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APPENDIXES

The interpretation of Citation Summary table's indicators

The All Databases Citation Summary table displays a breakdown of all Times Cited counts across all products and editions in Web of Knowledge, including citation counts from product databases that are not in your institution's subscription.

Citing Articles without Self-citations

This field displays the total number of citing articles minus any article that appears in the set of search results on the Citation Report.

Average Citations per Item

This field displays a simple formula that calculates the average number of citing articles for all items in a set. It is the sum of the Times Cited count divided by the number of results found.

h-index

This field displays the h-index count and is based on a list of publications ranked in descending order by the Times Cited count.

The h-index is indicated by an orange horizontal line going through the Year / Total Year columns. The number of items above this line, which is "h" have at least "h" citations. For example, an h-index of 20 means there are 20 items that have 20 citations or more. This metric is useful because it discounts the disproportionate weight of highly cited papers or papers that have not yet been cited.

Calculating the h-index Value - The h-index factor is based on the depth of years of your product subscription and your selected timespan. Items that do not appear on the Results page will not be factored into the calculation. If your subscription depth is 10 years, then the h-index value is based on this depth even though a particular author may have published articles more than 10 years ago. Moreover, the calculation only includes items in your product database - books and articles in non-covered journals are not included.

Main findings of the most cited articles

Article	Object (aspect)	Research area	Country	Results	Times sited
Grodsky, E. and Pager, D. (2001) The structure of disadvantage: Individual and occupational determinants of the black-white wage gap, American Sociological Review, 66 (4), pp. 542-567.	Racial wage gap	Sociology	USA	Racial wage gap is generated by systematic variation in the occupational structure (individual human capital and occupational placement)	84
Hirsch, B. and Schumacher, E. (2004) Match Bias in Wage Gap Estimates Due to Earnings Imputation, Journal of Labor Economics, 22 (3), pp. 689-722.	Labour market differentials	Business & Economics	Germany	Wage gap estimates are biased toward zero when the attribute being studied is not a criterion used to match donors to nonrespondents	46
Maume, D. (1999) Occupational segregation and the career mobility of white men and women, Social Forces, 77 (4), pp. 1433-1459.	Gender wage gap	Sociology	USA	For man, occupation was positively related to men's chances of receiving a wage promotion. For women, occupational segregation positively influenced movement to joblessness	30
Mastekaasa, A. and Olsen, K.M. (1998) Gender, Absenteeism, and Job Characteristics: A Fixed Effects Approach, Work and Occupations, 25 (2), pp. 195-228.	Gender wage differentials	Business & Economics; Sociology	Norway	The gender difference in absenteeism is reflected by general health and personality differences	28
Machin, S. and Puhani, P. (2003) Subject of degree and the gender wage differential: evidence from the UK and Germany, Economics Letters, 79, pp. 393-400.	Gender wage differentials	Business & Economics	England	Subject of degree explains higher wages of male over female graduates after controlling for age, industry, region, part-time and public sector employment	27
Jurajda, S. (2003) Gender wage gap and segregation in enterprises and the public sector in late transition countries, Journal of Comparative Economics, 31 (2), pp. 199-222.	Gender wage gap	Business & Economics	Czech Republic	Various forms of employment segregation are related to the overall gender pay difference. Much of the gap is due to violations of the equal pay policy	25
Martin, P. and Harkreader, S. (1993) Multiple Gender Contexts and Employee Rewards, Work And Occupations, 20 (3), pp. 296-336.	Gender wage differentials and satisfaction	Business & Economics; Sociology	USA	Women are less broadly dispersed than are man. Net of their human capital and responsibility, women and men who are situated in job ladders or at hierarchical levels with more women earn less	19
Melly, B. (2005) Public-private sector wage di?erentials in Germany: Evidence from quantile regression, Empirical Economics, 30, pp. 505-520.	Public-private sector wage di?erentials	Business & Economics; Mathematical Methods In Social Sciences	Germany	Conditional wages are higher in the public sector for women but lower for men. At the low end of wages, differences in characteristics explain less than the raw wage gap when it is the opposite at high wages. The most experienced employees and those with basic schooling do best in the public sector	18
Berger, M., Blomquist, G. and Peter, K. (2008) Compensating differentials in emerging labor and housing markets: Estimates of quality of life in Russian cities, Journal of Urban Economics, 63 (1), pp. 25-55.	Compensating differentials	Business & Economics; Urban Studies	USA	Compensating differentials exist even after controlling for the regional pay differences (“regional coefficients”) used by the Russian government to compensate public sector workers for living in regions that are designated as less desirable	17



