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Income-related inequality in children’s mental health in Norway: a

concentration index decomposition approach.

Ida Victoria Katjivena Pedersen

Master program: Economics 30 Credits

Faculty of Social Sciences

Department of Economics

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Preface

Working on this master thesis is without a doubt the most demanding and frustrating task I have embarked on during all my years as a student. However, it is also one of the most rewarding and I am grateful for the experience it has given me. First and foremost, I would like to send a huge thank you to my supervisor, Jonas Minet Kinge, for his impeccable advice, excellent guidance, and enthusiastic engagement. This thesis would have never come to fruition without your help. Thank you to Statistics Norway (SSB) and Norwegian Institute of Public Health (FHI) for assisting with clarifications and definitions.

Writing this thesis in the middle of a pandemic, from a home office has not always been easy.

So Rune, thank you for your patience, help, support, reminding me to go outside once in a while, and when all else fails, to meditate. Thanks to Bente, Kapombo and Nora, for reading, listening, supporting, and helping. And finally, a thanks to my fellow students, even though I haven’t seen you physically for a year, sharing the joys and sorrows via SoMe has been the next best thing, and it has definitely helped keep me sane.

The data used here are taken from " Levekårsundersøkelsen 2005 – Tverrsnitt Tema: Helse "

and " Samordnet levekårsundersøkelse om helse, omsorg og sosial kontakt 2012". Data are collected by Statistics Norway. Data is arranged and made available in anonymized form by NSD - Norwegian Center for Data Research AS. Neither Statistics Norway nor NSD are responsible for the analysis of data, or the interpretations made here.

Ida Victoria Katjivena Pedersen

Ida Victoria Katjivena Pedersen, Arendal, May 19th, 2021

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Abstract

Income-related inequality in children’s mental health in Norway:

a concentration index decomposition approach.

by

Ida Victoria Katjivena Pedersen, Master of Economics University of Oslo, 2021

Supervisor: Jonas Minet Kinge

The purpose of this thesis is to measure the degree of income related inequality in the mental health of children and adolescents in Norway and highlight possible associations that may explain this inequality. The data material used for the analysis, the 2005 and 2012 Surveys on Income and Living Standards (“Levekårsundersøkelsen 2005” and “Samordnet

levekårsundersøkelse 2012”), is collected by Statistics Norway, and made available by Norwegian Center for Data Research AS. Stata SE 16 is used for the analysis.

A concentration index decomposition approach is applied to measure the degree of inequality in mental health. I construct a measure for mental distress, based on sociological literature, and a concentration index is estimated for girls and boys, and decomposed.

I find income-related inequality in the distribution of mental distress at the disadvantage of the low-income households and the analysis shows that the income-related inequality is larger among boys than girls.

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PREFACE ... II ABSTRACT ... III TABLES AND FIGURES ... V

1 INTRODUCTION ... 1

1.1 SCOPE OF THE THESIS: ... 1

2 DEFINING MENTAL HEALTH AND MENTAL DISTRESS ... 2

3 CHILDREN’S MENTAL HEALTH; EMERGENCE, PREVALENCE, AND PREVENTION ... 4

3.1 PREVALENCE OF MENTAL HEALTH DISORDERS/ISSUES IN NORWAY ... 4

3.2 SYMPTOMS AND ONSET OF MENTAL HEALTH ISSUES ... 5

3.3 DETERMINANTS FOR MENTAL HEALTH ISSUES IN CHILDREN AND ADOLESCENTS ... 6

4 EXPLANATORY MODELS ... 9

4.1 ECONOMIC MODEL: ... 9

4.2 SOCIOLOGICAL EXPLANATORY MODELS: ... 11

5 ECONOMETRIC METHOD ... 13

5.1 THE CONCENTRATION INDEX ... 13

5.1.1 Standard errors to the concentration index ... 17

5.2 DECOMPOSITION OF THE CONCENTRATION INDEX ... 18

6 DATA AND VARIABLES ... 19

6.1 SURVEYS ON INCOME AND LIVING STANDARDS ... 19

6.2 BIASES ... 21

6.2.1 Potential selection bias: ... 21

6.2.2 Data collection and other potential biases: ... 23

6.3 THE PROCESSED DATASET ... 24

6.3.1 2005 Survey on Income and Living standards ... 24

6.3.2 2012 Survey on Income and Living standards ... 24

6.4 DEPENDENT VARIABLE:MENTAL DISTRESS SCORE ... 26

6.5 EXPLANATORY VARIABLES ... 27

6.5.1 Income: ... 27

6.5.2 Education: ... 28

6.5.3 Other explanatory variables ... 29

7 DESCRIPTIVE STATISTICS ... 30

7.1 DATA COMPOSITION ... 31

7.2 MENTAL DISTRESS ... 31

7.3 INCOME ... 33

7.4 EXPLANATORY VARIABLES: ... 35

8 ANALYSIS AND RESULTS ... 37

8.1 CONCENTRATION INDICES ... 37

8.2 REGRESSION COEFFICIENT ... 39

8.3 DECOMPOSING INEQUALITY IN CHILD AND ADOLESCENTS MENTAL DISTRESS SCORES ... 42

9 SUMMARY ... 47

10 REFERENCES ... 50

APPENDIX A: VARIABLE LIST ... 54

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Tables and Figures

Table 1 Sample composition ... 31

Table 2 Descriptive statisitic, Income ... 35

Table 3 Descriptive Statistics ... 36

Table 4 Concentration indices ... 38

Table 5 Regression Coefficients ... 40

Table 6 Decomposition of Mental Distress ... 44

Figure 1 Concentration Curve ... 14

Figure 2 2005 Survey On Income and Living Conditions ... 32

Figure 3 2012 Survey on Income and Living Conditions ... 32

Figure 4 Kernel Density Estimates, Income ... 34

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1 Introduction

In her 2020 New Year’s Address, the Norwegian Prime Minister, Erna Solberg, commented on the increasing number of children and adolescents suffering from mental health problems.

An increase that particularly appears to affect girls. The PM further said that the Government intended to initiate measures to identify the causes of the increasing trend, as this was key to effectuating responses (Solberg, 2020).

The Government’s proposition for increasing children and adolescent’s mental health and wellbeing (Prop. 121 S) was passed on the 4th of February 20201. In the proposition multiple steps are taken to combat the increasing trend of mental health problems in children and adolescents. While Norway, according to the world Happiness Report, is considered a great country to live in, there still might be social conditions in Norway that can contribute to decreasing mental health and wellbeing for children and adolescents. In proposition 121 S, growing up in low-income households is listed as one of them (Martela, Greve, Rothstein, &

Saari, 2020; Helse- og Omsorgsdepartementet, 2018-2019).

