is also evident from the graphs of mean mental distress score (figures 2 and 3) where the lower income groups have higher mental distress scores than those in the top income groups.
The household level of education is not significant for neither boys nor girls, however, the coefficients indicate different associations. For boys, all levels of educational attainment, except for when education is unspecified, is associated with an increase in the mental distress score. The coefficients for girls are associated with a decrease in the mental distress score for all levels of educational attainment, except for the highest. This however is likely due to the same effects as with log income, as a univariate regression (education regressed on mental distress) results in negative coefficients for all levels of educational attainment, except for the unspecified category for boys, which in this case is positive21.
contribution results in a positive (negative) percentage contribution, which in turn increases (reduces) the inequality at the disadvantage (advantage) of the individuals at the lower end of the income distribution.
When investigating the result of the decomposition, I will primarily focus on the factors that have the largest contributions to the estimated concentration index.
To give a more complete picture of this association both the elasticity and the explanatory variable’s concentration index will be examined. The elasticity indicates how sensitive the mental distress score is to changes in the explanatory variable, and explanatory variable’s CI explains how the variable is distributed over the income distribution. Interpreting these to factors together gives a more complete picture of how the respective explanatory variables affect the income-related inequality in the distribution of the mental distress score.
Both the estimated concentration indices for girls and boys showed that mental distress was unequally distributed to the disadvantage of the individuals at the lower end of the income distribution. Though only the concentration index for boys was statistically significant.
At first glance, the decomposition results show that there are differences in the magnitude of association for girls and boys. This is also evident in how the different explanatory variables contribute to the estimated CIs for girls and boys.
Table 6 Decomposition of Mental Distress
The table shows results from decomposing the concentration index into explanatory variables for girls and boys respectively
The largest contribution, percentagewise, throughout the entire table is attributed to the logarithm of income for girls. This contribution is predominantly driven by the elasticity of the mental distress score with respect to log income. As the estimated elasticity is larger than 1, the mental distress score, is very sensitive to changes in the income distribution. For boys,
VARIABLES
Elasticity CI contribution cont % Elasticity CI contribution cont %
log income 1,23436 0,01573 0,01942 -116,64 % -0,7605 0,0161 -0,0123 25,19 %
parental gender = male -0,00589 0,04744 -0,00028 1,68 % -0,0435 0,0293 -0,0013 2,62 %
parental age 30,65 % 5,81 %
age2544 -0,30205 -0,05218 0,01576 -0,2763 -0,0536 0,0148
age4566 -0,14256 0,14634 -0,02086 -0,1251 0,1421 -0,0178
age6779 - - - -0,0004 -0,3781 0,0001
child age 3,48 % 3,70 %
age8_9 0,0067 -0,00324 -0,00002 0,0133 -0,0622 -0,0008
age10_11 0,00469 -0,01138 -0,00005 0,0068 0,0272 0,0002
age12_13 -0,0185 0,03444 -0,00064 -0,0139 0,0336 -0,0005
age14_15 0,00342 0,0389 0,00013 -0,0108 0,0638 -0,0007
child disability/injury/illness 0,03147 -0,11375 -0,00358 21,50 % 0,0291 -0,1049 -0,0031 6,27 %
child needs extra care 0,01137 -0,09977 -0,00113 6,82 % 0,0298 -0,0906 -0,0027 5,55 %
parental depression 0,03709 -0,34497 -0,0128 76,87 % 0,0299 -0,2064 -0,0062 12,70 %
marital status: 59,43 % 22,73 %
married -0,07153 0,08161 -0,00584 -0,1316 0,0823 -0,0108
sepdiv 0,00903 -0,41198 -0,00372 -0,0001 -0,4345 0
widow 0,00076 -0,44361 -0,00034 0,0006 -0,4368 -0,0003
