4261.3 - Educational outcomes, experimental estimates, Queensland, 2011  
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SOCIOECONOMIC FACTORS AND EARLY CHILDHOOD DEVELOPMENT IN QUEENSLAND

INTRODUCTION

Children's early development is essential for their wellbeing; it impacts on a child's likelihood of successfully transitioning to school and affects educational, employment and health outcomes.1 Parents and families are children’s first educators and have a significant ongoing influence on their children’s development.2

An enduring goal of policy around child development in Australia is to improve outcomes for children, ensuring they have the skills and abilities in the early years that are fundamental to success and wellbeing later in life.3 Studies have shown that socioeconomic background plays an important role in determining development as a child progresses through school.4 Better understanding of child development across population groups is important for improving policy development, service delivery and evaluation.

Australian Early Development Census (AEDC, formerly the Australian Early Development Index) is an Australian Government Initiative, in recognition that "early childhood development is of central importance to the wellbeing of Australia's children and to the future wellbeing and productivity of the nation."5 The AEDC is a population measure of children's development as they enter school. The AEDC is conducted every three years, with the first two collections taking place in 2009 and 2012. The next collection will be conducted in 2015. While the AEDC provides some demographic data such as Indigenous status, country of birth and language spoken at home, there is limited parental, family and household information available to support understanding of child development.

For the first time, data from the Census of Population and Housing (Census) has been linked with AEDC data to produce a powerful new dataset that is capable of providing insights on the role of parental and other socioeconomic characteristics on child development. This approach leverages more information from the combined dataset than is available from the individual datasets taken separately. Importantly, it also enables analysis of the socioeconomic characteristics associated with developmental vulnerability for children from different population sub groups.

This article demonstrates how the integrated dataset can enhance the evidence base for social, economic and education policy in Australia. While there has been some data linkage undertaken between the AEDC and other datasets (such as the Longitudinal Study of Australian Children, perinatal and births data),6 this new integrated dataset continues to contribute to building the evidence base for measuring educational outcomes from the early years through to adulthood. Many of the findings outlined in this article are consistent with the findings from earlier ABS studies which assessed the extent to which particular parental and socioeconomic factors affect student achievement using integrated National Assessment Program – Literacy and Numeracy (NAPLAN) and Census data.7

Data was linked in accordance with the High Level Principles for Data Integration Involving Commonwealth Data for Statistical and Research Purposes.8 The data was linked using deterministic methods without the need for name or address information.


DATA IN THIS ARTICLE

AEDC is completed by teachers about children in their first year of full-time schooling, known as a Preparatory year or 'Prep' in Queensland. This article looks at 2012 AEDC results for children living in Queensland, combined with various personal and socioeconomic characteristics collected in the 2011 Census.

The AEDC measures five areas or 'domains' of early childhood development:

  • Physical health and wellbeing (which looks at whether children are healthy, independent, and physically ready for the school day, as well as their gross and fine motor skills).
  • Social competence (which looks at children's overall social competence as well as how they play, share and get along with other children).
  • Emotional maturity (which looks at whether children are able to concentrate during the school day, help others, are patient and not aggressive or angry).
  • Language and cognitive skills (which is mainly based on those skills necessary for school, including literacy, numeracy and memory).
  • Communication and general knowledge (which looks at whether children can communicate easily and effectively, and have adequate general knowledge).9
Domain scores are calculated based on teacher responses to a range of developmental and observational questions on each child. Based on the responses to these questions, children are classed as 'on track', 'developmentally at risk' or 'developmentally vulnerable' on each domain.10 Whilst the majority of children in Queensland were doing well on each of the five developmental domains in 2012, 27% of children in Prep were categorised as developmentally vulnerable on one or more domains, and 14% were categorised as developmentally vulnerable on two or more domains.

The AEDC also provides sub-domain and individual question level information. However, it is recommended that this information be used with caution as a measure of child development.11 Examples of this level of AEDC information have been included in this article with the aim of demonstrating the capacity of Census data to inform further research.

The data in this publication is based on linked AEDC and Census data and weighted to ensure the dataset is representative. There may be differences between figures in this publication and those published elsewhere. In addition, due to ABS Census data being collected in August 2011 and AEDC data between May and July 2012, there is a possibility that some children's family and household characteristics may have slightly changed between the time of the Census and the AEDC.

For more detailed information about data sources, definitions and linkage methodologies, see the Explanatory Notes tab. A number of resources to assist in understanding AEDC collection methodology, data and reporting are also available at www.aedc.gov.au.


