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 Creative Commons Attribution NonCommercial NonDerivative 4.0 International License (http://creativecommons.org/licenses/ by-nc-nd/4.0/), which permits unrestricted use, distribution, and reproduction without revision in any non-commercial medium, 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 Global Journal of Transformative Education (2020) Vol 2 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. Global Journal of Transformative Education (2020) Vol 2 Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies 81 Global Journal of Transformative Education (2020) Vol 2 References Adeyemi, B.A., Adediran, V.O., & Adewole, O.S. (2018). Influence of parental involvement, parental support and family education on pupils’ adjustment in lower primary schools in Osun State. International Journal of Humanities Social Sciences and Education, 5(4), 67-75. Bakken, L., Brown, N., & Downing, B. (2017). Ear- ly childhood education: The long-term benefits. Journal of Research in Childhood Education, 31(2), 255-269. Bashir, S., Lockheed, M., Ninan, E. & Tan, J-P. (2018). Fac- ing forward schooling for learning in Africa. Interna- tional Bank for Reconstruction and Development / The World Bank. Bhai, M., & Horoi, I. (2019). Teacher characteristics and academic achievement. Applied Economics, 51, 4781–4799. doi: 10.1080/00036846.2019.1597963 Blampied, C. et al. (2018). Leaving no one behind in the health and education sectors: an SDG stocktake in Ghana. London: Overseas Development Institute. Bofah, E. A., & Hannula, M. S. (2015). TIMSS data in an African comparative perspective: Investigating the factors influencing achievement in mathematics and their psychometric properties. Large-Scale As- sessments in Education, 3(1), Article number 4. Bofah, E. A., & Hannula, M. S. (2017). Home resources as a measure of socioeconomic status in Ghana. Large- scale Assessments in Education, 5(1), 1–15. Broer, M., Bai Y., & Fonseca, F. (2019). A review of the literature on socioeconomic status and educational achievement. In: Socioeconomic Inequality and Edu- cational Outcomes. IEA Research for Education (vol 5). Springer, Cham. Bronfenbrenner, U. (2005). Making human beings hu- man: Bioecological perspectives on human develop- ment. Thousand Oaks, CA: Sage. Çiftçi, K.S., & Cin, M.F. (2018). What matters for rural teachers and communities? Educational challenges in rural Turkey. Compare: A Journal of Comparative and International Education, 48(5), 686-701, Cochran-Smith, M., Cannady, M., McEachern, K., Mitch- ell, K., Piazza, P., Power, C., & Ryan, A. (2012). Teach- ers’ education and outcomes: Mapping the research terrain. Teachers College Record, 114(10), 1-49. Cochran-Smith, M. & Zeichner, K.M. (Eds.) (2005). Studying teacher education. The report of the AERA Panel on research and teacher education. Washing- ton DC: Lawrence Erlbaum Associates, Inc. Considine, G., & Zappala, G. (2002). Influence of social and economic disadvantage in the academic per- formance of school students in Australia. Journal of Sociology, 38, 129-148. Destin, M., Hanselman, P., Buontempo, J., Tipton, E., &Yeager, D.S. (2019). Do student mindsets differ by socioeconomic status and explain disparities in academic Achievement in the United States? AERA Open, 5(3), 1-12. Fan, X., & Chen, M. (2001). Parental involvement and students’ academic achievement: A meta-analysis. Educational Psychology Review, 13(1):1-22. Fredriksen, B., & R., Kagia, R. (2013). Attaining the 2050 vision for Africa: Breaking the human capital barri- er. Global Journal of Emerging Market Economies, 5 (3), 269–328. Ghana Statistical Service. (2012). 