Mental Health, Disruptive Behavior and Extrinsic

Motivation

Peter Rohde Skova, Anders Holma

Abstract

Using a randomized controlled experiment we show how an extrinsic reward motivates

children when completing cognitive test. The experiment consisted in financial rewards on

performance in two cognitive tests. We demonstrate how the overall effect of the experiment

is in part due to heterogeneous effects for sub-groups of children. For a subsample of children

with a high degree of hyperactivity/ inattention but with no further emotional problems we find

large and significant positive effects of being in the reward group. We also find that for children

with both high levels of hyperactivity/inattention and emotional problems, the effect of being

in the reward group is negative. The heterogeneous effects that are associated with being in

the reward group is further substantiated by the fact, that we find no effect of belonging to the

reward group on a cognitive test applied four years later, for which there was no reward on

performance.

aSFI – The Danish National Centre for Social Research, Herlufs Trolle Gade 11, DK-1052 Copenhagen K;

University of Copenhagen, Department of Sociology, Øster Farimagsgade 5, Bld. 16, 1014 Copenhagen K,

Denmark. Email: plo@sfi.dk, aho@sfi.dk. Telephone: +45 3369 7715

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1 Introduction

Using a randomized controlled experiment we show the effect of a financial reward on the outcome

of cognitive tests for a random sample of Danish eleven year old children. We find a small overall

significant effect on outcomes from being in the reward group. We further show that a substantial

part of this effect is due to heterogeneous effects on a subset of children with mental health prob-

lems. For a subsample of children with a high degree of hyperactivity/ inattention, but with no

further emotional problems, we find large and significant effects of being in the reward group. We

also find that for children with both hyperactivity/inattention and emotional problems, the effect

of being in the reward group is negative. Thus our study may point to a way of affecting cogni-

tive performance of children with hyperactivity/inattention and that for this subset of children, the

effect of the incentive depends further on their overall mental health profile.

The relationship between accumulation of human capital and mental health has been given

increasingly attention during the recent years. The reason for this is that cognitive as well as non-

cognitive abilities play an integral part in education and labor market outcomes and overall health

and well-being of individuals (Cunha et al., 2010; Heckman et al., 2006; Heckman and Rubinstein,

2001). Accumulation of human capital is especially important during childhood, underlining the

importance of mental health in child development (Currie, 2009).

Two of the most common childhood mental health diagnoses are emotional problems, such as

depression and anxiety, and “disruptive behavior”, such as hyperactivity/ inattention or the more

specific diagnosis of attention deficit/ hyperactivity disorder (ADHD) (Currie and Stabile, 2006,

2007; Fletcher and Wolfe, 2008). Emotional problems and hyperactivity/ inattention have impair-

ments on the overall health and well-being of individuals.

It is estimated that in 2011 around 9 per cent of US children aged 3 - 17 have symptoms of

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ADHD (Bloom et al., 2012). A systematic review has shown that around 5 per cent of the European

children and adolescents have an ADHD diagnosis (Polanczyk et al., 2007). These estimates show

that a relatively large proportion of children have mental health problems that can affect learning

outcomes. Emotional problems and hyperactivity/ inattention can be considered as non-cognitive

skills that are pertinent to learning outcomes. Hyperactivity/ inattention, more commonly known

under the psychiatric diagnosis of ADHD, is becoming more common among children, with an

annual growth of 3 per cent between 1997 and 2006 for the US (Pastor and Reuben, 2008).

Hyperactivity/ inattention and emotional problems lead to lower learning outcomes for chil-

dren, which in turn affect earnings and employment (Almlund et al., 2011; Borghans et al., 2008,?;

Cunha and Heckman, 2008; Cunha et al., 2010; Currie and Stabile, 2006, 2007; Fletcher and Wolfe,

2008; Hinshaw, 1992; McLeod and Fettes, 2007; McLeod and Kaiser, 2004; Miech et al., 1999).

The lower learning outcome for children with hyperactivity/ inattention is due to the lack of the

ability to focus at the task at hand (Adams et al., 1999; Barkley et al., 2001; Epstein et al., 2011;

McInerney and Kerns, 2003; Winstanley et al., 2006). This is due to a lack of control of inhibition

and executive functions, leading to “perverted” utility functions in which the individuals seeks in-

stant gratification, in the form of small, instant rewards (Antrop et al., 2006; Levitt et al., 2012;

Marco et al., 2009; Sonuga-Barke et al., 1992; Thorell, 2007; Wilson et al., 2011). Medication to

treat the children’s hyperactivity/ inattention can be costly, and the long run effects of medication

are not yet documented (Carlson and Bunner, 1993; Currie et al., 2013). Another way of treating

children with hyperactivity/ inattention is by rewarding them. Previous research on this group of

children suggests that they can be helped to keep their attention, if they are promised a reward for

completing a given task (Aase and Sagvolden, 2006; Barkley and Cunningham, 1979; Cunning-

ham and Barkley, 1979; Epstein et al., 2011; Kohls et al., 2009; Luman et al., 2008; McInerney

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and Kerns, 2003). This suggests that children with hyperactivity/ inattention are more responsive

to extrinsic motivation, than to intrinsic motivation.

Hyperactivity/ inattention are often associated with emotional liability, or emotional overreac-

tions, such as low frustration tolerance, hot temper or irritability (Barkley, 1997; Sobanski et al.,

2010; Wehmeier et al., 2010). This implies comorbidity between hyperactivity/ inattention and

emotional problems. Whereas the comorbidity and the description of the relationship between

emotional problems and hyperactivity/ inattention has been given much attention (e.g. Karustis

et al., 2000; Schatz and Rostain, 2006; Sobanski et al., 2010), only a few studies have used a large

scale representative sample to investigate how the relationship between extrinsic motivation, emo-

tional problems and hyperactivity/ inattention affect learning outcomes. Although children with

high levels of hyperactivity/ inattention have a higher risk of lower academic achievement, they

also tend to vary greatly in their social and academic impairments. This can be contributed to

membership of subtypes of hyperactivity/ inattention and emotional problems. Thus, there is a

further need to investigate the relationship between hyperactivity/ inattention and emotional prob-

lems, and how this relationship affects learning outcomes.

Our study contributes to the existing literature on learning outcomes for children with hyper-

activity/ inattention in three ways.

First, we use a randomized controlled experiment to examine how extrinsic motivation affects

learning outcomes for children with hyperactivity/ inattention problems.

Second, we use a longitudinal study of a large national birth cohort. This allows us to investi-

gate whether the effect of the reward has long run effects or whether the reward only had an effect

in conjunction with the experiment.

Third, we use a broad screening instrument for identifying children with hyperactivity/ inat-

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tention and emotional problems. This also mean that we are using a relatively broad description of

hyperactivity/ inattention and not just the diagnosis of ADHD. One of the problems of only using

data on diagnosis is the issue of “overdiagnosis” and “underdiagnosis”. Overdiagnosis is the case

when the children are diagnosed as having a mental disorder, when the disorder is not the under-

lying issue of the child’s behavior. This might be the case when the child is performing relatively

poorly in school or displaying disruptive behavior. Underdiagnosis is case when diagnosis is not

issued to the child, i.e. to avoid social stigma (Cuffe et al., 2005; Currie and Stabile, 2006, 2007).

The paper is organized as follows: Section 2 presents the literature on intrinsic and extrinsic

motivation that we address. Section 3 presents data and measures, while section 4 discusses the ex-

perimental setup and our methodological framework. Section 5 shows the results of our estimated

models and experiments. Finally, section 6 discusses our findings and concludes our paper.

2 Intrinsic/ extrinsic motivation and mental health

In this section we briefly outline the psychological and economics literature on extrinsic and extrin-

sic motivation important for our study. First, psychologists have shown in experiments that extrin-

sic motivation “crowd out” intrinsic motivation, (Deci, 1972). Furthermore Condry and Chambers

(1978) suggest that rewards often distract attention from the process of task activity to the product

of getting a reward. In response economists have then made contributions that show incentives

framed as losses have different (positive) effect as compared to incentives framed as gains (Levitt

et al., 2012). Furthermore Bénabou and Tirole (2003) proposed reasons for why extrinsic motiva-

tion crowds out intrinsic motivation. The apparent negative correlation between reward size and

performance is due to the fact that the principal offers the highest rewards to those least likely to

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work – or in our case – when rewards are fixed, they have the highest effect on those least likely

to work – those with low intrinsic motivation (children with hyperactivity/ inattention) (Bolle and

Otto, 2010). They also propose that rewards has larger effects (in the short run) for low ability

agents who would not exert any effort in the case of no reward.