Investing in children’s and adolescent’s mental health is beneficial for society. By working to increase and better children’s and adolescents’ mental health one lays a solid fundament for adulthood, as well as increasing the individual’s feeling of quality of life (Helse- og

Omsorgsdepartementet, 2018-2019). As such, understanding what associations may affect mental health problems in children and adolescents is of great importance for the individual, the public health, and society as a whole.

1.1 Scope of the thesis:

The objective of this thesis is to measure the degree of, and highlight possible associations that may explain, the inequality in mental distress among children and adolescent. This will

1 https://www.stortinget.no/no/Saker-og-publikasjoner/Saker/Sak/?p=76495

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be done by applying data from two Norwegian surveys on income and living standards that include information on parental wellbeing, socioeconomic status, and their children. A measurement for the child/adolescent’s mental distress is constructed, and a concentration index estimated, and decomposed for possible associations. I will apply a definition of mental health and mental distress, based on sociological literature, which encompasses individuals experiencing symptoms of mental distress that are not severe enough to constitute a formal diagnosis. As such, I will be able to assess associations for at-risk groups, and hopefully contribute to greater insight into the associations and conditions that characterize mental distress in children and adolescents, as well as the distribution of this over parental socioeconomic status.

It is necessary to state that I in no way will be assessing causal effects or correlations, as this is not possible with the available data.

2 Defining mental health and mental distress

The term ‘mental health’ and ‘mental health issues’ embraces a wide array of states ranging from an experience of a good and sturdy mental health and a feeling of wellbeing, to a reduced feeling of quality of life, ailments, and disorders (Major, 2011; Reneflot et al., 2018;

WHO, 2005).

When discussing mental health and mental illnesses it can also be tackled from two different ideas. According to Teresa L. Scheid and Tony N. Brown mental health can be viewed as a dichotomy or as a continuum. The dichotomous view, which Scheid and Brown associates to the medical field, perceives individuals as either sick or healthy, which is in line with formal diagnostic tools such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Disease (ICD). One either meets the criteria for a diagnosis or one does not (Brown & Scheid, 2010).

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Viewing mental health as a continuum rather considers mental health and mental illnesses as opposite sides of the pole. In this view all individuals can be placed on the continuum, and the majority of individuals fall somewhere between the two extremes (Brown & Scheid, 2010).

The definition of ‘mental health’ and ‘mental distress’ will be based on the writing of John Mirowsky and Catherine E. Ross and the concept of mental health as a continuum. In their book, Social Causes of Psychological Distress, Mirowsky and Ross define psychological distress and psychological well-being as opposite sides of the continuum.

While psychological well-being is associated with feelings of happiness, accomplishment and enjoying life2, psychological distress is described as an “unpleasant subjective state”

(Mirowsky & Ross, 1989, p. 23).

According to Mirowsky and Ross psychological distress takes on two forms, depression, and anxiety, which again can be expressed in two ways, mood and malaise. Depressive mood refers to states where the individual feels lonely, sad or worthless, to mention some, while depressive malaise refers to bodily discomforts like lack of appetite, experiencing difficulties with sleeping or concentrating and talking less. Similarly, anxiety also has a mood and a malaise component. The mood part of anxiety refers to feelings of tenseness, restlessness, worry and irritability, to mention some, while the malaise anxiety can manifest as

stomachaches, headaches, dizziness and shortness of breath (Mirowsky & Ross, 1989).

To summarize, when speaking of ‘mental health issues’ or ‘mental distress’ throughout this thesis, it will be in the context of the framework set forward by Mirowsky and Ross, unless specified otherwise. As such, mental health will, for the sake of this thesis, be viewed as a continuum ranging from an experience of ‘psychological well-being’, i.e. a state of happiness, contentment and buoyancy, to the opposite pole, ‘psychological distress’, characterized by sadness, lack of positive feelings and pessimism.

2 A definition which holds similarities to the definition employed by the World Health Organization, i.e.,

“Mental health is a state of well-being in which an individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and is able to make a contribution to his or her community”

(WHO, 2005, p. 2)

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Nevertheless, it will still be necessary to shed light on the prevalence of formalized mental disorders, as a lot of the research deals with this.

3 Children’s mental health; emergence, prevalence, and prevention

3.1 Prevalence of mental health disorders/issues in Norway

Every third year “Ungdata” publishes the findings from their nationwide survey about adolescents’ health and well-being. The 2020 report is based on survey responses from over 200 thousand adolescents from grades 8 to 13. While the majority of the adolescents asked reported that they are content with life, the data also showed that 15 % of lower secondary students, and 21% of upper secondary students had experienced mild symptoms of mental distress in the past week (Bakken, 2020). While this trend was evident for both genders, girls experienced more distress than boys, a discovery which corresponds with earlier findings that indicate an increase in psychiatric symptoms in teenage girls (Costello, Mustillo, Erkanli, Keeler, & Angold, 2003; Reneflot et al., 2018).

In Norway, mental disorders account for approximately 17% of the overall burden of disease (DALYs) for children aged 5-9, where anxiety disorders are the largest contributing factor (7%), followed by behavioral disorders. By ages 10-14 mental disorders make up more than a quarter, and by ages 15-19 the overall burden has risen to 30%. For the two latter age groups anxiety disorders are still the largest contributing factor, and depressive disorders play a larger part. Together, the two disorders make up approximately 20% of the disease burden for adolescent girls, while only accounting for approximately 12% for adolescent boys. In return adolescent boys have a larger disease burden from behavioral disorders (approximately 15%) (Skogen, Smith, Aarø, Siqveland, & Øverland, 2018).

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3.2 Symptoms and onset of mental health issues

To understand how mental distress can manifest in children and adolescents, it may be useful to understand how mental disorders onset and present in children and adolescents as this is not a “one size fits all”.

Neurodevelopment disorders (autism spectrum disorders – ASDs and Attention Deficit/Hyperactivity Disoreder – ADHD) typically appear early in childhood, while depression typically makes their appearance in the teen years. Anxiety disorders onset throughout, but there are clear patterns for what types of anxiety disorders onset at different ages3 (Maughan & Collishaw, 2015).

