THE EVALUATION AND MANAGEMENT OF RISKS IN FORECASTING OF SOCIO-ECONOMIC INDICATORS IN PUBLIC ADMINISTRATION SYSTEM
Abstract
The relevance of the researched problem is due to the high importance of forecasting and possible risks and consequences evaluation in the process of decision development and implementation at state and regional levels.
The purpose of this article is to develop a tool for evaluation the risk of socio-economic indicators deviations from the expected level in forecasting of regional development in the sphere of state decision-making.
As the main method for solving problems of system's socio-economic parameters forecasting the authors suggest a method used in forecasting the returns of financial assets in portfolio analysis.
The results of forecasting the most important indicators of the regional socio-economic sphere (per capita income and the composite index of population living standard) obtained with the use of proposed tool allow to evaluate the effects of administrative decisions and risks of their implementation, as the system response to the action of controlled and uncontrolled factors complex.
The article may be useful for government and municipal bodies when making administrative decisions and regulatory actions evaluation.
Keywords
socio-economic indicators forecasting, risk evaluation, state and municipal administration, regulatory actions evaluation
AUTHORS
Elena Lobkova
Candidate of Economic Sciences, Associate Professor, Socio-economic Planning Chair, Institute of Economics, Management and Nature Resources Use. Siberian Federal University. 79, Svobodny pr., Krasnoyarsk, 660041, Russia E-mail: angelinagilshtein@yandex.ru
Tatiana Melnichenko,
Graduate Student, Institute of Economics, Management and Nature Resources Use,
Siberian Federal University. 79, Svobodny pr., Krasnoyarsk, 660041, Russia E-mail: mel555-555@mail.ru
Introduction
The conditions of uncertainty and increased risks from political, economic, environmental, military and other factors demand from public authorities effective application of methods of strategic planning of socio-economic development of the state and its separate territorial units. Providing conditions for creating a controlled process of minimizing negative consequences of risks is a complex methodological
17 Modern European Researches No 5 / 2017 (from the viewpoint of development and implementation of the applied methods and tools apparatus) and application task.
Disproportional regional development is a permanent problem for Russia, which tends to become worse and demands for the formation of an effective policy aimed at reducing socio-economic, demographic, budgetary and other differences. For this reason, scientists are showing increasing interest in the working up evaluation methods of regions' socio-economic development. Due to the fact that in recent years state and municipal administrations use program indicative planning aimed at sustainable development of the country, regions and municipalities, the need to establish a high-quality system for evaluation, monitoring and analysis of territorial unit's socio-economic development level increases. This system must certainly take into account the probabilistic nature of different factors and processes influence that take place within and beyond the studied area.
All world regions and countries pay more and more attention to studying the problems of territories development, which are characterized by disproportion in the rate and characteristics of development. The successful functioning of regional and whole country economy directly depends on the sustainable development of individual territories and effective government policy. Scientists and practitioners in the field of public administration developed a considerable number of approaches aimed at sustainable regional development.
Forecasting the development of any socio-economic system should take into account the high probability of external and internal environment parameters deviations, providing the possibility of regulatory action risk estimation on Federal and regional levels. In this regard, there is a need in constant search of the most perfect methods of socio-economic system development forecasting (Balashova, Chernetsov, 2009).
In forecasting socio-economic development of a region, it is assumed that the key parameters and conditions of national and regional systems are stable: external and internal risk factors influencing socio-economic processes in the country or region; the planned programs and reforms are implemented according to the main indicators governing them by appropriate acts and documents; the reproduction process occurs within the framework of the economic cycle planned by authorities; manufactured products and income are distributed efficiently. This strategic planning does not take into account a set of factors arising from the instability of economic and political environment, behavior of economic agents, changing social situation, etc.
Risk in this study refers to the probability of territorial system (country, region, municipality) socio-economic development parameters deviations from the expected (planned and forecasted) levels, wherein the indicated deviation can have any meaning (negative and positive). The following indicators are used as parameters of the socioeconomic development: level of living (per capita income and expenditures of population, wages as the main component of income and other indicators, aggregated or not aggregated); dynamics of total product production and industry structure; investment activity of economic agents, estimated by the volume of investments in fixed capital; business activity of the subjects (the number of enterprises and organizations; the number of small and medium companies, etc.).
The traditional approach to territory development risk management includes the following stages: evaluation of risk level and qualitative analysis of a set of factors determining the emergence of the risk; development of measures preventing potential "threats" or "challenges" of environment and minimization of risk consequences in case of unfavorable situations for the system development; designing the system of territorial system management in a crisis.