2010 population and housing census. Summary report of final results. Accra: Ghana Statistical Service. Ghana Statistical Service. (2014). Ghana living stan- dards survey round 6: poverty profile in Ghana: 2005-13. Accra: Ghana Statistical Service. Ghana Statistical Service. (2015). Ghana poverty map- ping report. Accra: Ghana Statistical Service. Hanson, M. J., Miller, A. D., Diamond, K., Odom, S., Lieber, J., Butera, G., Horn, E., Palmer, S., & Fleming, K. (2011). Neighborhood community risk influences on preschool children’s development and school readiness. Infants & Young Children, 24(1), 87–100. Heck, R.H., & Thomas, S.L. (2015). An introduction to multilevel modelling techniques. MLM and SEM approaches using Mplus (3rd ed). Routledge. Taylor and Francis. Hill, H. C., Charalambous, C. Y., & Chin, M. J. (2019). Teacher characteristics and student learn- ing in mathematics: a comprehensive assess- ment. Education Policy, 33, 1103–1134. doi: 10.1177/0895904818755468 Hornby, G., & Lafaele, R. (2011). Barriers to parental involvement in education: an explanatory model. Educational Review, 63(1), 37-52. Hox, J. J., Moerbeek, M., & van de Schoot, R. (2017). Mul- tilevel analysis: Techniques and applications. Quanti- tative methodology series (3rd ed). Routledge. Kim, S.W., Cho, H., & Kim, L.Y. (2019). Socioeconom- ic status and academic outcomes in developing countries: A meta-analysis. Review of Educational Research, 20(10), 1–42. http://dx.doi.org/10.20431/2349-0381.0504007 http://dx.doi.org/10.20431/2349-0381.0504007 http://dx.doi.org/10.20431/2349-0381.0504007 https://www.tandfonline.com/doi/pdf/10.1080/02568543.2016.1273285 https://www.tandfonline.com/doi/pdf/10.1080/02568543.2016.1273285 https://openknowledge.worldbank.org/bitstream/handle/10986/29377/9781464813948.pdf?sequence=15 https://openknowledge.worldbank.org/bitstream/handle/10986/29377/9781464813948.pdf?sequence=15 https://www.odi.org/publications/11093-leaving-no-one-behind-health-and-education-sectors-sdg-stocktake-ghana https://www.odi.org/publications/11093-leaving-no-one-behind-health-and-education-sectors-sdg-stocktake-ghana https://www.odi.org/publications/11093-leaving-no-one-behind-health-and-education-sectors-sdg-stocktake-ghana https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-015-0014-y https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-015-0014-y https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-015-0014-y https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-015-0014-y https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-017-0039-5 https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-017-0039-5 https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-017-0039-5 https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-017-0039-5 https://largescaleassessmentsineducation.springeropen.com/articles/10.1186/s40536-017-0039-5 https://eprints.lancs.ac.uk/id/eprint/88428/1/What_matters_for_human_development_in_rural_education_in_Turkey.pdf https://eprints.lancs.ac.uk/id/eprint/88428/1/What_matters_for_human_development_in_rural_education_in_Turkey.pdf https://eprints.lancs.ac.uk/id/eprint/88428/1/What_matters_for_human_development_in_rural_education_in_Turkey.pdf