a new recognition and reward scheme provides managers to recognize commitment and performance immediately. A range of options is available, including bonus payments of up to and over £100 to be paid through salary, gift vouchers, team celebrations, etc

LR

`outstanding performers', £600 to £1,200

Table 17.

System of `box' marks for of UK's central government bonuses

Resource: IDS, 2009

Moreover, UK data refer to March 2011. Initial data of gross salary was divided on 12 (to provide monthly salary) and converted to rubles 2011 using exchange rate of the Central Bank of the Russian Federation (1 Pound = 46 rubles in March 2011). The table of comparability of Russian and UK FEBs based on comparability of functions performed is represented in Appendix 22. Appendix 23 illustrates data received.

It is evident from this data that civil servants' pay in UK has more unequal distribution across FEBs, which confirms the argument that the level of horizontal pay differentiation among FEBs in UK is higher than in Russia. Moreover, civil servants of all categories receive higher compensation than Russian civil servants in all FEBs. Pay gaps between Russian and UK's civil servants vary from 0.89 times (between SCS of Russian Treasury and UK's HMT) to 6.44 times (between SCS of Rorarhiv and UK's NA) (see Appendix 24).

With these caveats in mind, it is nonetheless interesting to consider pay gaps between the lowest and the highest categories of civil service among UK's and Russia's FEBs (see Appendix 25). In UK it varies from 2.88 times (DCLG of UK) to 6.21 times (OS of UK). However, in Russia these indicators vary from 1.81 times (FS MTS of Russia) to 7.91 times (FNS of Russia). It is worth to underline, that the level of vertical pay differentiation is higher in Russia than in UK.

The next section of research provides the regression analysis of the main determinants of civil servants' pay differentiation in FEBs of the Russian Federation.

Section 6. Regression analysis of the main determinants of civil servants' pay differentiation

The theoretical part of the thesis provides a brief overview of civil servants' pay differentiation determinants in accordance with theoretical models. The previous section of the practical part reviews some factors of civil servants' pay differentiation, identified through the analysis of some practical cases. However, we now return to research questions posed at the beginning of the master thesis:

· Is civil servants' pay differentiation justified?

· What determinants lie in the basis of civil servants' pay differentiation?

· Can civil servants' pay differentiation be attributed to any of the observable factors?

To answer these questions the regression analysis of the main determinants of civil servants' pay differentiation has been conducted. Therefore, this section provides the investigation of some factors, which have a significant impact on the emergence of civil servants' pay differentiation.

Taking into consideration width of the investigated problem, the research is based on the cross-sectional study of pay differentiation determinants. The choice of such research method can be explained by the availability of the most necessary data only on 2011. Moreover, there are some successful empirical studies of public sector pay differentiation which are also relied on cross-sectional data (see, for example, Emilio et al., 2012). Furthermore, a quantitative study was suggested as the best design for explaining relationships between variables (Cavana et al., 2001; Naeem et al., 2011). Hence, quantitative data were collected on the basis of disproportionate stratified random sampling that included 66 FEBs of the Russian Federation that have publicly available data on pay differentiation.

The dependent variable of our regression model is the magnitude of pay gap between Senior managers and Support professionals in the FEBs of the Russian Federation. The advantage of such variable is that it can explaine both vertical and horizontal pay differentiation of civil servants in FEBs considered.

As a first approximation, the pay gap between Senior managers and Support professionals in the FEBs can be estimated using a very simple econometric model. Let be a vector representing the observable characteristics of FEB `i' (e.g., monthly pay of civil servants, salary budget of FEB, type of the FEB, etc.) and be the pay gaps between Senior managers and Support professionals of this FEB. The relationship between individual characteristics of FEBs and pay gaps can be specified as (3):

where: - dependent variable;

- explanatory variables;

- unknown parameters of the model;

- unobserved error term.

The error term summarizes the effects of unobservable individual characteristics or connections. Assuming that is not correlated with the unobservable individual characteristics (, the parameters in vector can be estimated by ordinary least squares.

