Sage Journals: Discover world-class research

Abstract

Australian children from regional, rural and remote (RRR) areas exhibit lower educational outcomes than their peers in metropolitan areas. The mechanisms driving the comparatively poorer educational outcomes of children in RRR areas, however, are not well understood. This paper proposes and tests two sets of factors that may be responsible for these disparities: population socio-demographic composition and school climate. Using rich survey and linked administrative data from the Longitudinal Study of Australian Children (n = 9,248 observations), we estimate the relative contribution of these sets of factors to RRR children’s disadvantage in NAPLAN numeracy test scores. Our results indicate that both socio-demographic and school climate factors account for part of the educational disparities between children in RRR and metropolitan areas. These findings suggest that hybrid policy approaches that tackle both the social determinants of educational success and use schools as an intervention site are required to close the achievement gap.

Keywords

Australia equity NAPLAN numeracy remoteness schools test scores

Introduction

Australia is one of the most geographically diverse countries in the developed world, and geographic location constitutes a major factor shaping the personal, social and economic lives of Australian people. A key divide in the Australian geographical hierarchy is that separating major cities – the main hubs of Australian economic and socio-cultural life – and its more isolated and deprived regional, rural and remote (RRR) areas (Anti-Poverty Week, 2019; Baxter et al., 2011; McLachlan et al., 2013; Saunders & Wong, 2012). Typically, individuals residing in major cities tend to fare better than those residing in RRR area across a range of domains, including their education, employment prospects, financial wellbeing, and health outcomes.

Children are not impervious to the disadvantages stemming from living away from urban cores. This observation is in fact well recognised in Australian research on children’s developmental outcomes, including their cognitive and academic progress. For example, children who live in RRR areas exhibit worse educational outcomes than their peers living in metropolitan areas. In 2019, the average National Assessment Program Literacy and Numeracy (NAPLAN) reading score for Year 3 students was 441 scale points in metropolitan areas, compared to 419 in inner regional areas, 406 in outer regional areas, 384 in remote areas, and 311 in very remote areas (Australian Curriculum, Assessment and Reporting Authority (ACARA), 2019). These geographic disparities in student performance have been reported across school years, academic domains, and Australian states/territories, (ACARA, 2019), and their magnitude has increased since NAPLAN was first introduced in 2008 (Smith et al., 2019).

The vast and persistent pattern of educational disadvantage experienced by children from RRR areas has elicited significant policy concerns (ACARA, 2019; Halsey, 2018; Herbert, 2020; Lamb et al., 2015; 2020). Yet the factors driving differences in achievement gaps between children in RRR and metropolitan areas remain poorly understood – hampering progress towards the design and implementation of efficient and effective policy solutions. The present study fills this critical gap in scholarly knowledge by estimating the extent to which achievements gaps between RRR and metropolitan primary- and secondary-school students depend on two sets of factors: (i) children’s and families’ socio-demographic profiles and (ii) the quality and character of school life, as captured by ‘school climate’. Understanding the relative role of these sets of factors has important implications for the types of policies that are necessary to close the gap. To accomplish this, in the remainder of this paper, we leverage unique survey and linked administrative data from Growing Up in Australia: The Longitudinal Study of Australian Children and multiple regression models.

Literature Review

Diverging Educational Outcomes of Children from RRR and Metropolitan Areas

Despite policy efforts to increase equity in the Australian school system (e.g. Halsey, 2018; Herbert, 2020; Lamb et al., 2015; 2020), accumulating empirical evidence points to persistent developmental and educational disparities between children in RRR and metropolitan areas. Disparities favouring metropolitan over RRR students have been reported, for example, in relation to mental health (e.g. Bowman et al., 2017; Dray et al., 2016), academic performance (e.g. ACARA, 2019; Smith et al., 2019; Thomson et al., 2017), and enrolment in higher education (e.g. Chesters & Cuervo, 2021).

Remoteness has also been shown to be negatively associated with various post-school outcomes (Chesters & Cuervo, 2021; National Centre for Vocational Education Research., 2020; Universities Australia, 2020). For instance, despite a significant increase in the number of undergraduate students from RRR areas (from 110,124 in 2008 to 162,985 in 2018), in 2019, the percentage of people aged 25–34 years old with a university degree was 45.5% in metropolitan areas compared to less than a quarter (23.7%) in RRR areas (Universities Australia, 2020). Further, drawing on nationally representative data from the Longitudinal Surveys of Australian Youth (LSAY), Chesters and Cuervo (2021) showed that RRR students are less likely to enrol in higher education, even after controlling for academic achievement at age 15 and Australian Tertiary Admission Rank (ATAR) scores.

Research shows that RRR communities in Australia experience a ‘unique concentration of multiple forms of educational disadvantage’ (Smith et al., 2019, p.135). Young people in RRR areas face various barriers to access support (e.g. Aisbett et al., 2007; Smith et al., 2019) and constraints to achieve better outcomes (e.g. Halsey, 2018; Parker et al., 2016). As Smith et al. (2019) summarised, such barriers include distance to metropolitan areas, higher levels of poverty, a sparse population with limited access to resources, small school sizes with composite classes, and difficulties in attracting and retaining quality teachers. In addition, recent studies point to the unequal availability of cultural and social resources in RRR areas, which limit rural students’ aspirations for higher education options (Cuervo et al., 2019).

Population Composition as a Driver of Differences in Student Outcomes

A wealth of Australian social-science literature shows that children and families in rural areas ‘look’ demographically and socio-economically different to those residing in metropolitan areas (see e.g. Baxter et al., 2011). Critical to our arguments, these differences encompass factors that can act as barriers to, or facilitators of, children’s educational success. One example is parental education, a resource known to dramatically enhance children’s educational outcomes (see e.g. Black et al., 2005; Dickson et al., 2016). This enhancement occurs through channels such as parental ability and predisposition to help children with homework, transmission of educational aspirations and expectations and a shared genetic pool (Davis-Kean et al., 2021). However, the population composition in RRR areas features an overrepresentation of individuals with low levels of formal education. According to the Australian Bureau of Statistics [ABS] (2022), the share of individuals with a bachelor’s degree was 36% in major cities, 20% in inner regional areas, 16% in outer regional areas, and 17% in remote and very remote areas.