Research into children with high hyperactivity/ inattention, suggests that these children have

deficient behavioral inhibitions, leading to more impulsive behavior, and thus more short term

goals. According to a theory of ADHD, created by Barkley (1997), the behavioral inhibitions are

atop the executive functions in the brain. According to this theory there are four executive func-

tions that are subordinate of the behavioral inhibitions. These executive functions can only work

with an intact inhibitory system. The four executive functions are spatial working memory; verbal

working memory; self-regulation of affect, motivation and arousal; and reconstitution (Barkley,

1997; Barkley et al., 2001; McInerney and Kerns, 2003; Marco et al., 2009; Sonuga-Barke et al.,

1992; Scheres et al., 2006). These four executive functions contribute to the self-regulation of

behavior. Barkley’s theory hypothesizes that these executive functions work in sequence, creating

delays in which the executive functions can direct behavior away from immediate action to longer

term goals (i.e. postpone gratification). Collectively, the processes are critical for problem-solving.

Of particular interest for, although not testable in our study is the spatial working memory. The

spatial working memory is related to tasks such as mental arithmetic, digit span and imitations of

hand movement sequences; tasks that need concentration in order to be solved (McInerney and

Kerns, 2003). This executive function is also related to the perception of time, leading to dissim-

ilar motivational sets, for children with hyperactivity/ inattention disorders, compared to children

without hyperactivity/ inattention. As children with high levels of hyperactivity/ inattention per-

ceive time differently to other children, they also have a higher need for response contingencies

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and rewards, to keep them motivated (see McInerney and Kerns, 2003; Luman et al., 2005, for a

longer discussion on this). As an externalizing problem, hyperactivity/ inattention leads to poor

academic performance, and the internalizing nature of emotional problems leads to social prob-

lems, i.e. problems with acceptance from peers. According to research by psychologists and psy-

chiatrists emotional problems, such as anxiety and depression, have an inhibitory effect (Karustis

et al., 2000; Schatz and Rostain, 2006; Sobanski et al., 2010). Children with comorbid disorders of

hyperactivity/ inattention and emotional problems do not display the same levels of impulsiveness,

as the ones children with only high levels of hyperactivity/ inattention have (Garon et al., 2006).

Although children with comorbid disorders are less impulsive, they also show increased difficul-

ties in tasks that require attention, compared to children with only high levels of hyperactivity/

inattention.

Empirical findings on how extrinsic motivation affects task performance when individuals per-

form cognitive test are mixed. Some psychologists confirm earlier findings. Aase and Sagvolden

(2006) finds that there is no difference between children with ADHD and comparison children

when reinforces were given frequently. Only during infrequent reinforcement were statistically

significant differences on measures of sustained attention, but not hyperactivity and impulsiveness,

found. On the other hand some economists find that the presence of incentive payments seems to

be more important than the size of the reward (Borghans et al., 2013). This also means that we

expect an effect of the reward among the children with high levels of hyperactivity/ inattention,

as they have less intrinsic motivation, than the children who have lower levels of hyperactivity/

inattention. We expect negative effects for the children who have comorbid disorders, as they

will perceive both the tests and the rewards as being stressful. In summary it seems that extrin-

sic motivation might enhance performance during cognitive test, especially for the children with

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hyperactivity/ inattention, and that some groups are responding more than others.

3 Data and measures

We use data from the Danish Longitudinal Study of Children (DALSC). This unique data (initially)

follows 6,000 Danish children, born in the autumn of 1995. The data is nationally representative

of children born in this period. The first data collection was initiated in 1996, when the children

were six months old. The main aim of the longitudinal study was to track the physical and mental

development of the children, while supplying information on family background (e.g. Gupta

et al., 2013). The DALSC has been supplied with follow up surveys in 1999, 2003, 2007 and

2011. The DALSC covers rich information on parents rearing practices, parental educational level,

socioeconomic status and social life within the family. The survey offers a unique insight about the

critical early life of the children and how human capital is invested in the children, as suggested

by Heckman et al. (2006) and Cunha and Heckman (2008). Combined with the comprehensive

Danish administrative registers for the entire Danish population, this data provides unique insights

into the family and the development of children.

The response rate in 2007 was about 80% of the original sample. The overall attrition for

this survey has been low, and the response rates of families with low socioeconomic status has

been declining throughout the waves of DALSC, meaning that better educated parents have a

slightly higher representation in our sample, compared to the rest of birth cohort. This does have

the implication, that we might have fewer children with hyperactivity/ inattention and emotional

problems in our sample, compared to the rest of the birth cohort.

Due to the large scale of the DALSC, a brief psychometric instrument is used to assess the

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hyperactivity/ inattention and the emotional problems of the children. We specifically use infor-

mation from the Strength and Difficulty Questionnaire (SDQ). The SDQ, developed by Goodman

(1997), is a standardized questionnaire that has similar psychometric properties as the Child Be-

havior Checklist (CBCL) (Achenbach et al., 2008; Goodman and Scott, 1999). The SDQ contains

25 questions on the behavior the children may have exhibited within the previous six months. For

each child in the DALSC, the SDQ was rated by the parents, which in most cases were the mothers.

As most parents spend more time with children, than the teachers, the answers from the parents

provide a more reliable measure of the children’s behavior. The parent can answer “Not true”,

“Somewhat true” or “Certainly true” to each of the 25 questions on the SDQ.

The 25 questions are divided into five subscales of each five questions. The subscales re-

late to the children’s problems with peers, conduct disorders, hyperactivity/ inattention, emotional

problems and pro-social behavior (Goodman, 2001). The items that compose the scale for hyper-

activity/ inattention are designed to assess hyperactivity, inattention and impulsiveness, which are

the key symptoms of the Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV)

diagnosis of ADHD (Niclasen et al., 2012). Each of the subscales have a range in integers from 0

to 10, with 0 being no problems at all within the specific subscale and 10 indicating large problems

within the specific subscale. The validity of the SDQ has been assessed in Denmark and found to

have similar properties as those found in other Nordic countries as well as those found in Germany

and scores slightly lower than in the UK (Becker et al., 2006; Niclasen et al., 2012; Obel et al.,

2004). Although a high score on the SDQ is not the same as a clinical diagnosis, it does provide

valuable information on the difficulties that the children have.

The SDQ offers cut off scores for each subscale to assess whether the child is within a normal

range or the child is outside of what is considered the normal range of the scale. Danish 10-12-

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year-old children are considered as having a high level of hyperactivity/ inattention (abnormal), on

the parental questionnaire, if they have a score of 5 or more points on the scale for hyperactivity/

inattention. The Danish 10-12-year-old children are considered as having emotional problems out-

side the normal range if they score 4 or more points on the scale for emotional problems (Niclasen

et al., 2012).

The children have been screened using the SDQ in each wave of the DALSC since 2003.

We therefore have information on the children’s physical and psychological well-being and non-

cognitive traits in the age of 7 and 11. In order to assess whether the measures of hyperactivity/

inattention and the emotional problems are persistent with the child, we utilize the longitudinal

structure of the data. For the SDQ subscales for hyperactivity/ inattention and emotional problems,

we take the average of the scores on the two SDQ-subscales at the age of 7 and 11. A child’s level

of hyperactivity/ inattention tends to vary with the child’s age (e.g. Bertrand and Pan, 2013;

Biederman et al., 2000; Elder, 2010; Evans et al., 2010; Niclasen et al., 2012). Indeed this is also

true of our data. We illustrate this by using the normative cut-off scores that was outlined earlier.

As is seen in table 1, there is some variation in the levels of hyperactivity/ inattention between the

ages 7 and 11. From the table we find that 96.28 per cent of the children who had normal levels of

hyperactivity/ inattention at age 7 are also found to have similar levels of hyperactivity/ inattention

at age 11. The table also shows that around 40 per cent of the children, who had abnormal levels

of hyperactivity/ inattention at age 7, also have abnormal levels of hyperactivity/ inattention at age

11. This suggests that for at least the children with abnormal levels of hyperactivity/ inattention,

there is a large variation between ages.

Because of this, our strategy of using the average of the scores on the two SDQ-subscales at the

age of 7 and 11, gives us a more valid estimate of the children’s level of hyperactivity/ inattention

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and emotional problems, as we take measurement errors into account, by using this strategy. In the

cases in which we have missing observations on the SDQ-subscales at either age 7 or 11, we use

the information on the subscale without taking the average of two observations.