As it mentioned in Section 4, the independent variables are divided into three groups:

1. The group of explanatory variables considers the pay structure in general:

• - the average monthly pay of civil servants in FEBs, rubles;

• - the average monthly official salary of civil servants in FEBs, rubles;

• - the average monthly length of service allowance of civil servants in FEBs, rubles;

• - the average monthly bonuses of civil servants in FEBs, rubles;

• - the average monthly material assistance of civil servants in FEBs, rubles;

• - the annual salary budget of FEB in 2011, thousands rubles;

2. The group of explanatory variables accounts for the peculiarities of FEBs:

• - the type of the FEB (dummy variable: 1 - for ministry, 0 - for agency or service);

• - “presidential block” (dummy variable: 1 - federal executive body from the “presidential” block, 0 - otherwise)

• - “jurisdiction” (dummy variable: 1 - if the FEB is under ministerial jurisdiction, 0 - if the FEB under the direct jurisdiction of the President or the Government);

• - the budget allocations for the maintenance of the central apparatus of FEBs estimated for 2011, mn rubles;

• - the budget allocations for salaries of the central apparatus of FEBs estimated for 2011, mn rubles;

• - the Federal government expenditures under section 01 of the Federal Budget 2011, mn rubles;

• - the number of employees received further vocational education, civil servants;

3. The third group of explanatory variables explains the activities of FEBs:

• - the total number of powers established by the Government decrees on FEB, units;

• - the number of regulated (“reglamentated”) powers of FEB, units;

• - the number of the accounting systems of FEB's powers, units;

• - the number of governmental databases available on the official websites of FEB, units;

• - the number of services provided by FEB, units;

• - the number of support functions of FEB, units;

• -the number of the groups of functions according to Government decree of FEB, units.

The specific issues of civil servants' pay differences that are being covered by each of these variables are as follows:

The first five variables of the first group () characterize the components of pay of Federal civil servants of the Russian Federation. It is essential to note, that such variable as has a complex character, because in this case the average monthly bonuses includes some additional payments compiled from the economy of salary budget and undistributed material assistance. Therefore, this variable is unrelated to performance (like, for instance, the bonus pay for performance of critical and complex tasks). To this, the variable “bonuses” reflects the redistributed budget allocations for salaries of Federal civil servants. The data for variables have been employed from the Rosstat website.

The variable takes into account such indicators as the average monthly pay and the average number of employees of federal authorities simultaneously. The data also have been employed from the Rosstat website.

It should be noted that the dummy variables () describe the type of FEB, the “presidential bloc” belonging and the jurisdiction of federal authorities. Considering that each type of FEBs has particular executive functions and powers and the number of these functions and powers depends on the jurisdiction, reflect indirectly the activity of FEBs. These variables were determined by means of analysis of 66 Government decrees on FEBs.

The following three parameters of the second group of explanatory variables represent the expenses of the Federal budget on a specific FEB. The variables and reflect the budget allocations for the work of Central apparatus of FEBs as a whole and for civil servants' pay in particular respectively. This data provided by the Ministry of Finance of the Russian Federation. The variable reflects the expenses of Federal budget under section 01 “National Issues” (“Obschegosudarstvennye voprosy” in Russian) in 2011. This data have been employed from the Federal Budget 2011.

The section “National Issues” consists of thirteen divisions which accumulate the expenses for National Issues. These issues are not corresponded to the relevant sections and subsections of the budget classification of expenditures (including the cost of maintenance of the head of state - the President of the Russian Federation, the top official of the Russian Federation and the head of the municipal administration, the cost of providing the relevant state authorities, public bodies, local authorities and by institutions providing activities such officials and agencies). As an approximation, this indicator reflects the “significance” of a particular federal authority for the state, the so-called “political importance” of FEB for the public administration. In this respect it is assumed that the pay determination process reflects changing priorities of a government.

According to the government regulations, approved by the Government decree N 362 “On approval of state requirements for occupational training, professional development and training of civil servants of the Russian Federation”, dated by May 2008, further professional education of civil servants includes their professional training, professional development and internship. Therefore, the variable reflects the career growth opportunities in the federal authorities. The data in this parameter have been employed from the official website of Rosstat.