Similarly, the Australian household income of people living outside of capital cities in Australia was, on average, 18% lower than that of people living in capital cities, whereas household net worth was 29% lower (ABS, 2017). Yet evidence indicates that parental income is a major enabler to children’s educational achievements, through channels such as the ability to pay for tutors, enrol children in high-quality schools, or purchase expensive educational equipment (e.g. computer hardware and software) (Duncan et al., 1994; Duncan & Brooks-Gunn, 1999). Further, RRR areas tend to house greater shares of individuals from vulnerable population groups, including Aboriginal and Torres Strait Islander Australians – who face significant additional challenges to education, including systemic racism, intergenerational trauma and western-centric curricula (see e.g. Bodkin-Andrews & Carlson, 2016; Brown, 2019).

Collectively, the evidence discussed within this section suggests that socio-demographic factors may explain a non-negligible portion of the achievement gap between students from RRR and metropolitan areas. However, as we elaborate in the next section, other factors at the school level may also play an important role.

School Climate as a Plausible Mechanism Driving RRR Student Disadvantage

Besides differences rooted at the child or family levels, there are also differences between RRR and metropolitan areas in a critical component of the education system: schools. Differences in the characteristics of schools across geographical areas may thus be responsible for the diverging outcomes of students living in RRR and metropolitan areas. Which school characteristics may be at play? To guide this difficult question, we refer to a well-established conceptual framework in education research, namely that of school climate. School climate can be broadly defined as the quality and character of school life; it is ‘based on patterns of people’s experiences of school life and reflects norms, goals, values, interpersonal relationships, teaching and learning practices, and organisational structures’ (Cohen et al., 2009, p. 180).

School climate is a multidimensional construct encompassing many aspects of school life (Cohen et al., 2009; Thapa et al., 2013; Wang & Degol, 2016). Despite a lack of consensus about the specific indicators contributing to school climate (Cohen et al., 2009; Grazia & Molinari, 2020; Thapa et al., 2013; Wang & Degol, 2016), there is some agreement about four key domains. These are as follows: (i) safety (e.g. discipline and order, physical and emotional safety), (ii) academic climate (e.g. teaching and learning practice, quality of instruction and professional development), (iii) relationships and community (e.g. respect for diversity, quality of relationships) and (iv) institutional environment (e.g. size of school, availability of resources). For each of these domains, there is strong empirical evidence – predominantly from the US – indicating that a positive school climate is predictive of students’ academic achievement, school success and healthy development (see e.g. Cohen et al., 2009; Wang & Degol, 2016).

The distribution of school climate factors across the Australian geography is, however, not uniform. Instead, there is evidence that schools located in RRR areas feature less positive school climates than those located in metropolitan areas. For example, an Australian study by Sullivan and colleagues using Programme for International Student Assessment (PISA) data compared school climate factors across metropolitan and rural schools. Their findings indicated that, in rural areas, students reported less positive disciplinary climates, principals perceived less positive teacher and student behaviours, and both students and principals perceived less positive learning environments (Sullivan, et al., 2014).

Similarly, Lamb et al. (2014) argued that differences in educational outcomes between RRR and metropolitan students can be explained by schools in rural areas being smaller. As the authors note, small schools ‘tend to have fewer resources, are often less able to employ specialist staff or offer specialist subjects or programs, have to use composite multigrade classes, provide fewer opportunities for professional development, have more difficulty recruiting and retaining teachers, provide less support for special needs students and offer fewer options for courses’ (Lamb et al., 2014, p. 68).

The comparatively poorer average school climate in RRR compared to metropolitan schools may be traced back to lower levels of government and self-generated funding (relative to need) amongst schools in these areas (Independent Schools Council of Australia, 2017; Lamb et al., 2014), as well as challenges faced by such schools in attracting and retaining suitably qualified staff (Lamb et al., 2014; OECD, 2019; Young et al., 2018). Taken together, this evidence suggests that differences in school climate between RRR and metropolitan areas may be partially responsible for differences in student outcomes between these areas.

The Present Study: Aims and Contributions

The aim of the present study is to quantify the extent to which socio-demographic and school climate factors can explain differences in educational achievement between RRR and metropolitan students. In doing so, we make three key contributions to the literature.

First, we provide distinctly robust and up-to-date estimates of the true extent of the educational disadvantage experienced by RRR students, as denoted by NAPLAN numeracy test scores. These data come from administrative linkages from official records, which minimises the reporting errors and social desirability biases inherent to self- or proxy-reports of educational attainment.

Second, we offer a novel application of the concept of school climate within quantitative education research in the Australian context. Leveraging the rich data at hand, we document how multiple indicators tapping each dimension of school climate are empirically associated with Australian students’ test scores from Years 3–9.

Third, understanding the relative role of socio-demographic and school-level factors as drivers of differences in achievement between RRR and metropolitan students has critical implications for the design and implementation of equity policies that can successfully ‘close the gap’. A preponderance of socio-demographic factors would imply that differences in student outcomes are compositional and depend on the characteristics of families and children who reside in RRR areas. This would hint at the need for policies that address those socio-demographic factors that are malleable – for example, parental education and income. A dominance of school climate factors, however, would indicate that differences in student outcomes are driven by school characteristics and practices. Such a finding would suggest the need to intervene at schools.

Data and Methods

Dataset: The Longitudinal Study of Australian Children

The aim of this study is to examine the degree to which socio-demographic and school climate factors can explain the diverging academic performance of students in RRR and metropolitan areas. To accomplish this, we use data from a large-scale longitudinal survey, Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC). LSAC is a nationally representative study following the development of ∼10,000 children in Australia. The study began in 2004 with a representative sample of children from urban and rural areas of all states and territories in Australia (Mohal et al., 2022). Data are collected every 2 years from two cohorts of young people, one comprising children aged 0–1 year in 2004 (B cohort) and one comprising children aged 4–5 years in 2004 (K cohort). As of mid-2021, 8 full waves of LSAC data are available.

