Abstract
This paper suggests a new and comprehensive approach to the assessment of the material well-being at the individual level by constructing a multidimensional index. Using this approach, material well-being is understood as a generic notion that covers a number of different domains, whereas the concept of domain is used to distinguish between different aspects of people’s resources, including income security, basic needs, durables, housing and subjective material well-being. Each dimension is measured independently, using the best indicators available, to generate a score or domain index for each aspect of material well-being. The procedure of re-weighting the indicators within the separate domains enables us to account for the disparity in resources and consumer preferences across different population subgroups. The final domain scores, combined with explicit weighting, are then used to generate a summary material well-being index. The domain indices and the summary material well-being index are validated by exploring their relationships to key socio-economic attributes, which were previously shown to be strongly associated with individual material well-being. The results showed that the summary indices of material well-being are characterized by greater differentiation in relation to such measures, as occupational class and judgments of satisfaction with one’s life. This allows us to conclude that our summary indices capture the latent concept of material well-being better than any of our domain indices used separately. Although the index is constructed using the Russian Gender and Generation Survey data for 2007, the methodological approach that we applied can be easily replicated in other surveys which contain information on several aspects of material well-being.
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Notes
GGS is a part of the international research programme of UNECE. More details can be found at: http://www.unece.org/pau/ggp/Welcome.html.
This is something different from the hidden or the illegal economies. The latter comprise the activities related to tax fraud or tax evasion or illegal activities. The non-observed economy also comprises activities that have nothing to do with criminality or tax evasion, but that still remain unobserved because the traditional survey tools are not perfect and business registers are not always complete and up-to-date.
Persons living in institutional households (children’s homes, social care institutions, convents) are excluded. In geographical terms the sample covers 32 (out of 83) regions and is not representative at the regional level, which is the main limitation of the survey. On average the household response rate exceeds 80 %, but it is lower in Moscow and St-Petersburg (<60 %).
For more information about RLMS–HSE survey see: http://www.hse.ru/en/rlms/.
GGS-2007 was conducted in April–September 2007 by the Independent Institute for Social Policy with financial support from the Pension Fund of the Russian Federation, UNFPA, Sberbank of Russia, Children’s Foundation ‘Victoria’ and Ford Foundation.
Because of the conceptually different nature of our indicators we chose not to rely on statistical methods, such as Factor analysis (FA) or Principal Component Analysis (PCA), to define our dimensions for us. The OECD handbook on composite indicators stresses that PCA and FA should be used with caution, as they may “identify dimensions that do not necessarily help to reveal the clustering structure in the data and may actually mask the taxonomic information” (OECD 2008, p. 26). Generally speaking, PA and PCA, being non-probabilistic methods, are very sensitive to slight alterations in the structure of data; and there is no ‘objective’ procedure for judging the validity of their results.
For example, Eurostat treats ‘the presence of mortgage or rent payments, utility bills, hire purchase instalments or other loan payments’ in the household as a component of the EU material deprivation index.
In our case the options for the denominator are as follows: the proportion of households who have nominated an item as a necessity; the proportion of households who report that they do not have an item or who report an enforced lack of an item. All these options are essentially variations of the prevalence weighting approach. The first option which relies on judgments about the relative weights of items made by respondents themselves should be a preferred option, but these data are not available in our survey, and in fact are rarely collected in general purpose large-scale surveys. In the absence of the public opinion data, we relied on the information about actual consumption choices, and among the two options available we opted for accounting for the ‘enforced lack’ as it captures the preferences of individuals.
The sample yields functional values for a segment as small as 1.5 % of the total population. In this case the sample error is 20 % at 95 % confidence level.
It is noteworthy, the use of reference group-level weights generally leads to the reduction in inequalities between various population subgroups, because in this case the position of each household is estimated relative to the position of its reference groups, rather than relative to the average national standard. The more disadvantage groups are likely to have lower living standards. For example, if the national-level prevalence weights were applied in the durables domain instead of the reference group-level weights, the gap in average well-being scores between households from large cities and the rural area would be 1.2 times as high as it appears to be under the current weighting scheme, while the gap between households with children and households of pensioners would be 1.8 times as high as under the current scheme. This issues deserve further consideration in future research.
