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Socioeconomic status and the allocation of Government resources in Australia: How well do geographic measures perform?

Patrick Lim1, Sinan Gemici, John Rice & Tom Karmel

National Centre for Vocational Education ResearchLevel 11, 33 King William Street, ADELAIDE, SOUTH AUSTRALIA 5000

Ph: + 61 8 8230 8400, Fax: +61 8 8212 3436

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1Corresponding Author

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mailto:[email protected]



Socioeconomic status and the allocation of Government resources in Australia: How well do geographic measures perform?

Abstract

Purpose – The aim of this paper is to compare the performance of area-based vs. individual-level measures of socioeconomic status (SES).

Methodology - Using data from the Longitudinal Surveys of Australian Youth (LSAY), a multidimensional measure of individual SES is created. This individual measure is used to benchmark the relative usefulness of Socio-Economic Indexes for Areas (SEIFA), a geographic set of measures often used in Australia to assess the SES of individuals. Both measures are compared in terms of classification bias. The effects of using the different SES measures on participation in post-compulsory education are examined.

Findings – SEIFA measures perform satisfactorily with regard to the aggregate measurement of SES. However, they perform poorly when their use is aimed at channelling resources toward disadvantaged individuals. It is at the individual level, then, that our analysis reveals the shortcomings of area-based SES measures.

Research limitations/implications – While region based measures are relatively easy to collect and utilise, we suggest that they hide significant SES heterogeneity within regional districts. Hence, the misclassification resulting from the use of regional measures to direct support for low SES groups creates a risk for resource misallocations.

Originality/value – Our finding that region-based measures are subject to significant misclassification has important research and policy implications. Given the increasing availability of individual-level administrative data, we suggest that such data be used as a substitute for geographic SES measures in categorising the SES of individuals.

Keywords - Socio-economic status, young people, tertiary education, LSAY.

Paper type – Research paper

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1. Introduction

The persistent social and economic marginalisation of individuals and groups within society has significant detrimental direct and indirect impacts. Such marginalisation tends to create a ‘vicious cycle’ of disadvantage, limiting access to educational opportunities, which in turn leads to poor labour market outcomes and low earnings. Many governments, including Australia’s, have the policy aim of breaking this cycle of economic and social marginalisation by targeting educational assistance programs to lower SES groups.

Policy initiatives aimed at increasing the social inclusion levels of those of low SES rely, to a great degree, on accurate measures of SES. SES, while very real, is an abstract notion. Determining the specific criteria by which the SES of individuals, families and groups is established can be a complex and nuanced task. While access to financial or other tangible resources (in the form of income or assets) is of fundamental importance, SES is also influenced by the interaction, moderation and mediation of a variety of other social and economic determinants.

One easy to determine, and hence frequently-used, approach is based on an individual’s geographic location. Although geographic measures, such as the Socio-Economic Indexes for Areas (SEIFA; Australian Bureau of Statistics [ABS] 2008), are widely used to report the SES status of groups, these measures have been criticised for their poor construct validity in determining SES at the individual level (Coelli 2010; Jones 2001). In essence, the imprecision of the measure arises due to significant ‘within region’ variance in SES within geographical areas. When assistance is targeted to individuals or groups based on geographical measures alone, assistance may well flow to individuals of substantial economic and social means. This results in a sub-optimal mechanism for resource allocation.

This paper proceeds as follows. We begin with a brief discussion of variables that should be included in an ideal measure of SES. We then introduce four area-based SES measures that are frequently used in the Australian context. Subsequently, we use data from the Longitudinal Surveys of Australian Youth (LSAY) to create an individual measure of SES. We use our newly-created SES measure as a reference against which we assess the performance of the area based measures in terms of classification bias (i.e., the degree to which different SES measures correctly identify a respondent’s SES). We conclude by examining the effects of using the different SES measures on participation in post-compulsory education.

2. Approaches to Measuring SES

SES has been defined as ashort-hand expression for variables that enable the placement of persons, families, households and aggregates such as Statistical Local Areas (SLA’s), communities and cities into some hierarchical order, reflecting their ability to produce and consume the scarce and valued resources of society. (Western 1998, p. 5)

Occupational status, education, income and wealth, as well as general access to social and cultural resources all contribute to SES (Bourdieu 1973; Coleman 1987). Health, family structure, social contacts, and migration history are additional have also been shown to be covariates of an individual’s socioeconomic position (Pantazis 2006; Saunders 2007; Scutella, Wilkins & Horn 2009).

