There Is No Such Thing as a Free Lunch! Who Is Paying for Our Happiness?
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dominates the overall score’ (Jeffrey et al. 2016), where inequality adjusted values
of LEX and EWB are used and some scaling constants are incorporated.
H P I =
0.452 ∗ ((EW B I A − 0.158) ∗ LEX I A + 3.951)
(EF P + 2.067)
(3)
The subscript IA denotes that the EWB and LEX indicators have been ‘inequality
adjusted’ for economic inequalities in the countries. For details Jeffrey et al. (2016)
and nef (2016) should consulted.
It should be noted that in order to achieve a sensible ranking picture it is
mandatory that all indicators included have the same orientation, e.g., the larger
the better. Thus, in the case of the HPI the EFP indicator will be multiplied by −1
in order to guarantee co-monotony with the EWB and LEX indicators.
2.6 Data
The data used for the analysis can be found in the appropriate cited reports (HI 2016;
Carlsen 2018; Jeffrey et al. 2016). The full set of indicators and the complete set of
countries (approx. 150) have been used for the calculations.
3 Results and Discussion
3.1 The World Happiness Index
Let us initially look at what makes us happy. Here we take the onset in the Word
Happiness Index (HI 2016, 2017, 2018). As mentioned in the introduction this index
is calculated by a simple arithmetic aggregation of the 7 indicators mentioned above.
Obviously, such an aggregation of data may lead to more or less strange results due
to compensation effects (Munda 2008), roughly speaking adding apples and oranges
getting bananas. Hence, in a recently paper (Carlsen 2018) the happiness index
was revisited applying partial order methodology, among other things to disclose
the relative importance of the seven indicators. In Fig. 1 the relative importance
of the seven indicators are depicted as calculated applying the sensitivity module
sensitivity23_1 of the PyHasse software package (Bruggemann and Patil 2011;
Bruggemann et al. 2014) on the 2016 happiness index data (HI 2016).
The result summarized in Fig. 1 has in details been discussed by Carlsen (2018),
a discussion that shall not be reproduced here. However, it is worthwhile to mention
just 3 specific indicators, i.e., GPd, Gener and Dys, respectively.
First it can be noted that in an overall evaluation of happiness money, here
expressed as the gross domestic product or more precisely as the purchasing power
209
dominates the overall score’ (Jeffrey et al. 2016), where inequality adjusted values
of LEX and EWB are used and some scaling constants are incorporated.
H P I =
0.452 ∗ ((EW B I A − 0.158) ∗ LEX I A + 3.951)
(EF P + 2.067)
(3)
The subscript IA denotes that the EWB and LEX indicators have been ‘inequality
adjusted’ for economic inequalities in the countries. For details Jeffrey et al. (2016)
and nef (2016) should consulted.
It should be noted that in order to achieve a sensible ranking picture it is
mandatory that all indicators included have the same orientation, e.g., the larger
the better. Thus, in the case of the HPI the EFP indicator will be multiplied by −1
in order to guarantee co-monotony with the EWB and LEX indicators.
2.6 Data
The data used for the analysis can be found in the appropriate cited reports (HI 2016;
Carlsen 2018; Jeffrey et al. 2016). The full set of indicators and the complete set of
countries (approx. 150) have been used for the calculations.
3 Results and Discussion
3.1 The World Happiness Index
Let us initially look at what makes us happy. Here we take the onset in the Word
Happiness Index (HI 2016, 2017, 2018). As mentioned in the introduction this index
is calculated by a simple arithmetic aggregation of the 7 indicators mentioned above.
Obviously, such an aggregation of data may lead to more or less strange results due
to compensation effects (Munda 2008), roughly speaking adding apples and oranges
getting bananas. Hence, in a recently paper (Carlsen 2018) the happiness index
was revisited applying partial order methodology, among other things to disclose
the relative importance of the seven indicators. In Fig. 1 the relative importance
of the seven indicators are depicted as calculated applying the sensitivity module
sensitivity23_1 of the PyHasse software package (Bruggemann and Patil 2011;
Bruggemann et al. 2014) on the 2016 happiness index data (HI 2016).
The result summarized in Fig. 1 has in details been discussed by Carlsen (2018),
a discussion that shall not be reproduced here. However, it is worthwhile to mention
just 3 specific indicators, i.e., GPd, Gener and Dys, respectively.
First it can be noted that in an overall evaluation of happiness money, here
expressed as the gross domestic product or more precisely as the purchasing power