The following parameters of the model represent the activity of FEBs. The first four parameters of the third group of explanatory variables () enable to provide an analysis of the FEBs' powers in terms of their regulation (“reglamentation”) (), as well as in terms of their accountability (), including the publicly available governmental databases of powers (). By definition of IPAMM, the power (“polnomochie” in russian) is the right and/ or obligation of FEB, fixed in statute on the federal authority, as well as other legal acts (Zhulin et al., 2010).

Accounting systems of FEBs' powers include governmental databases of check-lists, land registries, registries, inventories containing information about the objects and subjects of accounting, legally significant events and actions, unified and systematically collected and fixed. In this case, the variable partly indicates the “degree of openness” of the FEB. The data of variables were provided by the IPAMM NRU HSE.

The variable describes the number of services provided by the FEBs, which is also an important factor of federal authorities' activity. The data of this variable were received from the Common Government Services Portal of Russian Federation.

The last two variables directly characterize the functions of FEBs: the support functions of the Central Apparatus of FEBs according to the Ministry of Economic Development data (), as well as the groups of functions in various fields of activity according to Government decrees on each FEB ).The last variable characterizes the degree of "functions' systemisation” in the FEBs.

Before we embark to the implementation of the regression analysis, it is worth to underline the limitations of this section. It was already pointed out that the main limitation is that only Federal civil servants (civil servants working in central apparatus of FEBs of the Russian Federation) have been considered. The next limitation that should be mentioned is that statistical data, essential for this part of research, are available only for 2011, therefore the cross-sectional study have been conducted. In addition, the scope of the study depends significantly on the availability of statistical data: the structure of FEBs have been changed since 2011. Thus, all conclusions can be partly adapted to the current structure of FEBs. Other limitation is linked with the absence of statistical data of some FEBs of “presidential block” and some of the new-established FEBs. Thus, in our sample we have only 66 FEBs from 79 FEBs.

It is essential to note, that all model parameters are taken in absolute values. The current study used the Eviews software with version 6.1. The descriptive statistics of common sample are represented in Appendix 26.

In order to get valid results about any individual predictor of our model it is essential to exclude the highly correlated variables. In this respect, for detection multicollinearity, the correlation matrix was developed (see Appendix 27). The following variables with high (more than 0,75) pair correlation coefficients were omitted from the model to eliminate the effects of multicollinearity: , , , , . The initial model is represented in Appendix 28. The high value of the adjusted coefficient of determination indicates the high quality of the resulting model.

At this stage, it is necessary to check coefficients for significance using t-statistic for the hypothesis H₀ and H₁:

.

The hypothesis H₀ is accepted if |t_comp|<t_table. The process of checking all hypotheses performed at a significance level of 0.05, hence probability of our findings is 95per cent. The data provided by Eviews computation shows the t_comp value (see Appendix 28).

The formula (4) for is:

Where n - number of observations, m - number of explanatory variables, - significance level. According to the Student's t-distribution tables equals 1,68. Hence, 9 variables of the initial model have insignificant coefficients. It is essential to note that if coefficients of explanatory variables are insignificant, such variables cannot explain the linear relation between the dependent variable and observable factors. Table 14 provides the significant and insignificant parameters of initial model.

Table 14.

Significant and insignificant parameters of initial model

Significant parameters	Insignificant parameters
- length of service allowance - bonuses - “jurisdiction” - budget allocations for the maintenance of the CA - government expenditures under section 01 - powers of FEB	- official salary - material assistance - type of the FEB - “presidential block” - further vocational education - regulated powers of FEB - accounting systems of FEB's powers - services provided by FEB - support functions of FEB

The explanatory variables with insignificant coefficients were subsequently omitted from the following model specifications. The final model is represented in Table 15.

Table 15.

The final model

Dependent Variable: Y
Method: Least Squares
Sample (adjusted): 1 65
Included observations: 55 after adjustments
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	-198042.2	59079.08	-3.352154	0.0016
X2	43.05675	14.01672	3.071813	0.0036
X3	30.66097	23.23068	1.319848	0.1934
X4	2.771526	0.290148	9.552109	0.0000
X9	9312.240	6827.367	1.363958	0.1792
X10	0.011598	0.005329	2.176202	0.0347
X12	0.038132	0.005985	6.371123	0.0000
X14	-151.1723	87.45763	-1.728520	0.0906
X19	10.52591	31.80770	0.330923	0.7422
R-squared	0.905091	Mean dependent var	73566.20
Adjusted R-squared	0.888585	S.D. dependent var	59126.28
S.E. of regression	19735.70	Akaike info criterion	22.76683
Sum squared resid	1.79E+10	Schwarz criterion	23.09530
Log likelihood	-617.0877	Hannan-Quinn criter.	22.89385
F-statistic	54.83426	Durbin-Watson stat	1.859632
Prob(F-statistic)	0.000000