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Most young people are in some way affected directly or indirectly by parental influences. Therefore, SES measures for children and youth should include variables based on parental attributes. Such attributes usually include one or more dimension of parental occupation, parental educational attainment, household income or wealth, and access to societal and cultural resources.

2.1 Area-based Measures

Area-based measures of SES determine socioeconomic position by considering the average economic profile of a given geographic location, and at the individual level attributing this measure to all of its residents. In Australia, region-specific data is derived from the five-yearly Census of Population and Housing. This data is aggregated into the SEIFA, comprising four separate indexes that each represent a slightly different approach to measuring SES.1. Index of Relative Socioeconomic Disadvantage (IRSD): This index summarises 17

area-based variables, including low income, low education, as well as high rates of unemployment and unskilled occupations. A low score reflects relative socioeconomic disadvantage.

2. Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD): This index features 21 area-based variables, including low or high income, internet connection, occupation and education. A high score reflects relative socioeconomic advantage.

3. Index of Economic Resources (IER): This index contains 15 area-based variables, including items such as household income, housing expenditure, and wealth. A high score indicates socioeconomic economic advantage.

4. Index of Education and Occupation (IEO): This index is composed of 9 area-based variables, including educational attainment, enrolment in further education, occupational information (such as skill level), and unemployment status. Higher scores indicate socioeconomic advantage.

2.2 Parental Occupation

Parental occupation has been shown to be a strong covariate of the SES of children and young people. Various measures exist for the measurement of parental occupation. Internationally, the International Standard Classification of Occupations (ISCO, International Labour Organisation 1990) is commonly used to categorise occupations. In Australia, occupations are classified using the Australian and New Zealand Standard Classification of Occupations (ANZSCO, ABS 2009). Both of these scales can be converted to a continuous scale. ISCO can be converted to ISEI1 (Ganzeboom, De Graaf, & Treima, 1992), while ANZSCO can be converted to AUSEI06 (McMillan, Jones, & Beavis 2008). These conversions convert the categorical classifications to a continuous scale of ‘occupational prestige’ based on the conversion of education, earnings, social standings, and other variables to a score.

Apart from which occupational classification scheme to use, the question of which parent’s occupation to measure has also to be decided. Three common options are outlined below:

1 The ISEI is a continuous measure of occupational prestige that converts the International Standard Classification of Occupations into a scale that ranges from 0 to 90.

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1. Focus on father’s occupation only: this approach assumes that the adult male in the household has the strongest attachment to the labour force. However, in the modern labour market this approach probably underestimates family SES, as females nowadays routinely make significant contributions to household income.

2. Focus on the higher-status occupation: this approach assumes that the adult with the higher-status occupation determines the family’s overall socioeconomic position.

3. Focus on father’s occupation or, if missing or unknown, the mother’s occupation: this approach is useful in that it helps to overcome missing values that may arise due to male detachment from the labour force. Moreover, it is likely that young people are able to identify the occupation of at least one of their parents or parent figures.

In this paper, we use the third option and map information on parental occupation to the continuous ISEI scale.

2.3 Parental Education

Parental educational attainment is another important element of an accurate SES measure for young people. In Australia, educational attainment is often classified according to the Australian Qualifications Framework (AQF; AQF Advisory Board 2007) or the Australian Standard Classification of Education (ASCED; ABS 2001). An alternative approach to measuring educational attainment is to focus on the length of formal education. However, in the Australian context substantial differences exist in the duration of qualifications, particularly for vocational education and training (VET) courses.

Internationally, educational attainment is usually classified using the International Standard Classification of Education (ISCED, UNESCO 1997). ISCED facilitates the comparison of education statistics and indicators within and between countries. Table 1 provides an overview of ISCED classifications.