LSAC study informants include the study child, their parents (both resident and non-resident), teachers and carers. With informant (parent) consent, the LSAC data have also been linked to several administrative databases. This includes NAPLAN scores capturing official assessments of children’s literacy and numeracy performance in Years 3, 5, 7 and 9, as well as school-level data from the My School dataset maintained by the Australian Curriculum, Assessment and Reporting Authority’s (ACARA). The latter provides information about key characteristics of the child’s school, such as location, sector, and student and staff numbers.

The LSAC data are ideal to achieve our research aims, as they incorporate rich information on key socio-demographic characteristics of students and their families, including their place of residence; multiple indicators tapping the four domains of school climate (including through linkages to administrative records on schools’ institutional features); and objective measures of students’ academic achievement (from linkages to NAPLAN). For the purposes of this project, we use data from Waves 2 to 5 for K-cohort children, for whom we were able to observe NAPLAN numeracy scores for school Years 3, 5, 7 and 9. After listwise deletion of records with missing data on the explanatory variables, our analytic sample includes 9,248 observations from 3,698 children.¹

Measures

Key Explanatory Variable: RRR Status

Our RRR measure is based on the Remoteness Areas Structure within the Australian Statistical Geography Standard (ASGS), which provides a statistical hierarchy of areas based on a measure of relative access to services (ABS, 2022). Consistent with current practice in Australian education research (see e.g. Halsey, 2018), we construct an indicator capturing regional, rural and remote (RRR) areas by combining the following categories of the ASGS: inner regional areas, outer regional areas, remote areas and very remote areas. The remaining category of major cities is therefore used as the contrast. Across LSAC survey waves, 11.7–15.2% of our sample resided in RRR areas.

Outcome Variable: NAPLAN Numeracy Test Scores

To measure academic performance, we use objective test scores obtained from LSAC’s administrative data linkages. Specifically, we use scores from the National Assessment Program – Literacy and Numeracy (NAPLAN), a national standardised testing program assessing students’ fundamental literacy and numeracy skills. NAPLAN is undertaken annually by students in Years 3 and 5 (primary school) and Years 7 and 9 (secondary school). In this paper, we primarily use results from NAPLAN numeracy tests, which assess students on their proficiency in understanding, fluency, problem-solving and reasoning across the three content strands of mathematics (numbers and algebra, measurement and geometry, and statistics and probability) (ACARA, 2017). The use of NAPLAN numeracy scores as a global indicator of academic achievement is common in Australian education research (see e.g. Smith et al., 2019; Tomaszewski et al., 2020). NAPLAN scores range from a theoretical minimum of 0 to a theoretical maximum of 1,000, with higher scores indicating greater performance.

Intervening Variables: Socio-Demographic and School Climate Factors

The socio-demographic variables used in the analyses represent factors known to affect children’s educational performance. They include child’s age (expressed in months), child’s sex (male/female), child’s Aboriginal and Torres Strait Islander background (yes/no), child’s non-English-speaking background (NESB) (yes/no), maternal educational qualifications (degree-level/below degree-level), weekly parental income (expressed in AU$1,000s), household type (two-parent family/one- or no-parent family) and presence of siblings (yes/no).

The richness of the LSAC data allow us to derive several indicators for each domain of school climate. This includes:

• two indicators of the school-safety domain (teacher-reported school discipline policy, and student-reported experiences of bullying);

• four indicators of the academic domain (teachers’ evaluations of the quality of their work environment, teachers’ self-efficacy, frequency with which teachers organise their class by achievement level, and teachers’ use of computers in class);

• six indicators for the domain of relationships and community (teachers’ perceptions of parent involvement in school activities, teachers’ perceived relationships with the students, parents’ perceptions of their interaction with the school, teachers’ perceptions of students’ behavioural engagement in school, students’ perceptions of their relationship with teachers, and students’ enjoyment of school); and

• four indicators of the institutional environment domain, retrieved from linked administrative data from the MySchool dataset (school sector, school size, staff-to-student ratio, and proportion of students from non-English speaking backgrounds).

Details on the derivation and properties of each of these school-level variables are presented in Table A1 in the Appendix. Due to data availability, some of these indicators are only used in analyses of primary school outcomes (NAPLAN scores for Years 3 and 5) and some are only used in analyses of secondary school outcomes (NAPLAN score for Years 7 and 9).²

Analytic Strategy

To estimate the degree to which (i) socio-demographic factors and (ii) school climate factors are responsible for the observed gaps in test-score outcomes between RRR and metropolitan students, we fit a series of ordinary least squares (OLS) regression models using Stata 17 software (StataCorp, 2021). We begin by fitting a base model (Model 1) in which the outcome variable is the NAPLAN numeracy test score for a given school year (3, 5, 7 or 9) and the only explanatory variable is an RRR indicator. This model gives us a base or raw estimate of the achievement gap between RRR and metropolitan students, as captured by the coefficient on the RRR variable.

We subsequently fit additional regression models in which we add to Model 1 socio-demographic factors (Model 2), school climate factors (Model 3), and both socio-demographic and school climate factors (Model 4). By comparing the magnitude and statistical significance of the coefficient on the RRR variable across models, we can ascertain the degree to which socio-demographic and school climate factors can account for differences in NAPLAN scores between RRR and metropolitan students. Specifically, any movement of the RRR coefficient towards zero or towards statistical non-significance indicates that the added variables are responsible for (part of) the differences. We repeat this process for the four outcome variables of interest, capturing NAPLAN numeracy test scores for school Years 3, 5, 7 and 9.³

Findings/Results

Unadjusted Differences in Test Scores, Socio-Demographic Factors and School Climate Factors between RRR and Metropolitan Students

Table 1 shows variable means for the complete sample and for students from RRR and metropolitan areas separately (for wave-in-wave sample means, see Table A2 in the Online Appendix). The statistics reveal marked differences in average NAPLAN numeracy scores between RRR and metropolitan students, favouring the latter. Average scores for RRR students were 406.5 (Year 3), 483.4 (Year 5), 544.3 (Year 7) and 592.5 (Year 9), compared to 427.4 (Year 3) 507.2 (Year 5), 563.5 (Year 7) and 608.4 (Year 9) for metropolitan students.

