The poor stay poor and the rich stay rich

A recently completed doctoral study (Peck, 2000) looked at ethnicity in education. It also involved several other variables such as socioeconomic status (SES), gender and rurality. These variables were statistically controlled in order to study the effect of ethnicity in particular. This paper gives a re-working of the data in which the associations between SES and a range of educational indicators is the focus of investigation.

It was found that on average, compared with students from high SES families, those from low SES families leave school earlier, have lower aspirations, tend to have different patterns of subject selection in post-compulsory schooling, achieve at a lower level at school, obtain lower Tertiary Entrance Scores, are less likely to go to university, and are more likely to enter occupations associated with low SES.

While associations between SES and educational indicators are far from new, this paper will describe the size of the effects in the context of the current Western Australian educational system.

Introduction

While investigating ethnicity a number of other variables had to be statistically controlled. Chief amongst these were gender, socioeconomic status (SES) and rurality. It was noticed that the effects of these variables, in particular SES, was often greater than that of ethnicity. This paper reports a re-working of the original data in which SES, rather than ethnicity, is made the focus of investigation.

In this investigation, rurality and ethnicity were crudely controlled by selecting a subset of the original data. This subset consisted of Anglo-Australians in Year 12 who lived in the Perth metropolitan area. It had been noticed earlier (Peck, 2000, p. 205) that SES variables functioned differently in rural areas compared with the city, and also amongst different ethnic groups. The elimination of ethnicity as a variable limited the scope of the conclusions but allowed much more straightforward statistical models to be used for investigating SES.

A number of investigations (for example: Banks, 1976; Gordon, 1976; Featherman and Carter, 1976; Kerckhoff and Campbell, 1977; Hoeben, 1989; Mortimore, 1988; Brandsma and Knuver, 1989; Blakey and Heath, 1992) found SES to be one of the strongest determinants of educational outcomes. In many studies, the only factor with a greater effect on educational outcomes than SES is prior educational achievement.

Attainments in both the social and economic domain contribute to SES. SES is determined by individual achievements, the most important of which are: educational attainment; employment and occupational status; membership of groups identified as disadvantaged; and income and wealth. In the case of children, SES is conventionally taken to be that of the family.

SES has been estimated by using the postcode of students or schools (Gentilli, 1996), or census statistical collection areas, for example the Ross index (Ross, Farish and Plunkett, 1988) which is currently accepted by most Australian educational systems for identifying the need for school funding to offset social disadvantage. According to Graetz, (1995, p. 42) these "aggregate level methods are efficient and unobtrusive, but concerns have been raised about ( [their] ( accuracy ( in comparison with the individual level attributes they purport to represent".

The most frequently used indicator in the United States is subsidised lunch status (for example, Levine, 1992). In Australia, this measure is not available and it is more usual to form a composite measure of an individual child's SES from information on their parents' education and occupation, and sometimes, additionally, on information about family possessions (for example, Hammond, 1995, p.123).

Data collected by the Student Survey include, for individual students, the highest educational level reached by each of the student's parents and the status (on the ASCO scale) of both parents' occupations. Additional data from the 1996 Australian census (ABS, 1998, private communication) gave household income. The usual approach to deriving a composite SES measure is to use principal components analysis to obtain the optimally weighted combination of these variables (and, if available, other measures of property, wealth, et cetera) for explaining variation in the dependent variable. In the present study, uncombined variables were analysed using multivariate techniques and the SES variables selected for the final models were those that gave what was judged to be the optimal combination of effect size and simplicity.

Methods

Since it was known that ethnicity and rurality have a significant effect on several educational outcomes, a subgroup (N = 1424) of Anglo-Australian students in the metropolitan area was selected. For this investigation, students were deemed Anglo-Australian if they were born in Australia, state that they were not Indigenous Australians, and had four grandparents whose main language at home was/is English. Additional data were obtained on the whole State population of this age cohort from the 1996 Australian Census of Population and Housing (ABS, 1996) in order to investigate participation in post-compulsory schooling.

The indicators of SES that were available were the occupational category of each student's mother and father (Table 1), and also the highest educational level reached by each student's mother and father. In the case of Census data the overall household income was used as an indicator of SES.