WHAT ROLE DO FAMILY CHARACTERISTICS HAVE ON CHILD DEVELOPMENT IN QUEENSLAND?

Developmental vulnerability differs across family types

Whilst the majority of children in Queensland across each family type were developmentally on track, children from couple families were less likely to be developmentally vulnerable on two or more domains than those from lone parent families (12% compared with 22%).

Of the children living in opposite sex couple families, those with parents in a registered marriage were less likely to be developmentally vulnerable on any of the domains than those whose parents were in a de facto relationship. There were not enough children living in same sex couple families in the Queensland dataset to allow for robust analysis.

Children living with a lone mother were less likely to be developmentally vulnerable across the domains than those living with a lone father, though there was little difference between these two groups in the Emotional maturity domain.

Across all of the selected family types, higher proportions of children were developmentally vulnerable in the Physical health and wellbeing and Social competence domains, while lower proportions were developmentally vulnerable in the Emotional maturity and Language and cognition domains.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY FAMILY TYPE
Graph: shows that children from opposite sex married families were least likely to be vulnerable across the domains. Children from opposite sex de facto families were next best, followed by children of lone mothers, then children of lone fathers.
Source: Integrated Queensland AEDC and ABS Census Dataset.

While they should be used with caution as measures of child development,12 sub-domain information can demonstrate the capacity of Census data to inform research. Emotional maturity, for example, is categorised into four sub-domains: Prosocial and helping behaviour, Anxious and fearful behaviour, Aggressive behaviour, and Hyperactivity and inattentive behaviour. Children whose parents were in a registered marriage were less likely to be developmentally vulnerable in any of these sub-domains than other children.

Although children living with a lone father were more likely to be vulnerable in three of the Emotional maturity sub-domains than other children, they were slightly less likely to be vulnerable in the Anxious and fearful behaviour sub-domain than children living with either a lone female parent or in a de facto family.

PROPORTION OF CHILDREN VULNERABLE IN EMOTIONAL MATURITY SUB-DOMAINS, BY FAMILY TYPE
Graph: shows that children from opposite sex married families were least vulnerable in these sub-domains, with little variability between the sub-domains. The other family types had greater vulnerability, and more variability between the sub-domains.
Source: Integrated Queensland AEDC and ABS Census Dataset.

While they are not measures of child development in their own right, it may be useful to explore responses to the individual questions that are part of the AEDC to determine how they vary for different population groups. For example, the AEDC provides additional information reported by the teacher about children, such as whether they were identified as having an emotional or behavioural problem. Only a small proportion of children were reported as having an emotional problem (3%) or a behavioural problem (4%) in 2012. Children of lone fathers were slightly less likely than those of lone mothers to be identified having an emotional or behavioural problem. These levels were higher than for children living in couple families.

PROPORTION OF CHILDREN REPORTED AS HAVING PARTICULAR PROBLEMS, BY FAMILY TYPE
Graph: shows that children from opposite sex married families were least likely to be reported as having an emotional or behaviour problem. The other family types were more likely to be identified as having these problems, particularly behaviour problems.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Natural or adopted children least likely to be developmentally vulnerable on most domains

Census data gives us the ability to be able to distinguish between different kinds of children and the family construct in which they live. Natural or adopted children were less likely to be developmentally vulnerable on most of the domains, compared with other children. In couple families that contain children from previous relationships (step-children) as well as natural or adopted children of both parents, the natural or adopted children were less likely to be developmentally vulnerable than the step-children. Foster children were more likely to be developmentally vulnerable in the Social competence and Emotional maturity domains, though interestingly, they were less likely than the other children to be developmentally vulnerable in the Communication and general knowledge domain.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY CHILD TYPE
Graph: shows that natural or adopted children generally fared best in each domain, followed by step-children. Children living with other relatives or friends and foster children were most likely to be vulnerable.
Source: Integrated Queensland AEDC and ABS Census Dataset.

At the sub-domain level, foster children's Prosocial and helping behaviour is on a par with other children, however they were more likely than other children to display Aggressive or Hyperactive and inattentive behaviour.

PROPORTION OF CHILDREN VULNERABLE IN EMOTIONAL MATURITY SUB-DOMAINS, BY CHILD TYPE
Graph: shows that natural or adopted children were least likely to be vulnerable in the Emotional Maturity sub-domains, followed by step-children, then children living with other relatives or friends, and then foster children.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Natural or adopted children were less likely to be identified as having an emotional or behavioural problem than other children. Foster children and children living in families with a grandparent, another relative, or an unrelated adult were more likely to be reported as having an emotional or behavioural problem.