https://digitalcommons.unl.edu/teachlearnfacpub/219/ https://digitalcommons.unl.edu/teachlearnfacpub/219/ https://digitalcommons.unl.edu/teachlearnfacpub/219/ https://d1wqtxts1xzle7.cloudfront.net/43251095/Considine_and_Zappala_2002_The_influence_of_social_and_economic_disadvantaged_in_the_academic_performance_of_school_students_in_Australi.pdf?1456868754=&response-content-disposition=inline%3B+filename%3DThe_influence_of_social_and_economic_dis.pdf&Expires=1602775528&Signature=SMZ9ekwaC4Lij~OwVCI5G-Sq2iE3j9vfimwaKBw4ryrBdzZbcxVMtuF3Fj~6jBgEFw2NeRRvSmCHk~PAr9IxcnMyKeEPWl6lV2G~0CNf31BN3kcKBDb9VskhQDCXoiyUkOtgj1ZQQMijBUkhlPDGugclreWag0MTdJcEDqUdAlMNOWz3Zxa7CmVBPnCsn8LBkPGyT~F-LAvm8beZdrfpnhZ8FRoS2vnb05t5~ax74O6t7w7q8jRyCSxVHAt0XBmRNbdJp1~SRCNaoXLv0lfuFrdtqzSOSeRx2X~VxFCL7KnZfJydYHAhI0gBDDB0aC1SAYoZK7xjHQGgEuttPbTcHQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA https://d1wqtxts1xzle7.cloudfront.net/43251095/Considine_and_Zappala_2002_The_influence_of_social_and_economic_disadvantaged_in_the_academic_performance_of_school_students_in_Australi.pdf?1456868754=&response-content-disposition=inline%3B+filename%3DThe_influence_of_social_and_economic_dis.pdf&Expires=1602775528&Signature=SMZ9ekwaC4Lij~OwVCI5G-Sq2iE3j9vfimwaKBw4ryrBdzZbcxVMtuF3Fj~6jBgEFw2NeRRvSmCHk~PAr9IxcnMyKeEPWl6lV2G~0CNf31BN3kcKBDb9VskhQDCXoiyUkOtgj1ZQQMijBUkhlPDGugclreWag0MTdJcEDqUdAlMNOWz3Zxa7CmVBPnCsn8LBkPGyT~F-LAvm8beZdrfpnhZ8FRoS2vnb05t5~ax74O6t7w7q8jRyCSxVHAt0XBmRNbdJp1~SRCNaoXLv0lfuFrdtqzSOSeRx2X~VxFCL7KnZfJydYHAhI0gBDDB0aC1SAYoZK7xjHQGgEuttPbTcHQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA https://d1wqtxts1xzle7.cloudfront.net/43251095/Considine_and_Zappala_2002_The_influence_of_social_and_economic_disadvantaged_in_the_academic_performance_of_school_students_in_Australi.pdf?1456868754=&response-content-disposition=inline%3B+filename%3DThe_influence_of_social_and_economic_dis.pdf&Expires=1602775528&Signature=SMZ9ekwaC4Lij~OwVCI5G-Sq2iE3j9vfimwaKBw4ryrBdzZbcxVMtuF3Fj~6jBgEFw2NeRRvSmCHk~PAr9IxcnMyKeEPWl6lV2G~0CNf31BN3kcKBDb9VskhQDCXoiyUkOtgj1ZQQMijBUkhlPDGugclreWag0MTdJcEDqUdAlMNOWz3Zxa7CmVBPnCsn8LBkPGyT~F-LAvm8beZdrfpnhZ8FRoS2vnb05t5~ax74O6t7w7q8jRyCSxVHAt0XBmRNbdJp1~SRCNaoXLv0lfuFrdtqzSOSeRx2X~VxFCL7KnZfJydYHAhI0gBDDB0aC1SAYoZK7xjHQGgEuttPbTcHQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA https://journals.sagepub.com/doi/pdf/10.1177/2332858419857706 https://journals.sagepub.com/doi/pdf/10.1177/2332858419857706 https://journals.sagepub.com/doi/pdf/10.1177/2332858419857706 https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/content/pdf/10.1023/A:1009048817385.pdf&casa_token=nYTeUZ5q22MAAAAA:OKNaAjjEOeJTSwdhlVOaGbiBOZ1YTIphxv-LScKxRzS81_-IOtGLrBSIIiqGVAlBs3ScmvimIBhkpbw https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/content/pdf/10.1023/A:1009048817385.pdf&casa_token=nYTeUZ5q22MAAAAA:OKNaAjjEOeJTSwdhlVOaGbiBOZ1YTIphxv-LScKxRzS81_-IOtGLrBSIIiqGVAlBs3ScmvimIBhkpbw https://journals.sagepub.com/doi/abs/10.1177/0974910113505794 https://journals.sagepub.com/doi/abs/10.1177/0974910113505794 https://journals.sagepub.com/doi/abs/10.1177/0974910113505794 https://doi.org/10.1097/IYC.0b013e3182008dd0 https://doi.org/10.1097/IYC.0b013e3182008dd0 https://doi.org/10.1097/IYC.0b013e3182008dd0 https://www.tandfonline.com/doi/abs/10.1080/00131911.2010.488049 https://www.tandfonline.com/doi/abs/10.1080/00131911.2010.488049 https://journals.sagepub.com/doi/10.3102/0034654319877155 