Median full-time gross weekly earnings in UK's regions, 2008

Resource: IDS, 2009

Hourly Wages (Median) by Private and Public Sectors in French Regions, 2002

Source: Meurs et al., 2007



Dynamics of interregional wage differentials in Russia

Resource: Oshchepkov, 2007a

The ratio of the annual average salary of the public sector to that of all sectors and per capita GDP in China

Resource: National Statistics Bureau of the People's Republic of China, 2009



Ratio of public to private compensation per employee in different countries

Resource: Giordano et al., 2011

Ratios between average salaries of selected top public sector management positions and bottom of workforce pay spine in UK, 2000 - 2009

Resource: Hutton, 2011



Systematization of the main theories of pay differentiation, factors, types and possible ways of optimisation

Theories	Factors of pay differentiation	Types of pay differentiation	Possible ways of optimization
Human capital theory Glass ceiling theory	Age, gender, ethnicity, education, experience	Gender pay differentiation	Minimal wages, Equal pay policy, Flexibility of working conditions, Monitoring of pay practices
		Racial (Ethnic) pay differentiation
Compensating differences theory	Risk, image, prestige, stability, working conditions, region, territory	Interregional pay differentiation	Regional coefficients
Opportunity cost theory	Occupation, sector of economy, industry, skills, education, competencies	Sectorial (occupational) pay differentiation	Market supplements, Wage indexation
Effective wage theory	Psychological factors, health, attitudes, perceptions, expectations, morale, interrelationships, values, ethics	Psychological pay differentiation	Personal involvement (collective bargaining), Transparency and accountability of wage setting process
-	Level of job position	Vertical (hierarchical) pay differentiation	Different types of pay systems, Talent management
-	Peculiarities of organization (structural effect)	Horizontal pay differentiation	PRP

The average monthly pay of Municipal civil servants by groups of civil service' positions, rubles per civil servant (2011)