PROPORTION OF CHILDREN REPORTED AS HAVING PARTICULAR PROBLEMS, BY CHILD TYPE
Graph: shows that natural or adopted children were least likely to be identified as having emotional or behavioural problems, followed by step-children, then those living with other relatives or friends, and then foster children.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Family size plays a role in children's development

Family size has been identified as one of many potential factors that may impact on children's development.13 Children in families with two or three children were less likely to be developmentally vulnerable than children in one child families, or those in families with four or more children. However, the pattern of vulnerability across the domains varied considerably. The domain with the greatest difference was Language and cognition, where 7% of children from two child families were developmentally vulnerable, compared with 16% in families with four or more children.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY NUMBER OF CHILDREN IN FAMILY
Graph: shows that families with two or three children fared best. For one and two child families the best domain was Language and cognition, while for families with three or more children the best domain was Emotional maturity.
Source: Integrated Queensland AEDC and ABS Census Dataset.

As well as having higher rates of developmental vulnerability than those in two or three child families, children with no brothers or sisters were also observed by teachers to be more likely to get along poorly with their peers, or be less likely to play with various children.

PROPORTION OF CHILDREN WITH SELECTED CHARACTERISTICS, BY NUMBER OF CHILDREN IN FAMILY
Graph: shows that children with siblings were less likely to get along poorly with peers, or not play with various children. For all children the rates of getting along poorly with peers was higher than for not playing with various children.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Children from families with two or three children were slightly less vulnerable than those from one child families in each of the Physical health and wellbeing sub-domains. Children from families with four or more children tended to be seen as being considerably more vulnerable when it comes to the Physical readiness for the school day sub-domain. This includes at least sometimes arriving to school inappropriately dressed, late, tired, sick, or hungry.

PROPORTION OF CHILDREN VULNERABLE IN PHYSICAL HEALTH AND WELLBEING SUB-DOMAINS, BY NUMBER OF CHILDREN IN FAMILY
Graph: shows that children from two and three child families fare best on these sub-domains. Those from families with four or more children were particularly vulnerable in the Physical readiness for school day sub-domain.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Children of employed parents least likely to be developmentally vulnerable

Children who lived with at least one employed parent were less likely to be developmentally vulnerable across the five domains, compared with children who did not live with an employed parent. The domain with the greatest difference between those with an employed parent and those without was Language and cognition.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY PARENTAL EMPLOYMENT STATUS
Graph: shows that children from couple families with both parents employed fared best across the domains, followed by those from couple families with one parent employed, then children with a lone parent who was employed.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Families with at least one parent employed were the most engaged with the school and more likely to regularly read to their child at home. In contrast, children who did not have at least one employed parent were less likely to be reported by their teacher as being regularly read to at home, and their parents tended to not be as actively engaged with the school.

PROPORTION OF CHILDREN WITH SELECTED CHARACTERISTICS, BY PARENTAL EMPLOYMENT STATUS
Graph: shows children from couple families with both parents employed were most likely to be regularly read to at home and have parents engaged with the school, followed by those from couple families with one parent employed.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Occupation of parents related to likelihood of the child being developmentally vulnerable

While children with employed parents were less likely to be developmentally vulnerable, there were considerable differences in rates of vulnerability by parental occupation. Generally, more highly skilled parental occupations that require higher educational levels were associated with lower rates of developmental vulnerability amongst children. For example, only 4% of children whose father was a Medical Practitioner were developmentally vulnerable on two or more domains. Likewise, just 4% of children whose mother was a Natural and Physical Sciences Professional were rated as developmentally vulnerable on two or more domains.

Children with highly educated parents less likely to be developmentally vulnerable

The Census provides detailed information about the level (and field) of parents' education that is not currently available on the AEDC. Where a child lives with both parents, this article presents details of the parent with the highest education level.

Generally, as parents' education levels increased their children were less likely to be developmentally vulnerable. Children whose parent (or parents) did not complete Year 12 were more likely to be developmentally vulnerable across the AEDC domains than other children. This pattern was strongest for the Language and cognition domain, where 3% of children who had a parent who completed a Bachelor Degree or higher were rated as developmentally vulnerable, compared with 20% of children whose parents' highest education level was Year 11 or below.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY HIGHEST PARENTAL EDUCATION
Graph: shows a clear trend indicating that the more highly educated the parent, the less likely the child was to be developmentally vulnerable on any of the domains.
(a) Includes Certificate I & II Level.
Source: Integrated Queensland AEDC and ABS Census Dataset.