Table 1.

Sample Means/Percentages for Model Variables.

	All	Metropolitan	RRR
Outcome variables
NAPLAN numeracy score, Year 3	424.9	427.4	406.5
NAPLAN numeracy score, Year 5	503.8	507.2	483.4
NAPLAN numeracy score, Year 7	560.6	563.5	544.3
NAPLAN numeracy score, Year 9	606.4	608.4	592.5
Key explanatory variable
RRR, %	13.7	0.0	100.0
Socio-demographic factors
Child’s age (in months)	119.9	119.8	120.8
Child is male, %	50.8	50.8	51.3
Child is Aboriginal or Torres Strait Islander, %	2.4	1.7	6.8
Child comes from NESB, %	9.0	9.9	3.5
Child’s mother has a degree, %	34.3	35.3	27.4
Weekly parental income, in AU$1,000s	1.9	2.0	1.6
One parent in the household, %	13.2	13.2	13.3
Child has siblings, %	91.7	91.9	90.0
School climate factors
Safety
School discipline index	4.0	4.0	3.9
Child experienced bullying, %	29.2	28.4	34.2
Academic
Teacher work environment index	4.2	4.2	4.1
Teacher efficacy index	4.4	4.4	4.3
Class in groups index	6.4	6.4	6.3
Teacher use of technology in class	3.0	3.0	2.9
Relational
Teacher’s parent involvement practice index	3.6	3.7	3.1
Carer relationship quality index	4.4	4.4	4.3
Parent interaction with teacher/school	3.7	3.7	3.8
School social capital index	3.5	3.5	3.5
School engagement index	4.2	4.2	4.1
Child enjoys school	3.8	3.8	3.8
Student–teacher relationship index	3.1	3.1	3.1
Institutional
School type, %
Government	60.6	59.7	66.7
Catholic	22.6	22.3	24.9
Independent	16.7	18.0	8.4
Small school (700 or fewer students), %	70.1	67.5	86.3
FTE staff-to-student ratio	15.8	16.0	14.9
Percentage of NESB students (in 10s), %	1.6	1.7	0.8
N (Children)	9,248	7,978	1,270

Notes: LSAC, Cohort K, Waves 2 to 5. RRR: Regional/remote/very remote area. NAPLAN: National Assessment Program – Literacy and Numeracy. NESB: non-English-speaking background.

As expected, there were also statistically significant differences in the socio-demographic characteristics of RRR and metropolitan students. For example, a greater share of RRR (6.8%) than metropolitan (1.7%) students were Indigenous, whereas a greater share of metropolitan (9.9%) than RRR (3.5%) students were from non-English speaking backgrounds. A higher percentage of mothers had university-level qualifications in metropolitan compared to RRR areas (35.3% compared to 27.4%) and weekly parental income was approximately $400 greater in metropolitan than RRR areas ($2,000 compared to $1,600). There were smaller geographical differences in household composition, children’s ages and gender, and presence of siblings.

The characteristics of schools also differed by area of residence. For example, compared to children in RRR areas, children in metropolitan areas faced greater levels of school discipline (4.0 compared to 3.9 units), school engagement (4.2 compared to 4.1 units), positive teacher work environments (4.2 compared to 4.1 units), teacher efficacy (4.4 compared to 4.3 units), use of technology in class (3.0 compared to 2.9 units), parental involvement practices (3.7 compared to 3.1 units), carer relationship quality (4.4 compared to 4.3 units) and staff-to-student ratios (16.0 compared to 14.9), and were more likely to attend independent schools (18.0% compared to 8.4%). Meanwhile, compared to children in metropolitan areas, children in RRR areas had greater levels of parents’ interaction with teachers (3.8 compared to 3.7 units) and were more likely to be bullied (34.2% compared to 28.4%) and to attend small (86.3% compared to 67.5%) and government (66.7% compared to 59.7%) schools.

Altogether, these descriptive statistics point to systematic differences of various magnitudes between children in RRR and metropolitan areas in socio-demographic and school climate factors. Typically, students in metropolitan areas were in more advantageous circumstances than their peers from RRR areas. These factors may act as intervening variables in the relationship between area of residence and children’s test scores. In the next section, we empirically examine this proposition.

Estimating the Contribution of Socio-Demographic and School Climate Factors to Test-Score Gaps between RRR and Metropolitan Students

Table 2 shows the results from our regression models, which are also represented graphically in Figure 1. The models in Panel 1 include the RRR indicator as the sole explanatory variable. These models describe the raw differences in NAPLAN numeracy test scores between RRR and metropolitan students. The results show that, in the absence of other covariates, RRR students attain lower test scores than those attained by their metropolitan peers. The differences amount to 20.90 units in school Year 3, 23.82 units in Year 5, 19.20 units in Year 7 and 16.0 units in Year 9, and they are all statistically significant (p < 0.001 in all cases). Measures of effect sizes using Cohen’s D indicated that these differences are moderate, ranging from D = 0.22 (Year 9) to D = 0.32 (Year 5).

Table 2.

Regression Models of NAPLAN Numeracy Test Scores.