Similar symptoms of mental disorders often materialize in different disorders. A diagnosis refers to patterns of these signs or symptoms that are prevalent amongst individuals (Rutter &

Pine, 2015; Stringaris, 2015). It is possible to show signs and symptoms without a diagnosis, as these are often an inherent part of being a human being, as such, it is often the magnitude, frequency and combination of symptoms that may lead to a diagnosis.

As an example, irritability is a symptom that is seen across multiple disorders, and is considered a symptom with major depressive disorder, specific phobias and oppositional defiant disorder (Stringaris, 2015). Disinterest, difficulty concentrating and changes in sleep patterns are just some of the additional symptoms needed for a major depressive diagnosis, in addition to a persistent mood changes that have lasted for at least 14 days (Brent & Maalouf, 2015).

Mental health issues in childhood and adolescence are not synonymous with adversity later in life. Nevertheless, links have been made between physical aggression at early ages, and antisocial behavior later in life. Depressive symptoms typically reoccur in 50-70% of individuals, and as such, adolescent depression can persist into adulthood. ADHD typically

3 Social phobias and obsessive-compulsive disorder (OCD) show increase rates during early adolescence, while separation anxiety and specific phobias onset at earlier ages.

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also persists into adulthood, but with declining symptoms. It is worth noting that prominent cases of ADHD in childhood, combined with antisocial behavior and substance misuse during adolescence has been linked to adverse effects in early adulthood. This is in accordance with findings indicating that clustering of adversities in childhood often lead to negative outcomes for adults (Maughan & Collishaw, 2015).

Research has also shown that the onset of mental disorders in childhood is associated with mental disorders later in life, less social mobility, and lower social positions in adulthood (Costello et al., 2003; Stansfeld et al., 2011). Some have argued that mental disorders in childhood may hamper the child’s ability to accumulate human capital, and as such the future acquisition of education and work. These found that mental disorders in childhood showed persistent effects on cognitive achievements, poorer academic results, and grade repetition (Currie & Stabile, 2006, 2007).

3.3 Determinants for mental health issues in children and adolescents

It is possible to prevent the development of mental health issues in children and adolescents, but the route of engagement is not unambiguous, partly due to the complexity and

interwovenness of the risk factors (National Research Council and Institute of Medicine, 2009; Skogen et al., 2018).

Preventing mental health issues (in the form of distress or disorders) is a matter of public health, and the measures designed to tackle these issues should be precise and effective as not to waste resources. As such, when working preventively in a public health framework it is usually advised that factors that constitute risks and protections be identified in the early years. The measures must further be applied where they can reach at-risk individuals, and for children and adolescents this is the local community, the family, leisure arenas and schools (National Research Council and Institute of Medicine, 2009; Skogen et al., 2018)

Health determinants, the factors that influencing mental health, can have an effect on their own, or in interaction with each other. It is not unusual to group characteristics together in

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into two categories, environmental and individual, whereupon environmental is further divided into three levels: society, local communities and social networks/family (Major, 2011).

The individual health determinants are those factors that relate to the individual and can be classified as genetic and environmental. As I will not attempt to define the boundaries

between what constitutes genetic and environmental factors, these will be viewed as a whole, and as such can encompass personality, self-efficacy4, physical health, disability, intelligence and the heritability of mental disorders (Franić, Middeldorp, Dolan, Ligthart, & Boomsma, 2010; Major, 2011; Mrazek & Haggerty, 1994). The three levels of environmental factors, society, local communities, and social networks will be addresses in depth.

The social level relates to relationships with peers, within the family, neighbors, and others one interacts with. A safe, stable, nurturing, and supportive family is an important preventive factor for child and adolescent mental health issues, and has been associated with lower probabilities for the development of internalizing and externalizing problems (Hannighofer, Foran, Hahlweg, & Zimmermann, 2017; Major, 2011; Skogen et al., 2018)

On the other hand, research has shown that family life characterized by hostility and/or

conflicts may increase the risk of the child or adolescent developing mental health issues. One study found that children from relationships where the mother perceived the relationship as medium to low on the Dyadic Adjustment Scale had a significant probability of developing internalizing problems, while others have seen an association between nontraditional families and emotional disorders in boys. The Disintegration of marriage has also been considered a risk factor for mental distress and/or disorders in children (Hannighofer et al., 2017;

Heiervang et al., 2007; Major, 2011; Reneflot et al., 2018).

Further, if the parents themselves also have mental health issues, are experiencing strains or negative life events while lacking social support, this can also increase the risk (Major, 2011;

4 As in the individuals beliefs in their own capabilities, their sense of mastery or control over themselves and their own life (Victor, 1992).

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National Research Council and Institute of Medicine, 2009; Mrazek & Haggerty, 1994;

Sletten & Bakken, 2016). As such, social networks, and confidants have been considered a preventive factor for major depression and as functioning as a sort of buffer for psychological stress (Cohen, 2004; Mrazek & Haggerty, 1994).

The children and adolescents’ peer relations are also considered as part of the social level of the environmental determinants, and relationships with peers are highlighted as important for social cohesion. While social interaction and spending time with peers in their free time is an important preventive factor, bullying and social exclusion are important risk factors. This was also found in a study from Tromsø where children and adolescents how reported that they were being bullied had higher occurrences of emotional difficulties, while close social

relations was the one factor that had the biggest preventive effect (Major, 2011; Skogen et al., 2018; Thorvaldsen, Westgren, Egeberg, & Rønning, 2018).

Environmental determinants at a societal level relates to social capital, socioeconomic status (in the form of education, employment, etc.) and social equality, and the subject area is widely researched. In an international comparative study, adolescents own perceived socioeconomic status was associated with 2, or more, health complaints in 31 of the 37 included countries5, a systematic review found a negative association between socioeconomic status and mental health issues in children and adolescents in 52 of the 55 included studies, and low

socioeconomic status has also been associated with having more psychological symptoms.

The same is also evident in Norway where the Bergen Child Study (BCS) found that low- family income was persistently associated with higher scores for self-reported mental health issues with respect to conduct-, emotional-, peer- , and hyperactivity problems, while low parental education was associated with conduct- and hyperactivity problems. (Bøe, Øverland, Lundervold, & Hysing, 2012; Due, Lynch, Holstein, & Modvig, 2003; Holstein et al., 2009;

Reiss, 2013).

Finally, local communities, or the local environment may also affect children and adolescents’

mental health. This through communities with little social interaction, where the population is

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unstable or there are other factors that lead to social isolation or marginalization. This level also encompasses individuals working environment and school or kindergarten environment, as this is information that has not been accessible/usable, little attention is given to this level of determinants (Major, 2011).