Table 1 ISCED Classification

ISCED Level Qualification Level

0 Pre-primary, kindergarten, pre-school1 Primary2A/B Certificate I and II (general enabling, bridging courses)2C Certificate I and II (basic vocational)3A/B Higher school certificate, university enabling courses, AQF certificate

III3C AQF statement of attainment4A/B Certificate IV5A Bachelor, bachelor with honours, master (research and coursework)5B Diploma, advanced diploma, graduate certificate, graduate diploma6 PhD, professional doctorate

Similar to the measurement of occupation categories previously discussed, considering parental education raises the question of which parent’s educational attainment to measure. Three common options are outlined below:

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1. Focus on mother’s education only: this approach is based on an argument of nurture versus nature. Traditionally, mothers provide guidance in child rearing, and a mother who values education is likely to instil this value in her child.

2. Focus on the higher level of education: this approach assumes that the adult with the higher educational attainment level exerts a leading influence over the family’s overall socioeconomic position. Problems arise when the contribution of the adult with the lower level of educational attainment to the family’s socioeconomic position is underestimated.

3. Focus on the mother’s education or, if missing or unknown, the father’s education: this approach helps to alleviate the problem of missing values because it is likely that young people are able to identify the educational attainment level of at least one of their parents or parent figures. It also includes single-parent households.

In this paper, we use the third option and map information on parental education to the ISCED scale.

2.4 Proxies for Income and Wealth

Household income and wealth are routinely used as indicators of SES because they represent a direct measure of access to economic resources. Surveys such as the Household, Income and Labour Dynamics of Australia (HILDA, Department of Families, Housing, Community Services and Indigenous Affairs 2011) survey and the Census of the Australian Population ask individuals to report their weekly income. In Australia, respondents frequently perceive income-related questions as intrusive, and that adolescents may not know their parents’ income or may be unwilling to disclose this information, LSAY refrains from collecting income-related information directly2.

As an alternative to direct questions on income and wealth, possession-based measures that enquire about the presence of consumer and cultural items in the household are often used as suitable proxies in survey research (Buchmann 2002). Examples of possession-based measures include the number of rooms, bedrooms, or bathrooms in the home, the presence of a dishwasher, or the presence of literature or art. It is implicitly assumed that parents who can afford certain household and cultural possessions are assumed to provide their children with a richer set of social and educational resources. The Longitudinal Surveys of Australian Youth (LSAY) asks a range of questions regarding the presence of social, cultural and educational possessions in the home; these questions are included in the individual measure of SES.

3. Method

We used data from the 2003 cohort of the Longitudinal Surveys of Australian Youth (LSAY) to create a reference measure for SES. LSAY is a nationally-representative survey that tracks young people in Australia from the ages of 15 to 25 as they move from school into further study, work, and other destinations. LSAY contains a rich set of individual background variables, which is an important prerequisite for creating an accurate SES reference measure. Although LSAY is affected by attrition bias, we remedied this issue by applying appropriate weights. Thus, LSAY attrition bias did not have a detrimental effect on our comparisons with regard to SEIFA. Furthermore, our measure of SES was derived using variables from the first LSAY wave from the 2003 base year, which was

2Direct income questions are avoided to minimise attrition. If questions are too intrusive, respondents will drop out of the survey in later years, which has a detrimental effect on overall survey quality.

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not influenced by longitudinal attrition effects. We used a set of 16 SES-related background variables as a basis for creating our SES reference measure (Table 2).

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Table 2 LSAY Variables Used in Creating an SES Reference Measure

Variable Type or Categories Valid n p̂ SE

Own desk at home Dichotomous 10366 0.902 0.003Own room at home Dichotomous 10366 0.915 0.003Own study place at home Dichotomous 10367 0.834 0.004Computer software at home Dichotomous 10366 0.669 0.005Internet at home Dichotomous 10366 0.849 0.003Calculator at home Dichotomous 10366 0.971 0.002Literature at home Dichotomous 10365 0.363 0.005Poetry at home Dichotomous 10366 0.406 0.005Art at home Dichotomous 10366 0.556 0.005Textbooks at home Dichotomous 10366 0.802 0.004Dictionary at home Dichotomous 10366 0.973 0.002Dishwasher at home Dichotomous 10362 0.594 0.005Number of books at home 0 – 10 447 0.043 0.002

11 – 25 883 0.085 0.00326 – 100 2848 0.275 0.004101 – 200 2347 0.226 0.004201 – 500 2205 0.213 0.004More than 500 1486 0.143 0.003

Parental occupation Continuous 9417 46.635* 17.246Parental education None 310 0.031 0.002

ISCED 1 84 0.084 0.001ISCED 2 2064 0.206 0.004ISCED 3B,C 319 0.032 0.002ISCED 3A, 4 3174 0.317 0.005ISCED 5B 1201 0.119 0.003ISCED5A, 6 2872 0.286 0.005

Own computer at home Dichotomous 10364 0.939 0.002Note: Sample sizes and proportions are unweighted. Proportions represent the per cent respondents to whom the listed variable condition applies.*Value represents the mean, not the proportion.

