and English 
language.      

School location and pupils’ academic achievement
Studies and reports across different contexts have, 

for the most part, concluded that pupils from urban 
schools achieved higher grades than those from ru-
ral schools (Destin, Hanselman, Buontempo, Tipton, 
Yeager, 2019; MOE, 2016a; Bashir, et al., 2018; Bofah 
& Hannula, 2015). Some studies have suggested the 
differences in the social-economic characteristics of 
children from rural and urban schools as the main 
driver of the existing achievement gaps (Ryan, Koczber-
ski, Curry & Germis, 2017; Broer, Bai & Fonseca, 2019). 
Parents of rural school children are generally unable to 
provide the educational needs for their children be-
cause of poverty. This constraint negatively affects the 
academic potentials of the child as evidenced by prior 
studies (Bofah & Hannula, 2017; Bofah & Hannula, 
2015). Thus, children from economically disadvantaged 
households are typically educationally disadvantaged 
right from the onset of their formal education (Bashir et 
al., 2018; Kim, Cho & Kim, 2019; Organization for Eco-
nomic Co-operation and Development [OECD], 2016). 
Moreover, available data from Ghana suggest that many 
of the rural dwelling parents are semi-literate, hence, 
unable to assist their children in school or academ-
ic-related tasks (Ghana Statistical Service [GSS], 2014). 
For instance, 33.1% of the population who had never 
attended school live in rural areas in contrast to 14.2% 
in the urban areas (GSS, 2012). 

Other studies have suggested that urban schools 
outperform their rural counterparts because they tend 
to have higher expectations and are more likely to have 
parents who participate in school activities (Adeyemi, 
Adediran & Adewole, 2018; Prasertcharoensuk & Tap-
khwa, 2016). The achievement gaps between rural and 
urban schools have also been linked to the differences 
in the professional characteristics of teachers serving 
in rural and urban schools (Nyatsikor, Sosu, Mtika & 
Robson, 2020; Hill, Charalambous & Chin, 2019; Bhai 
& Horoi, 2019). Urban schools are often staffed with 
teachers who are more experienced and possess higher 
academic and professional qualifications than teach-
ers in rural schools (MOE, 2018; Bashir et al., 2018). 
Even though there is a plethora of research showing 
significant differences in achievement between urban 
and rural schools, in some cases no significant differ-
ences were found. For instance, Considine and Zappala 
(2002) found that geographical location does not

significantly predict outcomes in school performance 
in Australia. This is because school children in rural 
Australia have access to an adequate number of edu-
cational facilities. Nonetheless, similar studies from 
equally developed nations, particularly the USA, reveal 
stark achievement differences between rural and urban 
communities (Cochran-Smith & Zeichner, 2005; Owens, 
2018; Reardon, 2011; Reardon et al., 2019). Though 
the focus on rural and urban school achievement has 
caught the attention of researchers and policymakers 
for decades, this current study extends prior studies by 
investigating the impact of school location on academic 
achievement in a methodologically different way. First, 
an estimation of the unique influence of school location 
on achievement was done, and secondly, the actual 
numbers of pupils who achieved or failed to achieve 
certain levels of competencies using the derived esti-
mates attributed to school location were determined. 
To achieve this purpose, the following research ques-
tions were formulated to guide the study.

Research Questions
1. To what extent do the inequalities in rural and

urban schools account for pupils’ achievement in
English language and mathematics?

2. How much of the variance in pupils’ achievement
can be attributed to schools in different localities of
a geographical region?

Context of the Study 
This study relied on the 2013 wave of the Ghana 

National Education Assessment [GNEA] data. The GNEA 
started in 2005 and it is held biennially by the RTI 
International and the USAID in collaboration with the 
Ghana Education Service. The purpose of the assess-
ment is to assess primary 3 (in 2016 primary 4 pupils 
examined) and primary 6 pupils’ competence in math-
ematics and the English language subjects (MOE, 2014; 
MOE, 2016a). The primary 3 (P3) and 6 (P6) pupils 
were assessed and scored over a 30-item and 40-item 
test respectively. Pupils’ achievement was assessed 
at three levels but different cut-off points for the two 
grade-levels. For the P3 mathematics and English 
language tests, pupils who answered a maximum of 
10 items correctly (i.e. below 35%) performed “below 
minimum competency”. Pupils who correctly answered 
a minimum of 11 and a maximum of 16 items (i.e. 35% 
- 54%) attained “minimum competency” while those
who correctly answered at least 17 items or better (i.e.
≥ 55%) were “competent”. In the case of the P6 sample,