Information on the family income, marital status, parent’s highest educational level and sex of

the child is obtained by the Danish administrative registers. We thereby minimize the amount of

attrition due to partial answers on the surveys.

4 Experiment

Our randomized controlled experiment was conducted in conjunction with two cognitive achieve-

ment tests in 2007 when the children in the sample were approximately eleven years old. The

children were randomly selected to participate in the reward experiment before the start of the data

collection of DALSC in 2007. All children of the 6,000 children of the DALSC were eligible

for the reward experiment. We use the term “reward group” for those children who won the lot-

tery, which is those who were randomly picked to receive a reward for completing two cognitive

achievement tests. The group of children who did not win the lottery is labeled as the “control

group”. The children in the reward group were not told that there was a control group and vice

versa. This should minimize the likelihood of Hawthorne or John Henry effects, i.e. that behavior

in either of the two groups in the experiment is affected by the fact that they are participating in an

experiment (Cook et al., 1979; Levitt and List, 2011).

A total of 1,008 children out of the original 6,000 children of the DALSC were randomly

selected to participate in a reward experiment. The remaining children were not informed on the

experiment. Out of the 1,008 randomly selected children, 823 participated in the experiment. 3,901

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children participated in the control group. The 185 children, who did not participate in the reward

group, did not participate due to usual attrition in the survey. In the reward group each child was

told that (s)he would be given an amount of money, depending on how many problems on both

tests that was solved correctly. Due to ethical concerns it was decided that the children in the

reward group was assured the equivalent of $8. The highest amount the children could earn was

equivalent of $40. The potential reward therefore has a range between $8 and $40. The children

were rewarded after all of the interviews in the DALSC had been conducted. Due to this delay in

the rewards, we are estimating the effect of the anticipation of the reward.

In our sample the children’s monthly allowances was on average $19.98 in 2007, with no

significant difference between the reward ($18.71) and the control group ($20.25). We therefore

expect that the rewardis sizeable enough to have an effect on the outcomes. The reward will yield

approximately twice the size of the average monthly allowances.

Our two main outcome variables are two cognitive achievement tests that were administered

to the children in the 2007 wave of DALSC. The first of the two tests is the Children’s Problem

Solving Test (CHIPS), which is a Raven-type progressive matrix (Kreiner et al., 2006; Hansen

et al., 2011). The CHIPS-test contains 40 problems with increasing difficulty to be solved by the

child within a timeframe of 17 minutes (McIntosh and Munk, 2013). The score on the CHIPS-test

range from 0 to 40, reflecting the total number of correct answers on the test. The CHIPS-test is a

non-verbal figure test. No child in our sample scored 40 correct answers on the CHIPS-test.

The second test that was administered to the child was a verbal test on the comprehension of

the Danish language. The language comprehension test is adapted to the 11-year old children. The

language comprehension test has been used on an older Danish cohort, born in 1954, and used in

numerous analyses of cognitive ability, educational and labor market outcomes in Denmark (e.g.

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Holm and Jæger, 2008; McIntosh and Munk, 2007; Ørum, 1971). The language comprehension

test is a proficiency test, and it therefore entails a human capital component. The language test

contains 34 problems to be solved within a timeframe of 25 minutes. By summing the number of

correct answers from the language test, the variable ranges from 0 to 34. In relation to our sample,

however, no child has more than 33 correct answers. Both of the cognitive achievement tests are

multiple choice tests.

Besides the two cognitive achievement tests in 2007, most of the children also participated in

the follow-up wave of DALSC in 2011. In this wave of the DALSC the children were administered

part of the Raven’s Standard Progressive Matrices (SPM) (Raven et al., 2004). The SPM consists

of five sets (A - E) of twelve questions. The SPM is a non-verbal figure test that is very similar to

the CHIPS-test (Kreiner et al., 2006; Hansen et al., 2011). In the 2011 wave of DALSC only the C

set of SPM was included for the children. This was due to time-restrictions on the overall survey.

A pilot study was conducted before the decision of only using the C set. The pilot study showed

that the C set of the SPM was the subset with most variance in the scores (Bingley et al., 2012).

There are at least three reasons as to why we consider the experiment and data ideal for studying

extrinsic motivation for children with hyperactivity/ inattention. The first reason is that, since

everyone in the reward-group was ensured at least $8, and thereby minimize the incentive to make

an extra effort, we consider the estimate a lower bound. This is due to the relatively small difference

between the base reward, given for participating in the experimental group and the value of making

an extra effort to do well on the cognitive achievement tests. Some of the participants in the reward

group might therefore see the promise of a reward as control or insulting, and therefore either

not respond to the extrinsic motivation or achieve negative outcomes, although this is still much

debated (Bénabou and Tirole, 2003; Borghans et al., 2013; Deci, 1972; Deci et al., 1999; Fryer,

13

2011; Gneezy and Rustichini, 2000; Gneezy et al., 2011). Furthermore the reward was given to the

children after the test had been administered to them, not during the test. This creates a realistic

measure of the motivation that can be achieved by applying rewards to this group of children. In

real life applications, rewards will often be given with some delay and this may be important for

students with a high discount rate and especially among children with hyperactivity/ inattention

(Bettinger and Slonim, 2007; Scheres et al., 2006, 2010). We therefore consider the experiment

transferable to real-life situations in for instance a classroom, in which the effect could also be

achieved by tokens of smaller magnitude, such as a sticker or a note on a job well done.

Second, the experiment is conducted on a nationally representative sample. This allows us to

make generalizations on the experiment.

Third, the longitudinal design of the data allows us to assess whether the effect of the reward

is temporary and only occurs in relation to the reward experiment. By using the information on

the SPM from the 2011 wave of DALSC we can investigate whether the scores on the cognitive

achievement test was successful only in conjunction with the experiment.

The descriptive statistics (means and standard deviations) of the observable characteristics for

the reward group and the control group are shown in table 2. The characteristics include child’s

sex, log of family equivalent scaled income in 2007, highest parental education in 2007, mothers

marital status, the average score on the SDQ-subscales of emotional problems and hyperactivity/

inattention, as well as scores on the language comprehension test, the CHIPS-test and the Raven-

test at age 15. To control for possible innate abilities, we also control for the mothers cognitive

ability. This is done by using the D set of the SPM from the 2011 wave of DALSC for the children’s

mothers1.1We have tested whether there is a difference in including or omitting the variable on the mothers Raven-test in

the models. We find no significant difference between these models and therefore use the information on the mothers

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The descriptive statistics show that there is no apparent difference between the two groups on

the observable characteristics.

< TABLE 2 ABOUT HERE >

In order to assess whether there are systematic difference between the reward and the control

group, we estimate a linear probability model of being in the reward group of the following form:

p(Yi = 1) = α +X′i β + εi (1)

where p(Yi = 1) represents the probability of being in the reward group, α is a constant and X′i

is a vector of observable characteristics. εi is the error-term. The results of this model are show in

table 3. As a control we estimate equation 1 for the data with all the observations we have on the

experiment, and we estimate equation 1 using the observations in which we both have observations

on the experiment and scores on the Raven-test in the follow-up wave of DALSC.

< TABLE 1 ABOUT HERE >

The results show that there are no significant differences between the reward group and the con-

trol group. This result shows that the randomization was independent of the individual’s character-

istics and that the two groups are compatible on observable characteristics and hence presumably

also on non-observable characteristics, due to the randomization2.

In the following section we estimate the effect of the reward on the two cognitive achievement

tests. Because of the experiment we can estimate unbiased results by using OLS regressions on

different subpopulations (Angrist and Krueger, 1999). The first regressions are simple regression

models using only a reward group dummy indicator:

Raven-test in our models, as a measure of innate ability.2Hence, we only include the observational characteristics for reasons of statistical efficiency.

15

YT EST SCOREi = α +DTi β1 + εi, (2)

in which YT EST SCOREi is the outcome on the cognitive achievement tests for child i, DTi is a

dummy variable indicating whether the child was in the reward group and εi is the error term in

the model. β1 measures the treatment effect of the reward on the outcome variables. The subpop-

ulations, on which we are estimating our models, are defined by using different configurations of

the mean scores on the SDQ-subscales for hyperactivity/ inattention and emotional problems. We

show using equation 2 how the effect of the reward changes for children with a high score of hy-

peractivity/ inattention by the score on the measure of the SDQ-subscale for emotional problems.