biological child -0,22106 -0,00209 0,00046 -2,78 % -0,1815 -0,0016 0,0003 -0,60 %
local friends -0,18986 0,00667 -0,00127 7,61 % -0,3392 0,0099 -0,0034 6,90 %
bullied 0,07598 -0,1099 -0,00835 50,16 % 0,0873 -0,134 -0,0117 24,05 %
confidants 0,3833 0,00968 0,00371 -22,30 % 0,2929 0,0036 0,001 -2,15 %
education: -30,53 % -16,94 %
upper sencondary, all levels -0,01111 -0,10303 0,00114 0,0456 -0,1166 -0,0053
lower level university or college -0,00815 0,08686 -0,00071 0,0504 0,0839 0,0042
higher level university or college 0,00783 0,3587 0,00281 0,023 0,4037 0,0093
uspecified -0,00329 -0,55948 0,00184 0 -0,4594 0
densely populated -0,03755 0,03294 -0,00124 7,43 % 0,0072 0,0358 0,0003 -0,53 %
Geography: 1,94 % 4,51 %
Akershus/ Oslo -0,00544 0,12562 -0,00068 -0,0152 0,1793 -0,0027
Eastern Norway (Østfold) -0,00638 -0,0343 0,00022 -0,0039 -0,0658 0,0003
Western Norway (Vestlandet) -0,02507 -0,00955 0,00024 -0,004 -0,0502 0,0002
Mid Norway (Trøndelag) -0,00715 -0,05566 0,0004 -0,0062 -0,052 0,0003
Southern Norway (Agder/Rogaland) -0,01136 0,01749 -0,0002 -0,0133 0,0419 -0,0006
Northern Norway (Nord-Norge) 0,004 -0,07404 -0,0003 -0,0079 -0,0385 0,0003
Total: -0,01587 95,30 % -0,0486 99,82 %
Estimated CI -0,01665 -0,04866
Residuals: -0,000800 4,70 % -0,000060 0,18 %
GIRLS BOYS
indices is completely different for boys and girls. As the elasticity of log income for boys is negative and the concentration index is positive, log income contributes to the inequality at the disadvantage of those at the lower range of the income distribution. On the other hand, as the components that make up the contribution are positive for girls, log income does not show the same tendency, and in this case reduces the inequality in favor of the poor.
The parents aged 25-44 and 45-66 also contributes a large proportion to the concentration indices of both girls and boys. For girls, parents belonging to the age group 45-66 contributes negatively, meaning it is associated with an increase in the inequality at the disadvantage of the less well off. Examining the concentration index of age4566 show that this age groups is more common in the top of the income distribution. This association might be related to the trend of Norwegian women and men postponing family life in favor of higher education (Kravdal & Rindfuss, 2008). As such, this explanatory variable may embody more than just parental age, it may also have such a large effect as it captures the economic return on
education. In contrast, the younger age group is associated with a positive contribution to the concentration index, for both girls and boys, suggesting that parents in this age groups help to hamper the unequal distribution of the mental distress scores.
Whether the child has been bullied or not in the past three months contributes close to a quarter of the estimated concentration index for boys, and approximately half to the concertation index for girls. The concentration index for bullying suggests that it
disproportionately affects children at the lower end of the income distribution. As a result, bullying contributes negatively to the estimated concentration indices and is associated with increasing inequality for those ranked lower on the income distribution.
Marital status predominantly contributed negatively to the concentration indices (the exception being separated or divorced which contributes approximately zero22). Examining the variables closer shows that marriage is the largest contribution factor for both girls and boys and is associated with an increase in inequality at the disadvantage of the poor.
22 The contribution is 0,000038.
However, this association does not entail that marriage increases mental distress. Rather, the negative elasticity of marriage indicates that mental distress decreases with marriage, but as there are more individuals at the top of the income distribution that are married, this increases the inequality at the disadvantage of the poorer.