At the time the AEDC was conducted, between May and July 2012, the majority of children in Prep were capable of performing a range of academic tasks, such as writing their own name and recognising numbers 1 to 10. However, young children's ability to perform selected academic tasks also increased as their parents' education levels increased. Nearly 30% of children who did not have a parent who had completed Year 12 could not recognise numbers 1 to 10, compared with just 7% of those with a parent who had completed a Bachelor Degree or higher. However, it is important to note that there can be individual variability in the specific timing at which children achieve developmental milestones, and delays in development may not necessarily equate to having later academic difficulties.14

PROPORTION OF CHILDREN UNABLE TO PERFORM SELECTED ACADEMIC TASKS, BY HIGHEST PARENTAL EDUCATION
Graph: shows a clear trend indicating that the more highly educated the parent, the more likely a child was to be able to perform the selected academic tasks.
(a) Includes Certificate I & II Level.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Parental engagement with the school also increased with parental education level, with only 2% of children where a parent had a Bachelor Degree or higher not actively engaged with the school, compared with 18% of children with a highest parental education of Year 11 or below. The same pattern was seen for whether children are regularly read to at home, with only 1% of children where a parent had a Bachelor Degree or higher not being regularly read to at home, compared with 16% of children with a highest parental education of Year 11 or below.

PROPORTION OF CHILDREN WITH SELECTED CHARACTERISTICS, BY HIGHEST PARENTAL EDUCATION
Graph: shows a clear trend indicating that the more highly educated the parent, the more likely they were to actively engage with the school and regularly read to their child at home.
(a) Includes Certificate I & II Level.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Children of younger mothers more likely to be developmentally vulnerable

Children whose mothers were in their early 30s at the time of the child's birth were less likely to be developmentally vulnerable on any of the domains than other children. Children of younger mothers were more likely to be developmentally vulnerable across all domains. While rates of developmental vulnerability on most domains began to increase again with the age of the mother at the time of child's birth after their mid to late 30s, this pattern was not so evident for the Language and cognition domain, where the proportion of children rated as vulnerable remained low compared with children of younger mothers.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY MOTHER'S(a) AGE AT TIME OF CHILD'S BIRTH
Graph: shows general trend for developmental vulnerability to decrease on all domains as mother's age increases, up to 30-34 years old. From 35-39 years old vulnerability increases again for most domains.
(a) Includes natural and adoptive mothers.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Developmental vulnerability less likely the more time migrants have spent in Australia

Previous research exploring the relationship between children’s language background and early developmental outcomes has concluded that "further research is needed to explore the impact of family disadvantage as well as other family characteristics, such as the circumstances and timing of immigration."15

Census data allows these factors, such as year of arrival in Australia, to be explored. While children who were born outside of Australia were more likely to be developmentally vulnerable in the Communication and general knowledge domain than those who were born in Australia, they compared more favourably on the other domains where all but the most recent migrants fared better than the children born in Australia.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY YEAR OF ARRIVAL IN AUSTRALIA
Graph: shows children who arrived in Australia from 2006 to 2009 fared best on most domains, except Communication and general knowledge where those born in Australia were less vulnerable.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Reporting of Indigenous status fairly consistent

As well as offering additional information to that collected in AEDC, the integrated dataset is also able to provide a comparison between variables that exist on both datasets, such as Indigenous status. This allows differential reporting of Indigenous status in different contexts to be explored.

There is generally good consistency between reporting of Indigenous status in the AEDC and Census. Approximately 90% of children identified in the AEDC as being Aboriginal or Torres Strait Islander were also identified as being Aboriginal or Torres Strait Islander in the Census, while 98% of children identified as non-Indigenous in the AEDC were also identified as non-Indigenous in the Census.

Consistent with national AEDC results,16 while the majority of Aboriginal or Torres Strait Islander children in Queensland were developmentally on track, they were much more likely than non-Indigenous children to be developmentally vulnerable across all five domains. Aboriginal and Torres Strait Islander children in Queensland were around twice as likely to be developmentally vulnerable on two or more domains (25%) compared with non-Indigenous children (13%).


WHAT ROLE DO HOUSEHOLD CHARACTERISTICS HAVE ON CHILD DEVELOPMENT?

Children in higher income families less likely to be developmentally vulnerable

Consistent with children of employed parents being less likely to be developmentally vulnerable (particularly those in more highly skilled occupations), a strong relationship is apparent between household income and children's developmental vulnerability.