We further extend our regression model for the entire population to:

YT EST SCOREi = α +DTi β1 +Hiβ2 +Eiβ3 + εi, (3)

where Hi is the score on the SDQ subscale on hyperactivity/ inattention for child i and Ei is the

score on the SDQ subscale on emotional problems for child i. We make this extension to see how

much hyperactivity/ inattention and emotional problems affect the cognitive outcome irrespective

of the effect of the reward. We also estimate equation 3 using a set of controls to gain statistical

efficiency:

YT EST SCOREi = α +DTi β1 +Hiβ2 +Eiβ3 +X

′i β3 + εi, (4)

where X′i is a vector of controls. The controls include sex of the child, log of family equivalent

scaled income, dummies indicating highest parental educational level and marital status and cog-

nitive ability of mother. We also estimate equation 4 on subpopulations of different configurations

16

of the mean scores on the SDQ-subscales for hyperactivity/ inattention and emotional problems.

Estimating both equation 2 and equation 4 on subpopulations makes sure that the more efficient

estimates of the effect of the reward (on subpopulations) in equation 4 are not distorted by inter-

actions effects between subpopulations and controls. This is the case if the effect of the reward in

subpopulations in equation 2 is different from the estimated effect in equation 4.

To further substantiate our findings we estimate models in which we include interaction terms

between the reward and the mean of the SDQ subscale for hyperactivity/ inattention and the SDQ

subscale of emotional problems. As extensions of equation 4, these models have the following

form:

YT EST SCOREi =α+DTi β1+Hiβ2+Eiβ3+DT

i Hiβ4+DTi Eiβ5+HiEiβ6+DT

i HiEiβ7+X′i β8+εi (5)

The coefficient that has our main interest in the models estimated by equation 5 is the coefficient

that measures the difference in the slopes of the reward and control group, and the scores on

hyperactivity/ inattention and emotional problems, β7.

To test the robustness of our results, we first estimate models of equation 2 on the two cognitive

testscores, CHIPS and language test, in 2007. We then proceed by estimating a model on the C

set of the Raven’s Standard Progressive Matrix (SPM), with the same children for the follow-up

wave of DALSC in 2011. A non-significant difference between the reward and control group on

the scores of the C set of the SPM, will show that the reward was only efficient in relation to the

two cognitive achievement tests, and that the children did not learn from the experiment.

17

5 Results

First, in order to assess the relationship between hyperactivity/ inattention and emotional problems,

we estimate pairwise correlations between the measures at the age of 7 and 11 and the composite

score of the measures for the two years. We furthermore estimate the pairwise correlations for the

cognitive achievement tests. The results are shown in table 4. The correlations show a moderate

positive correlation between the mean emotional problems and the mean of hyperactivity/ inat-

tention at the ages 7 and 11. We find correlations of the same magnitude between the measures

of hyperactivity/ inattention and emotional problems at the ages 7 and 11. These results show co-

morbidity between hyperactivity/ inattention and emotional problems, as we would expect (Adams

et al., 1999; Sobanski et al., 2010).

The table shows a negative correlation between the measures of hyperactivity/ inattention and

the cognitive achievement tests. The table also shows a negative correlation between measures

of emotional problems and the cognitive achievement tests. However, the negative correlations

between the cognitive achievement tests and the measures of emotional problems are of a slightly

lower magnitude than the corresponding measures of hyperactivity/ inattention and the achieve-

ment tests. The table also indicates a clear persistence of both cognitive achievement scores, on

the level of hyperactivity/ inattention and emotional problems across age.

< TABLE 4 ABOUT HERE >

In order to further examine the relationship between cognitive achievement, hyperactivity/ inat-

tention and emotional problems graphically, we create surface plots at ages 7 and 11 showing the

two cognitive achievement tests as a function of hyperactivity/ inattention and emotional problems.

The surfaces of the figures are created by using non-parametric LOWESS estimations (Altman,

1992; Fox, 2000). Figure 1 shows the profiles of scores on the language comprehension test that

18

was taken by the children, in relation to the reward, as a function of the mean scores of hyperac-

tivity/ inattention and emotional problems at ages 7 and 11.

The upper left panel of figure 1 shows the scores on the language comprehension test of the

reward group. The upper right panel shows the scores on the language comprehension test of the

control group. The lower panel of figure 1 shows the difference in the language comprehension

test for the two groups.

The red color shows a higher score on the language comprehension test for the children in the

reward group, while a blue color shows a lower score on the language comprehension test for the

children in the reward group, compared to the control group.

The lower panel shows that the children who have a high score on hyperactivity/ inattention, but

no emotional problems, have a stronger response to the reward, than the children who score lower

on hyperactivity/ inattention and score low on the scale for emotional problems. This indicate

that the reward have little effect on children who have few emotional problems and hyperactivity/

inattention. The lower panel of figure 1 also shows that children with a high score on emotional

problems and a low score on hyperactivity/ inattention, score lower on the language comprehension

test in the reward group than in the control group, suggesting negative effects of the reward for this

particular group of children.

<FIGURE 1 ABOUT HERE >

Figure 2 shows the response surface on the CHIPS-test for the children in the reward group (up-

per left panel), the children in the control group (upper right panel) and the difference in the scores

on the CHIPS-test for the two groups (lower panel). The lower panel shows that the children who

receive a reward and have a high score on hyperactivity/ inattention and low score on emotional

problems, also have a higher score on the CHIPS-test, than the comparable group of children in the

19

control group. The figure also shows that children who have a high score on emotional problems

and a low score hyperactivity/ inattention, have lower scores on the CHIPS-test in the reward group

than in the control group. This substantiates the previous finding of potential adverse effects of the

reward for this particular group of children. This might be attributed to anxiety and a feeling of

control for this particular group of children (Deci et al., 1999; Sobanski et al., 2010; Wehmeier

et al., 2010).

The lower panel of figure 2 shows that the children who receive the reward in general scores

lower on the CHIPS-test, except for the children with high scores on hyperactivity/ inattention

and a low score on emotional problems, suggesting that the effect of the reward is local around

those children, who are hyperactive/ inattentive. This result shows that only the children with

high levels of hyperactive/ inattention responds to this extrinsic motivation. The declining, and

at certain points negative, effect of the reward, as the scores on hyperactivity/ inattention and

emotional problems rises in tandem, shows the costs of the comorbidity of hyperactive/ inattention

and emotional problems.

We do not find a difference in the scores on the CHIPS-test for the children who have a low

score on hyperactivity inattention and a low score on emotional problems. This shows us that

the size of the reward might not be of magnitude to which children that fall within this category,

responds (Gneezy et al., 2011; Gneezy and Rustichini, 2000; Kreps, 1997). The CHIPS-test is a

figure test, which is somewhat different in its form as to what the children are used to from school.

The CHIPS-test might therefore be more labor-intensive than the language comprehension test,

which contains elements that the children are used to from their schooling3 (Hansen et al., 2004).

< FIGURE 2 ABOUT HERE>3Most of the children, 96 per cent, went in fourth or fifth grade when the experiment was conducted.

20

Before moving on to a statistical analysis of the significance of the differences between the

reward and the control group we further graphically substantiates that the difference between the

two groups is due toshort term effect of the reward by showing the differences between the two

groups in a non-experimental setting. Figure 3 shows the results of the response surface on the

RAVEN C-set-test for the children in the reward group (upper left panel), the children in the control

group (upper right panel) and the difference in the scores on the RAVEN-test for the two groups

(lower panel). The test was administered four years after the reward experiment and contained no

different treatment of the children who were previously in the control- and treatment group.

<FIGURE 3 ABOUT HERE>

From the figure we find the same relationship between hyperactive/ inattention and emotional

problems on the one side and test scores on the other as in the two previous figures from the reward

experiment. However, in contrast to figure 1 and figure 2 we do not find any detectable differences

between reward and control group in figure 3. This finding clearly supports the interpretation that

the differences between the reward and control group across different configurations of hyperac-

tive/ inattention and emotional problems during the experiment is in fact due to extrinsic incentives

created by the reward.

To further investigate whether the differences between the reward group and the control group

for each of the two cognitive achievement tests are statistically significant, we estimate regression

models of equation 2, both for the full sample as well as conditional on children who are hyper-

active and with different configurations of emotional problems. More specifically this means that

we estimate the slope of the lower panels of figure 1 and figure 2 in the direction of hyperactivity/

inattention for fixed values of emotional problems. We also present results of estimating equation

3 for the entire sample. The results are shown in table 5 for the language test and table 7 for the

21

CHIPS-test.