https://journals.sagepub.com/doi/10.3102/0034654319877155 https://journals.sagepub.com/doi/10.3102/0034654319877155 Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies 82 Lenhardt, A., Rocha Menocal, A., & Engel, J. (2015). Gha- na, the rising star: progress in political voice, health and education. London: Overseas Development Institute. Martin, M. O., Mullis, I. V. S., Foy, P., & Hooper, M. (2016). TIMSS 2015 international results in science. Chest- nut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. Meyer, H. & Benavot, A. (Eds) (2013). PISA, power, and policy: The emergence of global educational gover- nance. Symposium Books Ltd. Ministry of Education. (2014). Ghana 2013 national education assessment technical report. Accra: Ghana. Ministry of Education. (2015). Inclusive education policy in Ghana. Accra: MoE Ministry of Education. (2016a). Ghana national educa- tional assessment technical report. Accra:MoE. Ministry of Education. (2016b). Education sector perfor- mance report. Accra: MoE. Ministry of Education. (2018). Ghana education sector analysis. Accra: MoE. Mullis, I.V.S., Martin, M.O., & Loveless, T. (2016). 20 Years of TIMSS: International trends in mathematics and science achievement, curriculum, and instruc- tion. Chestnut Hill, MA: TIMSS & PIRLS Internation- al Study Center, Boston College. Nyatsikor. M.K., Sosu, E.M., Mtika, P. & Robson, D. (2020). Teacher characteristics and children’s educational attainment in Ghana: do some teacher characteristics matter more for children attend- ing disadvantaged schools? Frontiers in Education, 5(162). doi:10.3389/feduc.2020.00162 Organization for Economic Co-operation and Develop- ment. (2016). Low performing students: Why they fall behind and how to help them succeed. Paris:PI- SA. Organization for Economic Co-operation and Develop- ment. (2017). Starting strong: Key OECD indicators on early childhood education and care. Paris:Orga- nization for Economic Co-operation and Develop- ment. Owens, A. (2018). Income segregation between school districts and inequality in students’ achievement. Sociology of Education, 91(1), 1–27. Prasertcharoensuk, T., & Tapkhwa, N. (2016). Schools, parents, and community partnership enhancing students’ learning achievement. Contemporary Edu- cational Researches Journal, 6(1), 30-39. 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. Authors Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In R. Murnane & G. Duncan (Eds.), Whither opportunity? Rising inequality and the uncertain life chances of low-income children. New York, NY: Russell Sage Foundation Press. Reardon, S.F., Fahle, E.M., Kalogrides,D., Podolsky,A., Za´rate, R.C. (2019). Gender achievement gaps in U.S. School Districts. American Educational Research Journal, 56(6), 2474–2508. Rogošić, S., & Baranović, B. (2016). Social capital and educa- tional achievements: Coleman vs. Bourdieu. CEPS Journal, 6(2), 81-100. Ryan, S., Koczberski, G., Curry, G. N., & Germis, E. (2017). Intra-household constraints on educational attainment in rural households in Papua New Guinea. Asia Pacific Viewpoint, 58, 27–40. Tudge, J. R. H., Mokrova, I. L., Hatfield, B. E., & Karnik, R. B. (2009). Uses and misuses of Bronfenbrenner’s bioeco- logical theory of human development. Journal of Family Theory and Review, 1(4), 198–210. United Nations Educational Scientific Cultural Organisation. (2018). Learning at the bottom of the pyramid: