Positive Family Functioning - Department of Social Services · · 2012-03-21Positive Family Functioning 29 September 2010 Final Report by Access Economics Pty Limited for Department

Positive Family Functioning

29 September 2010

Final Report by Access Economics Pty Limited for

Department of Families, Housing, Community Services and Indigenous Affairs

© Access Economics Pty Limited

This work is copyright. The Copyright Act 1968 permits fair dealing for study, research, news reporting, criticism or review. Selected passages, tables or diagrams may be reproduced for such purposes provided acknowledgment of the source is included. Permission for any more extensive reproduction must be obtained from Access Economics Pty Limited through the contact officer listed for this report.

Disclaimer

While every effort has been made to ensure the accuracy of this document and any attachments, the uncertain nature of economic data, forecasting and analysis means that Access Economics Pty Limited is unable to make any warranties in relation to the information contained herein. Access Economics Pty Limited, its employees and agents disclaim liability for any loss or damage which may arise as a consequence of any person relying on the information contained in this document and any attachments. Access Economics Pty Limited ABN 82 113 621 361 www.AccessEconomics.com.au

CANBERRA MELBOURNE SYDNEY Level 1 9 Sydney Avenue Barton ACT 2600

Level 27 150 Lonsdale Street Melbourne VIC 3000

Suite 1401, Level 14 68 Pitt Street Sydney NSW 2000

T: +61 2 6175 2000 F: +61 2 6175 2001

T: +61 3 9659 8300 F: +61 3 9659 8301

T: +61 2 9376 2500 F: +61 2 9376 2501

For information on this report please contact Lynne Pezzullo T: 61‐2‐6175 2000 E: [email protected]

Report prepared by Lynne Pezzullo Penny Taylor Scott Mitchell Laze Pejoski Khoa Le Anam Bilgrami

2


Contents

Acknowledgements.........................................................................................................................i

Glossary ii

Executive Summary .........................................................................................................................i

1 Introduction ........................................................................................................................ 5

2 Methodological overview ................................................................................................... 6

2.1 Definition of family functioning and the outcomes of interest ............................................. 6

2.2 Data review............................................................................................................................ 7

2.3 Literature review.................................................................................................................. 10

2.4 Concepts and data underlying lifetime costing ................................................................... 10

2.5 Model construction.............................................................................................................. 11

2.6 Cost benefit/cost effectiveness analysis (CBA/CEA) and the process for selecting interventions for analysis..................................................................................................... 18

3 Findings from the LSAC data investigation ....................................................................... 21

3.1 LSAC variables ...................................................................................................................... 21

3.2 LSAC analysis........................................................................................................................ 24

4 Findings from the ATP data investigation......................................................................... 31

4.1 ATP variables........................................................................................................................ 31

4.2 ATP analysis ......................................................................................................................... 34

5 The costing model............................................................................................................. 38

5.1 Health outcomes.................................................................................................................. 38

5.2 Productivity outcomes......................................................................................................... 44

5.3 Criminality outcomes........................................................................................................... 45

5.4 Lifetime costing framework ................................................................................................. 49

6 Cost benefit analysis ......................................................................................................... 52

6.1 Communities for children (CfC)............................................................................................ 52

6.2 Positive Parenting Program (PPP) ........................................................................................ 56

6.3 Reconnect ............................................................................................................................ 62

6.4 Shocking all variables – the value of PFF ............................................................................. 69

7 Conclusions ....................................................................................................................... 71

References................................................................................................................................... 77

Appendix A : LSAC children and future addictive behaviours .................................................... 85

Appendix B : Literature review sources, ATP ............................................................................. 88

Appendix C : LSAC regression outcomes .................................................................................... 91

Appendix D : LSAC variable specification ................................................................................. 104

Appendix E : Mean and standard deviation of LSAC variables................................................. 108

Appendix F : ATP variable interpretation.................................................................................. 111

Appendix G : ATP Regression outcomes ................................................................................... 118

Appendix H : Mean and standard deviation of ATP variables................................................... 128

Appendix I : Detailed costing methodology and tables ........................................................... 133

Charts

Chart 3.1 : Explanatory power of regressions, by age................................................................ 26

Chart A.1 : Differences in cognitive development by socio‐economic status............................ 86

Tables

Table 2.1 : Characteristics of family functioning domains ........................................................... 6

Table 2.2 : FF variables used for each intervention .................................................................... 16

Table 3.1 : Regression constructs in LSAC .................................................................................. 23

Table 4.1 : Regression constructs................................................................................................ 32

Table 5.1 : Summary of total costs of obesity (2010)................................................................. 39

Table 5.2 : Summary of total costs of anxiety and depression (2010) ....................................... 41

Table 5.3 : Summary of total costs of daily smoking (2010) ...................................................... 42

Table 5.4 : Summary of total costs of alcohol abuse (2010) ...................................................... 43

Table 5.5 : Summary of total costs of illicit drug use (2010)...................................................... 44

Table 5.6 : Estimated effects* (%) of year 12 and undergraduate completion* on probability of participation and average earnings ............................................................................ 45

Table 5.7 : Social costs of crime by cost type (2010) ................................................................. 46

Table 5.8 : Net recurrent expenditure on criminal courts (2008‐09) and % criminal court finalisations by court type .......................................................................................... 47

Table 5.9 : Total real net operating expenditure on prisons and community corrections in Australia in 2008‐09.................................................................................................... 48

Table 5.10 : Discounted lifetime costs of adverse health outcomes (a) (2010 dollars) ............. 49

Table 5.11 : Discounted lifetime costs of adverse productivity outcomes (a) (2010 dollars).... 50

Table 5.12 : Discounted lifetime costs of criminality outcomes (a) (2010 dollars).................... 51

Table 6.1 : Communities for Children target areas and related LSAC variables ......................... 54

Table 6.2 : Outcomes of Communities for Children variables used in this report...................... 55

Table 6.3 : Outcomes of CfC as delivered over 2004‐05 to 2007‐08 .......................................... 56

Table 6.4 : PPP target areas and LSAC variables ......................................................................... 58

Table 6.5 : Impact of large scale Queensland PPP trial ............................................................... 58

Table 6.6 : Impact of large scale Western Australian PPP trial ................................................... 59

Table 6.7 : Improvement in means scores of European PPP trial .............................................. 60

Table 6.8 : Outcomes of PPP variables used in this analysis....................................................... 60


Table 6.9 : PPP costs ($US) .......................................................................................................... 61

Table 6.10 : Outcomes of PPP as delivered over 2004‐05 to 2007‐08 in Queensland................ 61

Table 6.11 : ATP variables associated with the Reconnect Program .......................................... 66

Table 6.12 : Estimate of effect size — young person’s perception of their ability to manage family conflict before Reconnect and now................................................................. 67

Table 6.13 : Estimate of effect size — young person’s perception of their family’s ability to manage family conflict before Reconnect and now................................................... 68

Table 6.14 : Outcomes of the Reconnect Program, as delivered over 2004‐05 to 2008‐09....... 69

Table 6.15 : The value of PFF....................................................................................................... 69

Table 7.1 : Summary of costs and benefits of modelled interventions ...................................... 72

Table B.1 : Antisocial behaviour ................................................................................................. 88

Table B.2 : Anxiety and depression ............................................................................................ 88

Table B.3 : Smoking .................................................................................................................... 89

Table B.4 : Alcohol...................................................................................................................... 89

Table B.5 : Illicit drug use ........................................................................................................... 89

Table B.6 : Productivity .............................................................................................................. 90

Table B.7 : Overweight/obesity.................................................................................................. 90

Table C.1 : Obesity B1 (ahs23c2) ................................................................................................ 91

Table C.2 : Obesity B2 (bcbmi) ................................................................................................... 92

Table C.3 : Obesity B3 (ccbmi).................................................................................................... 92

Table C.4 : Obesity K2 (dcbmi) ................................................................................................... 93

Table C.5 : Obesity K3 (ecbmi).................................................................................................... 93

Table C.6 : Productivity B1 (awlrnoi) .......................................................................................... 94

Table C.7 : Productivity B2 (bwlrnoi).......................................................................................... 94

Table C.8 : Productivity B3 (cwlrnoi) .......................................................................................... 95

Table C.9 : Productivity K2 (dwlrnoi).......................................................................................... 95

Table C.10 : Productivity K3 (ewlrnoi) ........................................................................................ 96

Table C.11 : Anxiety and depression B1 (apedsgc)..................................................................... 96

Table C.12 : Anxiety and depression B2 (bpedsef)..................................................................... 97

Table C.13 : Anxiety and depression B3 (cpedsef) ..................................................................... 97

Table C.14 : Anxiety and depression K2 (daemot) ..................................................................... 98

Table C.15 : Anxiety and depression K3 (eaemot) ..................................................................... 98

Table C.16 : Antisocial B1 (apedsgc)........................................................................................... 99

Table C.17 : Antisocial B2 (babitp) ............................................................................................. 99

Table C.18 : Antisocial B3 (caconda) ........................................................................................ 100

Table C.19 : Antisocial K2 (daconda) ........................................................................................ 100

Table C.20 : Antisocial K3 (eaconda) ........................................................................................ 101

Table C.21 : Addictions B1 (apedsgc) ....................................................................................... 101

Table C.22 : Addictions B2 (babitp) .......................................................................................... 102

Table C.23 : Addictions B3 (casdqta) ........................................................................................ 102

Table C.24 : Addictions K2 (dasdqta)........................................................................................ 103

Table C.25 : Addictions K3 (easdqta)........................................................................................ 103

Table D.1 : Standard LSAC variables......................................................................................... 104

Table D.2 : Combinations of LSAC variables............................................................................. 106

Illustrative distribution of categorical variables........................................................................ 109

Table G.1 : Underengagement predictor variables................................................................... 118

Table G.2 : Completion of high school(a)(b) ............................................................................. 118

Table G.3 : Completion of high school (only) v University degree(a)(b) ................................... 119

Table G.4 : Body mass index at 23‐24 years(a)(b)..................................................................... 120

Table G.5 : ATP anxiety/depression 2002 crosstabulation ....................................................... 122

Table G.6 : Logistic regression results — child age 19‐20 years (year 2002)(a)(b) ................... 122

Table G.7 : Logistic regression results — child age 19‐20 years (year 2002)(a)(b) ................... 124

Table I.1 : Obesity prevalence rates and estimated obese people (number) in 2010 ............. 136

Table I.2 : Anxiety and depression prevalence rates and estimated people with anxiety and depression (number) in 2010 ................................................................................... 136

Table I.3 : Smoking prevalence rates and estimated current daily smokers in 2010............... 137

Table I.4 : Prevalence rates for tobacco‐caused diseases and conditions(a)........................... 138

Table I.5 : Prevalence rates ‐ drinking at risky‐high risk(a) levels of long term health harm and estimated risky‐high risk drinkers in 2010................................................................ 138

Table I.6 : Prevalence rates for recent use(a) of illicit drugs and estimated recent users in 2010139

Table I.7 : Offender age‐gender prevalence profile and estimated offenders in 2008‐09 ...... 139

Table I.8 : Prisoner age‐gender prevalence rates and estimated prisoners in 2009 ............... 140

Table I.9 : Annual per‐person costs of obesity ‐ males (in 2010 dollars) ................................. 140

Table I.10 : Annual per‐person costs of obesity ‐ females (in 2010 dollars) ............................ 141

Table I.11 : Annual per‐person costs of anxiety and depression ‐ males (in 2010 dollars)...... 142

Table I.12 : Annual per‐person costs of anxiety and depression ‐ females (in 2010 dollars) .. 142

Table I.13 : Annual per‐person costs of current daily smoking ‐ males (in 2010 dollars) ........ 143

Table I.14 : Annual per‐person costs of current daily smoking ‐ females (in 2010 dollars)..... 143

Table I.15 : Annual per‐person costs of alcohol abuse ‐ males (in 2010 dollars)..................... 144

Table I.16 : Annual per‐person costs of alcohol abuse ‐ females (in 2010 dollars).................. 144


Table I.17 : Annual per‐person costs of illicit drug abuse ‐ males (in 2010 dollars)................. 145

Table I.18 : Annual per‐person costs of illicit drug abuse ‐ females (in 2010 dollars) ............. 145

Table I.19 : Age‐gender employment in the general population............................................. 146

Table I.20 : Age‐gender average weekly earnings(a) for the general population ($) .............. 146

Table I.21 : Annual costs of year 12 non‐completion ($ 2010) ................................................ 147

Table I.22 : Annual costs of undergraduate degree non‐completion ($ 2010)........................ 147

Table I.23 : Age‐specific undergraduate non‐completion rates in 2007.................................. 148

Table I.24 : Annual policing cost per offender ($) .................................................................... 149

Table I.25 : Annual court system cost per offender ($)............................................................ 149

Table I.26 : Annual prison system cost per prisoner ($)........................................................... 149

Table I.27 : Annual per‐person societal costs of crime for males ($)....................................... 150

Table I.28 : Annual per‐person societal costs of crime for females ($).................................... 150

Table I.29 : Crime under‐reporting multipliers and derived probabilities ............................... 151

Table I.30 : Probabilities of court action on reported crimes .................................................. 152

Table I.31 : Court finalisation outcome probabilities............................................................... 153

Table I.32 : Custodial sentence probabilities in guilty verdict court cases .............................. 153

Figures

Figure 2.1 : Approximate age of study cohorts and bridging the current information gap......... 8

Figure 2.2 : Diagram of data map................................................................................................. 9

Figure 2.3 : Conceptual map for valuing costs of NFF................................................................ 10

Figure 2.4 : Incidence versus prevalence approach .................................................................... 10

Figure 2.5 : Cost effectiveness analysis model map.................................................................... 17

Figure 2.6 : CEA model pathway for interventions .................................................................... 18

Figure 6.1 : Value of PFF by benefit type, 2010 (total $5.4 billion), $bn and % total ................. 70


Acknowledgements

Access Economics would like to acknowledge with gratitude the expert knowledge and inputs provided by members of the Expert Reference Group for this project. Professor Ann Sanson Department of Paediatrics, University of Melbourne, ARC/NHMRC Research Network Coordinator, Australian Research Alliance for Children and Youth Brian Babington Chief Executive Officer, Families Australia Carol Ey Branch Manager, Research and Analysis Branch, Department of Families, Housing, Community Services and Indigenous Affairs Dr Lance Emerson Chief Executive Officer, Australian Research Alliance for Children and Youth (ARACY) Dr Marian Esler Section Manager, Research Section, Family and Child Support Policy Branch, Department of Families, Housing, Community Services and Indigenous Affairs Dr Matthew Gray Deputy Director, Australian Institute of Family Studies Megan Shipley Research Section, Family and Child Support Policy Branch, Department of Families, Housing, Community Services and Indigenous Affairs Paula Mance Research Projects and Publications Section, Department of Families, Housing, Community Services and Indigenous Affairs Rachel Henry Research Section, Family and Child Support Policy Branch, Department of Families, Housing, Community Services and Indigenous Affairs Professor Stephen Zubrick Co‐Director for Developmental Health, Curtin University of Technology Access Economics would like to acknowledge in particular the staff of the Australian Institute of Family Studies (AIFS), who analysed the Australian Temperament Project (ATP) data and modelled those regressions. Apart from Dr Gray, particular thanks go to Dr Ben Edwards and Dr Diana Smart.

Glossary

ABS Australian Bureau of Statistics

AE‐DEM Access Economics Demographic Model

AEM Access Economics Macroeconomics Model

AIFS Australian Institute of Family Studies

AIHW Australian Institute of Health and Welfare

AWE average weekly earnings

ATP Australian Temperament Project

B1, B2, B3 Baby cohort, waves 1, 2 and 3 in LSAC

BOD burden of disease

CALD Culturally and Linguistically Diverse

CBA cost benefit analysis

CEA cost effectiveness analysis

CFC Communities for Children

DALY disability adjusted life year

DCBA disease cost‐burden analysis

DEEWR Department of Education, Employment and Workplace Relations

DOFD Department of Finance and Deregulation

DSM Diagnostic and Statistical Manual

DSP Disability Support Pension

DWL deadweight loss

FAHCSIA Australian Government Department of Families, Housing, Community Services and Indigenous Affairs

FF family functioning

FRS family relationship services

GP general practitioner

HILDA Household Income and Labour Dynamics in Australia

International Classification of Diseases (10th revision) ICD‐10

K1, K2, K3 Kindy cohort, waves 1, 2 and 3 in LSAC

LSAC Longitudinal Study of Australian Children

LSAY Longitudinal Study of Australian Youth

NHS National Health Survey

NDHS National Drug Strategy Household Survey

NPV net present value


NSA Newstart Allowance

NFF negative family functioning

MBS Medicare Benefits Schedule

OLS Ordinary Least Squares

PBS Pharmaceutical Benefits Scheme

PC Productivity Commission

PFF positive family functioning

PPP Postive Parenting Program

PS Parenting Scale

PSOC Parental Sense of Competency

PUP Parents under Pressure

QOL quality of life

REACH Responding Early Assisting Children

SCRGSP Steering Committee for the Review of Government Service Provision

SDAC Survey of Disability, Ageing and Carers (ABS)

SDQ Strengths and Difficulties Questionnaire

SES socioeconomic status

SFCS Stronger Families and Communities Strategy

TILA Transition to Independent Living Allowance

VSLY value of a statistical life year

WHO World Health Organization

W1,2,3 waves 1,2,3 of LSAC

YLD year(s) of healthy life lost due to disability

YLL year(s) of healthy life lost due to premature mortality


i

Executive Summary

Access Economics was commissioned by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) to quantify, in economic terms, the value of ‘goods and services’ provided by positive family functioning (PFF) and to conduct a cost benefit analysis (CBA) to establish the returns to government and society for investments made in supporting family functioning (FF). This report follows a scoping study, also conducted by Access Economics, to establish the methodology for the project. The scoping study explains the equivalence of measuring the value of PFF as the costs of negative family functioning (NFF). This study was overseen by a panel of experts.

Methods

FF is defined through a variety of domains – emotional, governance, cognitive, physical, intra‐familial and social (Table 2.1). Literature review revealed three broad areas of outcomes associated with FF.

■ Health outcomes were observed through the occurrence of anxiety and depression, obesity and substance abuse (smoking, alcohol and drug abuse) later in life. These are associated with health expenditures, productivity losses (through lower workforce participation and premature death), other financial costs, and loss of quality of life (QoL) (measured in disability adjusted life years or DALYs).

■ Productivity outcomes were reflected in secondary and tertiary educational achievement completion, flowing on to impact lifetime earnings.

■ Social outcomes were primarily measured through negative manifestations – antisocial behaviour such as delinquency and crime, resulting in criminal justice system costs.

Two longitudinal studies — the Longitudinal study of Australian Children (LSAC) aged up to 9 years and the Australian Temperament Project (ATP) for older children were selected to analyse the relationship between FF and child outcomes. Regression analysis was conducted to establish relationships between ‘transition’ health, productivity and social outcome variables in LSAC and FF variables. The latter were selected on the basis of literature evidence, after controlling for other factors such as socioeconomic status (SES). Transition variables were carefully selected to match similar or identical ATP variables. Further regression analysis was undertaken using the transition variables, together with ATP FF and control independent variables, to establish relationships with ATP ‘interim’ health, productivity and social outcomes in early adulthood. The interim outcomes were then used to predict lifetime health, productivity and social costs, based on an extensive costing process utilising multiple data sources (Chapter 5).

Findings

The net present value (NPV) of benefits from intervening in childhood and adolescence to prevent poor outcomes later in life are substantial, despite the fact that such intervention

incurs costs today but discounted benefits are realised a long time into the future.

In total, the potential NPV of benefits to be realised is in the order of $5.4 billion per annum in 2010 dollars. This can be considered the cost of NFF currently, or the value of PFF gains possible. Over half these gains (53% or $2.9 billion) are productivity gains, with a further 22% ($1.2 billion) of the benefits deriving from savings from fewer

■

ii

addictions. Fewer cases of anxiety and depression would save $0.6 billion (11%), while lower rates of criminality and antisocial behaviour would accrue $0.5 billion (10%). A reduction in obesity would save $0.3 billion per annum (5% of the total) ‐ Figure i.

Figure i: Value of PFF by benefit type, 2010 (total $5.4 billion), $bn and % total

261 , 5%

2,882 , 53%

581 , 11%

547 , 10%

1,176 , 22%

Obesity Productivity Anxiety and depression Anti‐social Addictions

Source: Access Economics calculations. Note: Shares may not sum to 100% due to rounding.

is has focused on three interventions selected on ba

■

efit:cost ratio

tio

benefit:cost ratio for this return on investment.

Costs and benefits are summarised in Table ii.

There are also marked social and economic benefits if cost effective prevention programs can be identified and implemented. This analysthe ses of a range of criteria (section 2.6)

The Communities for Children program, targeting pre‐school and primary school aged children, is one of the major Australian Government investments in families. The program improves outcomes in various FF areas including hostile parenting, parenting self‐efficacy, parent mental health, quality of the home learning environment, parental relationship conflict, child total emotional and behavioural problems, childhood overweight, receptive vocabulary achievement and verbal ability. The benfor this program was estimated as 4.8:1, a 377% return on investment.

■ The Positive Parenting Program is one of the best evaluated FF programs for younger children. The program improves FF outcomes in parental sense of competency, the dyadic adjustment scale, the Strengths and Difficulties Questionnaire (SDQ) emotional and conduct scales, the Eyberg Child Behaviour Intensity score, parental depression, parental laxness, parental over‐reactivity, and parental verbosity. The benefit:cost rafor this program was estimated as 13.8:1, a substantial 1,283% return on investment.

■ The Reconnect program targets an older cohort of children and was found to improve outcomes in school bonding and conflictual relationships, with proxied effect sizes estimated for attachment to parents and harsh parenting. The program was estimated as 1.8:1, an 81%


iii

Table ii: Summary of costs and benefits of modelled interventions

CfC PPP Reconnect

Program cost ($m)* 113.6 19.7 112.1

Unit cost ($) 840/child aged 0‐5 34/child aged 2‐12 3,800/person aged 12‐21

Benefit ($m, lifetime NPV) 541.4 272.4 202.8

Benefit:cost ratio 4.76 13.82 1.81

Source: Access Economics calculations. * Costs estimated over 2004‐05 to 2007‐08 except for Reconnect which extends to 2008‐09.

Many of the family ‘inputs’ incorporated in the analysis were found to be statistically significant explanators of child outcomes with the relationship consistent with that predicted by the literature.

■ Obesity was explained by key drivers such as previous obesity, parental obesity, lack of child persistence, and parent‐child conflict.

■ Anxiety and depression were dependent on previous emotional problems, difficult temperament, lower socioeconomic status (SES), harsh discipline, parental anxiety/depression, alienation from parents and lack of child persistence.

■ Smoking in young adulthood (19‐20 years) was determined by previous smoking in adolescence, parental permission to smoke at home and a conflictual parent‐teenager relationship. Alcohol abuse (binge drinking) in young adulthood was dependent on teen bingeing, lack of parental monitoring, father drinking and initiating drinking at an older age (over 15 compared to 14 or younger). Illicit drug use in 23‐24 year olds was dependent on the child’s temperament, lack of parental monitoring, and mother smoking.

■ Predisposition to smoking, alcohol abuse and illicit drug use was established in early years by parental smoking, temperament, harsh and/or inconsistent discipline, poor

nce,

king and low SES, along with parental anxiety/depression and the child’s temperament.

te these findings internationally as well as continue to enhance the evidence base in Australia.

Access Economics

family cohesion and parental anxiety depression.

■ Productivity was driven by previous learning outcomes, consistent discipline, temperament, socioeconomic status, parent education and, in adolescence, persisterelationship quality/warmth, parental monitoring and a positive attitude to school.

■ Antisocial behaviour and outcomes were determined by child lack of persistence, previous social/conduct problems and, importantly, were largely influenced by early life FF variables such as poor family cohesion, harsh discipline, parental smo

The greatest value in this project has been primarily to showcase how a broad, quantitative approach to social policy evaluation can work. With better quality data in the future, there is scope to refine and continue to develop the modelling and elaborate on findings further. The scope of this project has been both ambitious and challenging but, we believe, the methods developed and many findings and insights are of global significance. The novelty of the research inspires further work in this field that we hope can be used to triangula


5

1 Introduction

Access Economics was commissioned by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) to quantify, in economic terms, the value of ‘goods and services’ provided by positive family functioning (PFF) and to conduct a cost benefit analysis (CBA) and/or cost effectiveness analysis (CEA) to establish the returns to government and society for investments made in supporting family functioning.

This report follows a scoping study, also conducted by Access Economics. The scoping study in 2009 determined:

■ the feasibility of quantifying, in economic terms, the value of ‘goods and services’ provided by PFF;

■ a method for measuring the benefits of PFF; and

■ a method to conduct CBA and CEA of interventions to improve FF.

The methods developed in the scoping study form the basis for the analysis in this report. The scoping study explains the equivalence of measuring the value of PFF as the costs of negative family functioning (NFF).

Both the scoping study and this full study were overseen by a panel of experts, as listed in the acknowledgements section of each report. In addition, this study was undertaken with assistance from the Australian Institute of Family Studies (AIFS), which analysed the Australian Temperament Project (ATP) data.

The of this report is as follows. structure

■ The methodology is explained in chapter 2.

■ The findings from the investigation of the Longitudinal Survey of Australian Children

f the ATP are detailed in chapter 4.

nd the CEA results are explained.

etailed information is provided in the Appendices in relation to the methods, LSAC and ATP investigations, and the costing model.

(LSAC) are outlined in chapter 3.

■ The findings from the investigation o

■ The costing model is described in 5.

■ In chapter 6, the interventions analysed a

■ Conclusions are elaborated in chapter 7.

■ D

2 Methodological overview

2.1 Definition of family functioning and the outcomes of interest

The focus of the scoping study was on the outcomes of family functioning for the child, without pursuing any family ‘ideal’ or promoting any specific type of family structure. Children are not able to explicitly control their family environment and they are often viewed as the main victims of NFF.

The scoping study identified that while no simple definition of PFF exists, consistent themes (or ‘domains’) of FF emerged from the literature. These were developed and agreed, in consultation with the Expert Reference Group for the scoping study and with FaHCSIA. These domains provide an overarching definition of the FF environment. A summary is provided below.

Family functioning (FF) – positive and negative – is defined through a variety of emotional attributes, family governance frameworks, cognitive engagement and development characteristics, physical health habits, intra‐familial relationships and social connectedness. PFF is characterised by emotional closeness, warmth, support and security; well‐communicated and consistently applied age‐appropriate expectations; stimulating and educational interactions; the cultivation and modelling of physical health promotion strategies; high quality relationships between all family members; and involvement of family members in community activities.

The domains of FF are not mutually exclusive, but interact, complement each other and co‐exist.

Table 2.1: Characteristics of family functioning domains

Domain Characteristics / Proxies

Emotional Closeness of parent‐child relationships, warmth, responsiveness, sensitivity, perceived parental and family support as well as healthy open communication, and security/safety.

Governance Establishment of age‐appropriate rules, expectations and consistency

Engagement and cognitive development

Reading and verbal engagement, quality time fostering the development of educational, language and interaction skills.

Physical health

Healthy/unhealthy physical activities or environments as well as access – including in‐utero – to specific products (e.g. fruit and vegetables, cigarettes and alcohol).

Intra‐familial relationships (dyadic family relationships)

Quality of relationships between all members of the family. For example sibling rivalries, parent‐child relationships as well as the health of the parents’ relationship.

Social connectivity Involvement of parents and children in activities outside of the family unit (e.g. school, community service, volunteer work). Also includes relationships with extended family and work/life balance.

Source: Access Economics in consultation with the Expert Reference Group and FaHCSIA.

6


7

The literature review for the scoping study revealed three broad areas of outcomes associated with FF.

■ Health outcomes were mostly observed through the occurrence of mental illnesses such as anxiety and depression later in life, but also included eating disorders, health behaviours (e.g. unsafe sex, physical inactivity, overweight and obesity) and substance abuse (e.g. smoking, alcohol and drug abuse), with the consequent physical impacts of these risk factors on morbidity and mortality outcomes.

■ Productivity outcomes were reflected in rates of labour force participation, employment and hourly wage rates, with a number of intermediate measures reported in the literature, such as reduced levels of literacy and numeracy and other measures of educational achievement.

■ Social outcomes were measured primarily through their negative manifestations –involvement in antisocial behaviour such as delinquency, and the probability of criminal behaviour during youth and later in life. In contrast it was harder to quantitatively associate PFF with positive manifestations such as the quality of inter‐personal relationships and future community contributions.

The iteria used to select the specific outcomes for analysis in this report were: cr

■ each outcome domain was covered (health, productivity and social);

■ outcomes were associated with high economic and social costs, including burden of

were able to be measured by the data sets used as the basis for analysis (see below).

this

ree);

sion, obesity, smoking, drinking, illicit drug use); and

ile

aic of

thy study timeframes. For example, intermediate outcomes y and literacy cannot easily be converted into specific types of jobs and streams.

investigated but the LSAY did not

disease (BoD), based on prevalence and/or existing studies of costs and BoD;

■ the outcomes

In report, the correlation between FF is thus examined for the following outcomes:

■ productivity (school completion and completion of an undergraduate university deg

■ health (anxiety and/or depres

■ social (antisocial behaviour).

Wh the literature provided valuable insights into potential linkages between FF and child outcomes, the following cautions apply.

■ Many of the concepts are difficult to capture and measure and there is a mosdifferent and overlapping instruments and metrics.

■ Statistical techniques can be used to determine correlation rather than causation.

■ While measures of intermediate outcomes are available, it is difficult to convert these to final outcomes without lengsuch as numeraclifetime earnings

2.2 Data review

Two longitudinal studies — the Longitudinal study of Australian Children (LSAC) and the Australian Temperament Project (ATP) were selected to analyse the correlation between FF and child outcomes. The Longitudinal Survey of Australian Youth (LSAY) and the Household, Income and Labour Dynamics in Australia (HILDA) were also

8

col t information on FF variables and HILDA is limited to relationships between the family nment and parent’s participation in the labour force.

LSAC has the advantage of containing a breadth of data, valuable for testing confo

lecenviro

■ unding factors. However, a disadvantage is the relatively short timeframe of data

years. However, measures

gers du , however, eptual consistencies in measurement of FF between the two data sets.

collection as the eldest participants from the child cohorts are currently 10‐11 years of age.

■ ATP is currently the only study in Australia that allows the determination of long term impacts of FF on health, economic and social outcomes as the most recent data for participants in this study were collected at the age of 23‐24of FF and parenting have only been recorded since the participants were in their early teens, with no measures during infancy or early childhood.

Variables from the ATP and LSAC within each of FF domains were mapped to the specific outcomes selected for analysis so that the likelihood of one of the events of interest occurring could be established across different age groups (Figure 2.1) and linked with outcomes. The ability to join information from LSAC and ATP is limited by slightly different methods in each data set of measuring FF and differences in the generations (ATP children were teena

ring the 1990s, whereas LSAC children are growing up during the new century). There are conc

Figure 2.1: Approximate age of study cohorts and bridging the current information gap

LSAC (1)

LSAC (2) Current Age Cohort Data Gap

ATP

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Approximate Age of Cohort in 2009 LSAC (1) is the Birth cohort and LSAC (2) is the Kindy cohort.

Ideally, one or two more waves of information from the LSAC is required to successfully bridge the gap in ages between the two studies, to enable mapping outcomes for children aged

analys

■ observations in the oldest groups i.e. the third wave of the Kindy cohort in LSAC. Early and mid‐period FF and control

P ep

analys

9 years to those aged 13 years. This approach is considered acceptable, given the alternative is to wait some years for additional study waves to be completed.

Figure 2.2 shows how LSAC provides the dependent (end‐period ‘transition’) variables and independent (early and mid‐period family functioning and control) variables in regression

is of children aged up to 9 years.

End‐period transition variables are those that relate to

variables relate to observations in all the other age groups (the first and second wave observations and the third wave for the birth cohort).

AT data then provide the dependent (end‐period ‘interim outcome’) variables and ind endent (early and mid‐period family functioning and control) variables in regression

is of people aged up to 25 years.


9

■

ion completed, illicit drug use and body mass index were assessed at age 23‐24 years, while other variables (completing year 12, smoking, binge drinking,

n be modelled by ‘shocking’ the LSAC variables, while adolescent interventions1 can by ‘shocking’ the ATP variables. ‘Shocking’ the model refers to changing inpu simulating the effects of an intervention, and then observing the consequent change in model outputs (health,

End‐period interim outcome variables are those that relate to observations in the oldest groups of ATP relevant to the cost data and the circumstance. For example, highest level of educat

anxiety/depression, and anti‐social behaviour) were assessed at adulthood (18‐19 years). Early and mid‐period FF and control variables relate to younger age observations.

The interim outcomes can then be used to predict, in an Excel model, lifetime health costs, lifetime productivity losses, social costs of crime and disability adjusted life years (DALYs). The impact of childhood programs ca

be modelledt parameters from one level to another,

productivity and social outcomes).

Figure 2.2: Diagram of data map

LSAC ‐ early and mid‐period

CfC, PPP shock FF variables

control variables (confounding)

Regression analysis

LSAC ‐ end‐period ATP ‐ early and mid‐period

Transition variables FF variables shock Reconnect

control variables (confounding)

Regression analysis

ATP ‐ end‐period

Interim outcomes

Excel model

Lieftime health costs

Productivity (lifetime earnings)

Social (lifetime costs of crime)

DALYs

In laymen’s terms, LSAC was used to establish the impact of FF on children up to the age of 9 years, at which age transitional outcomes from childhood were spliced into the ATP analysis by matching this set of transitional outcomes across the two datasets. Health, productivity

ng adequate data to establish hip between family functioning and outcomes, collaborative relationships were

chers and FaHCSIA data specialists. Both were important participants in the project. Furthermore, AIFS currently manages the ATP data set and access

and social interim outcomes in early adulthood thus depended on FF during adolescence as well as the transitional outcomes from childhood. From the interim outcomes in early adulthood, lifetime cost impacts were predicted using a variety of other datasets.

Given the identification of the ATP and LSAC databases as providithe relationsestablished with AIFS resear

arrangements require that AIFS undertake any in depth analysis.

1 The Communities for Children (CfC) and Positive Parenting Program (PPP) were selected for younger children, while the Reconnect program was selected for adolescents (see Section 2.6).

10

2.3 Literature review

Evidence from the literature was used to edify the selection of family functioning inputs from es. The findings are briefly

outlined in chapter 3, and chapter 4, and evidence is summarised in Appendix A and Appendix

2.4 Concepts a

the ATP and LSAC that would be likely to be correlated with outcom

B.

nd data underlying lifetime costing

Costs were attached to each outcome as depicted in Figure 2.3.

Figure 2.3: Conceptual map for valuing costs of NFF

Emotional Health Health system expenditures

Governance Productivity losses

Cognitive Productivity Criminality costs

Physical Social/criminality DWLs

Intra‐familial Other financial impacts

Social Burden of disease

Source: Access Economics. Blue – health impacts. Red – productivity impacts. Green – Social/criminality impacts.

An incidence or ‘life aetiology of lagged outcomes – i.e. ‘lifetime’ costs (hazard model). An incidence approach is distinguished from a

time’ costing approach was adopted in line with the

prevalence approach in Figure 2.4.

Figure 2.4: Incidence versus prevalence approach

2000 2009 2018

A* A

B* B B**

C C*

Incidence costs (2009) = C + present value of C*

Prevalence costs (2009) = A + B + C

The lifdiseas

■ Cost Data Collection

(Department of Health and Ageing), the Pharmaceutical Benefits Scheme (PBS), the

Note: The years are illustrative and do not relate to this analysis.

etime costs associated with each outcome include the standard cost categories for e cost burden analysis from the health economics literature:

Direct health costs – estimated with cost data sourced primarily from the Australian Institute of Health and Welfare (AIHW) data, National Hospital


11

Medicare Benefits Schedule (MBS) and epidemiological data sourced from the Australian Bureau of Statistics (ABS) National Health Survey (NHS), AIHW and other specific epidemiological studies reported in the peer reviewed literature.

re (unpaid care provided by family and friends), health aids and appliances, deadweight

sources of these estimates are previous studies by Access Economics, and the ABS

and Carers (SDAC) (ABS, 2004), and Lattimore et al (1997).

is sthe co lected for analysis. The net

se

■ nt a direct input into the cost benefit analysis.

analyses) change as a result of the intervention

pact of a 'shock' to consistent

■ Costs of crime – estimated with data primarily from the Steering Committee for the Review of Government Service Provision (SCRGSP) Report on Government Services, and reports from the Australian Institute of Criminology and ABS. .

■ Productivity costs – are estimated using the human capital approach and reflect reduced labour force participation and absenteeism due to the outcomes selected. Parameters and labour force data were drawn primarily from the ABS and reports by the Productivity Commission (PC), as well as peer reviewed literature.

■ Burden of disease (BoD) – was estimated using DALYs and determined using the same disability weights and methodology used by the AIHW (Begg et al, 2007). Monetary values were estimated for the BoD using the value of a statistical life year from DOFD (2009).

■ Other financial costs – include costs associated with the provision of informal ca

losses (DWLs) (efficiency losses which arise due to transfer payments). The main

Survey of Disability, Ageing

Further detail on costing methods, cost categories and data sources is provided in Appendix I.

2.5 Model construction

Th ection provides an outline of the model developed to investigate the benefits of PFF and sts and benefits of the three family functioning programs se

benefits for each program were derived under a scenario with the intervention compared to a ‘ba case’ without the intervention.

The costs for the CfC program, PPP and Reconnect programs are reported in chapter 6 and represe

■ The benefits are based on the extent to which each intervention improves FF and reduces its associated costs. The effectiveness of each intervention is reported in chapter 6 while the associated costs (health, productivity and social) are detailed in chapter 5.

The underlying principle of the model (developed in Microsoft Excel 2007) is that outcomes (dependent variables in the regression affecting early and mid‐period family functioning (independent) variables in the regression. The size of the intervention is a direct function of the effectiveness of the program on impacted functioning variables (chapter 6) and the size of the coefficients derived in the regressions (Appendix C and Appendix G).

A simple example illustrates the model construction using the imdiscipline on anxiety in children aged 4‐5 (so the analysis starts with the B3 cohort of this age – incidentally the same age as the K1 cohort). The ‘shock’ in this example was derived from PPP improvements in ‘parental laxness’ and mapped directly to parental consistent discipline as both in practice are aspects of parental consistency (Table 6.20).

12

The 'shock' can be viewed as changing the input parameters (e.g. parental consistent discipline) from one level to another, simulated by the effects of an intervention. Before the shock, the multivariate regressions for anxiety are given below for age group 4‐5 (B3), 6‐7 (K2) and 8anxiet child, and discipline is towards that child).

‐9 (K3), respectively. In each case, i represents the observational child in the dataset (so y is that of the

Where:

B3 = Cohort of children aged 4‐5 in the ("Baby") group B2 = Cohort of children aged 2‐3 in the ("Baby") group

= Beta coefficient for 'Anxiety' in group B2

= Beta coefficient for 'Consistent discipline' in group B3

= Regression error term

Where:

K2 = Cohort of children aged 6‐7 in the ("Kindergarten") group B3 = Cohort of children aged 4‐5 in the ("Baby") group


= Beta coefficient for 'Consistent discipline' in group K2


Where: K3 = Cohort of children aged 8‐9 in the ("Kindergarten") group K2 = Cohort of children aged 6‐7 in the ("Kindergarten") group

= Beta coefficient for 'Anxiety' in group K2

= Beta coefficient for 'Consistent discipline' in group K3



13

As a result of the intervention, the new level of childhood anxiety for children aged 4‐5 includes changes in parental consistent discipline. The change in parental consistent discipline from the shock was calculated by multiplying the effectiveness of the program by the average onsistent discipline value for children aged 4‐5.

c

Where:

= New 'Anxiety' value after intervention

= (mean consistent discipline x effectiveness of program)





As a result of the shock, the consequent change in the model output (Anxiety') is given by a ercentage change.

p

Where:

= Percentage change in 'Anxiety'


= Baseline 'Anxiety' value


Consequently, the direct percentage change in anxiety levels for children in the 4‐5 age group is captured through to the next LSAC age group (6‐7 year group) through the use of the lagged ependent variable changing by the intervention effectiveness.

d

Where:

= Percentage change in 'Anxiety'


= Baseline 'Anxiety' mean value


Where:

= New 'Anxiety' value after intervention in the previous age group

= New 'Anxiety' lagged dependent variable = as above



K2 = Cohort of children aged 6‐7 in the ("Kindergarten") group B3 = Cohort of children aged 4‐5 in the ("Baby") group


Following this pattern, Anxiety’’K2,i and Anxiety’K3,i are similarly calculated. The change in the final period of LSAC compared with the actual outcome is then included in the ATP multinomial logit regression i.e. the new anxiety levels for K3 children aged 9 (the final regression in the LSAC analysis) is fed directly into the ATP logistic regression for children aged 13 years.

14


Where:

= New 'Anxiety' value in children aged 8‐9 in the ("Kindergarten") group

= Base 'Anxiety' value in children aged 8‐9 in the ("Kindergarten") group

This effectively assumes that no change occurs in outcomes between the ages of 10 to 12. As mentioned before, this approach is considered acceptable, given the alternative is to wait some years for additional study waves to be completed.

The multinomial logit regression models were used to analyse the impact of an intervention on the probability of each family functioning outcome for children aged 13‐23 years. Unlike the multivariate linear regressions, the results presented in the logit regression represent the probability of an outcome for a person with ‘average’ attributes. Like the LSAC multivariate linear regressions, the interventions were modelled as a deviation from the mean of a particular explanatory variable (e.g. anxiety). The baseline logistic regression is given below, where e is a mathematical constant, i is again each child observed (in ATP this time), βs are

again coefficients, and εs are again error terms. X is the sample mean.

After the percentage change from anxiety in the 8‐9 year old age group, the new logistic regression is given, with changes to the lagged dependent variable.

In using the multinomial logit model, coefficient estimates are not directly interpretable so do not provide the same type of information as coefficients from an Ordinary Least Squares (OLS) model. A more natural way of interpreting results from a multinomial logit model is to determine the impact on the probability of an outcome by changing the variables that would be impacted by the intervention while holding all others constant. The impact of the intervention on the probability can therefore be represented by:

The impact of the intervention was therefore measured as the difference between the probability of an outcome for an average person with and without the intervention. As such, the model projects the probabilistic change in the outcome as a result of the intervention and calculates the economic benefits attached (detailed in chapter 5). The net economic gains

15

16

between the scenario and the ‘base case’ (no intervention) can therefore be evaluated to determine the intervention’s overall return on investment using a dollar value.

Table 2.2 summarises all the FF variables in the model for each intervention modelled (i.e. found to be significant in the regression analysis reported later on). A conceptual map of the model is provided in Figure 2.5.

