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Modeling Employment on Regional Labor Markets(Through the Example of the Khabarovsk Territory of Russia)

Irina Valerievna Zikunova*

Khabarovsk State University of Economics and Law, 134, Tikhookeanskaya St., 680042, Khabarovsk, Russian Federation

Valentina Sergeevna Zikunova

Khabarovsk State University of Economics and Law, 134, Tikhookeanskaya St., 680042, Khabarovsk, Russian Federation

*Corresponding Author:
Irina Valerievna Zikunova
Khabarovsk State University of Economics and Law 134
Tikhookeanskaya St., 680042, Khabarovsk, Russian Federation
Tel: +74212765446; E-mail: [email protected]

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The disbalance of demand and supply on the labor market acquires special sense for remote and underpopulated settled lands. The lack of perspective estimations of the regional labor markets development leads to the outflow of human capital assets from regions, and their high concentration in central regions of the country. It results in the loss of competition effects on the labor market, aggravation of the mismatch of the demand and supply for labor resources according to the types of economic activity. Ultimately, the inertia policy in the area of planning regional employment can lead to the loss of skilled personnel and loss of effects related to regional specialization. The aim of this article is to substantiate the model to predict the number of the employed in the region. Firstly, the article generalizes regional and national tendencies of the labor market development. Secondly, based on the analysis of demographic and economic characteristics of the region, the model related to predicting the employment in the region is offered. Its quality is proved by subsequent approbation on the basis of real statistic data. The article displays the perspectives of applying such models in other regional economic systems taking into account their industry specialization.


Regional Labor Market; Modeling the Number of the Employed; Human Capital Assets; Multiple Regression


The development of the Far East is a top priority of the state policy of the Russian Federation. It is particularly regularized by the Concept for the Long-Term Socio-Economic Development of the Russian Federation until 2020 [1] and by a number of regional strategic documents. One of the current tasks of the state policy in the region is to activate business activity within the territories of the outrunning social and economic development (TOSED). The TOSED growth will result in an increase in the number of working places in the region. Meanwhile, over the last years the following negative tendency has been observed: the labor resources of the Far East fail to be adequately applied in the home region and move to central regions of the country. In its turn, it increases the disbalance on regional labor markets. According to the data of the research made by the All-Russian Center of Public Opinion Research (ACPOR), today about 40% of the employable population of Siberia and the Far East consider the opportunity to leave the macro-region. Most often they explain such intentions by low level of wages (44%), the lack of career perspectives (36%), and impossibility to buy proper residential property (28%) [2]. Over the last years it has also been possible to specify intense loss of the human capital assets by the Far East. Skilled young people get higher education and leave the region at the very beginning of their career. The region, which is as favorable as any in terms of social and economic situation, becomes the donor of the human capital asset for central regions of the country. The fundamental reason of the outflow of intellectual capital from remote regions lies in the fact that in comparison to other countries in Russia there are a lot of people with higher education and career ambitions, and simultaneously the quality of labor life – goodwill, wages, and social status – is not high enough [3].

In order to solve the problem related to the outflow of the human capital asset from the territory of the Far East, it is necessary to create the system of target preparation of personnel in the volume and in the sectoral structure that are currently demanded in the region. During the discussion of this theme at the meeting of the “2020 Strategy”, the expert group made a proposal about the necessity to develop a system of planning human resources of regions and to form the state demand for specific specialties [1]. Thus, in order to ensure success of people strategy of regions, a new vector - state participation in the professional preparation of human resources of regions - was formed. The mechanism of implementing such policy was also proposed: the form of new supporting universities is implemented. They are established by merging regional universities for the purpose of solving tasks on meeting regional needs in labor resources. On the one hand, the liquidation of small and inefficient higher educational establishments is reasonable in response to the demographic trend of the decrease in the number of students. At the same time to a greater extent the reduction of the number of universities allows to control the preparation of specialists according to the sectors demanded and required by a specific region and at the specific point of time.