Coefficient of the following variables are significant on significance level of 0.1: , ,, , , , . Hence, the probability of our findings is 90 per cent. In spite of the insignificance of the coefficient of the variable , this variable was not excluded due to the fact that it is an essential for our model.

The initial and final models were compared according to the criteria presented in the Table 15.

Table 15.

Comparison of the models

Specification	R2	Adj R2	AIC	SC	The number of insignificant coefficients
	0.91	0.88	22.91	23.49	9 of 15
	0.90	0.88	22.76	23.09	1 of 8



The second model is better than the first, because it has only one insignificant coefficient. Moreover, the magnitudes of Akaike info criterion and Schwarz criterion are lower than in previous model, which represents the higher quality of the model. The high value of the adjusted coefficient of determination also indicates the high quality of both resulting models.

Therefore, the final model have been tested on multicollinearity (using the Pearson Correlation), heteroscedasticity (White test) and autocorrelation (Durbin-Watson stat test) (see Appendix 28). Thus, the resulting regression model is as follows (5):

This model demonstrates the dependence of Federal civil servants' pay differentiation on the following parameters:

• - the average monthly official salary of civil servants in FEBs;

• - the average monthly length of service allowance of civil servants in FEBs;

• - the average monthly bonuses of civil servants in FEBs;

• - “jurisdiction” (dummy variable: 1 - if the FEB is under ministerial jurisdiction, 0 - if the FEB under the direct jurisdiction of the President or the Government);

• - the budget allocations for the maintenance of the central apparatus of FEBs estimated for 2011;

• - the Federal government expenditures under section 01 of the Federal Budget 2011;

• - the total number of powers established by the Government decrees on FEB;

• - the number of support functions of FEB.

Summarizing the findings above, we may conclude the following:

• The influence of such factors as official salary and length of service allowance are justified the model validity. It is readily seen that the pay gap between the lowest and the highest civil service' categories depends on the experience/ skills (length of service) and competencies/ knowledge (job position held).

• According to the Rosstat data, the magnitude of Federal civil servants monthly bonuses in about 40 per cent of total pay. Therefore, the influence of such variable as “average monthly bonuses” on the level of civil servants pay differentiation is evident. However, it was already mentioned, that this component is unrelated to performance of civil servants.

• The significance of “jurisdiction” variable explains the fact that the type of FEB matters in the context of pay differentiation. If FEB is under ministerial jurisdiction, the level of pay gap between Senior managers and Support professionals rises on 9 312 rubles.

• The following two significant parameters represent the expenses of the Federal budget on a specific FEB. The variable reflects that the higher the budget allocations for the work of Central apparatus of FEBs the higher the level of pay differentiation among categories of civil service. The influence of variable on civil servants' pay differentiation means that the so-called “political importance” of FEB plays a great role in the process of salary budget formation.

• The last two variables of our model (the number of powers and support functions of FEB) justify the importance of FEB's activity. Therefore, the area of FEBs' activities directly influences on the level of pay differentiation. Moreover, considering that each type of FEBs has particular executive functions and powers, the significance of variables mentioned underline the role of the type of FEB.

• It is essential to note, that there is an inverse relationship between the number of FEB's powers and the level of pay differentiation. It means that the higher the number of powers, the lower the pay gaps between Senior managers and Support professionals of FEB. These findings can be explained by the fact that civil servants' pay varies according to the budget of FEB. However, the salary budget is based on the number of authorised positions in FEB, calculated on a historical basis. Therefore, such factor as the number of powers of FEB can be neglected.