The natural logarithm of per capita income was regressed on a set of household characteristics, including household composition, education level, labour market status, presence of various sources of income and dummies for the region and the type of settlement to account for the unobserved parameters of the local market.
The minimum consumer basket—referred to as the minimum subsistence level (MSL)—is the official poverty threshold in Russia. The value of MSL is calculated separately for three socio-demographic groups—children up to 16 years, persons of active working age (men of 16–59 years and women of 16–54 years) and persons of pension age (men of 60+ years and women of 55+ years). It is set quarterly in all 83 regions, and for Russia as a whole.
We did not apply any equivalence scale, following the standard practice of the national statistics agency which uses per capita incomes. This is unlikely to alter our results significantly because the structure of household consumption in Russia differs from that in a mature market economy. The share of spending on rent and utilities is substantially lower, while the share of spending on food is higher, resulting in lower economies. Previous studies for Russia indicated that the elasticity coefficient based on the analysis of Engel curves (food shares) was equal to 0.9 in early 1990s (see: Klugman 1997). This has declined to 0.78 by 2003 (see: Ovcharova et al. 2006). More recent estimates were not available at the time of writing.
We have considered another indicator of income security which could be relevant for post-transition countries like Russia—the share of income from/consumption of home produced food in total household income/consumption. Home production of food was an important source of consumption in the 1990s, yet the share of this source in total income/consumption in Russia has substantially decreased since the beginning of 2000s. According to RLMS–HSE for 2008 the share of income from ‘in-kind home production and informal sector’ in total household income was 3.9 % on average, while the share of households that had this source of income was 44.3 %, with very little variation across income quintiles. The share of home produced food consumption in total household consumption was 3.4 %, with little variation by quintile (see: http://www.hse.ru/data/2015/10/09/1077435180/Vestnik%20RLMS-HSE_2015.pdf). This can be interpreted as follows. Quite a large number of Russian households have summer cottages and small land allotments where they do some gardening over the summer, but it does not have a substantial impact on their material well-being, maybe apart from the psychological effect, as home grown food is perceived as a better quality ‘organic’ food. There is a category of rural households that rely heavily on home production but this group is small and is not identifiable in GGS because consumption data was not collected.
It does not matter whether an item is owned, rented, or otherwise provided for household use.
For instance, according to the all-Russian household survey conducted in 2003, the market rent was 5 times as high as the sum of the bills paid by homeowners and tenants renting from the state or municipality (see: Ovcharova and Tesliuk 2006).
It is noteworthy, although households renting from the state/municipality pay rent which is substantially below the market rent, we cannot equalize them with owners, because home ownership is an important asset. Although this category of tenants have lower costs compared to those who rent private housing, the former cannot control their housing, which is a serious limitation on their opportunities, compared to homeowners.
These standards are applied to calculate the size of the housing subsidy, one of the most important federal means-tested social allowances. The federal social standard equals 36 sq m for a person living alone, 42 sq m for a two-person household and 18 sq m per each person in households with three and more members.
In a number of studies this item is included in the material deprivation measure, however, the difficulty in coping with expenses is likely to be influenced not only by the deficit of income, but also by individual coping skills, that is why we prefer to treat it as a subjective measure.
Weights are equal to the squared factor loadings taken relatively to the total eigenvalue of the first factor.
The demographic composition variable was used to derive weights for domain indices, but in a reduced form that included only three broad categories: households with children under 18 years/households without children under 18 years/households of pensioners.
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Acknowledgments
Support from the Basic Research Programme of the National Research University Higher School of Economics is gratefully acknowledged (TOR-36: The impact of changes in economic and social policies on consumer behavior of the population and fostering conditions for active aging, 2015). The views expressed are those of the authors. We are the only responsible for any errors as well.
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Popova, D., Pishniak, A. Measuring Individual Material Well-Being Using Multidimensional Indices: An Application Using the Gender and Generation Survey for Russia. Soc Indic Res 130, 883–910 (2017). https://doi.org/10.1007/s11205-016-1231-7
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DOI: https://doi.org/10.1007/s11205-016-1231-7