	The average mounthly pay salary
	total	by groups of civil service' positions in FEBs
		Senior staff	Chief officers	Leading officers	Senior officers	Junior officers
RF	26870	44512	37238	32029	23097	14282
Central federal district	25705	38313	34781	29951	21300	13279
Belgorod region	23383	45396	26304	18642	13311	11193
Bryansk region	18185	38130	23983	21320	15625	10717
Vladimir region	21688	35861	25494	20238	15890	10948
Voronezh region	27523	67837	57464	34171	22266	14363
Ivanovo region	19733	31109	29519	22610	15566	8590
Kaluga region	21887	29834	28290	25947	18982	11996
Kostroma region	19390	31545	24502	22505	13895	10459
Kursk region	18461	28614	22466	18983	15315	9919
Lipetsk region	23537	49706	34895	24866	18167	13428
Moscow region	33613	55877	50241	37273	27072	18127
Oryol region	20796	37388	27312	29866	16105	11942
Ryazan region	22873	51487	36146	25390	18042	12281
Smolensk region	19334	29508	25383	24068	17778	12494
Tambov region	15426	62012	25624	18961	13507	8642
Tver region	28471	33464	33792	39764	24295	10341
Tula region	23312	41716	26223	23656	17786	10927
Yaroslavl region	21982	38661	33046	26950	18425	12315
Moscow	46720	91140	82246	59681	41206	29395
North Western federal district	30000	50657	42195	35711	26261	18186
Republic of Karelia	29793	71117	44128	39128	27883	17364
Republic of Komi	35674	87746	54052	39711	25830	16292
Arkhangelsk region	33389	55733	70591	43966	25878	17396
Vologda region	22519	36220	25789	29373	18140	12589
Kaliningrad region	30090	50174	35790	32826	25113	18260
Leningrad region	30528	51287	49272	37734	27364	19725
Murmansk region	45976	113318	84690	58113	44702	30970
Novgorod region	25288	51148	36177	29384	21983	16943
Pskov region	12906	23617	16696	15150	11218	8065
St. Petersburg	40273	58347	54127	45162	36764	26039
Southern federal district	24098	47071	35884	28019	20351	13527
Republic of Adygea	21494	30417	31853	30441	20390	11420
Republic of Kalmykia	16304	24288	23095	17564	13459	9596
Krasnodar Krai	27940	57936	39811	30495	22969	16151
Astrakhan region	20495	39210	24606	17165	15796	9995
Volgograd region	23326	43348	36075	25252	18523	11503
Rostov region	22326	54958	38820	29270	21101	12782
North Caucasus federal district	18030	30194	22977	19947	15594	10143
Republic of Dagestan	17763	26476	20962	16133	14057	9096
Republic of Ingushetia	16696	24775	19357	16019	13398	11155
Republic of Kabardino-Balkariya	16293	29287	19937	13569	9893	7520
Republic of Karachaevo-Cherkessiya	16033	27230	22210	14208	12929	8926
Republic Northern Ossetia-Alania	17042	30370	20147	18192	13926	8458
Chechen Republic	15073	28082	22745	18031	15135	10302
Stavropol Krai	20359	39525	29231	28005	17488	10949
Volga federal district	21121	36248	30532	25978	17950	12006
Republic of Bashkortostan	23902	100404	53304	35882	24819	13593
Republic of Mari El	17563	31683	29052	18606	13031	9547
Republic of Mordovia	15006	23338	16217	14867	10955	8650
Republic of Tatarstan (Tatarstan)	21688	20905	28242	28926	19466	9303
Udmurt Republic	19141	51586	30751	21141	13833	9510
The Chuvash Republic - Chuvashia	13974	45267	25162	19340	14565	9537
Perm Krai	21288	51943	39048	24217	15080	8970
Kirov region	16573	29158	23619	18907	14060	9416
Nizhny Novgorod Region	25125	69052	39597	33893	19123	12806
Orenburg region	19232	35286	28928	24955	19218	11850
Penza region	20769	31469	28027	19897	15483	12104
Samara region	25873	44644	31397	26101	19442	11366
Saratov region	19753	36020	29308	31441	24351	13108
Ulyanovsk region	18476	31831	25392	18304	15992	9663
Ural federal district	45960	79697	50337	60982	39270	18977
Kurgan region	14953	22355	18076	13235	12159	10564
Sverdlovsk region	30733	48915	36758	42503	27306	17556
Tyumen region	72905	131788	105077	87856	56830	32738
Yamal-Nenets Autonomus region	99092	290040	179756	122412	73998	56449
Chelyabinsk region	23542	55078	29461	21918	19280	10076
Siberian federal district	24567	37330	37703	29687	21534	16179
Republic of Altai	21514	37701	34822	30746	17379	9220
Republic of Buryatia	25853	67357	44718	32491	22100	11486
Republic of Tyva	23773	50551	39109	26519	20576	12048
Republic of Khakassia	26827	57224	44008	32002	23620	16324
Altai region	16137	18915	22674	22171	13360	8448
Zabaykalsky Krai	22668	48996	33579	26064	19170	11084
Krasnoyarsk Krai	31030	63881	40913	30575	28462	17702
Irkutsk region	31918	83006	58857	45617	36668	23630
Kemerovo region	18287	30989	24236	22371	14678	8884
Novosibirsk region	21106	50370	41722	28647	19457	11500
Omsk region	22428	42286	33726	28220	19876	12065
Tomsk region	31723	101571	45561	44974	26011	15324
Far East federal district	38952	79970	52867	38009	30979	21323
Republic of Sakha (Yakutia)	38009	89026	67454	49181	32603	26602
Kamchatka region	56662	111582	54427	37133	26653	25363
Primorsky Krai	32252	57104	40653	29752	16813	12276
Khabarovsk region	40453	79152	70637	53845	37650	23584
Amur region	27638	55454	39471	32620	22495	12822
Magadan region	44922	104907	78005	50676	34777	20032
Sakhalin region	52944	125695	59971	35945	32430	24593
Jewish Autonomus region	28077	49594	41555	27818	22290	14544
Chukotka Autonomus region	69982	118199	86165	69078	55406	23963

Resource: Rosstat, 2011

Russian legislation containing the principles and recommendations on the implementation of PRP for civil servants:

· Federal law N 58 (2003) “On the system of Civil Service in the Russian Federation”.