We conducted a latent-class factor analysis to create an individual reference measure of SES. Traditional factor analysis assumes all of the variables included in the model to be continuous. It further assumes that the emerging factors of interest follow a continuous distribution. In our case, however, the majority of the 16 variables used in the model were dichotomous or categorical, thereby violating basic distributional assumptions for traditional factor analysis. We addressed this issue by using tetrachloric (dichotomous) and polychloric (categorical) correlations in a latent class factor analysis, which was a better fit for the particular distributional properties of our SES-related variables.

Our reference measure was designed to measure SES at the individual, rather than aggregate, level. For the purposes of this analysis, and due to its empirical foundation, we assumed our reference measure to achieve an unbiased classification of individual SES. Classification bias resulting from different SES measures was determined using standardized differences and cross-tabulations of quintiles.

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4. Results

4.1 Factor Analysis

Eigenvalues and related statistics for the latent-class factor analysis are provided in Table 3. The interpretation of results from latent-class factor analysis is similar to that of traditional factor analysis.

Table 3 Initial Latent-class Factor Analysis

Factor Eigenvalue Difference Prop. Explained Cumulative

1 6.28 NA 39.25 39.252 1.74 4.54 10.89 50.143 1.48 0.26 9.26 59.394 1.15 0.34 7.16 66.555 0.81 0.33 5.08 71.636 0.72 0.09 4.52 76.157 0.71 0.02 4.41 80.568 0.65 0.06 4.06 84.619 0.54 0.11 3.37 87.9810 0.48 0.06 2.97 90.9511 0.42 0.06 2.61 93.5612 0.35 0.07 2.18 95.7413 0.21 0.08 1.32 97.0614 0.29 0.06 1.81 98.8715 0.17 0.04 1.07 99.9416 0.01 0.16 0.06 100.00

Four factors had Eigenvalues in excess of 1. Collectively, these four factors explained over 65% of the total variance in the model. The four factors included income and wealth, study resources, computing resources, and cultural resources (Table 4).

Table 4 Loadings for the Four-factor Model (Varimax Rotation)

LSAYVariable

Study Resources

Income/Wealth Computing Resources

Cultural Resources

Desk 0.628 0.167 0.243 0.259Own room 0.524 0.312 -0.026 -0.052Study place 0.758 0.100 0.097 0.257Software 0.413 -0.043 0.547 0.377Internet 0.251 0.390 0.641 0.062Calculator 0.675 -0.033 0.321 0.206Literature 0.148 0.319 0.136 0.787Poetry 0.134 0.172 0.040 0.880Art 0.231 0.149 0.116 0.619Textbooks 0.384 0.023 0.214 0.559Dictionary 0.667 0.012 0.353 0.382Dishwasher 0.237 0.422 0.272 0.089Parental occupation 0.060 0.507 0.101 0.165

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No. of books 0.109 0.305 0.098 0.453Parental education 0.005 0.503 0.068 0.227Computer in home 0.198 0.279 1.109 0.171Note: The highest loading for each variable across all four factors is bolded.

Table 4 demonstrates that the variable capturing computer availability in the home produced a loading in excess of 1, indicating an estimation anomaly. A likely cause for this anomaly was possible collinearity between the presence of a computer in the home, the availability of software, and internet access. To obviate further estimation problems, we eliminated the ‘computer in the home’ variable from the model. Results from the latent-class factor analysis for the modified model are provided in Table 5 and Figure 1.