Table 2.2: FF variables used for each intervention

CfC PPP Reconnect

Hostile parenting Hostile parenting Harsh parenting

Parenting self‐efficacy Parental self‐efficacy

Consistent parental discipline

Attachment to parents Parental warmth*

Parental relationship conflict Family cohesion Conflictual relationships

Parent mental health Parental anxiety and depression

Child total emotional and behavioural problems (SDQ)

SDQ total score, SDQ emotional score, SDQ conduct problems

Quality of the home learning environment

Home learning environment*

Receptive vocabulary achievement and verbal ability

Inductive reasoning School bonding (positive affect towards school); Under‐engagement (not in education or training and not employed)*

Child overweight

Source: Access Economics (2010). Note: See Chapters 3, 4 and 6 for derivation. * These variables are in the Reconnect model but the effect size was estimated as zero.


Figure 2.5: Cost effectiveness analysis model map

Source: Access Economics (2010)

17

2.6 Cost benefit/cost effectiveness analysis (CBA/CEA) and the process for selecting interventions for analysis

The concept of CEA modelling of FF interventions is outlined in Figure 2.6. All analyses compare the outcomes for children with the interventions, against outcomes for children without the intervention. The efficacy of a selected intervention in improving FF is derived from previous evaluations of the programs. The Excel model is then used to explore how this improvement in FF (the ‘shock’) reduces lifetime costs (in dollars and DALYs). These lifetime benefits can then be compared with the intervention costs, in net present value (NPV) terms.

Figure 2.6: CEA model pathway for interventions

Intervention

Improvement in

family functioning in

Yr x (efficacy)

Reduces lifetime costs

(NPV in Yr x, discounted

DALYs saved )

Cost in 09‐10$ Benefit in 09‐10$ CBA $:$ Benefit:Cost ratio

DALYs saved CEA $/DALY saved

As noted in Section 2.2, ‘shocking’ the model for CBA and CEA involves comparing what happens in the absence of an intervention (the status quo), with what would happen if a particular target population received an intervention. The intervention improves FF based on evaluated effectiveness of the program, which in turn improves transition and/or interim outcomes (based on the coefficients derived from the modelling). Better outcomes are associated with lower costs, so the NPV of the benefits (lower costs of NFF) can then be compared with the costs of the intervention. Benefits minus costs provide the ‘net benefit’ in dollars, while benefits divided by costs provide the ‘benefit:cost ratio’.

The process of selecting appropriate FF interventions commenced in the scoping study, when a preliminary assessment was undertaken of 12 types of interventions:

1. family assistance and income support payments;

2. family relationship services (FRS);

3. Stronger Families and Communities Strategy, including Communities for Children and Invest to Grow;

4. Positive Parenting Program (PPP);

5. Early childhood education;

6. Peel Child Health Survey;

7. Responding Early Assisting Children (REACh);

8. Reconnect;

9. Youthlinx;

10. Transition to Independent Living Allowance (TILA);

11. SureStart; and

12. Parents under Pressure (PuP).

18


For this full study, criteria were established in the first Reference Group meeting for assessing appropriate interventions for CEA. These four criteria were:

1. Model data: the LSAC and ATP datasets will be able to accommodate ‘shocks’ to the intervention;

2. Target age: the interventions will target different age groups (e.g. pre‐school, primary school, youth)

3. Reach: the interventions have ‘reach’ i.e. they effectively target relevant (disadvantaged) groups; and

4. Efficacy data: adequate information is available from Australian (preferably) or international sources in order to provide an indication of the efficacy of interventions.

A follow‐up meeting with Steve Zubrick identified three further criteria.

5. Specificity: Interventions specifically target family functioning, rather than indirectly affect it (e.g. income supplementation can assist with FF, but is in essence a poverty alleviation method).

6. Sustainability: Interventions are current, and likely to continue into the future.

7. Relevance: Interventions have strong connections to or relevance for FaHCSIA.

We also reviewed literature provided by the Reference Group – Karoly et al (2007) and Wise et al (2005). The interventions selected from this process are summarised below.

2.6.2 Communities for children (CfC)

CfC meets all criteria for the CEA.

1. Model data: LSAC FF data can be linked with CfC interventions.

2. Target age: CfC is targeted to pre‐school and primary school age children.

3. Reach: CfC targets all Australian population sub‐groups.

4. Efficacy data: CfC has been evaluated with efficacy outcomes that can be imputed to the Excel model.

5. Specificity: CfC aims to improve family functioning and outcomes for children.

6. Sustainability: The CfC program has forward funding.

7. Relevance: CfC is a FaHCSIA program.

Conclusion: CfC is one of the major Australian Government investments in families. It has already been shown to be efficacious, and the CEA evaluation can also determine at what cost its effective outcomes are achieved.

2.6.3 Positive Parenting Program

PPP meets all criteria for the CEA.

1. Model data: LSAC FF data can be linked with PPP interventions.

2. Target age: PPP is targeted to pre‐school and primary school age children.

3. Reach: PPP targets all Australian population sub‐groups.

4. Efficacy data: PPP has been evaluated with efficacy outcomes that can be imputed to the Excel model.

19

20

5. Specificity: PPP aims to improve family functioning and outcomes for children.

6. Sustainability: The PPP program is successful and growing.

7. Relevance: PPP is a program relevant to FaHCSIA core business.

Conclusion: PPP is one of the best evaluated programs targeted at improving family functioning and outcomes for younger children. While its efficacy is well‐proven, there are fewer studies on its cost effectiveness and this CEA can also act as a tool to test/triangulate the power of the model.

2.6.4 Reconnect

In the Scoping Study Reference Group and in the 13 January Reference Group meeting the Reconnect program was identified as being a good candidate for CEA. Again it meets all criteria for the CEA.

1. Model data: ATP FF data can be linked with Reconnect interventions.

2. Target age: Reconnect targets youth aged 12‐18 (who are homeless or at risk of homelessness) and their families.

3. Reach: Reconnect targets all Australian population sub‐groups.2

4. Efficacy data: Reconnect has been evaluated with efficacy outcomes that can be imputed to the Excel model (based on two longitudinal studies).

5. Specificity: Reconnect aims to improve family functioning and outcomes for high school aged children.

6. Sustainability: The Reconnect program is funded into the future.

7. Relevance: Reconnect is a FaHCSIA program.

Conclusion: Reconnect provides a complementary intervention targeted at an the older cohort of children, which has been evaluated as effective, but where nothing is yet known regard cost effectiveness.

2 Indigenous youth comprised 9% of the respondents to the longitudinal survey that formed a key element of the 2003 evaluation by the Australian Government Department of Family and Community Services (FACS, 2003:34) and ‘No differences exist between entering and exiting clients in relation to country of birth or language background’ (FACS, 2003:35). Youth were represented from all jurisdictions, from both sexes and with varying levels of case complexity.


21

3 Findings from the LSAC data investigation

LSAC consists of two cohorts ‘B’ (for Baby) and ‘K’ (for Kindergarten). Each of these cohorts has three sets of time data ‐‘waves’ of survey data taken at two yearly intervals. Information is collected through self‐reporting and also observational measures (such as parent‐child interactions). Child outcomes are measured as: behavioural and emotional adjustment; language and cognitive development; and social competence.

LSAC contains specific research questions. One question focuses particularly on the impacts of family relationships, composition and dynamics on child outcomes, and changes to these over time. The question includes the analysis of:

■ the size and make‐up of family;

■ the involvement of extended family;

■ roles of family members;

■ character of parental relationships and level of conflict in the family;

ies, particularly in times of stress.

to represent se

Appendix D provides a detailed description of each individual variable.

Cases

■

lationships between children and parents. In

■ parenting practices;

■ child’s temperament;

■ impact of family break‐up and re‐formation; and

■ family coping strateg

3.1 LSAC variables

Evidence from the literature was used to edify the selection of family functioning inputs from the ATP and LSAC that would be likely to be correlated with outcomes. The evidence is

in Appendix B. In most cases, the choice of relevant LSAC variablessummarisedthe general literature categories in the model is fairly straightforward.

■ Appendix C provides a complete list of LSAC variables included in the model.

■ ■ Appendix E provides a distribution of LSAC variables.

that involved more consideration are discussed below.

In some cases, concepts linked in the literature to negative outcomes for children — either as control variables or family functioning variables — were not available from the datasets. In the LSAC, no variables were identified for child abuse, school bonding, enmeshment, or negative conflictual readdition, some literature concepts were not relevant to LSAC children such as study child’s alcohol consumption or smoking.

■ For the obesity regression, the category ‘Sedentary activities’ is not derived directly from the literature discussed in Appendix B. The FF variable used here (e‐tainment) measures total hours per week of electronic entertainment: using computers, playing video games and watching television. The concept is that too much sedentary activity contributes to obesity.

22

■ Also, under obesity, the control variable ‘needs extra medical care’, while a direct LSAC variable, is not derived directly from the literature in Appendix B. The rationale here is that some children may be physically unable to exercise, which could in turn contribute to weight gains.

■ Under productivity, academic competency (learning outcomes) is derived from the literature but, given the young age of the children involved, academic competency could vary considerably within the two‐year span of each wave. Accordingly, the LSAC variable ‘study child age in months’ was added as a control variable.

For the very young cohorts (particularly B1 and sometimes B2 and older), there were no distinct measures of addictions, antisocial behaviour, or anxiety and depression. In some cases the best proximate dependant variable was the same (such as apedsgc for the B1 cohort across all three regressions). Since the only impact of these early cohorts is to feed into older age groups as a general indicator of previous problems, this was considered sensible, since the same

■

predisposing factor can be a risk for two different

d

d highest level of parent education .

■ Alcohol consumption has been recoded from six categories down to three categories –

missinbetwePrefer ple sizes

outcomes later. Note that for B2, babitp is used as the dependent variable with identical independent variables except that parental overprotection is used to predict addiction but not antisocial behaviour, based on the literature (Emmelkamp an Heeres, 1988), which reported that parental overprotection was a significant factor in adult drug addiction, hence its inclusion there.

■ Parental anxiety and depression has been recoded from an average to a sum of component measures.

■ The literature category ‘socio‐economic status’ is proxied by the LSAC variables household income3 an 4

abstain, safe and unsafe.

■ In an effort to obtain a ‘cradle to grave’ coverage for the full model, where the same dependant variable for the regressions is not available for all age groups, close substitutes are used.

There were also issues around sample size. The initial sample for both cohorts was around 5,000 children. After excluding children who have not participated in all three waves, the sample size is reduced to between 4,000 and 4,500. Using standard regressions, for any givenvariable, participants with nil responses are excluded. That is, the variable with the most

g observations determines the sample size for the regression. Thus, there is a trade off en more variables (greater explanatory power) and fewer variables (larger sample size). ence has been given here to greater explanatory power, but this does limit sam

to between 2,000 and 3,000 observations.

■ Certain participants are more likely to drop out as the study proceeds or to be non‐responders (which will affect sample size and bias in the analysis sample) – e.g. people with low socioeconomic status (SES), Indigenous Australians and those from non‐English speaking backgrounds. To account for this, sample weightings were employed.

3 For some groups, weekly income had to be used, but for compatibility this was converted to annual income.

4 Highest level is ‘post secondary’ (which includes the majority of parents, so there may be a lack of precision in the high end of the scale).


23

■ The model did not adjust for complex survey design, as this is not usually an issue unless location‐specific factors are important.

■ Also, to maintain sample size, the primary carer was used to represent parents, rather than having separate (and thus smaller) equations for mothers and fathers. This is also consistent with the ATP, which generally does not differentiate parents by gender. However, to allow for gender differences, sex of parent was included as a control variable.

■ The LSAC variable ‘family cohesion’ was used as a proxy for the literature category LSAC ‘argumentative gh this ed of sole parent families, who are an important subsample to retain

from the Strengths (SDQ) re utilised from LSAC and depression, ).

Table 3.3: Regression constructs in LS

Concept

‘marital conflict’. also measures relationships’, althouvariable is not askin the modelling.

■ Scales and Difficulties Questionnaire wein relation to anxiety anti‐social behaviour and addictions (Table 3.3

AC

Literature LSAC Measure LSAC Variable Name

Dependent variables#

Obesity Body mass index *cbmi (ahs23c2)

Productivity Learning Outcome Index *wlrnoi

Anxiety and depression SDQ emotional symptoms scale pedsgc, bpedsef,

*aemot (acpedsef)1

Antisocial SDQ conduct problems scale (apedsgc, babitp) *aconda

Addictions SDQ total score *asdqta (apedsgc, babitp)

FF variables

Harsh discipline Hostile parenting scale *ahostc (caang K1)

Parental monitoring / supervision

Importance of monitoringsupervising child

/

*pa08a1

Relationship quality / warmth Parental warmth scale *awarm

Inductive reasoning Inductive reasoning scale *aireas

Inconsistent discipline Consistent parenting scale *acons

Parental self efficacy Global rating of self‐efficacy *pa01a

Family cohesion Family ability to get along wieach other

th *re06a

Eating behaviour (obesity) Food diary (fat) *hfat

Eating behaviour (obesity) Food diary (sugary drink) *hsdrnk

Exercise (obesity)‡ Choice of physical activity in free time

*hb14c4

Sedentary activities (obesity) Hours per week electronic entertainment†

*he06c1, *he07b2,*egweek (combo) *he06b1,

Parental involvement in education (productivity) index

Home (education) activities *ahact

Parental involvement in uctivity)

volvement in class activities assc education (prod

In *

24

Literature Concept LSAC Measure LSAC Variable Name

Parental smoking (addictions, tisocial)

ette smoking anxiety & depression, an

Frequency of cigar *hb15a1

Parental alcohol (addictions, anxiety & depression, antisocial)

Alcohol consumption groups *balcgp

Overprotection (anxiety & on scale depression, addictions)

Parental overprotecti *aoverp

Control variables

Temperament Temperament *se06

Parental anxiety & depression K‐6 depression scale *ak6

Gender Sex of study child zf02m1

Socioeconomic status Household annual income2 *hinc (*fn05)

Parental education level Highest level of education completed†

*fd08a1, *fd08b2 (combo)

Parent gender Sex of primary carer zf02m2

Parental body mass index (obesity)

primary carer body mass index *abmi

Disability (obesity)‡ Special health care needs *hs14d

Age (productivity)‡ study child age in months *scagem

Notes: In the left column, variables in normal font are universal, variables in italics are particular to the individual right column, variables outside brackets are those used mostly or exclusively and used when the main variable was not available. Appendix D provides greater

description of these variables.

ildcard (*) it covers all age groups (represented by first letters a,b,c,d,e) unless them d 1 in Excel).

■ While this should yield a total of six time series observations, the two cohorts overlap at

ual difficulties in splicing the two together to create one ‘continuous’ time

The sigreat ohort groups. This is to some extent

regressions in brackets. In thevariables in brackets are those

# Previous wave of the dependent variable is also used as a control variable. † = LSAC variables that have been combined to form new variables. ‡ = variable not directly suggested by literature search. * Where a variable code contains a wano r age‐specific variable code is included next to the variable name. 1 cae ot would have been preferred, but yielded nonsensical results (R‐squared of 0 in Stata an

2 Household annual income was categorical, converted to dollars per year.

3.2 LSAC analysis

The LSAC presents a number of methodological issues for time series analysis. As noted in the preamble to chapter 3, the Baby and Kindergarten cohorts each have three waves of time data taken at two yearly intervals.

ages 4‐5 years (intentionally), leaving only five time series observations.

■ Moreover, as each cohort of children are entirely distinct populations, this creates conceptseries.

tuation is further complicated by the fact that, despite having over 16,000 variables, the majority of them are not common across all wave/c

unavoidable given the great differences between newborns and nine year olds, but it still makes it difficult to track variables continuously over time periods.


25

Accordingly, a number of approaches were investigated for modelling LSAC data. A hypothetical obesity regression is used here to illustrate the differences between these approaches.

■ 1) a straightforward longitudinal analysis. This derives averages across each total age group (e.g. average obesity for 4‐5 year olds) and uses them as regression variables. However, this approach

was not satisfactory for a number of reasons, including the lack

to W3. Also, because

was usually around 1,500, which was low.

■ 3) a stepped nction of W2 diet, other W2 control and FF variables, and previous period (W1) obesity. While explanatory

of suitable variables which covered all age groups.

■ 2) a semi‐panel approach, combining elements of longitudinal and cross sectional. Illustratively, obesity in wave (W) 3 is a function of obesity in W2, obesity in W1, diet in W3, diet in W2, diet in W1. This had the advantage that if the same variable cannot be found for diet across all groups, it does not matter, as different diet variables can be used for each age group. On the other hand, the number of variables with significant explanatory power was not high, and most of them belongedeffective sample size for regression analysis was limited to the smallest variable among any of the constituent age groups, the number of observations

time series model. Illustratively, obesity in W2 is a fu

power was good for the older groups, and average for intermediate ages, it was verypoor for the B1 infants group (Chart 3.1). A further issue with this approach is that theK1 group could not be utilised, as they lacked a link to previous periods.5 Fortunately,this age group (four to five year olds) is the only one covered with two sets of surveydata, so B3 could be used instead to represent this age group.

While none of the methods were optimal, method three was adopted as it had the leastshortcomings. Outcomes for individual regressions are discussed below.

5 For the 6‐7 year old group (K2), the coefficient for the lagged variable is derived from regressing against the K1 4‐5 year olds, but in the final regression the values used for lagged obesity are from the B3 4‐5 year olds to enable continuity with B2 and B1.

26

Chart 3.1: Explanatory power of regressions, by age

‐

0‐1 2‐3 4‐5 6‐7 8‐9

Age Cohort

0.10

0.20

0.70

0.30

0.40

0.50

0.60

R squared

0.80

0.90

Obesity Productivity Anxiety Antisocial Addictions

2

n lowest e

always significant.

gend the B1 cohort, while male gender was a risk for K2

t,

of physical activity.

The explanatory power (R2) for productivity regressions ranged from .07 to .51.

3.2.2 Obesity

Overall, obesity had the best explanatory power (R ) among the regressions, ranging from .08 for B1, up to 0.77 for K3. As in all regressio s, B1 was and K3 high st in terms of explanatory power.

As with all regressions, the lagged dependant variable (in this case, last period’s obesity) was significant for every age group. Previous obesity also had the largest coefficients. Parental obesity was also

Female er for the child was a risk forand K3. Male gender of the primary carer was a risk factor in two cohorts.

Interestingly, fatty foods, electronic entertainment and exercise tended not to be significanand when they were, occasionally had an unexpected sign in the younger cohorts (when the children were aged 2 and 4). When children are young, body mass index is a less reliable indicator and this may explain why some of the findings are not intuitive. Moreover, children in the younger cohorts may be less likely to use electronic entertainment and eat fatty foods. It should also be noted that the model captures children’s preference to being active or not, not to actual measures

3.2.3 Productivity


27

Previous learning index outcomes were always significant, as were consistent discipline, temperament (easy temperament effect), SES and parent education6. SES, however, had an unexpected sign for the B1 cohort. This may reflect that higher income is associated with

g to be associated with less ability to spend time the baby and poorer early learning outcomes as a consequence.

nsistently strong coefficients.

K

bles also sometimes had unexpected signs – such as parental warmth (B2, B3, K2), parental monitoring (K2), and parental involvement in education (K3). This is discussed further

sion

o had a strong impact and SES had a significant impact in some age groups, mostly with the expected sign. Harsh discipline/hostile parenting and parental

dep gnificant. Family cohesion and over‐protection also sometimes had signs.

or functioning). This is important to understand when interpreting the coefficients in Appendix C.

had explanatory power ranging from 0.01 (B1) to 0.50 (K3).

Inductive reasoning was significant for children aged 4 years and older, but with an

returnin work which may, in turn,

Both temperament and child gender had co

Being a girl was an advantage up to the K2 age group, when being a boy was significant (neither gender was significant for 3).

Other varia

in Section 3.2.7.

3.2.4 Anxiety and depres

Anxiety and depression, as measured by the LSAC’s Strengths and Difficulties Questionnaire (SDQ) emotional symptoms scale had explanatory power ranging from 0.01 (B1) to 0.36 (K3).

Previous emotional problems was always significant with the expected sign, while difficult temperament als

anxiety/ ression were siunexpected

Note that some outcome measures used are coded in less than intuitive directions. For example, while in the PEDS global concerns item (apedsgc and *pedsef), lower scores represent concern, but for the SDQ high scores equate with more problems (i.e. po

3.2.5 Anti‐social behaviour

Antisocial behaviour, as measured by the LSAC’s SDQ conduct problems scale,

Previous conduct problems, family cohesion, harsh discipline, parental smoking, low SES and the child’s temperament were always significant with the expected signs. Difficult temperament had consistently large coefficients, along with parental anxiety/depression in the B2 cohort.

Parental efficacy, parental anxiety/depression, gender (with risk for boys), and household income were mostly significant. Consistent discipline was mostly significant but with an unexpected sign in the K2 cohort.

unexpected sign. It may be that parents are more likely to use inductive reasoning with children who have conduct issues (i.e. reverse causation). Parental alcohol consumption had

6 The scale of parental education is provided in Appendix D.

28

an unexpected sign in the two younger cohorts cases where it was significant, possibly due to the same issue7.

3.2.6 Addictions

‘Addictions’ is a composite measure covering the literature concepts of smoking, drinking and drug use. It is rare for children in the LSAC age groups to smoke, drink or take other drugs. Thus, some LSAC variable needed to be found as an intermediate outcome that could act as a

inal surveys, there is no focus on transitional outcomes in nine year old children, as is needed here to bridge between the LSAC and the ATP.

There is evidence from paediatric and neurological studies (Shonkoff et al, 2010) to suggest

emotions such as fear and aggression while simultaneously diminishing the forward‐thinking and reasoning capacities which normally moderate reactions to such

Appendix A.

was sometimes significant with the expected sign. Inductive reasoning was sometimes

pointer to future addictive problems when the children reach ATP age and later adulthood.

There is abundant literature showing clear links between poor FF inputs in childhood and crime and addiction outcomes in adulthood. However, because such literature is frequently based on contiguous longitud

that anti‐social behaviour in primary school children may be linked to both addictions and anti‐social behaviour in adulthood, with stress as the common factor. Continual environmental stress – such as that occasioned by NFF – can cause structural changes in the brain that strengthen negative

emotions. While longitudinal studies on the influence of high stress have only been covered in animals so far, retrospective studies show that people who experienced trauma as children are much more likely as adults to smoke, drink and use drugs (Felitti et al, 1998) and become criminals (Currie and Tekin, 2006).

High levels of childhood stress can manifest as psychological and emotional dysregulation in childhood (Evans and Kim, 2007). Thus, for the purposes of regression modelling, ‘addictions’ are represented by the LSAC variable ‘total SDQ score’. A detailed rationale for this choice can be found in

The addictions regression, thus defined, had explanatory power ranging from 0.01 (B1) to 0.62 (K3). No variables were significant in the youngest B1 cohort.

In the four older cohorts, previous total SDQ problems, harsh discipline, inconsistent discipline, family cohesion, smoking, temperament, gender (risk for boys), and parental anxiety/depression were always significant with the expected signs. Temperament and parental anxiety/depression consistently had the largest coefficients.

Parental warmth and overprotection were mostly significant with expected signs, while SES

significant, but with unexpected signs – possibly for the same reasons as for antisocial behaviour (e.g. reverse causation). Parental alcohol consumption had an unexpected sing in the B2 and B3 cohorts also. 7

7 Reverse causality with alcohol has also been discussed in the literature. That is, parents of problem children may use alcohol as a coping mechanism, as reported by Pelham and Lang (1999): “Studies strongly support the assumption that the deviant child behaviours that represent major chronic interpersonal stressors for parents of ADHD children are associated with increased parental alcohol consumption. Studies also have demonstrated that parenting hassles may result in increased alcohol consumption in parents of "normal" children.” However, this does not explain the significant inverse relationship. Another possible reason, although we could find no literature


29

3.2.7 General observations

A number of variables in the LSAC analysis had unexpected signs. Family cohesion and ren

indicahas go

Inducparen eing disciplined / talk it over’. Note that most studies of the PPP include a variable to measure parental verbosity, which has a

sitively associated with conduct and total emotional problems because parents are more likely to use this parenting technique with children with difficult

link between caring for pets, swimming and learning outcomes may not be as clear as activities

d sign in the older cohort is reverse causation. That is, if a child has learning difficulties, a parent may have to spend more time helping them.

■ Indeed, in general it should be noted that these analyses are largely cross‐sectional so direction/causation cannot be perfectly tested, although to some extent this is overcome by including lagged dependent variables in subsequent stages of the modelling. However, in some cases (notably this variable and inductive reasoning), rather than the parents’ behaviour causing the child’s problem, it may be that the parents’ behaviour is in reaction to the child.

Parental alcohol consumption often has an unexpected sign, being significantly negatively associated with anxiety and depression (K2, K3), anti‐social behaviour (B2, B3) and addictions (B2, B3). This might possibly be due to reverse causation together with other factors in these toddler and pre‐school cohorts.

Finally, income sometimes has unexpected signs. This may be improved by standardising for household size. That is, a household with higher income may not have better outcomes than another household with lower total income, if income per child is in the first house is actually

pa tal warmth may be in this category because of their skewed distributions; LSAC data tes that a large proportion of people report that they are warm parents and their family od cohesion.

tive reasoning usually has an unexpected sign. In the LSAC, ‘inductive reasoning’ asks ts if they ‘explain to children why they are b

consistent negative effect on behaviour. It is possible that ‘inductive reasoning’ is in fact capturing ‘verbosity’ instead. In other words, the parent may perceive a detailed explanation, while the child may perceive ‘going on about it’ (at worst, nagging or harassment). It may also be that inductive reasoning is po

behaviour.

Parental involvement in education at home was negatively associated for the K3 cohort but positively correlated for the children aged 2 and 4 as expected. It should be noted that the measure changes (due to developmental differences) between the cohorts. When the children are younger, the measure includes activities such as reading stories, drawing, music and singing. These activities are likely to have a link to educational outcomes. In the K3 cohort, there are only three home activities questions – everyday activities (e.g. cooking, caring for pets), outdoor activities (e.g. swimming) and an adult in the family reading to the child. The

such as drawing and music. By the age of 8‐9 a child may spend more time reading to themselves and/or their parents than vice versa, particularly higher performing children. Another possible explanation of the unexpecte

supporting this conjecture, might be that parents of toddlers and pre‐schoolers drink either little or nothing, rather than a lot. It is a time of life when mothers may be pregnant or breastfeeding and when the young family is not as involved in social activities. If children have difficult behaviours, parents may find themselves even less likely to be able to participate in social functions, whereas if children are well‐behaved, they may be more able to relax and enjoy a drink (without abusing). However, this conjecture is purely speculative.

30

lower than income per child in the second house. In two wave 1 regression variables, income sity. We think this can be

– so in this case the is as expected – higher income is associated with heavier babies (low SES with

income goes up, learning in babies may be negatively impacted by mothers returning to work earlier (Ruhm, 2000), which could explain the productivity finding.

ion achieved and family cohesion — and at first glance may appear to have unexpected signs.

coefficients had an unexpected sign – for B1 productivity and obeexplained as follows. For the babies, the obesity metric is in fact weightresultunderweight babies). As

Readers should note that some variables are reverse coded in the LSAC database, including parental depression, parental highest educat


31

4 Findings from the ATP data investigation

4.1 ATP variables

Access Economics and the Australian Institute of Family Studies (AIFS) worked to specify appropriate models of family functioning based on the literature and variables available from

In the literature, positive outcomes for young people were commonly associated with authoritative parenting mode of parenting in Baumrind’s categorisation (see Wise 2003) — authoritative parenting is characterised by high levels of both c eptance ( rents on n of affection) and control (which into control based on the use of inductive

d con

Authoritative parenting poorer outcomes are on acceptance), permissive arenting (low on both control

Other features of parenting or family functioni ed in the literature as influencing outcomes for children include:

■ family conflict and family cohesion;

■ marital relationships;

relati

child abuse (including physical, sexual and

■ parental smoking and/or drinking.

The tables in Appendix B provide a summary of

Confounding variables available in the ATP and

■ the child’s innate temperament (persistence, );

■ parents’ education (as a proxy for socio

■ family stress (e.g. death of a parent or divorce) — some of which may reflect family functioning (such as the circumstances surrounding marriage breakup).

the Australian Temperament Project (ATP).

ATP variables were mapped to family functioning domains (emotional, governance, engagement and cognitive development, physical health, intra‐familial relationships and social connectivity — and to outcomes (in the domains of health, productivity and criminal behaviour). A literature review was also conducted to inform the selection of family functioning and other variables from the ATP likely to be correlated with the selected outcomes. Statistical analysis determined the final model specifications, and only variables found to be significantly correlated with outcomes were included in the CEA model.

ls. Based on the two dimensions

a c which entails emphasis by pa can be divided

warmth and the expressio

reasoning an trol based on parents’ use of pow

is to be distinguished

er).

from alternatives often associated with for children — authoritarian p nting (high on control and low parenting (high on warmth and low on and acceptance) (see Wise 2003).

control) and neglectful p

ng commonly identifi

■ sibling

■ onships;

emotional abuse) and neglect; and

the literature reviewed.

included in the analysis included:

reactivity and approach

economic status);

32

In rare cases, variables o

con either as control r family functioning variables — were example,

in the ATP we were unable to allow for paren post natal depression or parent’s anxiety/depression linke pression (Rapee 1997, van Gastel et al, 2009) — or parent’s body mass ked to child’s overweight or obesity outcomes (Wardle et al, 2001, Danielzik nother variable not available from the ATP. However, s were found, for example, in place of family income, the ATP parent’s highest education level attained’. Positive affect towards indicator of school bonding based on AIFS advice about the findings

l regressi

It is importa mily functioning’ olated from other influences such as environmental factors or cult to measure. The ATP variables selected inevitably reflect a d other factors and are at times difficult to interpret as either tioning or some other factor external to the family. In addition, there s where variables do not completely capture the conc there are missing variables within the ATP. The statistical relationships in this context.

Table 4.4: Regressi

variables s

cepts linked in the literature to outcomes for children — not available from the datasets. For

tal ental health — mother’smd to child’s anxiety/de index as a factor lin

et al, 2004). Family income was a in some cases, proxie

parameter: ‘each resident school was selected as an of previous research.

The initia on constructs investigated are summarised in Table 4.4.

nt to note that ‘fa cannot always be is child temperament, and is diffi

an mixture of family functioning reflecting family func

is the potential for omitted variable biaept being measured or where

reported here need to be viewed

on constructs

Interim outcome (dependent) Family functioning variable

Productivity Underengagement (childrenemployed and not studying)

Did not complete Yr 12

Completed Yr 12, Post secondary(non‐University), University degree

ision

ctual parent‐child

an earlier age

n level

not Harsh discipline

Parental monitoring/superv

Inconsistent discipline

Attachment to parents

Negative conflirelationship

School bonding

Academic competence at

Parental educatio

Temperament

Overweight/ obesity

Body mass index (normal orunderweight/overweight/o

rents

e

er eating behaviours

g/supervision

‘intact’)

Parental education level

bese) Negative conflictual parent‐child

Attachment to pa

relationship

Inconsistent disciplin

Harsh discipline

Teenag

Parental monitorin

Inductive reasoning

Family status (i.e.


33

Anxiety and/or Anxdepression

iety and/or depression Attachment to parents

Relationship quality/warmth

Experience of child abuse/neglect

Marital relationship

Harsh discipline

Enmeshment

Inductive reasoning

Stressful life event

Temperament

Alcohol/ smoking

Alcohol consumption including binge drinking

Parental management of teen substance use

Daily Smoking Inductive reasoning


Harsh discipline

Parental monitoring/supervision


Negative conflictual parent‐child relationship

Parental smoking

Temperament

Illicit drug use Illicit drug use including marijuana Experience of child abuse/neglect

marital relationship (conflict)

Mother/Father smoking and drinking habits

Temperament


Parent adolescent conflict


Antisocial behaviour

3 or more antisocial acts in the past year including illicit drugs

Relationship with parents


Family cohesion

Relationship quality/warmth

Harsh discipline


History of parental separation and its effect

Parental smoking


Marital relationship

Parental use of alcohol

Temperament

34

4.2 ATP analysis

The statistical analysis was undertaken by AIFS using ordinary least squares, logistic and multinomial regressions. The variables are described in Appendix F and details of each of the

ndix H. In summary:

instances, composite variables were constructed which included data for were computed to assist

ables. Composite variables were

itial ATP sample comprised 2,443 families from urban and rural areas of Victoria in still participating after 24 years. However, were determined by the samples available

P for which data are available (2006) reports on ATP children at

r children aged 19‐20 were

■ For the first two (obesity and completion of a university degree), dependent variables e for this age group.

■

ent variable was a lot less — 574 children of whom only 58 children re and not studying.

Completed

12) based on young adult report at age 19‐

The

redictor variables were significant:

■ Relationship warmth (inverse relationship with completion of high school);

regression outcomes are in Appendix G. The means and standard deviations of the regression variables are in Appe

■ Variables were constructed at various child ages depending on the most appropriate sample size for that variable in any given year.

■ In somechildren at a range of different ages. Inter‐correlationsdecisions about the viability of using across‐time varionly constructed for variables which were highly correlated across time.

■ Tests for multicollinearity suggest this was not a problem in any of the final equations.

■ The in1983, approximately two‐thirds of whom arethe sample sizes for the regression analysesfor all of the variables included in the regression analysis — and were affected by missing data and drop outs over time.

The most recent wave of the ATthe age of 23‐24 years. While regression analysis was conducted on the transitional outcomes for children aged 19‐20 as well as 23‐24, dependent variables fogenerally preferred on the basis that family influences were likely to be stronger at this age. Exceptions are obesity, completion of a university degree and illicit drug use.

were not availabl

For illicit drug use, the regression equation for the older group was preferred because one of the independent variables was of an unexpected sign (see Appendix G).

4.2.1 Productivity

A logistic regression was also conducted for under‐engagement (children not employed and not studying versus other children) but none of the predictor variables was significant. The sample size for this dependwe not employed

high school versus did not complete

A logistic regression was conducted comparing children who completed secondary school (year with those who did not. The responses are

20 years. There were 687 children in the sample, 65 of whom did not complete high school. Nagelkerke R2 was 0.309.

The following p


35

■ Parental monitoring/supervision of teen’s activities/associates, teenager report 17‐18 years (an increase in supervision was associated with an increased chance of completing

■ with a greater

■

■ School bonding (better bonding was associated with a greater likelihood of completing

■ Persistent temperament style, composite mean score of parent report at 11‐12, 13‐14,

l versus completed university degree

The following predictor variables were significant:

are only completing high school);

ssociated with a greater likelihood of only

(higher educational achievement of the father was associated with a the child completing a university degree)

rs. The sample size was 513, with 44 obese, 128 overweight and

on analysis comparing the chance of being overweight or obese at age 23‐24 rweight found the following significant variables. The

school);

Mother’s education (higher educational achievement was associatedchance of the child completing high school);

The child’s academic competence at 11‐12 years based on teacher report (greater academic competence was associated with a greater likelihood of completing school);

8

school); and

& 15‐16 years (persistence was associated with a greater likelihood of completing school).

Completed high schoo

In the sample of 595 young adults at age 23‐24 included in a logistic regression, 143 had completed high school only, and 277 had completed a university degree. The Nagelkerke R2 was 0.276.

■ more likely to complete a university degree thanAttachment to parents child age 17‐18 (children with higher levels of attachment

■ Relationship warmth (greater warmth is acompleting high school);

■ Father’s educationgreater chance of

■ School bonding9 (better bonding was associated with a greater likelihood of completing a university degree).

4.2.2 Obesity

In the ATP, body mass index is generated from self report (rather than actual measurements) at 15‐16 years and 23‐24 yeathe rest underweight or normal.

A logistic regressicompared with ‘normal’ or undeNagelkerke R2 was 0.518.

■ Overweight

Body mass index in 1998 (child age 15‐16) (a high index at age 15‐16 is associated with a greater chance of being overweight at age 23‐24);

8 positive affect towards school, teenager report 15‐16 years

9 positive affect towards school, teenager report 15‐16 years

36

Father’s education (lower levels of education are associated with a greater chance of being overweight at age 23‐24); and

h a lower chance of being overweight at

ild age 15‐16) (a high index at age 15‐16 is associated obese at age 23‐24);

conflictual parent‐teenager relationship, (parent report 17‐18 years), with a greater chance of obesity at age 23‐24;

tion (lower levels of education are associated with a greater chance 23‐24); and

4.2.3 Anxiety and/or depression

pression were combined for this analysis. The ATP scales for anxiety and ), which lead to similar prevalence

istical Manual of Mental Disorders (DSM IV) used more

adults aged 19‐20 who were at risk of being in the ssion with those who were not was conducted using a

sample of 642 children, 166 of whom were anxious and/or depressed. The Nagelkerke R2 was

ely associated with:

■ previous experience of anxiety/depression.

4.2.4 Drinking

A logistic regression was conducted using a sample of 580 young adults aged 19‐20, divided into those who spent 1‐4 days per month binge drinking (245 young adults), those who spent 5 or more days per month binge drinking (196 young adults) and those who did not binge drink (0 days per month). The Nagelkerke R2 was 0.121.

Binge drinking on 1‐4 days per month at age 19‐20 was significantly positively associated with drinking at age 13‐14 years.

Binge drinking on 5 or more days per month at age 19‐20 was significantly positively associated with:

■ drinking at age 13‐14 years;

■ less parental monitoring at age 15‐16;

A persistent temperament style (composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years) is associated witage 23‐24.

■ Obesity

Body mass index in 1998 (chwith a greater chance of being

A negative/is associated

Father’s educaof obesity at age

A persistent temperament style (composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years) is associated with a lower chance of obesity at age 23‐24.

Anxiety and dedepression are based on Lovibond and Lovibond (1995results to the Diagnostic and Statbroadly in Australia.

A logistic regression comparing youngclinical range for anxiety and/or depre

0.213.

Anxiety and/or depression at age 19‐20 was significantly positiv

■ alienation of the child from his or her parents;

■ not having a persistent temperament; and


37

■ father drinking, (composite mean score of parent report at 13‐14, and 17‐18 years); and

enager was first allowed to drink at home (this variable has two categories — the teen was allowed to drink at home at 15 years or older or the teen

19‐20 years, 121 of whom smoked daily. The Nagelkerke R was 0.297.

ositively associated with:

■ ■

riables which were significantly positively associated with the dependent

A logistic regression was conducted using 755 young adults aged 19‐20, 112 of whom reported

(including illicit substance use). Tne Nagelkerke R was 0.145.

age 13‐14 years and not having a persistent temperament were

■ a higher age at which the te

was allowed to drink at home at 14 years or younger — (based on parent report at 17‐18 years).

4.2.5 Smoking

A logistic regression was conducted on a sample of 830 young adults aged2

Daily smoking in this group was significantly p

■ Smoking at age 13‐14 years;

■ Being allowed to smoke at home;

Negative conflictual parent‐child relationship at age 17‐18; and

Mother smoking.

4.2.6 Illicit drugs

A regression was based on a sample of 664 young adults aged 23‐24 years who reported the number of differing types of illicit drugs used on one or more day/s in past month (range 0‐4). The R2 was 0.076.

Independent vavariable were:

■ Less parental supervision/monitoring at age 17‐18;

■ A mother who smoked; and

■ An approaching temperament.

4.2.7 Antisocial behaviour

that they were involved in 3 or more differing types of antisocial activities in past 12 months2

Antisocial behaviour in 1996 atthe only independent variables significantly positively associated with antisocial behaviour.

38

5 The costing model

As explained in the introductory chapter, FF was defined as having six domains, and variables from the ATP and LSAC within each of these domains were mapped to specific outcomes. The outcomes were drawn from three domains: health (anxiety and/or depression, obesity, smoking, alcohol abuse and illicit drug use), productivity (not completing year 12 and not completing a university undergraduate degree) and social/criminality (antisocial behaviour). The cost of each adverse outcome was then estimated (recall Figure 2.3).

This chapter describes the method for estimating the social and economic costs associated with adverse outcomes attributable to NFF.

s of include:

elfare (AIHW) data and Australian Bureau of Statistics (ABS) National Health Survey (NHS) primarily.

■ Productivity costs – estimates of reduced participation and absenteeism related to

tion, TER and post‐secondary education outcomes are from the PC and other sources.

■ Burden of dis al (2007) BoD study he year 2003 (with extrapolations modelled to future d the value of a stical life year (based on DOFD, 2009).

costs – these include costs associated with the of informal and appliances, deadweight efficiency losses and other costs for

g term substance abuse and other health pro The main sources ious studies by Access Economics and the Surve sability, Ageing and

) 2003 (ABS, 2004).

in Appendix I.

5.1 Health outcomes

■ alcohol abuse; and

■ illicit drug abuse.

The cost each outcome

■ Health costs – of five main conditions: tobacco use, alcohol abuse, illicit drug use, obesity, and anxiety and depression. These were sourced from the Australian Institute of Health and W

■ Costs of crime – using data sources from the PC’s Report on Government Services for 2009, as well as reports from the Australian Institute of Criminology.

health impacts are based on Access Economics cost of illness methods, plus other sources for alcohol and illicit drugs. Productivity costs associated with school comple

ease (BoD)– was determined using the AIHW Begg et for t years) anstati

■ Other financialcare, aids

provision various

people with lon blems.comprise prev y of DiCarers (SDAC

Further detail on costing methods, cost categories and data sources is provided

Analysis of the LSAC and ATP data‐sets led to the identification of the following main health conditions and behaviours associated with NFF:

■ anxiety and depression;

■ obesity;

■ current daily smoking;


39

In line Access Economics’ Diseaseoutcomes

with Cost Burden Analysis (DCBA) framework, health are associated with health system costs, productivity losses, DWLs, other financial

an

Collinssely categories. It is noted that there are

te of 3.5% per annum (based on

(Table

costs and BoD.

For xiety and depression, obesity and smoking, past Access Economics studies were used to collate total costs in 2010 and calculate per‐person costs. For alcohol abuse and drug abuse,

and Lapsley (2008) was used. Total reported costs were disaggregated and matched, as as possible, with Access Economics’ DCBAclo

limitations matching costs from Collins and Lapsley (2008) with the DCBA categories, due to differences in costing methodologies. However, Collins and Lapsley (2008) is the most recent and relevant source for costs of drug abuse in Australia and is thus employed. This report can be referred to for details on the methodology it uses.

5.1.1 Obesity

Obesity is the accumulation of excessive body fat, defined here as a body mass index of over 30 for adults. For children and adolescents aged 2 to 18 years, a set of age‐gender specific thresholds was used.

The cost of obesity was estimated for this project using Access Economics (2008) — that report included the costs of type 2 diabetes, cardiovascular disease, osteoarthritis and cancer attributable to obesity.

The 2008 costs were inflated to 2010 using a health inflation rahistorical averages reported by the AIHW). and 3% per annum for non health expenditure

5.5).