The risk-factors parental depression, child disability, and the child’s extra care needs all contribute a substantial percentage of the concentration indices, especially for girls. Whether the child has any illnesses, disabilities or injuries has a negative concentration index, and contributes to the estimated concentration indices at the disadvantage of the poor. Parental depression shows the same associations (negative contribution and negative concentration index), as does the child’s extra needs. Unsurprisingly, they are also all associated with an increase in the mental distress score trough the elasticity. In Janet Currie’s model (chapter 4.1) the permanent individual specific productivity shifter (𝒖() enters into both the child health production function (equation 4), and the demand function (equation 8). As shifters are factors that influence how one uses the inputs that are available, child disability and whether the child has extra care needs can be interpreted permanent individual specific productivity shifter (Currie, 2009; Victor, 1966, 1992).
Though the regression coefficients for parental confidants was associated with an increase in the mental distress score. The decomposition results show that parents having people to confide in is associated with reducing the measured inequality at the advantage of the individuals at the lower and of the income distribution. The elasticity does however still indicate a positive association, meaning it increases the mental distress score, but as the concentration index is also positive it has a dampening effect on the inequality.
In subchapter 8.2 we saw that ‘higher level university’ households were associated with an increase in the mental distress score for both girls and boys. This was briefly commented on, but to get a more complete picture it is useful to investigate the associations between
household education and mental distress closer.
For boys, all the elasticities with respect to education are positive, indicating a negative association between mental distress and education. Simultaneously the concentration indices for households with university levels of education (lower and higher), indicate that these levels of education are predominantly found amongst the higher income households. As such, it is possible that the negative associations seen with the elasticities are due to the fact that in lower income households the risk factors cluster together (e.g., child disability, whether the child needs extra care and parental depression), and the inclusion of these variables in the regression results in adjusted estimates. This may also be the case with respect to the
logarithm of income for girls, as both education and income, when performing an unadjusted regression results in higher levels of education being associated with lower mental distress score for boys, and log income being associated with lower mental distress scores for girls.
Nevertheless, education, as a whole, contributes to the largest reduction in the measured inequality for boys, and the second largest for girls.
9 Summary
The purpose of this thesis has been 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.
This sample of data showed income-related inequalities in the distribution of mental distress, for both girls and boys. However, the inequality was considerably larger for boys than for girls, even though the girls had higher mean mental distress scores than boys.
The mechanisms driving the inequality in the distribution of mental distress measured in this sample are complex. It’s not simply a matter of how the explanatory variable affects the mental health, it is also a matter of how the explanatory variable is distributed in the sample.
Many of the explanatory variables that increased the income-related inequality in mental distress, were themselves unequally distributed across the income distribution. Usually at the disadvantage of the poorer.
Where the child had experienced bullying in the past three months unsurprisingly explained a large proportion of the income-related inequality in mental distress and contributed
negatively. Respectively, 50% for girls, and 24% for boys. The descriptive statistics showed that approximately 10% of both boys and girls had experienced bullying during this period.
Comparatively a study from Tromsø, investigating the effect of bullying and cyber bullying on mental disorders reported the same percentage of bullying in their sample of 9- to 16-year-olds in 2018. They also found that children and adolescents being bullied reported higher scores on all four areas of the Strength and Difficulties Questionnaire (SDQ) (Thorvaldsen et al., 2018).
While the variable applied in this thesis is not a formal screening tool for mental distress, the association between the mental distress score and bullying still showed positive and
significant coefficients in the regression (table 5), indicating a negative association between the child/adolescent’s mental health and bullying.
Assuming one can interpret the explanatory variable ‘parental depression’, ‘child disability’
and ‘childs need for extra care’ as individual risk factors for mental distress, or “permanent individual specific shifters” in Currie’s theoretical model, these variables together explain a large proportion of the measured inequality (Currie, 2009). ‘Parental depression’ accounting for ≈ 77% of the inequality for girls, and ≈ 13% for boys. While the child’s inherent
capabilities (disability/injury/illness and need for extra help to complete daily tasks) account for ≈28% for girls and ≈12% for boys. As these variables all have negative concentration indices, they also effect the more disadvantaged population to a larger extent, and it might be useful to examine these factors more closely.