Global Journal of Transformative Education (2020) Vol 2



Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  74 

Global Journal of Transformative Education (2020) Vol 2

pupils who correctly answered up to 13 items (i.e. 
below 35%) performed “below minimum competency”. 
Pupils who had 14 up to 21 marks (i.e. 35% - 54%) 
attained “minimum competency” while those who cor-
rectly answered at least 22 items or better (i.e. ≥ 55%) 
were “competent” in a subject (see Table 1 for perfor-
mance distribution by subject and grade-levels). 

The data in Table 2 show considerable differences 
in the achievement of rural and urban pupils in both 
subjects at the two grade-levels. The percentage of 
children from urban schools who were competent 
in both subjects for the two grade-levels was higher 
than those from rural schools. On the other hand, the 
percentage distributions of the performances below 
minimum competency were higher for pupils from the 
rural schools for both subjects and grade-levels. The 
percentage distributions of performance levels show 
significant connections between school locality and 
achievement. However, it is impossible to determine the 
unique effect of school location on the performance of 
pupils. This is because the influence of the location of 
schools on achievement is confounded by other factors 
including the characteristics of the pupils (e.g. age and 
gender), schools (e.g. type and class size), and districts 
(e.g. resource levels). 

Methodology
Design, Sample, and Sampling   

The study employed descriptive cross-sectional sur-
vey design. The study population comprised of primary 
school pupils from all the 10 regions in Ghana (current-
ly there are 16 regions following the re-demarcation of 
4 regions). The target population was the P3 and P6 pu-
pils. The P3 sample size was 16,481 pupils [equivalent 
weighted sample = 93,008] from 448 schools [equiva-
lent weighted sample = 12,734]. The P6 sample

comprised 14,495 pupils [equivalent weighted sam-
ple = 81,319] from 426 [equivalent weighted sample 
= 12,393]. The weighted school values are derived by 
dividing the total number of primary schools (which 
had a class size of at least ten pupils) in a region by the 
number of sampled schools from that region. For exam-
ple, there were 1,821 primary 3 schools in the Eastern 
region out of which 55 participated in the assessment. 
The sample weight for schools in the Eastern region 
is therefore 33.11 (i.e. 1,821÷55 = 33.11). Thus, each 
school that participated in the assessment from the

Table 2: Performance levels of P3 and P6 pupils by 
school location



Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  75 

Eastern region represented 33.11 schools. The weight-
ed pupils’ sample is derived by multiplying the weight-
ed school value for each region by the number of pupils 
sampled from that region. For instance, the weighted 
sample (57,312) for the P3 pupils from the Eastern 
region is derived by multiplying 33.11 by the total 
number of pupils (1,731) sampled from the 55 schools 
in the region. 

Schools were selected using stratified random sam-
pling. Schools were stratified by regions and sorted by 
district, locality (urban or rural), school type (public or 
private) and enrolment size within the regions (MOE, 
2014). Schools with less than 10 pupils in the class 
were excluded. Randomly, 55 schools were sampled 
with equal probability from each region except Ashan-
ti and Northern regions where 54 schools each were 
selected because a school from each of these 2 regions 
was not in session at the time the test was adminis-
tered. According to the MOE (2014), the reliability of 
the test was determined using SPSS Kuder-Richard-
son-20 (KR20) tests. Alpha values of 0.89 and 0.84 were 
achieved for the P6 mathematics and English language 
tests respectively. Alpha values of 0.82 and 0.84 were 
achieved for the P3 mathematics and English language 
tests respectively. The test questions were developed 
based on the specified topics in the national curricula. 
The English language test questions covered listen-
ing; reading comprehension; and usage (grammatical 
structure) domains. The mathematics test covered four 
domains namely: basic operations; numbers and nu-
merals; measurement, shape and space; and collection 
and handling of data. The return rate for the answered 
scripts was 100% (MOE, 2014).