Table 5.5: Summary of total costs of obesity (2010)

Cost type Cost ($m)

Health system expenditure 2,098

Productivity losses 3,850

Other financial costs(a) 2,860

52,935

Total financial costs 8,808

BoD (DALYs x VSLY)

Total costs 61,743

(a)‘Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics (2008 and 2010 calculations)

Obesity prevalence rates were also drawn from Access Economics (2008). The rates were applied to 2010 population projections from Access Economics Demographic Model (AE‐Dem). Total costs were divided by the estimated number of obese people in 2010 (around 4 million)

ed to the correct total, a minor level adjustment downwards was applied while preserving the prevalence profile.

to obtain a per‐person annual cost for each cost type. The age‐gender prevalence profile for obesity was then applied to these per‐person costs to further refine costs into per‐person annual costs by age and gender. To ensure per‐person costs multiplied by estimated obese people summ

40

5.1.2

d depression are defined consistent with the clinical de d for prevalence

orders are those in which ‘anxiety is a predom (American ciation, 2009) such as panic disorder, obsessi pulsive disorder, a

anxiety disorder.

related affective disorders are defined in the ional Classification seases Tenth Revision (ICD‐10) by the World Health Orga (WHO, 2010) as a

. Capacity for is reduced, and marked tiredness after even

minimum effort is common. Sleep is usually disturbed and appetite diminished. Self‐

king in the several hours before the usual time, depression worst in the morning, marked

rdation, agitation, loss of appetite, weight loss, and loss of libido.

ce abuse disorders, anxiety disorders, affective disorders, bipolar disorder, schizophrenia and other mental illness. The

nxiety and

ts of all mental illness were used to proxy anxiety and

nment welfare payment rates in 2009 (Centrelink, 2009)

one disease category due to a high degree of co‐

m for non health

Anxiety and depression

Anxiety an finitions usestudies as follows:

■ Anxiety dis inant feature’Psychiatric Asso ve‐comphobia, or generalised

■ Depression and Internatof Di nizationlowering of mood, reduction of energy, and decrease in activityenjoyment, interest, and concentration

esteem and self‐confidence are almost always reduced and, even in the mild form, some ideas of guilt or worthlessness are often present. The lowered mood varies little from day to day, is unresponsive to circumstances and may be accompanied by so‐called ‘somatic’ symptoms, such as loss of interest and pleasurable feelings, wamorningpsychomotor reta

Cost data were drawn from Access Economics (2009) — a study of the costs of six categories of mental illness in the 12 to 25 years age group including substan

costs were adjusted so that they applied to all age groups by:

■ applying 2003 age‐gender prevalence rates, relative risks of mortality attributable to anxiety and depression and applying the relevant disability weights for adepression from Begg et al (2007);

■ expanding productivity losses to incorporate all age groups using NHS data for 2004‐05 (special request from the ABS for Access Economics, 2009) ‐ parameters for employment/productivity effecdepression;

■ adjusting carer costs to apply to the prevalent group of those with anxiety and depression; and

■ applying average annual goverto total recipients with mental illness.

The prevalence profile for anxiety and/or depression was obtained from Begg et al (2007), which grouped anxiety and depression asmorbidity and similarity in psychological and drug treatment between these conditions. The number of people with anxiety and/or depression was calculated by applying the prevalence rates to 2010 population projections from the AE‐Dem.

The total aggregated cost of anxiety and depression in 2010 is presented in Table 5.6 by cost type. The 2009 costs were inflated to 2010 using a health inflation rate of 3.5% per annum (based on historical averages reported by the AIHW)) and 3% per annuexpenditure.


41

Table 5.6: Summary of total costs of anxiety and depression (2010)

Cost type Costs ($m)



Other financial costs(a) 3,491


BoD (DALYs x VSLY) 41,162

Total costs 66,463

(a)‘Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics (2008 and 2010 calculations)

Total costs were divided by the estimated number of people with anxiety and/or depression in 2010 (around 2 m The age‐gender prevalence profile for anxiety and depression was then applied to these per‐person costs.

ts were made to ensure per‐person costs summed to th otal.

being a smoker if he or she currently smokes daily basis. Others eople smoking less frequently are excluded.

ere estimating using the findings of Access Economics (2 usted as follows:

from all relevant y smoking rather than ‘excess’ costs;

e ABS) for

■ applying welfare payment rates (Centrelink, 2009) to estimated recipients with tobacco‐Economics, 2006a) and tobacco‐caused cardiovascular illness

(Access Economics 2006b) in 2005.

lence rates for current daily smoking for people aged 18 and over, from Access Economics (2007). This prevalence profile displays falling prevalence rates

and conditions. This profile displays higher prevalence rates for older age groups, and was

illion) to obtain a per‐person annual cost for each cost type.

Adjustmen e relevant t

5.1.3 Smoking

A person is defined as on a e.g. ex‐smokers or p

The costs w 007), adj

■ removing the stipulation of having a mental illness and being a smoker costs to obtain total costs of current dail

■ applying 2007 age‐gender prevalence rates for current daily smoking for those without a mental illness from Access Economics (2007);

■ calculating relative risks of mortality attributable to current daily smoking for all age groups using Begg et al (2007);

■ adjusting BoD estimates to incorporate average disability weights for tobacco‐caused diseases for males and females using unpublished data from the AIHW requested for preparation of Access Economics (2007);

■ calculating productivity losses using NHS 2004‐05 data (special request from thall current daily smokers; and

caused cancer (Access

Two separate prevalence profiles were applied to estimate per‐person costs of daily smoking. The first profile employed preva

with higher age groups and was applied to estimate per‐person productivity costs, DWLs from transfers, carer costs and indirect costs. This profile was also applied to AE‐Dem 2010 population estimates to calculate estimated current daily smokers in 2010.

The second prevalence profile employed prevalence rates for tobacco‐attributable diseases

42

applied to health system and BoD costs. An increasing‐prevalence profile was deemed to be more suitable for these categories due to the cumulative nature of health deficits from smoking with increasing age (Hubbard et al, 2009). Thus, annual health expenditures and loss of wellbeing costs are likely to increase with age.

4 prevalent cases of total tobacco‐caused diseases per age group by the 2004 population in that age group (from AE‐Dem).

te of 3.5% per annum (based on historical averages reported by the AIHW ) and 3% per annum for non health

Table 5.7: Summary of total costs of daily smoking (2010)

Prevalence data for this second profile was obtained from the AIHW via a special data request in 2004 for Access Economics’ past study (2007). Prevalence rates were calculated by dividing the 200

The total aggregated cost of current daily smoking in 2010 is presented in Table 5.7 by cost type. The 2005 costs were inflated to 2010 using a health inflation ra

expenditure.

Cost type Cost ($m)



Other financial cos


x VSLY)

1

ts(a) 2,261

BoD (DALYs 167,197

Total costs 83,637

(a)‘Other financial’ catSource: Access Economics

egory includes DWL from transfers, carer costs and other indir s. (2008 and 2010 calculations)

d other financial costs were divided by the esti mber of smokers r‐person annual cost for these categories. The smoker age‐

valence profile was then applied to these per‐person co rther refine costs

ated prevalent cases of tobacco‐caused disease in 2010 (692,523) to obtain a per‐person annual cost for these

Alcohol abuse

Collins and Lapsley (2008) note that the definition of ‘abuse’ in relation to alcohol is

and ‘high risk’ drinking were summed. ‘Risky’ drinking is defined by the AIHW as 29 to

ect cost

Total productivity an mated nuin 2010 (2.9 million) to obtain a pegender pre sts to fuinto per‐person annual costs by age and gender.

Total health system costs and the value of the BoD were divided by estim

categories. The tobacco‐caused disease prevalence profile was then applied to these per‐person costs to further refine costs into per‐person annual costs by age and gender.

Adjustments were made to ensure per‐person costs summed to the relevant total.

5.1.4

problematic, with the National Alcohol Strategy showing no preference or separation between the terms ‘misuse’ and ‘abuse’.

For the purposes of this report, alcohol abuse is defined as consumption of alcohol at a level that produces sufficient risk of long‐term harm to health. This definition was taken from the 2004 National Drug Strategy Household Survey (NDHS) (AIHW, 2005). The categories of ‘risky’ drinking


43

42 drinks per week for males and 15 to 28 drinks per week for females. ‘High risk’ drinking is defined as 43+ drinks per week for males and 29+ drinks per week for females. Short term risk was not included as the data were insufficient in LSAC and ATP to identify short term risk (e.g. from bingeing).

for costs was Collins and Lapsley (2008). The prevalence profile for long‐term risky drinking was obtained from the 2004 NDHS (AIHW, 2005). These rates were applied to AE‐De ople affected in 2010.

s of alcohol abuse in 2010 are in Table 5.8. 2004‐05 costs were inflation rate of 3.5% per annum (based historical averages

IHW) and 3% per annum for non health expenditure

s were excluded to avoid double‐counting — the of crime related to .

tes of the BoDfrom alcohol abuse appear small when ared to those for e

ey study categories with Access Economics disease cost burden analysis

The primary data source

m 2010 population projections to estimate the number of pe

The total aggregated cost a health

Theinflated to 2010 usingreported by the A

on.

Notably, crime cost costs substance abuse are included in section 5.3

The estima compsmoking, anxiety and depression, and obesity. This is partly due to limitations in matching thCollins and Lapslcategories due to differences in study methodologies.

Table 5.8: Summary of total costs of alcohol abuse (2010)

Cost type Costs ($m)


Productivity losses

Other

4,102

financial costs(a) 4,511

163


BoD (loss of life + pain and suffering) 5,204

Total costs 16,

(a)‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Collins and Lapsley (2008) – costs adapted to Access Economics DCBA categories

Total costs were divided by the estimated number of people with the condition in 2010 (1.8 million) to obtain a per‐person annual cost for each cost type. The age‐gender prevalence profile was then applied to these costs to develop a set of per‐person annual costs by age and gender group. Adjustments were made to ensure per‐person costs summed to the relevant

5.1.5 Illicit drug abuse

it dwith t

The p2005),were Dem.

total.

Illic rug abuse in this report is defined to include all illicit drugs (e.g. marijuana), consistent he source of the cost data (Collins and Lapsley, 2008).

revalence profile for recent use of illicit drugs was obtained from the 2004 NDHS (AIHW with recent use defined as use within the last 12 months. Recent drug users in 2010 calculated by applying prevalence rates to the 2010 population estimates from the AE‐

44

The toinflate torical averages

As witavoid

(2010)

Cost t

tal aggregated cost of illicit drug abuse in 2010 is in Table 5.9. The 2004‐05 costs were d to 2010 using a health inflation rate of 3.5% per annum (based on his

reported by the AIHW) and 3% per annum for non health expenditure.

h alcohol abuse, the costs of crime associated with illicit drug abuse were excluded to double‐counting.

Table 5.9: Summary of total costs of illicit drug use

ype Costs ($m)

He system expenditure 240 alth

Pro tivity losses 1,912

financial costs(a) 1,647

inancial costs 3,798

ss of life + pain and suffering from road nts) 1,477

osts

duc

Other

Total f

BoD (loaccide

Total c 5,275

(a)‘Other financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusive consuSource: Collins and Lapsley

Total costs were divided by estima ition in 2010 (around 2.8 million) n annual cost age‐g

then applied to these costs to develop annual costs by age and gender tments were made to ensure per‐ costs summed to the rele nt total.

oductivity outcomes

e LSAC and ATP data‐sets, and parameters available from the literature, ss of earnings from not complet 12 and not comp a tertiary

te) degree (if completed year 12) able to be costed.

ucation levels display higher Additionally, those with

ing these workers to the labour force (La Plagne et al, 2007).

e form of forgone income) associated with non‐completion s.

ses were estimated as follows:

the effect of not completing year 12 and/or a university degree on d productivity were drawn from La Plagne et al (2007) and Forbes et al

007), through econometric modelling, estimated the effects of attainment levels on the probability of participation in the labour

force, relative to the baseline of year 11 completion only. A more recent study (Forbes et al 2010) estimated the impact of different education levels on average hourly wages

mption costs. (2008) – costs adapted to Access Economics DCBA categories

ted people with the condfor each cost type. The

a set of per‐personto obtain a per‐perso ender prevalence profile was

group. Adjus person va

5.2 Pr

Based on analysis of th the lo ing year leting (undergradua were

Human capital theory supports the idea that people with higher edlabour productivity, as proxied by their earnings (Forbes et al, 2010).higher education are more likely to participate in the workforce, due to higher labour demand for these workers, and labour supply factors such as expectations of higher wages and derivation of greater utility from the social and intellectual stimulation work provides attract

Consequently, there are costs (in thof year 12 and/or undergraduate studie

A number cost components for productivity los

■ Parameters foremployment an(2010). La Plagne et al (2different educational


45

(taken as an indicator of labour productivity), relative to the baseline of year 11 completion. Parameters from these studies are presented in Table 5.10.

ion depending on educational attainment were used as a proxy r di loyment. This difference was combined with average weekly

, 2009b) and employment rates (ABS, 2009a) for each age‐gender st earnings. The ABS AWE data were for 2008 and were inflated to

).

ational attainment on (ABS, 2009b) and

lculating taxation revenue

arnings. The tax rates for 2010 (19.48% and 11.68% respectively) were drawn from Access Economics’ Macroeconomic Model (AEM). DWLs were then

tion

effects* (%) of year 12 and undergraduate completion* on probability of participation and average earnings

n probability of participation*

Effect on average earnings*

■ Differences in participatfo fferences in empearnings (AWE) (ABSgroup to estimate lothe end of 2009 using labour price index data (ABS, 2010a

■ Wage differences were used as a proxy for the impact of educlabour productivity. These calculations were also based on ABS AWEemployment rate data (ABS, 2009a).

■ DWLs arising from foregone earnings were estimated by caforegone by applying the average personal income tax rate and average indirect taxation rate to foregone e

calculated by applying a DWL rate of 28.75% of each tax dollar foregone (Lattimore, 1997; PC, 2003) and Access Economics estimates of Federal Government administracosts).

Table 5.10: Estimated

Level of attainment Effect o

Year 12

Males 5.7% 12.8%

7.7% 10.1% Females

Undergraduate degree

Males 8.7% 38.4%

Females 16.4% 36.7%

* Relative to the baseSource: Productivity

line of year 11 completion Commission ‐ La Plagne et al (2007) and Forbes et al (2010)

The proportion of ee was based on data on undergraduate award for 2007 n, Employment and Work EEWR) via a est in

comes

s of costs were included

cietal costs ‐ due to the impacts on v and society;

s;

and

■ prison system costs.

the population not completing an undergraduate degrunpublishedof Educatio

course completionsplace Relations (D

from the Departmentspecial data requ

2009.

5.3 Criminality out

Four broad categorie :

■ so ictims

■ policing cost

■ court system costs;

46

The cos ere matched withresulting

ts w the relevant probability e.g. of the crime being prosecuted and in a conviction and prison sentence.

property‐related costs incurred by individuals and businesses due to property

er services and those costs non‐separable into the categories.

Estimates of societal costs were taken from an Australian Institute of Criminology report

g individual, commercial, burglary, thefts of vehicles, thefts from vehicles, shop theft and other theft;

r annum (based on historical averages reported by the AIHW) and 3% per annum for non health expenditure. Societal costs were divided by th t type (Table 5.11).

Table 5.11: Social costs of crim 0)

Cost type os Per o

5.3.1 Societal costs

Societal costs can include:

■ medical costs for victims of violent interpersonal crimes;

■ productivity losses for hospitalised and non‐hospitalised victims;

■ the costs of pain, suffering and lost quality of life (Rollings, 2008);

■ damaged/destroyed and non‐recovered stolen property; and

■ other costs such as cost of volunteabove

(Rollings, 2008). Rollings (2008) quantified societal costs for the following types of crime:

■ homicide;

■ assault;

■ sexual assault;

■ robbery – includin

■ criminal damage;

■ arson;

■ fraud; and

■ drug offences.

The total social cost in 2010 is in Table 5.11. The 2005 costs from Rollings (2008) were inflated to 2010 using a health inflation rate of 3.5% pe

e number of offenders in 2008‐09 (344,274) to determine a set of per offender costs by cos

e by cost type (201

ts ($m) Total C ffender ($)

Medical costs 791 2,296

Lost output (productivity) 3,392 9,85

uffering 3,467 10,07

9,819 28,5

21,197

Total societal costs 24,767 71,939

3

Costs of pain and s(intangible costs)

damage/loss

2

Property 21

Other(a) 7,298

(a)Those costs non‐separable into above categories Source: Rollings (2008)


47

5.3.2 Policing costs

Policing costs are defined here as recurrent government expenditures on crime‐related serv Data on policing expenditures were obtained from SCRGSP (2010)

and adjusted down by 30% consistent with Rollings (2008) to account for the fact that not all

274 total offenders in Australia for all crime types in 2008‐09 (ABS, 2010b). An age‐gender distribution of offenders based on 2007‐08 data

stimates by population estimates from the AE‐Dem.

urt:

■■ District/County courts;

■ and

■ courts.

t expenditures on criminal courts were obta m SCRGSP (2010). timates of recurrent expenditur lisation for each

5.12, by court level. A weighted average net recurrent expenditure per finalisation for all courts was calculated by applying

base court finalisations at each court level. These weights are presented in the last column of Table 5.12.

ourt Net recurrent

expenditure per % of court

policing ices in a year.

police time is spent on crime.

A prevalence profile of offenders was developed using ABS data on offenders in Australia (ABS 2009c; 2010b). There were approximately 344,

(ABS, 2009c) was applied to this figure to estimate offenders by age‐gender group.

A set of offender prevalence rates was developed by dividing these offender e

5.3.3 Court system costs

Court system costs are defined here as recurrent government expenditures for all levels of criminal co

Supreme courts;

Magistrate courts;

Children’s

Data on total recurren ined froThis publication also contained es es per finacourt level.

Total recurrent expenditure on criminal courts in Australia in 2008‐09 and recurrent expenditure per court finalisation is presented in Table

weights d on the percentage of total criminal

Table 5.12: Net recurrent expenditure on criminal courts (2008‐09) and % criminal court finalisations by court type

Criminal cNet recurrent

expenditure ($m ) court finalisation ($)

finalisations

Suprem ts 85 15,118 0.6% e cour

District/County courts 204 7,553 3.0%

Magistrate courts 327 414 89.2%

85 ‐ 100.0%

Children’s courts 32 503 7.0%

Total

Source: Access Economics calculations using data from (SCRGSP, 2010)

48

Th ffender prevalence profile wae o s applied to the weighted average expenditure per ender annual court costs based on age‐gender group.

ures by the government on expenditure per prisoner were obtained from

figures for real net operating expenditure on prisons and in 2008‐09.

ped using data on prisoners in Australia from the ner prevalence rates was developed by dividing prisoner estimates

m the AE‐Dem.

community corrections in Australia in 3. Overall, total real net operating expenditure on prisons

s approximately $2.5 billion in 2008‐09. This figure was divided by the number of prisoners in 2008‐09 (29,319) to determine a cost per prisoner of

Expenditure type Amount ($’000)

finalisation estimate to develop per‐off

5.3.4 Prison costs

Prison costs are defined here as real net operating expenditprisoners in a year. Data on real net operatingSCRGSP (2010). This contained community corrections in Australia

An age‐gender profile of prisoners was develoABS (2009d). A set of prisoin each age‐gender group by age‐gender population estimates fro

Total real net operating expenditure on prisons and2008‐09 is presented in Table 5.1and community corrections wa

approximately $85,064.

Table 5.13: Total real net operating expenditure on prisons and community corrections in Australia in 2008‐09

Prisons 2,140,154

Community corrections 353,823

Total real net operating expenditure 2,493,977

Source: (SCRGSP, 2010)

The prisoner prevalence profile was applied to the cost per prisoner to attain a set of per‐prisoner annual prison system costs by age‐gender group.

. dependent on visibility of crime, severity),

reported. However a schematic set of probabilities was developed to g, court and prison system costs. This set

included:

■ em cost);

ability of court action on a reported e (thus attracting a court ;

■ of a ‘guilty verdict’ in a cou isation; and

of a guilty verdict involvin stodial sentence (thus attracting a prison ).

e types were based on ABS (2010b) for the cation of probabilities as

5.3.5 Probabilities of incurring costs

Since not all crimes incur all cost types (i.eprobabilities based on crime type were applied to costs within the final costing model. All crimes were assumed to attract the societal costs mentioned in section 5.3.1, regardless of whether they weredetermine whether a crime attracted policin

probability of a crime being reported to the police (thus attracting a policing syst

■ prob crim system cost)

probability rt final

■ probability g a cusystem cost

Crim appli follows:


49

■ homicide and related offences;

use inj

ssault and related offences;

or negligent acts endangeri ple;

abduction/harassment/other offences t the person;

robbery, extortion and related offenc

wful entry with intent;

d related offences;

and related offences;

illicit drug offences;

prohibited/regulated weapons and ex offences;

damage and environmental pollution;

rder offences; and

justice.

Lifetime costing framework

g framework was based on e application of each set of per‐head annual ender, across an estimated life Total lifetime costs were dent on an

set for the condition and assumed life‐span over which cos ld apply.

ese costs across life‐spans total lifetime, undiscoun st from the nce of that NFF outcome. Application scount rate across the life the tion of discounted lifetime costs (net values) in 2010 dollars.

‐completion, was 19 years. For undergraduate degree non‐

Lifetime costs were discounted back to 2010 at an annual real discount rate of 7%, consistent w interventions (Australian Government, 2010).

Assumed on life for males and females taken from ABS life

bles

Females

■ acts intended to ca ury;

■ sexual a

■ dangerous ng peo

■ agains

■ es;

■ unla

■ theft an

■ fraud, deception

■ ■ plosives

■ property

■ public o

■ offences against

5.4

The lifetime costin thcosts by age‐g time. depenassumed age of on ts wou

Summation of th gave a ted coprese of a di span allowedestima present

The assumed age of onset for all health, productivity and criminality outcomes, excludingundergraduate degree noncompletion, the assumed age of onset was 21 years.

ith the Australian Government’s choice of discount rate for assessing regulatory

life spans were basedtables (2009e).

expectancies

The costs are summarised in the ta below.

Table 5.14: Discounted lifetime costs of adverse health outcomes (a) (2010 dollars)

Cost type Males

Obesity

Health system 1,968 1,510

Productivity losses 3,617 2,777

50

Other financia 2,687 2,063 l(b)

BoD 49,728 38,186

Total 57,999 44,536




Other financial(b) 7,959 5,766

BoD 95, 75 69, 19 0 2

Total 152,522 110,771

Smoking




BoD 57 56 6,799 3,150

Total 652,059 643,069

Alcohol abuse



Other financial(c) 28,610 31,338

BoD 33,006 36,153

Total 102,413 112,188

Illicit drug abuse

Health system 904 1,045



BoD 5,671 6,540

Total 20,232 23,334

(a) Assuming an age of onset of 19 years, life expectancies from ABS and 7% discount rate (b) ‘Other financial’ category for obesity, anxiety and depression and smoking includes DWL from transfers, carer costs and other indirect costs. (c) ‘Other financial’ category for alcohol abuse and drug abuse includes accidents not elsewhere included, fires not elsewhere included and abusive consumption costs. Source: Access Economics calculations.

Table 5.15: Discounted lifetime costs of adverse productivity outcomes (a) (2010 dollars)

Cost type Males Females

Year 12 non‐completion

Total productivity losses(b) 95,865 54,963

Undergraduate non‐completion

Total productivity losses(c) 312,768 198,282

(a) Including costs from lower employment/participation, productivity and DWL due to lower taxation revenue. (b) Assuming an age of onset of 19 years, life expectancies from ABS and 7% discount rate. (c) Assuming an age of onset of 21 years, life expectancies from ABS and 7% discount rate. Source: Access Economics calculations.


51

Table 5.16: Discounted lifetime costs of criminality outcomes (a) (2010 dollars)

Cost type Males Females

Policing costs

Total 99,278 85,439 discounted costs

Court system costs

Total discounted costs 9,087 8,512

Prison system costs


Societal costs

Medical 15,869 13,660

Lost output 68,092 58,611

Intangible(b) 69,604 59,913

Property damage 197,108 169,664

Oth 126,09er(c) 146,493 6


(a) Assuming an age of onset of 19 years, life expectancies from ABS and 7% discount rate.

Those costs non‐separable into above categories. (b) ‘Intangible’ costs are those due to pain, suffering and lost QoL (Rollings, 2008).(c)Source: Access Economics calculations.

52

6 Cost benefit analysis

The Communities for Children (CfC), Positive Parenting Program (PPP) and Reconnect programs were selected for analysis. The reasons for selection of these is explained in section 2.6.

6.1 Communities for children (CfC)

sectionThis is based on the evaluation of the Strongerrep (FaHCSIA, 2009). CfC is one of the major Au

Families and Communities Strategy ort stralian Government investments in

CEA evaluation can also .

Underdisadvcomm ildhood development. CfC was one of three

munities, which is not

conducted in 2008, approximately one year after CfC program activities were under way.

, child and parent physical health, children’s experiences of emotional and behavioural problems, children’s prosocial behaviour, children being overweight, and parents’ mental health;

families. It has already been shown to be efficacious, and the determine at what cost its effective outcomes are achieved

The CfC program was an initiative funded by the Australian Government from 2004–2009. The program aimed to:

■ improve coordination of services for children 0 to 5 years and their families;

■ identify and provide services to address unmet needs;

■ build community capacity to engage in service delivery; and

which ■ improve the community context in children grow up.

the CfC, FaHCSIA funded non‐government organisations as ‘Facilitating Partners’ in 45 antaged geographic areas around Australia to develop and implement a whole‐of‐unity approach to enhancing early ch

models of service delivery funded under the Australian Government’s Stronger Families and Communities Strategy (SFCS) 2004–2009.

Most of the outcomes targeted by CfC are included as LSAC variables within Access Economics’ model (Table 6.17). The main exception is building child‐friendly comdirectly related to family functioning.

FaHCSIA (2009) presented the results of the evaluation of the short‐run impacts of the CfC initiative on child, family and community outcomes. The evaluation study was based on a three‐wave longitudinal study of 2,202 families living in 10 sites that had a CfC program and five sites that did not have a CfC program but were in other ways comparable to the CfC sites (contrast sites). The study had a large sample, representing 42 per cent of the population of 2 year‐old children in 10 CfC sites and five contrast sites at wave 1. Follow up (wave 3) was

The effects of the CfC initiative were estimated using statistical techniques that allowed child, family and community outcomes in the CfC sites to be compared to what they would have been in the absence of the CfC intervention (using outcomes in the contrast sites). The outcomes measures related to four priority areas:

■ healthy young families—child injuries requiring medical attention


53

■ supporting families and parents—harsh parenting, parenting self‐efficacy or self‐confidence, parent relationship conflict, and living in a jobless household;

needs.

The ove ositive impacts:

hildren were livi ess household;

■ parents reported less hostile or harsh parenting practices; and

parents felt more effec

t initial follow up, any impac all, given t was of short‐run effects and the i as intended to have an impact on all families living CfC sites and not just thos cessed services. As exp for many of the utcome measures, the estim s not statistically significant, which is not urprising given that any effe small in the short‐r epending on the

ated, b ds and three‐quarters of ome variables d a positive, although not necessarily statistically significant, effect.

Although non‐significa not possible to sa high level of confidence that the ind s not different from zero, wed pattern of results towards positiv rt for the conclu on that CfC has had

pacts i

The effect sizes of the es were small, be considered wh d in the early phase of Sure Start (a large‐scale

parable old

re developed programs from birth.

■ Also important is the extent to which CfC outcomes compare with alternative early

nd that most studies reported effect sizes on parenting and child outcomes that were negligible to small. It should also be noted that most of these evaluations measured

ident irrespective of whether parents and children in the CfC communities had actually received services, seems to point towards an additional effect over and above the provision of new, stand‐alone services,

■ early learning and care—children’s receptive vocabulary achievement and verbal ability, and the quality of the home learning environment; and

■ child‐friendly communities—parents’ involvement in community service activities, the level of support parents receive from others to raise children, the quality of the neighbourhood as a place to raise children, parents’ sense of community social cohesion, their perception of the quality of facilities in the community, and the level of unmet service

rall conclusion is that, on balance, there is evidence that CfC had the following p

■ fewer c ng in a jobl

■ tive in their roles as parents.

A t of CfC was expected to be smntervention w

hat the evaluation

in e who directly ac ected, o ated impact of CfC was cts were likely to be un. Dstatistical model estimindicate

etween two‐thir the outc

■ nce means that it is y with aividual effect wa the skee effects provides suppo si

some positive im n the short‐run.

■ CfC impacts on all outcom but can positive relative to at was observearea‐based initiative in the United Kingdom). The current results were also comin size to those found in the later impacts evaluation of Sure Start, where 3 year‐children were exposed to mo

childhood interventions that target specific client groups and seek to enhance child outcomes through other processes, such as centre‐based programs, home visiting programs, case management interventions and parenting programs. Wise et al (2005) fou

outcomes for children who were directly enrolled in the program, whereas CfC is aimed at improving outcomes for children in the whole community.

The fact that the effect sizes of CfC were comparable to, if not greater than, many alternative early childhood interventions, and that these effects were ev

54

possib d/or other enhancements to the community context in which children develop.

6.1.1

LSAC categories used as interim variables in this report e used as p in FaHCSIA (2009): emotional social problems, (obesiverbal ability (learning outco Four more C riables ‐ hcipline), parental self‐efficacy, tal mental he (depressio

esion) are as inputs to t her two intvariable, quality of th e learning environment (par

as an into the lear outcome ine (Table 6.17).

17: Communities for Children target s and related LSAC

LSAC variable

ly as the result of a better coordinated local system of early childhood services an

CfC outcomes

Three of the fivefor variables modelled

can b overweight

roxies ty) and

and vocabulary and mes). fC va ostile parenting (harsh dis paren alth n) and parental relational conflict (family coh used he ot erim outcomes. The last CfC e hom ental involvement in education at home) is used variabl

input ning terim

Table 6. area variables

Priority area Outcome modelled

Healthy young families Child injuries ‐

Child physical health ‐

Child emotional and behavioural *asdqta development

Child overweight *cbmi

Parent general health ‐

Parent mental health *ak6

Supporting families and parents

Hostile parenting *ahostc

Parenting self‐efficacy *pa01a

Parental conflict *re06a

Living in jobless household ‐

Early learning and care Vocabulary and verbal achievement *wlrnoi

Home learning environment *ahactd

Child‐frie communities Various ndly ‐

Note: Variables in bold are mapped to LSAC dependent variables. Variables in normal font (not bold) are explanatory variables. Variables with dashes do not occur in our model as the literature review did not support them as being significant for our model. Source: FaHCSIA (2009).

All of the relevant outcomes of the CfC, as modelled in FaHCSIA (2009) had the expected signs (that is, they were all improvements.) The LSAC sample includes 364 children who were also participants in the CfC program. The data were drawn from the K1 cohort to match these children.

By comparing CfC effect sizes against LSAC averages for the same variables, it is possible to estimate the magnitude of the average improvements under the program (Table 6.18).


55

Most of the e smallest effects were on child outcomes – but they were still all positive.

Table 6.18: Outcomes of dre

Domain CfC

outcomes

LSAC standarddeviation

Improvement

substantial effects from CfC were improvements to parenting10. Th

Communities for Chil n variables used in this report

s1

Hostile parenting ‐0.14 10.9% 1.28 ‐

Parenting self‐efficacy 0.11 0 13.1% .84

Parent mental health ‐0.07 12.5% 0.56 ‐

Quality of the home learning environment 0.02 0.56 3.6%

Parental relationship conflict ‐0.02 0.84 ‐2.4%

Child t(SDQ)

‐0.9% otal emotional and behavioural problems ‐0.04 4.68

Child o ‐2.5% verweight ‐0.04 1.59

Recepability

‐2.0% tive vocabulary achievement and verbal

‐0.19 9.73

Source: FaHCSIA (2009). Note: Variables in bold are mapped to LSAC dependent variables. Variables in normal font

of this investment was spent on service delivery, with Community Partners (the local service provider that delivered the services/activities) receiving 60% of the funding, Facilitating

the economic benefits and costs under a scenario with the

in the le affected by each outcome. Under the CFC, the resulting reductions in

ble 6.19, along with the intervention effect in percentage terms. The net

(not bold) are explanatory variables. 1 Obtained from LSAC database observations on CfC children.

6.1.2 CfC costs

The cost of funding CfC over the four years from 2004‐05 to 2008‐09 was $142 million. The majority

Partners receiving 7% of the funding and 3% was used for local evaluation. The remaining 30% was for community resource funding (development, implementation, project management and community development).

Based on the number of 0 to 5 year olds in each CfC site in 2006 (n=28,810), $840 was spent on each 0 to 5 year‐old child living in the CfC communities between 2004–05 and 2007–08. This four year period was thus selected for the costing – estimated as 4/5ths of $142 million or $113.6 million.

6.1.3 Cost benefit results

The difference betweenintervention and one without was estimated by using an incidence approach i.e. the difference

number of peopthe incidence of negative outcomes and the increases and improvements in positive outcomes are shown in Taeconomic gains between the scenario and the ‘base case’ (no intervention) were evaluated to determine the intervention’s overall return on investment.

a 2% change. 10 Defining substantial here as roughly

56

The total financial benefits represent discounted social and economic savings associated with improvements in outcomes attributable to CFC over the life of each individual. Consequently, the benefits were estimated to be $541.4 million, or $135.4 million per annum (assessed over 2004‐05 to 2007‐2008).

Table 6.19: Outcomes of CfC as delivered over 2004‐05 to 2007‐08

Intervention effect* (change in incidence rate compared with no intervention)

Difference in the number of people affected

Obesity ‐22.44% ‐8,546

Productivity (yr 12 completion) 1.42% 804

y and Depression ‐0.87% ‐46

cial ‐0.01% ‐1

Anxiet

Antiso

Addictions ‐0.5% ‐47

Source: Access Economics (2010). * The intervention as modelled targets children aged 0‐1 years.

Given the cost of funding over four years 2004–05 to 2007–08 was $113.6 million (Section 6.1.2), the average annual cost of the program was therefore $28.4 million per year. As a result, the total benefit cost ratio of the CfC program was calculated to be 4.77, suggesting a 377% return on investment from the CfC program.

6.2 Positive Parenting Program (PPP)

PPP is one of the best evaluated programs targeted at improving family functioning and outcomes for younger children. While its efficacy is well‐proven, there are fewer studies on its

A can also act as a tool to test/triangulate the power of the

discipthe taperiod e

ope of the interventi

11

o promote the development, growth, health and social competencies of children and

cost effectiveness and this CEmodel.

Developed at the University of Queensland, the PPP (also referred to as ‘Triple‐P’) is a multi‐level system of parenting and family support. It can be provided individually, in a group, or a

‐directedself format. It incorporates five levels of intervention on a tiered continuum of increasing strength for parents of children and adolescents from birth to age 16. The multi‐

linary nature of the program allows utilisation of the existing professional workforce in sk of promoting competent parenting. The program targets five different developmental s from infancy to adolescence. Within each developmental period, the reach of th

intervention can vary from being very broad (targeting an entire population) to quite narrow (targeting only high‐risk children). The PPP enables practitioners to determine the sc

on given their own service priorities and funding.

The aims of PPP are:

■ Tyoung people.

t/?pid=65 11 http://www26.triplep.ne


57

■ ent protective and nurturin for children.

independenc hancing paren ge,

competence, and self‐sufficiency of pa raising n.

ness.

nt; positive learning en and taking care of self as parent. The

■ Level 1 – provides universal access to parenting information through print and electronic media. l aim aw ne ng resources,

e parent participation in PPP create a sense of optimism

Level 2 – or tw pri health sessions th ovides pdevelopmen to parents who h h problems.

eets and videotapes are commonly used t this leve

Level 3 – Fo imary re intervention sessions for ren hildren ilmoderate beh u ems ions p id e sk ning for parents.

nsive program of eight to ten sessions that are either individual sessions,group base self‐directed. level is paren of children with sbehaviour p ms

n enhanced behavioural family intervention program for parents whosedifficulties mplicated by r issu h as r tion rdepression le refor gets ly ting but also distressing

ional reactions (including depression, anger and via cognitivebehavioura iques.

PPP m

has been th ect of zen eer‐reviewed journal art the .rity of the dependent variables ana been reported

ood problem solving skills (which is similar to the dependant variable

■ PPP also targets health, of which obesity is a factor. Again, no studies were found that reported on obesity directly, but a number of input variables have been studied.

To promote the developm of non‐violent, g environments

■ To promote the e and health of families by en ts' knowledskills and confidence.

■ To enhance the resourcefulness rents intheir childre

■ To reduce the incidence of child abuse, mental illness, behavioural problems, delinquency and homeless

PPP is prevention oriented, multi‐disciplinary and has five levels. The program has been developed through over 20 years of clinical trials. Five different developmental periods are targeted at each level – infants, toddlers, preschoolers, primary school aged children and teenagers. The program aims to promote parental competence and enable parents to become independent problem solvers. Five key principles of parenting – safe, engaging environme

vironment; assertive discipline; reasonable expectations program has five levels, as outlined below.

This leve s to increase community are ss of parenti encourag and .

■ One o mary care at pr ‘antici atory tal guidance’ ave c ildren with mild behaviour

Parenting tip sh a l.

■ ur pr ca pa ts of c with m d to avio r probl . The sess rov e activ ills trai

■ Level 4 – Inte d or This for ts more evere roble .

■ Level 5 – A are co othe es suc ela ship conflict and pa ental . This vel the e tar not on paren skills

parental emot stress) l techn

6.2.1 outco es

PPP e subj do s of p icles over years The majo LSAC regression in this lysis have directly in Australian PPP studies (Table 6.20).

■ PPP targets childhin the productivity regression (learning outcomes). While the literature search did not uncover any quantitative reporting for this outcome, several input variables (or common psychological variables that can be used as proxies for input variables) were reported.

58

Table 6.20: PPP target areas and LSAC variables

PPP target LSAC measure LSAC variable

Parental Sense of Competency Parental self‐efficacy *apa01a (PSOC)

Dyadic Adjustment Scale Family cohesion *re06a

SDQ emotional score SDQ emotional score *aemot

SDQ conduct problems SDQ conduct problems *aconda

ECBI SDQ total score *asdqta

Parental depression Parental anxiety and depression *ak6

Parental laxness1 Consistent parental discipline *pa01a

Parental overreactivity1 Hostile parenting *fhostc

Parental verbosity1 Inductive reasoning *areas

Note: Text in bold indicates LSAC regression dependent variables. 1 – these three variables are often reported upon collectively as the Parenting Scale (PS).

Sanders et al (2 all five stages of PPP for four to seven year old Australian children with follow up at two years. The authors found (Table 6.21) that PPP produced substantial improvem emotional difficulties

SDQ (b ch nt re in parental depression (26. es n con

T Impact of larg le Queensland trial

Control Tarones χ2 Sig

008) conducted a large scale (n = 3,000) controlled evaluation of

ents in SDQ ildren, and significa

o better, in the(by 17.6%) and total difficulties

2%). All of thy 21.6%) in

e worsened, or gotductions

trol groups.

able 6.21:

PPP

e sca PPP

% Clinical 95%CI ical OR %CI OR % Clin 95

SDQ emotional

Pre 15.3 1 0.653 12.1 1 909 4.96 26 0. 9* 0.0

Post 12.6 0.988 13.4 1.128 1.398 0.803*

SDQ conduct problems

Pre 18.7 0.685 16.8 1 661 0.0 0.85 1 0. 36

Post 16 1.001 14 .806* 0.822 0.828

SDQ total difficulties

Pre 13.9 1 0.608 9.7 1 0.855 4.783* 0.029

Post 10.9 .757* 0.941 10.4 1.085 1.37

Parental depression

Pre 26.70 1 .570 19.10 1 0.802 7.673* 0.006

Post 19.70 0.676 .802 18.60 0.963 1.157

Parenting consistency

Pre 91.10 1 .808 86.10 1 0.915 0.243 0.622

Post 91.40 1.04 1.34 87.50 1.13 1.397

Parental confid ence

Pre 99.70 0.148 61 1 99.50 1 0.263 0.193 0.6

Post 99.40 1 1.634 30 0.694 27 0.49 99. 1.8

Note: * indicates signSource: Sanders et a

ificant difference (at p=0.05) betw tervention and co oups. l (2008).

een in ntrol gr


59

De Graaf et al (2008) conducted a meta analysis on the impact of PPP Level 4 on two parenting first, Parenting Scale (PS) is a of laxne (inconsistent ne),

ity (harsh pare ) and verbosity uctive reasoni 12. The second, Competency (PSOC), approximates the LSAC variable Parental self‐efficacy. The

weighted e effect sizes 8 for PS and for PSOC, bo hich ated to be large.

PPP trial in Western icipants in the intervention group and the control group. This was

randomised risk

Attribute R

t Me

clinical e)

Estimated ffect

a onths

Implied improvem

factors. Theover‐reactivSense of

nting

composite (ind

ss ng)

discipli Parental

authors found averag of 0.6 0.65 th of wwere st

Zubrick et al (2005) conducted a large, community based Level Four Australia with over 800 partnot a trial, as the intervention was targeted towards at families (as measured by likelihood of having clinically significant issues), but it was a controlled trial. Results were reported immediately after the intervention and at 12 and 24 months. In all cases there were significant positive results directly after the intervention, and most of these gains were still preserved after two years. Not all of the measures used in the study had direct LSAC equivalents that used in our model, so closest substitutes were chosen. On this basis, child social and emotional problems, parental depression and family cohesion all recorded improvements equivalent to ten percent or better. As the authors reported means for the intervention group, the implied improvement in (Table 6.22) is simply the estimated intervention effect as a proportion of the mean.

Table 6.22: Impact of large scale Western Australian PPP trial

elevant LSAC equivalen

an (% in rang

intervention et 24 m

ent

Parental depression Parental depression

16.2 4.41 27.2%

Marital satisfaction Family Cohesion 10.0 0.73# 7.3%

Parental laxness Inconsistent discipline

8 % 39.6 0.3 1.0

Parental over‐reactivity

3 %

Hostile parenting

69.5 0.3 0.5

Parental verbosity Inductive reasoning

40.6 9 % 0.2 0.7

Eyberg Child Behaviour Intensity

92 %

(ECBI)

SDQ total problems

121.6* 12. 10.6

Notes: *ECBI mean is a score, not percent in clinical range, # Abbreviated Dyadic Adjustment Scale.

demonstrated P is cultures. For example, Bodenmann et al (2008) in a smaller n c l (n = 50 families) found significant improvements in parenting,

Source: Zubrick et al (2005).

While the focus of this report is on Australian parenting, several studies havethat PP effective in otherEuropea ontrolled triaparental esteem and child misbehaviour, most of which were maintained after 12 months.

12‘Inductive reasoning’ as recorded by LSAC often had counter‐intuitive results, so it is possible that what parents think of as inductive reasoning may in fact be closer to what clinicians would describe as verbosity.

60

Table 6.23: Impro ropean PPP trial

Post‐trial s % change

vement in means scores of Eu

Topic Pre‐trial After 12 month

PS laxness 5 13.2 12.3 12.3% 13.

PS over 24.7 18.7 21.0 reactivity 15.0%

Parental self e

26.9 28.7 28.8 7.1% fficacy

EC s 7.2 104.7 99.9 1BI problem 11 4.8%

DA

6.3 111.0 109.6 3yadic djustment Scale*

10 .1%

Note *measures quality of the couple’s relationship, approximates LSAC family cohesion.

ere available (SDQ emotional and total scores, parental anxiety and depression), as this was the largest study and Australian. Variables from the

Au second preference (used for family cohesion, parental parental over‐reactivity and parental verbosity), as this study was also Australian, and

than the total score so it is probably that the conduct score at least reflected the total score (or more).

variable improvement WA EU

Source: Bodenmann et al (2007).