· Federal law N 79 (2004) “On the State Civil Service in the Russian Federation”.

· Presidential decree (2010) N 261”On the Federal programme “Reforming and Development of the civil service in the Russian Federation (2009-2013)”.

· Presidential decree (2012) N 601 «On the main directions of perfection the system of public administration”.

· List of Presidents' instructions resulted in enlarged session of working group on preparation suggestions for development in the Russian Federation the system “Open Government” for the civil service's staff assistance on 14th of March 2012.



Pay gaps between Senior managers and Support Specialists in the FEBs of the Russian Federation, 2011

Based on the data of Rosstat, 2011

Current structure of FEBs of the Russian Federation

I. Federal Ministries, Federal Services and Federal Agencies, management of which activity is carried out by the President of the Russian Federation, Federal Services and Federal Agencies subordinated to these Federal Ministries

Ministry of the Interior of the Russian Federation

Ministry of Civil Defence, Emergencies and Disaster Relief of the Russian Federation

Ministry of Foreign Affairs of the Russian Federation

· Federal Agency for CIS (Commonwealth of Independent States) Affairs, Compatriots Abroad and International Humanitarian Cooperation

Ministry of Defense of the Russian Federation

· Federal Service for Military and Technical Cooperation

· Federal Technical and Export Control Service

· Federal Agency for Special Construction

· Federal Agency for Deliveries of Weapons, Military and Special Equipment and Materiel

Ministry of Justice of the Russian Federation

· Federal Penitentiaries and Correction Service

· Federal Bailiff Service

State Courier Service of the Russian Federation (Federal Service)

Foreign Intelligence Service of the Russian Federation (Federal Service)

Federal Security Service of the Russian Federation (Federal Service)

· National Antiterrorist Committee

Federal Service for Narcotics Traffic Control of the Russian Federation (Federal Service)

Federal Protection (Bodyguard) Service of the Russian Federation (Federal Service)

Chief Directorate for Special Programs of the President of the Russian Federation (Federal Agency)

Directorate of the President of the Russian Federation (Federal Agency)

Federal Financial Monitoring Service

II. Federal Ministries, Federal Services and Federal Agencies, management of which is carried out by the Government of the Russian Federation, Federal Services and Federal Agencies subordinated to these Federal Ministries

Ministry of Healthcare of the Russian Federation

· Federal Supervision Service for Healthcare

· Federal Biomedical Agency

Ministry of Culture of the Russian Federation

· Federal Archives Agency

· Federal Agency for Tourism

Ministry of Education and Science of the Russian Federation

· Federal Service for Education and Science Supervision

· Federal Agency for Youth Affairs

Ministry of Natural Resources and Environment of the Russian Federation

· Federal Service for Hydrometeorology and Environmental Monitoring

· Federal Supervisory Natural Resources Management Service

· Federal Water Resources Agency

· Federal Agency for Subsoil Resources Management

· Federal Forestry Agency

Ministry of Industry and Trade of the Russian Federation

· Federal Agency for Technical Regulation and Metrology

Ministry of Tele- and Mass Communications of the Russian Federation

· Federal Service for Communications, Information Technologies and Mass Communication Supervision

· Federal Press and Mass Communication Agency

· Federal Communications Agency

Ministry of Regional Development of the Russian Federation

· Federal Agency for Construction, Housing and Utilities

Ministry of Agriculture of the Russian Federation

· Federal Service for Veterinary and Phytosanitary Control

· Federal Agency for Fisheries

Ministry of Sport of the Russian Federation

Ministry of Transport of the Russian Federation

· Federal Transport Supervision Service

· Federal Air Transport Agency

· Federal Road Agency

· Federal Rail Transport Agency

· Federal Maritime and River Transport Agency

Ministry of Finance of the Russian Federation

· Federal Tax Service

· Federal Service for Financial and Budgetary Supervision

· Federal Treasury (Federal Service)