Table 5 Loadings for the Three-factor Model

LSAY Variable EducationalResources

Income/Wealth

CulturalResources

Desk 0.664 0.219 0.229Own room 0.396 0.291 -0.032Study place 0.685 0.130 0.231Software 0.608 0.091 0.324Internet 0.464 0.493 0.063Calculator 0.776 0.052 0.141Literature 0.210 0.302 0.791Poetry 0.176 0.128 0.883Art 0.287 0.146 0.608Textbooks 0.484 0.057 0.524Dictionary 0.788 0.091 0.323Dishwasher 0.299 0.482 0.083Parental occupation 0.047 0.515 0.188No. of books 0.132 0.293 0.463Parental education -0.014 0.495 0.256Note: The highest loading for each variable across all three factors is bolded.

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Figure 1 Scree Plot of Eigenvalues

The modified model identified three factors with Eigenvalues greater than 1 as underlying traits of SES, including educational resources, income and wealth, and cultural resources. However, the Eigenvalue difference between the first factor and the other two factors was substantial. Given the disproportionately large amount of variance explained by the first factor, we isolated the first factor to generate one composite measure of SES consisting of a variety of home resources and parental background dimensions (Table 6).

Table 6 Loadings for the Single-factor Model

LSAY Variable Composite SES Factor

Desk 0.628Own room 0.340Study place 0.618Software 0.598Internet 0.499Calculator 0.640Literature 0.844Poetry 0.803Art 0.653Textbooks 0.659Dictionary 0.782Dishwasher 0.426No. of books 0.523Parental occupation 0.359Parental education 0.380

We used the single-factor model as our SES reference measure for the remainder of this paper. Our decision was based on practical considerations related to the interpretability

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0 2 4 6 8 10 12 14 160.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Eigenvalues

and useability of subsequent analyses. Given that our primary interest centred on the evaluation of various different measures of SES, the composite SES measure from the single-factor model provided a less complex reference for comparison. For the remainder of this paper, we refer to our SES reference measure as SES-C (SES-Composite).3

4.2 Comparing SEIFA and SES-C

Given sustained criticisms surrounding the accuracy of area-based SES measures, this section compares SEIFA to SES-C in terms of classification bias. To carry out meaningful comparisons, we assumed our SES-C measure to be an unbiased classification variable against which to assess the extent of misclassification resulting from SEIFA.

Despite the availability of various SEIFA measures, our investigation exclusively considered the SEIFA Postal Area (SEIFA-POA) indexes. This limitation was necessary because information contained in the LSAY dataset is limited to respondents’ residential postcode. SEIFA-POA consists of four indexes, including education and occupation, economic resources, relative advantage and disadvantage, and relative disadvantage. Correlations between our SES-C reference measure and each of the four SEIFA-POA indexes were in close proximity to each other (Table 7), prompting us to choose the most-highly correlated Index of Education and Occupation (SEIFA-EO) for comparison purposes.

Table 7 Correlation of SEIFA-POA with SES-C

Variable Correlation with SES-CSEIFA-POA Index of Education and Occupation 0.30

SEIFA-POA Index of Economic Resources 0.26

SEIFA-POA Index of Relative Advantage 0.29

SEIFA-POA Index of Relative Disadvantage 0.29

The low correlations between SES-C and any of the SEIFA indexes indicated that the classification of individuals into SES groups was highly sensitive to the particular SES measure chosen.

4.2 Individual-level Comparison

In determining the performance of SEIFA-EO with that of SES-C we used three different approaches. In the first approach, the distributions of SEIFA-EO and SES-C were both standardized to a Gaussian with m = 1,000 and sd = 100. Each individual in the dataset thus had a standardised score for SEIFA-EO and SES-C. Standardised scores were then subtracted from one another (see distribution of differences depicted in Figure 2. While the majority of the differences fell between -1.5 (-150) and +1.5 (150) standard deviations, substantial differences were observed well beyond this range.

3 Our SES measure overlaps to some extent with the Index of Economic, Social, and Cultural Status (ESCS), a measure of SES developed by the Organisation for Economic Cooperation and Development (OECD 2005) for use in the Program for International Student Assessment (PISA). Similar to our measure, ESCS is derived from family background variables, including parental occupation, parental education, and home possessions. Despite the high correlation between our SES-C measure and ESCS (r = 0.75), important differences exist. ESCS scores are obtained as component scores for the first principal component from factor analysis, whereby 0 is the score of an average OECD student and 1 the standard deviation across equally weighted OECD countries. The need for multi-country adjustment renders ESCS less reliable when considering only the Australian context. This loss in reliability is reflected in the considerably lower reliability coefficient for ESCS compared with our SES-C reference measure (standardised Cronbach’s alpha for ESCS for Australia = 0.61; standardized Cronbach’s alpha for SES-C = 0.74).