Detailed information from the survey on parental occupations was collapsed (see Table 1) to variables with five levels. These are comparable with the census categories which are based on the ASCO skill levels (ABS, 1997). The categories Not in paid work, Unemployed and Unstated were recoded "Missing" because exploratory work indicated that these categories represent SES anomalously, especially in the case of mothers' occupations.

The highest educational levels reached by each parent were collapsed to a three-point scale, representing the highest educational level completed by students' fathers and mothers respectively. In Table 2, which shows the categories of the new variables, Apprenticeship has been combined with Secondary because this is the form that was taken a generation ago by vocational education, but is now undertaken in post-compulsory schooling. Students who did some secondary education without finishing it are counted in the Primary category since this is the highest level they completed.

Student Survey categories	Collapsed categories	Value (Level)	Number of parents by occupational category
			Fathers	Mothers
Some primary	Primary	1	366	544
Completed primary
Some secondary
Completed secondary	Secondary	2	342	265
Apprenticeship
University	Tertiary	3	530	455
Other tertiary
Don't know	Missing data	Missing	186	160

In the present study, the uncombined variables were initially used for analysis. This allowed the effects of parents' education and occupation to be investigated separately, and the contribution of mothers and fathers to be compared. However, for several of the dependent variables a composite parents' education variable, defined as the number of university educated parents (with values 0, 1 or 2) was used. In calculating this variable students who supplied data on less than two parents were omitted. In many cases this variable was found to explain approximately the same amount of variance as the separate variables for each parent, and since it led to more parsimonious models it was preferred in accordance with the principle of Occam's razor.

Model selection entailed a trial and error process of discovering which combination of significant effects maximised the amount of explained variance in the dependent variable. Gender was initially included since it was not assumed that the gender ratio was the same across all levels of SES. In addition, initial models included interactions of gender with the SES variables.

Since the aim of this study was to investigate the effect of SES in isolation from gender, multivariate methods were used for analysis. When the outcome was dichotomous (as in did/did not enter university) the method chosen was logistic regression (Kleinbaum, 1994). In other cases, where the outcome variable was continuous (as for Tertiary Entrance Score) the General Linear Model procedure in SPSS version 10 (SPSS, 1999) was used to carry out an analysis of variance (Norusis, 1997). The dependent variables, the educational indicators, will be described in more detail in the next section.

Findings

Educational participation

The data from the census also stated whether each 16-year old person in the State was still at school. Odds ratios for post-compulsory school participation were estimated using logistic regression, with household income and gender as independent variables (Table 4). In this study, odds ratios for SES are presented relative to the category of highest SES.

Both of the main effects were statistically significant. The odds of a person staying at school for post-compulsory education diminish as SES decreases, and males are significantly less likely to finish school than females. A person of low SES has about half the odds of finishing school as one in the group of high SES.

This model also included the interaction between gender and household income. There is a significant interaction between gender and SES at the lowest level of household income, in addition to the main effects (Table 5).

Educational aspirations

Logistic regression was used to determine the model which best accounted for this outcome. Starting with all the SES variables and gender, and their interactions, the final model contained only gender and an SES variable whose values were equal to the number of university educated parents. This model accounted for 15.7 percent of the variance. The odds ratios for gender and SES are shown in Table 6.

This model shows that males have significantly lower odds of aspiring to go to university than females. Students with no university educated parents had odds of aspiring to university about one seventh of those with two university educated parents.

Occupational aspirations

Logistic regression was used to determine the model which best accounted for this outcome (Table 7). Of all the SES variables and gender, and their interactions, the only significant effects were the educational levels of the student's parents. This model accounted for 12.4 percent of the variance.

It may be seen that students whose fathers have completed only primary or secondary education can expect to have odds of high occupational aspirations about half that of students whose fathers are university educated. A similar effect is seen in the case of mothers' occupations.

Aspirations for occupational prestige

The SES factors and gender were used to select the logistic regression model which best explained aspiration for high prestige. The only significant factors were father's education and mother's occupation (Table 8).

The results show that if a student's father has not been to university their odds of aspiring to occupations associated with high prestige are less than half of those of students with university educated fathers. The effect of mothers is different in that the mother's occupation has a significant effect, not her educational level. It may be seen that students with mothers in professional/managerial occupations have higher odds of aspiring to high prestige occupations than other students.