Table 6.24 shows the outcomes of PPP variables that were used in the modelling. Variables from Queensland study were used wh

Western stralian study were the laxness,larger than the European study. Note, however, that the effect sizes for laxness and over‐reactivity were much smaller in the Western Australian study than in the European study. The European study provided the inputs for parental self‐efficacy. The SDQ conduct problems score was based on the total score, since the emotional score was less

Table 6.24: Outcomes of PPP variables used in this analysis

PPP outcome Model concept Specific % QLD

ParenCompetency (PSOC) efficacy

tal Sense of Parental self‐ *apa01a 7.1% n/s n/a 7.1%

Dyadic Adj st nt Scale

Family co %u me

hesion *re06a 7.3 n/a 7.3% 3.1%

SDQ emotional score SDQ emotional score

28.4% 28.4% n/a n/a *aemot

SDQ conduct SDQ coproblems problems

nduct

*aconda 28.8% n/s n/a n/a

ECBI SDQ total score 28.8% 28.8% .6% 14.8% *asdqta 10

Parental depression Parental anxiety and 23.6% 23.6% .2% n/a depression

*ak6 27

Parental laxness Consistent parental *pa01a 1.0% n/s% 1.0% 12.3% discipline

Parentreactiv

0% al over‐ity

Hostile parenting *fhostc 0.5% n/a 0.5% 15.

Parent % n/a al verbosity Inductive reasoning *areas 0.7% n/a 0.7

Source: Based on Table 6.21, Table 6.22 and Table 6.23. QLD=Queensland. WA = Western Australia.


61

6.2 PPP costs

et al (2005) report that cost effectiveness analyses of PPP have found that that its costs rson range from 75c at Level 1 to $422.45 at Level 4. Foster et al (2007)

.2

Wise per pe reported the

Table 6.25: PPP costs ($US)

following estimated costs of PPP, by level, in the United States (Table 6.25). As an overall average, the authors estimated the costs of PPP were $US11.27 per child.

Level Provider costs

Participant costs

Attending Preparation Total Grand Total training

Primary care 707 500 32 532 1,239

L4 Group 719 726 44 769 1,488

L4 Standard 740 800 57 857 1,598

L5 Enhanced 660 587 18 605 1,266

Mihalopoulos et al (2007) state that the cost of implementing PPP in Queensland to 572,701

Cost benefit results

no intervention)

e number of people affected

children aged between 2 and 12 years of age (315,378 families) was A$19.7 million (Level 1, $240,000; Level 2, $5.8 million; Level 3, $5.7 million; Level 4, $4.4 million; Level 5, $3.6 million) with an average cost of $34 per child.

6.2.3

Similar to CfC in section 6.1.3, the difference between the economic benefits and costs under a scenario with the PPP intervention and one without was estimated using an incidence approach, and was based on the costs from the Queensland study as presented above. The change in outcomes and intervention effects are shown in Table 6.26.

The net economic gains between the scenario and the ‘base case’ (no intervention) were evaluated to determine the intervention’s overall return on investment. The benefits were estimated to be $68.1 million per year (assessed over 2004‐05 to 2007‐2008).

Table 6.26: Outcomes of PPP as delivered over 2004‐05 to 2007‐08 in Queensland

Intervention effect* (change in incidence rate compared with

Difference in th

Obesity 0.00% 0

Productivity (yr 12 completion) 0.00% 0

Anxiety and Depression ‐0.69% ‐161

‐0.13% ‐96 Antisocial



62

Given the cost of the intervention in Queensland over the four years 2004–05 to 2007–08 was $19.7 million, the average annual cost of the program was therefore $4.925 million. The total benefit cost ratio was therefore calculated to be 13.83, suggesting substantial 1, a 283% return on investment from the PPP program.

6.3 Reconnect

The Reconnect program aims to assist

who are homeless, or at risk of homelessness, and a number of specialist Reconnect services including

Indigenous, Culturally and Linguistically Diverse (CALD), Mental Health, Gay, Lesbian,

homeless or at risk of homelessness through the Newly Arrived Youth Specialist Services.

me’ rate (where appropriate) is available to young people who are

clude counselling, group work, mediation and practical support and are

h as specialised mental health services. Services are provided where the client is most comfortable, such as in homes, schools, and other sites in the community.

munity service

■ young people aged 12 to 18 yearstheir families. This includes

Bisexual, Transgender, Intersex and Newly Arrived Youth specialists; and

■ young people aged 12‐21 years who have arrived in Australia in the previous five years, focussing on people entering Australia on humanitarian visas and family visas, and who are

The objective of Reconnect is to assist young people to stabilise their living situation13 and improve their level of engagement with family, work, education, training and their local community through:

■ using family focussed early intervention strategies to achieve family reconciliation;

■ improving coordination and integration of services delivered by government and the community sector; and

■ working with Centrelink, young people, and their parents to ensure that income support at the ‘away from hoproperly entitled to it (FaHCSIA 2003a).

Reconnect services inprovided to the whole family. Reconnect providers also 'buy in' services to target individual needs of clients, suc

The program is funded by FaHCSIA and services are delivered by (generally) non‐government organisations. Participants may be self‐referred or referred from a range of sources, including: schools, education and training organisations; parents or family; community agencies; Centrelink; juvenile justice; police; child protection agencies; accommodation services; specialist services, such as English language centres; and state/territory comdepartments.14

g/funding/reconnect/recon_operation_guidelines/Pages/2_overview.aspx accessed August 2010

13 The aim is to achieve family reconciliation, wherever practicable, between homeless young people, or those at risk of homelessness and their family. Family reconciliation outcomes include: the young person returns home; ongoing positive family relationships are created which provide the young person with emotional and physical support; reconciling the young person with other family members e.g. grandparents or siblings; both parent (s) and the young person accept that independence is appropriate for the young person; and establishing a viable support system for the independent young person that includes a member of his/her family (http://www.fahcsia.gov.au/sa/housing/funding/reconnect/recon_operation_guidelines/Pages/2_overview.aspx accessed August 2010).

14 http://www.fahcsia.gov.au/sa/housin


63

Reconnect services are located in areas of high need having regard to factors such as the size of the adolescent population, indicators of socio‐economic disadvantage, particularly vulnerable population groups such as Indigenous people or newly arrived refugees or immigrants from non‐English speaking backgrounds, the range of other services in a region and

ere are Reconnect services located in major metropolitan tre

typica

Recon lia. In

tudies were conducted on Reconnect in 2001 and 2002 (reported in 2003) — a

ng

geographic location. As a result, thcen s, rural towns and more remote locations (FaHCSIA 2003). Reconnect services are small,

lly employing two to three practitioners with some administrative support.

nect services are located in disadvantaged communities throughout Austra2008‐09, there were 101 services, and around 5,215 cases15. The data available suggests activity each year between 2004‐05 and 2008‐09 was as follows:

■ 2004‐05: 6,301 cases;

■ 2005‐06: 6,025 cases;

■ 2006‐07: 5,890 cases;

■ 2007‐08: 6,078 cases; and

■ 2008‐09: 5,215 cases.

6.3.1 Reconnect outcomes

Two evaluative scommunity study investigating service structures and whether these had changed in accordance with Reconnect objectives, and a longitudinal survey of Reconnect clients which aimed to assess the potential benefits of Reconnect on the lives of young people and their parents.

The community study (FaHCSIA 2003a) investigated 12 Reconnect services over a one‐year period (end 2001 to end 2002) to assess their contribution to an improvement in community capacity for early intervention around youth homelessness. The 12 services were selected to be reflective of the broader Reconnect program, and were located in remote, rural and urban locations and included specifically targeted as well as generalist services. The study concluded that the 12 services had a significant impact, relative to their own capacity, on buildicommunity capacity for early intervention for youth homelessness in three key ways:

■ by building community infrastructure for early intervention for example, by improving access to services and through training of service providers, community members, parents and clients;

■ by strengthening service networks, collaboration and resource sharing between agencies; and

■ through assisting other organisations to have a greater focus on effective early intervention.

15 When a client engages with a service, a case is commenced for that client. The case work will involve providing whatever services that client needs. The case can continue to deal with the initial issues and/or any issues identified while working with the client. The case is closed when: a) all issues are resolved, ie as mutually agreed by the worker and the client, or b) can be closed if either the worker or the client feels that is appropriate. c) If a client does not have contact with the service for over 30 days, the case can also be closed. A new case would be opened if the client comes back to the service after the previous case has been closed. A new case cannot be created if there is an open case for that client.

64

The longitudinal survey (FaHCSIA 2003b) was sent to selected clients at two points in time, around 10 months apart. The first wave of the survey commenced in November 2001 and the second wave was administered in October 2002. At each time point, both new clients and clients who were exiting Reconnect were surveyed. 1,001 cases participated — 516 new

Cases could consist of a young person only,

■

■ es were received for 260 cases—this represents a

ificantly across entering and exiting client groups. It is

in conflict management are lasting, and indeed increase over time.

clients and 485 whose support period had ended. adult(s) only (in most cases, parents), or both a young person and adult(s).

In the first wave of the survey, responses were received for 455 cases, making an overall case response rate of 45.5%. The response rate for exiting cases (46.0%) was slightly higher than for entering cases (45.0%).

In the second survey wave, responswave 2 response rate of 57.1%. The response rate for entering cases (59.1%) was higher than for exiting cases (55.2%).

The longitudinal survey concluded:

■ There was no difference in the extent to which entering and exiting clients were participating in employment or education at wave 1. As entering clients are used as a de facto control group, this suggests that Reconnect intervention has little immediate impact on improving young people’s participation in education or employment.

■ Self‐reported school performance, interest in school, perceived importance of school subjects, expectations of educational attainment, and psychological sense of school membership did not vary signdifficult, therefore, to conclude that Reconnect significantly improves clients’ connectedness to employment or education using the measures studied.

■ There was a significant improvement across time in the extent to which young people felt liked and respected at school, which could not be explained by sex, age or case complexity. It is difficult to conclusively attribute this to Reconnect. However, clients were also asked separately about the impact of Reconnect, and the finding from the longitudinal survey is consistent with young clients’ self‐reported impact of Reconnect on how they feel about themselves, their school, and their ability to deal with their teachers.

■ Young people responding to wave 2 of the survey reported improvements in their own ability to manage family conflict. About two thirds of respondents rated their ability in this area as poor or very poor before entering the Reconnect program, but just 16% felt their ability was poor or very poor after receiving Reconnect support. The proportion who felt they had good or very good skills in managing family conflict increased from 12% before Reconnect to 44% after Reconnect. Young people also reported that their family, as a whole, was better able to manage conflict after Reconnect. Those rating their family’s abilities as good or very good increased from 14% to 37%. Exiting clients reported higher levels of improvement than entering clients, suggesting that improvementsSimilar levels of improvement in conflict management were also reported by parents.

■ Respondents reported positive shifts in communication between family members. About 7% of young people and 2% of parents felt their family communicated very well

Rebefore receiving connect support. Comparable figures reported at the time of the wave 2 survey were 17% and 13%. No significant difference was found in changes in communication between entering and exiting clients.


65

■ Among young people, although there was no significant impact of Reconnect found for family closeness or attachment to parents (measured by three scales of Trust and communication, Alienation, and Relationship with father), parents as a whole

epression and anxiety’ across the two

■ The extent to which parents felt their child was likely to engage in undesirable social behavi of entering and exiting parents, suggesting little, if any, impact by Reconnect services. However, it

d that li Avera th ering and exiting parents on the index of parental‐assessed lik f children

able 2.35, respectively out of a

IA, 2003c) concluded that:

t in the stability of young people’s living situations — Young temporary situations fell from 16.5% at Reconnect’s initial intervention m the ntion also increa stability of

ople’s living situations in relation to parents—young people living with parents a ort, ase which

ross all age categories.

en ic r nal survey above.

■

e evaluation found that, for the

ng been expelled. The longitudinal study found a significant improvement over time in the extent to which young people felt liked and respected at school.

experienced a significant decline in feelings of ‘dwaves. There were no statistically significant differences between exiting and entering clients, or between wave 1 and wave 2 clients in response to scales administered relating to ‘depression and anxiety’ and ‘self‐worth and coping’.

ours was also examined. There was no difference in the assessments

should be note ttle improvement was to be made. ge scores for boent elihood oengaging in undesirpossible 10).

The final report (FaHCS

■ There was improvemenpeople living in

behaviours were very low (2.39 and

to 5% at exit froyoung pe

services. Reconnect interve sed the

increased from 57.5% was found ac

t the start of support to 62% after supp an incre

■ There was improvemthe longitudi

t in young people’s ability to manage confl

t — as reported fo

■ There were improvements in communication within families — Young people and parents reported improvements in communication following the Reconnect intervention. The proportion of young people who felt their family communicated well increased from 22% before Reconnect intervention to 41% afterwards. Parents’ reported improvement was more pronounced: the proportion of parents reporting their family communicated well or very well increased from 11% before Reconnect intervention to 42% at the second survey. Parents reported feeling increased closeness with their children and less alienation after Reconnect intervention, although this effect was not sustained over time.

Improvements in young people’s attitudes to school — The majority of young people using Reconnect services were in full‐time education (76%) — a participation level which was unchanged by Reconnect intervention. However, thmajority of young people using Reconnect, experience of school was not a happy one. Reconnect clients tend to change school frequently: 52% of young people had been at their current school for less than two years. Just over one third of young people (36%) reported being bullied, 18% saying that it happened on ‘most days’; more than half (53%) of the young people surveyed reported hating school often or almost always; 43% reported having been suspended and 10% havi

■ Improvements in engagement with education and employment — The proportion of young people employed full time or part time increased from 2% at the start of support to 5% at completion. The proportion of young people who were not in education,

66

training or employment dropped from 15% at program entry to 11% at exit. Educationand unemployed (looking for work) statu

participation rates s remained unchanged. However, in the light of the findings on case complexity of Reconnect clients,

g people’ education can be vie s a positive achievement.

■ Engagement with com though s orted im in youn ple’s agement wit commu ere found, o easures u the longitu

study to track increases in ation in com y activitie nd no signiimprovements over time.

Clients’ views o — e quarters o ng peopl parents repall improvement in the s n that led t Recon ore than h

e (55%) and parents attribut lot’ of this improvement tontervention.

P d

Based pselecttable.

Evaluavariab

maintaining youn s participation in wed a

munity — al elf‐rep provements g peoeng h the nity w ther m sed in dinal

particip munit s fou ficant

■ f outcomes Thre f you e and orted over ituatio hem to nect. M alf of young peoplReconnect i

(52%) ed ‘a the

AT ata for the Reconnect cost effectiveness analysis

on the findings of the evaluation, ATP variables relevant to the Reconnect rogram were ed (see Table 6.27). The method for estimating the effect sizes is described after the

Table 6.27: ATP variables associated with the Reconnect Program

tion outcome le

ATP variable(s) tested in regressions Effect size on FF variable

Youngschoolwagginexpulsion, hated school, cnot respect students)

people's attitudes to (school changes, g, suspension or

School bonding (positive affect towards school)

18.6%

urriculum irrelevant, teachers

Young people's ability to Conflictual relationships 55% manage conflict

Communication within families and family closeness

nt to enti 37%Attachme parents, harsh par ng

Engagementployme

with educat nt

Unde ement (young in education training and no nt)

nservatively

ionand em

r‐engag adult not or t in employme modelled

Co not

Source: Australian Governm Department and Community (2003)

School bonding: e longitudi rvey of Reco ents m clients’ seng to school (or students’ in across

ensions (FaHCSIA, 2003b:5 espect and (school membership concets get along with, and are viewed ir peers) and elongingne

a given student feels they ‘fit in’ to the school. There was a significant difference in mean scores on the ‘respect and affect’

not reported. While scores on the ‘belongingness scale’ did not vary significantly

ent of Family Services

■ Th nal su nnect cli easured the

se of belongindim

psychological3): ‘R

membershipaffect’

school) two rning

how studen by, the ‘b ss’ (a more internal measure of the extent to which

scale across time (scores on this scale increased from wave 1 to wave 2), across all categories of client status, age, sex and case complexity. However, the effect size was

between the two waves, there was a significant effect for cases classed as medium


67

complexity16 where the mean increased from 5.06 in wave 1 to 6.00 in wave 2. The effect size was therefore modelled as 6/5.06‐1 = 18.6%.

■ Conflictual relationships: The longitudinal survey found that young people perceived an improvement in their ability to manage family conflict before Reconnect and when

, 2003b). While the extent of the change was derived and clear (see the reporting around the analysis of variance,

surveyed (‘now’) (FaHCSIAreported, the units were notFaHCSIA. 2003b:70). Hence, we used data from the report shown in Table 6.28 to estimate an effect size of 55% (3.326/2.144‐1).

Table 6.28: Estimate of effect size — young person’s perception of their ability to manage family conflict before Reconnect and now

Likert scale categories Before Reconnect Now

% survey ts

Score multiplied by %

% survey respondents

Score multiplied by % responden

Very poor 1 35.0% 0.350 6.8% 0.068

Poor 2 30.7% 0.614 9.3% 0.186

OK 3 22.7% 0.681 39.5% 1.185

Good 4 8.6% 0.344 33.3% 1.332

Very good 5 3.1% 0.155 11.1% 0.555

Weighted average 2.144 3.326

Source: FaHCSIA (2003b:76) and Access Economics calculations.

■ s

for young people who connect and those who were exiting Reconnect.17 However, increa % . communica ely

family’s ability to resol , and is also th reduce harsh parenting, to the extent th is indicative of and associated with poorer mmunication. As such, we h d a proxy for attachment harsh parenting

similar t for conflictual relationsh ere the child’s eir family’s abili manage conflict is utilised a proxy for intra‐

lial attachment, in the absen a more specific direct effec and given the ficant improvement in com ion and parental wellbeing d. We used data the report shown in Table 6.29 to estimate an effect size of 37% (3.070/2.234‐1).

Communication within families and family closeness. For ‘attachment to parents’specifically, the longitudinal survey found no significant difference between the score

were entering Regood communicationtion is considered likve conflict improvedat such parenting

sed significantly, by 86% (22% toto be related to the finding that the

ought to 41 ) Good

co ave use and based on a method o that ip, whperception of th ty to as fami ce of t sizesignifrom

municat foun

(30.3% of respondents) or high (34.9% of dents). Case managers measured complexity according to conflict with authority, physical or emotional

parent‐child attachment over time, but for young people no significant differences were found. The nature of the

16 Complexity was measured as low (34.8% of respondents), mediumresponviolence, sexual abuse, mental illness, substance abuse, disability, child protection, poverty, homelessness or living situation, living skills, and identity conflict.

17 Analysis of variance was used to examine whether Reconnect clients experienced a change in their perceived

reporting did not allow use of the means in the model.

68

Table 6.29: Estimate of effect size — young person’s perception of their family’s ability to manage family conflict before Reconnect and now

Likert scale categories Before Reconnect Now





Very poor 1 34.0% 0.335 11.6% 0.116

Poor 2 27.4% 0.548 15.9% 0.318

OK 3 25.6% 0.768 36.0% 1.080

Good 4 9.2% 0.368 27.4% 1.096

Very good 5 9.2% 0.460 4.3% 0.215

Weighted average 234 3.070 2.

Source: (2003b:76) an ics

t with education an yment w rvatively not led. Althougome variable imp % (4%/1 connectio ot through

se. be m aluating Rec overall, to int as well. Howeve is on the inte ns that lead nts which in turn provemen erim outco direct ben that bypass the FF been excl from this analysis.

The budget for Reconnect is around $20 million per year (FaHCSIA portfolio Budget papers from 2001‐02 to 2006‐07). Unpublished data from FaHCSIA suggest the average cost per case across all services and all cases in 2008‐09 was $3,800, i.e. an average of $22.4 million per annum over all cases 2004‐05 to 2008‐09. The distribution of costs per case across each service was skewed, with 50% of services having a cost per case less than $4,000 and 92% with a cost per case less than $10,000. This is likely to reflect location and case mix, together with other factors.

6.3.3 Cost benefit results

Similar to CfC and PPP, the difference between the economic benefits and costs with and without Reconnect was estimated using an incidence approach, with the change in outcomes and intervention effects shown in Table 6.30.

The net economic gains between the scenario and the ‘base case’ (no intervention) were evaluated to determine the intervention’s overall return on investment. Benefits were estimated to be $40.6 million per year (assessed over 2004‐05 to 2008‐09).

FaHCSIA d Access Econom calculations.

Engagemen d emplo as conse model h this interim outc roved 27 5%), the n was n family functioning variables per A case could ade in ev onnect clude this benefi r, as this analysis focused rventio to FF improveme

nectlead to imdomain

ts in intuded

mes, the efits of Recon have

6.3.2 Reconnect costs


69

T able 6.30: Outcomes of the Reconnect Program, as delivered over 2004‐05 to 2008‐09

Intervention effect (change in incidence rate compared with no intervention)

Difference in the number of people affected

Obesity ‐6.73% ‐377

Productivity (yr 12 completion) 13.68% 1,690

Anxiety and Depression 0% 0

Antisocial 0% 0



With the cost of Reconnect estimated to be $22.4 million per year (Section 6.3.2), this yields a benefit cost ratio of 1.81, suggesting an 81% return on investment from the Reconnect program.

6.4 Shocking all variables – the value of PFF

A final economic shock was specified which set the values of all the consistently significant18 FF variables to their optimum values, some being maxima and others minima19 – depending on the specific relationship to the health, productivity and social outcome variables

the variables. By setting the transition variables in ATP to their optimum values,and the

coding of this equated to optimal FF in the early age groups.

The ta od in 2010 in the absence of the shock, 6.5% would be obese, 41.9% would have completed secondary school, 8.0% would have anxiety/depression, 5.2%

viours, and 19.2% would have addictions (4.3% abusing alcohol, % u d

Table 6.31: The value of PFF

ople Benefit $m

rget population was set as the population of Australian children entering adultho(313,577 people turning 19) of whom,

would exhibit antisocial beha2.3 sing illicit drugs and 12.6% smoking). The effect on each outcome from optimal FF anthe l value of the benefit is shown in Tab e 6.31 and Figure 6.7.

NPV lifetime PFF

% pecost per case effectiveness*

Obesity $51,268 ‐24.75% 6.55% 260.6

Productivity $75,414 29.06% 41.93% 2,881.7

Anxiety and depression $131,647 ‐17.66% 7.97% 581.1

Anti‐social $462,556 ‐7.28% 5.18% 546.7

Addictions $258,883 ‐7.53% 19.23% 1,175.5

Total 5,445.7

Source: Access Economics calculations. * The maximum that PFF can affect each outcome.

18 Variables shoes coefficient signs changed across different age groups were excluded from the shock.

19 For ATP variables, maxima and minima were proxied by taking two standard deviations from the mean.

70

PFF by benefit type, 2010 (total $5.4 billion), $bn and % total Figure 6.7: Value of

261 , 5%

1,176 , 22%

2,882 , 53%

581 , 11%

547 , 10%

Obesity Productivity Anxiety and depression Anti‐social Addictions

Source: Access Economics calculations. Note: Shares may not sum to 100% due to rounding.

The outcomes for health, productivity and social costs were then compared to the base case of no shock to estimate the total value of gains from PFF in 2010 dollar NPV terms as $5.4 billion per annum.

Of the total annual benefits:

productivity gains comprise the largest component (52.9■ % or $2.9 billion per annum);

■ savings from fewer addictions comprise the second largest component (21.6% or $1.2 billion);

■ savings from fewer cases of anxiety and depression are 10.7% or $0.6 billion;

■ reduced costs of criminality comprise 10.0% or $0.5 billion; and

■ reduced costs of obesity represent 4.8% or $0.3 billion per annum.


71

7

The s we believe, the

ve and detailed ‘cost of

t that such intervention incurs costs today as a down‐payment for discounted benefits realised a long time into the

■ In to billion per annu value of PFF gains possible. Over h gains (53%) productivity gain rther 21% of the benefits deriving savings from fewer Fewer

ion would sa .6 billion (11%), e lower rates of criminality and cial behaviour bil ctio

um (5% total).

ts accrue from:

er rates of smoking, alcohol abuse and illicit drug abuse);

ime earnings due to better secondary and tertiary education outcomes); and

erventions selected on the basis that they specifically target

■ The Communities for Children program, targeting pre‐school and primary school aged children, is one of the major Australian Government investments in families. The

Conclusions

cope of this project has been both ambitious and challenging but, methods developed and many findings and insights are of global significance. The novelty of the research inspires further work in this field that we hope can be used to triangulate these findings internationally as well as continue to enhance the evidence base in Australia.

Key findings

The main finding of this report is that positive family functioning is economically and socially valuable, and that value can be quantitatively measured using extensiillness’ methodology traditionally adopted to analyse interventions in the health sector. By demonstrating and quantifying the value of PFF, it is then also possible to systematically and consistently evaluate interventions employed by the Government and the community to enhance FF in Australia.

Value of PFF: The potential benefits of intervening in childhood and adolescence to prevent poor outcomes later in life are substantial, even (or despite) the fac

future.

tal, the potential NPV of benefits to be realised is in the order of $5.4 m in 2010 dollars. This can be considered the cost of NFF currently, or the

alf these from

are addictions.

s, with a fu cases of anxiety

and depress ve $0 whilantiso would accrue $0.5 lion (10%). A redu n in obesity would save $0.3 billion per ann of the

■ The benefi

health benefits (from lower levels obesity, anxiety and depression, and low

productivity benefits (from improvements in lifet

social benefits (from lower rates of criminality and reduced court and prison system costs).

Value of interventions to enhance FF: There are clearly marked social and economic benefits to be gained if cost effective prevention programs can be identified and implemented. This analysis has focused on three intFF in a mixture of age groups, they have reach, relevance and sustainability, and they have been previously evaluated and shown to be efficacious in the short term in improving particular aspects of FF. Moreover, these particular FF aspects were ones that, in the main, were able to be measured in our analysis of FF variables in LSAC and ATP, and were, at least in part, found to be significant factors ultimately influencing to improved health, productivity and social outcomes in early adulthood – with lifelong impacts.

72

program has been shown to improve outcomes in various FF areas including areas significant in our analysis as impacting lifelong outcomes – namely, hostile parenting, parenting self‐efficacy, parent mental health, quality of the home learning environment, parental relationship conflict, child total emotional and behavioural problems, childhood overweight, receptive vocabulary achievement and verbal ability. The benefit:cost ratio for this program was estimated as 4.8:1, a 377% return on investment.

d as 13.8:1, a substantial 1,283% return on investment.

r under‐engagement (in education and employment) and the home

ary of costs and benefits of modelled interventions

■ The Positive Parenting Program is one of the best evaluated programs targeted at improving FF and outcomes for younger children. The program has been shown to improve outcomes in FF areas significant in our analysis – namely, parental sense of competency, the dyadic adjustment scale, the SDQ emotional and conduct scales, the Eyberg Child Behaviour Intensity score, parental depression, parental laxness, parental over‐reactivity, and parental verbosity. The benefit:cost ratio for this program was estimate

■ The Reconnect program targets an older cohort of children and was found to improve outcomes in school bonding and conflictual relationships, with proxied effect sizes estimated for attachment to parents and harsh parenting. No impact was modelled for the program folearning environment, which may be conservative, since these impacts were found to be direct, rather than occurring through FF variables. The benefit:cost ratio for this program was estimated as 1.8:1, an 81% return on investment.

Costs and benefits are summarised in Table 7.32.

Table 7.32: Summ

CfC PPP Reconnect

Program cost ($m)* 113.6 19.7 112.1

Un st ($) 840/child aged 0‐5 34/child aged 2‐12 3,800/person aged 12‐21

($m, lifetime NPV) 541.4 272.4 202.8

it co

Benefit

efitBen :cost ratio 4.76 13.82 1.81

Sou : Access Economics calculations. * Costs estimated over 2004‐05 to 2007‐08 except for Reconnect which to 2008‐09.

ing the evidence b

rceextends

Build ase – critical assets and challenges

ett, 2002; Schweinhart et al, 1993, Wise et al, 2005; Weissberg et al, 2003) there has historically been little investment in rigorous economic evaluation in the sector.

ic gaps in evidence of the long term benefits and cost effectiveness of programs.

prove FF provide useful tools to assist in policy choices but that ongoing emphasis needs to continue to supply evidence of individual program efficacy and effectiveness in Australia, and development of appropriate data sets and collection of longitudinal data to feed in to policy making and program development. Moreover, program

While evidence of the success of family programs is slowly growing (Nation et al, 2003; Manning et al, 2010; Katz, 2009; Aos et al, 2004; Durlak and Wells, 1997; Hawkins et al, 2009; Masse and Barn

Wise et al (2005) concluded there are specif

The analysis in this report provides further confirmation that current economic methods for comparing interventions to im

evaluation needs to have a greater focus on the inputs required for economic analysis.


73

Th p in economic evaluation has meant that many program evaluations do not measure or concepts required for economic analysis.

e gareport An example is the Reconnect program for

of the

■ n the oldest LSAC children and the youngest ATP children TP, FF

variables remain unchanged over this gap in the modelling.

was attempting to map the impact of parenting in

family income, and parental anxiety/depression are not available. Parents’ educational achievement was used as a proxy for the former in this

Findings from the econometric analysis

orporated in the analysis were found to be statistically significant explanators of child outcomes with the relationship consistent with that

which the evaluation found substantial program related benefits (FaHCSIA, 2003b) but which did not report well on some of the key effect sizes necessary for quantitative economic analysis.

The LSAC and ATP data sets are rich and offer many avenues for research. Their complexity can potentially diminish their accessibility. There are important implications for governments in continuing to fund longitudinal data sets so that quality and accessibility endure through the maintenance of specialist and corporate knowledge about these data sets. Those managing the ATP and LSAC were extremely important collaborators for this project. Advice and inputs from the experts on the Reference Group for this project were also critical to the success of the analysis. ‘

■ Family functioning’ can be difficult to measure and LSAC and ATP variables reflect a mixture of FF and other factors which, at times, can be difficult to interpret. In addition,

there is the potential for omitted variable bias where variables do not completely capture the concept being measured or where there are missing variables within the LSAC and ATP.

■ There were a number of obstacles to overcome with the LSAC dataset, to try to consistently capture desired information (such as depression/anxiety, antisocial behaviour or addiction tendencies in babies, toddlers or pre‐schoolers). This largely arises due to the vast developmental differences between newborns and nine year olds, and means that many of the LSAC regressions had to use three different versionsdependent variable.20

■ Also, there were a few unexpected but statistically significant results. Most of these could be explained on reflection, while a small remainder were less intuitive (see Section 3.2.7).

There is a four year gap betweefor whom FF data are available. To transition between the LSAC and the A

■ Another difficulty encounteredchildhood on addictive behaviour (use of alcohol, nicotine and other drugs). LSAC has childhood FF data, but no drug use data, while the ATP has drug use data, but no childhood FF data.

■ In the ATP, variables measuring

analysis and its significance for child outcomes was demonstrated via the statistical analysis.

Not surprisingly, many of the family ‘inputs’ inc

predicted by the literature.

20 (For example, anxiety and depression is proxied by apedsgc in 0 to 1 year olds, babitp in 2 to 3 year olds, and caemot in 4 to 5 year olds).

74

■ Obesity was explained by key drivers such as previous obesity, parental obesity, lack of child persistence, and parent‐child conflict.

■ Anxiety and depression were dependent on previous emotional problems, difficult temperament, lower SES, harsh discipline, parental anxiety/depression, alienation from parents and lack of child persistence.

■ Smoking in young adulthood (19‐20 years) was determined by previous smoking in

king and initiating drinking at an older age (over 15 compared to 14 or younger). Illicit drug use in 23‐24 year olds was dependent on the child’s temperament, lack of parental monitoring, and mother smoking.

■ Predisposition to smoking, alcohol abuse and illicit drug use was established in early years by parental smoking, temperament, harsh and/or inconsistent discipline, poor family cohesion and parental anxiety depression.

■ Productivity was driven by previous learning outcomes, consistent discipline, temperament, socioeconomic status, parent education and, in adolescence, persistence, relationship quality/warmth, parental monitoring and a positive attitude to school.

■ Antisocial behaviour and outcomes were determined by child lack of persistence, previous social/conduct problems and, importantly, were largely influenced by early life FF variables such as poor family cohesion, harsh discipline, parental smoking and low SES, along with parental anxiety/depression and the child’s temperament.

There were, however, some notable exceptions.

■ An ATP variable denoting parental ‘warmth’ was inversely related to high school outcomes for children, but can potentially be understood in the context of being task‐focused, which may come across as less ‘warm’ but (without harshness) may achieve better high school outcomes.

■ None of the ATP FF variables were significant explanators of antisocial behaviour.

Areas for further research

It would be desirable in the future to bridge the gap between the age of children in the most recent wave of the LSAC and the youngest age of children involved in the ATP for which FF variables are available. A repeat analysis might be conducted in five years time, re‐estimating equations and triangulating findings.

In addition, some of the transition variables available for matching LSAC with ATP participants are not perfectly matched in this analysis, and there might be scope to address this in future waves of LSAC, to provide a more seamless transition between the two datasets.

Moreover, probabilistic analysis around the realisation of future benefits would be an important further step, but is out of scope here.

However, the finding in relation to the PPP suggests the model constructed for the project provides a sound basis for development, since this finding is consistent with other CBA/CEA studies of this program.

adolescence, parental permission to smoke at home and a conflictual parent‐teenager relationship. Alcohol abuse (binge drinking) in young adulthood was dependent on teen bingeing, lack of parental monitoring, father drin


75

Due to the compsome variables,

lexities of the analysis and issues such as potential two‐way causation for we caution readers in making overly strong statements about conclusions

to refine and continue to develop the modelling and elaborate on

We coand globally to bring an evidence basis fully in focus in social policy decision making, even in

presented. Rather, the greatest value in this project has been primarily to showcase how a broad, quantitative approach to social policy evaluation can work. With better quality data in the future, there is scopefindings further.

mmend the ongoing research focus of FaHCSIA, which is proving so valuable in Australia

areas previously deemed unquantifiable or intangible.


77

Access udy, Report for the Australian

‐ fectiveness of early

‐

‐ ANE Australia.

‐ iabetes Australia.

Disorders

d early Washington State Institute for Public Policy.

rinking: a longitudinal analysis’, Subst Abuse Treat Prev Policy, 7:3:6.

Austra arch 2010, ABS Cat No 6345.0.

‐ 008‐09, ABS Cat No 4519.0.

‐

‐ 2009b, Employee earnings, benefits and trade union membership, Australia, August

‐ c, Recorded Crime – Offenders, selected states and territories, 2007‐08, ABS Cat No 4519.0.

‐ Prisoners in A , ABS Cat No 4517.0.

‐ 2009e, Life tables – Australia, 2006‐2008, ABS Cat No 3302.0.55.001.

‐ ber, and associated data cubes.

References

Economics 2009a, Positive family functioning ‐ scoping stGovernment Department of Families, Housing, Community Services and IndigenousAffairs (FaHCSIA).

2009b, The economic impact of youth mental illness and the cost efintervention, Report for Orygen Research Centre.

2008, The growing cost of obesity in 2008: three years on, Report for Diabetes Australia.

2007, Smoking and mental illness: costs, Report for S

‐ 2006a, Cost of cancer in New South Wales, Report for the Cancer Council of NSW.

2006b, The economic costs of obesity, Report for D

American Psychiatric Association 2009, Diagnostic and Statistical Manual of Mental(DSM) IV, http://psych.org/MainMenu/Research/DSMIV.aspx.

Aos S, Lieb R, Mayfield J, Miller M, Pennucci A 2004, Benefits and costs of prevention anintervention programs for youth, Olympia:

Arria AM, Kuhn V, Caldeira KM, O'Grady KE, Vincent KB, Wish ED 2008, ‘High school drinking mediates the relationship between parental monitoring and college d

lian Bureau of Statistics 2010a, Labour price index, Australia, M

2010b, Recorded Crime – Offenders 2

‐ 2010c, Criminal Courts Australia 2008‐09, ABS Cat No 4513.0.

2009a, Australian Labour Market Statistics, Australia, July, 2009, ABS Cat No 6105.0.

2009, ABS Cat No 6310.0.

2009

2009d, ustralia

2004, Survey of Disability, Ageing and Carers: Summary of Findings, Australia Cat No4430.0, Septem

Australian Government 2010, Best Practice Regulation Handbook, Canberra.

78

Australian Government Department of Family and Community Services, 2003, ’I'm looking at the future’, Evaluation Report of Reconnect, Final Report, October.

tegy No 16.

Bahr SJ, Hoffmann JP, Yang X 2005, ’Parental and Peer Influences on the Risk of Adolescent

Begg B, Stevenson C, Stanley L, Lopez AD 2007, The burden of disease and injury in Australia 2003, Canberra, AIHW PHE 82.

Berthe ldren’s education’ Family Matters, No 79, Australian Institute of Family Studies.

Betts, of emotion regulation, temperament, and parenting style as potential predictors of adolescent depression risk status: A

2, 3)

Bierut Dependence: Alcohol,

Marijuana, Cocaine and Habitual Smoking’, Archives of General Psychiatry, 55:982‐988.

le P‐Positive Parenting Program in improving parenting and child behaviour: a comparison with two

,

/10225/458 (Accessed: 19 February, 2010)

BrensMen and Women Receiving Detoxification Services’, The American Journal of Drug and

Brown ntal rearing behaviors, attachment style, and worry in anxious children. J Anxiety Disord;22(2):263‐

Bruce AE, Cole DA, Dallaire DH, Jacquez FM, Pineda AQ, LaGrange B 2006, Relations of

):321‐33. Epub 2006 May 17.

of

Australian Institute of Health and Welfare (AIHW) 2005, 2004 National drug strahousehold survey: detailed findings, AIHW Cat No PHE 66, Drug Statistics Series,

Drug Use’, The Journal of Primary Prevention, 26(6):529‐551.

S, Vos T, Barker

lsen D, and Walker S (2008) ‘Parents’ involvement in their chi

J; Gullone E, Sabura Allen J, An examination

correlational study British Journal of Developmental Psychology, Volume 27, NumberJune 2009 , pp. 473‐485(1

LJ, Dinwiddie SH, Begleiter H, Crowe RR, Hesselbrock V, Nurnberger JI Jr, Porjesz B, Schuckit MA, Reich T 1998, ‘Familial Transmission of Substance

Bodenmann G, Cina A, Ledermann T, Sanders R 2008, ‘The efficacy of the Trip

other treatment conditions.’ Behaviour and Research Therapy, 46: 411‐427.

Bögelsand SM and Brechman‐Toussaint ML 2006, Family issues in child anxiety: Attachment, family functioning, parental rearing and beliefs Clinical Psychology Review Volume 26Issue 7, November 2006, Pages 834‐856

Brechting EH 2004, ‘Family Environment and Substance Use in Adolescent Males’, University of Kentucky Electronic Theses, Available (Online) URL: http://hdl.handle.net

C, Johnson ME, Freemon M 2004, ‘Childhood Abuse History and Substance Use Among

Alcohol Abuse, 30(4):799 – 821.

AM, Whiteside SP 2008, Relations among perceived pare

72. Epub 2007 Feb 23.

parenting and negative life events to cognitive diatheses for depression in children. J Abnorm Child Psychol. Jun;34(3

Bureau of Transport Economics 2000, Road Crash Costs in Australia: Report 102, Bureau Transport Economics: Canberra.


79

Campbell K, and Crawford D 2001, ‘Family Food Environments as Determinants of Preschooaged Children’s Eating Behaviours: Implications for Obe

l‐sity Prevention Policy, A Review’,

Australian Journal of Nutrition and Dietetics, 58(1):19–26.

CampFood Environments of 5‐6‐year‐old‐children: Does socioeconomic status make a

Catalaence and Self‐reported Crime in Emerging Adulthood: Results

from the Raising Healthy Children, Presented at the American Society of Criminology,

Centre Australian Government payments, Department of Families, Housing, Community Services and Indigenous Affairs and Department of Education,

Clark adolescence: developmentally specific interactions’ J Dev Behav Pediatr, 29(4):285‐92.

Cohen

Collins ustralian society in 2004/05, Department of Health and Ageing: Canberra.

Currie nal Bureau of Economic Research, USA.

Danie ight, Socioeconomic Status and High Birth Weight are the Major Determinants of Overweight

De Graaf I, Speetjens P, Smit F, de Wolf M, Tavecchio L 2008, ‘Effectiveness of the Triple P

life,

http://www.finance.gov.au/obpr/docs/ValuingStatisticalLife.pdf.

Dick D ng environmental influences on substance use across development’, Twin Res Hum Genet.

Dornb PL, Leiderman PH, Roberts DF, Fraleigh MJ 1987, ‘The relation of parenting style to adolescent school performance’ Child Development, 58: 1244‐1257.

bell K, Crawford D, Jackson M, Cashel K, Worsley A, Gibbons K, Birch LL 2002, ‘Family

difference?’, Asia Pacific Journal of Clinical Nutrition, 11:S553–S561.

no R, Fleming C, Haggerty K, Abbott R 2007, The Relationships between the Family Environment during Adolesc

Atlanta Marriott Marquis, Atlanta, Georgia, Nov 14.

link 2009, A guide to

Employment and Workplace Relations, Canberra.

DB, Kirisci L, Mezzich A, Chung T 2008, ‘Parental supervision and alcohol use in

DA, and Rice J 1997, ‘Parenting styles, adolescent substance use, and academic achievement’ J Drug Educ. 27(2):199‐211.

Cohen DA, Richardson J, LaBree L 1994, ‘Parenting behaviours and the onset of smoking and alcohol use: a longitudinal study’, Pediatrics,. September (3):368‐75.

DJ, Lapsley HM 2008, The costs of tobacco, alcohol and illicit drug abuse to A

J, Tekin E 2006, Does child abuse cause crime? Working Paper No. 12171, Natio

lzik S, Czerwinski‐Mast M, Langnase K, Dilba B, Muller MJ 2004, ‘Parental Overwe

and Obesity in 5‐7 y‐old Children: Baseline Data of the Kiel Obesity Prevention Study (KOPS)’, International Journal of Obesity, 28:1494–1502.

Positive Parenting Program on Parenting: A Meta‐ Analysis’. Family Relations, 57: 553‐566

Department of Finance and Deregulation (DOFD) 2009, Best Practice Regulation GuidanceNote: Value of statistical

M, Pagan JL, Viken R, Purcell S, Kaprio J, Pulkkinen L, Rose RJ 2007,‘Changi

10(2):315‐26.

usch SM, Ritter

80

Drevets WC, Price JL, Simpson JR Jr, et al 1997, ‘Subgenual prefrontal cortex abnormalities in mood disorders’ Nature 386(6627):824‐827.

Dube SR, Felitti VJ, Dong M, Chapman DP, Giles WH 2003, ‘Childhood Abuse, Neglect, and

of Paediatrics, 111(3):564 – 572.

of Community Psychology, 25(2):115‐152.

Emme.

FaHCSIA 2009, Stronger families in Australia study: the impact of Communities for Children,

‐ g Community Capacity for Early Intervention, Report prepared by RPR Consulting, April.

