Holistic System Approach to Assess the Older Generation's Well-Being in Russia: the Need for the Russian Elderly Well-Being Index
The demographic shift in the population structure, associated with the increase in the proportion of the older ages, has a very serious impact on the lives of individuals, communities and the entire country. There is a necessity to develop and practically implement new tools and mechanisms for the integrated assessment of the main aspects of the elderly generation economic and social well-being.
For example, the Tomsk region at the beginning of 2014 accounts 224400 people over retirement age (men - 60 years and older, women - 55 years and older). Compared to 2011, the number of older adults increased by 17800 (or 8.6% increase) since the generation of 50s began to reach retirement age. During this period, the share of the older retired generation in total population of the Tomsk region increased from 19.7% to 21.0%. The population of the Tomsk region, according to international criteria, is considered to be “old” since the number of the people 65+ years is higher than 7% of the total population. In the beginning of 2014 this figure was 11% (every ninth resident). Also, the aging of the population is evidenced by the increase in the average age of citizens. Over the past three years, the average age of the population increased by one year from 37 years to 38 years.
In general, there are several trends in evaluating the well-being of the elderly population at the macro level:
Development of composite indices as a universal tool for cross-country comparisons in order to solve a whole range of socio-economic and political problems;
Combination of objective statistical indicators and subjective assessments in a single measurement scale that characterizes the well-being of the elderly population as a phenomenon that requires a multidisciplinary assessment approaches;
Development of national statistics, national indices and scales of subjective well-being of the elderly population monitoring which allows to assess, analyze, compare the results in different timelines and in different regions in order to develop relevant national social and economic policies aimed at improving the well-being of the elderly population.
Our model for assessing well-being of elderly adults rests on the following prerequisites:
1. Well-being is a combination of objective and subjective economic, social, physical and psychological criteria determined by a specific quality of life and characteristics socio-cultural environment deeply rooted within economic, social, cultural subsystems of a country.
2. There are significant and important, often informal, support institutions for elderly adults such as social connections, communication, family, friends, neighborhood environment, etc.
3. Any socio-economic system requires formal procurement institutions acting through institutionalized structures which are responsible for resources allocation (governmental and public organizations, social welfare system, policies, etc).
While developing the structural model of the researched phenomenon, we used the hierarchy of functions related to the management and life procurement support or, in fact, to the livelihoods of various groups of older people (Fig.1). This approach allows researching multiple target groups and generating modifications of the model, depending on the research goals. Invariant parts of all models are functions of governance and procurement support. The governance functions are performed by such structural elements of the system as the “Government” and the “Economy”, creating a balance between the desirable and the possible. The function of procurement support is performed by institutions and organizations, creating and supporting living environment for the elderly people. The variable part of the model is a social target group itself in a variety of individual characteristics, problems, needs and opportunities.
Fig. 1 Holistic model of the elderly population’s well-being (Pavlova et al., 2015)
We also introduced the idea of using (1) the definition of “model”, (2) the concept of “identity of the model and the object” and “adequacy of the model and the object” which is applicable to any model type: structural, functional, institutional, mathematical, statistical, etc. Each model type has its advantages and disadvantages. Since they can be applied to the same object, the integrated evaluation becomes much more accurate. Fig. 2 shows a matrix of subjective assessments and objective indicators, where the columns represent a hierarchy of needs of the elderly people and lines represent scale of interactions.
Fig. 2 Matrix of objective and subjective indicators (Pavlova et al., 2015)
As the result, the model of practical level includes the following domains of needs: (1) health and physical activities; (2) income and employment; (3) accommodation, assets and living conditions; (4) education and training; (5) social connections; (6) social activities; (7) entertainment and free time; (8) mobility. Scale of interaction is represented by institutions and organizations performing functions of procurement: (1) legislation (national and regional levels); (2) budget and financial government responsibilities; (3) formal government institutions of healthcare, social welfare, education and so on; (4) non-government organizations and informal institutions; (5) close social environment (friends, neighbors, acquaintances); (6) household/family (spouses; parents, children, relatives).