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Figure 2 SEIFA-EO and SES-C Distribution of Differences

In the second comparison, SES-C scores were plotted against SEIFA-EO scores (Figure 3). The low correlation between the two measures was clearly reflected in the shape of the plot. Assuming that a score of 900 or below (i.e., one standard deviation or more below the SES mean score) represents low-SES, we examined the classification of individuals classified as low-SES by either of the two measures (Quadrant 2). Quadrants 1 and 3 depict the number of misclassified individuals when using SEIFA-EO. Specifically, Quadrant 1 shows actual high-SES individuals who were misclassified as low-SES using SEIFA-EO, whereas Quadrant 3 contains actual low-SES individuals who were misclassified as high-SES using SEIFA-EO.

Figure 3 SEIFA-EO and SES-C Classification Plot

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Q1

Q2

Q4

Q3

Cross-tabulation of quintiles was a third approach to determining classification bias between SEIFA and SES-C. Table 8 presents the cross-tabulation of SES-C and SEIFA-EO quintiles.

Table 8 SEIFA-EO and SES-C Quintiles

SEIFA

SES-C 1 2 3 4 5 Total

1 = lowest 5.53 5.19 4.09 3.38 1.80 202 4.57 4.51 4.34 4.04 2.52 203 4.20 4.24 4.29 4.03 3.30 204 3.54 3.72 4.04 4.03 4.65 20

5 = highest 2.03 2.42 3.59 4.30 7.64 20Total 20 20 20 20 20 100

The diagonal sum of Table 8 yielded a correct classification rate of only 26.3%. A further 35% of individuals were slightly misclassified (first-order off-diagonals). Almost 40% of individuals in this sample were, however, severely misclassified (second-order and above off-diagonals). Our results were very similar to those reported by Coelli (2010) who compared SEIFA and income levels using the Household, Income and Labour Dynamics in Australia (HILDA) dataset.

4.3 Aggregate-level Comparison

Cross-tabulations at the individual level illustrated the strength of classification bias caused by using SEIFA indexes. However, these analyses did not lend themselves to an examination of classification at the aggregate level. To assess aggregate-level misclassification, we conducted two logistic regression analyses. The first examined whether an individual participated in a Bachelor or higher degree by age 19. The second examined whether an individual participated in Vocational Education and Training (VET) by age 19. We note that these two regressions were not mutually exclusive and that in individual may have indeed participated in both. Both regression analyses were undertaken twice for each outcome, using our SES-C reference measure in the first run, and SEIFA-EO in the second4.

Our focus on participation in post-school education as an outcome measure was motivated by the considerable policy interest in enhancing higher education access for low-SES youth. Table 9 provides the aggregate-level predicted probabilities of participation in higher education and VET by age 19 that resulted form the regression analyses. Predicted probabilities were categorised into low, medium and high SES (details for the regression models are provided in the Appendix).

4 The regression models also included covariates that controlled for Indigenous status, regionality, family structure and academic achievement.

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Table 9 Probability of Higher Education Participation by Age 19

SES Measure Low SES Predicted

Probability

Medium SES Predicted

Probability

High SES Predicted

Probability

Higher Education ParticipationSES-C 0.230 0.374 0.570SEIFA 0.267 0.383 0.542

VET ParticipationSES-C 0.314 0.243 0.170SEIFA 0.301 0.241 0.173

Despite the considerable strength of classification bias resulting from the use of SEIFA and SEIFA composites at the individual level, both measures performed reasonably well at the aggregate level. However, SEIFA over-stated participation probabilities for the low and medium SES quintiles, and under-stated them for the high SES quintile for higher education. For VET participation, the difference between SES-C and SEIFA was smaller, with either measure of SES providing reasonably accurate aggregate VET participation rates. Regardless of the differences between SEIFA and SES-C, it was evident that low-SES individuals are much more likely to participate in VET than in higher education, even after controlling for academic ability.

