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The Quality-of-Life Measurement with a Stochastic Choice of Parameters of the Weighted Principal Component

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  • Alexey A. Mironenkov
  • Alexey N. Kurbatskii
  • Marina V. Mironenkova

Abstract

The quality of life of the population is a latent category, and due to the impossibility of direct measurement it has to be assessed as an integral indicator of many variables. According to the established methodology, one of the main tools in this case is the first principal component, that is, a linear convolution of variables, which has the property of minimizing the variation of the original characteristics. The fact that parameters’ variation is taken into account with equal weight may cause criticism from economists. The use of a weighted principal component which is free of this drawback can be considered as a development of the initial method. In this case to minimize the total variation, the weighting coefficients of features are set expertly. However, in this case, a logical question arises: won’t expert subjectivity have a significant impact on the final integral indicator, as it happens in the case of simple linear convolution with expert weights? Thus, the purpose of this work is to test the applicability of the weighted first principal component as the main tool in constructing an integral indicator of the population quality of life. In particular, it is necessary to test the hypothesis that the influence of heterogeneity in the weights of expert assessments on the final integral indicator is insignificant. In this case, it would be useful not only to illustrate the presence or absence of this influence, but also to estimate its extent. To do this, the simulation modeling is carried out to assess the latent variable “quality of life of the population”, based on empirical expert weights and macro statistics data. Moreover, in contrast to most works related to the topic, the values of the integral indicator (and, accordingly, the ranking of observations) are presented as an interval estimate. In other words, the result is presented as a random variable where the element of randomness is the subjectivity of the expert choice of weights for the weighted principal component. It turns out that even in this case it is possible to obtain robust and meaningful results that are in good agreement with the conclusions of well-known research in this area.

Suggested Citation

  • Alexey A. Mironenkov & Alexey N. Kurbatskii & Marina V. Mironenkova, 2024. "The Quality-of-Life Measurement with a Stochastic Choice of Parameters of the Weighted Principal Component," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(1), pages 82-109.
    • Handle: RePEc:aiy:jnjaer:v:23:y:2024:i:1:p:82-109
    DOI: https://doi.org/10.15826/vestnik.2024.23.1.004
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    References listed on IDEAS

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    1. Reichlin, Lucrezia & Giannone, Domenico & De Mol, Christine, 2006. "Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?," CEPR Discussion Papers 5829, C.E.P.R. Discussion Papers.
    2. Slottje, Daniel J, 1991. "Measuring the Quality of Life across Countries," The Review of Economics and Statistics, MIT Press, vol. 73(4), pages 684-693, November.
    3. Matteo Mazziotta & Adriano Pareto, 2019. "Use and Misuse of PCA for Measuring Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 451-476, April.
    4. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    5. Antonio Tomao & Walter Mattioli & David Fanfani & Carlotta Ferrara & Giovanni Quaranta & Rosanna Salvia & Luca Salvati, 2021. "Economic Downturns and Land-Use Change: A Spatial Analysis of Urban Transformations in Rome (Italy) Using a Geographically Weighted Principal Component Analysis," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
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    7. Blomquist, Glenn C & Berger, Mark C & Hoehn, John P, 1988. "New Estimates of Quality of Life in Urban Areas," American Economic Review, American Economic Association, vol. 78(1), pages 89-107, March.
    8. Volkova, Maria, 2010. "Comparison of objectivistic and subjectivist approaches to measurement of synthetic la-tent categories of Quality of Life of the population: results of the empirical analysis of Russian data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 19(3), pages 62-90.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    integral indicator; the quality of life; weighted first principal component; stochastic selection; simulation modeling; expert weights.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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