Evaluation of the developed model was done in several stages following the procedure presented in the Fig. 3. M0 is the initial two-level model including system and practical levels. Mobj and Msubj stand for sub-models integrating objective or subjective indicators.
Fig. 3 Procedure of the two-level model approbation (Pavlova et al., 2015)
As the main sources used for this study we focus on numerous databases and documents of official governmental and public authorities such as the UN, WHO, OECD, Eurostat, Russian Federal State Statistics Service (FSSS). The official statistics for Russia are withdrawn on the basis of the List of Indicators from Federal Statistical Observation Forms and the List of Indicators of the FSSS. On a special request of the authors, the information on 116 selected indicators was withdrawn by the Tomsk Regional Statistics Service (Tomskstat) which, finally, led to the release of a unique report “Evaluation and improvement of the social, economic and emotional well-being of elderly people in 2000-2013” by Tomskstat in 2015. For subjective assessments analysis we developed a survey with 78 questions resulting into 324 variables. The questionnaire was designed on the basis of the matrix of subjective and objective indicators covering 8 domain of needs and 6 functions of institutional procurement. The survey embraced 400 older adults living in the Tomsk region. At the present stage, the survey enters the stage of information synthesis and aggregation.
For this study, there are two normative models (Mnorm) selected – the Global Age Watch Index (AW) and the Active Ageing Index (AAI). Both indices the AW and the AAI are to serve as political tools as they focus on sustainable development of societies. The AAI is aimed at “providing a new tool for policy makers to enable them to devise evidence-informed strategies in dealing with the challenges of population ageing and its impacts on society” (Zaidi et al., 2013) in order to monitor (and compare) active aging outcomes at international, national, and subnational levels; to indicate older people’s potential for a better inclusion in social and economic life as well as to advocate most appropriate policy measures. The AW is aimed at measuring and improving the quality of life and well-being of older people, indicating population challenges in order to generate evidence for policymakers in the first place. The AW demonstrates strong affiliation to pension watch as a tool to guarantee income security. The AAI represents a generally universal approach to measuring active ageing according to well-built methodology and its application to high-comparability data. At the same time, there is a strong limitation to such an index reconstruction for Russia, since the current data comparability is questioned. As a normative model, the index can be calculated for Russia, but with significant aberrations due to different data sources and necessary methodology modification. Nevertheless, the research team, which developed the AAI, stressed the flexibility of the index usage. We suppose that for the correct development of the AAI for Russia, it is necessary to introduce national statistical into the computation of this index or to develop a new methodological approach basing on existing data sources. This may be quite a challenging, but a very promising further research direction. Both these two indices represent a combination of objective indicators and subjective assessments. The Global Age Watch Index comprises 13 indicators (8 objective and 5 subjective ones). The Active Ageing Index has 22 indicators with 20 objective and 2 subjective ones.
Russia lacks overall general monitoring surveys of the older generation and ageing problems, though recently some new monitoring forms have been introduced by the FSSS for the nation-wide monitoring in the domain of older generation well-being for a limited number of objective indicators. In general, there is a very narrow scope of data collection on many indicators, comparable with those of foreign countries. Since the monitoring of the elderly population level of life is relatively recent for Russia, it is still impossible to trace the dynamics for the most of the indicators for significantly lengthy time periods. Therefore, while testing the the systemic model (Fig. 1), within the project Evaluation and enhancement of social, economic and emotional wellbeing of older adults (the Agreement No.14.Z50.31.0029), we developed the database devoted to the assessment of the elderly population well-being. The database structure is developed according to the proposed model and includes: (1) Types of reserched objects such as Government, Procurement, Economy, Retired, Friends, etc.; (2) Needs and resources: Health and physical activity, Revenue, Accommodation, Development, Communications and social connections, Social activity, Leisure, Mobility; (3) Indicators (45 source indicators plus computed indicators); (4) Connections between objects, needs and resources, and indicators.
Web-interface of the database is the part of web-site http://statlwl.tpu.ru/. It allows to input and edit the data and to display data (the user needs to login in order to have an access to the database). As for the web-interface, we obtained a computer program registration certificate «The control program for database of indicators of well-being of older people», certificate of the Russian Federations’ state registration № 2015619013, authors: Barysheva G.A., Monastyrny E.A., Spitsin V.V., Shabaldina N.V., Gumennikov I.V. Physical model of the database is shown on Figure 4.
