Analysis Findings of the Depth of Social and Economic Problems Across the Regions of the Russian Federation Volkova Maria Igorevna, Candidate of Economic Sciences, Associate Professor The Central Economic Mathematical Institute of the Russian Academy of Sciences

Social and Economic Problems Decrease in Quality of Life and Quality of Living Conditions Increase in Stratification and Differentiation Search for Alternative Sources of Income Potential Involvement in Criminal Organizations

Examples of Simulation Models Where the Population Acts as One of the Main Agents Genkin M., Gutfraind A. (2008): How Do Terrorist Cells Self-Assemble? Insights from an Agent-Based Model. Annual Meeting of the American Sociological Association, Sheraton Boston and the Boston Marriott Copley Place, Boston, MA, Jul 31, 2008. S.Ya. Sushchy, G.A. Ugolnitsky, V.K. Dyachenko. Simulation Modeling of Struggle Against Extremism in the Northern Caucasus. Sociology: 4М. 2013. No. 37. P. 126-150. Steve Kiser. Financing Terror. An Analysis and Simulation for Affecting Al Qaeda's Financial Infrastructure. – 2004. 247 р.

Groups of the Most Acute Social and Economic Problems Labor Unemployment Long-term unemployment Arrears in wages Work-related injuries Unemployed people per vacancy And other criteria Health Infant mortality Mortality caused by certain diseases Incidence of certain diseases Rate of natural increase Security Crime rate Social security level Income and Welfare Personal and household incomes (relative parameters) Poverty headcount Material security Infrastructure development level

Key Focuses of National Projects Quality of life Demography, education, healthcare Social sphere Ecology Employment support Small and medium-sized entrepreneurship Science, digital economy, culture Income, housing, roads

Composite Indicator Calculation Methodology as Part of the Principal Component Analysis. Flowchart Transformation of initial variables Block partitioning (correlation relationships) Calculation of information content criteria (using eigenvalues of the covariance matrix) Estimation of eigenvalues, eigenvectors and weighting factors Calculation of principal components and indicators of certain categories Estimation of the weighted Euclidean distance to the reference point and the composite indicator value

List of Variables (by Russian Region) Demography Rate of natural increase (per 1,000 people) Infant mortality rate (Infant deaths per 1,000 live births) Mortality from malignant neoplasms (per 100,000 people) Cardiovascular mortality rate (per 100,000 people) Number of invalids (per 1,000 people) Social sphere Percent of the population with money incomes below the minimum subsistence level (%) Long-term unemployment level (people seeking employment for more than 12 months, % of all unemployed) Labor market tension (number of unemployed people per one vacancy) Total number of grave crimes (per 10,000 people) Welfare Number of cars in private hands (per 1,000 people) Housing commissioning (square meters per 1,000 people) Ratio of per capita income to the minimum subsistence level (%) Cumulative volume of retail turnover and chargeable services (per capita) Investments in fixed capital (per capita) Length of paved motor roads (per 1,000 square meters of the regional area)

Criterion Weights Within Categories (2018) Demography Rate of natural increase (0,31) Number of invalids (0,08) Infant mortality rate (0,1) Mortality from malignant neoplasms (0,25) Cardiovascular mortality rate (0,26) Social pathology 1(0,69): Poverty headcount (0,41); long-term unemployment (0,28); unemployed people per vacancy (0,31) 2 (0,31): Number of grave crimes Welfare 1 (0,27): Turnover and chargeable services (0,41); housing commissioning (0,4); roads (0,19) 2 (0,46): Income (0,45) and investments (0,55) 3 (0,27): Cars

“Demography” Indicator Values, 2018

Ranks of the Leading and Low-Performing Regions (Demography), 2015-2018

“Social Pathology” Indicator Values, 2018

Ranks of the Leading and Low-Performing Regions (Social Sphere), 2015-2018

“Welfare” Indicator Values, 2018

Ranks of the Leading and Low-Performing Regions (Welfare), 2015-2018

Weights of Group Indicators in the Composite Indicator, 2018 J=1 (Demography), J=2 (Welfare), J=3 (Social Sphere)

Chart of Normalized Weights of Synthetic Categories of the Quality of Life of the Population of the Russian Federation, 2018

Composite Indicator Values, 2018

Summarized Principal Component Flowchart (for Three-Dimensional Data)

Eigenvalues of the compromise matrix

Object Projection (Altai Krai) (Onto the First Axis of Compromise Space)

Object Projection (Republic of Khakassia) (Onto the First Axis of Compromise Space)

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