Science, measurement, and policy in low income countries. Interna- tional Institute for Educational Planning. UNESCO. United Nations International Cultural Educational Fund and Centre for Democratic Development (CDD)-Ghana. (2016). District league table 2016: calling for central gov- ernment to better target district support. Accra:CDD. United Nations International Cultural Educational Fund and Centre for Democratic Development (CDD)-Ghana. (2019). District League Table II. Monitoring social devel- opment in Ghana. Accra:CDD. United Nations. (2015). Helping governments and stakehold- ers make sustainable development goals a reality. New York: United Nations. World Development Report. (2018). Learning to realize edu- cation’s promise: Overview. Washinton, DC: World Bank Group. http://nacca.gov.gh/wp-content/uploads/2019/04/2016-NEA-Findings-Report_17Nov2016_Public-FINAL.pdf http://nacca.gov.gh/wp-content/uploads/2019/04/2016-NEA-Findings-Report_17Nov2016_Public-FINAL.pdf http://www.moe.gov.gh/assets/media/docs/ESPR2016_Final_Version_Final.pdf http://www.moe.gov.gh/assets/media/docs/ESPR2016_Final_Version_Final.pdf https://www.globalpartnership.org/content/ghana-education-sector-analysis-2018 https://www.globalpartnership.org/content/ghana-education-sector-analysis-2018 http://timss2015.org/timss2015/wp-content/uploads/2016/T15-20-years-of TIMSS.pdf http://timss2015.org/timss2015/wp-content/uploads/2016/T15-20-years-of TIMSS.pdf http://timss2015.org/timss2015/wp-content/uploads/2016/T15-20-years-of TIMSS.pdf http://timss2015.org/timss2015/wp-content/uploads/2016/T15-20-years-of TIMSS.pdf https://read.oecd-ilibrary.org/education/starting-strong-2017_9789264276116-en#page1 https://read.oecd-ilibrary.org/education/starting-strong-2017_9789264276116-en#page1 https://journals.sagepub.com/doi/10.1177/0038040717741180 https://journals.sagepub.com/doi/10.1177/0038040717741180 https://un-pub.eu/ojs/index.php/cerj/article/view/593 https://un-pub.eu/ojs/index.php/cerj/article/view/593 https://un-pub.eu/ojs/index.php/cerj/article/view/593 https://journals.sagepub.com/doi/10.3102/0002831219843824 https://journals.sagepub.com/doi/10.3102/0002831219843824 https://cepsj.si/index.php/cepsj/article/download/89/37 https://cepsj.si/index.php/cepsj/article/download/89/37 https://doi.org/10.1111/apv.12143 https://doi.org/10.1111/apv.12143 https://doi.org/10.1111/j.1756-2589.2009.00026.x https://doi.org/10.1111/j.1756-2589.2009.00026.x https://unesdoc.unesco.org/ark:/48223/pf0000265581 https://unesdoc.unesco.org/ark:/48223/pf0000265581 https://www.unicef.org/ghana/District_League_Table_Report_Final_for_web_Dec_2016.pdf https://www.unicef.org/ghana/District_League_Table_Report_Final_for_web_Dec_2016.pdf https://www.unicef.org/ghana/reports/201819-district-league-table-ii https://www.unicef.org/ghana/reports/201819-district-league-table-ii https://issuu.com/world.bank.publications/docs/9781464810961 https://issuu.com/world.bank.publications/docs/9781464810961 Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies 83 Supplementary Materials Global Journal of Transformative Education (2020) Vol 2 ST1 ST2 Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies 84 Supplementary Materials Global Journal of Transformative Education (2020) Vol 2 ST3 ST4 Nyatsikor, Abroampa & Esia-Donkoh, Impact of School Locale on Competencies 85 Supplementary Materials Global Journal of Transformative Education (2020) Vol 2 ST5 ST6