Migration And Welfare In The Far East

Analysis of industrial production index

The volume of industrial production is affected by indicators of the degree of depreciation of fixed assets and the number of credit organizations in the region (Aksyonova & Barysheva, 2016). For calculation, depreciation rates of fixed assets in the construction industry were taken as the most in need of updating in comparison with other branches of production. Also, the volume of industrial production is affected by investment policy, namely, the creditworthiness of the population of the Far Eastern Region (Silifonova et al., 2017). Over the past 15 years, due to the steady decline in the number of credit institutions, the availability of cash for the population is declining. The dynamics of the above indicators are shown in Table 1 .

When regression analysis is carried out, the P-value (probability of error) of the depreciation ratio is close to 5%, the number of credit institutions is less than 2%, which indicates the sufficient reliability of the obtained model. The R-square (the coefficient of determination) is 96.9%. The results of the regression analysis are presented in Table 2 . Consequently, the share of the variance of the dependent variable, explained by the constructed model, characterizes a high level of accuracy. Thus, the equation of dependence has the form:

$Y_1=\mathrm{117,1749}-0.48703\times X_1+\mathrm{0,230897}\times X_2$,

Where $Y_{\mathrm{1}}$ – industrial production index,

$X_{\mathrm{1}}$– degree of depreciation of fixed assets in the construction industry in the Far Eastern Region,

$X_{\mathrm{2}}$– number of credit institutions in the Far Eastern Region.

Figure 1 shows the dynamics of the change in the index of industrial production. The trend line has the form of a polynomial function of the second degree

with an accuracy level of 93.5%. With the help of this equation, the predicted values of the industrial production index until 2020 are calculated.

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The volume of shipped goods of own production in manufacturing industries

Regression analysis of the exported goods of manufacturing industries in the Far Eastern Region shows that this indicator is a dependent value of the number of scientific personnel in the region and the degree of depreciation of fixed assets for mining (Barsukov, 2015).

The increase of human potential, understood as a set of abilities and opportunities for the performance of a certain type of labour activity, is now an unconditional factor in increasing the competitiveness of products and services (Iremadze & Kulinich, 2013). The knowledge applied by producers of goods obtained in scientific fields allows manufacturers to use effective means of organizing production. Increasing exports of goods from the region presupposes a stable variability of highly developed industries, which in turn generates the need for continuous improvement of the skills of workers. At the same time, the scientific personnel is the mechanism for the formation of innovative potential in the region to increase the competitiveness of exported goods (Barysheva et al., 2016).

The state of the processing industries depends not only on the state of fixed assets in this sector (this indicator has already been taken into account in analyzing the index of industrial production). The efficiency and degree of development of the manufacturing industry is directly proportional to the position of the production of minerals (Ronova & Yakovlev, 2011). When the goods with high added value pass through the full technological cycle, the intermediate phase is the extraction of raw materials. Therefore, the productivity of products, their uniqueness and competitiveness, and therefore, the volume of their exports to other regions and beyond the Russian Federation, directly depends on the state of fixed assets in the extractive industries. In this case, this indicator is the degree of depreciation of fixed assets.The dynamics of the indicators used in the analysis is presented in Table 3 .

When carrying out the regression analysis, the P-value (probability of error) of depreciation of fixed assets is 0.3%, the number of scientific personnel is 0.04%, which indicates the high reliability of the model obtained. The R-square (the coefficient of determination) is 68.2%. The results of the regression analysis are presented in Table 4 . Therefore, the proportion of the variance of the dependent variable, explained by the constructed model, characterizes an acceptable level of accuracy. Thus, the equation of dependence has the form:

where $Y_{\mathrm{2}}$ – industrial production index,

$X_{\mathrm{3}}$– degree of depreciation of fixed assets in the construction industry in the Far Eastern Region,

$X_{\mathrm{4}}$– number of credit institutions in the Far Eastern Region.

Figure 1 shows the dynamics of changes in the growth rates of shipped goods of own production in the manufacturing industries. The trend line has the form of a polynomial function of the second degree

with a level of accuracy of 61,8%. With the help of this equation, the forecasted values of the growth rates of the shipped goods of own production until 2020 are calculated.

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Analysis of the level of migration from the Far Eastern Region to other regions of the Russian Federation

The analysis of the industrial production index is due to the fact that it reflects a change in the real level of production in the Far Eastern Region, in contrast to the domestic regional product discounted to the level of inflation. Due to the fact that there is a significant disproportion between the statistical data of the consumer price index and the opinions of people living in the given territory, there can be doubts about the reliability of the inflation indicator (Kundakchyan & Zulfakarova, 2013).

The volume of industrial production is an indicator of the standard of living in the region due to the fact that it is a derivative of the level of incomes of the population, the number of jobs, social payments due to changes in tax revenues (Tchigirinskaya, Tchigirinsky & Chesnokov, 2015). Thus, the increase in migration flows to the western regions of the Russian Federation is due to a slowdown in the dynamics of industrial production.

The volume of shipped goods of its own production affects the welfare of the population and emigration flows through capital inflows from export transactions. Inflow of capital promotes increase of investment activity of the population (Masaya & Atsushi, 2014). According to the law of diminishing marginal return on capital (Chicherina & Tolkacheva, 2017), the Far East as an underdeveloped region will bring a greater return on investment than a more developed region, which will have a beneficial effect on the economy of the Russian Federation as a whole (Platonova & Panina, 2015).

Over the past 15 years there has been a steady positive dynamics of migration from the Far Eastern Region to other regions of the country. Table 5 shows the dynamics of the level of migration to other regions, the index of industrial production and the volumes of shipped goods of own production.

In the regression analysis, the P-value (probability of error) of the industrial production index is 0.8%, the growth rate of the volume of shipped goods of own production by processing industries is 2.3%, which indicates the high reliability of the model obtained. R-square (coefficient of determination) is equal to 80.9%. The results of the regression analysis are presented in Table 6 . Therefore, the proportion of the variance of the dependent variable, explained by the constructed model, characterizes an acceptable level of accuracy. Thus, the equation of dependence has the form:

$$Z=\mathrm{535505.4}-\mathrm{2605.85}\times Y_{\mathrm{1}}-\mathrm{1981.75}\times Y_{\mathrm{2}}$$

Where $Z$ – migration to other regions of the Russian Federation, persons,

$Y_{\mathrm{1}}$– the index of industrial production,

$Y_{\mathrm{2}}$– the rate of growth in the volume of shipped goods of own production by processing industries.

Using formula (3), using the obtained predictive data of factor characteristics, we calculate the migration rates from the Far Eastern Region to other regions until 2020. The dynamics of migration flows with the current economic situation of the Far East is presented in Figure 3 .

The analysis shows that with the state policy unchanged in relation to the increase in living standards and welfare of the Far Eastern Region, migration to other regions of the Russian Federation will exceed 100 thousand people by 2020, which is a critical mark for the region.

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Dynamics of changes in the growth rates of shipped goods of own production in processing industries and the trend in the Far Eastern Region.