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Erschienen in: Social Indicators Research 3/2017

24.06.2016

The Cost of Well-Being

verfasst von: Vincenzo Patrizii, Anna Pettini, Giuliano Resce

Erschienen in: Social Indicators Research | Ausgabe 3/2017

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Abstract

It is by now common knowledge that in switching from GDP to alternative, multidimensional, measures of collective well-being one can provide a better account of a country’s socio-economic conditions. Such a gain, however, comes at the price of losing output-to-input type of link between well-being and the resources necessary to make it available. Since well-being measures are not meant to be only an exercise in documentation, but also to inform policies and priorities, we propose a method to build a measure of well-being in the form of a single index, as for GDP, which takes into account: (1) the social and environmental costs, not considered in the GDP, and (2) the use of conventional resources (capital and labour), not considered in the currently available multidimensional measures of well-being. We use a Data Envelopment Analysis type of model, integrated with Principal Component Analysis, to evaluate OECD countries’ relative efficiency in providing well-being. Our results show that the costs of producing well-being have a large and significant impact on the resulting index of well-being. Therefore, high efficiency in providing well-being and high income cannot be considered a proxy to each other. In addition, it is shown that countries react differently to the different costs of well-being: poor countries are, on average, more efficient in terms of conventional inputs (labour and capital), while rich countries have higher efficiency indices relative to social and environmental costs. The close to zero correlation between GDP and well-being indices for rich countries provides new support to the “Easterlin paradox”.

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Fußnoten
1
Dea is a non-parametrical technique, originates from the work of Farrel (1957) and further developed by Charnes et al. (1978), see Cooper et al. (2007) for a comprehensive treatment of the subject.
 
2
The term Dmu in is used Dea literature to generally indicate the decision center responsible for converting inputs into outputs.
 
3
Conventional inputs measures have been considered in Golany and Thore (1997), Zaim et al. (2001), Despitos (2004), Mizobuchi (2014), while have not been considered in Hashimoto and Ishikawa (1993), Hashimoto and Kodama (1997), Zhu (2001), Mahlberg and Obersteiner (2001), Ramanathan (2006), Murias et al. (2006).
 
4
Lovell et al. (1995) use the same quantity of input for all Dmus, or Hashimoto and Ishikawa (1993) treat only bad outputs as inputs.
 
5
Interestingly, Zhu (2001) uses the three best cities selected by Fortune magazine to describe the efficient frontier. It is clear, however, that the results are strongly affected by the choice made by the analyst.
 
6
On the other hand we include labour inputs, not considered in Golany and Thore (1997) who include as bad-output, “Infant mortality”, by taking the complement to one of the variable. Against this procedurea wide range of Dea literature (for instance Färe and Grosskopf 2004), has made it clear that such a transformation can lead to absurd conclusions. Moreover, Lovell and Pastor (1999) show that even the value relative to which the complement is taken affects the results.
 
7
Mizobuchi (2014) in using the 11 Bli indices (topics) from OECD (2013a, b) ends up dealing with bad output as in Golany and Thore (1997). A further source of differentiation concerns labour inputs. While Mizobuchi (2014) uses “Intangible wealth” World Bank (2011) we choose to use “per capita worked hours” (Oecd 2015a). Our choice relies on two observations: i) “intangible capital” estimates contain the effects on human capital of variables such as, for instance, education, health expenditure, role of institutions, consumption and wealth which are well-being measures contained in the Bli database; ii) in the World Bank’s own words intangible capital estimates contain some risk of the “black box” type (World Bank 2011, p. 94).
 
8
Input and output measures in Fig. 2 anticipate the contents of the Bli database described further in Sect. 5.
 
9
For a detailed discussion of these points see Cooper et al. (2007).
 
10
We did actually run Pca on the two input variables. However the eigenvectors showed loadings of similar size with opposing signs, suggesting that labour and capital original variables tend to be orthogonal (higher capital input correspond to lower labour input). In this case, limiting the choice to one latent variable would heavily reduce explained variation.
 
11
Mix inefficiency measures the inefficiency due to the “wrong” combination of inputs and outputs.
 
12
For the sake of generality in model presentation we keep including the eigenvector matrix for input variables (\(L_{X}\)) despite the fact that, as mentioned above, in the model we actually run input variables are left unchanged.
 
13
A detailed list of input and output variables contained in X, B and G matrices is in Fig. 2.
 
14
This is how Oecd (2011) refers to the aggregate output variables.
 
15
Pastor (1996) shows that in the cases where there are translated variables, it is only safe the use of the old version of Bcc input oriented or the additive Vrs models.
 
16
As succinctly stated by Oecd: “It is also noteworthy that neither the net nor the gross stock are the conceptually correct measure to capture capital inputs into production—these are best reflected through measures of the flow of capital services” OECD (2013a, b).
 
17
Out of the 35 countries in the Bli database only 20 can be found in the Oecd Vics database.
 
18
For instance, Ons (2004, 2006, 2008a, b) and Atkinson (2005) choose to rely on “Consumption of fixed capital” in their assessment of Uk public sector productivity. We thank a referee for calling our attention on the need to clarify the point about capital input selection.
 
19
Bli is available for 36 countries. As Brasil lacks data on labour, we exclude it from our analysis.
 
20
Gdp data is in the form of purchasing power parities (http://​stats.​oecd.​org/​index.​aspx?​DatasetCode=​PDB_​LV).
 
21
Slacks are indeed computed over a linear combination of input and outputs of the peer countries, as if we got an ideal country to be compared with each inefficient unit.
 
22
Decreasing returns for “mature” countries is a result we have in common with Golany and Thore (1997).
 
23
The bad-outputs in Fig. 2. The undesirable effects of economic activity.
 
24
To be noticed that Russia excellent performance is affected, in the principal component analysis, by a large loading in “Housing expenditure” which is relatively low, compared to western standards, due to the high frequency of council houses.
 
25
Table 5 shows that zero rank correlation is within the bounds of the 95 % confidence interval.
 
26
We thank a referee for calling our attention on this way to look at the results.
 
27
Gdp and Ste are positively rank correlated at 95 % (see further down Table 5).
 
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Metadaten
Titel
The Cost of Well-Being
verfasst von
Vincenzo Patrizii
Anna Pettini
Giuliano Resce
Publikationsdatum
24.06.2016
Verlag
Springer Netherlands
Erschienen in
Social Indicators Research / Ausgabe 3/2017
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-016-1394-2

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