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Erschienen in: Social Indicators Research 3/2014

01.09.2014

Measuring World Better Life Frontier: A Composite Indicator for OECD Better Life Index

verfasst von: Hideyuki Mizobuchi

Erschienen in: Social Indicators Research | Ausgabe 3/2014

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Abstract

The OECD Better Life initiative recently released a comprehensive set of 11 indicators of well-being covering a group of countries. Each individual indicator corresponds to a key topic that is essential to well-being. However, the problem of aggregating them is left to users of this dataset. Using these as individual indicators, we propose a composite indicator of overall well-being, which is intended to measure the performance of each country in terms of providing well-being to its people. The ‘benefit of the doubt’ approach (BOD), a well-known aggregation tool based on a weighed sum, assigns the most favourable weights for each entity under investigation. BOD may also be considered to evaluate the performance of each entity in terms of its efficiency. Regarding individual indicators as outputs, it constructs the benchmark production frontier from observed individual indicators. A composite indicator based on BOD equals the distance between each entity’s individual indicator and the production frontier, indicating its efficiency. It is widely considered that the well-being of a country’s people stems from its productive base, which is characterized by capital assets and social infrastructures. Thus, the productive base can be considered the input used to produce well-being, which is reflected by individual indicators. Therefore, when we apply BOD to aggregate individual well-being indicators across countries, we implicitly assume that all countries have the same productive base, as BOD addresses only the output and neglects the input. This inaccurate assumption leads to a distorted performance measure. Data envelopment analysis (DEA), in which BOD has its roots, is a tool to measure the efficiency of each entity by allowing for differences in inputs as well as outputs across entities. DEA also measures efficiency by using the distance to the production frontier; however, unlike BOD, DEA constructs the production frontier more accurately by utilizing the information of inputs as well as outputs, leading to a better performance measure. We apply DEA to aggregate 11 individual well-being indicators into a composite indicator using the World Bank’s estimates of each country’s productive base. The composite indicator based on BOD is distributed similarly to and is highly correlated with the existing Human Development Indicator (HDI). It is also positively correlated with GDP per capita. On the other hand, we show that the composite indicator based on DEA is negatively correlated with HDI as well as GDP per capita.

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Fußnoten
1
The number of countries covered was 34 in 2011. The revised dataset released in 2012 includes 36 countries, incorporating Brazil and Russia.
 
2
Your Better Life Index (http://​www.​oecdbetterlifein​dex.​org/​) was designed as an interactive tool that allows users to assign the importance of each of the 11 topics and track the performance of countries.
 
3
In addition to the problem of paternalism, Cherchye et al. (2007) also indicate that if weights are fixed, the eventual country ranking depends on the measurement unit and the particular normalization option adopted for individual indicators, such as rescaled scores, distances to goalposts, or z-scores.
 
4
Lovell et al. (1995) interpret the dummy input as a helmsman that pursues several policy objectives.
 
5
Despotis (2005a, b) also addresses the process of converting countries’ resources to well-being or human development. However, they are concerned with countries’ capabilities of converting economic prosperity into better lives for their people. Thus, while income is considered as input, life expectancy and adult literacy rate are considered as outputs.
 
6
The definition, based on the ratio of the distance functions, prevents us from interpreting the achievement index as a weighted average of individual indicators of well-being. On the other hand, the index we propose is an efficiency score simply defined by the distance function; it is the weighted average of individual indicators.
 
7
Fleurbaey and Gaulier (2009) deal with leisure, unemployment, health and inequality in addition to income. Jones and Klenow (2010) deal with life expectancy, leisure and inequality in addition to consumption.
 
8
Luxembourg also ranks relatively low among OECD countries in Jones and Klenow (2010).
 
9
This interpretation is rooted in Lovell et al. (1995).
 
10
Technically speaking, the minimal extrapolation principle constructs \(\varPsi_{BOD}\) so that \(\varPsi_{BOD} = \left\{ {\varvec{y} \in {\mathbb{R}}_{ + }^{M} :y_{m} \le {{\Upsigma}}_{k = 1}^{K} \lambda_{k} y_{mk} {\text{ for }}m = 1, \ldots ,M;\lambda_{k} \ge 0 {\text{ for }}k = 1, \ldots ,K} \right\}\).
 
11
Social infrastructures of country k, such as law and community ties, also constitute its productive base. They are considered social capital, which is a component of the wealth vector \(\varvec{x}_{k}\).
 
12
Technically speaking, the minimal extrapolation principle constructs \(\varPsi_{DEA}\) so that \(\varPsi_{DEA} = \left\{ {\left( {\varvec{x},\varvec{y}} \right) \in {\mathbb{R}}_{ + }^{M + N} :y_{m} \le {{\Upsigma}}_{k = 1}^{K} \lambda_{k} y_{mk} \,{\text{ for }}m = 1, \ldots ,M;x_{n} \ge {{\Upsigma}}_{k = 1}^{K} \lambda_{k} x_{nk} \,{\text{ for }}n = 1, \ldots ,N;\lambda_{k} \ge 0\,{\text{for}}\,k = 1, \ldots ,K} \right\}\).
 
13
Since individual well-being indicators are considered to be data from 2009, there is a difference in data periods between individual well-being indicators and the productive base. Because of data limitations, we omit this problem. One justification is that the magnitude of the comprehensive wealth of each country is significantly large because of the accumulation of past investment; thus, it is rather stable over several years. We leave extending the data of well-being indicators or the productive base in order to match their data period to future research.
 
14
The 11 subcomponents of natural capital are as follows: (1) Crop, (2) Pasture land, (3) Timber, (4) Non-timber forest, (5) Protected areas, (6) Oil, (7) Natural gas, (8) Hard coal, (9) Soft coal, (10) Coal and (11) Minerals.
 
15
The fact that the share of intangible capital is higher in middle- than high-income countries seems to contradict the finding by World Bank (2011), which indicates that its share increases as an economy grows. Since middle- and high-income countries in our sample are considered as high-income countries in World Bank (2011), the differences in per capita income and the level of economic development between these two groups are neglected in that study.
 
16
All calculations are done in R using the ‘Benchmarking’ package. See Bogetoft and Otto (2011, 2012).
 
17
Note that cases 1 and 2 adopt DEA with single input while case 3 adopts DEA with two inputs.
 
18
From now onwards, \(CI_{DEA}\) refers to the composite indicator based on DEA under case 3, which ideally assumes the productive base comprising produced, natural and intangible capital.
 
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Metadaten
Titel
Measuring World Better Life Frontier: A Composite Indicator for OECD Better Life Index
verfasst von
Hideyuki Mizobuchi
Publikationsdatum
01.09.2014
Verlag
Springer Netherlands
Erschienen in
Social Indicators Research / Ausgabe 3/2014
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-013-0457-x

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