The effect of unskilled mothers appears slightly anomalous, since their effect on their children's aspirations for prestige are not significantly different from that of mothers in professional/ managerial occupations. For the unskilled category, is it possible that mother's occupation is not a good indicator of SES.

Academic self-concept

SES was not significantly associated with mathematics self-concept. All of the available measures of family SES were added to general linear models but the only significant variable was gender (the average mathematics self-concept for males was significantly higher than that for females).

English self-concept was found to be significantly associated with SES. Table 9 summarises the results of the model which explains most of the variance in English self-concept while containing only statistically significant terms.

Of the SES variables available, the one that best explained the variance in English self-concept was found to be the number of parents who had gone to university. No significant interaction between SES and gender was found. It can be seen from the values of the partial correlations in Table 9 that the effect of this measure of SES is about one fifth of the effect of gender. Although the effect of SES was statistically significant, this factor accounts for only 2 percent of the variance in English self-concept.

The estimated marginal means shown in Table 10 show that there is no significant difference in mean English self-concept between students with zero or one university educated parent, but those with two university educated parents have a significantly higher English self-concept.

Subject choice in post-compulsory education

The variable used to indicate SES is the number of university educated parents for each student. Gender was also included in the logistic regression models, but the interaction between gender and SES was omitted as it was found in every case to be non-significant. Table 11 summarises these models.

It is clear from Table 11 that, relative to students with two university educated parents, those whose parents are not university educated have significantly lower odds of participating in programs of study such as High Maths, Maths & Science and High English, all of which consist of TEE subjects and are manifestly related to university entrance. They also have significantly lower odds of participating in the programs of study Humanities and Social Studies. In addition, they had nearly 6 times the odds of participating in a Non-TES program of study (meaning that they had ruled themselves out of contention for obtaining a Tertiary Entrance Score) than students with two tertiary educated parents.

Year 12 achievement

A file of grades achieved by each Year 12 student in 1996 was obtained from the Secondary Education Authority. Students who completed two or fewer subjects were deleted, since many of them would not have been typical Year 12 students. Subjects completed by fewer than 100 students were deleted, and the remaining dataset was analysed using the RUMM2010 software (Andrich, Sheridan, and Luo, 1999).

The Rasch Model analysis, using 65 subjects and data from 16,201 students, indicated that the data lay on a highly unidimensional scale, since the person separation index, which is analogous to a traditional Cronbach alpha reliability index, was 0.859. Amongst the output of this analysis was a table of estimates of student locations on the achievement scale. Achievement estimates produced by the Rasch Model analysis take into account the difficulties of the subjects chosen by each student.

This measure of overall achievement differs from the Tertiary Entrance Score in that the grades were derived from non-TES subjects as well as those used for entry to university. Thus, overall estimates of achievement were obtainable for all students whether they were university-bound or not.

The number of university-educated parents was used as an independent variable SES indicator. Model selection involved this variable and gender. The General Linear Model procedure in SPSS version 10 (SPSS, 1999) was used to carry out this analysis of variance. The results are summarised in Table 12.

Table 12 shows that both gender and SES are statistically significant factors in this model of academic achievement. SES accounts for about 11 percent of the variance in this variable. There was no significant interaction between SES and gender.

It can be seen that the mean achievement measures of students increase with the number of university educated parents (Table 13). The means of each category are significantly different from each other.

Tertiary entrance score

A general linear model analysis of factors thought to be related to Tertiary Entrance Score revealed that while SES was a significant factor, gender had no significant effect (Table 14). In this model SES was represented by two variables, the highest educational level of the student's mother and father. The father's educational level explains about 5 percent of the variance in Tertiary Entrance Score, while the mother's explains about 1 percent.

The significant interaction in this model is further illustrated in Table 15 which shows the estimated marginal means for each level of father's and mother's educational level. The main effect of father's education is manifested in every column of the table, which shows mean Tertiary Entrance Score increasing as the highest educational level increases. This effect is not apparent in the changes across rows of Table 15. In fact, mother's education only has a substantial effect when the father is university educated.

Admission to university

As Table 16 shows, the odds of getting in to university for students with no university educated parents are 0.18 of those for students with two university educated parents.