While this is in no way an extensive list of potential risk and prevention factors it is a short introduction to some of the determinants that may influence the mental health of children and adolescents. Consequently, when determining variables for the decomposition of the

concentration index, variables that may indicate risk factors or preventive factors will be included in the analysis.

4 Explanatory models

The purpose of this thesis is to attempt to shed light on what factors may explain differences in mental health due to parental socioeconomic status, in this case measured by income. As such, it is necessary to explain how parental income may affect child health.

4.1 Economic model:

In the paper “Healthy, wealthy, Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development” Janet Currie presents an extension of the human capital model for health as defined by Michael Grossman (Currie, 2009). The below model is obtained from this paper.

Currie’s starting point is an intertemporal utility function:

(2) % 𝐸!(1 𝜎⁄ )!

"

!#$

𝑈!+ 𝐵(𝐴"%$)

Where s is the discount rate, B is a bequest function and 𝐴 are assets.

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The parents’ utility function, 𝑈!, is given by:

(3) 𝑈! = 𝑈(𝑄!, 𝐶!, 𝐿!; 𝑿𝒕, 𝒖$, 𝜀$!)

It is assumed that the parents’ desired utility bundle consists of consumption (𝐶!), leisure (𝐿!), and the stock of the child’s health (𝑄!). In addition, it also comprises a vector of permanent individual specific taste shifters (𝒖$) and exogenous taste shifters (𝑿𝒕), while 𝜀$!

encompasses potential shocks that might occur and affect preferences.

Parental utility is maximized subject to the child health production function 𝑄! and three additional constraints.

(4) 𝑄! = 𝑄(𝑄!'$, 𝑮!, 𝑉!; 𝒁!, 𝒖(, 𝜀(!)

(5) 𝑌! = 𝐼!+ 𝑤!𝐻!+ 𝑟𝐴!

(6) 𝐶! = 𝑌!− 𝑷)!𝑮!− (𝐴!%$− 𝐴!)

(7) 𝑉!+ 𝐿!+ 𝐻! = 1

The stock of child health, or the child health production function, equation (4) is a function of the child’s health in the previous period (𝑄!'$), material inputs into health production (𝑮!), a vector of exogeneous productivity shifters (𝒁!), individual specific productivity shifters (𝒖(), a productivity shock 𝜀(!, and 𝑉! represents time inputs into health

Equation (5) is the budget constraint, where total income is represented by 𝑌!, 𝐼! is unearned income, 𝑤! is wage, 𝐻! is hours designated to work, r is the interest rate and 𝐴! denotes assets as before. The consumption function (6) is a function of prices 𝑷)!, material inputs (𝑮!) and assets (𝐴!). The final equation, (7), is a constraint consisting of time variables.

The child health production function can be solved for a Marshallian demand function:

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(8) 𝑄!= 𝑄∗∗∗(𝑄+,𝐴-, 𝑱!, 𝑿!, 𝒁!, 𝑤!, 𝐼!, 𝑷!, 𝑴!, 𝑟, 𝜎, 𝒖$, 𝒖(, 𝜀$!, 𝜀(!)

Where 𝑴! denotes a vector of moments of the distribution of (𝑿., 𝒁.𝑤., 𝐼., 𝑷., 𝜀$., 𝜀(.), of future time periods, i.e., k = t+1,…, T, and 𝑱! = (𝑿., 𝒁., 𝑤., 𝐼., 𝑷., 𝜀$., 𝜀(.), where k = 1,…, t-1. The other variables are as previously defined.

The above model explains how parental SES may affect a child’s health. If health is produced by way of material inputs, parents with more money will have greater possibilities of

acquiring these, and parents may be better at producing child health due to increased levels of education with is viewed as a productivity shifter (𝒖() (Currie, 2009).

4.2 Sociological explanatory models:

Currie’s model provides a theoretical economic framework for examining the relationship between parental SES and children and adolescent’s mental health. Sociology has also contributed theoretical frameworks that attempt to explain the associations between parental SES and child/adolescent mental health.

Sociology, as a social science discipline, often differentiates between a social determinant perspective, i.e., that health affects the available socioeconomic resources, and a social selection perspective, that socioeconomic resources affect health (Bøe, 2015). Below I will now outline two of the dominating theories explaining the relationship between lower socioeconomic status and children’s mental health. They each represent one of the perspectives mentioned above.

4.2.1 The family stress model

The family stress model, also known as the family process model, describes how economic hardships can affect children and adolescent through parental stress. If the economic

hardships are severe, they might put a strain on the family’s interactions as low income might

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result in families having to reduce their spending, or not reaching their material needs and desires as they attempt to live within their means. This pressure may affect the relationship between the parents, the internal family relation, or the parents own emotional state as well as their own mental health (Bøe, 2015; Conger, Ge, Elder, Lorenz, & Simons, 1994).

As the pressure increases, the family can become prone to conflict. The parents might be less involved with each other and their children, and the relationships may suffer as a result. If the relation in addition is characterized by hostile confrontations, and this is a persistent factor, it may lead to the adolescent experiencing a reduced sense-of-self which may present as anxiety or depression (Conger et al., 1994).

4.2.2 The family investment model

The family investment model is often used to explain the association between socioeconomic status and the child’s school achievements and development of cognitive skills. The general idea behind the model is that families with lower income will have limited resources to acquire food, housing, stimulating toys, and after school activities (Bøe, 2015).

While the underlying assumption of the model is not necessarily linked to mental health in the extent that is defines for this thesis, it is still of interest. Arguably, if one wants to prevent the occurrence of mental health issues in children and adolescents, to the extent that that is possible, one needs to reach them where they are. From ages 3-19 organized after school activities/leisure activities are an important area for the development and growth of the individual (Skogen et al., 2018). However, if children from low-income households do not have the resources to participate in these activities, they lose double. First, they are excluded from the social environment where the potential preventive measures are in place. Second, as they are excluded from their peers, the children might feel that their family is different, which may impact their own sense-of-self negatively. The exclusion might also result in teasing or bullying, the latter having strong associations to mental health issues in children and

adolescents (Sletten, 2011; Thorvaldsen et al., 2018).

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5 Econometric method

The models explained in the previous chapter described possible explanations for why parental socioeconomic status may affect child and adolescent mental health. To quantify the possible association an estimation strategy is needed. In this thesis a concentration index approach will be applied to assess the magnitude of the association between parental socioeconomic status and child/adolescent mental health.