Variables 
Independent variable - The independent variable 

for the study is ‘school location’ and was designated 
rural (0) or urban (1). In Ghana, urban localities are 
those with more than 60% of its residents engaged 
in non-agricultural activities in addition to having a 
minimum population size of 5,000. Otherwise, it is not 
urban (GSS, 2012). Urban schools have relatively better 
educationally-relevant resources than rural schools in 
terms of quantity and quality. There are more qualified 
teachers and better teaching and learning resources in 
urban than rural schools (MoE, 2014; MoE, 2018). The 
availability and quality of social amenities like potable 
water, functional electricity, , accessible roads, medical 
care, and internet connectivity are relatively better in 
urban communities when compared with rural commu-

nities (Blampied, et al, 2018; United Nations Interna-
tional Cultural Educational Fund [UNICEF] and Centre 
for Democratic Development [CDD]-Ghana, 2019). Par-
ents of rural school children are predominantly peasant 
farmers who live in poverty (GSS, 2012; Blampied et al., 
2018). Levels of education, incomes and employment 
opportunities in the rural areas are significantly low 
and limited compared with those living in urban areas. 
As a result, rurality in Ghana connotes low socioeco-
nomic status whereas urbanicity is associated with 
higher socioeconomic status (GSS, 2012; MoE, 2018; 
Blampied et al., 2018). Hence, in this study context, ru-
ral schools are used as a proxy for low SES while urban 
schools are used as a proxy for high SES.  According to 
the GSS (2012) approximately 37.0% of children from 
primary grade 1 to 6 live in rural areas compared with 
22.2% in urban areas. In this study, 77.9% of the P3 
schools were located in rural communities compared 
with 22.1% in urban communities while 77.7% and 
22.3% of the P6 schools were in rural and urban com-
munities respectively. 

Covariates - Five variables were controlled to ac-
count for their influence on pupils’ achievement. These 
were pupils’ gender (coded; male = 0; female = 1) and 
age, class size, school type (coded; public = 0; private = 
1) and district type (coded; deprived = 1; non-deprived
= 0).

Dependent variables - The two dependent vari-
ables are mathematics and English language achieve-
ment scores for the primary 3 and 6 pupils who 
participated in the 2013 wave of the Ghana National 
Education Assessment test.  

Data Screening and Management
Data were analysed using a multilevel modelling 

technique. Two exclusion criteria were applied to 
arrive at the final sample size. The first criterion was 
the exclusion of all schools not labelled as either ru-
ral or urban. The second criterion, which is based on 
statistical and methodological considerations was the 
exclusion of all schools which had less than 10 pupils in 
a class (see Hox, Moerbeek, van de Schoot, 2017; Heck 
& Thomas, 2015). Applying these two criteria, data on 
100 P3 schools (equivalent weighted sample = 2,843) 
comprising 2,977 pupils (equivalent weighted sample 
= 80,004) and 122 P6 schools (equivalent weighted 
sample = 3,468) comprising 2,952 pupils (equivalent 
weighted sample = 79,990) from the ten regions were 
excluded from the analysis. All the variable codes for 
the dichotomous variables were grand mean centred. 

Global Journal of Transformative Education (2020) Vol 2



Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  76 

Likewise, the continuous and dependent variables were 
grand mean centred. The grand mean centred achieve-
ment score for each pupil is the difference between 
a pupil’s raw score and the grand mean achievement 
score derived from all pupils involved in the study. 
Grand mean centering ensured that the variances of the 
intercept and the slopes in the regression have a clear 
interpretation when all explanatory variables are equal 
to zero (Hox et al., 2017).  