Occupational attainment

In order to study the effect of education on occupational attainment, a logistic regression analysis was carried out to relate each parent's education to their occupation. The outcome variable, parent's occupation, was dichotomised to two levels, Professional/managerial and Other.

It was found that about half of the variance in occupational level was explained by educational level. For both males (Table 17) and females (Table 18) the odds ratios of attaining a professional/managerial occupation are extremely low for individuals who do not complete tertiary education relative to those of people who do so.

Discussion and conclusions

However, despite the plausibility of this line of reasoning, there is little evidence to suggest that this is what occurs. On the contrary, many writers have discussed the role of the education system in perpetuating social inequality (Fitzgerald, 1976; Meade, 1978; Branson and Miller, 1979). For example, the Fitzgerald Report concluded, after reviewing the evidence on disadvantage in Australia

The structural inequalities in our society are nowhere more evident than in our school systems. Far from being a way out for poor people, schools act as a sorting, streaming mechanism helping to maintain the existing distribution of status and power. (Fitzgerald, 1976, p. 227)

For the most part, the effects of privilege on educational outcomes are only observed in their crudest forms-a good word put in, the right contacts, a well resourced home, extra tuition, information on the educational system and job outlets. However, this is only because the most extensive and substantial effects of privilege and power are invisible. The dominant culture is in a position to select, structure, evaluate and deliver 'appropriate' knowledge through its education system.

According to Bourdieu and Passeron (1977), schools do not transmit culture but they play an essential part in reproducing the dominant culture. This thesis was elaborated by Taylor (1982) who argued that schools are reality-defining institutions where there is a dialectical relationship between identity and social structure, mediated through school culture.

As institutions, schools have the power to structure the objective reality of those people who are part of them. Thus the reality internalized by students tends to be a particular definition of reality imposed on them through the structuring of school experience... This process of imposition of reality is hidden beneath an ostensibly neutral system which favours those with power in society. (Taylor, 1982, p. 152)

In this study, the measured educational outcomes and indicators were the culmination of twelve years of schooling in Western Australia. The widespread and significant effect of variables representing SES provides evidence that education is part of a positive feedback cycle for the reproduction of inequality. This does not imply that education is necessary for social class to reproduce itself, simply that it is currently involved in the reproduction of class.

It was found that low SES, as compared with high SES, was associated with

• leaving school early,
• low English self-concept,
• avoiding subjects on which entry to university is based,
• low educational aspirations,
• low aspirations for skill and prestige in occupations,
• low academic achievement at school, and
• not going to university.

The effect of SES on Tertiary Entrance Scores was such that students from high SES backgrounds could expect to gain access to many university courses which are not accessible to students from less privileged backgrounds. University admission is based on a student's Tertiary Entrance Rank (TER) which is a percentile rank based on the 17 year-old age cohort in the general population.

Students with two university educated parents had an average score of 342 (TER approximately 90), while those with no university educated parents had an average of 298 (TER approximately 79). Many university courses have entry cut-offs at TERs between 79 and 90 (TISC, 2001, pp. 25-34) and hence would tend to discriminate between these two groups of students. Since many university courses are aligned with occupations, this score at the end of schooling is a major determinant of future occupational attainment.

Current plans to implement the Curriculum Framework (Curriculum Council, 1998; 2000) and the Post-compulsory Review constitute what is arguably the most radical reform of Western Australian schooling in history. Social inequity has been identified as one of the targets to be addressed by this change. In view of the extensive theorisation of society's resistance to change, this is a very ambitious undertaking. The history of educational measures to redress social inequity is a record of numerous failures; if our new policies are to succeed, they will have to do so against this trend. During and after the Curriculum Framework's implementation, inclusivity and access will be monitored with interest. This work provides a baseline against which to measure future indicators of access.

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Author: Bob Peck is Senior Consultant Measurement and Research at the Curriculum Council of Western Australia and Adjunct Research Fellow at Edith Cowan University, Perth. His research interests are applications of the Rasch model and educational assessment in the paradigm of an outcomes-focussed standards framework. Email: peckb@curriculum.wa.edu.au Please cite as: Peck, B. (2001). The poor stay poor and the rich stay rich. Issues In Educational Research, 11(2), 45-64. http://www.iier.org.au/iier11/peck.html