As the income of a child's household increased, the likelihood that the child was developmentally vulnerable on two or more domains decreased. For those living in households with an income under $600 a week, one in five children were developmentally vulnerable on two or more domains compared with around one in twelve children in households with a weekly income of $3000 or more.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE ON TWO OR MORE DOMAINS, BY WEEKLY HOUSEHOLD INCOME
Graph: shows that 8% of children in households with a weekly income of $3000 or more were developmentally vulnerable on two or more domains, trending up to 21% of children where household income was less than $600 a week.
(a) Includes negative or nil income.
Source: Integrated Queensland AEDC and ABS Census Dataset.

There is a strong relationship between the socioeconomic status of the area in which the child lives (as measured by Socio-Economic Indexes for Areas or SEIFA) and their likelihood of being developmentally vulnerable.17

However, it is important to consider that each household within a given area has their own level of relative socioeconomic advantage and disadvantage that may differ from the local average. There may be highly advantaged households in an area that is generally disadvantaged, and vice versa. A major benefit of this integrated dataset is that socioeconomic measures can be explored at a household or family level, rather than merely drawing assumptions based on the socioeconomic conditions of a particular area.

There was a substantial difference by household income in the proportion of children that are considered developmentally vulnerable within a SEIFA quintile. Overall, 21% of children living in the most disadvantaged SEIFA quintile (Quintile 1) were developmentally vulnerable on two or more domains, however this figure varied from 24% for those in a household with a weekly income of less than $1000, to 15% for those in a household with an income of $2000 or more. In the most advantaged quintile (Quintile 5), 9% of children were developmentally vulnerable on two or more domains, varying from 14% for those in households with the lowest income to 8% for those in households with the highest income. It is interesting to note that those in high income households in the lowest SEIFA quintile fared similarly to those in low income households in the highest SEIFA quintile (15% and 14% respectively).

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE ON TWO OR MORE DOMAINS, BY SEIFA (a) INDEX OF ADVANTAGE/DISADVANTAGE AND WEEKLY HOUSEHOLD INCOME
Graph: shows a general trend of vulnerability decreasing as the socioeconomic status of the area in which the child lives in improves. Also shows within each SEIFA Quintile, children in higher income households fared considerably better.
(a) Based on the 2011 Socio-Economic Indexes for Areas (SEIFA) Index of Relative Socio-economic Advantage and Disadvantage (IRSAD).
(b) Includes negative or nil income.
Source: Integrated Queensland AEDC and ABS Census Dataset.

While children in households in the lowest income category were more likely to be developmentally vulnerable across all of the domains, and those in the highest income category were least likely, it is interesting to note the difference between the domains. In the low income households, children were most likely to be developmentally vulnerable in the Communication and general knowledge domain and least likely to be vulnerable in the Emotional maturity domain. In contrast, children in high income households were most likely to be developmentally vulnerable in the Physical health and wellbeing and Social competence domains and least likely to be vulnerable in the Language and cognition domain.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY WEEKLY HOUSEHOLD INCOME
Graph: shows considerable difference in rates of developmental vulnerability within each domain when looking at household income, with children in higher income households faring better.
(a) Includes negative or nil income.
Source: Integrated Queensland AEDC and ABS Census Dataset.

For the Physical health and wellbeing sub-domains, children in households in the lowest income group were more likely to be vulnerable when it came to the Physical readiness for school sub-domain. As mentioned previously, this includes factors such as arriving at school inappropriately dressed, tired, sick, or hungry, all of which may be related to having a lower income.

PROPORTION OF CHILDREN VULNERABLE IN PHYSICAL HEALTH AND WELLBEING SUB-DOMAINS, BY WEEKLY HOUSEHOLD INCOME
Graph: shows considerable difference in rates of vulnerability within each of these sub-domains when looking at household income, with children in higher income households faring better.
(a) Includes negative or nil income.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Children living in homes that are owned less likely to be developmentally vulnerable

Consistent with children from families with higher incomes and more educated parents having lower rates of developmental vulnerability, those living in houses which were owned either outright or with a mortgage were less likely to be developmentally vulnerable on any of the domains. Conversely, children living in public housing were more likely to be rated as developmentally vulnerable on any of the domains.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY TENURE AND LANDLORD TYPE
Graph: shows children living in a house that was owned, either outright or with a mortgage fared best on all domains, followed by those in homes rented from a private landlord, or other landlord type.
Source: Integrated Queensland AEDC and ABS Census Dataset.