18 Modern European Researches No 5 / 2017 The authors analyzed the factors of threats emergence, restricting the development of regional system (testing was conducted on example of Krasnoyarsk region), identified the main groups of risks affecting socio-economic processes in the region, proposed algorithm of evaluating the consequences of decision-making in strategic planning of regional development.
The introduction of decision-making and evaluation and management of territorial (specific) and systematic (general) risk mechanisms supporting system allows to raise awareness among authorities about the possible "threats" and "challenges" of environment in conditions of high uncertainty, and to improve the efficiency of the territorial administration.
Methods and ways of carrying out experiment
The authors propose modeling of regional socio-economic development with the use of risk evaluation tools as one of the methodological approaches to forecasting of socio-economic indicators within the framework of state and municipal public administration.
The theory and practice have at their disposal different methods for forecasting socio-economic development of regions and countries (Alexandrov, 2006; Koroleva, 2008; Pridvorova, 2013). The most commonly used and traditional forecasting methods do not take into account the instability of the economic and political environment, the behavior of economic actors, the social situation. They are based mainly on pessimistic, baseline and optimistic scenarios (Mukin, 2009; Nazarenko, 2012). The methodological approaches also differ by way of the description of the object and the environment in which it is evolving, by ratio of initial information and the method applied, by temporary possibilities of forecasting and its probability and by original purpose of the procedure (Ginis, 2009).
As the primary tool for evaluation of influence on complex and poorly formalized socio-economic systems, it is advisable to use dynamic simulation models that allow to study the behavior of systems in conditions of information uncertainty and a large number of stochastic nature factors activity, to play a large number of alternatives, scenarios and development strategies (Romashchenko, 2014).
Risk evaluation system in forecasting of regional socio-economic development includes: monitoring of system's socio-economic and financial parameters; the procedure for the identification of trends and patterns of territorial system development based on the statistical information analysis; forecasting regional production complexes condition as a basis for development of the entire socio-economic system; identification of market disproportions and growth points in the socio-economic system; analysis and quantification of different factors influence on the socio-economic situation with the aim of quantitative risk evaluation; system simulation of socioeconomic development of the object based on different methods of risk evaluation; designing a mechanism for developing measures to prevent risks and minimize them; information and analytical support system of managerial decision-making, including multivariant scenario-type calculations of regions' socio-economic development and decision-making consequences evaluation.
The construction of a decision-making consequences evaluation system for public authorities, in contrast to business, is complicated by huge scale of information flow in the space of the entire country and its territorial units; high probability of random elements (events), which prediction is almost impossible; difficulty and complexity of the socio-economic problems; high probability of opposing trends that reduce the effectiveness of decisions; necessity of considering the conjugate action of several factors of different nature and orientation. (Chaplinsky, Plaksin, 2016)
The process of risks identification and analysis in the framework of socio-economic system development forecasting on macro level is the basis of the decision-making risk evaluation process for administrative bodies. (Maslova & Kuznetsova, 2012)
At present, three main methods of maximum risk characteristics evaluation, index VaR (Value at Risk), are elaborated. These include: delta-normal method, historical modelling method, method of stochastic simulation.
The authors evaluated risk by delta-normal method of calculating VaR index to predict possible (probable) changes in per capita income and the summary index of population living standard in the region (Krasnoyarsk region). This method is used in modern portfolio theory for financial assets. (Ufimtsev, 2012)
Availability of population living standard summary index calculations is associated with the availability of initial data, which is the standard for official statistics. The population living standard summary index can be built at different territorial levels (from municipalities via regional units to RF subjects) and different temporal scales (month, quarter, year). The index can be interesting for monitoring the socio-economic situation in a region and solving problems of state and municipal management; can serve as a quantitative measure of regional administration effectiveness; to be used as a criterion of regional investment attractiveness evaluation and in the development of Federal and territorial programs; can be used for the purposes of regions' socioeconomic situation forecasting.
Evaluation of the territory development level requires the most accurate forecasts for the establishment of state regional policy effective measures, which necessitates the search for reliable methods of modeling. With integrated approach that allows to solve the main task of socio-economic policy - improving the population living standard.
The present study used as a measure of risk RMS deviation of per capita income. A set of risk factors for income changes represents a vector showing the linear sensitivity of average per capita monetary income of population to changes in selected risk factors (VaR-representation). Revealing influencing risk factors and plotting VaR-representation are key components of delta-normal method (decomposition of income by risk factors).