Finally, as mentioned in chapter 4.1, education is, in the context of Janet Currie’s model, viewed as a productivity shifter affecting how one applies available inputs. Examining the results of education from the decomposition results, the overall effect of education is
mental health of children. However, as previously pointed out, there are discrepancies concerning the results of education, and further investigation might be useful.
As a conclusive remark, it is necessary to highlight the large effects seen in the decomposed concentration index for girls. I have briefly attempted to explain possible reasons for the large and unexpected contributions from both education and the log of income. In addition, as the data is respectively 16 and 9 years old, the results may be outdated, however they still show possible associations, and may be useful when someone hopefully attempts to explain the increasing trend of mental health issued for girls as addressed by Erna Solberg in the beginning of 2020 (Solberg, 2020).
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Appendix A: Variable list
variable name type description
Mhscore discrete
chidisabill binary Does the child have any illnesses/disabilites/injuries thaht you expect to be of a permanent nature?
lninfadjinc continous natural log of income adjusted for purchasing power and householdsize infadjin continous income adjusted for purchasing power and householdsize
IOgender binary IO's gender, 1= male, 0=female gener binary child's gender, 1=boy, 0 = girl
age2544 binary Is IO between 25 and 44 years old? 1 = yes, 0 = no age4566 binary Is IO between 45 and 66 years old? 1 = yes, 0 = no
age6_7 binary Is the child between 6 and 7 years old? 1 = yes, 0 = no age8_9 binary Is the child between 8 and 9 years old? 1 = yes, 0 = no age10_11 binary Is the child between 10 and 11 years old? 1 = yes, 0 = no age12_13 binary Is the child between 12 and 13 years old? 1 = yes, 0 = no age14_15 binary Is the child between 14 and 15 years old? 1 = yes, 0 = no
xtracarechild binary Does the child need extra care, supervision or help to complete daily chores due to illness, disability or high age?
depression binary Has the IO experienced depresion during the past 3 months? 1 = yes, 0 = no married binary Is the IO married? 1 = yes, 0 = no
sepdiv binary Is the IO seperated or divorced? 1= yes, 0 = no widow binary Is the IO a widow/widover? 1 = yes, 0 = no
locfriends binary Does the child have any good friends in the area he/she lives?, 1= yes, 0 = no confidants binary Do you have someone close to you who you can talk to in confidence? 1 = yes, 0 = no
scndry binary The highest level of educational attainment in the household is secondary and/or vocatinal school, 1 = yes, 0 = no unilwr binary The highest level of educational attainment in the household is up to fours years of university or college, 1 = yes, 0 = no
unihi binary The highest level of educational attainment in the household is a mastersdegree or a Ph.D, 1 = yes, 0 = no unspced binary The highest level of educational attainment in the household is unspecified, 1 = yes, 0 = no
bullied binary Has the child experiened bullying during the past 6 months?, 1 = yes, 0 = no denspop binary Does the IO live in a densly populated area?, 1 = yes, 0 = no
akhosl binary Does the IO live in Akershus or Oslo?, 1 = yes, 0 = no
E_Nor binary Does the IO live in the eastern part of Norway? "Østlandet", 1 = yes, 0 = no W_Nor binary Does the IO live in the western part of Norway? "Vestlandet", 1 = yes, 0 = no M_Nor binary Does the IO live in the mid part of Norway? "Trøndelag", 1 = yes, 0 = no
S_Nor binary Does the IO live in the southern part of Norway? "Agder" and "Rogaland", 1 = yes, 0 = no N_Nor binary Does the IO live in the eastern northern of Norway? "Nord-Norge", 1 = yes, 0 = no biochild binary is the child the IO's biological child?, 1 = yes, 0 = no