	NAPLAN numeracy Y3^a	NAPLAN numeracy Y5^b	NAPLAN numeracy Y7^c	NAPLAN numeracy Y9^d
Panel 1: RRR only
Regional/remote/very remote	−20.90^***	−23.82^***	−19.20^***	−15.93^***
N (Children)	1,619	2,939	2,309	2,381
R² Overall/R²	0.01	0.01	0.01	0.01
Panel 2: RRR & socio-demographic factors
Regional/remote/very remote	−11.01^*	−14.62^***	−10.03^*	−8.50^*
N (Children)	1,619	2,939	2,309	2,381
R² Overall/R²	0.09	0.10	0.13	0.14
% decrease relative to Panel 1	47.3%	38.6%	47.8%	46.6%
Panel 3: RRR & school climate factors
Regional/remote/very remote	−15.91^**	−19.88^***	−10.78^*	−3.08
N (Children)	1,619	2,939	2,309	2,381
R² Overall/R²	0.05	0.06	0.09	0.16
% decrease relative to Panel 1	23.9%	16.5%	43.9%	80.7%
Panel 4: RRR, socio-demographic & school climate factors
Regional/remote/very remote	−8.16	−12.47^**	−4.54	−0.28
N (Children)	1,619	2,939	2,309	2,381
R² Overall/R²	0.12	0.13	0.18	0.25
% decrease relative to Panel 1	61.0%	47.6%	76.4%	98.2%
Wald test: β _RRR (Panel 1) = β _RRR (Panel 4); p-value	0.11	0.04	0.01	0.01

Notes: LSAC, Cohort K. Ordinary least squares models.

^aWave 2 data.

^bWave 3 data.

^cWave 4 data.

^dWave 5 data.

RRR: Regional/remote/very remote area. NAPLAN: National Assessment Program – Literacy and Numeracy. Statistical significance: ^* p < 0.05, ^** p < 0.01, ^*** p < 0.001.

Figure 1.

Differences in NAPLAN numeracy test scores between students from RRR and metropolitan areas. Notes: LSAC, Cohort K, Waves 2 to 5. Based on the results presented in Table 2. RRR: Regional/remote/very remote area. NAPLAN: National Assessment Program – Literacy and Numeracy.

The addition of the socio-demographic factors in the models within Panel 2 reduces these differences by 38.6% (Year 5) to 47.8% (Year 7), relative to the models in Panel 1. However, all differences remain statistically significant (at, at least, p < 0.05). Similarly, the addition of the school climate factors in Panel 3 reduces the differences in NAPLAN numeracy scores between RRR and metropolitan students by 16.5% (Year 5) to 80.7% (Year 9), relative to the models in Panel 1. Yet again, the RRR coefficients remain statistically different from zero.

The models in Panel 4 incorporate both socio-demographic and school climate factors simultaneously. In these models, differences between RRR and metropolitan students are reduced even further, from 47.6% (Year 5) to 98.2% (Year 9), relative to the raw estimates in Panel 1. In these fully specified models, the test-score gaps are thus much smaller: 8.16 units in school Year 3, 12.47 units in Year 5, 4.54 units in Year 7 and 0.28 units in Year 9. Only the difference for school Year 5 remains statistically significant. Results from Wald tests indicated that differences in the RRR coefficients in the models in Panel 1 and Panel 4 were statistically different from each other, at p < 0.05, in 3 of 4 cases. The only exception was the difference for the Year 3 NAPLAN scores, which only approached but did not reach statistical significance (p = 0.11). This is likely due to the smaller sample sizes in these models.

Collectively, these results suggest that, consistent with expectations, both socio-demographic factors and school climate factors contribute to the academic disadvantage experienced by RRR students.

Individual, Family and School Factors that Improve Student Test Scores

Reviewing the results from our fully specified models (Model 4), we can determine which specific factors are significantly associated with students’ NAPLAN test scores. The full sets of results from these models are presented in Table 3.

Table 3.

Regression Models of NAPLAN Numeracy Test Scores, Fully Specified Models.

	NAPLAN numeracy Y3^a	NAPLAN numeracy Y5^b	NAPLAN numeracy Y7^c	NAPLAN numeracy Y9^d
Key explanatory variable
Regional/remote/very remote	−8.16	−12.47^**	−4.54	−0.28
Socio-demographic factors
Child’s age (in months)	1.13	0.09	−0.21	−0.16
Child is male	8.02^*	12.60^***	12.64^***	24.16^***
Child is Aboriginal or Torres Strait Islander	−40.63^***	−39.39^***	−38.57^***	−31.69^***
Child comes from NESB	11.63	24.64^***	26.82^***	19.97^***
Child’s mother has a degree	27.50^***	24.56^***	29.74^***	25.96^***
Weekly parental income, in AU$1,000s	7.03^***	6.08^***	4.97^***	4.76^***
One parent in the household	−4.38	−5.87	−4.83	−1.59
Child has siblings	−10.30	−2.71	1.16	3.71
School climate factors
Safety
School discipline index	8.14^**	4.75^*	−4.42	2.59
Child experienced bullying	−12.44^**	−13.26^***	−12.90^***	−3.35
Academic
Teacher work environment index	−0.28	1.03	3.24	−6.55^*
Teacher efficacy index	3.26	−1.47	9.03^**	5.61
Class in groups index	1.44	0.21	−0.19
Teacher use of technology in class				0.46
Relational
Teacher’s parent involvement practice index	1.31	0.06	0.42
Carer relationship quality index	6.65	4.91	6.24^*
Parent interaction with teacher/school	2.43^*	4.28^***	4.29^***
School social capital index	−1.24	1.30	2.35
School engagement index				40.69^***
Child enjoys school				0.62
Student–teacher relationship index				1.42
Institutional
School type (Ref. Government)
Catholic	2.89	−7.27^*	−13.26^***	0.20
Independent	9.11	2.63	11.63^*	17.15^***
Small school (700 or fewer students)	−8.02	−2.08	−12.31^**	−6.72^*
FTE staff-to-student ratio	0.02	0.47	1.47^**	−0.39
Percentage of NESB students (in 10s)				3.87^***
N (Children)	1,619	2,939	2,309	2,381
R ²	0.12	0.13	0.18	0.25

Notes: LSAC, Cohort K. Ordinary least squares models.

^aWave 2 data.

^bWave 3 data.

^cWave 4 data.

^dWave 5 data.

OLS: RRR: Regional/remote/very remote area. NAPLAN: National Assessment Program – Literacy and Numeracy. NESB: Non-English-Speaking Background. Statistical significance: ^* p < 0.05, ^** p < 0.01, ^***p < 0.001.