Elements of the vector VaR-representation in a specified multidimensional space are the indicators of income components sensitivity to risk factors changes. Sensitivity indices allow us to calculate the variance and RMS deviation of average income changes (in relative or absolute terms) through known variance and risk factors covariance.
In forecasting average per capita income values by delta-normal method, the following calculation steps were made:
1. The calculation of per capita income relative growth rate with the use of logarithms to reduce to normal distribution law:
Wherein C^^ t and C^ t-1 - are per capita cash incomes in the current and prior periods.
2. The calculation of mathematical expectation, standard deviation and variance of the forecasted index.
3. Calculating of normal distribution function quantile (Gaussian function).
4. Calculation of the per capita income with a probability of 99% for the next time interval forecasting:
Tp+1 = (Q + 1)* Tp\
Where Q - quantile value for normal distribution of per capita income;
T pl - value of per capita income relative growth rate at the current time;
Tpt+1 - value of per capita income relative growth rate in the next moment.
5. Calculation of per capita income values for several forecast periods with a predetermined probability:
T£+n = (1 + Q*^)*Tpt,
Where Q - quantile value for normal distribution of per capita income;
Tpt - value of per capita income relative growth rate at the current time;
Tpt+1 - value of per capita income relative growth rate in the next moment;
n - number of forecast periods (forecast interval).
The authors investigated the effect of factors on the change in the average per capita income of the population at 95% value of VaR. The following factors were chosen: index of industrial production (in percent to previous year), organizations accounts payable (excluding small business subjects, in mln. rubles) and accounts receivable (excluding small business subjects, in mln. rubles).
VaR index at 95% significance level for each component of the per capita income is calculated as follows:
VaR = 2,326 *ai*Vi
Wherein V i - component value of per capita income, which includes wages, income from business activities, social benefits and other incomes;
o i - volatility of income component (standard RMS deviation of index);
2,326 - coefficient corresponding to 95% confidence level.
Proposed analytical method can be generalized to the per capita income index with arbitrary number of different components. In this case 95% VaR is calculated using the formula:
VaRcM = 2,326* o^yx * Vt
Where CTcaa= JX? If Ojotjcovij;
covij - covariance of i and j factors influencing income index (C^);
ai,aj - linear coupling coefficients between the components of per capita income and risk factors, evaluated by regression method by constructing linear univariate models;
n - the number of per capita income components (wages, social transfers, income from business activities, etc.).
Results and conclusions on the research topic
The resulting forecasted values of per capita income for the population in the region say that the value of the per capita income will not be above 34 723.6 rubles, 26 401.5 and 28 712.1 rubles in the first, second and third periods respectively.
TABLE 1. RESULTS OF AVERAGE PER CAPITA MONETARY INCOME FORECASTING FOR THE POPULATION OF THE REGION
Forecast period Forecasting of the maximum value of average per capita money income (rubles) Change of per capita income (rubles)
First (t +1) 34 723.6 -
Second (t + 2) 26 401.5 - 8 322.1
Third (t + 3) 28 712.1 + 2 310.6
21 Modern European Researches No 5 / 2017 Calculations of minimum VaR value (probabilistic value change in per capita income) for forecast periods with probability of 99% showed that in the first forecast period, a quantitative evaluation of deviation from expected income level risk is 2 537.4 rubs., in the second - 2 625.2 rubs., in the third - 2 841.7 rubles.
The maximum value of the deviation is forecasted in the ranges: up to 3 567.1 rubles - in a first forecast period; up to 3 892.5 rubles - in a second period; up to 3 901.3 rubles - in a third period.
Actual values of population per capita monetary income were 32 223.7 rubles, 25 789.1 rubles and 27 534.9 rubles in each of the three forecast periods.
The prognostic value of population living standard summary index for the region is calculated in a similar manner. VaR value in this case is considered as a minimum value of region's population living standard summary index change. For the factors were chosen shipped products volume indicators from the section "Minerals mining" and expenses of the regional consolidated budget, that exerted a positive influence on the population living standard index.
TABLE 2. RESULTS OF LIVING STANDARD SUMMARY INDEX FORECASTING FOR THE POPULATION OF THE REGION
Forecast period Forecasting of the maximum value of the population living standard summary index Change of the living standard summary index
First (t +1 ) 1.78 -
Second (t + 2) 1.67 - 0.11
Third (t + 3) 1.73 + 0.06
The actual values of population living standard summary index were 1.78, 1.64 and 1.71 respectively for three forecast periods - all values are within a forecast range.