‐ , Longitudinal survey of ReConnect clients, Statistical report of the Longitudinal Survey of Reconnect Clients, Prepared by RPR Consulting, September.

‐ Reconnect, Final report, October.

ng

hol.’ Psychiat. 360(6):929‐064.

d

Felitti VJ, Anda RF, Nordenberg D et al 1998, ‘Relationship of childhood abuse and household ood

Fergusson DM, Horwood LJ 2002, ‘Male and female offending trajectories’, Development and

Franci al care and the development of stress responses’ Curr Opin Neurobiol, 9(1):128‐134.

GableFamily Relations, 49(3):293–300.

Household Dysfunction and the Risk of Illicit Drug Use: The Adverse Childhood Experiences Study’, American Academy

Durlak JA, Wells AM 1997, ’Primary prevention mental health programs for children and adolescents: a meta‐analytical review‘, American Journal

lkamp PM, Heeres H 1988, ‘Drug addiction and parental rearing style: a controlled study’. The International Journal of Addictions, 23(2):207‐16

Evans GW, Kim P 2007, ‘Childhood poverty and health: cumulative risk exposure and stress dysregulation’ Psychol Sci. 18(11):953‐957.

Occasional Paper No 25.

2003a, Report of the Reconnect Longitudinal Study: Buildin

2003b

2003c, ‘I’m looking at the future’ Evaluation Report of

Farrington DP 1995, ‘The Twelfth Jack Tizard Memorial Lecture, The Development of Offendiand Antisocial Behaviour from Childhood: Key Findings from the Cambridge Study in Delinquent Development/Child Psyc

Forbes M, Barker A, Turner S 2010, The effects of education and health on wages anproductivity, Productivity Commission Staff Working Paper, Melbourne.

Foster E, Prinz R, Sanders M, Shapiro C 2007, ‘The costs of a public health infrastructure for delivering parenting and family support’ Children and Youth Services Review 30:493‐501.

dysfunction to many of the leading causes of death in adults: the adverse childhexperiences (ACE) study’ Am J Prev Med, 14(4):245‐258.

Psychopathology, 14:159‐177.

s DD, Meaney MJ (1999) ‘Matern

S, and Lutz S 2000, ‘Household, Parent and Child Contributions to Childhood Obesity’,


81

Garasky S, Stewart SD, Gundersen C, Lohman BJ, Eisenmann JC 2009, ‘Family Stressors andChild Obesity’, Social Science Research, (38):755–766.

Gibson LY, Byrne SM, Davis EA, Blair E, Jacob P, Zubrick SR 2007, ‘The Role of Family and

Hawk terle S, Brown EC, Arthur MW, Abbott RD, Fagan AA, Catalano RF 2009, ’Results of a type 2 translational research trial to prevent adolescent drug use and

cent

Heider D, Bernert S, Matschinger H, Haro JM, Alonso J, Angermeyer MC; ESEMeD/MHEDEA

umulation secondary analysis from the Canadian

study of health and aging’, The Journal of Nutrition, Health and Aging, 13(5):468‐472.

Isgor C on cognitive and stress

axis functions in rats’ Hippocampus, 14(5):636‐648.

Karoly CP, Sanders M, Chiesa J 2007, Investing in Our Children: What We Know and Don't Know About the Costs and

January at http://www.rand.org/pubs/monograph_reports/MR898/index.html

Katz Dw of Public Health, 30:253‐272.

nnet macaques’ Diabetes, 56(5):1382‐1386.

mple’, Journal of Consulting and Clinical Psychology, 68(1):19–30.

Kishiy arities rosci. 21(6):1106‐15.

La Plagne P, Glover M, Shomos A 2007, The effects of health and education on labour force

Latendresse SJ, Rose RJ, Viken RJ, Pulkkinen L, Kaprio J, Dick DM 2008, ‘Parenting mechanisms

Maternal Factors in Childhood Obesity’, Medical Journal of Australia, 186(11):591‐595.

ins JD, Oes

delinquency: A test of Communities that Care‘, Archives of Pediatrics and AdolesMedicine, 163 (9):789‐798.

2000 Investigators Parental bonding and suicidality in adulthood..Aust N Z J Psychiatry. 2007 Jan;41(1):66‐73.

Hubbard RE, Searle SD, Mitnitski A, Rockwood K 2009, ‘Effect of smoking on the accof deficits, frailty and survival in older adults: A

, Kabbaj M, Akil H, Watson SJ (2004) ‘Delayed effects of chronic variable stress duringperipubertaljuvenile period on hippocampal morphology and

LA, Greenwood PW, Everingham SS, Hoube J, Kilburn MR, Rydell

Benefits of Early Childhood Interventions, RAND Monograph Report last accessed 25

L 2009, ’School‐based interventions for health promotion and weight control: Not just waiting on the world to change‘, Annual Revie

Kaufman D, Banerji MA, Shorman I et al 2007, ‘Early life stress and the development of obesityand insulin resistance in juvenile bo

Kilpatrick DG, Acierno R, Saunders B, Resnick HS, Best CL 2000, ‘Risk factors for adolescent substance abuse and dependence: data From a national sa

ama MM, Boyce WT, Jimenez AM, Perry LM, Knight RT 2009, ‘Socioeconomic dispaffect prefrontal function in children’ J Cogn Neu

Kratt P, Reynolds K, Shewchuk R 2000, The Role of Availability as a Moderator of Family Fruitand Vegetable Consumption, Health Education Behaviour, 27:471‐484.

participation’, Productivity Commission Staff Working Paper, Melbourne.

in links between parents' and adolescents' alcohol use behaviors’ Alcohol Clin Exp Res, 32(2):322‐30.

82

Lattimore R 1997, Research and development fiscal incentives in Australia: impacts and polessons, Paper presented to the OECD Conference on Policy Evaluation inParis, 26 27 June, 81:574‐577.

licy Innovation,

Lissau I, and Sorensen TIA 1994, ‘Parental neglect during childhood and increased risk of

Lovibo , ‘The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety

Manni opmental prevention programs in at‐risk populations on non‐health outcomes in adolescence’,

Masse LN, Barnett WS 2002, A Benefit Cost Analysis of the Abecedarian Early Childhood

df.

:185‐

Marmot M (chair) 2010, Fair society, healthy lives, Report of the Strategic Review of Health

Mihalopoulos C, Sanders M, Turner K, Murphy‐Brennan M, Carter R 2007 ‘Does the Triple‐P

l

Moens E, Braet C, Bosmans G, Rosseel Y 2009, ‘Unfavourable family characteristics and their

Moens E, Braet C, Soetens B 2007, ‘Observations of family functioning at mealtime: a

Nation M, Crusto C, Wandersman A, Kumpfer KL, Seybolt D, Morrissey‐Kane E, Davino K 2003, ‘What works in prevention: Principles of effective prevention programs’, American Psychologist, 58(6/7): 449‐456.

Pelham WE Jr, Lang AR 1999, ‘Can your children drive you to drink? Stress and parenting in adults interacting with children with ADHD’. Alcohol Research and Health. 23(4):292‐8.

Letcher P, Smart D, Sanson A, Toumbourou JW, 2009, Trajectories of internalising behaviour, Social Development, 18, 3, 2009

obesity in young adulthood’ The Lancet, 343:324–27.

nd PF, Lovibond SH 1995

Inventories’ Behaviour Research and Therapy, 33:335‐343.

ng M, Homel R, Smith C 2010, ‘A meta‐analysis of the effects of early devel

Children and Youth Services Review, 32:506‐519.

Intervention, National Institute for Early Education Research: New Brunswick, http://nieer.org/resources/research/AbecedarianStudy.p

McLoyd VC 1998, ‘Socioeconomic disadvantage and child development’ Am Psychol, 53(2)204.

Inequalities in England post‐2010, UK Government.

Positive Parenting Program represent value for money?’ Australian and New Zealand Journal of Psychiatry, 41: 239‐246.

Miller P, Mulvey C, Martin N 2001, ‘Genetic and environmental contributions to educationaattainment in Australia’, Economics of Education Review, 20(3):211‐224.

associations with childhood obesity: a cross‐sectional study’, European Eating Disorders Review, 17:315–323.

comparison between families of children with and without overweight’, Journal of Pediatric Psychology, 32(1):52–63.


83

Pires P and Jenkins JM 2007, ‘A growth curve analysis of the joint influences of parenting of

on A, Smart D, Oberklaid F 2000, Pathways from infancy to adolescence: Australian Temperament Project 1983‐2000, Australian Institute of Family Studies

development of anxiety and ‐67.

‘Parenting style and adolescent nic, gender, and SES

Sanders M, Ralph A, Sofrnoff K, Gardiner P, Thompson R, Dwyer S, Bidwell K 2008, ‘Every

revent, 29(3):197‐222.

ts from the 2002 New Zealand national survey of Year 10 students’, Aust N Z J Public Health, 31:217‐223.

ith

Steeri of Government Service Provision (SCRGSP) 2010, Report on Government Services 2010, Productivity Commission, Canberra.

affect, child characteristics and deviant peers on adolescent illicit drug use’, JournalYouth Adolescence, 36:169‐183.

Plomin R, Fulker DW, Corley R, DeFries JC 1997, Nature, nurture, and cognitive development from 1 to 16 Years: a parent‐offspring adoption study, Association for Psychological Science.

Prior M, Sans

Research Report No 4.

Productivity Commission 2003, Evaluation of the Pharmaceutical Industry Investment Program, Research Report, AusInfo, Canberra.

Rapee RMPotential role of childrearing practices in the depression. Clin Psychol Rev. 1997;17(1):47

Radziszewska B, Richardson JL, Dent CW, Flay BR 1996, depressive symptoms, smoking, and academic achievement: Ethdifferences’ Journal of Behavioural Medicine, 19(3):289‐305.

Rollings K 2008, Counting the costs of crime in Australia: a 2005 update, Australian Institute of Criminology, Research and Public Policy Series No 91, Canberra.

Ruhm CJ 2000, ‘Parental leave and child health’. Journal of Health Economics, 19(6):931‐60.

family: a population approach to emotional problems in children making the transition to school’ J Primary P

Savage JS, Fisher JO, Birch LL 2007, ‘Parental Influence on Eating Behaviour’, Journal of Law, Medicine and Ethics, 35(1):22–34.

Schweinhart LJ, Barnes HV, Weikhart DP 1993, Significant Benefits, The High/Scope Perry Pre‐School Study through Age 27, Ypsilanti: HighScope Press.

Scragg R, Laugesen M 2007, ‘Influence of smoking by family and best friend on adolescent tobacco smoking, resul

Shonkoff P, Boyce T, McEwen B (2010) ‘Neuroscience, molecular biology and the childhood roots of health disparities: building a new framework for health promotion and disease prevention’ JAMA 301(21):2252‐2259.

Sm CA, Ireland TO, Thornberry TP 2005, ‘Adolescent maltreatment and its impact on young adult antisocial behaviour’ Child Abuse and Neglect, 29:1099–1119.

ng Committee for the Review

84

Steinberg L, Lamborn SD, Dornbusch SM, Darling N 1992, ‘Impact of parenting practices on adolescent achievement: authoritative parenting, school involvement, and encouragement to succeed’, Child Dev. 63(5):1266‐81.

Taylor P, Moore P, Pezzullo L, Tucci J, Goddard C, De Bortoli L 2008, The cost of child abuse in Australia, Access Economics, Australian Childhood Foundation and Child Abuse

02, Patterns and precursors of adolescent antisocial behaviour, The first report of a collaborative partnership between the Australian Institute of Family St

Wardle J, Guthrie C, Sanderson S, Birch L, Plomin R 2001, ‘Food and Activity Preferences in

Zubrick S, Ward K, Silburn S, Lawrence D, Williams A, Blair E, Robertson D, Sanders M 2005,

Prevention Research Australia: Melbourne.

van Gastel W, Legerstee JS, Ferdinand RF 2009 The role of perceived parenting in familial aggregation of anxiety disorders in children. J Anxiety Disord. Jan;23(1):46‐53. Epub 2008 Mar 26.

Vassallo S, Smart D, Sanson A 20

udies and Crime Prevention Victoria, Department of Justice, December.

Children of Lean and Obese Parents’, International Journal of Obesity, 25:971–977.

Weatherburn D 2001, ‘What causes crime?’ Contemporary issues in crime and justice No 54, NSW Bureau of Crime Statistics and Research, February.

Webb JA, Bray JH, Getz JG, Adams G 2002, ‘Gender, perceived parental monitoring, and behavioral adjustment: influences on adolescent alcohol use’. Am J Orthopsychiatry July 72(3):392‐400.

Weissberg RP, Kumpfer KL, Seligman MEP 2003, ‘Prevention that works for children and youth’, American Psychologist, 58(6/7): 425‐432.

Wise S 2003, An overview of the development in diverse families study, Australian Institute of Family Studies Research Paper No 30, January.

Wise S, da Silva L, Webster E, Sanson A 2005, The Efficacy of Early Childhood Interventions, Report prepared for the Australian Government Department of Family and Community Services, Australian Institute of Family Studies.

World Health Organization 2010, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, available at http://apps.who.int/classifications/apps/icd/icd10online/ accessed 22 September 2010.

‘Prevention of child behaviour problems through universal implementation of a group behavioural family intervention’, Prevention Science, 6(4):287‐304.


85

Appendix A: LSAC children and future addictive behaviours

the li tle

arents did so when they were little.

entify some factor that is:

dictive behaviour in adulthood;

our (reported in both the LSAC and the ATP). In the long‐term, these changes make adults more prone to engage in anti‐social behaviour and/or become addicted.

Because there is no one piece of literature that makes all these connections, a potted summary is laid out below.

Recent work by paediatricians and neuroscientists (Shonkoff et al, 2010) shows that childhood stress – such as that caused by poor FF – can have permanent effects that cause deleterious outcomes in adulthood. And, of relevance to this study, these childhood stresses also cause negative social and emotional outcomes, of the types that are captured by the LSAC and the ATP. In the brain, under such high ‘allostatic loads’ the amygdala grows more neurons, causing the brain to generate more fear and anxiety. Simultaneously, the prefrontal cortex loses neurons, reducing the brain’s ability to plan and prioritise – and also to keep the amygdala’s negative emotions in check.

Studies of animals show that such early, stress‐related changes in brain circuitry can persist into adult life and alter emotional states, decision‐making capacities and processes which in humans contribute to substance abuse, aggression, obesity and stress‐related mental disorders (Isgor et al, 2004 and Kaufman at al, 2007).

■ The extent to which such temperaments are genetic or environmental is debated. Animal studies have shown that higher levels of environmental stress can develop into a generational susceptibility to stress through epigenetic changes in DNA methylation (Francis and Meaney, 1999).

As indicated in the scoping study, there are numerous studies showing that the impact of family functioning variable x is that children who experience it will become adults with outcome y. For example, parents who have substance abuse problems (tobacco, alcohol, drugs) are likely to see their children grow up to have similar abuse problems (e.g. Latendresse et al, 2008).

However, there are some unusual aspects of the current study that are not directly covered by terature. For example, the LSAC has parents who drink and smoke, but only lit

children, who do not; the ATP has teenagers who may drink and smoke, but no record of whether their p

Our approach was thus to id

■ a consequence of poor family functioning;

■ contributes to ad

■ has symptoms that are recorded in young children (LSAC); and

■ those symptoms are also recorded in teens / young adults (ATP).

The factor identified was stress (allostatic load). It is caused by (among other factors) poor parenting. Stress causes changes in brain structure and functioning which, in the short term, can make children more prone to anti‐social behavi

86

No longitudinal studies have been conducted on the influence of high stress in humans. tween traumatic childhoods

smoking, depression and other mental illnesses, 1998). Further, adults with depression also exhibit smaller prefrontal cort

ork shows that there are c differences in brain circuitries between children low SES (Kishiyama, 2009). In particular, low SES children have smaller prefrontal

be the ng itself from chronic stress, but may at

78 months f h (Chart A.1). low SES children also have greater

rs.

es in cognitive development by socio‐economic status

However, retrospective studies have shown strong associations beand adverse adult outcomes including alcoholism, drug abuse, obesity,

(Felitti et al, exes (Drevets, 1997).

Recent w systematiof high and cortexes, which appear to

developmentbrain’s way of protecti

affect their intellectual22 months of age, by

— low SES children who have high cognitive (Q) scores all below the Q scores of high SES children who started wit

low 22‐month scores Also, adults who wereamygdale activity than othe

Chart A.1: Differenc

documented that children with low SES are i) exposed to high degrees of itive parental behaviour and ii) have more prevalent mental disorders and

cLoyd, 1998).

n that low SES er disease‐specific and all‐cause morbidity rtality rates that ca r

lower access to health serv erts are beginning to consider that the long‐term ress a explained gap.

so, an important implicat tions to reduce childhood stress may be trying to target risky health behaviour later in life. For example, in

Source: Marmot (2010).

It is also wellconflictive / punsubstance abuse (M

■ It well know groups have highand mo nnot be explained just by material deprivation, illiteracy o

ices. Expeffects of childhood st ccount for much of this hitherto un

■ If ion is that intervenmore effective than


87

the UK only 15% of men w ications smoke, but over 40% of unskilled workers do – despite quit campaigns and punitive taxation.

also have lity. This stands to reason as both s, while reducing reasoning and

Empirically, there link between childhood stressful events and s an adult. Cu at the US National Bureau of Economic

national s the effects of childhood maltreatment use) on adult crime. They found that:

ately doubled the probability of engaging in many types of crime n other effects such as unemployment, low wages, or access to guns);

dren are both more likely to be mistreated and suffer more damaging

the largest effects, and

■ the probability of engaging in the experience of multiple forms of maltreatment.

Finally, and of direct relevance for this study, high levels of childhood stress manifest in chological and emotional dysregulation in chronically stressed children (Evans and Kim,

to ca ional difficulties is the Strengths and s Questionnaire (SDQ) Total Score, which is a composite measure of social and

ment. A highe w

ith professional qualif

Childhood stress appears tochildhood stress increases

an impact on adult criminaanger and aggressivenes

control mechanisms.committing crime aResearch used a large(including neglect as well as ab

■ maltreatment approxim(far greater tha

is a strongrrie and Tekin (2006)ample to examine

■ low SES chileffects;

■ (arguablysexual abusenegative

most stressful form of abuse) appears to have the

crime increases with

psy2007). The variable chosenDifficultie

pture social and emot

emotional developthis LSAC measure is compatible

r SDQ score represents more difficulties. AIFS advised that ith ATP measures.

88

Appendix B: Literature review so

Table B.1: Antiso

Evidence

urces, ATP

cial behaviour

Variable

FF variables

Relationship with parents Vassallo et al (2002)

Attachment to parents Weatherburn (2001), Catalano et al (2007), Vassallo et al (2002)

Family cohesion Vassallo et al (2 02) 0

Relationship quality/warmth Harsh discipline Parental monitoring/supervision Inconsistent discipline

Farrington (199 ), Prior et al (2000), Vassallo et al (2002) 5

Control variables

Marital relationship History of parental separation and its effect

Vassallo et al (2002) Fergusson and Horwood (2002)

Parental smoking FeParental alcohol

rgusson and (2002), Vassallo et al (2002) Horwood

Temperament AIFS advised (from previous ATP analysis)

Social and emotional difficulties Shonkoff et al (2010) and others, see discussion above

Table B.2: Anxiety sion

Variable

and depres

Evidence

FF variables

Attachment to parents Prior et al (2000)

Relationship quality/wInductive reas

armth oning

Unpublished rk by Primose Letcher at University of Melbourne and AIFS Heider et al (2Whiteside (20

Harsh discipline

wo

007), Betts et al (2009), Rapee (1997), Brown and 08), Bruce et al (2006)

Enmeshment Unpublished ourne and AIFS Betts at al (2009), Rapee (1997), Bogels and Bergman‐Toussaint (2006)

work by Primose Letcher at University of Melb

Experience of child(retrospective)

abuse/neglect Evidence cited in Taylor et al (2008)

Control variables

Marital relationship Bogels and Bergman‐Toussaint (2006)

Stressful life event Bruce et al (2006)

Temperament Betts et al (2009), Bogels and Bergman‐Toussaint (2006), Prior et al (2000)

* Parental anxiety/depression Rapee (1997), van Gastel et al (2009)

Gender Letcher et al (2009)

* Not available in the ATP


89

Table B.3: Smoking

Variables Evidence

Parental management of teen substance use

Scragg and Laugesen (2007)

Inductive reasoning Harsh discipline Attachment to parents Parental monitoring/supervision

Dick et al (2007) Cohen et al (1994)

Negative conflictual relationship Inconsistent discipline

Control variables

Parental smoking Latendresse et al (2008)


Temperament Clark et al (2008)

Table B.4: Alcohol

les Evidence Variab

FF

Parental management ofdrinking

teen Latendresse et al (2008)

Inductive reasoning Attachment to parents Harsh discipline Parental monitoring/supervision Inconsistent discipline Negative conflictual relationship

Latendresse et al (2008), Dick et al (2007), Arria et al (2008), Webbet al (2002), Cohen et al (1994)

Control variables

Parental use of alcohol Latendresse et al (2008)


Temperament Clark et al (2008)

Table B.5: Illicit drug use

Variables Evidence

Family functioning

Experience of child abuse/neglect (retrospective) Dube et al (2003), Brens et al (2004), Kilpatrick et al, (2000), Smith et al, (2005), Pires and Jenkins, (2007)

Attachment to parents Parent adolescent conflict

Brechting (2004)

Parental monitoring/supervision Bahr et al (2005)

Marital relationship (conflict — proxy for witnessing violence)

Dube et al (2003), Kilpatrick et al, (2000)

Control

90

Variables Evidence

Mother/Father smoking and drinking habits Dube et al (2003), Brechting (2004), Kilpatrick et al (2000), Bierut et al (1998)


Temperament AIFS advised

Table B.6: Productivity

Productivity variables

Family functioning variables

Harsh discipline Parental monitoring/supervision Inconsistent discipline Attachment to parents Negative conflictual relationship

Parental involvement in education

Dornbusch et al (1987), Steinberg et al (1992), Cohen and Rice (1997) Radziszewska et al (1996), Berthelsen and Walker (2008)

Control variables

Academic and social progress at school

School bonding


Academic competence and reading Plomin et al (1997) and Miller et al (2001)

Temperament AIFS advised

Table B.7: Overweight/obesity

Variables Evidence

FF variables

Attachment to parents Negative conflictual re

eenager ea

nd Crawford (2001) Campbell et al (200 , Gabel lationshipInconsistent discipline Harsh discipline Parental monitoring/supervision Inductive reasoning T ting behaviours

Moens et al (2009), Savage et al (2007), Campbell a ; 2)and Lutz (2000), Kratt et al (2000), Lissau and Sorensen (1994); Moens et al (2009)

Control variables

Family status (single parent household etc) Gabel and Lutz, 2000; Gibson et al. 2007

Socioeconomic status Garasky et al, (2009), Campbell et al, (2002), Danielzik et al, (2004), Gabel and Lutz, (2000)


91

Appendix C: LSAC regression outcomes

Va P>|t| [95% Conf Intervals]

Harsh discipline a 0.01 0.02 0.13

Relationship quality / warmth aawarm 0.17‐ 0.08 2.02‐ 0.04 0.33‐ 0.01‐

Results in bold are significant. Results in bold red are significant, but have unexpected signs.Several variables, including parental depression are reverse coded in the LSAC database. Giventhe experimental nature of this exercise, significance was chosen to equal 0.10 in order to castthe net relatively broadly for possible substantiative FF effects. In the tables in this appendix,p values of ‘‐‘ represent values less than 0.001 (which is how Stata reports the output).

As K1 has no antecedents, lagged dependent variables cannot be derived, thus B3 (for whichprior observations are available) was chosen instead to represent this 4‐5 year age group.

Temperament does not have a clearly expected sign and is not coloured red. Wheretemperament is significant, for normally coded dependent variables, a negative sign indicateseasy temperament is operative, and a positive sign indicates difficult temperament.

■ It could be argued that an easy temperament should lead to better outcomes. Butconversely, if difficult temperament results from perfectionism, for example, that childmight do better academically than a child with an easy temperament but less attentionto detail. Similarly, a fussy eater (difficult temperament) may be less prone to obesitythan an easy‐going omnivore.

Table C.1: Obesity B1 (ahs23c2)

riable Coef. Std. Err t

ahost 0.08 0.03 2.53

Parental efficacy apa01a 0.03 0.04 0.89 0.37 0.04‐ 0.10

Temperament ase06i 0.00‐ 0.06 0.02‐ 0.98 0.13‐ 0.12

Parental anxiety & depression aak6 0.05 0.06 0.84 0.40 0.07‐ 0.16

Gender zf02m1 0.72‐ 0.06 11.64‐ ‐ 0.84‐ 0.60‐

Socioeconomic status afn05 0.08 0.00 1.88 0.06 0.00‐ 0.17

Parental education level acomboed 0.00 0.03 0.06 0.95 0.06‐ 0.06

Parent gender zf02m2 0.03‐ 0.32 0.10‐ 0.92 0.66‐ 0.60

Parent BMI aabmi 0.02 0.01 3.10 0.00 0.01 0.03

Needs extra medical care ahs14d 0.05 0.09 0.58 0.56 0.13‐ 0.24

Constant _cons 9.96 0.83 11.98 ‐ 8.33 11.59

R2=0.08, F = 14.44, No. observations = 2,155.

92

Table C.2: Obesity B2 (bcbmi)

Variable Coef. Std. Err t P>|t| [95% Conf Intervals]

Prev dep var ahs23c2 0.32 0.03 12.36 ‐ 0.27 0.37

Harsh discipline bahost 0.00 0.03 0.05 0.96 0.06‐ 0.06

Parental monitoring / supervision bpa08a1 0.00 0.07 ‐ 1.00 0.14‐ 0.14

Relationship quality / warmth bawarm 0.12 0.09 1.25 0.21 0.07‐ 0.30

Inductive reasoning baireas 0.03‐ 0.06 0.48‐ 0.63 0.15‐ 0.09

Parental efficacy bpa01a 0.03 0.04 0.58 0.56 0.06‐ 0.11

Family cohesion bre06a 0.02‐ 0.05 0.50‐ 0.62 0.11‐ 0.07

fatty foods bhfat 0.07‐ 0.04 1.69‐ 0.09 0.15‐ 0.01

Sugary Drinks bhsdrnk 0.03‐ 0.03 0.97‐ 0.33 0.09‐ 0.03

Exercise bhb14c4 0.08 0.05 1.64 0.10 0.02‐ 0.17

0.00‐ 0.00 0.64‐ 0.52 0.01‐ 0.01 E‐tainment betain

Temperament bse06a 0.08‐ 0.06 1.34‐ 0.18 0.21‐ 0.04

Parental anxiety & depression 0.92 0.14‐ 0.15

Gender zf02m1 0.05‐ 0.07 0.66‐ 0.51 0.19‐ 0.09

Socioeconomic status bhinc 0.05‐ 0.00 1.57‐ 0.12 0.11‐ 0.01

bak6 0.01 0.07 0.11

Parental education level bcomboe 0.08‐ 0.03 2.33‐ 0.02 0.14‐ 0.01‐

Parent gender zf02m2 0.59 0.24 2.45 0.01 0.12 1.07

Parent BMI babmi 0.06 0.01 7.20 ‐ 0.04 0.07

Needs extra medical care bhs14d 0.01‐ 0.09 0.14‐ 0.89 0.18‐ 0.16

Constant _cons 11.25 0.93 12.13 ‐ 9.43 13.07 R2=0.15, F = 13.49, No. observations = 2,156.

Table C.3: Obesity B3 (ccbmi)

Variable Coef. Std. Err t P>|t| [95% Conf Intervals]

Prev dep var bcbmi 0.68 0.02 28.13 ‐ 0.63 0.73

Harsh discipline cahostb 0.02 0.02 1.00 0.32 0.02‐ 0.07

Parental monitoring / supervision cpa08a1 0.00 0.02 0.20 0.84 0.04‐ 0.05

Relationship quality / warmth cawarm 0.02‐ 0.07 0.25‐ 0.80 0.15‐ 0.11

Inductive reasoning caireas 0.05 0.05 1.02 0.31 0.04‐ 0.14

Consistent discipline cacons 0.02‐ 0.05 0.46‐ 0.64 0.12‐

Parental self efficacy cpa01a

0.07

0.08 0.03 2.36 0.02 0.01 0.15

Family cohesion cre06a 0.02 0.04 0.61 0.54 0.05‐ 0.09

fatty foods chfat 0.02‐ 0.03 0.60‐ 0.55 0.07‐ 0.03

Sugary Drinks chsdrnkb 0.01 0.02 0.47 0.64 0.03‐ 0.05

Exercise chb14c4 0.05‐ 0.04 1.30‐ 0.19 0.13‐ 0.03

E‐tainment cetain 0.00‐ 0.00 1.86‐ 0.06 0.01‐ 0.00

Temperament cse06a 0.01 0.05 0.18 0.86 0.09‐ 0.10

Parental anxiety & depression cak6 0.06 0.06 1.14 0.26 0.05‐ 0.17

Gender zf02m1 0.01‐ 0.05 0.19‐ 0.85 0.12‐ 0.10

Socioeconomic status chinc 0.01 0.00 0.56 0.58 0.02‐ 0.04

Parental education level ccomboed 0.01‐ 0.03 0.44‐ 0.66 0.07‐ 0.05

Parent gender zf02m2 0.22 0.20 1.08 0.28 0.18‐ 0.62

Parent BMI cabmi 0.05 0.01 8.06 ‐ 0.03 0.06

Needs extra medical care chs14d 0.13‐ 0.10 1.38‐ 0.17 0.32‐ 0.06

Constant _cons 3.36 0.84 4.03 ‐ 1.73 5.00 R2=0.45, F = 50.58 No. observations = 2,955.


93

Table C.4: Obesity K2 (dcbmi)

Var Coef. Std. Err t P>|t| [95% Conf Intervals]

Prev dep var ccbmi 1.01 0.03 31.56 ‐ 0.95 1.07

Harsh discipline dahostc 0.03‐ 0.02 1.25‐ 0.21 0.08‐ 0.02

Parental monitoring / supervision dpa08a1 0.09 0.06 1.52 0.13 0.02‐ 0.19

Relationship quality / warmth dawarm 0.01‐ 0.07 0.10‐ 0.92 0.14‐ 0.13

Inductive reasoning daireas 0.10 0.05 1.80 0.07 0.01‐ 0.20

Consistent discipline dacons 0.10‐ 0.05 2.19‐ 0.03 0.19‐ 0.01‐

Parental self efficacy dpa01a 0.07 0.04 1.90 0.06 0.00‐ 0.15

Family cohesion dre06a 0.01 0.04 0.35 0.73 0.07‐ 0.10

fatty foods dhfat 0.03 0.03 1.06 0.29 0.03‐ 0.09

Sugary Drinks dhsdrnk 0.09 0.03 3.31 0.00 0.03 0.14

Exercise dhb14c4 0.05 0.04 1.47 0.14 0.02‐ 0.13

E‐tainment detain 0.00‐ 0.00 1.23‐ 0.22 0.01‐ 0.00

Temperament dse06a 0.05 0.05 0.99 0.32 0.05‐ 0.15

Parental anxiety & depression dak6 0.06‐ 0.05 1.23‐ 0.22 0.17‐ 0.04

Gender zf02m1 0.09 0.05 1.69 0.09 0.01‐ 0.19

Socioeconomic status dhinc 0.00‐ 0.00 0.11‐ 0.92 0.05‐ 0.04

Parental education level dcomboed 0.04 0.03 1.34 0.18 0.02‐ 0.10


Parent BMI 0.03 0.01 5.15 ‐ 0.02 0.05

Needs extra medical care 0.40 0.11‐ 0.27

Constant cons 1.85‐ 0.74 2.50‐ 0.01 3.30‐ 0.40‐

dabmi

dhs14d 0.08 0.10 0.84

R2=0.67, F = 98.73 No. observations = 2,701.

Table C.5: Obesity K3 (ecbmi)

Var Coef. Std. Err t P>|t| [95% Conf Intervals]

Prev dep var dcbmi 1.14 0.02 72.98 ‐ 1.11 1.17

Harsh discipline eahostc 0.01‐ 0.02 0.55‐ 0.58 0.06‐ 0.03

Parental monitoring / supervision epa08a1 0.03 0.03 0.91 0.37 0.03‐ 0.08

Relationship quality / warmth eawarm 0.02 0.06 0.37 0.71 0.09‐ 0.13

Inductive reasoning eaireas 0.03‐ 0.04 0.66‐ 0.51 0.11‐ 0.06

Consistent discipline eacons 0.06‐ 0.05 1.22‐ 0.22 0.15‐ 0.03

Parental self efficacy epa01a 0.02‐ 0.03 0.46‐ 0.64 0.08‐ 0.05

Family cohesion ere06a 0.01‐ 0.03 0.23‐ 0.82 0.08‐ 0.06

fatty foods ehfat 0.03 0.03 1.18 0.24 0.02‐ 0.08

Sugary Drinks ehsdrnkb 0.02 0.02 1.00 0.32 0.02‐ 0.07

Exercise ehb14c4 0.09‐ 0.04 2.37‐ 0.02 0.16‐ 0.02‐

E‐tainment eetain 0.00‐ 0.00 0.99‐ 0.32 0.01‐ 0.00

Temperament ese06a 0.05 0.05 0.92 0.36 0.05‐ 0.14

0.08 0.05 1.40 0.16 0.03‐ 0.18

Gender zf02m1 0.10 0.05 1.79 0.07 0.01‐ 0.20

Parental anxiety & depression eak6

Socioeconomic status ehinc 0.02‐ 0.00 0.97‐ 0.33 0.06‐ 0.02

Parental education level ecomboed 0.04 0.03 1.55 0.12 0.01‐ 0.09

Parent gender zf02m2 0.28 0.14 1.97 0.05 0.00 0.56

Parent BMI eabmi 0.03 0.01 5.83 ‐ 0.02 0.05

Needs extra medical care ehs14d 0.11 0.07 1.64 0.10 0.02‐ 0.25

Constant _cons 2.86‐ 0.68 4.20‐ ‐ 4.19‐ 1.52‐ R2=0.77, F = 326.88 No. observations = 3,018.

94

Table C.6: Productivity B1 (awlrnoi)

Category Var Coef. Std. Err t P>|t| [95% Conf Intervals]

Harsh discipline aahost 0.22 0.16 1.34 0.18 0.10‐ 0.54

Parental monitoring / supervision aawarm 5.66 0.51 11.17 ‐ 4.67 6.66

Relationship quality / warmth apa01a 0.52 0.21 2.41 0.02 0.10 0.94

Parental anxiety & depression aak6 0.35‐ 0.34 1.04‐ 0.30 1.01‐ 0.31

Gender zf02m1 0.66 0.35 1.86 0.06 0.03‐ 1.36

Age ascagem 0.00 0.08 ‐ 1.00 0.16‐ 0.16

Socioeconomic status afn05 0.64‐ 0.00 2.58‐ 0.01 1.13‐ 0.16‐




Table C.7: Productivity B2 (bwlrnoi)


Learning outcome index awlrnoi 0.22 0.02 10.67 ‐ 0.18 0.26

Harsh discipline bahost 0.01‐ 0.16 0.04‐ 0.97 ‐0.31 0.30

Parental monitoring / supervision bpa08a1 0.39‐ 0.45 0.88‐ 0.38 ‐1.28 0.49

Relationship quality / warmth bawarm 0.98‐ 0.54 1.81‐ 0.07 ‐2.04 0.08

nductive reasoning baireas 3.17 0.38 8.36 ‐ 2.43 3.92

Parental self efficacy bpa01a 0.37‐ 0.26 1.38‐ 0.17 ‐0.88 0.15

Family cohesion ‐1.08 0.02

Parental involvement in education bahact 2.57 0.39 6.63 ‐ 1.81 3.33

Temperament bse06a 2.15‐ 0.38 5.70‐ ‐ ‐2.89 1.41‐

I

bre06a 0.53‐ 0.28 1.90‐ 0.06

Parental anxiety & depression bak6 1.30 0.42 3.07 0.00 0.47 2.13

Gender zf02m1 3.04 0.38 7.99 ‐ 2.30 3.79

Age bscagem 0.00‐ 0.07 0.06‐ 0.95 ‐0.15 0.14

Socioeconomic status bhinc 0.92 0.00 4.99 ‐ 0.00 0.56

Parental education level bcomboe 0.42‐ 0.22 1.92‐ 0.06 ‐0.86 0.01

Parent gender zf02m2 2.03 1.39 1.46 0.15 ‐0.70 4.75

Constant _cons 58.06 5.42 10.72 ‐ 47.44 68.68 R2=0.21, F = 42.85 No. observations = 2,678. Note, the productivity dependent variable (learning outcome index) is normally coded, while parental education level is reverse coded (lower score = higher education). Thus, the expected sign of parental education is negative.


95

Table C.8: Productivity B3 (cwlrnoi)


Learning outcome index bwlrnoi 0.30 0.02 15.94 ‐ 0.26 0.34

Harsh discipline cahostb 0.02 0.15 0.11 0.91 ‐0.28 0.31

Parental monitoring / supervision cpa08a1 0.01‐ 0.15 0.06‐ 0.96 ‐0.31 0.29

Relationship quality / warmth cawarm 0.92‐ 0.47 1.97‐ 0.05 ‐1.83 0.01‐

Inductive reasoning caireas 0.21 0.33 0.65 0.52 ‐0.43 0.85

Consistent discipline cacons 1.03 0.32 3.24 0.00 0.41 1.65

Parental self efficacy cpa01a 0.34 0.23 1.46 0.14 ‐0.11 0.79

Family cohesion cre06a 0.06‐ 0.23 0.27‐ 0.79 ‐0.52 0.40

Parental involvement in education cahactd 0.73 0.33 2.19 0.03 0.08 1.38

Temperament cse06a 1.40‐ 0.33 4.24‐ ‐ ‐2.05 0.75‐

Parental anxiety & depression cak6 0.02 0.35 0.05 0.96 ‐0.67 0.70

Gender zf02m1 3.33 0.35 9.51 ‐ 2.65 4.02

Age cscagem 0.22 0.06 3.38 0.00 0.09 0.34

Socioeconomic status chinc 0.98 0.00 8.71 ‐ 0.00 0.75

Parental education level ccomboe 1.11‐ 0.20 5.53‐ ‐ ‐1.50 0.72‐

Parent gender zf02m2 0.61 1.44 0.42 0.67 ‐2.22 3.44


Table C.9: Productivity K2 (dwlrnoi)


Learning outcome index cwlrnoi 0.54 0.02 29.15 ‐ 0.50 0.57

Harsh discipline dahostc 0.28‐ 0.12 2.31‐ 0.02 ‐0.53 0.04‐

Parental monitoring / supervision dpa08a1 0.98‐ 0.41 2.40‐ 0.02 ‐1.79 0.18‐

Relationship quality / warmth dawarm 0.73‐ 0.40 1.81‐ 0.07 ‐1.51 0.06

Inductive reasoning daireas 0.43 0.29 1.46 0.15 ‐0.15 1.00

Consistent discipline dacons 0.77 0.28 2.75 0.01 0.22 1.31

Parental self efficacy dpa01a 0.54‐ 0.22 2.47‐ 0.01 ‐0.97 0.11‐

Family cohesion dre06a 0.07‐ 0.21 0.35‐ 0.72 ‐0.48 0.34

Parental involvement in education dahact 0.15‐ 0.32 0.47‐ 0.64 ‐0.78 0.48

Parental involvement in school dassc 0.23 0.15 1.47 0.14 ‐0.08 0.53

Temperament dse06a 1.18‐ 0.29 4.10‐ ‐ ‐1.75 0.62‐

Parental anxiety & depression dak6 0.12 0.33 0.36 0.72 ‐0.52 0.75

Gender zf02m1 1.52‐ 0.33 4.64‐ ‐ ‐2.16 0.88‐

Age dscagem 0.01 0.06 0.24 0.81 ‐0.10 0.12

Socioeconomic status dhinc 1.09 0.00 7.58 ‐ 0.00 0.81

Parental education level dcomboe 0.32‐ 0.16 2.02‐ 0.04 ‐0.63 0.01‐

Parent gender zf02m2 0.29‐ 1.18 0.25‐ 0.80 ‐2.62 2.03


96

Table C.10: Productivity K3 (ewlrnoi)


Learning outcome index dwlrnoi 0.66 0.02 42.26 ‐ 0.63 0.69

Harsh discipline eahostc 0.09‐ 0.11 0.88‐ 0.38 ‐0.30 0.11

Parental monitoring / supervision epa08a1 0.11 0.13 0.81 0.42 ‐0.15 0.37

Relationship quality / warmth eawarm 0.38‐ 0.29 1.33‐ 0.18 ‐0.95 0.18

Inductive reasoning eaireas 0.17‐ 0.23 0.75‐ 0.46 ‐0.62 0.28

Consistent discipline eacons 0.60 0.25 2.38 0.02 0.11 1.10

Parental self efficacy epa01a 0.02‐ 0.17 0.12‐ 0.90 ‐0.36 0.32

Family cohesion ere06a 0.19 0.17 1.16 0.25 ‐0.13 0.52

Parental involvement in education eahacte 0.67‐ 0.23 2.95‐ 0.00 ‐1.11 0.22‐

Parental involvement in school eassc 0.55 0.11 5.14 ‐ 0.34 0.76

Temperament ese06a 1.27‐ 0.25 5.00‐ ‐ ‐1.77 0.78‐

Parental anxiety & depression eak6 0.17‐ 0.25 0.67‐ 0.50 ‐0.66 0.32

Gender zf02m1 0.25 0.27 0.95 0.34 ‐0.27 0.78

Age escagem 0.01 0.05 0.11 0.91 ‐0.09 0.10

Socioeconomic status ehinc 0.17 0.00 1.69 0.09 ‐0.03 0.38

Parental education level ecomboe 0.55‐ 0.13 4.33‐ ‐ ‐0.80 0.30‐



Anxiety and depression B1 (apedsgc) Table C.11:


Harsh discipline aahost 0.00‐ 0.00 0.63‐ 0.53 0.01‐ 0.01

Relationship quality / warmth aawarm 0.00 0.01 0.39 0.69 0.02‐ 0.03

Parental Efficacy apa01a 0.01 0.00 1.90 0.06 0.00‐ 0.02

Smoking ahb15a1 0.01‐ 0.01 0.83‐ 0.41 0.02‐ 0.01

Alcohol consumption aaalcgpm 0.01 0.01 1.12 0.26 0.01‐ 0.04

Parental anxiety & depression aak6 0.02 0.01 1.97 0.05 0.00 0.04

Gender zf02m1 0.01 0.01 1.03 0.30 0.01‐ 0.03

Socioeconomic status afn05 0.00‐ 0.00 0.56‐ 0.57 0.02‐ 0.01


Parent gender zf02m2 0.00 0.03 ‐ 1.00 0.07‐ 0.07

Constant _cons 1.77 0.10 17.62 ‐ 1.58 1.97 R2=0.01, F = 2.07 No. observations = 2,584. Note: parental anxiety and depression (*ak6) is reverse coded, such that a higher score indicates less anxiety and depression. For childhood anxiety and depression in B1, apedsgc is also reverse coded, thus the expected sign of *ak6 is positive. However, all other childhood anxiety and depression dependent variables (B2 through K3) are coded normally, thus the expected sign of *ak6 is negative.