Data Analysis Procedure 
The first stage of the analysis was to partition 

pupils’ achievement as a null or intercept-only model 
to estimate the Intra-class Correlation Coefficient (ICC). 
The outcome of this partitioning helped to determine 
whether multilevel modelling was required to analyse 
the data (Hox et al., 2017; Heck & Thomas, 2015). At 
stage two, the five covariates (pupils’ gender, age, class 
size, school, and district types) were introduced into 
the model to quantify their influence on pupils’ achieve-
ment. The third and final stage of the analysis was the 
introduction of the independent variable (school loca-
tion) into the model to estimate its unique influence 
on pupils’ achievement. From the estimates derived, 
the numbers of pupils who attained specific levels of 
proficiencies in both subjects on merit or as a result of 
the inequalities between rural and urban schools are 
determined. The second part of the analysis examined 
the unique influence of the location of schools on the 
achievement of pupils in each of the ten regions. 

achievement for the P3 pupils from rural schools was 
11.2 and 11.6 while that for urban schools was 14.0 and 
15.9 for Mathematics and English Language. The mean 
mathematics and English language achievement for the 
P6 pupils from rural schools was 14.3 and 16.8 while 
that for urban schools was 17.1 and 24.0 for Mathe-
matics and English language. Preliminary analysis of 
the data using the t-test showed statistically significant 
(p-value = 0.000) mean differences in achievement be-
tween rural and urban schools for both subjects. 

In Supplemental Table 1 [use link to view table], 
the fixed effects estimates show that the P3 pupils who 
attended rural schools on average, earned 2 marks (b 
= -2.085, χ2 = 16.8, -2LL = 21, df = 6, p = 0.000) and 1 
mark less (b = -1.498, χ2 =16.8, -2LL =16, df = 6, p = 
0.000) in the English language and mathematics respec-
tively when compared with those who attended urban 
schools. The unconditional models for the P3 sample 
revealed that a greater proportion of the achievement 
variances in the English language (54.8%) and mathe-
matics (63.5%) remained unexplained by the number 
of variables investigated. School-level inequalities 
accounted for 29.7% and 28.4% of achievement differ-
ences in English language and mathematics respective-
ly. District level differences also influenced achievement 
variances in the English language (15.5%) and mathe-
matics (8.9%). 

In Supplemental Table 2, the fixed effects estimates 
show that the P6 pupils who attended rural schools 
were predicted to have attained approximately 4 marks 
(b = -3.654, χ2 = 16.8, -2LL = 35, df = 6, p = 0.000)

Global Journal of Transformative Education (2020) Vol 2

Results
Descriptive Analysis 

The descriptive information about the 
school and pupil characteristics is presented 
in Table 3. The samples from public schools for 
both grade-levels were at least 5 times more 
than those from private schools. Similarly, the 
number of rural schools was more than thrice 
of urban schools. The mean class size for the 
P3 sample was bigger for urban schools (62.7) 
than the rural schools (47.2). The respective 
class sizes for the P6 urban and rural schools 
were 64.8 and 43.9. The average ages for the P3 
pupils from the rural and urban schools were 
10.9 and 10.4 respectively. As well, the average 
ages for the P6 pupils from the rural and urban 
schools were 13.8 and 13.3 respectively. The 
mean mathematics and English language



Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  77 

and 1 mark less (b = -1.401, χ2 = 16.8, 2LL = 17, df = 6, p 
= 0.000) in the English language and mathematics tests 
respectively. The unconditional models for the P6 
sample indicated that respectively 54.2% and 70.1% of 
the variances in achievement for English language and 
mathematics remained unexplained given the number 
of variables investigated. School-level differences ac-
counted for 29.1% and 20.5% of the achievement vari-
ances in English language and mathematics respectively 
while district-level differences accounted for 16.7% and 
9.4% of achievement variances in English language and 
mathematics respectively. 

The next stage of the analysis was to determine the 
number of pupils who attained or failed to attain specif-
ic levels of competency after accounting for the existing 
inequalities between rural and urban schools. As an 
example, the estimate derived for P3 English language 
achievement is used to calculate the number of pupils 
who attained specific levels of proficiency. From the 
results, 2 marks are added to the initial scores obtained 
by each pupil. As a result, pupils who initially scored 0 
to 8 mark(s) would now earn between 2 and 10 marks; 
a performance below minimum competence (see Table 
1). Pupils who initially scored 9 and 10 marks (i.e. be-
low minimum competence) would now earn between 
11 and 12 marks to attain “minimum competency”. 
Next, we add 2 marks to the minimum mark (i.e. 11) for 
attaining minimum competency level to get 13 marks. 
This means all pupils who initially scored 11 to