Ministry of Economic Development of the Russian Federation

· Federal Service for State Registration, Cadastre and Cartography

· Federal State Reserves Agency

· Federal Agency for State Property Management

· Federal Accreditation Service

· Federal Service for Intellectual Property

Ministry of Energy of the Russian Federation

Ministry of Labour and Social Security of the Russian Federation

· Federal Labour and Employment Service

Ministry for the Development of the Russian Far East

Federal Antimonopoly Service

Federal Customs Service

Federal Tariff Service

Federal Financial Markets Service

Federal Space Agency

Federal Agency for the Development of the State Border Facilities of the Russian Federation

Federal Service for Alcohol Market Regulations

Federal Service for Environmental, Technological and Nuclear Supervision

Federal Migration Service

Federal Supervisory Service for Consumers Rights Protection and Human Welfare

Federal Service for Defence Contracts

Federal State Statistics Service

The average monthly pay of Federal civil servants of FEBs by categories of civil service' positions, rubles per civil servant (2011)

The level of average monthly compensation of civil servants in central government as % of OECD average level, 2011

ISCO-08 occupations	Senior managers	Middle managers	Professionals	Secretarial positions
ISCO-08 positions/ Countries	D1 position	D2 Position	D3 Position	D4 Position	Economists, policy analysts, statisticians	Administrative executive secretaries	Secretaries (general office clerks)
AUT	86%	100%	86%	85%	101%	81%	-
BEL	131%	105%	129%	134%	128%	127%	131%
CHL	91%	67%	61%	-	55%	32%	-
DNK	222%	171%	109%	102%	95%	125%	118%
EST	45%	-	51%	59%	48%	57%	55%
FIN	74%	-	91%	100%	106%	136%	121%
HUN	65%	78%	72%	79%	56%	46%	60%
ISL	47%	71%	79%	-	89%	-	99%
IRL	136%	111%	126%	115%	128%	115%	95%
ITA	155%	-	132%	-	76%	96%	-
KOR	80%	83%	84%	86%	93%	108%	96%
NLD	102%	101%	137%	143%	138%	144%	140%
NZL	184%	151%	111%	108%	74%	81%	95%
NOR	86%	90%	89%	88%	93%	114%	123%
SVN	53%	-	70%	-	81%	79%	58%
ESP	69%	93%	99%	90%	141%	104%	113%
SWE	74%	69%	74%	71%	78%	89%	91%
GBR	154%	133%	140%	116%	75%	90%	77%
USA	98%	132%	148%	140%	165%	156%	123%
BRA	68%	78%	93%	101%	109%	111%	82%
OECD	100%	100%	100%	100%	100%	100%	100%
Russia	54%	33%	70%	44%	53%	67%	48%

Model with 15 explanatory variables

Dependent Variable: Y
Method: Least Squares
Sample(adjusted): 1 65
Included observations: 55
Excluded observations: 10 after adjusting endpoints
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	-156925.2	71950.87	-2.181005	0.0353
X2	28.62352	18.53719	1.544113	0.1306
X3	49.01216	27.66101	1.771886	0.0842
X4	2.856971	0.325595	8.774611	0.0000
X5	9.743899	11.05721	0.881226	0.3836
X7	22245.34	13569.61	1.639351	0.1092
X8	7166.496	11904.71	0.601988	0.5507
X9	15564.74	8211.922	1.895384	0.0655
X10	0.012097	0.006031	2.005637	0.0519
X12	0.035743	0.006673	5.356309	0.0000
X13	-18.48538	25.41963	-0.727209	0.4714
X14	-240.8212	107.0226	-2.250191	0.0302
X15	-111.7522	649.8397	-0.171969	0.8644
X16	-689.3000	603.8590	-1.141492	0.2606
X18	174.9058	341.3807	0.512348	0.6113
X19	34.25804	37.23861	0.919960	0.3632
R-squared	0.914620	Mean dependent var	73566.20
Adjusted R-squared	0.881781	S.D. dependent var	59126.28
S.E. of regression	20329.39	Akaike info criterion	22.91557
Sum squared resid	1.61E+10	Schwarz criterion	23.49952
Log likelihood	-614.1782	F-statistic	27.85193
Durbin-Watson stat	2.097509	Prob(F-statistic)	0.000000