We will mention that it is impossible to receive effects of such policy in the system of education without creating the system of planning and predicting the employment in the region. For these purposes, they usually use data of regional employment services – official statistics about the level of unemployment, the number of vacancies, and their allocation according to economic types of activity. However, using such sources of information, it is necessary to take into account, firstly, considerable differences between official indicators and real state of the labor market, and, secondly, factors related to changes in the economic environment that were not considered during statistical profile. Specialists in the area of regional marketing researches also say about the drawbacks of applying official statistics, because the prediction of needs in personnel made only on the basis of data from employment services does not effectively reflect the actual situation on the labor market [1]. Those who support applying sources think it is more efficient to use the data of enterprises managers’ questionnaires – ultimate consumers of labor resources. Based on the data of the Human Resources Department of a specific enterprise, it is actually possible to predict approximate volume of vacancies that will be offered by this enterprise in the future. However, there are also restrictions that do not allow to rely exceptionally on the data of questionnaires. So, there is a risk of annihilating the structural subdivision of the company in the future, reorganization inside the enterprise, terminating large-scale projects that involve working places that were specially established for them. We must not exclude the possibility of changes in the company management, and consequently, its policy, including that in the area of HR management. Remote regions also have a vexed problem related to the movement of head offices of local companies to central regions of the country as a consequence of the business absorption by the competitors from the capital or within the acquisition of new promising regions. Subjectivity of the estimation, the human factor is the last and not less important argument against using the data of questionnaires. The above circumstances do not allow to refuse from the official statistics in favor of the questionnaire details. At the same time it is not correct to rely only on statistical profile of regional employment services.

Thus, the employment of the population of regions must be predicted on the basis of retrospective data of regional economic statistics that takes into account the dynamics of the economic environment, regional demographic specificity, and peculiarities of the level of people’s life. The latter factors considerably differ in various regions of the country: among others, the demographic situation depends on migration processes. It is especially crucial for the Far Eastern regions. The level of life of the population in remote regions of the country is considerably lower than that in large cities of federal importance. Thus, we make a hypothesis about the reasonability to implement multifactor models to predict regional employment based on the indicators of the regional social and economic specificity. We think that the requirements to the factors involved in such models lie in 1) reflecting the peculiarities of the demographic situation in the region, 2) describing the level of the welfare of the population in the region, 3) illustrating economic development of the region, its investment climate, and development of crucial industries. The aim of this research is to create the multifactor model to predict the employment of the population in the region through the example of the Far Eastern region of the Russian Federation – the Khabarovsk Territory.


In order to develop the model related to predicting the employment of the population on the Khabarovsk Territory, we used the data of the official economic statistics of the region – in particular, passport of the Khabarovsk Territory [4] and indicators of the region development in official releases of the Territorial body of the Federal State Statistics Service for the Khabarovsk Territory (Khabarovskstat) [5]. The time duration of the selection for getting the model (2003-2004) includes the years of economic growth (2003-2007), periods of slowing the tempos of the economic growth in the crisis years (2008-2009), and the data for 2014 was used to check out the deviation of the predicted result from the actual indicator.

Various factors influencing the final feature have occasional (stochastic) character of allocation, so multiple regression was chosen as the method to study the model. The total number of the occupied working places in the region is accepted as the final variable. In order to allocate factors that are specific for the region, a great number of indicators were tested. They can be classified into the following groups: 1) indicators of the population life level: real available cash incomes of the population, the share of population with cash income lower than the living wage, etc. 2) indicators of the demographic development of the region: the number of people that were born per thousand people, net/decrease migration per thousand people, the number of registered diseases with the diagnosis determined for the first time, etc., 3) characteristics of regional investment climate: structure of investments in capital asset according to the forms of incorporation and types of financing sources, etc., 4) indicators of the development of separate industries of the region economy: passenger turnover, volumes of cargo traffic, commissioning of residential houses, turnover of retail trading, etc., 5) indicators of general economic development: indexes of physical volume of the gross regional product in constant prices, the number of organizations in sole proprietorship, etc., 6) characteristics of social welfare of the population: the number of registered crimes, the share of population that reads, the frequency of visiting theaters and museums, etc., 7) indicators of social infrastructure of the region, for example the capacity of outpatient organizations. Not only initial data of sources but also symbiontic features (derivatives from two and more initial variables) are used among the indicators under analysis; for example, the share of graduates of higher educational establishments from the number of economically active population, etc. Having chosen the specified combination of indicators to test, we pursued the goal to characterize the region in as many details as possible. Consequently, at the stage of estimating the influence of factors on the final feature, the majority of factor data was eliminated, and 17 indicators remained at the next stage of developing the model of 40 factors. They characterize predominately the following aspects of the region development: demographic situation, tendencies of the economic development, and level of the population welfare. The analysis of inter-factor correlations decreased the number of variables down to 4-7. It allowed to decrease the risk of “retraining” the model, because a great number of variables is acknowledged as unreasonable [6].