In 1991 Wagstaff, Paci, & van Doorslaer wrote a paper discussing different measures of socioeconomic inequalities in health, and how accurately these measurements describe the inequality. They introduced three minimum requirements that a measure for inequality should encompass, the Concentration Index (CI) was one of three measurement methods that met all three. It quantified the degree of inequality for the whole population, acknowledged the socioeconomic aspect of this inequality, and showed sensitivity to distributional changes in- between socioeconomic groups. Since then, the CI has been a widely used tool for assessing and describing the level of inequality for a given measure of health (Mangalore, Knapp, &

Jenkins, 2007; O'Donnell, O'Neill, Van Ourti, & Walsh, 2016; O'Donnell, van Doorslaer, Wagstaff, & Lindelow, 2008; Wagstaff, Paci, & van Doorslaer, 1991).

5.1 The concentration index

The CI is a bivariate rank-dependent indicator, which quantifies the degree of socioeconomic- related inequality in the health variable. By ranking individuals, based on their socioeconomic status, from poor to rich, one is able to see if there is a difference in how the level of health (or ill health, as in the case of this thesis) is distributed amongst the individuals in the sample.

As such, the concentration index quantifies the relationship between the health variable, and the rank of the socioeconomic variable, not the variable in itself, while it still encompasses both distributions (Erreygers & Kessels, 2017; Mangalore et al., 2007; O'Donnell et al., 2008).

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In our case the CIs measure inequality in children’s mental distress related to the ranking of parental income. The CI is then measured as a function of the share of total mental distress accounted for by children with parents in different parts of the income distribution. This provides a measure of inequality that includes children from all parts of the income distribution. The CI is derived from the concentration curve which plots the cumulative proportion of mental distress (y-axis) against the cumulative proportion of parental income, ranked from poorest to richest (x-axis) as seen in Figure 1. If everyone has equal mental health Lh(p), the concentration curve, coincides with the diagonal. In the example in Figure 1, Lh(p), measuring the degree of ill-health in a population, lies above the diagonal, which means that inequalities in mental distress exist and are more prevalent in the poor.

Figure 1 Concentration Curve

For illustration: concentration curve of ill-health concentrated amongst the disadvantaged

As a graphical presentation of the concentration curve does not give a measure of the

magnitude of inequality and it does not illustrate the shape of the association, little attention is given to graphical representations of the CI it in this thesis. Rather, to depict how the health variable is distributed across the socioeconomic variable, mean health is estimated for the socioeconomic groups and graphed in the chapter 7.2 (O'Donnell et al., 2016; O'Donnell et al., 2008).

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The formal definition of the CI is:

(9) 𝐶 = 1 − 2 E 𝐿/(𝑝)𝑑𝑝

$

+

Which is twice the area between the concentration curve, 𝐿/(𝑝), and the 45° line (Figure 1).

The standard concentration index ranges from 1 to -1. When the health variable measures the magnitude of ill-health (or mental health distress in children) a negative value of the

concentration index means that mental distress is higher among the poor (Mangalore et al., 2007; O'Donnell et al., 2008).

The below explanation of the concentration index is based on Mangalore et al., 2007;

O'Donnell et al., 2016; O'Donnell et al., 2008; Wagstaff et al., 1991.

The standard concentration index is defined as:

(10) 𝐶(ℎ|𝑦) =1

𝑛% Mℎ0

ℎN (2𝑅0− 1)P

1

0#$

= 2𝑐𝑜𝑣(ℎ0, 𝑅0) ℎN

Where ℎ0 is the health variable and ℎN its mean (mean mental distress). 𝑅0 is the fractional rank of individual i (𝑅0 = 𝑖 𝑁V ) where the poorest individual is equal to i = 1. The second equality is known as the “convenient covariance” result which is closely related to the “convenient regression” result which gives an identical estimate for the concentration index:

(11) 2𝜎2(Wℎ0

ℎNX = 𝛼 + 𝛽𝑟0 + 𝜀0

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The concentration index is here attained trough regressing the rank of individual i (𝑟0), by use of OLS, on two times the variance of the fractional rank and health divided by mean health (2𝜎2([/!

/3\). The coefficient 𝛽 then provides an estimate for the concentration index.

When the data is weighted, the fractional rank should be extended to encompass this. For weighted data the fractional rank is defined as:

(12) 𝑟0 = % 𝑤4

0'$

4#+

+𝑤0 2

This is created by summing all the sample weights and dividing each individual weight by the total sum. This produces 𝑤0 which is a scaled weight which sums to 1. 𝑤4 is the cumulative sum of 𝑤0, and 𝑟0 is, as before, the rank of individual i. As the survey data applied in this thesis provides sample weights, the weighted fractional rank will be used, and whenever fractional rank is used in this thesis the weighted fractional rank is implied.

Ultimately, there are multiple other ways of estimating the concentration index, based on the measurement level of the health variable, whether the variable is bounded or not, and the views on inequality underlying the estimation (O'Donnell et al., 2016). In the strictest sense the health variable should be continuous and on a ratio scale, but data on mental health are often ordinal in nature, and strictly adhering to the issue of measurement level may limit possible insights. Therefore, heeding the words of the psychologist S. Stevens who claimed that “there can be invoked a kind of pragmatic sanction (as) in numerous instances it leads to fruitful results” (Stevens, 1946, p. 679), a standard concentration index approach will be applied in thesis regardless of the measurement characteristics of the health variable (Jamieson, 2004; Stevens, 1946).

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5.1.1 Standard errors to the concentration index

It is possible to use the standard error of the coefficient 𝛽 as the standard error of the concentration index, this however does not account for the sampling variability of the estimated mean mental distress score. Another method is instead to estimate standard errors by the delta method, where the rank coefficient is transformed to acknowledging that the coefficient, 𝛽, is in itself a random variable.

Regressing the rank of individual i (𝑟0) on the mental distress score (ℎ0):

(13) ℎ0 = 𝛼$+ 𝛽$𝑟0+ 𝑢0

The concentration index can be estimated as:

(14) 𝛽_ = `2𝜎2( 𝜇̂ c 𝛽_$

𝛽_, representing the estimated concentration index, 𝛽_$ the coefficient from equation (12), 𝜎2( the variance of the fractional rank, and 𝜇̂ the mean mental distress score. As the predicted values (ℎd0) and the dependent variable (ℎ0) has the same mean (𝜇̂), and the fractional rank, by design, has a mean equal to 0.5, equation (13) can be rewritten as:

(15) 𝛽_ = f 2𝜎2( 𝛼g$+ 𝛽_$

2 h 𝛽_$

The estimated concentration index is now a function of the regression coefficient, and estimating standard errors, by the delta method, will consider the variability of the sample.