We observe some significant associations for socio-demographic factors. Across models, boys achieve higher NAPLAN numeracy test scores than girls, and this difference increases over time. Aboriginal or Torres Strait Islander students receive consistently lower scores than non-Indigenous students, whereas students from non-English speaking backgrounds attain consistently higher scores than students from English-speaking backgrounds. Both maternal education and parental income exhibit large, positive and statistically significant associations with children’s NAPLAN numeracy test scores.

The estimated effects on the school climate factors are less patterned and, in almost all cases, they are modest in terms of effect size (Cohen’s D <0.20). However, many are statistically significant in ways that align with theoretical expectations. For example, in the domain of safety, greater levels of school discipline are associated with greater test scores in Years 3 and 5, whereas experiences of bullying are associated with decreases in test scores in Years 3, 5 and 7. In the academic domain, teachers’ self-efficacy are associated with increases in Year 7 test scores. In the relational domain, greater carer relationship quality (in Year 7) and parental interactions with teachers (in Years 3, 5 and 7) are associated with increases in test scores, and so does the child’s degree of school engagement (in Year 9). In the institutional domain, there are some statistically significant differences in students’ test scores by school type (government, catholic or independent), some penalties to attending small schools (in Years 7 and 9), and a premium to ethnic diversity (in Year 9).

Altogether, these results are largely consistent with expectations, and point to issues with equity in student achievement along the lines of socio-demographic and school climate factors.

Discussion and Conclusion

Our results revealed that, if RRR students came from the same socio-demographic backgrounds and went to schools with the same school climate characteristics as metropolitan students, their disadvantage in NAPLAN numeracy performance would narrow from approximately 16–24 units (depending on the year) to approximately 0.3–12.5 units, or by 48%–98%. Socio-demographic factors alone explained 38.6%–47.8% of the gaps, compared to 16.5–80.7% for school climate factors alone. While the explanatory power of socio-demographic factors was consistent across school years, that of school climate factors was substantially greater in secondary school (Years 7 and 9). In relation to theory, our results confirm the applicability of the concept of school climate and its different dimensions to quantitative education research in Australia. In this regard, we document that associations that had been previously reported in research based in the United States of America (e.g. Cohen et al., 2009; Wang & Degol, 2016), are also apparent in Australia. For example, we found that school-level factors such as school discipline, bullying prevention, teachers’ self-efficacy, teacher–student relationship quality and parental engagement were positively associated with academic outcomes. These results point to avenues to improve schools in ways that can both lift the educational outcomes of all students and reduce achievement disparities between metropolitan and RRR students.

Limitations

Despite the high representativeness of the LSAC dataset, children residing in remote and very remote areas are under-represented in the sample and more likely to experience panel attrition (see Mohal et al., 2022). Therefore, the findings presented here may not account fully for the experiences of the relatively small number of children in remote and very remote areas. Second, the small sample sizes for these groups precluded analyses of differences between different types of regional areas (i.e. inner regional, outer regional, remote and very remote areas). Future studies may wish to replicate our findings using data that do not suffer from these shortcomings. Third, despite the many school climate indicators available in LSAC, some potentially important ones are missing. For example, we are unable to incorporate information on school resources or teacher experience, both of which have been shown to be important school climate factors contributing to students’ academic success (see e.g. Wang & Degol, 2016). The omission of these variables may result in an under-estimation of the role of school climate factors in explaining differences in school outcomes between RRR and metropolitan students. Future research should therefore aim to incorporate these and other school climate factors into the analyses.

Implications

Despite these limitations, our results bear important implications for policy and practice. Since both population socio-demographic traits and school characteristics played a prominent role in our analyses, we interpret our findings as calling for hybrid policy approaches to reducing RRR student disadvantage.

On the one hand, interventions should continue targeting the social determinants of children’s educational performance. For example, broad policies that increase access to (parental) education and lift incomes in RRR areas are likely to have indirect, positive effects on the school outcomes of children in these areas. Similarly, policies aimed at improving Indigenous access to education may have downstream effects by lifting the school outcomes of children in RRR areas to a greater extent. Increases in the quality of education and educational attainment of young people in regional areas are likely to result in long-term effects not only on their own skills and earnings capacity but also on their children’s developmental and educational outcomes. It is nevertheless possible that policies aimed at lifting parental education and income in regional areas would not immediately change the educational outcomes of the current cohort of children, as parental influences on their children’s development occur early within the life course and begin prior to school entry (Fay-Stammbach et al., 2014).

On the other hand, our results confirm that schools are a social environment ripe for policy intervention (see also Halsey, 2018). In this regard, programs that target bullying (e.g. the National Safe Schools Framework, Positive Behaviour for Learning, Friendly Schools, KidsMatter and MindMatters), teacher self-efficacy (see George et al., 2018; Klassen & Tze, 2014; Morris et al., 2017; Zee & Koomen, 2016); and parental engagement (see Barger et al., 2019; Castro et al., 2015; Willis et al., 2021), amongst others, may result in better educational outcomes for children in RRR areas.

Nonetheless, in implementing any of the aforementioned strategies to lift the outcomes of students in RRR areas, it is important to recognize the role of marketisation dynamics – including school choice and sector dynamics – in generating segregated school contexts in many RRR communities. For example, well-off families often send their children to boarding schools in metropolitan areas, or to non-government schools in nearby regional towns (see e.g. Perry, 2018). These dynamics, which are arguably supported and incentivised by federal and state policies, might lead to residual school contexts in regional areas (Perry, 2018). It follows that successfully improving educational outcomes in regional areas may also require efforts to make schools less socially segregated.

Supplemental Material

Supplemental Material - Explaining achievement gaps between students from regional and metropolitan areas: Accounting for socio-demographic and school climate factors

Supplemental Material for Explaining achievement gaps between students from regional and metropolitan areas: Accounting for socio-demographic and school climate factors by Francisco Perales, Melissa Johnstone, Ning Xiang, and Wojtek Tomaszewski in Australian Journal of Education

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Queensland Department of Education through the Education Horizon research grant scheme. Any claims made in this article represent the authors’ views, and do not necessarily represent the views of the Department. The research was also supported by the Australian Research Council Centre of Excellence for Children and Families over the Life Course (project number CE200100025).