Traditional statistical characteristics of multiple regression (determination coefficient, Fischer test, deviation of calculation values from actual during the whole interval of the model development) and deviation of the prediction of the final feature from its actual value for the period that was not included in the basis for the model development were used as the criteria of selecting the optimal model to predict the regional employment. As a result, we chose the model that demonstrated the utmost high indicators of the quality of the prediction and practically gives minimum deviation of the prediction from the fact.


Multifactor Model to Predict the Employment of Population and Its Economic Importance

The model to predict the employment of the population on the Khabarovsk Territory that was received in the process of analysis is as follows:

Y=0.782 × A – 1.662 × B – 0.318 × C – 4.105 × D+836,915 (1)


Y is the number of people involved in economy, thous. persons,

A is the tempo of the growth of the real accrued wages, in percent as to the previous year,

B is the tempo of the growth of actually granted pensions (as of the end of the year), in percent as to the previous year,

C is the index of actual volume of Gross Regional Product in constant prices, in percent as to the previous year,

D is the level of covering basic needs of the population with profits received from labor activity, %.

Real accrued wages as the price factor on the labor market naturally have a considerable influence upon the formation of employment. Herewith, the remoteness of the Khabarovsk Territory from the central regions plays an important role, because the metropolitan companies are known to run remote business by using relatively cheap labor forces of remote regions. According to the researches made by the ANKOR human resources holding, the remuneration of top managers in the Far East is by a third lower than that in Moscow. The difference of the middle managers’ remuneration is even higher-wages in the Far East is three times lower than that in Moscow [7]. Thus, in this case it goes mainly about skilled specialists whose competitive advantages to their Moscow colleagues lie not only in the decreased labor remuneration but also in the difference of clock zones – the Far Eastern subdivisions manage to provide results of their work actually one day earlier than the ones from the capital.

In the proposed model the wages are the factor that demonstrates the most considerable direct impact on the dynamics of the goal variable. Firstly, the goal variable is not a net demand for labor force but a stationary point of the demand and supply on the labor market – the number of the occupied working places, i.e., the number of cases when both parties of the market came to an agreement. Accordingly, in this case the price has a traditionally important role as a regulator of the market balance. Secondly, rises and falls of the wages and the number of the occupied working places are cyclically interrelated, since during crisis periods enterprises use various methods to survive. So, some companies go toward downsizing, while in other companies it is possible to see the growth of the past due wages debt or the decrease in the duration of the working day [8]. The correlation coefficient of this factor is 43%. It says about the availability of the direct connection of the moderate power between the indicator and the goal variable.