97

Table dsef)


09

C.12: Anxiety and depression B2 (bpe

Lag dep var apedsgc 3.50 1.18 2.97 0.00 1.19 5.81

Harsh discipline bahost 2.11‐ 0.24 8.95‐ ‐ ‐2.57 ‐1.65

Parental monitoring / supervision bpa08a1 0.25‐ 0.60 0.42‐ 0.68 ‐1.44 0.93

Relationship quality / warmth bawarm 1.83 0.72 2.54 0.01 0.42 3.24

Inductive reasoning baireas 0.47‐ 0.49 0.95‐ 0.34 ‐1.44 0.50

Parental efficacy bpa01a 0.55 0.35 1.55 0.12 ‐0.15 1.24

Family cohesion bre06a 1.44‐ 0.37 3.89‐ ‐ ‐2.16 ‐0.71

Smoking bhb15a1 0.01 0.37 0.02 0.99 ‐0.72 0.74

Alcohol consumption baalcgpm 0.46‐ 0.65 0.71‐ 0.48 ‐1.73 0.82

Overprotection baoverp 1.22‐ 0.39 3.13‐ 0.00 ‐1.98 ‐0.45

Temperament bse06a 3.73‐ 0.51 7.30‐ ‐ ‐4.73 ‐2.73

3

90

Parental anxiety & depression bak6 5.12 0.62 8.30 ‐ 3.91 6.3

Gender zf02m1 0.09‐ 0.50 0.18‐ 0.86 ‐1.08 0.

Socioeconomic status bhinc 0.59‐ 0.00 2.31‐ 0.02 ‐1.09 ‐0.

Parental education level bcomboe 0.52 0.28 1.87 0.06 ‐0.02 1.07


Constant ‐ 49.73 76.95_cons 63.34 6.94 9.13 R2=0.19, F = 31.85 No. observations = 3,000.

Table C.13: Anxiety and depression B3 (cpedsef)


Lag dep var bpedsef 0.36 0.02 17.78 ‐ 0.32 0.40

Harsh discipline cahostb 1.25‐ 0.25 4.93‐ ‐ ‐1.74 ‐0.75

Parental monitoring / supervision cpa08a1 0.03 0.21 0.13 0.90 ‐0.39 0.45

Relationship quality / warmth cawarm 1.90 0.65 2.91 0.00 0.62 3.18

Inductive reasoning caireas 1.44‐ 0.48 3.02‐ 0.00 ‐2.38 ‐0.51

Consistent discipline cacons 0.39 0.52 0.75 0.45 ‐0.63 1.41

Efficacy cpa01a 0.08 0.34 0.24 0.81 ‐0.58 0.74

Cohesion cre06a 0.63‐ 0.33 1.94‐ 0.05 ‐1.28 0.01

Smoking chb15a1 0.36‐ 0.37 0.99‐ 0.32 ‐1.08 0.36

Alcohol consumption caalcgpmo 0.08‐ 0.76 0.10‐ 0.92 ‐1.56 1.40

Overprotection caoverp 0.29‐ 0.39 0.73‐ 0.47 ‐1.06 0.49

Temperament cse06a 2.94‐ 0.52 5.70‐ ‐ ‐3.95 ‐1.93

Parental anxiety & depression cak6 4.55 0.73 6.23 ‐ 3.12 5.98

Gender zf02m1 0.46‐ 0.51 0.89‐ 0.37 ‐1.46 0.55

Socioeconomic status chinc 0.39‐ 0.00 2.13‐ 0.03 ‐0.75 ‐0.03

Parental education level ccomboed 0.19 0.30 0.61 0.54 ‐0.41 0.78



98

Table mot) C.14: Anxiety and depression K2 (dae


Lag dep var caemot 0.39 0.02 19.03 ‐ 0.35 0.44

Harsh discipline dahostc 0.08 0.02 3.39 0.00 0.03 0.13

Parental monitoring / supervision dpa08a1 0.04 0.07 0.48 0.63 ‐0.11 0.18

Relationship quality / warmth dawarm 0.05‐ 0.07 0.73‐ 0.46 ‐0.20 0.09

Inductive reasoning daireas 0.06 0.05 1.29 0.20 ‐0.03 0.15

Consistent discipline dacons 0.07‐ 0.05 1.35‐ 0.18 ‐0.18 0.03

Efficacy dpa01a 0.04 0.04 0.92 0.36 ‐0.04 0.11

Cohesion dre06a 0.04 0.04 1.06 0.29 ‐0.04 0.12

Smoking dhb15a1 0.01‐ 0.04 0.23‐ 0.82 ‐0.09 0.07

Alcohol consumption daalcgpm 0.26‐ 0.09 3.00‐ 0.00 ‐0.42 ‐0.09

Overprotection daoverp 0.05 0.04 1.30 0.19 ‐0.03 0.14

Temperament dse06a 0.32 0.06 5.53 ‐ 0.21 0.43

Parental anxiety & depression dak6 0.48‐ 0.07 7.21‐ ‐ ‐0.61 ‐0.35

Gender zf02m1 0.14 0.06 2.57 0.01 0.03 0.25

Socioeconomic status dhinc 0.01‐ 0.00 0.38‐ 0.71 ‐0.07 0.05

Parental education level dcomboed 0.00‐ 0.03 0.01‐ 0.99 ‐0.05 0.05


2.28 0.79 2.88 0.00 0.73 3.84Constant _cons R2=0.29, F = 42.86 No. observations

Table C.15: Anxiety and depression K3 (eaemot)

= 2,939.


Lag dep var daemot 0.50 0.02 23.24 ‐ 0.46 0.55

Harsh discipline eahostc 0.06 0.02 2.32 0.02 0.01 0.11

Parental monitoring / supervision epa08a1 0.00 0.03 0.07 0.94 ‐0.05 0.06

Relationship quality / warmth eawarm 0.01 0.06 0.18 0.86 ‐0.11 0.14

Inductive reasoning eaireas 0.04 0.05 0.96 0.34 ‐0.05 0.13

Consistent discipline eacons 0.04‐ 0.05 0.82‐ 0.41 ‐0.15 0.06

Efficacy epa01a 0.02 0.04 0.67 0.51 ‐0.05 0.09

Cohesion ere06a 0.03 0.04 0.96 0.34 ‐0.04 0.10

Smoking ehb15a1 0.02 0.04 0.46 0.65 ‐0.06 0.09

Alcohol consumption eaalcgpm 0.16‐ 0.08 1.93‐ 0.05 ‐0.31 0.00

Overprotection eaoverp 0.09 0.04 2.19 0.03 0.01 0.17

Temperament ese06a 0.36 0.06 5.85 ‐ 0.24 0.47

Parental anxiety & depression eak6 0.40‐ 0.06 7.25‐ ‐ ‐0.51 ‐0.29

Gender zf02m1 0.05 0.06 0.96 0.34 ‐0.06 0.16

Socioeconomic status ehinc 0.04‐ 0.00 1.74‐ 0.08 ‐0.08 0.00

Parental education level ecomboed 0.00‐ 0.03 0.07‐ 0.95 ‐0.05 0.05

0.06‐ 0.18 0.33‐ 0.74 ‐0.41 0.29Parent gender zf02m2

Constant _cons 1.61 0.59 2.73 0.01 0.45 2.77 R2=0.36, F = 55.04 No. observations = 3,191.


99

Table C.16: Antisocial B1 (apedsgc)




Parental Efficacy apa01a 0.01 0.00 1.90 0.06 0.00‐ 0.02

Smoking ahb15a1 0.01‐ 0.01 0.83‐ 0.41 0.02‐ 0.01



Gender zf02m1 0.01 0.01 1.03 0.30 0.01‐ 0.03




Constant _cons 1.77 0.10 17.62 ‐ 1.58 1.97 R2=0.01, F = 1.77 No. observations = 3,476. Note: Parental anxiety and depression (*ak6) is reverse coded, such that a higher score indicates less anxiety and depression. For childhood antisocial behavior (B1), apedsgc is also reverse coded, thus the expected sign of *ak6 is positive. However, all other childhood antisocial behaviour dependent variables (B2 though K3) are coded normally, thus the expected sign of *ak6 is negative.

Table C.17: Antisocial B2 (babitp)



Harsh discipline ‐ 0.41 0.68

Parental monitoring / supervision bpa08a1 0.24 0.16 1.49 0.14 0.07‐ 0.55

Relationship quality / warmth bawarm 0.20‐ 0.22 0.93‐ 0.35 0.63‐ 0.22

bahost 0.55 0.07 7.86

Inductive reasoning baireas 0.21‐ 0.14 1.49‐ 0.14 0.49‐ 0.07

Parental efficacy bpa01a 0.05 0.10 0.49 0.63 0.15‐ 0.25

Family cohesion bre06a 0.61 0.10 5.97 ‐ 0.41 0.81

Smoking bhb15a1 0.21 0.11 1.90 0.06 0.01‐ 0.42

Alcohol consumption baalcgpm 1.02‐ 0.20 5.20‐ ‐ 1.40‐ 0.63‐

Temperament bse06a 1.80 0.16 11.57 ‐ 1.50 2.11

Parental anxiety & depression bak6 2.32‐ 0.19 12.13‐ ‐ 2.70‐ 1.95‐

Gender zf02m1 0.54‐ 0.15 3.68‐ ‐ 0.82‐ 0.25‐

Socioeconomic status bhinc 0.14‐ 0.00 1.90‐ 0.06 0.28‐ 0.00

Parental education level bcomboe 0.15 0.09 1.66 0.10 0.03‐ 0.32



100

Table C.18: Antisocial B3 (caconda)


Lag dep var babitp 0.06 0.01 6.85 ‐ 0.04 0.08

Harsh discipline cahostb 0.36 0.03 11.99 ‐ 0.30 0.42

Parental monitoring / supervision cpa08a1 0.01‐ 0.03 0.47‐ 0.64 0.07‐ 0.04

Relationship quality / warmth cawarm 0.35‐ 0.07 4.73‐ ‐ 0.50‐ 0.21‐

Inductive reasoning caireas 0.31 0.05 5.74 ‐ 0.20 0.41

Consistent discipline cacons 0.36‐ 0.06 6.36‐ ‐ 0.47‐ 0.25‐

Parental efficacy cpa01a 0.11‐ 0.04 2.82‐ 0.01 0.19‐ 0.03‐

Family cohesion cre06a 0.09 0.04 2.02 0.04 0.00 0.17

Smoking chb15a1 0.18 0.05 3.90 ‐ 0.09 0.27

Alcohol consumption caalcgpm 0.18‐ 0.08 2.27‐ 0.02 0.34‐ 0.02‐

Temperament cse06a 0.83 0.06 13.48 ‐ 0.71 0.95

Parental anxiety & depression cak6 0.13‐ 0.07 1.88‐ 0.06 0.26‐ 0.01

Gender zf02m1 0.02‐ 0.06 0.31‐ 0.75 0.13‐ 0.09

Socioeconomic status chinc 0.04‐ 0.00 1.86‐ 0.06 0.08‐ 0.00

Parental education level ccomboed 0.03 0.04 0.98 0.33 0.04‐ 0.10


Constant _cons 0.92 0.86 1.07 0.29 0.77‐ 2.61 R2=0.37, F = 76.00 No. observations = 3,120.

Table C.19: Antisocial K2 (daconda)


Lag dep var caconda 0.23 0.01 15.41 ‐ 0.20 0.26

Harsh discipline dahostc 0.21 0.02 11.01 ‐ 0.18 0.25

Parental monitoring / supervision dpa08a1 0.05 0.06 0.83 0.41 0.06‐ 0.16

Relationship quality / warmth dawarm 0.14‐ 0.06 2.33‐ 0.02 0.26‐ 0.02‐

Inductive reasoning daireas 0.07 0.04 1.95 0.05 0.00‐ 0.15

Consistent discipline dacons 0.25‐ 0.05 5.28‐ ‐ 0.34‐ 0.16‐

Parental efficacy dpa01a 0.01 0.03 0.26 0.79 0.05‐ 0.07

Family cohesion dre06a 0.14 0.03 4.59 ‐ 0.08 0.20

Smoking dhb15a1 0.12 0.03 3.61 ‐ 0.06 0.19

Alcohol consumption daalcgpm 0.07 0.06 1.13 0.26 0.05‐ 0.19

Temperament dse06a 0.42 0.04 9.32 ‐ 0.33 0.51

Parental anxiety & depression dak6 0.07‐ 0.05 1.37‐ 0.17 0.18‐ 0.03

Gender zf02m1 0.18‐ 0.05 4.06‐ ‐ 0.27‐ 0.09‐

Socioeconomic status dhinc 0.05‐ 0.00 2.56‐ 0.01 0.09‐ 0.01‐

Parental education level dcomboed 0.04 0.02 1.48 0.14 0.01‐ 0.08


Constant _cons 0.84 0.66 1.29 0.20 0.44‐ 2.13 R2=0.41, F = 90.99 No. observations = 2,042.


101

Table C.20: Antisocial K3 (eaconda)


Lag dep var daconda 0.42 0.02 20.00 ‐ 0.38 0.46

Harsh discipline eahostc 0.19 0.02 8.99 ‐ 0.15 0.23

Parental monitoring / supervision epa08a1 0.02‐ 0.02 0.93‐ 0.36 0.06‐ 0.02

Relationship quality / warmth eawarm 0.04‐ 0.05 0.97‐ 0.33 0.13‐ 0.04

Inductive reasoning eaireas 0.06 0.03 1.87 0.06 0.00‐ 0.13

Consistent discipline eacons 0.18‐ 0.04 4.25‐ ‐ 0.26‐ 0.09‐

Parental efficacy epa01a 0.05‐ 0.03 1.84‐ 0.07 0.11‐ 0.00

Family cohesion ere06a 0.06 0.03 2.22 0.03 0.01 0.12

Smoking ehb15a1 0.08 0.03 2.50 0.01 0.02 0.14

Alcohol consumption eaalcgpm 0.03 0.06 0.50 0.62 0.09‐ 0.15

Temperament ese06a 0.45 0.04 10.65 ‐ 0.37 0.53

Parental anxiety & depression eak6 0.16‐ 0.05 3.49‐ ‐ 0.25‐ 0.07‐

Gender zf02m1 0.15‐ 0.04 3.55‐ ‐ 0.23‐ 0.07‐

Socioeconomic status ehinc 0.03‐ 0.00 2.21‐ 0.03 0.06‐ 0.00‐

Parental education level ecomboed 0.00 0.03 0.01 0.99 0.05‐ 0.05


Constant _cons 1.25 0.43 2.91 0.00 0.41 2.09 R2=0.50, F = 96.27 No. observations = 3,194.

Table C.21: Addictions B1 (apedsgc)




Parental efficacy apa01a 0.01 0.00 1.90 0.06 0.00‐ 0.02

Smoking ahb15a1 0.01‐ 0.01 0.83‐ 0.41 0.02‐ 0.01



Gender zf02m1 0.01 0.01 1.03 0.30 0.01‐ 0.03




Constant _cons 1.77 0.10 17.62 ‐ 1.58 1.97 R2=0.01, F = 1.77 No. observations = 3,476. Note: Parental anxiety and depression (*ak6) is reverse coded, such that a higher score indicates less anxiety and depression. For childhood addictive behavior (B1) , apedsgc is also reverse coded, thus the expected sign of *ak6 is positive. However, all other childhood addictive behaviour dependent variables (B2 though K3) are coded normally, thus the expected sign of *ak6 is negative.

102

Table C.22: Addictions B2 (babitp)

P>|t| [95% Conf Intervals]


Harsh discipline ‐ 0.42 0.69

Parental monitoring / supervision bpa08a1 0.12 0.16 0.75 0.45 0.19‐ 0.42

rmth rm 0.40‐ 0.22 0.83 0.03

eas as 0.22‐ 0.14 0.49 0.06

l efficacy bpa01a 0.03 0.10 5 0.73 0.16‐ 0.23

he 9 0.10

Smoking 8 0.11 1.68 0.40

Alcohol cons baalcgp

Category Var Coef. Std. Err t

bahost 0.56 0.07 7.97

Relationship

Inductive r

Parenta

quality / wa

oning

bawa

baire

1.83‐

1.52‐

0.3

0.07 ‐

0.13 ‐

Family co sion

umption

bre06a 0.5

bhb15a1 0.1

5.77 ‐ 0.39 0.79

0.09 0.03‐

m 0.91‐ 0.20 4.65‐

Overprotection baoverp 0.72 0.12 6.24 5

Temperament bse06a 1.81 0.15 11.66 1

Parental anxiety & depression bak6 2.26‐ 0.19 11.86‐ 2.63 1.89‐

r 0.86 0.29‐

conom 0 0.24 0.04

Parental educ

‐ 1.30‐ 0.53‐

‐ 0.50 0.9

‐ 1.50 2.1

‐ ‐

Gende

Socioe

zf02m1 0.57‐ 0.14

bhinc 0.10‐ 0.0

bcomboe

3.98‐

1.40‐

‐ ‐

0.16 ‐ ic status

ation level d 0.11 0.09

zf02m2 1.07‐ 0

_cons 37.61 2.

1.21 0.23 0.07‐ 0.28

Parent gender .72 1.48‐ 0.14 2.49‐ 0.35

Constant 17 17.35 ‐ 33.36 41.86 2 , F = 5 ervati

3: Addictions B3 (casdqta)

Category t P>|t| [95% Conf Intervals]

Lag dep var 2 12.54 ‐ 0.24 0.33

Harsh discipline 0.07 8.67 ‐ 0.50 0.79

Parental monitoring / supervisio 0.07 0.48 0.63 0.11‐ 0.17

Relationship quality / warmth warm 0.70‐ 0.21 3.28‐ 0.00 1.12‐ 0.28‐

ve re 0.14 0.18 0.72

Consistent d 0.16 4.11‐ ‐ 0.96‐ 0.34‐

Parental eff a01a 0.20‐ 0.10 1.89‐ 0.06 0.40‐ 0.01

ohe 0.03 0.40

ng 0.09 0.52

Alcohol cons

R =0.31 5.66 No. obs ons = 3,003.

Table C.2

Var Coef. Std. Err

babitp 0.29 0.0

cahostb 0.64

n cpa08a1 0.03

ca

Inducti asoning

iscipline

icacy

caireas 0.45

cacons 0.65‐

cp

3.29 0.00

Family c

Smoki

sion

umption

cre06a 0.18 0.11

chb15a1 0.30 0.11

caalcgp

1.65

2.73

0.10 ‐

0.01

m 0.47‐ 0.2

caoverp 0.24 0.1

cse06a 1.95

cak6

1 2.25‐ 0.03 0.87‐ 0.06‐

Overprotec 2 2.05 0.04 0.01 0.47

Temperame 0.16 12.40 ‐ 1.64 2.26

Parental anxiety & depression 1.00‐ 0.20 4.89‐ ‐ 1.40‐ 0.60‐

0.15 0.73 0.16‐

Socioeconom chinc 0.16‐ 0.00 3.13‐ 0.00 0.26‐ 0.06‐

l edu vel 0.10 0.08 0.45

Parent gende zf02m2 0.02‐ 0.82 0.02‐ 0.98 1.62‐ 1.59

Constant _cons 2.95 2.33 1.27 0.21 1.62‐ 7.53

tion

nt

Gender zf02m1 0.44‐ 3.04‐ 0.00 ‐

ic status

Parenta cation le

r

ccomboe 0.27 2.75 0.01

, F = 7 servatiR2=0.41 3.14 No. ob ons = 3,116.


103

Table C.24: Addictions K2 (dasdqta)

Cate

Lag de

gory r P> Conf Intervals]

p var 02 0.37 0.45

Harsh discip 0.06 7.08 ‐ 0.32 0.56

mo rvisio .21 0.25 0.55

Relationship h dawarm 0.47‐ 0.19 2.52‐ 0.01 0.83‐ 0.10‐

ive re 12 42 0.05 0.52

Consistent d dacons 0.44‐ 0.15 .96 0.00 0.73‐ 0.15‐

al effi 0.10 89 0.29

ohe 0.10 78 0.46

Smoking 15a1 0.27 0.10 .55 0.01 0.06 0.47

cons on 0.20 7 0.12

Overprotection daoverp 0.16 0.11 4 0.15 0.06‐ 0.38

erame 4 .87

Parental an n 0.17

Gender 0.14 4.75‐ 0.40

Socioeconomic status 1.60‐ 0.11 0.24‐ 0.02


Var Coef. Std. Er

casdqta 0.41 0.

dahostc 0.44

t

20.39

|t| [95%

‐

line

Parental nitoring / supe

quality / warmt

n dpa08a1 0.15 0 0.73 0.47 ‐

Induct asoning

iscipline

daireas 0.29 0. 2.

2‐

0.02

Parent

Family c

cacy

sion

dpa01a 0.09

dre06a 0.27

dhb

0.

2.

2

0.37 0.11‐

0.01 0.08

Alcohol umpti daalcgpm 0.28‐ 1.3‐

1.4

0.17 0.68‐

Temp nt

xiety & depressio

dse06a 1.57 0.1

dak6 1.02‐

zf02m1 0.68‐

dhinc 0.11‐ 0.00

dcomboe

10

5.95‐

‐ 1.29 1.86

‐ 1.36‐ 0.68‐

‐ 0.96‐ ‐

d 0.05

zf02m2 0.79‐

0.07 0.70 0.48 0.09‐ 0.19

Parent gender 0.59 1.34‐ 0.18 1.95‐ 0.37 R2=0.49, F = 105.48 No. observat

K3 (easdqta)

ions = 2,938.

Table C.25: Addictions

Category t P> s]

Lag dep var dasdqta 0.59 0.02 32.91

Harsh discipline eahostc 0.38 0.06 6.60

Parental monitoring / supervision epa08a1 0.02‐ 0.07 0.36‐ 0.11

Relationship quality / warmth eawarm 0.18‐ 0.14 1.34‐

Inductive reasoning eaireas 0.17 0.11 1.58

Consistent discipline eacons 0.40‐ 0.13 3.04‐

Parental efficacy epa01a 0.08‐ 0.09 0.90‐

Family cohesion ere06a 0.14 0.08 1.71 0.02‐ 0.31

0.10 8

Alcohol cons 0.25 0.18 1.38‐

Overprotec eaoverp 0.22 0.10 2.25 0.41

erame 13 .23 1.34 1.85

Parental anxiety & depression eak6 1.02‐ 0.13 ‐ 1.28‐ 0.76‐

13 4.72 0.86 0.36‐

nom 00 0.17 0.01

Parental edu el

Var Coef. Std. Err |t| [95% Conf Interval

‐ 0.56 0.63

‐ 0.26 0.49

0.72 0.16‐

0.18 0.45‐ 0.09

0.11 0.04‐ 0.37

0.00 0.66‐ 0.14‐

0.37 0.25‐ 0.09

0.09

Smoking ehb15a1 0.19

eaalcgpm ‐

1.90 0.06 0.01‐ 0.3

0.17 0.60‐ 0.10

0.02 0.03

umption

tion

Temp nt ese06a 1.60 0. 12

7.69‐

‐

Gender

Socioeco

zf02m1 0.61‐ 0.

ehinc 0.08‐ 0.

ecomboe

‐

1.74‐

‐ ‐

0.08 ‐ ic status

cation lev d 0.01

zf02

0.06 0.90 0.12‐ 0.13

Parent gender m2 0.12 0.44 0.28 0.78 0.73‐ 0.98

nt 43 3.98 8.48

0.12

Consta _cons 5.68 1. ‐ 2.88 R2=0.49, F = 1 serva05.48 No. ob tions = 2,938.

104

Appendix D: LSAC variable specification

Table D.1: Standard LSAC variables

Variable Name

Person Label

Measure Question Values

aalcgp* n

t 1 (not in the last year); 2 Moderate (occasional to <4 day men, or to <2 day women); 3 Harmful (>=4 men, >=2

Alcohol consumptiogroups

primary carer alcohol consumption groups

Paren 1 Abstain

women)

abitp

05a2a, b, c, d, e, f, g, u,

Number

abmi Body mass index

Parent 1 Number

aconda SDQ Conduct problems scale

e be a integer

er items are

Parent 1 Number

acons Consistent

5. se

Parent 1

aemot Mean of cse03a3a, b, c, d and e r fewer

nent items are ing

Parent 1 Number

hacte Home activities index

Mean of che02a1d, 6d, 7d Parent 1 Number

ahostc Hostile parenting sc

umber

Inrescale

Mean of cpa09a1 and cpa09a2 P Number

ak6** K‐6 Depression Sc

ah a6 Parent 1 Number

aoverp Parental overprotection scale

P N

asdqta SDQ total score

Sum of cahy eer and cacond

Parent 1 N

BITSEA problems scale

Sum of bseh, i, j, k, l, m, n, o, p, q, r, s, t, v, w where fewer than 5 are missing (no contact counts as missing)

Weight in kg/(height in metres)**2

Mean of cse03a4a, b, c, d andrescaled to

Parent 1

between 0 and 10 where fewthan 3 componentmissing, cse03a4b reverse coded

Mean of cpa11a1, 2, 3, 4,parentingscale

SDQ

With cpa11a3, 4 &5 revercoded

Number

Emotional symptoms scale

rescaled to be a integebetween 0 and 10 wherethan 3 compomiss

a

ale

ductive

Mean of apa04a1, 2 and 4 Parent 1 N

aireas asoning

arent 1

ale

Mean of s24a1 to ahs24

Mean of bpa15a1 to 3

pr, caemot, capa

arent 1 umber

umber


105

VariabName

le Measure n Person Label

VQuestio alues

assc Involvement inclass activities

to

Parent 1 N

Number ofdhe15a1a

'yes' responses to dhe15a5a

umber

awarm Parental warmth scale

Mean of ap Parent 1 N

cbmi Body mass index

Weight in kg Study Child

N

f02m1 Sex Is the Studyfemale?

Study Child

1 Male; 2 Female

2m2 Se 1 m Parent 1

1 emale

fn05 Combined yearly income before tax

cohat is you

income (forcombined)? pensions

lowan , annua

e) ( om

Not Applicable

Categories were converted to dollars taking the mean of each category.

hb14c4 Choice of physical activity in free time

What does ly do when she/h about how to spend free time?

Study Child

1pcr t as likely to c as inactive pastimes; 3 Usually chooses active pastimes like bike riding, dancing, games or sports; (‐2 Don't know)

smoke at all; 2 3 At

least once a day

hfat Food diary Sum of chb21c1a to chb21c4a Not Applicable

Number

hinc*** Usual weekly income

Sum of bfn09a, b and o (if not missing any, other than skipped)

Not Applicable

Number

hs14d Special health care needs screener

Does child need or use more medical care than is usual for most children of the same age?

Not Applicable

1 Yes; 2 No; (‐2 Don't know)

hs23c2 Weight Weight of child Study Child

Number

hsdrnk Food diary sum of bhb21b1a & bhb21b2a Not Applicable

Number

pa01a Global rating of self‐efficacy

Overall, as a parent, do you feel that you are…

Parent 1 1 Not very good at being a parent; 2 A person who has some trouble being a parent; 3 An average parent; 4 A better than

a03a1 to apa03a6

/(Height in m)**2

Child male or

umber

umber

f0 x Is Parent

Before inw

ale or female?

me tax is taken out, r present yearly you and partner (Include

@W1 Male; 2 F

and alsuperinsurancPartner c

ces) (before taxtion or health Parent 1 andbined) (Before tax)

child usuale has a choice

Usually chooses inactive astimes like TV, omputer, drawing or eading; 2 Jushoose active

hb15a1 Frequency of cigarette smoking

How often do you currently smoke cigarettes?

Parent 1 1 Do notLess than once a day;

106

Variable Measure Question Person Values Name Label

average parent; 5 A very good parent

pa08a1 Importance of monitoring / supervising child

It is im pknow whe ir childwhat he/s g alltime. Do

P S reDisag eithernor disagree; 4 AgreStron e

DS QL emotional

Mean of c 1a to cgd04b1e so th1=100, 2= 50, 4=25=0, only value if than 3 ite missin

NotAppl

Number

c PEDS Global n

agd01a w codwith yes.

Not App

1 Yes/ ; 2 No

Family ability along

other

Sometime ily membmay have lty gettialong with another. don't alw ree and tmay get ang . In genera would you rate your famability to with another?

Pare 1 Exce 2 Very g Good; ; 5 Poor

d's age (months)

Study child in monthtime of survey

Not Appl

Numb

temperament Overall, com ared to othechildren of t e same age, do you think is...?

Pare 1 Easi averagAbout ge; 3 Modifficu averag

wlrnoiOutcome

Learning Outcome Indexcontinuou e

Not Appl

Number

portant that arents re the is and he is doin the you:

arent 1 1 trongly disag e; 2 ree; 3 N agree

e; 5 gly agre

pedsef PE

functioning

gd04b recoded at 75, 3= 5, and has a fewer ms are g

icable

pedsgConcer

ith 'a little' ed licable

A little

re06ato getwith each

s fam ers difficu ng oneays ag

Theyhey

ry l, howily's

get along one

nt 1 llent; ood; 3 4 Fair

scagem Study Chil age s at icable

er

se06a General p r histh child

nt 1 er than e; 2 averalt than

re e

Learning

Index

s scor icable

Notes: Variable name is in the LSAC ‘without age’ format. s from the original six.

converted to a sum, original is mean. annum.

Table D.2: Combinatio LSAC variables

put

variables Measure uestion Pe

LabValu

* truncated to three categorie***** converted to dollars per

ns of

New Invariable

Q rson el

es

etain1 egweek Amount of weekend electronic games

otal minutlaying an estem on verage we

NotApplicable

NumT es p game sy an a ek

ber.

he06b1 Amount of weekday TV

ut how rs on a t

would u that chiches TV

ideos at ho

NotApplicable

1 Do watch Tvide thanhour; to 3 hours; 4 3 up urs; 5 5mor s; (‐2 Doknow

houAbo many

ypicalweekday,say

yold

wat or v me?

es not V or os; 2 Less one 3 1 up to 5 ho

or

e hour n't )


107

New variable

Input variables

Measure Question Person Lab

Values el

he06c1 Amount of weekend TV

ut how rs on a t ekend da s d watchos at hom ? (If

for urday and day, givrage hou

NotApplicable

1 Do watch Tvide thanhour to 3 h 3 up urs; 5 5mor s; (‐2 Don'tknow)

houAbo many

ypicalwe y, doechil TV or vide edifferentSatSun e ave rs)

es not V or os; 2 Less one ; 3 1 up ours; 4 to 5 ho or e hour

he07b2 Amount of weekday computer use

ut how rs on a t ekday wo u that study a comp at e?

NotApplicable

Num

dn2 fd08a1 Highest

level of schooling

at was theest yeaary or

ondary sent 1

Paren 1 1 Ye equivalent; 2 Year quivalYear equivaleYear alenYear w; 6 attended school; 7

(‐

post‐secondary

completed a trade certificate or any

qualification?

Abo many hou ypicalwe uld yosay child

uter useshom

ber

comboe

completed

Wh high r of primsec chool Parcompleted?

t ar 12 or 11 or e ent; 3 10 or nt; 4 9 or equiv t; 5 8 or belo

NeverStill at

school; (‐2 Don't know);3 Refused)

fd08a2a Completed Has Parent 1 Parent 1 1 Yes; 2 No

qualification other educational

N am ’1 etain is the sum of the four electronic entertainment variables measured in hours per week.

de e 8a1 with tion of 0 parent has co ry tion from fd08a2.

otes: Variable N e is in the LSAC ‘without age format.

2 comboedn is coeduca

d as the sam as fd0 the addi if mpleted post‐seconda

108

Appendix E n d stan deviation of les

Category 0‐1 years 2‐3 years 4‐5 yea 7 years 8‐9 years

: Mea an dard LSAC variab

rs 6‐

Obesity 9.22 16.84 16.84 16.49 17.59

1.44 1.59 1.59 2.13 2.84

00

9.72 10.18 9.73 9.81 9.90

Anxiety and depression 1.94 74.06* 74.66 1.58* 1.57

0.24 14.06 14.18 1.68 1.75

Antisocial 1.94 30.21* 2.14* 2.43 1.45

0.24 4.46 1.78 1.98 1.47

Addictions 1.94 30.21* 8.18* 7.81 7.49

0.24 4.46 4.68 5.02 5.32

Harsh discipline 1.93 3.09 3.17 3.36 3.29

1.14 1.30 1.28 1.45 1.41

Parental monitoring 4.82 4.51 4.85 4.64

0.44 1.07 0.40 0.94

Relationship warmth 4.55 4.61 4.50 4.44 4.32

0.41 0.42 0.47 0.48 0.55

Inductive reasoning 4.24 4.23 4.24 4.14

0.65 0.63 0.64 0.68

consistent discipline 4.19 4.16 4.19

0.61 0.61 0.61

Parental self‐efficacy 4.08 4.09 3.85 4.07 3.84

0.96 0.80 0.84 0.82 0.86

Family cohesion 1.99 2.05 2.17 2.16

0.81 0.84 0.85 0.87

Parental home education 1.96 1.69 1.36 1.46

0.55 0.56 0.53 0.63

Parental involve school 3.79 3.32

1.15 1.38

Fatty foods 1.14 1.69 1.22 1.88

0.84 1.12 0.85 1.18

Sugary Drinks 1.28 1.33 1.41 1.37

1.11 1.30 1.15 1.32

Exercise 2.16 2.02 1.99 1.96

0.72 0.76 0.75 0.77

E‐tainment 41.80 54.75 55.17 57.48

10.35 27.95 10.90 11.58

Smoking 1.35 1.30 1.34 1.32 1.38

0.74 0.70 0.74 0.73 0.77

Alcohol consumption 1.82 1.80 1.81 1.83 1.82

0.38 0.41 0.40 0.39 0.40

Overprotection 3.65 3.58 3.51 3.54

Productivity 99.63 100.49 100.94 101.06 101.


109

Category 0‐1 years 2‐3 years 4‐5 years 6‐7 years 8‐9 years

0.69 0.69 0.73 0.72

Temperament 1.73 1.72 1.73 1.68 1.67

0.51 0.56 0.56 0.59 0.59

depression 4.43 4.52 4.47 4.47 4.42

0.56 0.53 0.56 0.59 0.61

nths 5

8

income ($'0 0.6

.0

Parental education leve 1.57 1.52 1.47 1.65 1.58

1.13

Parent gender

Parent body mass index 25.44 25.03 25.70 25.42 25.88

Needs extra medical ca 0 1.89 1.88

&Parental anxiety

Gender 1.49 1.49

Age in mo

0.50 0.50

8.78 33.89 57.58 81.87 105.5

Household

2.56 2.91 2.83 2.94 2.8

00 pa) 52.2 80.8 99.0 83.4 10

35.1 51.1 70.8 53.9 68

l

1.08 1.05 1.00 1.18

1.99 1.97

0.12 0.16

5.39 5.06 5.50 5.12 5.29

re 1.92 1.92 1.9

0.37 0.35 0.34 0.35 0.38

Notes. Numbers in ordinar in italics are standard deviations of sample. Text i

text in the ‘Category’ colum 4‐5 year cohort is B3 not K1 (see Appendix C anriable.

ries Varia

y text are means of the analysis sample. Numbersthe analysis n bold indicates dependent variables. Ordinary text is common input variables. Italic

n is variables peculiar to specific regressions. Thepreamble tochange of va

d footnote 5). Appendix D provides greater description of these variables. *indicates

Illustrative distribution of categorical variables

Catego ble Answer=1 Answer=2 Answer=3 Values

infant anxiety and

*

apeds

depression

gc 267 3,985 1 Yes/A little; 2 No

smoking ahb1 1 Do not smoke at all; 2 Less

once a day

consumption aalcg r);

derate ( <4 drinks /day

Hazardous (>=4 men, >=2

Choice of ivity

hb14c 1939 1506 1 Usually chooses inactive

to choose active as tive pastimes; 3 Usually

bike riding, dancing, games or

gender child zf02m

5a 3034 116 600 than once a day; 3 At least

alcohol pm 649 3,010 6 1 Abstain (not in the last yea2 Momen, <2 women); 3

women)

4 808 physical actin free time

pastimes like TV, computer, drawing or reading; 2 Just aslikelyinacchooses active pastimes like

sports; (‐2 Don't know)

1 2178 2,075 1 Male; 2 Female

110

Categories Varia er=1 Answer=2 Answer=3 Values ble Answ

gender adult zf02m2 58 4,195 1 Male; 2 Female temperament ase06 2,181 98 1 Easier than average; 2 About

average; 3 More difficult than average

xtra medical care

ahs14d 264 3,987 1 Yes; 2 No; (‐2 Don't know)

a 984

e

Note: Only the first age at which a given variable is used is shown in the table. * Also used for infant antisocial t addic

behaviour and infan tions.