ORCID iDs

Francisco Perales

Ning Xiang

Supplemental Material

Supplemental material for this article is available online.

Notes

References

Aisbett

Boyd

Francis

Newnham

(2007). Understanding barriers to mental health service utilization for adolescents in rural Australia. Rural and Remote Health, 7(1), 624. https://doi.org/10.22605/RRH624

Anti-Poverty Week. (2019). Poverty in Rural & Regional Australia. https://antipovertyweek.org.au/wp-content/uploads/2019/10/Poverty-in-Rural-Regional-Aus-APW-2019.pdf

Australian Bureau of Statistics . (2017). Household income and wealth, Australia, 2015–16. (No. ABS. 6523.0).

Australian Bureau of Statistics. (2022). Education and Work, Australia. https://www.abs.gov.au/statistics/people/education/education-and-work-australia/may-2022

Australian Curriculum, Assessment and Reporting Authority. (2017). The Australian National Assessment Program Literacy and Numeracy (NAPLAN) Assessment Framework: NAPLAN online 2017-2018. https://www.nap.edu.au/docs/default-source/default-document-library/naplan-assessment-framework.pdf?sfvrsn=2

Australian Curriculum, Assessment and Reporting Authority. (2019). National Assessment Program—Literacy and Numeracy: Achievement in reading, writing, language convention and numeracy: National report for 2019. https://nap.edu.au/docs/default-source/default-document-library/2019-naplan-national-report.pdf

Barger

M. M.

Kim

E. M.

Kuncel

N. R.

Pomerantz

E. M.

(2019). The relation between parents’ involvement in children’s schooling and children’s adjustment: A meta-analysis. Psychological Bulletin, 145(9), 855–890. https://doi.org/10.1037/bul0000201

Baxter

Gray

Hayes

. (2011). Families in regional, rural and remote Australia. Australian Institute of Family Studies. https://aifs.gov.au/sites/default/files/publication-documents/fs201103_0.pdf

Black

S. E.

Devereux

P. J.

Salvanes

K. G.

(2005). Why the apple doesn’t fall far: Understanding intergenerational transmission of human capital. American Economic Review, 95(1), 437–449. https://www.doi.org/10.1257/0002828053828635

10.

Bodkin-Andrews

Carlson

(2016). The legacy of racism and Indigenous Australian identity within education. Race Ethnicity and Education, 19(4), 784–807. https://doi.org/10.1080/13613324.2014.969224

11.

Bowman

McKinstry

McGorry

(2017). Youth mental ill health and secondary school completion in Australia: Time to act. Early Intervention in Psychiatry, 11(4), 277–289. https://doi.org/10.1111/eip.12357

12.

Brown

(2019). Indigenous young people, disadvantage and the violence of settler colonial education policy and curriculum. Journal of Sociology, 55(1), 54–71. https://doi.org/10.1177/1440783318794295

13.

Castro

Expósito-Casas

López-Martín

Lizasoain

Navarro-Asencio

Gaviria

J. L

(2015). Parental involvement on student academic achievement: A meta-analysis. Educational Research Review, 14, 33–46. https://doi.org/10.1016/j.edurev.2015.01.002

14.

Chesters

Cuervo

(2021). (In)equality of opportunity: Educational attainments of young people from rural, regional and urban Australia. The Australian Educational Researcher, 49, 1–19. https://doi.org/10.1007/s13384-021-00432-0

15.

Cohen

McCabe

Michelli

N. M.

Pickeral

(2009). School climate: Research, policy, practice, and teacher education. Teachers College Record, 111(1), 180–213. https://doi.org/10.1177/016146810911100108

16.

Cuervo

Chesters

Aberdeen

(2019). Post-school aspirations in regional Australia: An examination of the role of cultural and social capital. The Australian Educational Researcher, 46(5), 843–861. https://doi.org/10.1007/s13384-019-00305-7

17.

Davis-Kean

P. E.

Tighe

L. A.

Waters

N. E.

(2021). The role of parent educational attainment in parenting and children’s development. Current Directions in Psychological Science, 30(2), 186–192. https://doi.org/10.1177/0963721421993116

18.

Dickson

Gregg

Robinson

(2016). Early, late or never? When does parental education impact child outcomes? The Economic Journal, 126(596), F184–F231. https://doi.org/10.1111/ecoj.12356

19.

Dray

Bowman

Freund

Campbell

Hodder

R. K.

Lecathelinais

Wiggers

(2016). Mental health problems in a regional population of Australian adolescents: Association with socio-demographic characteristics. Child and Adolescent Psychiatry and Mental Health, 10(1), 1–11. https://doi.org/10.1186/s13034-016-0120-9

20.

Duncan

G. J.

Brooks-Gunn

(1999). (Eds.). Consequences of growing up poor. Russell Sage Foundation.

21.

Duncan

G. J.

Brooks-Gunn

Klebanov

P. K.

(1994). Economic deprivation and early childhood development. Child Development, 65(2), 296–318. https://doi.org/10.2307/1131385

22.

Fay-Stammbach

Hawes

D. J.

Meredith

(2014). Parenting influences on executive function in early childhood: A review. Child Development Perspectives, 8(4), 258–264. https://doi.org/10.1111/cdep.12095

23.

George

S. V.

Richardson

P. W.

Watt

H. M.

(2018). Early career teachers’ self-efficacy: A longitudinal study from Australia. Australian Journal of Education, 62(2), 217–233. https://doi.org/10.1177/0004944118779601

24.

Grazia

Molinari

(2020). School climate multidimensionality and measurement: A systematic literature review. Research Papers in Education, 1–27. https://doi.org/10.1080/02671522.2019.1697735

25.

Halsey

(2018). Independent review into regional rural and remote education—Final report. Department of Education and Training. https://www.education.gov.au/quality-schools-package/resources/independent-review-regional-rural-and-remote-education-final-report

26.

Herbert

(2020). Contextualising policy enactment in regional, rural and remote Australian schools: A review of the literature. Australian and International Journal of Rural Education, 30(1), 64–81. https://journal.spera.asn.au/index.php/AIJRE/article/view/272

27.