The values of VaR, as a quantitative measure of researched and forecasted parameter deviation risk made (with a probability of 99%), 0.07, 0.07 and 0.04: for the first forecast period the range of quantitative measure for the population living standards index deviation risk from the expected level was [1.71; 1.78]; for the second forecast period - [1,60; 1.67]; for the third forecasted period - [1,69; 1.73].
The implementation of proposed method for regional socio-economic development modeling is necessary in the process of working out activities and making effective decisions in the economic policy sphere by administrative bodies.
According to the results of the research, the authors formulated conclusion that the regional population living standards modeling, identifying significant factors on the basis of econometric analysis, allows to evaluate the risk of each of these factors influence on the forecast value of the researched feature. In the presented examples of Krasnoyarsk region population living standards modeling they demonstrated the effective use of risk evaluation system for the study of socio-economic development of the territory.
The process of government decisions development and implementation traditionally includes a system of goals that contribute to the achievement of a single general purpose of the whole state and municipal administrative system, and a set of risk factors (risk system), the evaluation of which is restricted by the lack of information and the limitations of the methods and tools to identify these factors. So, the whole decision-making process is complicated, requiring the government to attract other subjects of social and economic system which are interested in the management process success. As a mechanism for economic agents attraction to the process of public decision-making, they use now the institution of regulatory action evaluation. This institution is an important component of the work to prevent
22 Modern European Researches No 5 / 2017 the emergence of new and reduce existing risks and barriers to social and economic development of the territory. Regulatory action evaluation is invented to prevent and eliminate obstacles and risks on the way to improving the living standards of the population, the growth of manufactured products and surplus value, effective management of business activity, rational allocation of funds in the economy, development of competitive mechanisms, etc.
Regulatory action evaluation has been introduced in the practice of Western European countries since 70-ies of the XX century and is conducted to determine and quantify the effects of the proposed regulation before decision making. According to the authors, the risk evaluation system and the mechanisms of its management should be introduced by administrative bodies at the stage of the draft decisions preparation and included in the implementation practice of regulatory action evaluating mechanism.
The procedure of the problems and goals of government regulation analysis, search for alternative options to achieve these goals and associated with them benefits and costs for different social groups exposed to regulation is necessary to determine the most effective version of the regulatory action and evaluation of some risk factors of system-wide and specific nature strength and influence direction.
The concluding result of regulatory action evaluation is the improvement of public administration efficiency. An important element of this procedure is carrying out public consultations with the interested parties, that allows to define their position, to make the process of legal act draft consideration transparent, to provide free announcement of positions and taking them into account. All the above mentioned contributes to achieving the main goal - to minimize the negative effects of the stochastic factors combined action.
The process of regulatory action evaluation includes the following steps: studying of possible consequences and risks of decision-making; identifying causal relationship between the regulation implementation and the problem solution; the forecasted effects of the considered measures of administrative regulation is related to budget costs and costs of other economic parties (businessmen and consumers). In this regard, the most complex of the problems here is evaluation and analysis of costs (including probable) and benefits (effects) of the decision; the ability to predict social consequences and changes.
Carrying out complete and detailed analysis of costs and benefits for each of the possible regulation alternatives is complicated by the lack of established forecasting system based on a strict and accurate mathematical apparatus.
Regulatory action evaluation is a procedure during which the projects of government decisions are analyzed in order to identify their provisions, leading to the emergence of specific risks (the risks specific to particular sphere, territory and subjects), acting at the same time with the systemic risks (risks characteristic for the entire socio-economic system).
The use of evaluation tool allows to achieve significant results in the process of public administrating: to improve the business climate and the investment attractiveness of country or region; to ensure selection of the most effective options for regulatory decision-making; to make regulatory decisions exercisable for potential recipients of regulation; to restrict unreasonable costs for business and other parties; to reduce the risks associated with the introduction of new regulation; to increase the confidence of citizens and business in the decisions taken by the State. These parameters are the most important factors determining sustainable socioeconomic system, reliability and stability of the business environment, well-being of population.
At the state level, risks can be expressed in the form of unstable conditions for authorities' activity, administrative system deformation, government programs and reform failure, obtaining budget revenues not in full, excess of expenditure over income, government debt and its servicing costs growing etc. Regarding system and specific risks, various public institutions (regional, local, sectoral) develop their own mechanisms and models to identify threats and calculate the risks, which are used in their practice.