Heteroskedasticity Test

Heteroskedasticity Test: White
F-statistic	2.844786	Prob. F(43,11)	0.0324
Obs*R-squared	50.46224	Prob. Chi-Square(43)	0.2023
Scaled explained SS	51.05004	Prob. Chi-Square(43)	0.1867
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Sample: 1 65
Included observations: 55
Collinear test regressors dropped from specification
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	9.98E+10	5.05E+10	1.976576	0.0737
X2	-51411569	27861904	-1.845228	0.0921
X2^2	6638.881	3818.267	1.738716	0.1100
X2*X3	-13644.42	9724.166	-1.403146	0.1882
X2*X4	-50.35545	121.9868	-0.412794	0.6877
X2*X9	3507194.	2189743.	1.601646	0.1375
X2*X10	2.533057	2.884781	0.878076	0.3987
X2*X12	-21.23178	22.23771	-0.954765	0.3602
X2*X14	-14129.24	41614.56	-0.339526	0.7406
X2*X19	20249.85	15525.57	1.304290	0.2188
X3	51966592	40987155	1.267875	0.2310
X3^2	6345.508	7042.576	0.901021	0.3869
X3*X4	123.9413	148.0876	0.836946	0.4204
X3*X9	-4683562.	6204444.	-0.754872	0.4662
X3*X10	1.585379	6.016397	0.263510	0.7970
X3*X12	10.66252	37.28587	0.285967	0.7802
X3*X14	31608.97	67490.14	0.468349	0.6487
X3*X19	-33716.16	35395.79	-0.952547	0.3613
X4	165329.9	539375.7	0.306521	0.7649
X4^2	0.873329	1.636814	0.533554	0.6043
X4*X9	-24561.10	68176.36	-0.360258	0.7255
X4*X10	0.109848	0.108783	1.009790	0.3343
X4*X12	-0.170331	0.659635	-0.258220	0.8010
X4*X14	-514.0387	1634.624	-0.314469	0.7590
X4*X19	-445.1944	560.2340	-0.794658	0.4436
X9	-1.23E+10	7.92E+09	-1.556989	0.1478
X9*X10	5580.774	1789.889	3.117944	0.0098
X9*X12	-8687.580	11241.19	-0.772835	0.4559
X9*X14	-17299549	23142442	-0.747525	0.4704
X9*X19	-1510907.	5479440.	-0.275741	0.7879
X10	-20428.70	10390.14	-1.966162	0.0750
X10^2	-0.001307	0.001216	-1.074537	0.3056
X10*X12	0.010057	0.018439	0.545431	0.5963
X10*X14	58.32305	36.99704	1.576425	0.1432
X10*X19	-4.643716	14.23961	-0.326113	0.7505
X12	103966.9	105440.4	0.986025	0.3453
X12^2	-0.002437	0.006581	-0.370362	0.7181
X12*X14	-138.0589	100.8796	-1.368552	0.1984
X12*X19	-43.48792	46.78392	-0.929548	0.3726
X14	82933548	2.01E+08	0.413113	0.6875
X14^2	-336161.0	294085.7	-1.143072	0.2773
X14*X19	58399.07	107775.7	0.541858	0.5987
X19	-59120086	70980975	-0.832900	0.4226
X19^2	13984.21	29842.94	0.468594	0.6485
R-squared	0.917495	Mean dependent var	3.26E+08
Adjusted R-squared	0.594977	S.D. dependent var	5.59E+08
S.E. of regression	3.56E+08	Akaike info criterion	42.20844
Sum squared resid	1.39E+18	Schwarz criterion	43.81431
Log likelihood	-1116.732	Hannan-Quinn criter.	42.82944
F-statistic	2.844786	Durbin-Watson stat	2.284592
Prob(F-statistic)	0.032435

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