13 marks were influenced by the existing inequalities 
between rural and urban schools. Those who scored 
14 marks up to 16 marks attained minimum compe-
tency on merit. By the same analogy, all pupils who 
were competent but had between 17 up to 19 marks 
attained this level of achievement due to specific factors 
associated with the location of schools. Consequently, 
pupils who correctly answered a minimum of 20 out of 
the total 30 items in the test were those who achieved 
competency on merit after accounting for the impact of 
school location. The same procedure is used to estimate 
the number of pupils who attained or failed to attain 
specific competencies in the remaining subjects across 
both grade-levels as presented in Table 4.

The existing inequalities in rural and urban schools 
had cascading effects on pupils’ achievement at all 
levels of expected competencies in both subjects. Con-
trolling for the inequalities in school localities, the re-
sults indicated that 2,873 (70.3%) out of the 4,089 and 
2,256 (69.6%) out of the 3,243 pupils were meritori-
ously competent in P3 English language and mathemat-
ics respectively. The remaining 1,216 (29.7%) and 987 
(30.4%) pupils were competent in P3 English language 
and mathematics respectively as gains derived from the 
location of the schools they attended. A total of 3,060 
(63.3%) of the 4,837 and 2,424 (42.7%) of the 5,680 
pupils attained minimum competency in P3 English 
language and mathematics respectively as a unique 
contribution from the location of the schools they

Global Journal of Transformative Education (2020) Vol 2

attended. Respectively, 1,777 
(36.7%) and 3,256 (57.3%) attained 
minimum competency in English 
language and mathematics on merit. 
A total of 4,869 (64.4%) out of the 
7,555 and 6,219 (82.3%) out of 
the 7,558 pupils performed below 
minimum competency in P3 English 
language and mathematics tests on 
merit. The remaining 2,686 (35.6%) 
and 1,339 (17.7%) pupils performed 
below minimum competency in En-
glish language and mathematics tests 
respectively as a result of the defi-
ciencies associated with the location 
of the schools they attended. 

In respect of the P6 sample, 
3,339 (63.8%) out of the 5,232 and 
878 (58.4%) out of the 1,504 pupils 
were competent in English language 
and mathematics respectively on



Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  78 

merit. The remaining 1,893 (36.2%) and 626 (41.6%) 
pupils achieved competency level in English language 
and mathematics respectively as a result of the advan-
tages associated with the location of the schools they 
attended. Similarly, 3,088 (70.5%) of the 4,378 and 
2,417 (34.0%) of the 7,117 pupils attained minimum 
competency in English language and mathematics 
respectively as an influence from the location of the 
schools they attended. Precisely, 1,290 (29.5%) and 
4,700 (66.0%) pupils attained minimum competency in 
English language and mathematics on merit. Likewise, 
1,781 (36.5%) of the 4,885 and 4,671 (79.5%) of the 
5,874 P6 pupils performed below minimum competen-
cy in English language and mathematics respectively on 
merit. The remaining 3,104(63.5%) and 1,203(20.5%) 
of the P6 pupils performed below minimum competen-
cy in English language and mathematics respectively 
as a result of the deficits associated with the location of 
the schools they attended.  

The second objective of the study was to determine 
the extent of the influence of school location on pupil’s 
achievement in each of the regions. The results for both 
subjects and grade-levels are presented in Supplemen-
tal Tables 3-6. In Supplemental Table 3, the fixed effects 
estimates show that the impact of the rural schools on 
the P3 English language achievement for the Ashante 
(b = -1.004), Upper West (b = -1.484), Upper East (b = 
-1.968) and Western (b = -.254) regions were less than
the national average of 2 marks (b = -2.085). The im-
pact for Brong Ahafo (b = -2.893), Eastern (b = -6.477),
Greater Accra (b = -3.846), Northern (b = 3.038), Cen-
tral (b = -2.093), and Volta (b = -2.355) regions were
greater than the national average.