< TABLE 5 ABOUT HERE>

< TABLE 7 ABOUT HERE>

From model (1) in both tables we see that the reward group on average does have a higher

score on both the language and CHIPS-test. However, only the difference for the language test is

statistically significant. We further find that both emotional problems and hyperactive/ inattention

is associated with lower scores on both cognitive tests as seen for the model (2). When we inspect

the models with interaction effects, model (3) in both tables,between different configurations of

emotional problems and hyperactive/ inattention we find that the average effect of the reward in

both tables decline, most pronounced for the CHIPS test. Thus, to some extent, the effect of the

reward is concentrated on specific subpopulations of the data. This is in line with our expectations;

a higher degree of comorbid disorders leads to a lower score on the cognitive achivement tests. To

see this more clearly and inspired from figures 1 and 2 we show regression results for hyperactive

children with different configurations of emotional problems, shown in column (4) to (9). For

both test we find that students with hyperactive/ inattention has a higher effect of the reward,

thelower the score on emotional problems. Thus it seems that rewarding children with hyperactive/

inattention has a positive effect on the effort on the cognitive tests but also that this effect depends

on their level of emotional problems such that children with hyperactive/ inattention experience

the largest effect when their level of emotional problems are low. Only the effect of the reward for

children with no emotional problems for the CHIPS-test is significant (4) in table 7, but using a

Chow-test we reject that all effects are zero (not shown) and also that they are of similar size (not

shown). The effects of the reward therefore decline over increasing emotional problems.

Table 6 shows the results of the reward on the language comprehension test, while controlling

22

for sex of child, log of family equivalent scaled income, dummies indicating highest parental

educational level and marital status and cognitive ability of the mother and table 8 shows similar

results for the CHIPS-test.

< TABLE 6 ABOUT HERE>

< TABLE 8 ABOUT HERE>

Model (1) in table 6 show that the reward had a positive and statistical significant effect on the

language comprehension test, for the full sample of children using the full set of controls (model

(1) in table 6). The estimate is in line with the estimate without controls in table 5. For the CHIPS-

test we find that using the full set of controls the effect of the reward now become statistically

significant (model (1) in table 8 for the total sample. The interaction effects between hyperactivity/

inattention, emotional problems and being in the reward group implies that the overall effect of the

reward becomes insignificant. However, while it diminished in size for the CHIPS-test it remains

about the same size for the language test. Thus, for the CHIPS test it seems that the effect of being

in the reward group is pertinent to particular subgroups of children with hyperactivity/ inattention

and emotional problems, whereas for the language test it seems that at least to some extend that

there is an overall effect that is not heterogeneous over observed characteristics.

When we move to subgroups of children with hyperactivity/ inattention and varying degrees of

emotional problems (models (3) to (8)) we find the same pattern as in tables 5 and 7; a large effect

for children with hyperactivity/ inattention and no emotional problems and a decreasing effect for

increasing values of emotional problems. However, the only statistical significant effect is for the

CHIPS-test for children with no emotional problems. We use a Chow-test to reject that all effects

are zero (not shown) and also that they are of similar size (not shown).

Our claim that the effect of the reward is causal is based on the fact that allocation into the

23

reward and control groups where random. The causal claim is supported by the analysis of attrition

from the survey in the two groups in table 3. However, we can further substantiate the claim that the

reward had a causal effect and that it was unevenly distributed among sub-populations of children

with hyperactivity/ inattention and emotional problems. We do this by estimating the same models

as in tables 5 to 8 using the outcome of the RAVEN C-set-test which was given to the full sample

four years after the reward experiment. Neither the control nor the reward group where given any

rewards for participating in the Raven test. The estimates are reported without controls in table 9

and with controls in table 10.

< TABLE 9 ABOUT HERE>

< TABLE 10 ABOUT HERE>

From both tables we do not find detectible differences between the reward group and the control

group. Only in the model with controls and interaction effects (model (2) in table 10) do we find

a significant effect of being in the reward group. However, this model is over-fitted and once

insignificant interaction terms (involving the reward dummy) are removed, there is no difference

between the reward and control group. This point strongly towards that the differences created by

the reward four years earlier was indeed causal. It also indicates that being in the reward group and

thus receiving an extrinsic motivation has no lasting effects.

6 Conclusion and Discussion

In this paper we have shown that offering a economic reward and thus increasing the extrinsic

motivation for completing two cognitive test has an positive effect of the overall test score on both

cognitive tests. We have further demonstrated that, especially for the CHIPS-test the effect seems

24

to be larger, if not only present, among children with hyperactivity/ inattention and only small or

none emotional problems. Our two identification strategies point to that both the overall effect

of the reward and also its specific effect for sub-populations with special mental health problems

are causal. We demonstrate this by using the two identifiaction strategiess. First, being offered

the reward was delivered through a randomized controlled trial with no subsequent significant

attrition on observed background characteristics. Second, using a test four years after the reward

experiment, without a reward shows no differences between children in reward and control group.

The results of our analyses also show that extrinsic rewards on intrinsic motivation has different

meanings for subgroups of children, with different mental health profiles. These findings support

other studies of the effects of extrinsic motivation on intrinsic motivation for the subgroups of

children, who have comorbid disorders.

Our finding that the reward has a large effect for children with hyperactive/ inattention for

the CHIPS-test than the language test has two interpretations. The first interpretation is that the

language test requires previous knowledge, something that may be lacking for children with high

levels of hyperactivity/ inattention. Hence, even if the reward motivates them, they do not possess

the skills to turn their motivation into a higher test score. This is different with the CHIPS-test. This

is a test, which should be context independent and therefore to a much lesser degree dependents

on learned skills. Hence, for this test it should be easier for less skilled children, such as children

suffering from hyperactive/ inattention to turn extrinsic motivation into higher test scores. The

other interpretation is one of test fatigue. The CHIPS-test is the second test in the experiment.

If taking the second test is more demanding in terms of human effort, extrinsic motivation may

be more important when completing this test. There is evidence that executing will power (to

complete a test) is exhausting (Bucciol et al., 2010) and that rewards enforcing extrinsic motivation

25

will improve performance.

We are not able to distinguish between these two hypotheses in our study, they may have the

same policy implications in terms of motivation children with hyperactive/ inattention. It would

never the less be interesting to conduct experiments that would be able to separate the two hy-

potheses.

Our results show that children with different mental helath profiles react differently to extrinsic

incentives.

26

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37

Table 1: Level of hyperactivity/ inattention for children aged 7 and 11.

Hyperactivity/ inattention age 11

Hyperactivity/ inattention age 7 Normal level ofhyperactivity/ inattention

Abnormal level ofhyperactivity/ inattention Total

Normal level ofhyperactivity/ inattention 96.28 3.72 100

Abnormal level ofhyperactivity/ inattention 59.85 40.15 100

N 4,093 306 4,399

38

Tabl

e2:

Mea

nsan

dSt

anda

rdde

viat

ions

.A

llR

ewar

dgr

oup

Con

trol

grou

p

NM

ean

Stan

dard

devi

atio

nN

Mea

nSt

anda

rdde

viat

ion

NM

ean

Stan

dard

devi

atio

n

Rew

ard-

grou

p4,

724

0.17

40.

379

Sex

(1=

girl

)4,

724

0.48

20.

500

823

0.48

10.

500

3,90

10.

482

0.50

0L

ogof

fam

ilyeq

uiva

lent

scal

edin

com

e4,

673

12.1

160.

432

814

12.1

170.

398

3,85

912

.116

0.43

9M

othe

r’s

Rav

en-t

est

3,87

39.

529

2.43

368

19.

562

2.38

83,

192

9.52

22.

443

Hig

hest

pare

ntal

educ

atio

nL

ower

seco

ndar

y4,

682

0.09

40.

292

818

0.08

70.

282

3,86

40.

095

0.29

4U

pper

seco

ndar

y4,

682

0.03

00.

172

818

0.02

10.

143

3,86

40.

032

0.17

7Vo

catio

nalt

rain

ing

4,68

20.

418

0.49

381

80.

416

0.49

33,

864

0.41

80.

493

Low

erte

rtia

ryed

ucat

ion

4,68

20.

077

0.26

781

80.

072

0.25

93,

864

0.07

80.

268

Inte

rmed

iate

tert

iary

educ

atio

n4,

682

0.24

20.

428

818

0.25

70.

437

3,86

40.

238

0.42

6H

ighe

rter

tiary

educ

atio

n4,

682

0.13

90.