The second factor - the growth of the size of the granted pensions – describes the level of life and a demographic component in the formation of the demand for the labor force on the Khabarovsk Territory. High closeness of the connection (correlation coefficient - 77%) says about the power of impact of the demographic structure in the region. The population ageing is the All-Russian tendency. According to the demographic report of the UNO for 2015, the share of the Russian population above 60 makes up approximately 20% [9]. This is considerably higher than the international criteria of the age estimation of the country population - 7% [10]. Beginning from 1990, the population of the Russian Federation has grown old in two stages. The first stage was observed in the period from 1990 to 1998. And even at that time the Far Eastern Federal District showed the highest tempos of the increase in the share of senior citizens, but as before it maintained the first place in the rating of the “youngest” regions of the country. The second stage was observed in the period from 2007 to 2013. And again the Far Eastern regions demonstrated the maximum tempos of the increase in the number of senior citizens. Such dynamics is firstly stipulated by the migration processes inside the country, the departure of the working age people [11]. The events related to refraining skilled personnel in the Far East include the establishment of the Agency on the Development of the Human Resources Asset in the Far Eastern Federal District, reimbursement program on supporting young people, indexation of wages by using regional coefficients and many other events. However, in spite of the efforts of the state related to the creation of attractive conditions of life in the Far East, the Khabarovsk Territory annually loses 2-3% of economically active population as a result of the outflow of labor resources to central regions of the country. The decrease in the death rate on the Khabarovsk Territory is an additional factor of maintaining a high level of the population of the pensionable age. So, only from 2011 to 2014 the death rate from diseases of the blood circulation system on the Territory decreased by 23%. [12]. The real amount of the granted pensions is the indicator derived from the number of pensioners and the amount of the granted pension payments. Accordingly, we can see several components that stipulated the high impact of this factor on the number of the occupied working places in the Khabarovsk Territory. Firstly, notwithstanding the inclination of pensioners to continue their labor activity, the change of their number itself partially reflects demographic processes inside the region and includes the number of the working population. Even taking into account positive dynamics of the tempos of the natural growth of the population, the increase in the number of pensioners we spoke about before confirms the above tendencies of the population aging, and consequently, witnesses about the decrease in the number of economically active population. Secondly, the higher the amounts of the granted pensions are, and what is more important, the less the arrearage of the level of indexation from the tempos of inflation is, the less inclined senior citizens are to search for partial/full employment to meet their needs. It is necessary to note that in relation to pensioners of the Khabarovsk Territory such effect can be leveled by additional payments for working pensioners introduced by measures of social support this July. Herewith, the amount of the compensation depends on the level of the obtained wage. Currently, the share of working pensioners on the Khabarovsk Territory is 47% [13]. It surpasses the All-Russian indicator more than by 10%. [14]. Thirdly, pensioners make up the segment with specific needs that are peculiar of the senior age. In comparison with the young population, they show low consumer’s demand (as a result of both low level of profits and less active lifestyle) and indirectly contribute to a decrease in the business activity, and finally stagnation on the labor market. And, finally, the population ageing can affect the character and essence of the labor potential in the future: “physical capital” (technological progress) will be increased in slower tempos, while “the human resources asset” (the level of education and qualification) will be increased in faster tempos [15]. The correlation coefficient of this factor and the goal variable makes up 77%. It proves the availability of strong reaction: the more the number of pensioners and the more the pension payments they get are, the less number of working places are occupied in the region.

The third factor - index of actual volume of Gross Regional Product in constant prices – is characterized by direct relation to the dynamics of the number of the occupied working places. Synchronous dynamics is stipulated by natural flow of economic cycles, when during the recession period the demand for goods and services is decreased, and enterprises reduce the volumes of production and expenses including those for human resources. In its turn, it leads to the growth of the unemployment and a decrease in the buying capacity of the population, a further decrease in the demand, and reaching the bottom of the crisis. During the period of economic growth the development of the situation is similar but in a positive way. In 2015 the expected decrease in the Gross Regional Product of the Khabarovsk Territory is planned in the context of worsening of the economic environment of the country. However, it is expected that the tempos of the decrease will be less scaled (2% for the region and 3% for the Russian Federation) [12], including due to the development of industrial parks, infrastructure support of the current investment projects such as the “Power of Siberia” gas transmission system, and modernization of the Baikal-Amur Mainline and Trans-Siberian Railway. The level of Gross Regional Product is also supported due to forming territories of the outrunning social and economic development (TOSED) in Khabarovsk and Komsomolsk-on-Amur. It will allow to create in general 5,600 new working places.

Analyzing the structure of the employment on the Khabarovsk Territory according to the areas of the economic activity and contribution of relevant industries to the Gross Regional Product of the Territory, it is possible to notice that the creation of the greatest added value is ensured in such areas of the activity as manufacturing, machine building, and trading. Herewith, the greatest number of vacancies is registered for the above areas of the activity. Thus, there are grounds for believing that there is a disbalance in the structure of employment of the population from a perspective of industrial sectors, because the sectors driving economic growth experience noticeable employee scarcity. This problem can be solved due to the short-term involvement of foreign human resources and labor resources from other regions of the country. However, a more fundamental approach intends to plan the need of the region in specific human resources and carry out a complex of events on preparing the required specialists within the region and by using regional educational establishments. The demand for specialists in the strategically important area of the state management and provision of military safety (17% of all vacancies as on the end of October 2015) remains unsatisfactory [16]. The model to predict regional employment we offered takes into account the general dynamics of the Gross Regional Product. It is enough for the general prediction. Along with this, for stable social and economic development of the region it is necessary to plan the provision of human resources in the context of specific areas of activity. The level of the correlation coefficient between the described factor and final feature is 33%. It witnesses about the availability of the direct connection of moderate closeness between the indicators.