With respect to the P3 mathematics achievement, 
the fixed effects estimates in Supplemental Table 4 
show that the Ashante (b = -.348), Volta (b = -.968), Up-
per East (b = - 1.191) and Western (b = -.481) regions 
had average values less than the national average of 1 
mark (b = - 1.498). The remaining regions including 
Brong Ahafo (b = -2.259), Central (b = -1.838), Eastern 
(b = -5.486), Greater Accra (b = -2.908), Northern (b = 
2.219), and Upper East (b = -2.279) had greater average 
values than that of the nation.

In respect of the P6 English language achievement 
the fixed effects estimates in Supplemental Table 5 
show that the Ashante (b = -1.376), Greater Accra (b = 
-2.769), Northern (b = .963), Upper East (b = -2.970)
and Western (b = -.989) regions recorded less average
impact than the national average of 4 marks (b = -
3.654). The Central (b = -4.544), Eastern (b = -8.038)

and Upper West (b = -5.952), Brong Ahafo (b = -4.474), 
and Volta (b = -4.112) regions had greater mean values 
than the national average. 

In Supplemental Table 6, the fixed effects estimates 
show that the estimates for Brong Ahafo (b = -1.962), 
Central (b = -2.321), Eastern (b = -3.534), Upper West 
(b = -2.335), Volta (b = -2.623) were greater than the 
national average for P6 mathematics (b = - 1.401). The 
Ashante (b = -.243), Greater Accra (b = -.220), North-
ern (b =.270) and Western (b =.130) and Upper East 
(b = -1.036) regions recorded lesser impact than the 
national average. Consistently across grade-levels and 
subjects, rural schools contributed negatively to pupils’ 
achievement except for the Northern and Western (P6 
mathematics achievement only) regions where the av-
erage achievement of rural schools was higher than ur-
ban schools. The Eastern and Western regions respec-
tively came up as the regions with the most and least 
variances in achievement attributed to inequalities in 
rural and urban schools in Ghana. The implications of 
the results are discussed in the next section.

Discussion of Results 
The unconditional models for both grade-levels 

and subjects indicated significant differences between 
school and district-level achievements. For the P3 sam-
ple, three of the covariates (i.e. pupils’ age, district type, 
and school type) contributed significantly to achieve-
ment in both subjects (see Supplemental Table 1).  The 
average achievement for relatively younger pupils was 
higher than older pupils while schools in deprived 
districts performed poorer compared with those in 
non-deprived districts. The types of schools pupils at-
tend had the greatest impact on achievement. Children 
from private schools significantly outperformed those 
from public schools. Gender was significant only for 
mathematics achievement where boys did better than 
girls. There was no significant difference in achieve-
ment between genders with respect to the English lan-
guage. The impact of class size on achievement in both 
subjects was not statistically significant. 

For the P6 sample, all the five variables that were 
controlled made statistically significant impacts on 
pupils’ achievement in both subjects. Children from 
private schools outperformed their counterparts from 
public schools, boys performed better than girls while 
relatively younger children outperformed the relatively 
older peers. Moreover, children from deprived schools 
performed poorer than those from non-deprived 
schools. Relatively smaller class sizes were not

Global Journal of Transformative Education (2020) Vol 2



Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  79

associated with improvement in achievement. The 
specific degrees of impact for each of the covariates are 
presented in Supplemental Table 2.

The advantages and disadvantages associated 
with the location of schools in specific communities 
produced corresponding gains and deficits in pupils’ 
achievement. The results suggested that if conditions 
and educational resources in rural schools were im-
proved by 1 unit, 2,686, and 1,339 more pupils would 
have at least attained minimum competency in the P3 
English language and mathematics tests respectively. 
In same vein, 3,104(63.5%) and 1,203(20.5%) more 
of the P6 pupils would have at least attained minimum 
competency in English language and mathematics 
respectively had they attended schools in urban lo-
calities. The advantage of attending schools in certain 
parts of the country is evidenced by the 1,216(29.7%) 
and 1,893(36.2%) pupils who were predicted to be 
competent in the P3 and P6 English language tests 
respectively solely by attending urban schools. Like-
wise, 987(30.4%) and 626(41.6%) pupils were com-
petent in P3 and P6 mathematics solely by attending 
urban schools.  The results suggest that pupils from 
rural schools achieved less than their peers from urban 
schools as a result of certain unfavourable conditions 
associated with attending schools in rural localities.