At the basis of application models are predominantly principles of statistical (evaluation of events probability and quantification of the effects, calculated according to statistics of previous periods), economic (calculation of costs and effects ratio), social and psychological (forecasting the behavior of the decision maker, social reaction) analysis. The choice of approach sets the framework for risk parameters evaluation and determines the tactics of developing the structures of system administrating.
The reaction of administrative bodies to the results of the controlled system development forecasting should be in adaptation of decision-making processes structure, adjustment plans, objectives, programs indicators, time limits for their implementation, the amount of funding, specific activities. Creating a system of risks consequences compensation due to actions of probabilistic events is one of the most problematic aspects of forecasting and evaluating the development level and administrating socioeconomic system.
Control and risk management at the national and regional levels are aimed at minimizing unpredictable consequences, as well as reasonable restriction of forecasting corridor in order to clarify the parameters of the plan. Control and risk management procedures include socio-economic monitoring activities; system tools for hypothetical risks foresight and avoiding them; risk preventing and risk insurance; decision-maker training for the most probable risks by developing a system of alternative solutions etc.
In a situation of instability in the development and implementation of the decisions at the state and municipal levels, an important point is to keep the priorities and goals set by the state on the way to achieving the general objectives of social and economic processes regulation. The main objectives of the national social system public administrating are meeting the needs of people, establishment of a civilized way of life adequate to modern conditions, formation of appropriate conditions for creative activity of society members, establishment of rational relationships between person, state and society.
They distinguish political methods of risk management at the national level, which are expressed in improving administrative bodies and their decisions publicity forms, including already mentioned institution of regulatory action evaluation; reducing the level of confrontation and conflict within the government machinery; increasing intensity of interaction with society and deepening the process of feedback from the controlled system; establishing reasonable and safe level of economy openness and its dependence on the outside world.
Analytical methods of risk management are based: on the results of ongoing socioeconomic system monitoring; identifying trends, depending on the action of permanent and temporary factors; on simultaneous consideration of complex events that determine progressive cyclical development of the state or region; on the study of past experience and the consequences of taken decisions.
In addition, the risk management system includes the so-called social technologies designed to reduce the threshold of sensitivity and the reaction of certain social and economic groups to changes in environmental conditions and possible losses. The tools used within this approach framework are informing society about importance and effectiveness of the decisions, including unpopular ones; establishing adequate
24 Modern European Researches No 5 / 2017 attitude of the subjects to the risks and development chances that are not familiar and desirable to the mainstream members of society.
The introduction of decision-making support system and regional risk management mechanisms evaluation system, described by the authors, can increase the awareness level of potential "dangers" and "challenges" of environment in conditions of high uncertainty, and at the same time, improve the efficiency of public administration.
REFERENCES
Alexandrov, A.M. (2006). Forecasting socio-economic development of the region. Bulletin of Chelyabinsk State University, 4, 174 - 177.
Balashova, E.A. Tchernetsov, V.I. (2009). The index method in problems of factor analysis of socioeconomic processes. Bulletin of Saratov State Technical University, 1, 139 - 144.
Ginis, L.A. (2009). Review of scientific forecasting methods. News of South Federal University. Engineering, 3, 231- 236.
Koroleva, N.A. (2008). Methodological monitoring research base as a tool of socio-economic processes forecasting in modern society. Society: politics, economics, law, 2.
Maslova, N.S., Kuznetsova, I.V. (2012). Evaluation of risks as the institution for state needs management. Moscow: Higher School of Economics Publishing House, 482-489.
Mukin, S.V. (2009). The methodology for developing socio-economic scenarios. Bulletin of Tomsk State University, 7, 75 - 82.
Nazarenko, A.V., Zvyagintseva, O.S. (2012). Scenario forecasting of socio-economic systems development. Scientific journal of Kuban State Agrarian University, 84, 575 - 587.
Pridvorova, E. S. (2013). Comparative analysis of regional social-economic development forecasting methods. Scientific News of Belgorod State University, 1-1 (144) 5 14.
Romaschenko, V.A. (2014). Financial risks and their evaluation methods. Kant, 2 (11), 56 - 59.
Ufimtsev, A.A. (2012). Currency risks evaluation using Value-at-Risk methodology. Bulletin of Chelyabinsk State University, 8 (262) 137 - 142.
Chaplinsky, A.V., Plaksin, S.M. (2016). Risk management in the implementation of state control in Russia. Issues of state and municipal administration, 2, 7-29.