5. Conclusion

Given sustained policy interest in improving the social inclusion of disadvantaged youth, we examined two fundamentally different approaches to measuring SES. The first approach was based on the average economic profile of a given geographic location (i.e., SEIFA). The second approach was based on a mixture of possessions and individual background variables. Our investigation was motivated by the frequent use of SEIFA in directing economic resources to disadvantaged youth. Doing so assumes that SEIFA can properly identify youth from low-SES backgrounds. Results from our analysis question the adequacy of this assumption.

SEIFA measures perform satisfactorily with regard to the aggregate measurement of SES, as evidenced by our model for overall participation in higher education. Yet, the foremost concern of social inclusion policies lies with the ability to channel resources toward disadvantaged individuals rather than aggregate population averages. It is at the individual level, then, that our analysis reveals the shortcomings of area-based measures as SES classifiers.

SEIFA and other region-based measures are often used by governments to assess the SES of individuals and groups as they are readily derivable and do not raise concerns regarding privacy and intrusiveness. For example, the Australian Government has allocated $168.5 million to increase enrolment in higher education among low-SES youth. The allocation of these funds is primarily based on SEIFA. Given the extent to which SEIFA misclassifies the SES of young individuals, the efficiency of policies that seek to increase access to higher education and other critical resources for disadvantaged stakeholders may suffer.

Results from our investigation prompt us to discourage the use of SEIFA in the context of social inclusion. Instead of employing geographic measures, recently-developed social

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inclusion frameworks for Australia (Saunders 2007; Scutella 2009) and the European Union (Atkinson 2004) collectively emphasise the importance of individual SES parameters, including education, access to services, as well as economic, social, and political participation.

A potential solution to this measurement conundrum may lie in the use of extensive administrative and other individual-level data held by, or available to, governments. All developed economies have various forms of social support systems in place that are criteria-based and allow, to a greater or lesser degree, some insight into the ‘true’ SES of households and individuals. Given the increasing availability of individual-level administrative data, we suggest that such data be used as a substitute for geographic SES measures in categorising the SES of individuals. Achieving a better understanding of the SES of individuals and groups is the first step to effectively improving the SES of marginalised groups within the community.

6. Implications for Practice

Our analysis suggests that SES is complex and multifaceted, and clearly, as such, will be challenging to measure. Any measurement of SES is somewhat subjective, as indeed is the measure proposed in this paper. The measure that we have adopted takes a possession-based approach to cultural and educational resources in addition to parental occupation and education.

An implication of our work is that governments should utilise a measure of SES that is focused on an assessment of individual household attributes and resources. Governments do have some of this information available, and indeed in Australia undertake household-level assessments of financial resources to determine eligibility for a variety of welfare benefits (Mendes, 2009). This ‘administrative data’ includes a significant amount of detail allowing governments to identify low SES households. For example, where households contain recipients of Australia’s Disability Support Pension, details of the nature and severity of the illness are held, along with information relating to (a) an independent determination of the claimant’s capacity to undertake work, (b) their financial resources, and (c) disposable income (Cai, Vu and Wilkins, 2008).

More commonly, for recipients of unemployment benefits, Australia’s government collects detailed information regarding address, age, assets and income, whether the claimant has children and, if so, whether the claimant pays or receives payments for these children. When claimants are young people (under 25) the government also, in some cases, collects information on parental assets and income.

In different jurisdictions, the use of this data is controlled in different ways. Few governments, however, preclude the matching of anonymised data for group or population level comparisons (for example, at the institutional level) (Jutte, Roos and Brownell, 2011). Indeed, in Australia, part of the allocation of low SES participation funding for universities is based on an assessment of the proportion of an institution’s students whose parents are in receipt of various welfare benefits.

Given increasing access to relevant data for measuring individual SES, governments and other stakeholders may benefit from using such data to devise more accurate measures of SES. In the absence of such measures, institutions or individuals who might be beneficiaries of targeted government policies may miss out due to misclassification. Likewise, situations in which government resources are misdirected can be avoided. We thus argue for better use of administrative data to foster the development of more

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accurate SES measures for individual classification. This would require negotiating issues of privacy and data-security.