Level of meeting basic needs of the population with profits received from labor activity is a symbiontic indicator calculated by us as the relation of the average monthly nominal accrued wages of employees of organizations to the determined living wage of the employable population in the specific region (on the Khabarovsk Territory within this model). This indicator has a positive correlation connection with the final feature, i.e., the more free cash the population has, the more the employment is. On the one hand, the limitation of profits to meet basic needs make people look for additional employment and alternative sources of profit. However, in the majority of cases such employment is hidden, not officially registered, and involves partial working day. That’s why its value in the target indicator is not significant. Meaningful employment depends on the level of the business activity because the revival of the economy and the increase in the buying capacity of the population contributes to the increase in the number of working places. In the context of studying the employment in the Far Eastern region, the importance of this indicator is especially high: over the last year the level of the basic index of the customer prices has surpassed the tempos of the growth of average wage on the Territory 2.7 times [17]. Residential property as the most important thing to meet the demand for personal safety is the most expensive. The Khabarovsk Territory was repeatedly ranked as the third in the rating of the regions of the Russian Federation with the most expensive residential space and gave place only to the cities of federal importance – Moscow and Saint-Petersburg. That’s why even under evident availability of free cash the majority of employable population of the region spends it for the mortgage payment. So, as a result it affects the level of the consumers’ demand [18]. The lack of savings, in other words, low propensity to save indirectly makes it difficult to establish and develop small and medium-size business in the region, because primary financial investments are required for establishing a business. The consequence of this tendency is low production of working places by the entrepreneurship sector.

Estimation of Indicators of the Quality of the Model and Quality of Its Prediction Power

Basic data for developing the multifactor regression models are shown in Table 1. The model was developed during the time interval of 11 years – from 2003 to 2013. The 2014 data was used to check the quality of the prediction.

Indicator Role Years
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Involved in economy, thous. persons Goal variable 705.9 701.2 704.5 711.4 688.8 696.4 664.4 677.4 701.1 698 704.6 700.7
Real accrued wages, in % as to the previous year Factor 1 113 106.5 111 102.9 114 105.6 95.6 103.2 106.4 113.1 103.3 100.5
Actual amount of the granted pensions (as of the end of the year), in % as to the previous year Factor 2 104.4 106.2 110.1 103.5 118.8 107.9 123.8 111.5 103 104.9 103.4 94.5
Indexes of the actual volume of Gross Regional Product in constant prices, in % as to the previous year Factor 3 104.1 105.5 104.2 105.3 105.1 102.6 93 111 102.5 100.5 101.3 101
Level of meeting basic needs of the population with profits received from labor activity, times Factor 4 2.28 2.37 2.52 2.49 2.81 2.64 2.42 2.47 2.70 3.13 3.24 3.11

Table 1: Basic Data for Developing a Model Related to Predicting Regional Employment on the Khabarovsk Territory.

The coefficient of determination of the obtained model to predict human resources is 80.2%. Thus, it is possible to claim that the obtained model explains a considerable share of variations of final feature. Corrected for the number of variables participating in the model and the number of observations, the standardized coefficient of determination is 67%, i.e., the potential decrease in the quality of prediction when noticing new observations is estimated at 13%. So, we cannot see any considerable difference between the determination coefficient and its standardized level. The model is not reeducated, i.e., the number of factors included in it is not excessive [19].

The level of the standard error of the regression says that the actual observations spread around the regression by 7.99. Thus, during the development interval the model makes an error approximately by 1%. We will further see it at the graph that compares the actual and the calculated values of the goal variable when estimating balances.

The obtained relation of the dispersion of the model estimation to the balance dispersion (Fisher test) is 6.07. F-criteria is used to check the hypothesis that states that there is no linear dependence between the dependent variable and the factors participating in the model. The critical value of the statistics with the significance point of 5% under the set number of factors and observations is 3.70. It does not surpass the calculated criteria of Fischer. Thus, the described regression model is acknowledged as statistically relevant, and the error probability of the model is not big.