346

818

0.14

80.

355

3,86

40.

138

0.34

5M

othe

r’s

mar

itals

tatu

sin

2007

Nuc

lear

fam

ily4,

692

0.70

70.

455

818

0.73

60.

441

3,87

40.

701

0.45

8M

othe

rliv

ing

with

new

part

ner

4,69

20.

123

0.32

981

80.

112

0.31

63,

874

0.12

60.

332

Sing

lem

othe

r4,

692

0.17

00.

375

818

0.15

20.

359

3,87

40.

173

0.37

9Sc

ore

onat

tain

men

ttes

tsL

angu

age

scor

e4,

655

20.9

555.

158

807

21.3

544.

958

3,84

820

.872

5.19

6C

HIP

S-sc

ore

4,70

929

.082

5.38

581

929

.358

5.49

73,

890

29.0

245.

360

Rav

en-s

core

atag

e15

3,94

68.

303

2.13

769

18.

359

2.13

23,

255

8.29

12.

138

Aver

age

scor

eon

SDQ

,ata

ges

7an

d11

Em

otio

nalp

robl

ems

4,72

32.

132

1.73

682

32.

043

1.72

63,

900

2.15

11.

738

Hyp

erac

tivity

/ina

ttent

ion

4,72

32.

662

2.24

582

32.

547

2.11

13,

900

2.68

62.

272

39

Table 3: Linear probability model of allocation into reward and control group.

Independent variable Reward groupReward group

balanced sample

Sex (1= girl) 0.002 0.004

(0.013) (0.013)

Log of family equivalent scaled income -0.026 -0.023

(0.018) (0.018)

Highest parental education

Lower secondary (ref.)

Upper secondary -0.053 -0.056

(0.043) (0.043)

Vocational training 0.004 0.001

(0.025) (0.026)

Lower tertiary education -0.016 -0.022

(0.033) (0.033)

Intermediate tertiary education 0.005 -0.003

(0.027) (0.028)

Higher tertiary education 0.009 -0.001

(0.030) (0.031)

Mother’s marital status in 2007

Nuclear family (ref.)

Mother living with new partner -0.021 -0.020

(0.020) (0.021)

Single mother -0.040∗∗ -0.031

(0.020) (0.021)

Average score on SDQ

Emotional problems -0.005 -0.007∗

(0.004) (0.004)

Hyperactivity/ inattention -0.003 -0.002

(0.003) (0.003)

Mother’s Raven-test 0.001 0.001

(0.003) (0.003)

Constant 0.501∗∗ 0.478∗∗

(0.215) (0.218)

N 3,829 3,695

Standard errors in parentheses∗ p < 0.05 , ∗∗ p < 0.01 , ∗∗∗ p < 0.001

40

Tabl

e4:

Cor

rela

tions

betw

een

mea

sure

sof

emot

iona

lpro

blem

s,hy

pera

ctiv

ity/i

natte

ntio

nan

dco

gniti

veac

hiev

emen

tte

sts.

CH

IPS-

scor

eL

angu

age-

scor

eR

aven

-tes

t

Mea

nof

emot

iona

lpr

oble

ms

age

7an

d11

Mea

nof

hype

ract

ivity

/in

atte

ntio

nag

e7

and

11

Hyp

erac

tivity

/in

atte

ntio

nag

e7

Em

otio

nal

prob

lem

sag

e7

Hyp

erac

tivity

/in

atte

ntio

nag

e11

Em

otio

nal

prob

lem

sag

e11

Mot

her’

sR

aven

-tes

t

CH

IPS-

scor

e1

Lan

guag

e-sc

ore

0.449∗∗∗

1

Rav

en-t

est

0.393∗∗∗

0.293∗∗∗

1M

ean

ofem

otio

nal

prob

lem

sat

age

7an

d11

-0.11

1∗∗∗

-0.15

8∗∗∗

-0.09

7∗∗∗

1

Mea

nof

hype

ract

ivity

/in

atte

ntio

nat

age

7an

d11

-0.20

5∗∗∗

-0.25

8∗∗∗

-0.12

6∗∗∗

0.285∗∗∗

1

Hyp

erac

tivity

/in

atte

ntio

n,ag

e7

-0.15

9∗∗∗

-0.18

9∗∗∗

-0.07

9∗∗∗

0.238∗∗∗

0.918∗∗∗

1

Em

otio

nal

prob

lem

s,ag

e7

-0.07

7∗∗∗

-0.08

7∗∗∗

-0.06∗∗∗

0.869∗∗∗

0.201∗∗∗

0.203∗∗∗

1

Hyp

erac

tivity

/in

atte

ntio

n,ag

e11

-0.20

6∗∗∗

-0.26

7∗∗∗

-0.14

7∗∗∗

0.276∗∗∗

0.906∗∗∗

0.619∗∗∗

0.148∗∗∗

1

Em

otio

nal

prob

lem

sag

e,11

-0.10

7∗∗∗

-0.17

0∗∗∗

-0.10

7∗∗∗

0.873∗∗∗

0.288∗∗∗

0.206∗∗∗

0.450∗∗∗

0.300∗∗∗

1

Mot

her’

sR

aven

-tes

t0.1

68∗∗∗

0.159∗∗∗

0.164∗∗∗

-0.03

1∗∗

-0.04

0∗∗∗

-0.03

1∗-0

.013

-0.03

8∗∗

-0.04

3∗∗∗

1∗

p<

0.1

,∗∗

p<

0.05

,∗∗∗

p<

0.01

41

Figure 1: Score on language test as a function of hyperactivity/ inattention and emotional problems.Separate for reward and control groups, and difference in scores between the two groups.

42

Figure 2: Score on CHIPS-test as a function of hyperactivity/ inattention and emotional problems.Separate for reward and control groups, and difference in scores between the two groups.

02

46

8

02

46

8

0

10

20

30

40

Mean ofEmotional Problemsat age 7 and 11

Reward group

Mean ofHyperactivity/ Inattention

at age 7 and 11

CH

IPS

−sc

ore

02

46

8

0

5

100

10

20

30

40

Mean ofEmotional Problemsat age 7 and 11

Control group

Mean ofHyperactivity/ Inattention

at age 7 and 11

CH

IPS

−sc

ore

0 1 2 3 4 5 6 7 8

01

23

45

67

89

−10

−5

0

5

10

Mean ofEmotional Problemsat age 7 and 11

Difference in scores for Reward & Control group

Mean ofHyperactivity/ Inattention

at age 7 and 11

CH

IPS

−sc

ore

43

Figure 3: Score on the C set of the Raven’s Standard Progressive Matrix at age 15 as a functionof hyperactivity/ inattention and emotional problems. Separate for reward and control groups, anddifference in scores between the two groups.

02

46

8

02

46

8

0

5

10

15

Mean ofEmotional Problemsat age 7 and 11

Reward group

Mean ofHyperactivity/ Inattention

at age 7 and 11

Rav

en−

scor

e

02

46

8

0

5

100

5

10

15

Mean ofEmotional Problemsat age 7 and 11

Control group

Mean ofHyperactivity/ Inattention

at age 7 and 11

Rav

en−

scor

e

0 1 2 3 4 5 6 7 8

01

23

45

67

89

−10

−5

0

5

10

Mean ofEmotional Problemsat age 7 and 11

Difference in scores for Reward & Control group

Mean ofHyperactivity/ Inattention

at age 7 and 11

Rav

en−

scor

e

44

Table 5: Results of OLS regressions on language comprehension test. Full sample and modelswith children with hyperactivity/ inattention and different configurations of emotional problems.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Fullsample

Fullsample

Fullsample

withinteractions

Emotionalproblems=0

Emotionalproblems=1

Emotionalproblems=2

Emotionalproblems=3

Emotionalproblems=4

Emotionalproblems=5

Independent variables Language Language Language Language Language Language Language Language Language

Reward group 0.483∗∗ 0.371∗ 0.539 1.969 1.119 -1.079 1.292 1.052 -0.521(0.200) (0.192) (0.461) (1.481) (1.287) (1.234) (1.194) (1.528) (2.615)

Hyperactivity/ inattention -0.271∗∗∗ -0.222∗∗∗

(0.044) (0.077)Emotional problems -0.539∗∗∗ -0.536∗∗∗

(0.034) (0.058)Reward*Emotional problems -0.006

(0.018)Reward*Hyperactivity/ inattention -0.186

(0.191)Emotional problems*

Hyperactivity/ inattention 0.070

(0.148)Reward*Emotional problems*

Hyperactivity/ inattention 0.006

(0.046)Constant 20.872∗∗∗ 22.881∗∗∗ 22.812∗∗∗ 18.914∗∗∗ 19.381∗∗∗ 19.338∗∗∗ 19.250∗∗∗ 18.226∗∗∗ 18.188∗∗∗

(0.083) (0.137) (0.191) (0.705) (0.429) (0.498) (0.462) (0.615) (0.766)Adjusted R2 0.001 0.074 0.074 0.010 -0.001 -0.001 0.001 -0.005 -0.014N 4,655 4,654 4,654 75 198 166 160 111 70Standard errors in parentheses∗ p < 0.1 , ∗∗ p < 0.05 , ∗∗∗ p < 0.01

45

Table 6: Results of OLS regressions on language comprehension test. Full sample and modelswith children with hyperactivity/ inattention and different configurations of emotional problems.With controls.