The standard errors of the concentration indices presented in chapter X will be estimated by this method.

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5.2 Decomposition of the concentration index

It is possible to explain the variation of the health variable over the distribution of the socioeconomic variable by decomposing the index into explanatory variables.

The procedure and method outlined below is based on the writings by O’Donnell et al., and Wagstaff et.al. (O'Donnell et al., 2008; Wagstaff, van Doorslaer, & Watanabe, 2003).

To decompose the concentration index into the explanatory variables one first needs to tie the health variable, y, to them. This is done by fitting a linear regression and estimating it. For the sake of this thesis a clustered OLS approach is applied as it is highly unlikely that

observations within families are independent. As such correlation within families are expected, while correlations between families are not. The regression model is of the form:

(16) 𝑦0 = 𝛼0+ % 𝛽.𝑥.0+ 𝜀0

.

Where 𝑦0 is the mental distress score for child i, 𝛼0 is the intercept, 𝑥.0 are the explanatory variables (variable k for child i), 𝛽. is the coefficient for variable k and 𝜀0 is an error term.

The coefficients are assumed to be constant across the sample, i.e., variation in the observed mental distress score is due to the distribution of the explanatory variables vary by income.

Due to the association between the explanatory variables and the observed mental health variable seen in equation (16), the concentration index can be defined as:

(17) 𝐶 = %(𝛽.𝑥̅.⁄ )𝐶𝜇 .+ 𝐺𝐶5⁄𝜇

.

The association between 𝑦0 and 𝑥.0 means that the concentration index C can be treated as

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variables, 𝑥̅., and the concentration index for each of the explanatory variables, 𝐶.. This is the deterministic component, where the concentration index of y, i.e., the mental health variable, corresponds to the weighted sum of the concentration indices of the k explanatory variables. Furthermore, the weights are equal to the elasticity of the mental health variable with respect the explanatory variable 𝑥..

The second component is the residual component equal to:

(18) 𝐺𝐶5 = 2

𝑛% 𝜀0𝑅0

1

0#$

Also known as the generalized concentration index for 𝜀0. This captures the inequality in the distribution of mental distress that is not captured by variations in income, i.e., variations that do not have a systematic social profile.

6 Data and Variables

6.1 Surveys on income and living standards

To investigate the association and distribution of family income on children and adolescents’

mental distress, the data needs to contain both parental socioeconomic characteristics and some form of information about the mental health of children.

Statistics Norway (SSB) has on two occasions collected data that includes both

socioeconomic characteristics (e.g. income, marital status etc.) and self-reported evaluations of physical and mental health for the interviewee and other family members. This was done in

“Levekårsundersøkelsen 2005, Tverrsnitt Tema: Helse (NSD0842)” and in “Samordnet levekårsundersøkelsen om helse, omsorg og sosial kontakt 2012 (NSD2034)”. Consequently,

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and information about their mental health and other characteristics are attained trough the interview object.

The interviewees are selected from BeReg6, Statistics Norway’s demography and population database based on information from the national population register (“folkeregisteret”) from the Norwegian Tax Administration (“Skattedirektoratet») (Amdam & Vrålstad, 2014; Hougen, 2006).

The 2005 survey on income and living standards (NSD0842) was conducted by way of in- person and phone interviews. As a result, SSB applied their two-step sampling procedure. The first step is a stratified sampling procedure where primary sampling units are drawn from 109 strata. The strata consist of municipalities. If the municipality has more than 30 000 inhabitants it is assigned its own strata7. Smaller municipalities are grouped together in strata and designed to be as homogeneous as possible. In the second step individuals are weighted, and randomly drawn from the 109 primary sampling units8. Thus, individuals have equal probability of being selected the 109 primary sampling units. (Bjørnstad, 2000, 2009; Hougen, 2006; Vedø &

Solheim, 2006).

The gross sample consisted of 10 000 individuals (5000 respectively for the main- and additional sample). Due to non-response, death or individuals living abroad, the net sample consisted of 6766 individuals (Hougen, 2006).

For the 2012 survey on income and living standards (NSD2034), interviewees are randomly drawn in a one-step procedure. First 14 085 individuals of all ages are drawn from a nationwide representative sample where all municipalities are eligible. After removing all individuals under the age of 16, the gross sample consisted of 10 000 individuals. These constitute a representative

6 BeReg was in 2005 called BEBAS (Falnes-Dalheim, 2009, p. 57)

7 This is also the case for some municipalities with between 25 000 and 30 000 inhabitants.

8 Information about the sampling units were not include in the survey data and are therefore not taken into

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distribution of the Norwegian population with respect to gender, age groups, and regional habitation. Due to non-response, death or individuals living abroad, the net sample consisted of 5660 individuals (Amdam & Vrålstad, 2014).

6.2 Biases

When collective survey data errors may occur in data collection or with regard to interview responses. These errors may result in the data being biased. As there is no way of altering the collection of data for this analysis, the data is taken as granted. It is however necessary, for the integrity of the analysis, to highlight possible biases that might be present.

6.2.1 Potential selection bias:

While the gross sample is assumed to be representative with respect to the Norwegian population, the net sample, consisting of the remaining interviewees after defection and the departure of unqualified interviewees, may not be.

In both the 2012 and 2005 surveys the number of unqualified interviewees is considered so low as to not affect the selection bias. The numbers of interviewees that defected is on the other hand substantial and is corrected for.

Register data is used to examine the extent to which defection might have resulted selection bias. As any deviation between the gross sample and the population can be attributed to sample variation, the difference between the gross and net sample is applied. As such register data is applied to examine how the gross sample differs from the net sample. For the 2005 survey three characteristics were used for this analysis: age, gender, and region. These three were also applied for the 2012 survey, but here they also included education.

In the 2005 survey the percentage of defection, or dropouts, were 30,2 percent, and the most common cause overall was that the interviewee did not want to participate. The second most common cause was that one was unable to get ahold of the interviewee. The youngest age

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groups (“16-24” and “25-44”) accounted for the highest percentages within this cause for defection. While the age group “80+” were responsible for most of the individuals defecting on the basis of being prevented from participating in the survey.