Thus, the current study has explained 90% of the variance of pay differentiation through salary, length of service allowance, bonuses, “jurisdiction”, budget allocations for CA of FEB, Federal budget expenditures for FEB as whole, powers and functions of FEBs. However, the inclusion some of the remaining variables may provide a better explanation of the level of civil servants pay differentiation. Since the study is conducted on civil servants of central apparatus of FEBs, there is the limitation that the generalization of this study to other civil servants of regional and municipal authorities may not be appropriate. It is also important to include other public sector employees to a similar study to understand the regional and municipal levels of civil servants' pay differentiation.



CONCLUSIONS

During the research the author of the Master's thesis has come to the following conclusions:

· The theories of pay differentiation can partly explain the phenomenon of civil servants' pay differentiation. Each theory justifies particular type of pay differentiation: Human capital theory and Glass ceiling theory explain the gender and racial pay differentiation; Compensating differences theory accounts for the interregional pay differentiation; Opportunity cost theory elucidates the sectorial pay differentiation and Effective wage theory clarifies the psychological pay differentiation. Moreover, the horizontal and vertical pay differentiations are not substantiated by theoretical models. However, these aforementioned types are widespread in organisations of public and private sector.

· The factors of pay differentiation are different for each theory and each type of pay differentiation. Furthermore, the ways which can be involved in optimization are appropriate for certain types of pay differentiation. The author-established systematization provides the correspondence of the main theories, factors, types and possible ways of optimization civil servants' pay differentiation.

· The expert opinion that the current remuneration structure of Russian civil servants is complicated and unrelated to performance is readily confirmed by the analysis of pay structure and pay system of Federal civil servants of the Russian Federation.

· Concerning the ways of optimisation Russian civil servants' pay system it is worth to underline some pilot projects of performance indicators implementation and the recent initiatives of the President on perfection of Federal civil servants' pay in the Administration and in the Government. Nevertheless, there is no data available to confirm the increase of civil servants' performance after the pilot projects. Moreover, the initiatives of the President are also unrelated to civil servants' performance.

· The civil servants' pay has unequal distribution across FEBs. There is a large pay gap between different levels of executive bodies, which illustrate the vertical pay differentiation. However, there is little incentive for staff to take on higher level responsibilities because the differences in total pay and allowances between different categories of civil service in FEBs are insufficient.

· According to the analysis of pay compression, the following factors determine the level of pay differentiation in FEBs: civil service' category, type of FEB, pay components, area of FEBs' activity.

Considering the calculations of chain and basic pay compa-ratios, we can conclude the following:

· In the case of career advancement from “Support Professionals” to “Professionals” civil service category the average monthly pay increases on approximately 50 per cent in all FEB. However, in case of further career advancement the increase of pay varies from 96 to 139 per cent according to the type of FEB. Thus, civil servants of Federal ministries have more benefits than civil servants of the rest FEBs. This phenomenon illustrates the horizontal pay differentiation, then employees of the same civil service' categories receive different levels of pay in different FEBs.

· The level of civil servants' pay is appropriate for the category of civil service in most FEBs. However, the level of civil servants' pay is appropriate for all categories (except for Support Professionals in 3 Ministries) in all Federal ministries, but in most Federal services and agencies the actual remuneration of employees is lower than the reference level for all civil service' categories.

Summarizing the findings of regression analysis, we may conclude the following:

• The influence of such factors as official salary and length of service allowance are justified the model validity. It is readily seen that the pay gap between the lowest and the highest civil service' categories depends on the experience/ skills (length of service) and competencies/ knowledge (job position held).

• According to the Rosstat data, the magnitude of Federal civil servants monthly bonuses in about 40 per cent of total pay. Therefore, the influence of such variable as “average monthly bonuses” on the level of civil servants pay differentiation is evident. However, it was already mentioned, that this component is unrelated to performance of civil servants.

• The significance of “jurisdiction” variable explains the fact that the type of FEB matters in the context of pay differentiation. If FEB is under ministerial jurisdiction, the level of pay gap between Senior managers and Support professionals rises on 9 312 rubles.

• The following two significant parameters represent the expenses of the Federal budget on a specific FEB. The variable reflects that the higher the budget allocations for the work of Central apparatus of FEBs the higher the level of pay differentiation among categories of civil service. The influence of variable on civil servants' pay differentiation means that the so-called “political importance” of FEB plays a great role in the process of salary budget formation.