(1) (2) (3) (4) (5) (6) (7) (8)

Fullsample

Fullsample

withinteractions

Emotionalproblems=0

Emotionalproblems=1

Emotionalproblems=2

Emotionalproblems=3

Emotionalproblems=4

Emotionalproblems=5

Independent variables Language Language Language Language Language Language Language Language

Reward group 0.409∗∗ 0.404 1.056 1.261 -1.265 0.884 2.271 -0.016(0.201) (0.481) (1.398) (1.392) (1.317) (1.299) (1.945) (2.929)

Sex (1=girl) 0.860∗∗∗ 0.865∗∗∗ 2.201 0.300 0.803 1.591∗ 1.504 2.727(0.155) (0.156) (1.634) (0.951) (0.998) (0.937) (1.459) (1.890)

Log of familyequivalent scaled income

0.548∗∗ 0.552∗∗ 2.669 0.152 -0.139 1.647 0.637 1.526(0.218) (0.219) (2.115) (1.296) (1.327) (1.491) (1.944) (2.064)

Highest parental educationLower secondary (ref.)Upper secondary 2.316∗∗∗ 2.320∗∗∗ 2.986 3.727 2.472 -2.338 -4.151 2.815

(0.527) (0.527) (3.839) (2.640) (2.821) (2.601) (6.515) (4.975)Vocational training 1.151∗∗∗ 1.146∗∗∗ 1.174 0.762 0.526 0.868 -1.657 -0.670

(0.314) (0.314) (2.014) (1.726) (1.558) (1.359) (2.076) (2.619)Lower tertiary education 1.612∗∗∗ 1.603∗∗∗ -1.492 1.497 -2.239 -0.230 -4.675 4.650

(0.406) (0.406) (3.780) (2.123) (2.198) (1.885) (3.317) (4.247)Intermediate tertiary education 2.086∗∗∗ 2.081∗∗∗ 2.359 4.267∗∗ 3.374∗ -0.369 2.142 0.742

(0.337) (0.338) (2.210) (1.965) (2.003) (1.660) (2.450) (3.217)Higher tertiary education 3.358∗∗∗ 3.359∗∗∗ 1.030 5.300∗∗ 3.255 4.504∗∗ 1.638 2.570

(0.374) (0.374) (2.497) (2.266) (2.545) (2.011) (3.027) (3.705)Mother’s marital status in 2007Nuclear family (ref.)Mother living with new partner 0.425∗ 0.420∗ 4.527∗ -0.018 0.740 -0.397 0.616 -3.772

(0.251) (0.251) (2.331) (1.404) (1.382) (1.207) (1.767) (2.811)Single mother 0.705∗∗∗ 0.712∗∗∗ 0.196 0.767 -0.289 2.078 1.243 -0.023

(0.249) (0.250) (2.419) (1.470) (1.429) (1.431) (2.010) (2.410)Mother’s Raven-test 0.249∗∗∗ 0.248∗∗∗ 0.203 0.199 0.771∗∗∗ 0.450∗ 0.427 0.756

(0.032) (0.032) (0.254) (0.202) (0.217) (0.252) (0.258) (0.644)Emotional problems -0.405∗∗∗ -0.401∗∗∗

(0.037) (0.063)Hyperactivity/ inattention -0.265∗∗∗ -0.224∗∗∗

(0.047) (0.082)Reward*Emotional problems -0.075

(0.200)Reward*Hyperactivity/ inattention 0.107

(0.153)Emotional problems*

Hyperactivity/ inattention -0.005

(0.020)Reward*Emotional problems*

Hyperactivity/ inattention -0.019

(0.048)Constant 11.435∗∗∗ 11.326∗∗∗ -16.659 13.699 12.952 -6.162 6.314 -8.186

(2.665) (2.672) (25.843) (15.539) (15.917) (17.726) (23.141) (25.282)

N 3,776 3,776 61 159 137 129 87 53Adjusted R2 0.132 0.132 0.034 0.059 0.117 0.093 0.044 0.007Standard errors in parentheses∗ p < 0.1 , ∗∗ p < 0.05 , ∗∗∗ p < 0.01

46

Table 7: Results of OLS regressions on CHIPS-test. Full sample and models with children withhyperactivity/ inattention and different configurations of emotional problems.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Fullsample

Fullsample

Fullsample

withinteractions

Emotionalproblems=0

Emotionalproblems=1

Emotionalproblems=2

Emotionalproblems=3

Emotionalproblems=4

Emotionalproblems=5

Independent variables CHIPS CHIPS CHIPS CHIPS CHIPS CHIPS CHIPS CHIPS CHIPS

Reward group 0.333 0.253 0.012 3.510∗∗∗ 0.045 -1.021 -0.469 -0.368 -4.812(0.207) (0.203) (0.486) (1.286) (1.463) (1.089) (1.407) (1.620) (2.993)

Hyperactivity/ inattention -0.451∗∗∗ -0.399∗∗∗

(0.036) (0.061)Emotional problems -0.163∗∗∗ -0.094

(0.046) (0.081)Reward*Emotional problems 0.227

(0.201)Reward*Hyperactivity/ inattention 0.152

(0.155)Emotional problems*

Hyperactivity/ inattention -0.016

(0.019)Reward*Emotional problems*

Hyperactivity/ inattention -0.096∗∗

(0.048)Constant 29.024∗∗∗ 30.587∗∗∗ 30.409∗∗∗ 27.254∗∗∗ 27.781∗∗∗ 28.021∗∗∗ 27.617∗∗∗ 26.737∗∗∗ 26.955∗∗∗

(0.086) (0.144) (0.202) (0.608) (0.495) (0.437) (0.564) (0.661) (0.921)Adjusted R2 0.000 .0.044 0.045 0.079 -0.005 -0.001 -0.005 -0.008 0.021N 4,709 4,708 4,708 76 201 174 168 114 74Standard errors in parentheses∗ p < 0.1 , ∗∗ p < 0.05 , ∗∗∗ p < 0.01

47

Table 8: Results of OLS regressions on CHIPS-test. Full sample and models with children withhyperactivity/ inattention and different configurations of emotional problems. With controls.

(1) (2) (3) (4) (5) (6) (7) (8)

Fullsample

Fullsample

withinteractions

Emotionalproblems=0

Emotionalproblems=1

Emotionalproblems=2

Emotionalproblems=3

Emotionalproblems=4

Emotionalproblems=5

Independent variables CHIPS CHIPS CHIPS CHIPS CHIPS CHIPS CHIPS CHIPS

Reward group 0.372∗ -0.411 2.906∗∗ 0.572 0.572 -1.175 -0.933 -1.183(0.214) (0.513) (1.436) (1.583) (1.583) (1.568) (2.067) (3.310)

Sex (1=girl) 0.882∗∗∗ 0.896∗∗∗ 2.892∗ 0.524 0.524 2.177∗ 1.148 -1.327(0.166) (0.166) (1.676) (1.103) (1.103) (1.149) (1.584) (2.100)

Log of familyequivalent scaled income 0.332 0.367 1.001 1.920 1.920 0.648 -1.120 1.299

(0.233) (0.234) (2.166) (1.505) (1.505) (1.831) (2.124) (2.322)Highest parental educationLower secondary (ref.)Upper secondary 1.296∗∗ 1.249∗∗ 2.557 3.031 3.031 -2.347 -7.720 5.716

(0.561) (0.561) (3.947) (3.085) (3.085) (2.975) (7.171) (5.698)Vocational training 0.580∗ 0.537 3.001 2.183 2.183 -1.369 -2.717 2.842