Independent Schools Council of Australia. (2017). Independent Review into Regional, Rural and Remote Education: Submission. https://www.education.gov.au/system/files/documents/document-file/2020-12/independent-schools-council-of-australia.pdf

28.

Klassen

R. M.

Tze

V. M. C.

(2014). Teachers’ self-efficacy, personality, and teaching effectiveness: A meta-analysis. Educational Research Review, 12, 59–76. https://doi.org/10.1016/j.edurev.2014.06.001

29.

Lamb

Glover

Walstab

(2014). Educational disadvantage in regional and rural schools. Quality and equity: What does research tell us? Australian Council for Educational Research Conference. Melbourne: Australia. https://research.acer.edu.au/cgi/viewcontent.cgi?article=1228&context=research_conference

30.

Lamb

Huo

Walstab

Wade

Maire

Doecke

Jackson

Endekov

(2020). Educational opportunity in Australia 2020: Who succeeds and who misses out. https://www.vu.edu.au/sites/default/files/educational-opportunity-in-australia-2020.pdf

31.

Lamb

Jackson

Walstab

Huo

(2015). Educational opportunity in Australia 2015: Who succeeds and who misses out. https://www.vu.edu.au/sites/default/files/educational-opportunity-australia-2015-who-succeeds-who-misses-out-mitchell-institute.pdf

32.

McLachlan

Gilfillan

Gordon

(2013). Deep and persistent disadvantage in Australia. Australian Government Productivity Commission. https://www.pc.gov.au/research/supporting/deep-persistent-disadvantage/deep-persistent-disadvantage.pdf

33.

Mohal

Lansangan

Gasser

Howell

Duffy

Renda

Scovelle

Jessup

Daraganova

Mundy

(2022). Growing up in Australia: The Longitudinal Study of Australian Children – Data user guide, release 9.0C2. Australian Institute of Family Studies. https://growingupinaustralia.gov.au/sites/default/files/publication-documents/lsac_w9_data-user-guide_2022_0.pdf

34.

Morris

D. B.

Usher

E. L.

Chen

J. A.

(2017). Reconceptualizing the sources of teaching self-efficacy: A critical review of emerging literature. Educational Psychology Review, 29(4), 795–833. https://doi.org/10.1007/s10648-016-9378-y

35.

National Centre for Vocational Education Research. (2020). Student equity in VET: Participation, achievement and outcomes. https://www.ncver.edu.au/research-and-statistics/publications/all-publications/student-equity-in-vet-participation-achievement-and-outcomes

36.

OECD. (2019). Attracting teachers to schools in rural and remote areas in Australia. https://web.archive.org/web/20221220115542/http://www.oecdteacherready.org/promising-practice/attracting-teachers-to-schools-in-rural-and-remote-areas-in-australia/

37.

Parker

P. D.

Jerrim

Anders

Astell-Burt

(2016). Does living closer to a university increase educational attainment? A longitudinal study of aspirations, university entry, and elite university enrolment of Australian youth. Journal of Youth and Adolescence, 45(6), 1156–1175. https://doi.org/10.1007/s10964-015-0386-x

38.

Perry

L. B.

(2018). Educational inequality in Australia. In Committee for Economic Development of Australia (Ed.) How unequal? Insights on inequality, (pp. 56–67). https://cedakenticomedia.blob.core.windows.net/cedamediacontainer/kentico/media/general/publication/pdfs/ceda-how-unequal-insights-on-inequality-april-2018-final_web.pdf

39.

Saunders

Wong

(2012). Promoting inclusion and combating deprivation: Recent changes in social advantage in Australia. University of New South Wales.

40.

Smith

Parr

Muhidin

(2019). Mapping schools' NAPLAN results: A spatial inequality of school outcomes in Australia. Geographical Research, 57(2), 133–150. https://doi.org/10.1111/1745-5871.12317

41.

StataCorp. (2021). Stata statistical software: Release 17.

42.

Sullivan

Perry

L. B.

McConney

(2014). How do school learning environments differ across Australia’s rural, regional and metropolitan communities? The Australian Educational Researcher, 41(5), 521–540. https://doi.org/10.1007/s13384-014-0144-1

43.

Thapa

Cohen

Guffey

Higgins-D’Alessandro

(2013). A review of school climate research. Review of Educational Research, 83(3), 357–385. https://doi.org/10.3102/0034654313483907

44.

Thomson

De Bortoli

Underwood

(2017). PISA 2015: Reporting Australia’s Results. Australian Council for Education Research (ACER). https://research.acer.edu.au/cgi/viewcontent.cgi?article=1023&context=ozpisa

45.

Tomaszewski

Xiang

Western

(2020). Student engagement as a mediator of the effects of socio-economic status on academic performance among secondary school students in Australia. British Educational Research Journal, 46(3), 610–630. https://doi.org/10.1002/berj.3599

46.

Universities Australia. (2020). Higher education facts and figures 2020. https://www.universitiesaustralia.edu.au/publication/higher-education-facts-and-figures-2020/

47.

Wang

M. T.

Degol

J. L.

(2016). School climate: A review of the construct, measurement, and impact on student outcomes. Educational Psychology Review, 28(2), 315–352. https://doi.org/10.1007/s10648-015-9319-1

48.

Willis

Povey

Hodges

Carroll

(2021). Principal leadership for parent engagement in disadvantaged schools: What qualities and strategies distinguish effective principals? Springer. https://doi.org/10.1007/978-981-16-1264-0

49.

Young

K. D.

Grainger

James

. (2018). Attracting preservice teachers to remote locations. Australian Journal of Teacher Education, 43(2), 157–170. http://doi.org/10.14221/ajte.2018v43n2.9

50.

Zee

Koomen

H. M. Y.

(2016). Teacher self-efficacy and its effects on classroom processes, student academic adjustment, and teacher well-being: A synthesis of 40 years of research. Review of Educational Research, 86(4), 981–1015. https://doi.org/10.3102/0034654315626801