111

Appendix F: AT

Productivity Variable name

P variable interpretation

Description

attach96 parents, high score = high attachment, adolescent report, 13‐

attach00 report, 17‐

alientn96 gh alienation, adolescent report,

alientn00 igh alienation, adolescent report,

totrelqual th, quality rship,

chrqual00 ager report at 17‐18

totharshd

superv96 , parent report 13‐14 years

Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 15‐16 years

Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, teenager report 17‐18 years

cipline, high score = inconsistent, parent report 17‐18 years

n, parent report at 17‐18 years, high score = high cohesion

/conflictual parent‐teenager relationship, parent report 17‐18 years

7‐18 years, high score = low education

7‐18 years, high score = low education

2 years

s

15‐

acht98 e = lower achievement, teenager

totpersist very persistent, composite mean

enter02 e, available for 766 cases only

e, ‐20 years

, university), young adult report

Attachment to14 years

Attachment to parents, high score = high attachment, adolescent 18 years

Alienation from parents, high = score = hi13‐14 years, 2 ITEMS ONLY

Alienation from parents, high = score = h17‐18 years

Relationship quality/warmth, high score = high warmcomposite mean score of parent report at 13‐14, 15‐16 & 17‐18 years

Relationship quality, high score = high quality rship, teenyears

Harsh discipline, high score = high harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years

Parental monitoring/supervision of teen’s activities/associates, high score = high supervision

superv98

parsup00

inconst00 Inconsistent dis

hesiocohes00

negpt00

Family co

Negative

totmothed Mother’s education, composite mean score, parent report at 13‐14, 15‐16 &1

totfathed Father’s education, composite mean score, parent report at 13‐14, 15‐16 &1

tacp94 Academic competence, high score = high competence, teacher report at 11‐1

totmschprb Academic and social school problems, composite mean score,s parent reportat 13‐14 & 15‐16 years, high score more problem

posaff98 Positive affect towards school, high score = lower affect, teenager report16 years

Perceived achievement at school, high scorreport 15‐16 years

Persistent temperament style, high score =score of parent report at 11‐12, 13‐14, & 15‐16 years

Tertiary entrance scor

cmplsch # Did, or did not, complete secondary school (year 12), dichotomous variablyoung adult report 19

educ06_2 + Highest level of education completed‐ ‐ 4 level variable (less than year 12, year 12, post‐secondary but not university

112

23‐24 years

ed_empl_2 # ous variable, young adult Fully engaged vs. not fully engaged, dichotomreport 23‐24 years

# = dichotomous outc

outcome v

Obesity me

ome score

+ = ordinal ariable

Variable na Description

attach96 port, 13‐

attach00 lescent report, 17‐

Alientn96 olescent report,

alientn00 from parents, high = score = high alienation, adolescent report,

negpt00 onflictual parent‐teenager relationship, parent report 17‐18 years, high score = high conflict

ve reasoning, composite mean score of parent report at 13‐14, 15‐16 7‐18 years, high score = high use of reasoning

hd Harsh discipline, composite mean score of parent report at 13‐14, 15‐16 and 17‐18 years, high score = high harsh discipline

arental monitoring/supervision of teen’s activities/associates, parent report igh score = high supervision

t high score = high supervision

ion

*

‐16 years

*

* years

* ent

*

2 *

Attachment to parents, high score = high attachment, adolescent re14 years

Attachment to parents, high score = high attachment, ado18 years

Alienation from parents, high = score = high alienation, ad13‐14 years, ONLY 2 ITEMS

Alienation17‐18 years

Negative/c

totusereas Inducti& 1

tothars

superv96 P13‐14 years, h

superv98 Parental monitoring/supervision of teen’s activities/associates, parent repor15‐16 years,

parsup00 Parental monitoring/supervision of teen’s activities/associates, teenager report 17‐18 years, high score = high supervis

menc98_2 Mother encourages child to lose weight – 1 = no, 2 = yes responses, parent report 15‐16 years

fenc98_2 * Father encourages child to lose weight ‐ 1 = no, 2 = yes responses, parent report 15

mdiet98_2 Mother diets to lose weight ‐ 1 = no, 2 = yes responses, parent report 15‐16 years

fdiet98_2 * Father diets to lose weight ‐ 1 = no, 2 = yes responses, parent report 15‐16 years

curr9801_2 Child overeats at mealtimes currently, 1 = no, 2 = yes responses, parent report 15‐16

curr9802_2 Child eats a lot at between meals currently, 1 = no, 2 = yes responses, parreport 15‐16 years

past9801_2 Child overate at mealtimes in the past, 1 = no, 2 = yes responses, parent report 15‐16 years

past9802_ Child ate a lot at between meals in the past, 1 = no, 2 = yes responses, parentreport 15‐16 years

soc9830 ** Child participates in school sports teams, 3 level var, high = high participation, parent report 15‐16 years

soc9834 ** Child participates in community sports teams, 3 level var, high = high


113

participation, parent report 15‐16 years

diets98 ** Child has dieted to lose weight, 4 level var (no, once, 2‐3 times, many times),

hen upset, eats moderately in public but not in private, eats in ,

t able

# young adult report at 23‐24

high = high dieting, teenager report, 15‐16 years

eatprb98 Composite variable assessing eating behaviours and perceptions of weight (eats wsecrecy, thinks self is too fat, dissatisfied with body shape), teenager report15‐16 years

Intactf * Intact/non‐intact family over child’s lifetime to 18 years (non‐intact = separated, divorced, death of parent), parent report 17‐18 years

totmothed Mother’s education, composite mean score, parent report at 13‐14, 15‐16 &17‐18 years, high score = low education

totfathed Father’s education, composite mean score, parent report at 13‐14, 15‐16 &17‐18 years, high score = low education

totreact Reactive temperament style, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years, high score = very reactive

totpersist Persistent temperament style, composite mean score of parent report at 11‐12, 13‐14, & 15‐16 years, high score = very persistent,\

totapprch Approach/sociability temperament style, composite mean score of parenreport at 11‐12, 13‐14, & 15‐16 years, high score = very approaching/soci

Weight_3 Body mass index at 23‐24 years, 3 level outcome var in which 1 = underweight/normal, 2 = overweight, 3 = obese,years (n = 902)

* = dichotomous var

r

# = ordinal outcome var

Anxiety and/or d Desc

** = ordinal va

epression Variable name ription

attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐14 years

to parents, high score = high attachment, adolescent report, 17‐18

from parents, high = score = high alienation, adolescent report, 17‐18 years

Relationship armth, high score = high quality/warmth, composite mean score ort at 13‐14, 15‐16 & 17‐18 years

Rela

totusereas Indupare

Hars f pare years

Enmpare

Maripare ‐18 years

Mariqual ship, parent report 17‐18 years

attach00 Attachmentyears

alientn00 Alienation

totrelqual quality/w of parent rep

chrqual00 tionship quality, high score = high quality, adolescent report, 17‐18 years

ctive reasoning, high score = high use of reasoning, composite mean score of nt report at 13‐14, 15‐16 & 17‐18 years

totharshd h discipline, high score = high harsh discipline, composite mean score ont report at 13‐14, 15‐16 and 17‐18

enmesh00 eshment/overprotection, high score = high enmeshment/overprotection, nt report 17‐18 years

mconflic tal conflict over life of ATP child to 17‐18 years, high score = high conflict, nt report 17

marqual tal relationship quality over life of ATP child to 17‐18 years, high score = high ity relation

114

totmaltr* Expe lt retro rt at 23‐24 years, range 0 to 3

rience of child maltreatment/neglect during first 18 years of life, young aduspective repo

totfamstrss Occucom report at 13‐14, 15‐16 & 17‐18 years

Reac n score of pare

totpersist Pers igh score = very persistent, composite mean score of pa

Appr re = very approaching/sociable, com

anx_dep02** Anxi r very severe levels of depr

anx_dep06** Anxi severe levels of depression,

meananxdep02# Anxiety/depression outcome: mean of depression and anxiety, young adult report at 19

meananxdep06# Anxiety/depression outcome: mean of depression and anxiety, young adult report at 23

rrence of family life events (changes, problems) in previous 12 months, posite mean score of parent

totreact tive temperament style, high score = very reactive, composite meant report at 11‐12, 13‐14, & 15‐16 years

istent temperament style, hrent report at 11‐12, 13‐14, & 15‐16 years

oach/sociability temperament style, high sco totapprchposite mean score of parent report at 11‐12, 13‐14, & 15‐16 years

ety/depression outcome: moderate, severe oession, and/or anxiety, young adult report at 19‐20 years

ety/depression outcome: moderate, severe or very and/or anxiety, young adult report at 23‐24 years

‐20 years

‐24 years

= ordinal score

omous outcome

# = continuous outcome sc

SmokingVariable name

** = dichot score

ore

Alcohol/ Description

smokeh_2* nt report 17‐18

Agedrh* allowed to drink at home, 1 = 15 years or older, 2 = 14

Talkdr** little, 3 = a lot, parent

attach96 report, 13‐

attach00 lescent report, 17‐

alientn96 t report, 13‐14 years, 2 ITEMS ONLY

ienation from parents, high = score = high alienation, adolescent report, 17‐18 years

reasoning, high score = high reasoning, composite mean score of at 13‐14, 15‐16 & 17‐18 years

‐16 and 17‐18 years

rvision, parent report 13‐14 years

rvision, parent report 15‐16 years

Teenager is allowed to smoke at home, 1 = no, 2 = yes, pareyears

Age teenager was firstyears or younger, parent report 17‐18 years

Parent talks with teenager about drinking 1 = no, 2 = a report 17‐18 years

Attachment to parents, high score = high attachment, adolescent14 years

Attachment to parents, high score = high attachment, ado18 years

Alienation from parents, high = score = high alienation, adolescen

alientn00 Al

totusereas Use of

parent report

totharshd Harsh discipline, high score = high harsh discipline, composite mean score of parent report at 13‐14, 15

superv96 Parental monitoring/supervision of teen’s activities/associates, high score = high supe

superv98 Parental monitoring/supervision of teen’s activities/associates, high score = high supe

parsup00 Parental monitoring/supervision of teen’s activities/associates, high score =


115

high supervision, teenager report 17‐18 years

inconst00 s

s

totmsmoke 13‐14, & 17‐18

years

daysm02_1 the past month, continuous outcome,

daysm02_2 # port at 19‐20 years

2 +

1

daysm06_2 remainder of sample, young adult report at 23‐24 years

e, young adult report at 23‐24 years

at 23‐24 years

Inconsistent discipline, high score = inconsistent, parent report 17‐18 year

negpt00 Negative/conflictual parent‐teenager relationship, parent report 17‐18 year

Mother smoking, composite mean score of parent report atyears

ttotfsmoke Father smoking, composite mean score of parent report at 13‐14, & 17‐18 years

totmdrink Mother drinking, composite mean score of parent report at 13‐14, & 17‐18 years

totfdrink Father drinking, composite mean score of parent report at 13‐14, & 17‐18 years

totreact Reactive temperament style, high score = very reactive, composite mean score of parent report at 11‐12, 13‐14, & 15‐16

totpersist Persistent temperament style, high score = very persistent, composite meanscore of parent report at 11‐12, 13‐14, & 15‐16 years

Number of days smoked cigarettes inyoung adult report at 19‐20 years

Daily smoker vs remainder of sample, young adult re

binged02 Number of days in past month had 5+ drinks in a session, continuous outcome, young adult report at 19‐20 years

binged02_ 0, 1‐4, and 5+ days in past month had 5+ drinks in a session, young adult report at 19‐20 years

daysm06_ Number of days smoked cigarettes in the past month, continuous outcome, young adult report at 23‐24 years

Daily smoker vs

binged06 Number of days in past month had 5+ drinks in a session, continuous outcom

binged06_2 0, 1‐4, and 5+ days in past month had 5+ drinks in a session, young adult report

* = dichotomous var

** = ordinal variable

mous outcome

+ = ordinal outcome variab

Illicit Drugs ame

# = dichoto score

le

Variable n Description

totmaltr * 18 years of life, young ent

cohes00

marqual TP child to 17‐18 years, high score = high quality relationship, parent report 17‐18 years

ent to parents, high score = high attachment, adolescent report, 13‐14 years

attach00 Attachment to parents, high score = high attachment, adolescent report, 17‐18 years

Experience of child maltreatment/neglect during first adult retrospective report at 23‐24 years, range 0 to 3 types of maltreatm

Family cohesion, parent report at 17‐18 years, high score = high cohesion

mconflic Marital conflict over life of ATP child to 17‐18 years, high score = high conflict, parent report 17‐18 years

Marital relationship quality over life of A

attach96 Attachm

116

alientn00 Alienation from parents, high = score = high alienation, adolescent report, 17‐18 years

negpt00 Negative/conflictual parent‐teenager re

high scolationship, parent report 17‐18 years,

pospt00 Positive, communicative, good problem solving, parent‐teenager relationship, parent report 17‐18 years, high score = high communication/closeness

ivities/associates, high score = high supervision, parent report 13‐14 years

parsup00 iates, high score = high supervision, teenager report 17‐18 years

e n score of parent report at 13‐14, & 17‐18

ttotfsmoke

totmdrink Mother drinking, composite mean score of parent report at 13‐14, & 17‐18

totfdrink Father drinking, composite mean score of parent report at 13‐14, & 17‐18

totreact Reactive temperament style, high score = very reactive, composite mean

totpersist Persistent temperament style, high score = very persistent, composite mean

14, & 15‐16 years

+

totilldrg02_2 + l outcome variable: number of illicit drugs used on one or more day/s rt at 19‐20 years

+ (range 0‐4), young adult report at 23‐24 years

2 + month: 0, 1, 2+ drugs; young adult report at 23‐24 years

re = high conflict

superv96 Parental monitoring/supervision of teen’s act

superv98 Parental monitoring/supervision of teen’s activities/associates, high score = high supervision, parent report 15‐16 years

Parental monitoring/supervision of teen’s activities/assoc

totmsmok Mother smoking, composite meayears

Father smoking, composite mean score of parent report at 13‐14, & 17‐18 years

years

years

score of parent report at 11‐12, 13‐14, & 15‐16 years

score of parent report at 11‐12, 13‐14, & 15‐16 years

totapprch Approach/sociability temperament style, high score = very approaching/sociable, composite mean score of parent report at 11‐12, 13‐

totilldrg02 Number of differing types of illicit drugs used on one or more day/s in past month (range 0‐4), young adult report at 19‐20 years

3 levein past month: 0, 1, 2+ drugs; young adult repo

totilldrg06 Number of differing types of illicit drugs used on one or more day/s in past month

totilldrg06_ 3 level outcome variable: number of illicit drugs used on one or more day/s in past

* = ordinal variable

Antisocial behaviouVariable name Description

+ = ordinal outcome variable

r

selfcptpar conce about h pare ts, child t at 1 = c

attach96 Attachment to parents, high score = high attachment, adolescent report, 13‐ars

hme are h sco = hig chment, scen rt, 17ars

ation pare igh = re = lienation esce rt,

Selfscore

pt high self

relationsoncept

ip with n repor 1‐12 years, high

14 ye

attach00 Attac nt to p nts, hig re h atta adole t repo ‐18 ye

alientn00 Alien from nts, h sco high a , adol nt repo


117

17‐18 years

cohes00 Family cohesion, parent report at 17‐18 years, high score = high cohesion

ionsh ality/ th, hi scor qualit compositen sco ar rt at 14, 1 17‐18

ionsh ality, h q , adoles port ye

h disc , high = hi hars pline, c te me orent re 1 16 an 17‐1 s

ntal m ring/ vision f tee ivities/ es, hi ore supervision, parent rt 13‐14 ye

8 ntal m rin vision f tee ivities/ es, ore supervision, parent rt 15‐16 ye

monitoring/s teen’s hi ore =supervision, te repo ‐18

sis cipl h sco inconsistent, pa ort 17‐18 rs

conflict, parent report 17‐18 years

to 17‐18 years, high score =

Intactf* = separated, divorced, death of parent), parent report 17‐18 years

fdivsep Effect ntal h on high scparen ort at

divsep00 Effect of paren al separation, divorce, death on child, high score = bad effect, age at 1 s. IS US UT IF MISSIN PARENT PORT TITU

er smoking, co te mean scor re 13 7‐1s

er smoking, composite mea score rent rep ‐14 ‐18

drinki com mean score report 13‐14 ‐18s

er dri composite mea score rent repo 3‐14 ‐18s

tive t rame le, hi score ry reactiv posi n of p re ‐12, ‐14, 16 years

stent eram tyle, high sco ery persis omp me of paren report ‐12, ‐14, ‐16 years

e/an l behaviour outc e: involvement in 3 re differing s of antisocial s in past 12 months (includ it s e youn lt re t 19‐2

fering types of antisocial activities in past 12 months (including illicit substance use), young adult report at 23‐24 years

totrelqual Relat ip qu warm gh e = high y/warmth, mea re of p ent repo 13‐ 5‐16 & years

chrqual00 Relat ip qu high score = hig uality cent re , 17‐18 ars

totharshd Hars ipline score gh h disci omposi an sc of pare port at 3‐14, 15‐ d 8 year

superv96

Pare onito super o n’s act associat gh sc = high repo ars

superv9 Pare onito g/super o n’s act associat high sc = high

Parental

repo

upervision

ars

activities/associates,parsup00 of gh sc high enager rt 17 years

inconst00 Incon tent dis ine, hig re = rent rep yea

mconflic Marital conflict over life of ATP child to 17‐18 years, high score = high

marqual Marital relationship quality over life of ATP child high quality relationship, parent report 17‐18 years

Intact/non‐intact family over child’s lifetime to 18 years (non‐intact

ef of paret rep

separation,17‐18 years

divorce,

deat child, ore = bad effect,

tr reportteen 7‐18 year TH ED, B G, RE

SUBS TED

totmsmoke Moth mposi e of parent port at ‐14, & 1 8 year

ttotfsmoke Fath n of pa ort at 13 , & 17 years

Mothertotmdrink ng, posite of parent at , & 17 year

totfdrink Fath nking, n of pa rt at 1 , & 17 year

totreact Reac empe nt sty gh = ve e, com te meascore arent port at 11 13 & 15‐

totpersist Persiscore

tempt

ent s at 11

re = v& 15

tent, c osite an 13

anti02** Crim tisocia om or motype activitie ing illic ubstancuse), g adu port a 0 years

anti06** Crime/antisocial behaviour outcome: involvement in 3 or more dif

* = dichotomous var

** = dichotomous outcome score

118

Appendix G: ATP

Descriptions of the variable names below are in Appendix F.

Productivity

agement (children no oy no tudyi

predictor variables in G.1 found to be cant logistic

G.1: ren en dic ariab

Attachm pare ild 18;

Regression outcomes

Undereng t empl ed and t s ng)

None of the Table was signifi in a regression.

Table Unde gagem t pre tor v les

attach00 ent to nts ch age 17‐

totrelqual Relation ualit th, sit ean s par ort at age14, 15‐1 ‐18 y nterp as w ithou iplin

Harsh d e, co ite m ore paren rt at , 15‐1 17‐1years

Parental teen’s activities/associates, nt re 13‐14years

Parental orin rvi tee activi soci rent 15‐1years

Parental orin rv tee activi socia enage rt 17years

Negative ictua nt‐teenager re ionsh ent ‐18

Inconsisten scipline, gh score inconsistent, pa nt report ‐18 years

Mother atio posit n s e, par port a , 15 ‐1years

Father’s tion osite sc , pare ort at , 15‐1 7‐18

Academic a social school problems, composite mean scores parent repor 13‐14 &15‐16 y

Academ pet ache rt ‐12

Positive aff towards hool, teenage port 15‐1 years (Note that this s reversecoded)

Persiste pera style, osi mean of pa port a , 1& 15‐16 yea

ship q y/warm compo e m core of ent rep child 13‐6 & 17 ears (Note that this is i reted armth w t disc e)

totharshd isciplin mpos ean sc of t repo 13‐14 6 and 8

superv96 monitoring/supervision of pare port

superv98 monit g/supe sion of n’s ties/as ates, pa report 6

parsup00 monit g/supe ision of n’s ties/as tes, te r repo ‐18

negpt00 /confl l pare lat ip, par report 17 years

inconst00 t di hi = re 17

totmothed ’s educ n, com e mea cor ent re t 13‐14 ‐16 & 17 8

totfathed educa , comp mean ore nt rep 13‐14 6 & 1 years

totmschprb_2 nd t at ears

tacp94 ic com ence, te r repo at 11 years

posaff98 ect sc r re 6 wa

totpersist nt tem ment comp te score rent re t 11‐12 3‐14, rs

Completion of high sch

ble G.2: ompleti of gh school(a)(b)

95 for E

ool

Ta C on hi

% C.I. XP(B)

B S.E. Wald df Sig. Exp(

Lowe Upper B)

r

attach00 .3 .2 1.1 . 02 86 14 1 291 1.353 .772 2.370

totrelqual ‐1.0 .3 7.9 . 93 87 93 1 005 .335 .157 .715

totharshd ‐.4 .3 1.9 . 73 41 20 1 166 .623 .319 1.217

superv96 ‐.092 .461 .040 .842 .912 .36 2.25 1 9 4


119

95% C.I.f EXP(B) or

superv98 .145 .369 .154 1 . 1.1 .56 2.38695 56 1 1

parsup00 .7 .2 1 . 29 11 1.941 1 001 2.073 1.371 3.134

negpt00 .3 .5 . 83 03 .580 1 446 1.467 .547 3.936

inconst00 .0 .2 .0 . 39 92 18 1 894 1.040 .587 1.842

totmothed ‐. .1 12 . 480 37 .329 1 000 .619 .473 .809

totfathed .0 .1 .1 . 42 05 57 1 692 1.043 .848 1.281

totmschprb_2 ‐.4 .4 1.1 . 97 71 15 1 291 .608 .242 1.531

tacp94 . .0 9 . 023 07 .659 1 002 1.023 1.008 1.037

posaff98 ‐.6 .2 6 . 30 53 .196 1 013 .533 .324 .875

totpersist .8 .2 9 . 72 80 .717 1 002 2.392 1.382 4.139

Constant 3.3 3.2 1.0 . 72 42 82 1 298 29.128

(a) Base for comparison is not ting school. iables highlighted in bold nificant 0.05.

n of high s rsu ple university e

G.3: pletio high (only) v Uni

95% or

comple high (b) Var are sig at

Completio chool ve s com ting a degre

Table Com n of school versity degree(a)(b)

C.I.f EXP(B)

B df Sig. Exp(

Lo US.E. Wald B)

wer pper

Intercept ‐1.434 2.439 .346 .557 1

attach00 ‐.5 4.8 . 22 .237 39 1 028 .593 .373 .945

totrelqual21 . 4.7 . 607 .278 52 1 029 1.835 1.063 3.166

totharshd .099 .258 .147 .701 1.1 .66 1.83 1 04 6 0

superv96 .0 .0 . 02 .339 00 1 995 1.002 .516 1.947

superv98 ‐. . 202 .300 .453 1 501 .817 .454 1.471

parsup00 ‐.119 .167 .509 .476 .888 .63 1.23 1 9 2

negpt00 ‐.1 .3 . 16 55 .106 1 745 .891 .444 1.785

inconst00 .0 .2 .0 . 36 21 26 1 872 1.036 .672 1.598

totmothed ‐.016 .082 .038 .846 .984 .83 1.15 1 9 5

totfathed .3 .0 15.4 . 05 78 30 1 000 1.357 1.165 1.579

totmschprb_2 ‐.3 .5 . 01 91 .259 1 611 .740 .232 2.359

tacp94 ‐.007 .005 1.953 .162 .993 .98 1.00 1 3 3

posaff9822 .7 14.8 . 98 .207 55 1 000 2.220 1.480 3.331

totpersist ‐.3 3.1 . 77 .214 17 1 077 .686 .451 1.042

(a) Base for comparison is university de ) Vari in e sig at 0.05

gree. (b ables highlighted bold ar nificant .

21 This is interpreted as warmth without discipline.

22 This was reverse coded.

120

Obesity

Table G.4: Body mass index at 23‐24 years(a)(b)

B Error Wald df Sig. Exp(B)

Bound

Upper Bound

Std. Lower

Overweight

Intercept ‐9.801 3.29 .853 4 8 1 .003

bmi98 .409 .058 50.528 1 .000 1.505 1.685 1.345

eatprb98 .1 .187 98 .29 1.2 .843 1.96 1.0 1 5 16 753

attach00 ‐.2 .253 92 .40 .81 .493 1.11 .6 1 6 0 331

negpt00 ‐.4 72 .27 .6 .285 1.46 .412 1.1 1 9 40 436

totusereas .2 .255 94 1 .25 1.33 .811 2.290 1.2 5 7 05

totharshd .2 99 15 1 .339 1.3 .74 2.386 .2 .9 31 1 94

superv96 .4 .392 1.286 1 .257 1.560 .723 3.364 45

superv98 ‐.202 .338 .549 .817 .421 1.5.359 1 83

parsup00 .083 .189 .193 1 .660 1.087 .750 1.573

totmothed ‐.009 .094 .010 1 .921 .991 .824 1.191

totfathed 1.581 .279 .091 9.311 1 .002 1.322 1.105

totreact 1.278 ‐.247 .251 .970 1 .325 .781 .477

totpersist ‐.740 .234 10.021 1 .002 .477 .302 .755

totapprch .173 .203 31 .7 1 .393 1.189 .799 1.768

[intactf=1.00] 09 9 4 1 .3 1.3 .33 .83 61 .362 .702 2.646

[intactf=2.00] 0b . . 0 . . . .

[losewt2=.00] 60 0 7 1 .7‐.4 1.24 .13 11 .631 .056 7.178

[losewt2=1.00] 56 0 5 1 .7 .‐.4 1.29 .12 24 634 .051 7.942

[losewt2=2.00] 0b . . 0 . . . .

[diet2=.00] ‐.209 9 8 1 .81.06 .03 45 .811 .100 6.597

[diet2=1.00] 23 1 0 1 .9 1.0 1.07 .00 83 .024 .126 8.346

[diet2=2.00] 0b . . 0 . . . .

[overeat2=.00] ‐ 92 5 1 .2 .553 .221 1.38.5 .469 1.59 07 7

[overeat2=1.00] 64 7 6 1 .8‐.0 .49 .01 98 .938 .354 2.487

[overeat2=2.00] 0b . . 0 . . . .

[soc9830=0] .122 1 1 1 .7 1.43 .08 76 .130 .486 2.629

[soc9830=1] 96 5 0 1 .7‐.0 .30 .10 52 .908 .499 1.651

[soc9830=2] 0b . . 0 . . . .

[soc9834=0] 49 2 2 1 .6 1..1 .33 .20 53 161 .606 2.226

[soc9834=1] 21 3 1 1 .1 1. .809 2.87.4 .32 1.70 92 524 0

[soc9834=2] 0b . . 0 . . . .

[diets98=1] .276 .601 .211 1 .646 1.318 .406 4.284

[diets98=2] ‐.518 .631 .673 1 .412 .596 .173 2.054

[diets98=3] .041 .588 .005 1 .944 1.042 .329 3.299

[diets98=4] 0b . . 0 . . . .


121

Obese

Intercept ‐32.698 7.322 19.941 1 .000

bmi98 .992 .119 69.293 1 .000 2.696 2.135 3.405

eatprb98 ‐.477 .36 904 1.716 1 .1 .621 .304 1.267

attach00 ‐.333 .506 .433 1 .510 .717 .266 1.932

negpt00 1.940 .75 6.673 1 .010 6 2 .597 .346 1 .96 1 30

totusereas .475 1 1 1.607 .568 4.55.53 .799 1 .37 1

totharshd ‐.009 8 6 .9.51 .000 1 .98 91 .359 2.736

superv96 1.487 3 2 5 4.4 .8 2.86 .971 1 .08 26 15 4.020

superv98 ‐.819 5 1 0 .44 .1.62 .718 1 .19 1 29 1.501

parsup00 .727 1 3 6 2.07 .9.38 .647 1 .05 0 81 4.367

totmothed ‐.158 9 4 .8 .5.18 .697 1 .40 54 90 1.237

totfathed .831 18 2.2 1.5.194 .408 1 .000 96 71 3.356

totreact ‐.879 8 3 2 .41 .1.48 .248 1 .07 5 60 1.080

totpersist ‐1.055 4 .3 .1.476 .921 1 .027 48 37 .884

totapprch .235 4 8 1.2 .5.43 .293 1 .58 65 40 2.960

[intactf=1.00] .189 3 9 1.2 .3.67 .079 1 .77 08 23 4.520

[intactf=2.00] 0b . . 0 . . . .

[losewt2=.00] .362 2 7 1.4 .0 342.79 .017 1 .89 36 06 1.757

[losewt2=1.00] ‐1.236 9 0 .29 .0 72.80 .194 1 .66 1 01 1.418

[losewt2=2.00] 0b . . 0 . . . .

[diet2=.00] .106 6 5 1.1 .0 122.42 .002 1 .96 12 10 9.144

[diet2=1.00] 1.039 9 .18 1 9 2.827 .024 329.922.42 3 .66 1

[diet2=2.00] 0b . 0 . . . . .

[overeat2=.00] .407 7 2 1.503 .095 23.691.40 .084 1 .77 5

[overeat2=1.00] 1.750 7 1 0 5.7 91.42 .503 1 .22 54 .351 4.412

[overeat2=2.00] 0b . . 0 . . . .

[soc9830=0] .781 7 1 9 2.1 .4.76 .037 1 .30 84 86 9.823

[soc9830=1] .390 2 2 1.47 .4.58 .450 1 .50 8 73 4.619

[soc9830=2] 0b . . 0 . . . .

[soc9834=0] ‐.077 2 1 .9 .2.62 .015 1 .90 26 74 3.130

[soc9834=1] .868 3 2 0 2.38 .7 7.60 .071 1 .15 1 30 .761

[soc9834=2] 0b . . 0 . . . .

[diets98=1] .061 6 2 1.06 .11.02 .004 1 .95 3 42 7.938

[diets98=2] ‐.558 4 3 .5 .01.07 .270 1 .60 72 70 4.697

[diets98=3] .373 9 2 1.4 .2 11.00 .137 1 .71 52 01 0.493

[diets98=4] 0b . . 0 . . . .

(a) Base for comparison is normal/underweight. (b) les te sign at 0

analysis of ATP

Variab highligh d in bold are ificant .05.

Source: AIFS

122


‐20 years is inTable G.5.

lovibond clinical depression levels 2002

The overlap between anxiety and depression at 19

Table G.5: ATP anxiety/depression 2002 crosstabulation

moderate severe

s

normal mild extremely

evere

Total

normal 68 18 4 823 2 62 57

mild 56 17 20 5 4 102

moderate 4 7 3 1 32 40 13 13

severe 9 3 10 9 4 35

lovibond clinical anxiety

extremely

re

3 8 9 12 22 54

levels 2002

seve

Total 79 1 122 136 57 41 1147

Dependent variable is anxious an depressed.

G.6: Logis ession resul child age 19‐ 2002)(a)(b)

B .E. Wal d S Ex9 L

d/or

Table tic regr ts — 20 years (year

S d f ig. p(B)

5% CIower

95% CIUpper

anx_dep96 .809 .16 23.1 8 89 1 .000 2.245 1.615 3.120

alientn00 .636 .18 11.8 1 5 21 .001 1.888 1.314 2.712

relqual00 .090 .24 .1 1 3 37 .711 1.094 .680 1.761

totusereas ‐.083 .22 .1 1 1 42 .706 .920 .597 1.419

totharshd ‐.242 24 .9 1 .487 1.264 . 3 92 .319 .785

enmesh00 .055 .131 .174 1 .677 1.056 .816 1.367

mconflic .220 .134 2.707 1 .100 1.246 .959 1.619

marqual ‐.157 .169 .871 1 .351 .854 .614 1.189

totmaltr .045 2 .978

totmaltr(1) .069 .376 .033 1 .855 1.071 .513 2.238

totmaltr(2) .091 .043 .835 1.095 .438 1 .464 2.586

totfamstrss .083 4 81 7 .151 .30 1 .5 1.08 .809 1.461

totreact .337 6 3.295 1 .070 1.401 .973 2.017 .18

totpersist ‐.329 3.166 .920 1 .048 .720 .519 .997

totapprch ‐.208 1 1.166 .569 1 .210 .813 .587 .124

Constant ‐2.461 11.894 .687 1 .194 .085

(a) Base for comparison is

Source: AIFS ana

not anxious/depressed. (b) Variables highlighted in bold are significant at 0.05.

lysis of ATP.


123

Alcohol

Dependent variable is no/some/high binge drinking(a) age 19‐20

Std r d . Exp(B) 9 L

95Up

B . Erro Wald f Sig5% CIower

% CIper

1‐4 days binge drinking

Intercept .334 2.515 .018 1 .894

drink96 .645 .234 1 37.587 .006 1.905 1.204 .014

Talkdr .048 .195 .060 1 .806 1.049 .715 1.539

attach00 ‐.087 .212 2 .168 1 .68 .917 .606 1.388

Totusereas ‐.079 .226 .122 1 .727 .924 .594 1.438

Totharshd .345 .274 1.577 1 .209 1.411 .824 2.416

superv96 .072 .351 6 2.043 1 .83 1.075 .541 .137

superv98 ‐.408 .320 1.628 1 .202 .665 .355 1.244

parsup00 .100 6 1.5.162 .383 1 .536 1.10 .804 20

inconst00 .100 .222 2 1.204 1 .65 1.105 .716 .707

negpt00 .013 .360 .001 1 .970 1.014 .501 2.052

Totmdrink .046 .159 4 1.082 1 .77 1.047 .767 .428

Totfdrink .091 .159 .327 1 .567 1.095 .802 1.497

Totreact ‐.252 .206 1 11.495 1 .22 .777 .519 .164

Totpersist .037 .188 .039 1 .844 1.038 .718 1.499

[agedrh=1.00] .259 .283 0 2.837 1 .36 1.295 .744 .256

[agedrh=2.00] 0b . . . . . 0 .

5+ days binge drinking

Intercept 1.283 2.682 .229 1 .632

drink96 .841 .248 11.479 1 .001 2.319 1.426 3.773

Talkdr .160 .213 .566 1 .452 1.174 .773 1.781

attach00 .103 .232 .196 1 .658 1.108 .703 1.748

Totusereas ‐.293 .242 1.472 1 .225 .746 .464 1.198

Totharshd ‐ 57 .299 .037 1 .848 .944 .526 1.695 .0

superv96 59 .3 35 .333 .432 .3 71 .9 1 1 .692 2.967

superv98 .813 .3 8 1 .013 .444 3 ‐ 28 6.12 .23 .844

parsup00 ‐.294 3.038 81 5 .169 1 .0 .74 .535 1.037

inconst00 ‐.343 32 54 0 1.241 2.0 1 .1 .71 .443 .137

negpt00 .377 54 29 8 3.386 .9 1 .3 1.45 .684 .106

Totmdrink .261 .178 2.153 1 .142 1.298 .916 1.839

totfdrink .355 .175 4.105 1 .043 1.426 1.012 2.009

Totreact ‐.228 .222 54 1.21.0 1 .305 .796 .515 30

Totpersist ‐.134 .199 1.2.448 1 .503 .875 .592 94

[agedrh=1.00] .899 .333 5 07 8 4.7.27 1 .0 2.45 1.279 726

[agedrh=2.00] 0b . . . . 0 . .

(a) The reference category is: 0 days binge drinking.

124

Smoking

Dependent variable: Daily smoker at 19‐20 years

gistic ts — age years r 2002)(a)(b)

B S.E. Wald df Sig. Exp(B)

9 Lower

95Upper

Table G.7: Lo regression resul child 19‐20 (yea

5% CI % CI

smoke96 1.384 .250 30.738 1 .000 3.989 2.446 6.505

smokeh_3 2.87 4 0 2 .000

smokeh_3(1) Missing compared to allowed

‐2.459 .653 14.197 1 .000 .085 .024 .307

smokeh_3(2) Not allowed compared to

‐1.8 .292 9.417 .0 .28

allowed

35 3 1 .000 .160 90 3

attach00 .180 .215 .699 1 .403 1.197 .785 1.824

Totusereas ‐.21 .239 80 . 1.21 .7 1 .377 .809 506 94

Totharshd ‐.063 .281 .541 1.6 .050 1 .822 .939 28

superv96 .371 .350 1.124 1 .289 1.449 .730 2.878

superv98 ‐.4 .293 .984 . 1.113 1 1 .159 .662 372 76

parsup00 .288 .166 3.029 1 .082 1.334 .964 1.846

inconst00 ‐.321 .236 1.856 1 .173 .725 .457 1.151

negpt00 1.1 .365 .840 1 6.45 9 1 .002 3.142 .537 425

totmsmoke .286 .113 6.426 1 .011 1.331 1.067 1.660

Totfsmoke ‐.16 .107 .335 . 1.04 2 1 .126 .849 687 47

Totpersist ‐.272 .187 2.099 1 .147 .762 .528 1.101

Totreact .068 .214 .100 1 .751 1.070 .704 1.627

Constant ‐2.853 2.413 1.398 1 .237 .058

(a) Base for comparison is not a daily smoker. (b) Variables highlighted in bold are significant at 0.05.

Illicit drugs

sed in the past month, child aged 23‐24 years(a).

Unstandd

Standardised B

Lo Upp

Source: AIFS analysis of ATP

Dependent variable: number of illicit drugs u

ardise B

SE T Sig. 95% CI

wer 95% CI

er

(Constant) ‐ ‐1.009 .714 ‐.012 .990 .410 1.393

init_illicit96 .06 1.184 .107 7 .728 .084 ‐.025 .394

total n of types of nt/neglecd

.01maltreatme t experience

.025 .059 7 .430 .667 ‐.090 .141

marital conflict ‐.00‐.002 .034 2 ‐.053 .958 ‐.069 .065

attachmen2000

t to paren .03ts .061 .067 8 .916 .360 ‐.070 .193


125

Unstandardised

SE Standardised B

T Sig. 95% CI Lower

95% CIUpper

B

parent‐teen conflict .06 12000 .154 .104 8 .478 .140 ‐.051 .359

new monitoring/scale 1996

supervis‐.00

ion ‐.004 .102 2 ‐.042 .966 ‐.205 .197

new monitoring/supervisi

8

.00on

scale 199

.002 .089 1 .027 .979 ‐.173 .178

parental supervision/monito

‐.10 ‐2ring

M & F 2000

‐.120 .048 0 .496 .013 ‐.214 ‐.026

composite mother smoking 96 & 00

.084 .037 .096 2.255 .024 .011 .158

composite father smoking 96 & 00

.044 .033 .058 1.336 .182 ‐.021 .110

composite mother drinking 96 & 00

.087 .046 .081 1.880 .061 ‐.004 .177

composite father drinking 96 & 00

.056 .047 .053 1.200 .231 ‐.036 .149

composite reactivity temperament 94, 96 & 98

‐.096 .062 ‐.072 ‐1.533 .126 ‐.218 .027

composite persistent temperament 94, 96 & 98

‐.103 .058 ‐.077 ‐1.782 .075 ‐.216 .010

composite approach temperament 94, 86 & 98

.104 .053 .078 1.962 .050 .000 .207

(a) Variables highlighted in bold are significant at 0.05.


Dependent variable: number of illicit drugs used in the past month, child aged 19‐20 years(a). This equation was not used because attachment to parents has an unexpected sign.

Unstandardised B

SE Standardised B

T Sig. 95% CI Lower

95% CIUpper

(Constant) 1.246 .568 2.194 .029 .131 2.361

init_illicit96 .143 .084 .067 1.699 .090 ‐.022 .309

total n of types of maltreatment/neglect experienced 0‐18 years

.041 .048 .035 .856 .392 ‐.054 .136

marital conflict hi=hi ‐.007 .027 ‐.010 ‐.245 .806 ‐.060 .047

attachment to parents 2000

.116 .053 .091 2.182 .029 .012 .221

parent‐teen conflict 2000

.161 .083 .090 1.944 .052 ‐.002 .324

126

dardise SE Standardised B

T Sig. 95% CI Lower

9U

Unstan

d B

5% CIpper

new monitoring/supervisio

‐.102 .081 ‐.053 ‐1.258 .209 ‐.262 .057

n scale 1996 ‐ hi = hi

new monitoring/supervision scale 1998 ‐ hi = hi

‐.096 .071 ‐.058 ‐1.350 ‐ .044.177 .236

parental ‐.115 .038 supervision/monitoring M & F 2000 ‐ hi = hi

‐.122 ‐3.016 3 ‐.00 ‐.190 .040

composite mother smoking 96 & 00

.080 .030 .115 2.656 8 .00 .021 .139

composite father .022 .027 smoking 96 & 00

.037 .835 ‐ .075.404 .030

composite mother .013 .037 .016 drinking 96 & 00

.352 5 .72 ‐.059 .085

composite father .005 .038 .0drinking 96 & 00

07 .144 6 .88 ‐.069 .080

composite reactivity ‐.065 .04temperament 94, 96 & 98; hi = very reactive

9 ‐.063 ‐1.333 3 .18 ‐.162 .031

composite persistent ‐.125 .046 ‐.119 ‐temperament 94, 96 98; hi = persistent

2.701 7 ‐

&

.00 ‐.215 .034

composite approach temperament 94, 86 &

.045 .042 .043 1.072 4 ‐ 8 8

98; hi = approaching

.28 .03 .12


at age 19‐20 years. rator the c

Sig. ) 9L

95%Up



Dependent variable is antisocial behaviour Compa is that hild does not engage in antisocial behaviour (a).

B S.E. Wald df Exp(B

5% CI ower

CIper

anti96 .273 .072 14.445 1 .000 4 1.1.31 1.141 513

attach00 ‐.214 .212 1.023 1 .312 7 1.80 .533 .222

cohes00 ‐.026 .016 2.459 1 .117 5 1.97 .944 .006

totrelqual .058 .275 .044 1 .834 9 1 1.05 .617 .817

totharshd .275 .274 1.014 1 .314 7 21.31 .771 .251

superv96 .167 .317 .278 1 .598 2 21.18 .635 .201

superv98 ‐.086 .287 .089 1 .765 .918 .523 1.612

parsup00 ‐.270 .157 2.962 1 .085 .764 .561 1.038

inconst00 ‐.299 .222 1.807 1 .179 .742 .480 1.147


127

B S.E. Wald df Sig. Exp(B)

95% CI r

95% CIUpper Lowe

mconflic ‐.068 .123 .304 1 .581 1.1 9 .935 .735 8

intactf .062 .292 .045 1 .832 4 11.06 .601 .884

totmsmoke .212 .114 3.460 1 .063 6 11.23 .989 .546

totfsmoke ‐.005 .108 .002 1 .965 5 1 .99 .806 .230

totmdrink .061 .160 .145 1 .703 3 11.06 .777 .454

totfdrink .154 .164 .885 1 .347 6 11.16 .846 .607

totreact ‐.248 .200 1.533 1 .216 0 1.78 .527 .156

totpersist ‐.457 .180 6.433 1 .011 .633 .445 .901

Constant 1.975 2.426 .663 1 .416 6 7.20



128

Appendix H: Mean and standard deviation of ATP variables

vity

school Devia

N

Producti

Completed Mean Std.

tion

completed year 12 ‐ yes/no .9054 .29289 687

attachment to parents 2000 ‐ hi = hi 3.1398 .54840 687

composite relationship quality/warmth 96, 98 & 00 4.1655 .50427 687

composite harsh discipline 96, 98 & 00 2.1112 .47871 687

new monitoring/supervision scale 1996 ‐ hi = hi 4.7496 .32698 687


parental supervision/monitoring M & F 2000 ‐ hi = hi 3 .0963 .71075 687

parent‐teen conflict 2000 1 .4620 .38030 687

new inconsistent discipline scale 2000 ‐ hi = inconsistency 2.1219 .55519 687

composite across‐time mother education 96, 98 & 2000 4.2120 1.57588 687

composite across‐time father education 96, 98 & 2000 4.1218 1.70045 687

high school problems .0728 .25996 687

Academic competence teacher report 1994 54.3333 22.87801 687

Positive affect towards school 1998 2.2098 .59730 687

composite persistent temperament 94, 96 & 98; hi = persistent 3.7486 .65941 687

DeNUniversity degree Mean

Std. viation

highest level of education completed by 23‐24 years 3.1277 .94128 595

attachment to parents 2000 ‐ hi = hi 3. .51464 3231 595

composite relationship quality/warmth 96, 98 & 00 4. .41811 9797 595




parental supervision/monitoring M & F 2000 ‐ hi = hi 3.0953 .71004 595

parent‐teen conflict 2000 1.4493 .37525 595




high school problems .0538 .22578 595

Academic competence teacher report 1994 5 24.5849 2.67426 595

Positive affect towards school 1998 2.1934 .57797 595



129

Obesity

Mean Std.

Deviation N

bmi 3 level variable at 23‐24 years 1.4211 .64507 513

parental separation/divorce/death 1. 1930 .39502 513

bmi98 2 1.2532 3.07313 513

mother encourages child to lose weight 1.0585 .23488 513

father encourages child to lose weight 1.0468 .21138 513

child overeats at meal times ‐ currently 1.0390 .19375 513

child eats large amounts between meals currently 1.1520 .35942 513

Participates in school sports 1.36 .730 513

Participates in community sports 1.09 .870 513



composite use of reasoning 96, 98 & 00 4.1118 .52027 513







composite reactivity temperament 94, 96 & 98; hi = very reactive 2.7694 .63242 513

composite persistent temperament 94, 96 & 98; hi = persistent 9 53.837 .62523 13

composite approach temperament 94, 86 & 98; hi = approaching 3.4076 .63324 513

130

Anxiety and/or depression

N MeanStd.

Deviation

anx_dep02 .2586 .43819 642

anx_dep96 1.0280 .63240 642

alienation from parents 2000 ‐ hi = hi 2.0366 .59188 642

new relationship quality/warmth scale 00 ‐ hi = hi 4.1555 .51807 642


totharshd 2.0945 .50073 642

enmeshment 2000: hi = hi 3.0466 .79637 642

marital conflict hi=hi 4.3032 1.06109 642

marqual 4.1509 .73759 642

totmaltr .2617 .57785 642

composite family stress 96, 98 & 2000 .6225 .64771 642

composite reactivity temperament 94, 96 & 98; hi = very reactive .62.7746 5687 642

composite persistent temperament 94, 96 & 98; hi = persistent .63.8017 4665 642


Drinking

Mean St

Deviation N

d.

no/some/high binge drinking 2.0983 .75426 580

age teen was first allowed to drink at home: 0‐14 yrs (=2) vs 15+ yrs (=1)

1.1603 .36724 580

drunk 3+ alcoholic drinks in life 1996 1.45 .498 580

talk with teen about drinking 2.12 .564 580









composite mother drinking 96 & 00 2.8888 .70414 580

composite father drinking 96 & 00 3.2836 .71330 580




131

Smoking .

Deviation N Mean

Std

daily smokers vs rest of sample 2002, 2 = smoker, 1 =non 1.1458 .35310 830

smoked 3+ cigarettes in life 1.20 .397 830

teen allowed to smoke at home, 1 = no, 2 = yes, 3 = missing item 1.0518 .42363 830









composite mother smoking 96 & 00 1.6934 1.01063 830

composite father smoking 96 & 00 1.9627 1.16174 830



IllicitStd. tion

N

drugs

Variable MeanDevia

number of illicit drugs used in past month, 23‐24 years .3840 .86587 664

init_illicit96 .1130 .31677 664

total n of types of maltreatment/neglect experienced 0‐18 years .2726 .58599 664



parent‐teen conflict 2000 1. 54 .37971 664 44




composite mother smoking 96 & 00 1.6506 .98207 664







132


Mean Std.

Deviation N

highly antisocial (3+ anti‐social acts) 2002, including drug use .1470 .35436 755

anti96 .8464 1.38300 755

alienation from parents 2000 ‐ hi = hi 2.0507 .60259 755

Family cohesion scale 2000 62.4993 8.25190 755

composite relationship quality/warmth 96, 98 & 00 4.1424 .50802 755







parental separation/divorce/death 1.1735 .37894 755

composite mother smoking 96 & 00 1.6609 .98207 755




composite reactivity temperament 94, 96 & 98; hi = very reactive

2.8076 .65792 755



133

Appendix I: Detailed costing methodology and tables

Disease‐cost burden analysis (DCBA) cost categories

Health system expenditures

Financial costs to the Australian health system comprise the costs of running hospitals and

ivate), allied health services, research, and ‘other’ costs such as health administration. Australian Institute of Health and

er employment and workforce participation, including those due to

absenteeism and premature death result in reduced taxation revenue collected by the

product of the average

n revenue is considered a transfer payment, rather than an economic cost per se. However, raising additional taxation revenues imposes real efficiency costs on the

as deadweight losses (DWLs), which are described below.

This section provides further detail on Access Economics’ standard DCBA categories used in past studies, and the data sources employed to estimate different cost components.

nursing homes (buildings, care, consumables), General Practitioner (GP) and specialist services reimbursed through Medicare and private funds, the cost of prescribed and over‐the‐counter pharmaceuticals (Pharmaceutical Benefits Scheme and pr

Welfare (AIHW) data are used to estimate health expenditures relating to a condition, using a top‐down approach which aggregates the total cost of different elements.