(0.333) (0.333) (2.067) (2.009) (2.009) (1.675) (2.275) (2.821)Lower tertiary education 1.142∗∗∗ 1.111∗∗∗ -1.003 2.679 2.679 -2.143 -1.221 5.408

(0.429) (0.429) (3.887) (2.481) (2.481) (2.301) (3.657) (4.354)Intermediate tertiary education 1.129∗∗∗ 1.087∗∗∗ 2.994 3.833∗ 3.833∗ -0.157 -1.612 0.900

(0.358) (0.358) (2.273) (2.296) (2.296) (2.052) (2.697) (3.525)Higher tertiary education 2.043∗∗∗ 2.020∗∗∗ 1.368 4.300 4.300 1.213 -0.316 3.908

(0.397) (0.397) (2.566) (2.614) (2.614) (2.488) (3.327) (4.158)Mother’s marital status in 2007Nuclear family (ref.)Mother living with new partner 0.134 0.120 1.503 2.576 2.576 0.041 1.062 -0.330

(0.268) (0.267) (2.394) (1.635) (1.635) (1.482) (1.927) (3.001)Single mother 0.365 0.370 -1.655 2.324 2.324 1.995 1.301 0.964

(0.266) (0.266) (2.482) (1.705) (1.705) (1.765) (2.188) (2.631)Mother’s Raven test 0.312∗∗∗ 0.313∗∗∗ 0.041 0.340 0.340 0.709∗∗ 0.319 1.777∗∗∗

(0.034) (0.034) (0.261) (0.236) (0.236) (0.307) (0.285) (0.573)Emotional problems -0.325∗∗∗ -0.276∗∗∗

(0.040) (0.067)Hyperactivity/ inattention -0.162∗∗∗ -0.115

(0.050) (0.088)Reward*Emotional problems 0.472∗∗

(0.213)Reward*Hyperactivity/ inattention 0.260

(0.162)Emotional problems*

Hyperactivity/ inattention -0.016

(0.021)Reward*Emotional problems*

Hyperactivity/ inattention -0.134∗∗∗

(0.050)Constant 21.986∗∗∗ 21.444∗∗∗ 12.660 -1.822 -1.822 13.085 39.013 -7.748

(2.849) (2.852) (26.457) (18.041) (18.041) (21.777) (25.266) (28.687)

N 3,817 3,817 62 163 163 132 90 56Adjusted R2 0.082 0.084 0.001 0.011 0.011 0.031 .-0.043 0.109Standard errors in parentheses∗ p < 0.1 , ∗∗ p < 0.05 , ∗∗∗ p < 0.01

48

Table 9: Results of OLS regressions on Raven’s Standard Progressive Matrix at age 15. Full sampleand models with children with hyperactivity/ inattention and different configurations of emotionalproblems.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Fullsample

Fullsample

Fullsample

withinteractions

Emotionalproblems=0

Emotionalproblems=1

Emotionalproblems=2

Emotionalproblems=3

Emotionalproblems=4

Emotionalproblems=5

Independent variablesRaven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15

Reward group 0.070 0.030 0.298 0.475 -0.225 -0.052 -1.034∗ -1.186 -0.075(0.088) (0.087) (0.207) (0.546) (0.629) (0.506) (0.539) (0.715) (0.861)

Hyperactivity/ inattention -0.107∗∗∗ -0.073∗∗∗

(0.016) (0.027)Emotional problems -0.091∗∗∗ -0.052

(0.020) (0.035)Reward*Emotional problems -0.046

(0.086)Reward*Hyperactivity/ inattention -0.069

(0.066)Emotional problems*

Hyperactivity/ inattention -0.010

(0.009)Reward*Emotional problems*

Hyperactivity/ inattention 0.000

(0.021)Constant 8.235∗∗∗ 8.717∗∗∗ 8.608∗∗∗ 8.125∗∗∗ 7.975∗∗∗ 7.822∗∗∗ 7.817∗∗∗ 7.936∗∗∗ 7.932∗∗∗

(0.037) (0.063) (0.087) (0.280) (0.212) (0.207) (0.216) (0.295) (0.280)Adjusted R2 -0.000 0.020 0.021 -0.003 -0.005 -0.006 0.018 0.019 -0.016N 4,270 4,185 4,185 76 177 155 143 94 66Standard errors in parentheses∗ p < 0.1 , ∗∗ p < 0.05 , ∗∗∗ p < 0.01

49

Table 10: Results of OLS regressions on Raven’s Standard Progressive Matrix at age 15. Fullsample and models with children with hyperactivity/ inattention and different configurations ofemotional problems. With controls.

(1) (2) (3) (4) (5) (6) (7) (8)

Fullsample

Fullsample

withinteractions

Emotionalproblems=0

Emotionalproblems=1

Emotionalproblems=2

Emotionalproblems=3

Emotionalproblems=4

Emotionalproblems=5

Independent variablesRaven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15Raven

at age 15

Reward group 0.047 0.316 0.570 -0.355 0.004 -0.974∗ -1.011 -0.019(0.087) (0.207) (0.605) (0.645) (0.527) (0.523) (0.802) (0.834)

Sex (1=girl) -0.278∗∗∗ -0.274∗∗∗ -0.254 -0.455 0.030 -0.389 -0.802 0.963∗

(0.068) (0.068) (0.704) (0.445) (0.414) (0.408) (0.614) (0.562)Log of familyequivalent scaled income 0.013 0.013 0.400 -0.895 0.710 0.104 -0.247 -0.866

(0.095) (0.095) (0.743) (0.569) (0.541) (0.651) (0.874) (0.619)Highest parental educationLower secondary (ref.)Upper secondary 0.803∗∗∗ 0.799∗∗∗ 0.186 1.769 -1.693 -1.776∗ -1.090 -0.341

(0.228) (0.228) (1.758) (1.260) (1.200) (1.018) (2.848) (2.260)Vocational training 0.458∗∗∗ 0.451∗∗∗ 0.873 0.516 0.577 -0.145 0.191 -0.109

(0.135) (0.135) (0.880) (0.814) (0.630) (0.594) (0.823) (0.807)Lower tertiary education 0.688∗∗∗ 0.677∗∗∗ 2.068 1.652 0.753 0.422 -0.206 0.418

(0.175) (0.175) (1.489) (1.025) (0.901) (0.833) (1.422) (1.233)Intermediate tertiary education 0.705∗∗∗ 0.700∗∗∗ 0.907 0.725 0.673 0.793 1.007 1.593∗

(0.145) (0.145) (0.927) (0.916) (0.779) (0.744) (1.023) (0.930)Higher tertiary education 1.198∗∗∗ 1.198∗∗∗ 0.917 0.845 0.663 -0.384 0.760 2.147∗

(0.161) (0.161) (1.056) (1.023) (1.064) (0.908) (1.305) (1.162)Mother’s marital status in 2007Nuclear family (ref.)Mother living with new partner 0.114 0.118 0.557 0.300 0.112 0.252 0.509 1.035

(0.110) (0.110) (1.057) (0.668) (0.559) (0.534) (0.757) (0.904)Single mother 0.141 0.141 -0.679 -0.075 0.605 0.414 0.657 -0.786

(0.107) (0.107) (0.828) (0.681) (0.591) (0.613) (0.842) (0.681)Mother’s Raven-test 0.119∗∗∗ 0.118∗∗∗ 0.129 0.136 0.188∗∗ 0.393∗∗∗ 0.135 0.332∗∗

(0.014) (0.014) (0.116) (0.092) (0.091) (0.108) (0.113) (0.149)Emotional problems -0.091∗∗∗ -0.052∗

(0.016) (0.027)Hyperactivity/ inattention -0.080∗∗∗ -0.039

(0.020) (0.035)Reward*Emotional problems -0.035

(0.086)Reward*Hyperactivity/ inattention -0.083

(0.065)Emotional problems*

Hyperactivity/ inattention -0.011

(0.009)Reward*Emotional problems*

Hyperactivity/ inattention 0.002

(0.021)Constant 6.858∗∗∗ 6.752∗∗∗ 1.405 16.985∗∗ -3.153 2.823 9.337 14.374∗

(1.153) (1.156) (9.050) (6.737) (6.502) (7.732) (10.386) (7.649)

N 3,969 3,969 70 168 151 137 93 61Adjusted R2 0.066 0.067 -0.064 .-0.005 0.016 0.113 0.009 0.194Standard errors in parentheses∗ p < 0.1 , ∗∗ p < 0.05 , ∗∗∗ p < 0.01

50