Abstract
The scope of this study ranges from the identification of key drivers of living conditions from a wide spectrum of context-based indicators to the development of a concrete composite measure of living conditions within the framework of a multivariate analysis. The Living Conditions Index (LCI) is a standardized aggregate score that summarizes five components and 18 indicators in a single number. Three different approaches, principal component analysis (PCA), range equalization (RE), and division by mean (DM) are used to assess the impact of different methods of weighting and standardization procedures on the composite. Between the RE and DM methods, the RE method is preferred because it accounts for wider variations and strong correlations to the PCA composite. In general, the PCA method appears promising, particularly for cross-community comparisons as it is based on a weighting scheme. Extreme variability between quintiles that comprise the LCI indicates that the score represents a summary of economic, housing, and cultural diversities. The paper advocates for a future plan of research in the light of identified gaps in data, and more emphasis on disparities in economic conditions. A major implication of the study is that the composite provides a new tool in child development research for characterizing community-based living conditions and detecting disparities in the distribution of child developmental outcomes.
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Notes
The concept of a community is loosely defined for the present purpose, using a number of factors and/or geopolitical units, such as census dissemination areas (census-defined boundaries), archival neighborhood/community maps, and postal codes.
The value of certain variables, but not all, can have equal importance at various levels of the variable; the relative impact of variables, such as deprivation, can increase as the level of deprivation becomes sharper (Anand and Sen 1997). Based on this argument, some measures of living conditions can have a diminishing return, while others can have increasing returns (Salzman 2003).
While it is possible that such differences can vary according to cultural and/or personal preferences, they can serve as proxies for leisure and well-being. For an assessment of the impact of leisure time and income inequality on well- being, see Beckerman (1978).
This paper, which presents some of the data gathered through Census in 2006, provides an overview of the factors that make up the living conditions index. The most recent data, collected in 2011 was a National Household Survey to which the participation was voluntary. The new survey was collected and analyzed using different rules, and it also didn’t contain all of the same pieces of information we used here. Because the two are so different, we can’t make any comparisons between 2006 and 2011 information at this point. What this really means is that we can’t make a “true” picture of what has changed or what has stayed the same between 2006 and 2011 and how this will impact the index itself.
A peculiarity of our data is that some indicators have very little variation across areas. Considering this, the rescaling approach seems more suited for our purpose so that the interpretations can be more meaningful and easier.
Instead of a division by an indicator range, fixed range, computed on the basis of pre-determined ‘goalposts’ with set upper- and lower limits has also been in use in the computation of composites (e.g., HDI). Fixing the goalposts for indicators can be tricky because not all indicators can be assumed to reach the upper value in the same fashion across time and space.
The Coefficient of Variation (CV) is obtained by dividing the standard deviation of a variable by its mean. Graphically, it describes the peakedness of a unimodal distribution; the peak will be high and the CV will be small when the data points are bunched around the mean, and vice versa. A more equitable distribution has a smaller CV.
Reference may be made to the technique of power-averaging, developed by Anand and Sen (1997). Here, variables are raised to a power alpha, summed with weights, usually equal, and then the alphath roots is taken.
According to Nardo and Saisana (2005, p.11), “In both linear and geometric aggregations weights express trade-offs between indicators… With linear aggregations the compensability is constant, while with geometric aggregations compensability is lower when the composite contains indicators with low values. In policy terms if compensability is admitted (as in the case of pure economic indicators) a country with low scores on one indicator will need much higher score on the others to improve its situation if the aggregation of information is geometric. Thus in a benchmarking exercise, countries with low scores should prefer a linear rather than a geometric aggregation. On the other hand the marginal utility of an increase in the score would be much higher when the absolute value of the score is low. The resulting lesson is that a country should be more interested in increasing those sectors with the lowest score in order to have the highest chance to improve its position in the ranking if the aggregation is geometric. The opposite is true, i.e. a country has interest in specializing along its most effective dimensions, when the aggregation is linear.” (see also, Munda and Nardo 2009).
Cronbach’s Alphas are dependent on the number of indicators in a dimension. When the indicators are fewer than 10, which is, of course, the case with all our components, it is recommended to calculate the mean inter-indicator correlation for the indicators. Optimal mean inter-indicator correlation values range from 0.2 to 0.4 (Briggs and Check 1986).
Some may argue that many of those who fall below or above the average may have had incomes above or below the average at some point in the past and, therefore, relative measure of income does not reflect reality. However, in capitalist economies, the cutting edge of poverty is the perceived gap that exists between the poor and the rich. As Wilkinson and Pickett (2009) argued, inequality “gets under the skin” and makes everyone worse off, not just the poor (see also, The Economist 2011).
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Acknowledgments
The author thanks the Faculty of Extension, University of Alberta, Edmonton, for funding this project. The contents, however, are solely the responsibility of the author and do not represent the official views of the funding agency. The author would like to thank Dr. Susan Lynch, Director, Early Child Development Map** Project (ECMap) Alberta and the ECMap team for their support in carrying out this project. Special thanks are also due to Shea Betts and ** Project (ECMap), Community-University Partnership (CUP), Faculty of Extension, University of Alberta, Edmonton, AB, Canada
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Krishnan, V. Development of a Multidimensional Living Conditions Index (LCI). Soc Indic Res 120, 455–481 (2015). https://doi.org/10.1007/s11205-014-0591-0
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DOI: https://doi.org/10.1007/s11205-014-0591-0