The advantages and disadvantages inherent with 
urban and rural schools have been explored in many 
contexts (MOE, 2016a; Bashir, et al. 2018; Çiftçi & Cin, 
2018; Cochran-Smith et al., 2012). Unanimously, these 
studies found that pupils from urban schools achieved 
higher grades than those from rural schools. Rural and 
urban communities serve as different psychological 
environments for children who share different resourc-
es, hazards, and opportunity structures. They may also 
have different life course options, and patterns of social 
interactions unique to the school catchment area (Bron-
fenbrenner, 2005; Tudge, Mokrova, Hatfield & Karnik, 
2009). As aptly observed by Rogošić and Baranović 
(2016), the differences in educational success can be 
attributed to different levels of existing social capital, 
which is produced in networks and connections of 
families that the school serves. A significant number of 
the rural schools in Ghana are disproportionately locat-
ed in districts deprived of basic social amenities (e.g. 
electricity, internet), educational resources (e.g. less 
qualified teachers, teaching and learning materials), 
and economic opportunities to provide regular incomes 
for parents. Parents of rural school pupils are primarily 
peasant farmers and are characterised by high levels

of poverty and illiteracy (GSS, 2015). These constraints 
significantly affect schools, parents and communi-
ty-wide abilities to support the pro-academic activities 
of rural school children. 

The current Covid-19 pandemic with its concomi-
tant negative effects on education has further exacer-
bated the woes of the largely rural communities and 
schools in Ghana. Children in rural schools are unable 
to participate in the various remote digital learning 
initiatives rolled out by the Ghana Education Service. 
For the most part, internet connectivity is poor in rural 
communities and, where connectivity is possible par-
ents are unable to afford data for internet use. Unlike 
urban communities, some rural communities do not 
have electricity to benefit from the government’s strat-
egy of teaching children via national television stations. 
The differing opportunities for children living in dif-
ferent parts of the country contribute to the widening 
of the achievement gaps (Nyatsikor et al., 2020; MOE, 
2016b). 

The second research question explored the impact 
of school location on the achievement of pupils within 
specific regions and the results reveal two important 
educational concerns. First, rural schools in the North-
ern and Western (for P6 mathematics achievement 
only) regions positively contributed to achievement in 
contrast with extant literature which suggests other-
wise (Bashir et al., 2018; MOE, 2018). Like the rural 
localities, many of the urban localities in the Northern 
Region are equally deprived of basic social, econom-
ic and educational amenities that support effective 
teaching and learning (Blampied et al., 2018; MOE, 
2018; UNICEF and CDD-Ghana, 2016). As a result, 
school locale becomes inconsequential since school 
children experience similar challenges and opportuni-
ties. Though this deprivation is prevalent in many parts 
of the country, available data suggest it is acute in the 
Northern region (MOE, 2016a; MOE, 2018, UNICEF and 
CDD-Ghana, 2016). Thus, the communities in which
children attend schools serve as achievement oppor-
tunities and in the case of the Northern region, these
opportunities are significantly similar for all schools
regardless of their status as rural or urban.

In sharp contrast to the Northern region, there was 
a strikingly wide gap in achievement by pupils from ru-
ral and urban schools in the Eastern region, particularly 
for English language achievement. Consistently, rural 
children from the Eastern region were significantly dis-
advantaged. The persistent underachievement of rural 
schools (except for the Northern region) suggests

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Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  80

inherent handicapping conditions associated with rural 
schools which create a “deficit model” for children’s in-
ability to receive superior education (Fan & Chen, 2001; 
Hornby & Lafaele, 2011). The significant gaps in pupils’ 
achievement linked to school location may confirm the 
existence of extreme differences in the characteristics 
of the factors (e.g. socio-economic and educational 
resources) affecting school attainment in this part of 
the country. From the study results, it can be concluded 
that a major part of the generally low academic achieve-
ment among primary school children in Ghana is the 
uneven resources available to rural and urban schools 
as well as their socioeconomic backgrounds. 