• The last two variables of our model (the number of powers and support functions of FEB) justify the importance of FEB's activity. Therefore, the area of FEBs' activities directly influences on the level of pay differentiation. Moreover, considering that each type of FEBs has particular executive functions and powers, the significance of variables mentioned underline the role of the type of FEB.

• It is essential to note, that there is an inverse relationship between the number of FEB's powers and the level of pay differentiation. It means that the higher the number of powers, the lower the pay gaps between Senior managers and Support professionals of FEB. These findings can be explained by the fact that civil servants' pay varies according to the budget of FEB. However, the salary budget is based on the number of authorised positions in FEB, calculated on a historical basis. Therefore, such factor as the number of powers of FEB can be neglected.

Thus, the current study has explained 90% of the variance of pay differentiation through salary, length of service allowance, bonuses, “jurisdiction”, budget allocations for CA of FEB, Federal budget expenditures for FEB as whole, powers and functions of FEBs. However, the inclusion some of the remaining variables may provide a better explanation of the level of civil servants pay differentiation. Since the study is conducted on civil servants of central apparatus of FEBs, there is the limitation that the generalization of this study to other civil servants of regional and municipal authorities may not be appropriate. It is also important to include other public sector employees to a similar study to understand the regional and municipal levels of civil servants' pay differentiation.

RECOMMENDATIONS

On the grounds of the findings and conclusions the recommendations on improvement civil servants' pay system have been developed. These recommendations tend to optimize the horizontal and the vertical pay differentiation of Federal civil servants. It is essential to note, that in this context the notion of “optimisation” is closely related with justification for certain type of pay differentiation. In other words, ways of optimisation proposed enables to explain the level of pay differentiation from the efficiency perspective

Optimisation of the horizontal pay differentiation

The Horizontal pay differentiation between the comparable levels (categories) of civil servants in different FEBs can be optimised by the introduction of PRP. In fact, the remuneration structure of Russian civil servants is complicated and unrelated to performance. Moreover, according to the research provided, such components of civil servants total pay as Confidentiality Allowance, Special Conditions Allowance and Monthly allowance have not any influence on the level of pay differentiation. These components should be omitted from the total pay of civil servants in order to form the basis for performance pay budget. Furthermore, taking into account the significance of official salary, it is worth to introduce salary rates (“vilki”) in addition to fixed part of official salary.

Optimisation of the vertical pay differentiation

The appropriate type of pay structure can optimize the vertical pay differentiation. In particular, it is critical to ensure that pay levels are well defined, thereby making it easier to differentiate between them. To this, it is worth to consider the best practice of grade systems in foreign countries and to transfer some successful experience to Russian civil servants' pay system/

Therefore, talent management can also optimize vertical pay differentiation by attracting, retaining and development of high calibre employees because it ensures the adequate competition for top job positions.

The application of recommendations mentioned can help to implement the proper economic policy on the state level in order to avoid the problem of negative selection of future civil servants and to increase the motivation of current civil servants. The analysis carried out in the frames of this research leaves a room for the further research on the same subject.



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Government Decree of the Russian Federation (2011) ¹717 “The issues about governmental regulation in the financial market sphere in the Russian Federation” (along with “The approval of the Federal Service for Financial Markets”) from 29 August 2011 (ed. 30 April 2013).

Government Decree of the Russian Federation (2004) ¹332 “The approval of the Federal Tariff Service” from 30 June 2004 (ed. 23 March 2013).

Government Decree of the Russian Federation (2006) ¹459 “The Federal Customs Service” from 26 July 2006 (ed. 16 February 2013).

Government Decree of the Russian Federation (2011) ¹590 “The Russian Ministry of culture” (along with “The Approval of the Russian Ministry of culture”) from 20 July 2011 (ed. 18 February 2013).

Government Decree of the Russian Federation (2008) ¹437 “The Ministry of Economic Development” from 5 June 2008 (ed. 3 April 2013).

Government Decree of the Russian Federation (2008) ¹400 “The Ministry of Energy” from 28 May 2008 (ed. 18 April 2013).

Government Decree of the Russian Federation (2004) ¹329 “The Ministry of Finance” from 30 June 2004 (ed. 30 April 2013).