The Figure 1 shows the deviations of the predicted indicators from the actual ones. The error of the model is calculated as the relation of the module of difference between the prediction and fact to their maximum. The average error of the model during the interval of development is 0.7% taking into account the prediction in 2014 – 0.8%. In 2014 the prediction of the number of the employed in economy of the Khabarovsk Territory is estimated on the level of 713.5 thousand people. The difference with the actual indicator is 12.8 thousand people. Herewith, the standard deviation for the whole combination of observances is 12.77, i.e., the error of the model during the prediction period practically does not exceed the deviation that is particular for the development selection. As a whole, the error of the model does not exceed 2% from the actual value of the predicted indicator. It characterizes the quality of the developed multifactor model as high.


Figure 1: Comparison of Actual and Predicted Goal Variable Values.


The introduced factor model allows to predict general potential employment of the population of the Khabarovsk Territory taking into account social and economic characteristics of the region. It is reasonable to apply it when developing the internal migration policy, planning financing of state-funded places in educational establishments, investing in the development of the education system, and for analysis and prediction of social and economic development of the region. With the course of time the model must be adapted for new characteristics of the Territory, take into account changes on the level of the welfare of the population of the region, demographic structures, directions of economic growth as well as inflation, credit and many other factors, including external economic ones. So, in 2015 the change of the political situation (embargo for products from the EU and Ukraine, a wide range of economic sanctions in relation to Turkish suppliers), the decrease in the exchange rate of the national currency led to the acceleration of the inflation tempos and the decrease in the purchasing power of the Russians’ incomes. Any inflation risks for the Khabarovsk Territory are especially notable due to considerable surpass of the prices dynamics over the dynamics of the average wage.

It is also necessary to take into account the expected positive effect of demographic and migration policy of the state in relation to Far Eastern regions, i.e., currently the government takes active measures on stimulating the birth rate and decreasing the migration of the Far East population. Events related to the increase in the attractiveness of life in the region included, for example, the introduction of certificates for financing the movement of citizens from central regions to the Far East [20], formation of territories of the outrunning social and economic development, support of large-scale investment projects with attractive conditions of labor remuneration (Vostochniy spaceport). Finally, in 2015 the Agency on the Development of the Human Resources Asset was established. It aims at direct control over the situation on the labor market of the Khabarovsk Territory, promoting specific events on restraining valuable human resources on the Territory, and attracting specialists from other regions of the Russian Federation. The above measures confirm the high priority of the development of the Territory labor market in the strategy of the development of the Far East. The model to predict regional employment offered in this article can give a high quality estimation of the human resources policy of the region. Based on it, it is possible to form benchmarks when planning projects on developing the economy of the Khabarovsk Territory.

Further improvement of the system of estimation and prediction of human resourcing in regions of the Russian Federation assumes the development of a great number of individual models for separate economic areas of activities. It will allow, firstly, to more accurately estimate the need of the region in labor force taking into account the peculiarities of the development of separate areas; secondly, to analyze the balance of the system of forming and developing the human capital asset from the stage of professional orientation of school leavers to providing graduates and experienced specialists with working places in the target area of activity. Finally, the employment must become the point of crossing and meeting interests of both the citizens of the region (compliance of the direction of labor activity with the received qualification, stability of employment, and adequate level of labor remuneration) and the government (following the set vectors of the economic growth, development of priority sectors, population density and safety of remote regions). We have mentioned before that one of the most wide-spread reasons why employable citizens from the Far East are ready to leave the region is the non-availability of career perspectives. It goes not so much about low wages as the lack of vacancies that the applicant’s education or formed professional experience can suit. It happens due to various circumstances: focused specialization of the received education, acquisition of the employer’s business by the metropolitan companies and liquidation of the Far Eastern subdivisions, termination of organizations activity in remote and marginally profitable regions. Planning of the employment of the region population from a perspective of types of economic activity as well as understanding of the needs of areas which are of top priority for the region development in human resources will define benchmarks for the implementation of investment and social projects, formation of state orders for target specialties, planning events on attracting specialists of specific qualification from other regions. The maintenance of the sector profile and social and economic welfare of the region, solving the problem of its population density must become the long-term result of using such prediction models.


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