Productivity losses

Productivity losses are costs of lost production imposed when a person with a condition is unable to work because of that condition. Productivity losses include:

■ costs of lowpremature death;

■ costs of absenteeism;

■ costs of lower productivity of a person at work (‘presenteeism’ costs); and

■ additional search and hiring costs due to premature death of a person.

Reduced earnings resulting from reduced workforce participation, lower productivity,

Government. This includes both forgone income (personal) tax, and indirect (consumption) tax, as those with lower incomes spend less on the consumption of goods and services.

Personal income tax forgone was estimated in this report as thepersonal income tax rate of 19.48% and forgone earnings. Consumption tax foregone was estimated by applying an indirect taxation rate of 11.68% to foregone earnings. These average taxation rates were derived for 2010 from the Access Economics Macroeconomic Model (AEM). Lost taxatio

Australian economy, known

Access Economics adopts a human capital approach in estimating productivity losses in developed countries. Productivity loss estimates are informed by employment participation and absenteeism data from the National Health Survey (NHS), produced by the ABS

134

DWLs from transfers and foregone taxation revenue

Peop ent, including Newstart Allowance (NSA) or the Disability Support Pension (DSP), and carers of

requir ents. These ft of sources from one econo entity to anothe and are therefor ke taxation revenue lost),

themselves econom ts but rather transfer om taxpayers to are ients.

al resource cost nsfer payme d taxation foregone the ted DWL. DWLs r costs of administering welfare s and raisin onal n revenues. The resent the l consumer ducer surp to ion to the equilibr ciety preferr l of output

te of DWL applied sfer paymen ccess Econo A studies nts of tax revenue plus 1.25 cen dollar of tax raised for Office administra based on Productivity Commission ) in turn derived om

ore (1997), i.e. 28. erall.

costs

are people who p informal care ers in need tance or su ost al carers are fami iends of the receiving c rers may t off to accompany peopl a particular h condition cal appointme stay em in hospital, o for them at . Carers m take time k to ake many unpaid the pers h a conditio do thems the e of the health condition.

al care is distingu rom services ed by peo yed in th the

recipient and is not regulated by the government. While informal care is provided free of conomic sense, as time spent caring is time that cannot be directed

to other activities such as paid work, unpaid work (such as housework or yard work) or leisure.

Economics DCBAs use three p timate costs of informal care provision:

ity cost: the

replacement valuation: the cost of buying a similar amount of services from the formal care sector; and

self‐valuation: what s themselves should be

ccess Economics D ave utilised m the ABS f Disability, and (SDAC) to estim al number hours prov people wi alth ion, employment s f carers and weekly ear regone by c

financial costs

financial costs res a healt on can inc eral costs s of home modifica If prematu h results, t an ‘additio t of

le with a health condition may require income support payments from the governm

these people could e carer‐related paym payments represent a shire mic r, e (linot ic cos a financial fr welfrecip

The re of tra nts an revenue is only associa efer to pension g addititaxatio se rep oss of and pro lus, duedistort ium (so ed) leve and prices.

The ra to tran ts in A mics DCB is 27.5 ceper dollar raised, ts per revenue AustralianTaxationLattim

tion, 75% ov

(2003 fr

Carer

Carers rovide to oth of assis pport. Minform ly or fr person are. Ca ake time

work with th

e withr care

healt home

to mediay also

nts,off wor

undert tasks that on wit n would elves inabsenc

Inform ished f provid ple emplo e health andcommunity sectors (formal care) as this care is generally provided free of charge to

charge, it is not free in an e

As such, informal care has associated costs in the use of economic resources. Past Access otential methodologies to es

■ opportun value of lost wages foregone by the carer;

■

■ carer feel they paid.

Past A CBAs h data fro Survey o AgeingCarers ate tot of care ided to th a hecondit tatus o average nings fo arers.

Other

Otheraids and

ulting from h conditi lude fun and costtions. re deat here is nal’ cos


135

funerals borne by family and friends of people with the condition, due to increased likelihood th. Since death a lting funera ses are eve inevitable son r without a health the true the funeral forward e likelihood of dy way in a g ar). The B Transpo oad

(2000) calcul weighted cost of a l across all and ries as an average $3,200 per 1996.

rson with a partic ndition requ s or home tions, asso s t captured in form lth sector or lity services Past Access mics have estimated co and modifications, often bas ABS SDAC

of disease (BoD

le costs of alth condition or injury. These costs of disability, loss of re difficult to measure.

esents a year of perfect health, while a DALY of 1 represents by exper alth

l DALYs lost from sum of mortality ents – Life Lost due LLs) and the Yea to

isability (YLDs).

ain monetary val this non‐ approach, ess Econo dies ed DALYs i ar figures b ng an estim lue of a S Life

SLY). This represents estimate of society places an anonymous of is financial conve nds to utili gness to p k‐based la rket Thorough expla the appr istory and logy of Bo g is

ned in the earlier study for tive family ing (Acces ics

partment of Fina Deregulati D, 2009) ha ided an esti the appears to represent a fixed est f the net VS estimate w ,000

6 which inflates to $166,604 2010 dollars.

oted that the BoD od for deter measures ty of life o eing ng from aspects o y functionin nservative a ly measures and s and excludes as f overall fam and ing not asso with outcomes.

lence profiles

This section presents prevalence profiles for all the identified outcomes in the health, productivity and social/criminality domains. These profiles assisted the estimation of numbers of people with a condition in 2010 (for health and criminality outcomes) and to develop a set of per‐person costs by age and gender for each outcome.

of dea nd resu l expen ntually for a perwith o condition,

ing any cost is iven ye

cost broughtureau of

(adjustedrt and Rfor th

Economicsterrito

ated a average funera states cost of person in

If a pe ular co ires aid modifica ciated costare no al hea disabi costs. EconoDCBAs sts of aids ed from data.

Burden )

Burden of disease refers to the qualitative and quantitative analysis of the intangibpain and suffering from a particular hewellbeing and premature death are mo

In the last decade, a non‐financial approach to valuing human life has been derived, where loss of wellbeing and premature mortality – called the ‘BoD and injury’ – are measured in Disability Adjusted Life Years (DALYs). A DALY of 0 repr

death. Other health states are attributed values between 0 and 1 as assessedts on the basis of literature and other evidence of the quality of life in relative he

states. Totathe Year(s) of

a condition are theto premature death (Y

and morbidity componr(s) of healthy life Lost due

D

To obt ues from financial past Acc mics stuhave convert nto doll y applyi ated Va tatistical Year (V an

rsion te the valuese ‘willin

onay’ or ris

yearbour malife. Th

studies.contai

nation of oach, h methodo D costin scoping on posi function s Econom

2009).

The De nce and on (DOF ve prov mate ofVSLY, which imate o LY. This as $151in 200 approximately in

It is n meth mining of quali r wellbresulti f famil g is co s it on illness injurie pects o ily happiness wellbe ciated health

Preva

136

Obesity

Table I.1: Obesity prevalence rates and estimated obese people (number) in 2010

Age group Prevalence rate (%) Number of obese people

M F M F

0‐4 0.0% 0.0% 0 0

5‐9 7.8% 54,846 005 6.2% 41,

10‐14 7.8% 56,438 ,609 6.2% 42

15‐19 7.8% 172,013 716 6.2% 45,

20‐24 11.1% 92,429 232 9.3% 73,

25‐29 19.4% 161,874 09,684 13.5% 1

30‐34 19.4% 148,512 02,624 13.5% 1

35‐39 19.9% 160,138 72,271 21.2% 1

40‐44 19.9% 154,016 65,360 21.2% 1

45‐49 23.2% 181,347 31,562 29.2% 2

50‐54 23.2% 168,478 16,549 29.2% 2

55‐59 28.5% 35.6% 186,882 238,237

60‐64 28.5% 35.6% 172,046 216,496

65‐69 22.2% 31.9% 99,989 146,567

70‐74 22.2% 31.9% 76,382 117,054

75‐79 14.2% 16.9% 36,254 49,869

80‐84 14.2% 16.9% 26,871 42,280

85‐89 14.2% 16.9% 14,340 28,662

90+ 14.2% 16.9% 5,644 15,960

Total 2,055,736 16.8% 18.5% 1,968,499

Source: Access Economics calculations using Access Economics (2008) and AE‐Dem 2010 population estimates.

d depression

I.2: Anxiety and prevale s and estim ople with a nd depression r) in 2010

group alence rate Peop ty and ion

Anxiety an

Table depression nce rate ated pe nxiety a (numbe

Age Prev (%) le with anxie depress

M F M F

0‐4 0.00% 0.00% 0 0 5‐9 0.25% 0.53% 1,733 3,494 10‐14 2.52% 3.45% 18,225 23,724 15‐19 6.72% 8.43% 148,283 62,168 20‐24 9.24% 11.99% 76,937 94,451 25‐29 9.74% 13.96% 81,303 113,401 30‐34 9.81% 15.11% 75,066 114,840 35‐39 9.66% 15.78% 77,758 128,197 40‐44 9.38% 16.40% 72,623 127,935 45‐49 8.93% 16.68% 69,810 132,310


137

Age group Prevalence rate (%) People with anxiety and depression

50‐54 8.22% 16.34% 59,698 121,163 55‐59 7.36% 15.16% 48,274 101,452 60‐64 6.52% 13.33% 39,354 81,039 65‐69 5.9 % 3% 11.38 26,711 52,274 70‐74 5 9. 19 3 34, 1 .55% 30% ,11 1175‐79 5.23% 7.47% 13,358 22,041 80‐84 4.89% 6.02% 9,246 15,054 85‐89 4.58% 4.85% 4,626 8,230 90+ 4.35% 3.85% 1,730 3,639 Total 6.73% 11 1 .16% 843,848 ,239,522

Source: Access Economics cal Begg et a d AE‐Dem 2 on estimat

ed to estimate the number of current daily smokers in 2010 and estimate per‐person annual costs by age and gender for the categories of productivity los

I.3: Smoking prevalence da

oup Prevalence rate Current da ers

culations using l (2007) an 010 populati es.

Smoking – prevalence profile one

These prevalence rates were us

ses, indirect costs, DWLs and carer costs.

Table rates and estimated current ily smokers in 2010

Age gr (%) ily smok

M M F F 18‐19 28.01% 89,257 029 15.94% 48,20‐24 24.37% 202,902 3,883 15.73% 1225‐29 23.92% 199,595 6,341 15.55% 1230‐34 24.18% 185,096 3,229 14.90% 1135‐39 24.36% 196,042 9,566 19.64% 1540‐44 24.62% 190,582 3,404 19.67% 1545‐49 23.17% 181,083 7,283 14.79% 1150‐54 22.87% 166,068 5,604 15.59% 1155‐59 13.48% 88,405 1,459 12.17% 860‐64 16.29% 12.37% 98,316 75,247 65‐69 9.57% 7.53% 43,106 34,596 70‐74 9.40% 32,349 580 7.52% 27,75‐79 3.67% 9,381 607 3.59% 10,80‐84 2.90% 5,487 9,046 3.62% 85‐89 3.64% 3,677 6,133 3.62% 90+ 3.64% 1,447 3,375 3.57% Total 19.97% 1,692,793 205,383 13.85% 1,

Source: Access Economics calculations using Access E and AE‐Dem 2010 popu timates. conomics (2007) lation es

138

Smoking – prevalence profile two

These disease in 2010 and estimate per‐person annual costs by age and gender for the categories of

m costs and BoD cos

Table I.4: Prevalence tes for to used diseas and conditions(a)

group Females

prevalence rates were used to estimate the number of cases of tobacco‐attributable

health syste ts.

ra bacco‐ca es

Age Males

15‐24 0.06% 0.08%

25‐34 0.04% 0.14%

35‐44 1.20% 0.73%

45‐54 7.42% 2.76%

55‐64 11.81% 4.58%

65‐74 11.92% 5 .01%

75+ 16.30% 7

2

.97%

Total 4.61% .86%

(a) Included diseases and conditions: asthma, bladder ronic obstructive pulmonary d e injuries, flammatory bowel disease, kidney cancer, larynx cancer, low birth‐weight, lower respiratory infections, lung

and orapharynx cancer, oesophagus cancer, otitis media, pancreatic cancer, Parkinson’s disease, fant death syndrome, stomach uterus cancer, age‐related vision disorders, cervical cancer,

mic heart disease, peripheral arterial disease, stce: Access Economics calculations using AIHW Bo 04 special request) for Access Economics (2007), AE‐Dem 2010 population estimates.

l abuse

: Prevalence rates ‐ drinking at ris a) levels of long term h harm and estimated risky‐h drinkers in 2010

oup Prevalence ratePersons drinking a y/high risk

levels of long‐ harm

cancer, ch isease, firincancer, mouthsudden in cancer,ischae roke. Sour D data (20and

Alcoho

Table I.5 ky‐high risk( healtigh risk

Age gr (%) t riskterm

M M F F 14–19 7.70% 71,312 7,863 12.30% 1020–29 14.40% 15.10% 240,062 241,586 30–39 10.30% 9.80% 161,735 154,132 40–49 9.30% 10.30% 144,672 162,021 50–59 10.70% 7.40% 147,866 104,400 60+ 8.00% 5.20% 158,672 116,679 14+ 10.10% 9.60% 917,526 890,619

(a) ‘Risky’ drinking defined as 29 to 42 drinks per week for males, and 15 to 28 drinks per week for females. ‘High defined as 43+ drinks per week for males and 29+ drinks per week for females. ess Economics calculations using AIHW’s ‘2004 National Drug Strategy Household Survey’ (2005) and AE‐

Dem population estimates (2010).

risk’ drinkingSource: Acc


139

Drug abuse

le I.6: Prevalence for recen a) of illicit ugs and estima d recent use 2010

oup Prevalenc e (%) Recen of illicit

Tab rates t use( dr te rs in

Age gr e rat t users drugs

M F M F 14–19 20.90% 21.80% 193,5 1962 1,172 20–29 37.50% 25.60% 625,1 4061 9,576 30–39 25.50% 15.10% 400,4 2311 7,489 40–49 15% 9.50% 233,34 142 9,437 50–59 0% 4.80% 105,0 67.6 26 7,719 60+ 4.10% 4% 81,32 80 9,753 14+ 18.20% 12.50% 1,653, 1,1363 59,660

(a) ‘Recent use’ is defined within the l onths. Access Economics ations using NDSHS (2005) and AE‐Dem estimates.

der prevalenc ofile

ble I.7: Offend e‐gender p ence profil estimated ders in 2

group preval ted offe

as use ast 12 mSource: calcul AIHW 2004 2010 population

Offen e pr

Ta er ag reval e and offen 008‐09

Age Offender ence Estima nders

Male 10‐14 2.286% 16,474 15‐19 8.821% 67,695 20‐24 25‐29 4.452% 35,764

34 00% 27,655 07 24,099

2.286% 17,361 9 1.539% 11,975 4 0.955% 6,804

0.644% 4,167 0.494% 2,889 0.136% 1,815 2.838% 268,525

1.096% 7,497 2.950% 21,427 1.471% 11,351 1.104% 8,626 0.984% 7,322 0.856% 6,932 0.666% 5,110 0.449% 3,556 0.257% 1,866 0.155% 1,023 0.102% 599 0.028% 440 0.786% 75,749

6.398% 51,828

30‐35‐39

3.73.0 %

40‐4445‐450‐555‐59 60‐64 65+ Total

Female10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65+ Total

Source: Access Economics ations using AB (2009c; 2010b) and AE‐Dem estimates. calcul S 4519.0 2009 population

140

Prisoner prevalence profile

Table I.8: Prison age‐gender valence rates d estimate ers in 2009

prisoners

er pre an d prison

Age group Prisoner prevalence rate (%) Estimated

Male <18 0.001% 37 18 0.175% 273 1920–24 0.545% 4,418 25–29 0.633% 5,088

34 16 4,602 39 0.526% 4,214

0.390% 2,963 0.255% 1,987

0.181% 1,290 0.112% 724

0.084% 492 0.037% 492 0.250% 27,192

0.000% 3 0.009% 14 0.025% 38 0.034% 266 0.053% 416 0.052% 390 0.045% 363

0.033% 256 0.024% 186 0.014% 102 0.007% 49 0.005% 30 0.001% 14

0.019% 2,127

0.382% 612

30– 0.6 % 35–

40–4445–4950–54 55–59 60–6465+ Total

Female<18 18 19 20–24 25–29 30–34 35–39 40–4445–49 50–54 55–59 60–64 65+ Total

Source: Access Economics calculations using ABS 4517.0 (2009) and AE‐Dem 2009 population estimates.

H

The sections below present the sets of calculated per costs for each health outcome, nd gender. osts w ing costs

sity

Table I.9: nual per‐pe costs of obesity ‐ males (in dollars)

H Pro ity Other

financi l(a)

ealth outcomes – per person annual costs

‐person to the modellby age a These c ere used as inputs of lifetime .

Obe

An rson 2010

ealth ductiva

BoD Total

0‐4 ‐ ‐ ‐ ‐ ‐ 5‐9 225 415 308 5,701 6,649


141

Health Productivity Other

financial(a) BoD Total

10‐14 226 415 309 5,712 6,661 15‐19 256 472 350 6,484 7,562 20‐24 324 596 443 8,195 9,557 25‐29 534 981 729 13,492 15,737 30‐34 530 975 724 13,403 15,633 35‐39 544 999 742 13,735 16,020 40‐44 542 996 740 13,694 15,972 45‐49 612 1,124 835 15,450 18,020 50‐54 608 1,117 830 15,364 17,920 55‐59 698 1,282 953 17,632 20,566 60‐64

16,853

1,039 772 14,291 16,669

386 710 527 9,760 11,383

384 705 524 9,697 11,310 85‐

90+ 378 695 516 9,555 11,144

694 1,275 947 17,533 20,450 65‐69 572 1,051 781 14,449 70‐74 566 75‐79

80‐84

89 380 699 519 9,613 11,212

(a) ’Other finaSource: Access

ncial’ cate des DW , carer other indir Econom ions.

Table I.10: ual per‐pe osts of obe females ( dollars)

Heal Pro ty Other

fina

gory incluics calculat

L from transfers costs and ect costs.

Ann rson c sity ‐ in 2010

th ductivincial(a)

BoD Total

0‐4 0 0 0 0 0 5‐9 146 269 200 3,704 4,320 10‐14 146 270 201 3,715 4,332 15‐19 147 272 202 3,734 4,355 20‐24 228 420 312 5,772 6,732 25‐29 329 604 449 8,310 9,692 30‐34 327 601 447 8,265 9,640 35‐39 486 893 664 12,285 14,328 40‐44 484 890 661 12,241 14,277 45‐49 613 1

1 1

19,816

621 1,141 848 15,687 18,297 70

75‐79 380 699 519 9,610 11,208

85‐89 375 689 9,474 11,050

,126 837 15,485 18,060 50‐54 609 ,119 831 15,389 17,948 55‐59 678 ,246 925 17,129 19,978 60‐64 1,236 918 16,990 673 65‐69

‐74 614 1,127 837 15,499 18,077

80‐84 378 695 517

512

9,561 11,151

142



90+ 371 683 507 9,393 10,955

(a) ’Other financial’ catego cludes DWL fr sfers, carer c nd other indi s. Access Economics cal ulations.

and depression

le I.11: Annu person co anxiety an pression ‐ n 2010

H Pro ity Other

fina

ry in om tran osts a rect costSource: c

Anxiety

Tab al per‐ sts of d de males (i dollars)

ealth ductivncial(a)

BoD Total

0‐4 ‐ ‐ ‐ ‐ ‐ 5‐9 71 333 65 763 1,231 10‐14 401 1,787 359 4,532 7,080 15‐19 1,723 8,016 1

1 2

39,517

10,447 2,040 24,346 39,072

2,166 10,105 1,973 23,562 37,806

2,059 9,601 1,876 22,411 35,947 50‐54 75 55‐59 1,655 7,696 1,506 18,050 28,907

1

1,270 5,882 13,901 22,207

,568 18,783 30,089 20‐24 2,144 9,998 ,953 23,319 37,414 25‐29 2,264 10,568 ,063 24,621 39,516 30‐34 2,265 10,568 2,063 24,621 35‐39 2,239 40‐44

45‐49

1,877 8,743 1,709 20,446 32,7

60‐64

651,438 6,673 1,307

1,154

5,710 25,128 ‐69

70‐74 1,163 5,378 1,056 12,748 20,345 75‐79 1,072 4,949 973 11,767 18, 76180‐84 979 4,508 887 10,757 17,131 85‐89 893 4,106 809 9,838 15,646 90+ 830 3,809 752 9,158 14,549

(a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations.

2: Annual per‐person costs of anxiety and depression ‐ females (in 2010 dollars)


fin ) BoD Total

Table I.1

ancial(a

0‐4 ‐ ‐ ‐ ‐ ‐ 5‐9 ‐ ‐ ‐ ‐ ‐ 10‐14 91 431 84 986 1,592 15‐19 276 1,194 244 3,176 4,889 20‐24 1,053 4,857 955 11, 18, 25‐29 1,566 7,277 1,425 17,093 27,361 30‐34 1,833 8,534 1,669 19,968 32,004 35‐39 1,969 9,175 1,793 21,435 34,372

557 423


143



40‐44 2,065 9,626 1,880 22,467 36,038 45‐49

1,955 9,110 1,780 21,287 34,133

1,710 7,953 1,556 18,639 29,858

1,426 6,617 1,297 15,583 24,923 75‐79 5 80‐84 861 3,952 779 9,486 15,078

464 2,080 5,203 8,163

2,134 9,953 1,944 23,214 37,244 50‐54 2,172 10,129 1,978 23,618 37,897 55‐59 2,117 9,872 1,928 23,030 36,947 60‐64

65‐69

70‐74

1,128 5,213 1,024 12,370 19,73

85‐89

90644 2,931 581

416

7,150 11,306 +

(a) ’Other financial’ catego cludes DWL fro nsfers, carer c nd other indir Access Economics lations.

ng

I.13: Annua er‐person co current da oking ‐ ma (in 2010 doll )

financial(a)

ry in m tra osts a ect costs.Source: calcu

Smoki

Table l p sts of ily sm les ars

Age group Health Productivity Other

BoD Total

18‐19 48 4,473 815 4,197 9,533 20‐24

25‐29 83 4,240 773 7,260 12,356 30‐34 7 12,

37, 42,

4 3

1 1

1 4 1 1

2 2 2 23 2 3 23 0 23

4,623 774 141 404,218 409,756 0‐84 4,623 613 112 404,218 409,565

404,218 409,736

404,218 409,732

48 4,295 783 4,197 9,323

83

424

4,244

4,283

774

781

,260

047

360

53435‐39

40‐44 424 ,302 784 7,047 42,557 45‐49 1,598 4,123 752 39,737 46,20950‐54 ,598 ,063 741 39,737 46,13855‐59 ,653 ,650 483 32,030 7,81760‐64 ,653 ,089 563 2,03 8,33665‐69 2,907 1,928 351 254,182 259,368 70‐74 2,907 1,880 343 254,182 259,312 75‐79

8

85‐89 4,623 758 138 90+ 4,623 754 137

(a) ’Other financial’ category includes DWL from transfers, carer costs and other indirect costs. Source: Access Economics calculations.

Table I.14: Annual per‐person costs of current daily smoking ‐ females (in 2010 dollars)


financial* BoD Total

18‐19 46 4,271 779 3,987 9,082

144


BoD financial*

Total

20‐24 9,281 46 4,439 809 3,987 25‐29 79 804 6,897 12,186

79 ,89735‐39 402 96 35,194 41,884

40 5,306 967 35,1 41,871

1,518 ,218 769 132,750 255

1,518 ,386 800 132,750 453

2,521 ,529 643 220,429 121

2,521 ,558 649 220,429 156

2,761 ,247 410 241,473 891

2,761 ,224 405 241,473 863

4,391 ,100 201 384,007 699

4,391 ,102 201 384,007 701

4,391 ,094 199 384,007 691

4,391 196 384,007 668

4,407

4,230

5,318

30‐34 771 6

9

11,977

40‐44 2 9445‐49 4 139,50‐54 4 139,55‐59 3 227,60‐64 3 227,65‐69 2 246,70‐74 2 246,75‐79 1 389,80‐84 1 389,85‐89 1 389,90+ 1,074 389,

(a) ’Other financial’ category DWL from transfers, carer costs and other indirect sts. cess Economics cal ations.

l abuse

Table I.15: Annual per‐p costs of alcohol ab males (in 2010 d

up Health uctivity Othe

financiaBoD al

includes coSource: Ac cul

Alcoho

erson use ‐ ollars)

Age gro Prodr l(a)

Tot

14–19 837 ,481 1,629 1,879 27 1 5,820–29

1,680 ,953 3,248 3,747 28 1,204 ,121 2,33 2,691

1,083 2,10 2,423 17

6,666

2 11,630–39 2 2 8,34840–49 1,910 1 7,5

,52950–59 1,230 2,167 2,383 2,749 860+ 959 1,694 1,863 2,149

(a) ‘Ot ve consuSource: Access Economics calculations using Collins and Lapsley (2008) – costs adapted to Access Economics DCBA

Table I.16: Annual per‐person costs o hol abuse ‐ females (in 201 llars)

up Health ProductivitOther

nancial(a) BoD Total

her financial’ category includes accidents not elsewhere included, fires not elsewhere included and abusimption costs.

categories.

f alco 0 do

Age gro y fi

14–19 1,440 2,534 2,787 3,215 9,975 20–29 1,877 3,298 3,626 4,183 12,984

1,233 2,173 2,389 2,757 8,552 1,297 2,284 2,511 2,897 8,989

30–39 40–49


145



50–59 6,288 905 1,598 1,758 2,028 60+ 660 1,171 1,288 1,485 4,603

(a) ‘Other financiaconsumption costs

l’ category i cidents not el fi include .

Access Economics calculations using Collins and Lapsley (2008) – costs adapted to Access Economics DCBA es

rug abuse

I.17: Annua erson costs o drug abuse ‐ s (in 2010 do

up Healt ProductivitOther

nancial(a) BoD

ncludes ac sewhere included, res not elsewhere d and abusive

Source:categori

Illicit d

Table l per‐p f illicit male llars)

Age gro h y fi

Total

14–19 65 528 455 408 1,45720–29 114 919 791 710

84 684 589 528 0–49 48 398 343 307 1

16 143 123 111 93 1 19 17 15

2,53430–39 1,8854 ,09650–59 360+ 52

(a) ‘Other financial’ category in not els included, fires no where included a ive tion costs.

ss Economics calc using Collins an y (2008) – costs adapted to Access Econ CBA s .

ble I.18: Annual rson costs of drug abuse ‐ females (in 2010 do

up Healt ProductivitOther

al(a) BoD l

cludes accidents ewhere t else nd abusconsumpSource: Acce ulations d Lapsle omics Dcategorie

Ta per‐pe illicit llars)

Age gro h y financi

Tota

14–19 109 877 756 678 2,420 20–29 136 1,095 943 847 3,021

668 576 516 1,842 40–49 48 393 338 304 1,082

131 113 101 360 60+ 11 102 88 79 280

30–39 82

50–59 15

(a) ‘Other usive consumptionSource: Access Economics calc using Collins and (2008) – cos Access Economics DCBA

ctivity outcom per perso ual costs

tions below pre e sets of calculated per‐perso s for each vity e, by age and ge s well as th urces used rive these. ed as inputs to th elling of lifet ts.

financial’ category includes accidents not elsewhere included, fires not elsewhere included and ab costs.

ulations Lapsley ts adapted to categories.

Produ es – n ann

The sec sent th n cost productioutcom nder a e data so to de These costswere us e mod ime cos

146

Data sources: age‐gender employment rates and average weekly earnings

Table I.19: Age‐gender employment in the general population

up people

yed in 2008(‘000)

opulation in 2(‘000)

Employ te Age groTotal

emplo P 008

ment ra

Males 15 ‐ 19 361 757 47.

592 783 75.652 766 85.637 736 86.678 794 85.

4 632 754 83.7768 84.45%

569 699 81.45% 456 639 71.46%

Females 15 ‐ 19 0% 20 ‐ 24 542 748 72.56%

516 4 499 737 67.70%

39 540 804 67.18% 44 552 762 72.47% 49 593 782 75.82% 54 500 711 70.39% 59 384 646 59.40% 64 204 563 36.30% 78 1,546 5.05% l 4,767 8,762 54.41%

77% 20 ‐ 24 64% 25 ‐ 29 11% 30 ‐ 34 57% 35 ‐ 39 42% 40 ‐ 4 9% 45 ‐ 49 648 50 ‐ 54 55 ‐ 59 60 ‐ 64 311 564 55.18% 65 + 168 1,286 13.03% Total 5,705 8,544 66.77%

357 715 50.0

25 ‐ 2930 ‐ 3

747 69.04%

35 ‐40 ‐45 ‐50 ‐55 ‐60 ‐65 +Tota

Source: Access Economics calculations using ABS 6105.0 (2009) and AE‐Dem 2008 population estimates.

Table I.20: Age‐gender average weekly earnings(a) for the ge opulation ($)

oup Males Females

neral p

Age gr

15 ‐ 19 336 236 20 ‐ 24 750 598

1,038 849 1,264 905 1,406 865

823 45 ‐ 49 1,380 861

848 55 ‐ 59 1,239 807

25 ‐ 2930 ‐ 3435 ‐ 3940 ‐ 44 1,452

50 ‐ 54 1,345

60 ‐ 64 1,251 749 65 + 336 632

(a) 2008 figures for full‐time and part‐time workers were inflated to December 2009. Source: ABS 6310.0 (2009b) and ABS 6345.0 (2010a).


147

Year 12 non‐completion

Table 010)

p Lower

employment(a) prod y(b) Deadweight

losses l cost

I.21: Annual costs of year 12 non‐completion ($ 2

Age grouLower uctivit

Tota

Males 15 ‐ 19 475 133 ,614

1,683 470 ,715 2,619 731 8,895 3,242 906 11,013 3,560 994 12,092 3,606 1,007 12,249 3,454 965 11,733 3,248 907 11,033 2,624 733 8,912 2,046 4 572 6,951

789 104 1,266

15 ‐ 19 1,138 20 ‐ 24 4,182

2,348 465 ,655 2,452 486 906

2,326 2 461 5,602 2,387 473 5,750 2,615 518 6,298 2,389 473 5,755 1,918 2 380 4,621 1,089 1 216 2,622

128 25 308

1,006 120 ‐ 24 3,563 525 ‐ 29 5,545 30 ‐ 34 6,866 35 ‐ 39 7,538 40 ‐ 44 7,636 45 ‐ 49 7,314 50 ‐ 54 6,878 55 ‐ 59 5,556 60 ‐ 64 ,334 65 + 373

Females 472 572 94 1,736 2,102 344

25 ‐ 29 30 ‐ 34

2,842 2,969

55,

35 ‐ 39 ,816 40 ‐ 44 2,890 45 ‐ 49 3,166 50 ‐ 54 2,893 55 ‐ 59 ,323 60 ‐ 64 ,318 65 + 155

(a) Proxied by reduced probability of workforce participaied by reduced AWE. Access Economics calculations.

ergraduate non‐completion

Table I.22: Annual costs of undergraduate degree non‐completion ($ 2010)

Age group Lower Lower Deadweight

Total cost

tion. (b) ProxSource:

Und

employment(a) productivity(b) losses

Males 20 ‐ 24 2,568 1,157 ,074

3,997 1 1,801 21,904 34 4,949 2,230 120 39 5,433 2,448 777

5,504 2,480 163 5,272 2,376 8,892 4,958 2,234 7,169 4,005 1,804 946 3,123 1,407 118 569 256 ,117

10,349 1425 ‐ 29 6,10730 ‐ 19,942 27,35 ‐ 21,895 29,40 ‐ 44 22,179 30,45 ‐ 49 21,245 250 ‐ 54 19,978 255 ‐ 59 16,137 21,60 ‐ 64 12,587 17,65 + 2,292 3

148

Age group Lower

employment(a) Lower

prod (b) Deadweight

losses Total cost

uctivity

Females 951 11,566

9,354 1,286 15,640 5,223 9,770 1,343 16,336 4,953 9,267 1,274 15,494

40 ‐ 44 15,904 45 ‐ 49 17,420 50 ‐ 54 5,089 9,521 1,309 15,918 55 ‐ 59 4,086 12,

2,319 6 765 + 272 70 85

20 ‐ 24 3,698 25 ‐ 29 5,000

6,918

30 ‐ 34 35 ‐ 39

5,084 9,512 1,308 5,569 10,419 1,432

7,644 4,338 509

1,05159

781 ,253 1

60 ‐ 64

(a) Proxied by reduce bility of workforce participied by reduce Access Econ lculation

graduate n pletio were derived to as applica under e mpletion the A ort who ned TE compl r 12). d present le I.2

Table : Age‐sp ergr non‐completion rat 007

% who t comp dergrastudi

d proba ation. (b) Prox d AWE.Source: omics ca s.

Under on‐com n rates sist the tion of graduatnon‐co costs to TP coh obtai Rs (i.e. eted yea Deriverates are ed in Tab 3.

I.23 ecific und aduate es in 2

Age did no lete un duate

es

20 72.6% 21 58.1%

53.3% 23 24 25 68.4% 26 72.

75.28 76.

77.79.79.78.78.77.77.377.877.8

22 56.3% 64.0%

3% 3% 5%

27

29 3% 30‐34 1% 35‐39 1% 40‐44 1% 45‐49 1% 50‐54 3% 55‐59 % 60‐64 % 65+ %

Source: Access Econo culations a DEEWR uest from

inality outcomes – p rson ual cos

lated per‐person costs in terms of policing, prison and societal costs, by age and gender. These costs were used

as inputs to the modelling of lifetime costs.

mics cal using data req 2009.

Crim er pe ann ts

The sections below present the sets of calcusystem costs, court system costs


149

Policing costs

Table I.24: Annual policing cost per offender ($)

Age group Males Females

10‐14 6,438 10,234 15‐19 24,848 27,554

18,023 13,739 25‐29 12,540 10,316 20‐24

30‐34 10,423 9,187 35‐39 8,471 7,993 40‐44 6,439 6,225 45‐49 4,335 4,196 50‐54 2,689 2,402 55‐59 1,814 1,452 60‐64 1,393 956 65+ 384 259

Source: Access Economics calculations.

Court sys

Table I.25: Annual court system (

Males Fem

tem costs

cost per offender $)

Age group ales

10‐14 589 1,020 15‐19 20‐24

2,275 2,7451,650 1,3

1,0954 915 775 79589 620

45‐49 7 41850‐54 6 2355‐59 6 14

127 9535 26

69

25‐29 1,148 28 30‐34 35‐39 6 40‐44

3924

9

16 5 60‐64 65+


Prison system costs

Table I.26: An co r prisoner ($)

Males Femal

nual prison system st pe

Age group es

<18 253 262 18 31,155 20,41

964 54,3,037 74,2

112,682 114,109,548 112,93,558 96,51

71,899 50,615

6 19 67,20–24 97

07 37

25–29 718 30–34 840 35–39 0 40–44 69,412 45–49 45,434

150

Age group Males Females

50–54 32,206 30,269 55–59 19,910 60–64 14,982

16,037 11,036

65+ 6,580 1,899


Societal costs

Table I.27: Annual per‐person societal costs of crime for males ($)

Age group Medical output cost(a)

damage/ cost(b)

societal Lost Intangible

Property Other

Total

loss cost

10‐14 1,029 4,416 4,514 12,783 9,501 32,243 15‐19 3,972 17,043 17,421 49,334 36,666 124,436 20‐24 2,881 12,362 12,636 35,783 26,595 90,257

24,898 18,504 62,800 30‐34 1,666 7,149 7,308 20,694 15,380 52,196 35‐39 42,422 40‐44 4,416 4,515 12,784 9,501 32,246

693 2,973 8,607 10 430 1,845 5,340 68

1,244 3,602 2,67 9,086 955 2,765 2,05 6,975 264 269 763 567 1,924

25‐29 2,005 8,601 8,792

1,354 5,810 5,939 16,819 12,500 1,029

45‐49 50

3,039 1,886

6,397 21,73,968 13,4‐54

55‐59 290 1,272 7 60‐64 223 976 5 65+ 61

(a) ‘Intangible’ costs are those due(b) Those c

to pain, suffering a L (Rollings, 2008). osts non‐separable into above categories.

ual per‐person societal costs of crime for females ($)

Medical Lost

output Intangible cost(a)

Property damage/

loss

Other cost(b)

Total societal cost

nd lost Qo


Table I.28: Ann

Age group

10‐14 7,021 20,323 15,10 51,261 1,636 7,177 4 15‐19 4,405 20‐24 2,196 25‐29

18,902 19,322 54,717 40,667 138,014 9,425 27,282 20,2 68,814

1,649 7,077 20,485 15,22 51,669 6,302 6, 18,244 13,559 46,017

35‐39 1, 5,483 15,872 11,7 40,035 40‐44 4,270 12,361 9,18 31,178 45‐49 6 2,879 8,333 6,19 21,019 50‐54 38 1,647 4,769 3,544 12,029 55‐59 2 996 2,883 2,14 7,271

656 671 1,899 1,411 4,790 41 177 514 382 1,296

9,634 7,234

77 5

30‐34 1,469 442278 5,605 96 995 4,365 7 71 2,943 3 4 32

1,684 1,018

3

60‐64 153 65+ 181

(a) ‘Intangible’ costs are those due to pain, suffering a L (Rollings, 2008). ove categories. s.

nd lost Qo(b) Those costs non‐separable into abSource: Access Economics calculation


151

Crime probabilities

detail on the derivation of crime probabilities. Crime rtain which crime c s (i.e. policing, court, would be crime type. Soc ts were assumed to ap all crimes,

Probability of crime being reported

Probabilities of a crime being reported to police were derived from the Australian Institute of

s’ fear of reprisal. The inverse of the relevant multiplier was taken as a proxy for the probability of the crime being reported to p tched and adapted as best as possible to the ABS crime categories presented in section 5.3.5. The final set of crime categories, multipliers and attached probab sented in Table I.29.

Table I.29: Crime under‐reporting multip nd derived probabilities

ory lier of

rep

The sections below providewere derived to asce

probabilitiesprison) ost type

incurred, dependent on ietal cos ply to regardless of the crime being reported.

Criminology report (Rollings 2008). Rollings (2008) presented a set of multipliers for each crime type, which were applied to crime frequencies in her study, to account for crimes which were not reported to police due to non‐detection, being ‘victimless’ or victim

olice. The crime categories in Rollings (2008) were ma

ilities are pre

liers a

Crime categ MultipProbability crimebeing orted

Homicide and related 100%offences 1.0

Acts intended to cause injury* 5.2 19%

Sexual assault and related offences 5.3 19%

eople* 5.2 19%

Abduction/harassment/other offences against 5.2

‐ against commercial 1.2 83%

Unlawful e

Theft and related offences

‐ o

‐ from vehic 2.8

‐ shop the 100

‐ other the 2.7

4.0

Illicit drug offences*** ‐ ‐

Prohibited/regulated weapons and explosives offences***

‐ ‐

Property damage and environmental pollution** 4.3 23%

Public order offences** 4.3 23%

Offences against justice** 4.3 23%

Miscellaneous offences*** ‐ ‐

Dangerous or negligent acts endangering p

the person* 19%

Robbery, extortion and related offences

‐ against individual 7.2 14%

ntry with intent (ie: burglary) 3.4 29%

f vehicles 1 100%

les 36%

ft 1%

ft 37%

Fraud, deception and related offences 25%

* These crimes were all assumed to have the same multiplier – ‘physical assault’. ** These crimes were all assumed to have the same multiplier – ‘criminal damage’.

152

*** No relevant multipliers given for these crime categories in Rollings (2008). Source: Access Economics calculations using Rollings (2008) and crime categories from ABS 4519.0 (2010b).

118BProbability of court action on a reported crime

Probabilities of court action for a reported crime were derived from 2008‐09 data on police proceedings from the ABS publication, ‘Recorded Crime – Offenders, 2008‐09’ (ABS 2010b).

Some limitations are noted, which were due to indivisibilities in the available ABS data:

■ It was assumed both commercial and individual robberies had the same court action probability.

■ It was assumed that all theft and related offences had the same court action probability.

Since robbery and theft types can differ in their seriousness and thus probability of court action, assuming the same probabilities may not be the ideal approach, but is the best approach warranted by the available data.

The final set of probabilities is presented in XTable I.30 X.

Table I.30: Probabilities of court action on reported crimes

Crime category Court action Non‐court action

Homicide and related offences 99.19% 0.81%

Acts intended to cause injury 94.09% 5.91%

Sexual assault and related offences 92.63% 7.37%

Dangerous or negligent acts endangering people 86.18% 13.82%

Abduction/harassment/other offences against the person 92.30% 7.70%

Robbery, extortion and related offences*

‐ against individual 96.49% 3.51%

‐ against commercial 96.49% 3.51%

Unlawful entry with intent (ie: burglary) 84.31% 15.69%

Theft and related offences**

‐ of vehicles 58.26% 41.74%

‐ from vehicles 58.26% 41.74%

‐ shop theft 58.26% 41.74%

‐ other theft 58.26% 41.74%

Fraud, deception and related offences 92.57% 7.43%

Illicit drug offences 57.33% 42.67%

Prohibited/regulated weapons and explosives offences 90.18% 9.82%


153

Crime category Court action Non‐court action

Property damage and environmental pollution** 71.86% 28.14%

Public order offences 58.71% 41.29%

Offences against justice 81.77% 18.23%

Miscellaneous offences 20.67% 79.33%

* Same court‐action probability assumed for both types of robbery – individual and commercial. ** Same court‐action probability assumed for all types of theft. Source: Access Economics calculations using ABS 4519.0 (2010b).

119BProbability of ‘guilty’ verdict in a court finalisation

Probabilities of a guilty verdict in court finalisations were derived from 2008‐09 data on criminal court finalisations from the ABS publication, ‘Criminal Courts, Australia, 2008‐09’ (ABS 2010c). The final set of probabilities by court level is presented in XTable I.31 X.

Table I.31: Court finalisation outcome probabilities

Court level Guilty Acquitted Transferred to other court

Withdrawn Other

Higher courts 6.64% 78.61% 0.81% 13.64% 0.30%

Magistrate 3.96% 86.86% 2.07% 7.03% 0.08%

Children’s 3.88% 77.15% 3.47% 10.27% 5.23%

All courts 4.02% 86.07% 2.12% 7.39% 0.40%

Source: Access Economics calculations using ABS 4513.0 (2010c).

120BProbability of a custodial sentence

Probabilities of a custodial sentence being given in ‘guilty verdict’ court finalisations were derived from 2008‐09 data on criminal court finalisations from the ABS publication, ‘Criminal Courts, Australia, 2008‐09’ (ABS 2010c). The final set of probabilities by court level is presented in XTable I.32 X.

Table I.32: Custodial sentence probabilities in guilty verdict court cases

Court level Custodial sentence Non‐custodial sentence

Higher courts 84.57% 15.43% Magistrate 8.63% 91.25% Children’s 9.36% 90.14% All courts 10.39% 89.48%

Source: Access Economics calculations using ABS 4513.0 (2010c).

Documents

Positive Family Functioning - Department of Social Services · · 2012-03-21Positive Family Functioning 29 September 2010 Final Report by Access Economics Pty Limited for Department