This trend was also present for the 2012 survey, which had a defection rate of 42%. In this survey a two-sided T-test was applied to test the difference between gross and net sample for statistical significance. A 5 % significance level was used, and both age and education produced significant differences. All age groups, except “16-24” were significant, and “25- 44” accounted for the largest negative difference, while “45-66” accounted for the largest positive difference. The latter was also the case for the 2005 survey9.

The difference between net and gross sample had significant results for all five levels of educational attainment10. Evident throughout the four levels that account for acquired

education was the trend that individuals with lower education were less prone to answer than those with higher education. With the largest negative difference was found for individuals with primary education, while the largest positive difference was provided by individuals with up until 3 years of higher education.

Due to the deviation in distribution of characteristics between net and gross sample weights were constructed to give more weight to individuals who were underrepresented in the study.

For the 2012 survey the four above mentioned characteristics in addition to family size was used for weight construction. No information is given in the documentation report about the construction of weights for the 2005 survey.

9 The documentation report for the 2005 survey does not include results from a T-test. It rather listed the difference in percentage (net – gross), and age group “45-66” accounts for the largest positive difference with an increase in one percentage point(Hougen, 2006, p. 16).

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6.2.2 Data collection and other potential biases:

When constructing a questionnaire or survey, its goal is to produce data of high quality. The importance of how to structure the interview and the questions is widely debated as it might lead to potential biases11. As the subject of this thesis might be one that the interviewees find vexing or troublesome, there might be cases of social desirability bias. The length of the interview might also result in satisficing, this is especially true for the questions regarding children’s ails, ills and bullying which is placed toward the end of the interview and include the data that represent the backbone of the variable of interest in this thesis (Krosnick, 1991;

Streiner, Norman, & Cairney, 2015).

Both in 2005 and the 2012 survey was conducted by way of telephone – and in-person interviews. The degree to which in-person interviews were conducted varies greatly between the two surveys. In 2012 in-person interviews constituted 0,2 percent of the net sample of 5660 participants. For the 2005 survey, in-person interviews amounted to 13 percent of the 6766 participants that establish the net sample. In these cases, parents may be inclined to answering questions about one’s children’s well-being in a way that is socially desirable, and as such underreport, resulting in biased data (Amdam & Vrålstad, 2014; Hougen, 2006;

Streiner et al., 2015).

Questions that might be considered very divulging were issued by way of a questioner the respondents could answers either digitally or by post. These questionnaires contained questions related to the interviewee’s mental health, alcohol, and drug consumption etc.

Although this may mitigate a potential desire to be viewed in a favorable light, the possibility is still present. It also worth noting that the 2005 questionnaire had a monetary incentive attached to it in that all interviewees that responded were eligible to win one of twelve cash prizes (Hougen, 2006).

11 Evident by the Google search “survey construction” resulting in approximately 828 million results.

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6.3 The processed dataset

As the processed dataset is composed of two different datasets, the final result is a pooled cross- sectional dataset. As mentioned above, information about the children and adolescents were linked to the interview object, so all observations (interview objects) whom did not have children were dropped from the dataset as a starting point for the data cleaning. In the next step each child was extracted from the respective survey dataset consecutively, starting with child 3 and ending with child 1. This resulted in three different datasets for each survey year. These were then appended to construct one long dataset. All three children were extracted from both surveys as the 2012 survey listed child 1 as the youngest child in the household, and to avoid a skewed sample all children in the household were included. In the dataset from both surveys, observations that gave unspecified answers to the questions related to the mental distress score were dropped. Due to the process in which the children/adolescent’s information is extracted from the survey data, the interview object is assumed to be a parental figure and will be referred to as this throughout the thesis.

6.3.1 2005 Survey on Income and Living standards

The starting sample contained 6766 observations, and of these 1445 interview object had children between the ages of 6 and 15. The processed sample consists of 111 observations for the third child, 620 observations for the second child, after 7 observations were dropped due to dissatisfactory answers to the mental distress questions, and 1424 observation for the first child, 20 observations dropped by the mental distress questions. The final sample of the 2005 dataset consists of 2155 observations divided on 1431 family units (interview objects/original observations). This indicates that many of the observations dropped were from the same family units. Finally, three observations were dropped due to negative income.

6.3.2 2012 Survey on Income and Living standards

The household mapping done in this survey was a little different, and to get the two datasets

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number link them to the household mapping. To circumvent this issue a reference number was constructed linking information about each child mental health to their demographic characteristics. This was possible since the interview object was asked questions about the youngest child in the household first, then the second youngest and so on (Ramm, J. SSB, personal communication, March 2, 2021).

The original survey data consisted of 5660 observations. Removing all interview objects that did not have children left 989 observations with one or more children. 52 of these observations included no information about any on the variables needed to construct a mental distress score, and no demographic characteristics of the household members and were therefore removed, reducing the sample to 937 observations (i.e., interview objects).

In the creation of the mental distress variable 5 observations were dropped from the answers for child 3, leaving 79 observations. The observations on child 2 was reduced from 418 observations to 406, and child 1 went from 938 observation to 910. This left a total of 1395 observations for children divided on 917 family units.

As mentioned above, to link the mental distress score to the demographic characteristics stated in the household mapping a reference number was constructed. In the construction of these, an additional 51 children were dropped. These were dropped due to discrepancies between register data on gender and gender listed in the household mapping12, they were the same gender and had the same birthday13, and one of the children was not a child/stepchild of the interview object, but a sibling. In the end the processed sample contained 901 family units, and information about 1344 children.

12 The child was listed as a boy in the register data, but listed as a girl in the household mapping, and as such it was difficult to determine the actual gender of the child.

13 The children were same sex twins and were indistinguishable and randomly assigning a reference number may

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6.4 Dependent variable: Mental distress score

A variable, encompassing the definition of mental health/distress (see chapter 2), was created to measure the degree of mental distress that the children and adolescents experienced. This was done in council with senior researcher at the National Institute of Public Health, Fartein Ask Torvik (Torik, F and Kinge, J. National Institute of Public Health, personal

communication, May 3, 2021)

The variable measuring mental distress applied in this thesis is generated by summing together the answers from 10 ordinal questions relating to the child’s wellbeing. The

questions ask how often the child has exhibited or communicated certain behaviors over the past three months. Specifically, the parents are asked about whether they have experienced the child as: i) Having stomachaches, ii) Having headaches, iii) Having difficulties sleeping, iv) Being restless, v) Having difficulty concentrating, vi) Being angry or in a bad mood, vii) Being worried or anxious, viii) Being sad, ix) Feeling nervous or insecure, and x) Feeling lonely or alone. With answer alternatives being: “Each day”, “Multiple times a week”, “Once a week”, “Once a month”, “less often than once a month” or “never”.