Fig. 4 Elderly population database’ physical model
We consider regional social and economic comparisons in terms of their dynamics as an acute problem on the rise of a new agenda. At the time, inter-regional comparisons in Russia were mainly associated with the economic situation, basing on objective criteria. However, improving quality of life of older people is becoming one of the governmental priorities. The approaches to the statistical data collection are being reconsidered on the national level. Despite the fact, that new forms for life assessment of the older generation are introduced, this research issues bring up challenging and sensitive problems. In previous years, a comparative analysis of the elderly has been underdeveloped, so the direction for future research is a very promising comparative study of the older people well-being dynamics in different regions of the Russian Federation. Despite the fact that Russia specifically shows some dynamics in national policy on ageing, it still lacks comprehensive tools for older generation well-being measuring and analysis both on national and regional levels.
Based on the foregoing, it should be noted that the conceptual framework in creating the Russian index of the elderly population well-being should be the approach based on the needs of older generation (needs-driven approach). There are several basic principles for developing a composite index (Korchagina, 2012): data should be available and measurable, presented in official documents and regularly updated; the indicators/variables should be simple enough for interpretation and reflect the actual processes and their dynamics; the indicators/variables must be scientifically grounded and justified, be based on international standards, can be used in econometric models to measure, assess and predict development of the present situation.
In order to develop a composite index for assessing the elderly population well-being for the Russian Federation, we have formulated additional principles such as (1) possibility and necessity of heterogeneous indicators/variables aggregation in a comprehensive evaluation scale under the single methodology; (2) indicators/variables relevance implies the validity and justification of the integration into the composite index of any metric; (3) adequate, fair and reasonable allocation of weights between variables, indicators and domains; (4) needs of the elderly population non-excludability; (5) non-excludability of territories and regions; (6) differentiation of regions and territories with the possibility of multi-level differentiation in Russia (federal districts, regions, territories) due to significant distinctions in socio-economic development levels; (7) combination and usage both the objective and subjective measures; (8) valid and fair international and inter-regional comparisons.
In addition, the research of composite indices methodologies leads to understanding the basic variables/indicators assessment groups, which are tightly connected to elderly population needs. These groups, as a rule, include the following domains: (1) health; (2) income; (3) work/employment; (4) education; (5) living conditions/dwelling; (6) family; (7) social life; (8) political life; (9) emotional state of being/subjective perception; (10) community; (11) safety and security. Also, we can find the following areas of assessment which are more rarely used such as (1) opportunity; (2) climate/geographical conditions of living (3) gender equality and gender issues; (4) ecological conditions.
The research results allow us to select necessary domains with the relevant variables to develop the methodology for computing and developing the Russia Elderly Well-being Index (REWI) for the Russian regions. The necessity of the REWI development is justified by the current economic and political needs for the comparison of elderly population will-being for different Russian regions and territories due to the scarce elderly population well-being metrics presented in Russian statistics as well as due to significant differentiation of Russian regions in terms of demographics, income, employment, climate, dwelling specifics, etc. We intend to complete second part of the research with the analysis of subjective assessments basing on the survey conducted and compare objective and subjective criteria results. The cross-regional comparisons within the territory of the Russian Federation on the basis of holistic system approach are previewed as the next research phase.
Korchagina, E.V. (2012), Methods for evaluation of sustainable development of regional social and economic systems. Problems of modern economy, 1, 67-71 (in Russian).
Pavlova, I., Monastyrny, E. & Gumennikov, I. (2015). Developing a Holistic System Approach for MeasuringWell-being of the Older Generation in Russia. Proceedings of The 26th International Business Information Management Association Conference, 2755 – 2767.
Zaidi, A. et al. (2013), Active Ageing Index 2012. Concept, Methodology and Final Results. Research Memorandum/ Methodology Report, European Centre Vienna, March 2013. Режим доступа: www.euro.centre.org/data/aai/1253897823_70974.pdf (Accessed: 25.12.2015).