Government Decree of the Russian Federation (2008) ¹418 “The Ministry of Communications and Media” from 2 June 2008 (ed. 27 March 2013).

Government Decree of the Russian Federation (2010) ¹337 “The Ministry of Education and Science” (along with “The Approval of the Ministry of Education and Science”) from 15 May 2010 (ed. 1 December 2012).

Government Decree of the Russian Federation (2008) ¹404 “The Ministry of Natural Resources and Environment” from 29 May 2008 (ed. 13 April 2013).

Government Decree of the Russian Federation (2008) ¹438 “The Ministry of Industry and Trade” from 5 June 2008 (ed. 15 April 2013).

Government Decree of the Russian Federation (2005) ¹40 “The approval of the Ministry of Regional Development and on the amendments to certain acts of the Government of the Russian Federation” from 26 January 2005 (ed. 30 April 2013).

Government Decree of the Russian Federation (2008) ¹450 “The Ministry of Agriculture” from 12 June 2008 (ed. 6 March 2013). Government Decree of the Russian Federation (204) ¹395 “The approval of the Ministry of transport of the Russian Federation” from 30 July 2004 (ed. 16 March 2013).

Government Decree of the Russian Federation (2008) ¹423 “The issues of the Ministry of Public Health and Social Development and the Federal Bio-Medical Agency” from 2 June 2008 (ed. 28 June 2012).

Government Decree of the Russian Federation (2009) ¹154 “The Federal Service for the Regulation of the Alcohol Market” (along with “The approval of the Federal Service for the Regulation of the Alcohol Market”) from 24 February 2009 (ed. 9 August 2012).

Government Decree of the Russian Federation (2004) ¹278 “The approval of the Federal Service for Financial and Budgetary Supervision” from 15 June 2004 (ed. 25 December 2012).

Government Decree of the Russian Federation (2004) ¹372 “The Federal Service for Hydrometeorology and Monitoring of the Environment” from 23 July 2004 (ed. 24 March 2011).

Government Decree of the Russian Federation (2009) ¹228 “The Federal Service for Supervision of Telecom, Information Technologies and Mass Communications” (along with “The approval of the Federal Service for Supervision of Telecom, Information Technologies and Mass Communications”) from 16 March 2009 (ed. 26 October 2012).

Government Decree of the Russian Federation (2012) ¹604 “The Federal Service for Defence Order” from 19 June 2012 (ed. 18 February 2013)

Government Decree of the Russian Federation (2004) ¹300 “The approval of the Federal Service for Supervision in the Sphere of Science and Education” from 17 June 2004 (ed. 24 October 2011).

Government Decree of the Russian Federation (2004) ¹322 “The approval of the Federal Service on Customers' Rights and Human Well-being Surveillance” from 30 June 2004 (ed. 30 January 2013).

Government Decree of the Russian Federation (2004) ¹400 “The approval of the Federal Service for Supervision of Natural Resource Management and changes of the Government Decree ¹370 from 22 July 2004” from 30 July 2004 (ed. 8 October 2012 with changes from 30 April 2013).

Government Decree of the Russian Federation (2009) ¹457 “The Federal Service for State Registration, Cadastre and Cartography” from 1 June 2009 (ed. 13 December 2012).

Government Decree of the Russian Federation (2004) ¹327 “The approval of the Federal Service for Veterinary and Phytosanitary Surveillance” from 30 June 2004 (ed. 17 October 2011).

Government Decree of the Russian Federation (2008) ¹420 “The Federal State Statistics Service” from 2 June 2008 (ed. 15 April 2013).

Government Decree of the Russian Federation (2004) ¹398 “The approval of the Federal Transportation Inspection Service” from 30 July 2004 (ed. 2 May 2012).

Government Decree of the Russian Federation (2004) ¹324 “The approval of the Federal Service for Labour and Employment” from 30 June 2004 (ed. 19 June 2012).

Government Decree of the Russian Federation (2004) ¹323 “The approval of the Federal Service on Surveillance in Healthcare” from 30 June 2004 (ed. 29 April 2013).

Government Decree of the Russian Federation (2004) ¹703 “The Russian Federal Treasury” from 1 December 2004 (ed. 26 December 2011).

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