7. References

ABS (Australian Bureau of Statistics) 2001, Australian standard classification of education (ASCED), Commonwealth of Australia, Canberra.

—— 2008, An introduction to socio-economic indexes for areas (SEIFA), Commonwealth of Australia, Canberra.

——2009, ANZSCO – Australian and New Zealand Standard Classifications of Occupations, Canberra.

Atkinson, AB, Marlier, E & Nolan, B 2004, ‘Indicators and targets for social inclusion in the EU’, Journal of Common Market Studies, vol. 42, pp. 47-75.

Bourdieu, P 1973, ‘Cultural reproduction and social reproduction’, in Papers in the sociology of education, ed R Browen, Tavistock, London, pp. 71-112.

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7. Appendix

This appendix presents the regression co-efficients for the regression of participation in VET and higher education against SES-C, SEIFA and a range of control variables.

Table A1 Logistic Regression Co-efficients - VET Participation for SES-C.

Parameter df Estimate SE P-value

Intercept 1 -0.094 0.605 0.876

SES-C 0.000 0.000 0.050

Males 1 -0.785 0.067 <0.001

Females Reference Category

Non-Indigenous 1 0.028 0.202 0.890

Indigenous Reference Category

Non-Metropolitan 1 -0.293 0.065 <0.001

Metropolitan Reference Category

Government 1 -0.741 0.107 <0.001

Catholic 1 -0.657 0.117 <0.001

Independent Reference Category

Single-Family 1 -1.250 0.458 0.006

Nuclear-Family 1 -1.262 0.455 0.006

Mixed Family 1 -1.394 0.465 0.003

Other Family structure 1 -0.660 0.503 0.190

Unknown Family structure Reference Category

Maths Achievement score 1 0.003 0.001 <0.001

Reading Achievement score 1 0.003 0.001 <0.001

Science Achievement score 1 -0.000 0.001 0.980

Note. The probability modelled is the probability of NOT undertaking VET study by age 19

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Table A2 Logistic Regression Co-efficients - VET Participation for SEIFA.


Intercept 1 -5.260 1.354 <0.001

SEIFA 0.006 0.001 <0.001

Males 1 -0.786 0.067 <0.001


Non-Indigenous 1 0.019 0.202 0.925


Non-Metropolitan 1 -0.191 0.069 0.005


Government 1 -0.659 0.110 <0.001

Catholic 1 -0.595 0.119 <0.001


Single-Family 1 -1.260 0.458 0.006

Nuclear-Family 1 -1.246 0.455 0.006

Mixed Family 1 -1.387 0.465 0.003





Science Achievement score 1 7.4E-06 0.001 0.991

Note. The probability modelled is the probability of NOT undertaking VET study by age 19

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Table A3 Logistic Regression Co-efficients – HE participation for SES-C.


Intercept 1 -8.523 0.587 <0.001

SES-C 0.002 0.000 <0.001

Males 1 -0.630 0.065 <0.001


Non-Indigenous 1 0.389 0.271 0.152


Non- Metropolitan 1 -.034 0.068 <0.001


Government 1 -0.747 0.082 <0.001

Catholic 1 -0.309 0.093 0.001


Single-Family 1 -0.286 0.357 0.424

Nuclear-Family 1 0.163 0.351 0.643

Mixed Family 1 -0.623 0.370 0.092





Science Achievement score 1 0.001 0.001 0.066

Note. The probability modelled is the probability of undertaking Bachelor or higher by age 19

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Table A4 Logistic regression co-efficients – HE participation for SEIFA.


Intercept 1 -14.778 1.248 <0.0001

SEIFA 1 0.008 0.001 <0.0001

Males 1 -0.640 0.065 <0.0001


Non-Indigenous 1 0.3944 0.272 0.147


Non- Metropolitan 1 -0.205 0.073 <0.0001


Government 1 -0.688 0.085 <0.0001

Catholic 1 -0.269 0.0947 0.004


Single-Family 1 -0.333 0.358 0.352

Nuclear-Family 1 0.180 0.352 0.609

Mixed Family 1 -0.638 0.370 0.085





Science Achievement score 1 0.002 0.0007 0.024

Note that probability modelled is the probability of undertaking Bachelor or higher by age 19

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