The answer alternatives are given a numerical value from 0 – 5 (“never” responding to 0, and

“each day” responding to 5), and summed across all questions. The mental distress variable can thus be viewed as a scale which ranges from 0 – 50. “0” indicating the child, from the parents’ perspective, has not exhibited any of the behaviors or emotions in the past 3 months.

“50” indicating that the child has exhibited all these traits every day for the past three months.

In the construction of the mental distress variable, respondents that answered any of the 10 questions that make up the total mental distress score with “I don’t know” or “I don’t want to answer” were removed. Many of these consisted of individuals that had negated to respond to any of the 10 questions. As this can indicate satisficing the decision was made to simply exclude them from the analysis.

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As previously mentioned (see subchapter 5.1), the generated mental distress variable is based on ordinal data, and it is assumed that individuals can properly rank answer alternatives from good to bad (or from often to seldom) when answering the questions that make up the scale.

This is also the case for most scales used in psychology, where verbal statements are given a numeric value and a cut-off value, which is applied to indicate the possibility of a mental disorder14. It would, in the strictest sense, be incorrect to apply statistics such as means, standard deviations or coefficients of variation, as ordinal data do not inhabit the information needed to interpret these statistics in a meaningful manner15. As such, these statistics might be flawed as the intervals (“Each day”, “Multiple times a week”, “Once a week”, “Once a

month”, “less often than once a month” or “never”), cannot be argued to be equal.

Nevertheless, as stated in subchapter 5.1, for the sake of the possible insights that might be gained, these statistics will be applied regardless of the measurement characteristics of the generated mental distress score. As such, the above paragraph is included to acknowledge the potential shortfalls, and possible problems associated with the generated mental distress score.

6.5 Explanatory variables 6.5.1 Income:

As described in chapter x there are many socioeconomic variables that can be applied to rank the individuals from poorest to richest in the concentration index. For the sake of this thesis, the variable total household income after taxes is used. For both the 2005 data and the 2012 data the income is adjusted for purchasing power. Statistics Norway’s (SSB) consumer price

14e.g., The SCL-25 (Hopkins Symptom Check List), used to measure the psychological distress, consisting of 25 questions where answers are ranked from 1 to 4. For this scale, 1 indicates “not at all” and 4 responds to

“extremely”, with 2 and 3 falling somewhere between these two extremes. A cut-off value of 1.75 is then applied as a valid indicator of mental disorder as assessed independently by clinical interview (Strand, Dalgard, Tambs, &

Rognerud, 2003, p. 114).

15e.g., one of the questions from SCL-10 is ‘Have you, during the last week, experienced sudden fear without reason?’, and the answer alternatives are 1= ‘not bothered’, 2 = ‘slightly bothered’, 3= ‘very bothered’, and 4=

‘extremely bothered’ (Strand et al., 2003). If the population mean for this question is 2.5 what does this entail?

What is the mean of ‘slightly bothered’ and ‘very bothered’? How much larger is this than ‘not bothered’? These questions are, if not impossible, very difficult to answer, and thus, in the strictest sense, the statistics are

nonsensical (Jamieson, 2004; Stevens, 1946).

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index is used to adjust the respective incomes to NOK 2019. Further, the income is

“equivalized” by taking the square root of the total number of household members. A method applied by OECD in recent years (OECD, 2008). While this form of ‘equalization’ does not differ between adults and children, it does assume that larger households might have greater financial needs, and as such accounts for the size of the household. The ‘equalized’ and purchasing power adjusted variable is labeled infadjinc.

The natural logarithm of the equivalized purchasing power adjusted income variable, lninfadjinc, is included in the decomposition. In Currie’s model (see chapter X) material inputs that affect health enters the production of child health. In the family investment model (see section Y) higher income is assumed to buy material good or access to activities that may enhance the child’s cognitive development. As the material goods, or access to activities, are subject to the budget constraint, an increase in the available income may positively affect the child’s health. The log of income is used as this is often applied when examining the

relationship between income and health, and viewing income as a proportional change (i.e., +2 %) is closer connected to the real world than viewing it as a linear change (+ NOK 2,-) (Carrieri & Jones, 2017).

6.5.2 Education:

Instead of using the parental figure’s education, the highest level of educational attainment in the household is used. This means that the educational level could be attributed to the

interview object (i.e., the ‘parental figure’), their spouse or other members of the household.

The original data is coded in 9 levels16, this division is simplified in this thesis.

The 2012 dataset included a variable for highest level of attained education in the household (utdnivaa_h). For 155 observations the highest attained education was listed as “unspecified”, even if there were individuals in the household with higher levels of specified education (i.e., lower university degrees, higher university degrees and Ph.D’s). To correct for this, the

16 0 = no education, 1 = primary school (‘barneskole’), 2= lower secondary (‘ungdomsskole’), 3 = upper

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highest level of educational attainment in the household not equal to “unspecified” was used to indicate educational status. In the cases where no other educational attainment was listed for any members of household, or all individuals were coded as “unspecified”, this was recorded as the highest level of education. As a result, there are a total of 27 observations denoted by the binary variable uspeced, when taking into account interview objects from both surveys.

Other levels of educational attainment are also included in the decomposition. scndry denotes upper levels of secondary and vocational school, unilwr captures educational attainment up to four years of university or college. Finally, unihi denotes households where an individual has attained a masters or Ph.D level of education.

6.5.3 Other explanatory variables

As mentioned in subchapter 3.3, peer relationships and social interactions are important for the child’s mental health. To see if there is an association between the mental distress score and interpersonal relationships the variables locfriends, bullied are included in the

decomposition. Locfriends (local friends) indicates whether the child has any good friends in the area they live and bullied indicated whether the child has been bullied in the past six months. As these variables fit within the framework of environmental risk/preventive factors, chidisabill and xtracarechild are included to highlight some of the individual risk factors that may affect the child’s mental health. Chidisabill indicates whether the child has any illnesses, injuries or disabilities that are assumed to be permanent, and xtracarechild indicates whether the child has illnesses or disabilities that results in the child needing extra care, supervision or help to complete day to day tasks.

As interpersonal relationships and the environment the child is raised in may have an effect on the child’s mental health, the parental figure’s support network might function as a mitigating factor for the parents if they were to experience stressors that could again affect the child. To capture this the variable confidants is included as an explanatory variable indicating whether the IO has anyone they can talk to or confide in.

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