Children in crowded houses more likely to be developmentally vulnerable

By looking at the age and sex of people in a household and the relationships between them, it is possible to determine whether there are sufficient bedrooms in that dwelling to reasonably house the occupants without that dwelling being considered crowded.

Children who lived in a house where there were insufficient bedrooms for all of the occupants were more likely to be developmentally vulnerable on two or more domains than children who lived in a house where they were not crowded (22% compared to 13%). Where the houses were more severely crowded, the rates of developmental vulnerability were higher.

PROPORTION OF CHILDREN DEVELOPMENTALLY VULNERABLE IN EACH DOMAIN, BY NEED FOR EXTRA BEDROOMS
Graph: shows a similar pattern for all domains, with the least vulnerability across any of the domains where there were bedrooms spare, trending upwards as the number of extra bedrooms needed increased.
Source: Integrated Queensland AEDC and ABS Census Dataset.


LOOKING AHEAD

A wealth of personal, parental and socioeconomic information is available on the Census that is not currently collected as part of the AEDC. Through integrating these two datasets, the Census variables help to understand and explain differences in child development without the additional burden, cost and complexity of collecting them directly as part of the AEDC.

Using data for Queensland children in the first year of formal schooling, this article has demonstrated that socioeconomic and contextual factors, such as family composition, parental employment and income, have a strong influence on developmental vulnerability. Children from families with better socioeconomic circumstances are considerably less likely to be developmentally vulnerable.

There is extensive scope for further work in this area. Further analysis of the integrated AEDC and Census dataset using regression analysis would allow investigation of the extent to which particular personal, family and household factors influence developmental vulnerability. Expanding this analysis to cover national data rather than a specific jurisdiction would also be beneficial to determine if factors associated with developmental vulnerability vary across Australia, as well as to improve the ability to report on small population sub groups.

Recent data integration work performed by the ABS has linked data on student literacy and numeracy performance as measured in NAPLAN testing to the Census of Population and Housing.18 This work yielded similar results to those seen in this analysis. Future research could look at integrating the AEDC and Census data with data from NAPLAN to provide information on the relationship between children's school readiness and later performance, and how these are moderated by various socioeconomic contextual factors. Particular areas of interest might include how early childhood development and educational outcomes can be optimised for children from disadvantaged backgrounds.

Maximising the value of existing administrative data in conjunction with collections such as the Census, using data linkage techniques, has the potential to substantially enhance the evidence base for social, economic and educational policy in Australia. This can be achieved in a cost effective and efficient way without increasing the burden on the general public.


ENDNOTES

1. AEDC, The importance of early childhood development, Accessed 21 January 2015
2. AEDC, Working with communities, Accessed 21 January 2015
3. AEDC, How the AEDC assists policy reform, Accessed 3 February 2015
4. AEDC, The impact of socio-economics and school readiness for life course educational trajectories, Accessed 21 January 2015
5. Council of Australian Governments, Early Childhood, Accessed 21 January 2015
6. AEDC, Research snapshots, Accessed 21 January 2015, and AEDC, Research projects, Accessed 6 February 2015
7. Australian Bureau of Statistics, 2014, Educational outcomes, experimental estimates, Queensland, 2011, and Educational outcomes, experimental estimates, Tasmania, 2006-2013, Accessed 3 February 2015
8. National Statistical Service, 2010, High Level Principles for Data Integration Involving Commonwealth Data for Statistical and Research Purposes, Accessed 21 January 2015
9. AEDC, The AEDC domains, Accessed 21 January 2015
10. AEDC, FAQ for researchers, Accessed 21 January 2015
11. AEDC, AEDC User Guide, Accessed 21 January 2015
12. See end note 11.
13. AEDC, AEDI Community Profile 2012, p. 30, Accessed 21 January 2015
14. 'BetterStart' Child Health and Development Research Group, 2014, Five by Five, A Supporting Systems Framework for Child Health and Development, Accessed 21 January 2015.
15. Goldfeld et al., 2013, Early development of emerging and English-proficient bilingual children at school entry in an Australian population cohort, International Journal of Behavioural Development, p. 8.
16. Australian Government, 2013, A Snapshot of Early Childhood Development in Australia 2012 - AEDI National Report Re-issue November 2013, Accessed 21 January 2015
17. Brinkman et al., 2012, Jurisdictional, socioeconomic and gender inequalities in child health and development: analysis of a national census of 5-year-olds in Australia, Accessed 21 January 2015
18. See end note 7.