Conclusion
The study investigated the impact of school location 

on primary grades 3 and 6 pupils’ academic achieve-
ment and found significant differences in achievement 
attributable to existing inequalities in rural and urban 
schools. Rural schools are deficient in many resourc-
es needed to facilitate effective teaching and learning 
leading to improved outcomes. Rural schoolchildren are 
characterised by high levels of poverty (low SES) rela-
tive to their urban counterparts (high SES). The cumu-
lative impact of the disparities in social, economic and 
educational resources and opportunities for schools 
and communities is the achievement gaps between 
rural and urban school children. 

The results for the Northern region call for a more 
comprehensive and robust investigation including the 
assessment of the threshold of indicators (e.g. econom-
ic, social, educational, and infrastructural resources) 
used to classify localities into rural and urban. The 
gaps in the achievement due to the location of schools 
in different parts of the country appear to suggest that 
children’s present and possibly future educational 
fortunes depend on which part of the country they live 
in. This may be a testament to suggest that Ghana may 
not be achieving the goal of providing an equitable and 
inclusive education for all children as required by the 
country’s educational policy and international goals 
(United Nations, 2015; MOE, 2015). With approximate-
ly 37.0% of primary school level children living in rural 
areas compared with 22.2% in urban areas (GSS, 2012), 
it presupposes that more children in rural communities 
may continue to record low achievements in selected 
school subjects if the disadvantages associated with 
attending rural schools are not addressed.

Ghana may therefore need to invest more resourc-
es per pupil to bridge the achievement gap between the

rural and urban primary school children. It is impera-
tive to suggest that any attempt aimed at closing the ru-
ral and urban achievement gaps must not gloss over the 
more fundamental preschool level. Many studies have 
found statistically significant connections between the 
quality of preschools and primary school achievement 
(Bakken, Brown & Downing, 2017; OECD, 2017).  

Implications of the Study
The findings from the study have enduring im-

plications for all stakeholders in education.  Some 
rural school children failed to be competent, attained 
minimum competency or performed below minimum 
competency in both subjects because of the inequal-
ities between rural and urban schools. Data from the 
Ghana Statistical Service provides evidence of the 
significant differences between the socioeconomic and 
educational resources and opportunities for urban and 
rural dwellers. As developing country, majority of the 
population in the country are characterised by high 
levels of poverty. However, rural schools and children 
experience more disadvantages (e.g. educational and 
economic) and live in higher levels of poverty than their 
urban counterparts. It is incumbent on stakeholders, 
therefore, to ensure that the existing disparities be-
tween rural and urban parts of the country are bridged 
to close the achievement gaps between rural and urban 
school children. 

Limitations of the Study 
This study examined the influence of school lo-

cation on primary level achievement in Mathematics 
and English language subjects and found significant 
achievement gaps between rural and urban schools. 
However, the unavailability of certain data to account 
for (e.g. pupils’ prior achievement, teacher effect, school 
attendance history, household wealth, and parental 
support and community resources) may have over- or 
under-estimated the effects attributed to school loca-
tion.   

Acknowledgment
The lead author is grateful to the USAID and RTI 

International (the primary owners of the dataset) for 
the permission to use the Ghana National Education As-
sessment data for this research publication.   All results 
from this research are the responsibility of the authors 
and do not implicate the custodians of the primary data 
in any way.

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Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies  81

Global Journal of Transformative Education (2020) Vol 2

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1 Maxwell Kwesi Nyatsikor (mnyatsikor@uds.edu.gh) is a 
Lecturer in the Faculty of Education at the University for 
Developmental Studies, Tamale, Ghana.

2 Winston Kwame Abroampa (wkabroampa@knust.edu.
gh) is a Senior Lecturer in the Faculty of Educational 
Studies at Kwame Nkrumah University for Science and 
Technology, Kumasi, Ghana.
3 Kweku Esia-Donkoh (ke_donkoh@yahoo.com) is a 
Senior Lecturer in the Faculty of Educational Studies at 
University of Education, Winneba, Ghana.

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