For a qualitative research method, it is necessary to check the concept to identify certain results, so this is a deductive approach. The purpose of the study of this article is also deductive. This method will refer to the existing theory and show how to evaluate the impact of the World Cup on the development of the enterprise, using dependent and independent variables.

For a quantitative method, it is necessary to use an econometric regression model and use the OLS estimation method to determine the impact of government orders on the development of small and medium-sized businesses. "OLS (ordinary least squares) - regression analysis methods for estimating the unknown parameters of regression models from sample data" (Wooldridge, 2015). The regression model is a kind of hypothesis that will have a statistical test throughout the study. This model is a parametric function group and is displayed as:

,

where are the parameters,

- is an independent variable space,

- is a dependent variable space.

In the formula, - acting as an independent variable, will be presented as a volume of state order, the amount of government contracts (rubles), - acts as a dependent variable, will characterize the indicator of development of small and medium enterprises. Y of this work are revenue figures for 2013, 2014 and 2015, and revenue growth figures for 2013-2014 and 2014-2015. Using these variables, a regression will be constructed that will determine the dependence of these variables. The regression model is the most convenient tool for conducting this study, since it contains many universal functions necessary to solve the problems posed in the work. Do-file with an independent variable the amount of state orders - in Appendix 1, with an independent variable the number of state orders in Appendix 2.

e) Description of the methods and procedures used to analyses data and of the statistical software used to analyses data.

An empirical analysis of the collected data was performed in the STATA program. First of all, regressions will be constructed in which the independent variables are the number and amount of government contracts, and the dependent ones are the revenues of small and medium-sized enterprises in 2013, 2014 and 2015 and the revenue growth rate for 2013/2014 and 2014/2015 using the formula: growth rate revenue = ln (revenue of companies in 2014) - ln (revenue of companies in 2013) and revenue growth rate = ln (revenue of companies in 2015) - ln (revenue of companies in 2014). All regressions are ordered by type of construction activity and city of registration of small and medium enterprises. After the conclusions are made on the basis of the data obtained. The regression parameters should be checked for the multicollinearity of the parameters with the help of the correlation of variables and the VIF - test. "VIF-test - method of detecting multicollinearity using the study of factors of inflation dispersion" (Wooldridge, 2015). It is also necessary to build descriptive statistics to check for normal distribution. All these operations will be made according to the companies that performed the state contract in 2013, 2014 and 2015.

f) Limitations of the data and methods

The lack of data on private contracts of small and medium enterprises is the most important limitation in this work. Having the necessary information, it was possible to conduct a better analysis comparing the conditions, quantity and quality of private and public contracts.

To continue this work, it would be possible to investigate the percentage of failed government contracts and find out the reason why many small and medium-sized companies do not take up government orders or cannot fulfill them and break the contract. Using a quantitative data collection method, you can take information from official government procurement portals for failed and successfully completed contracts for certain years in several regions of Russia.

Description of the results

The practical part of the study is to conduct a regression analysis in which the impact of the number of government contracts, the amount of government contracts (rubles) and the average cost of a contract for revenues and the rate of revenue growth will be assessed. For this, it is necessary to use the program STATA and the method of the OLS to estimate the parameters. For a more accurate analysis, all government contracts executed in the construction sector for 2013, 2014 and 2015 were added to control variables such as total assets as an indicator of company size, the type of construction activity and the city of registration of the company hosting the state order. The first variable was grouped by such activities as: construction of residential and non-residential buildings, construction works (finishing works, installation, demolition of buildings, etc.), architectural works, engineering works and production works (production of material necessary for construction). The second control variable was sorted by such regions as: Moscow, St. Petersburg, the Republic of Tatarstan, Krasnodar Region, Nizhny Novgorod, Rostov-on-Don, Sverdlovsk Region, Volgograd, the Republic of Mordovia, Samara Region and Kaliningrad. All cities have hosted the World Cup 2018 in Russia. After designing regressions, it is necessary to check all the variables for multicollinearity and construct descriptive statistics. To test for multicollinearity will be constructed correlations and VIF-tests.

Government contracts for 2013

At this stage, data are collected for all construction small and medium enterprises in Russia for 2013. Regressions were constructed, as dependent variables - revenue of companies for 2013, 2014, 2015 and the revenue growth rate for 2014/2013 and 2015/2014. As independent variables - the amount of state contracts (RUB) and the number of state contracts.

Table 1

Analysis of the impact of the number of state contracts on revenue and revenue growth rate

VARIABLES	Revenue 2013	Revenue 2014	Revenue 2015	Revenue Growth rate 2014/2013	Revenue Growth rate 2015/2014
Amount of government contracts (RUB)	0.0633***	0.0506***	0.0651***	-0.0888***	-0.111***
	(0.0132)	(0.0134)	(0.0174)	(0.0174)	(0.0160)
Saint Petersburg	-0.0969	-0.0935	-0.125	0.115	0.126
	(0.0684)	(0.0835)	(0.0843)	(0.0832)	(0.0820)
Republic of Tatarstan	-0.251*	-0.182	-0.0700	0.200	0.238
	(0.151)	(0.191)	(0.287)	(0.170)	(0.188)
Krasnodar region	-0.220**	-0.186	-0.00474	0.0426	-0.0121
	(0.0882)	(0.115)	(0.163)	(0.109)	(0.127)
Nizhniy Novgorod	0.0273	-0.108	-0.137	-0.124	0.0823
	(0.146)	(0.226)	(0.215)	(0.153)	(0.279)
Rostov on Don	-0.223**	-0.387***	-0.0880	0.306***	0.237*
	(0.0951)	(0.114)	(0.135)	(0.111)	(0.123)
Sverdlovsk region	-0.633***	-0.399**	-0.234	0.754***	0.425**
	(0.226)	(0.200)	(0.166)	(0.259)	(0.208)
Volgograd	-0.282**	-0.204**	0.00917	0.429***	0.367**
	(0.129)	(0.104)	(0.130)	(0.160)	(0.153)
Republic of Mordovia	-0.637**	-0.307*	0.122	0.735***	0.315*
	(0.257)	(0.170)	(0.216)	(0.278)	(0.176)
Samara Region	-0.212	-0.0911	-0.112	0.133	-0.0544
	(0.129)	(0.124)	(0.177)	(0.127)	(0.113)
Kaliningrad	-0.670	-0.120	-0.466	0.800	0.193
	(0.537)	(0.195)	(0.421)	(0.535)	(0.182)
Construction works	0.0292	-0.0740	-0.0447	0.0395	0.0501
	(0.0571)	(0.0683)	(0.0787)	(0.0674)	(0.0705)
Architectural works	-0.208***	-0.289***	-0.182**	0.275***	0.165**
	(0.0739)	(0.0738)	(0.0854)	(0.0839)	(0.0788)
Engineering works	0.120	0.0844	0.185	-0.172*	-0.150
	(0.0894)	(0.107)	(0.137)	(0.0988)	(0.113)
Production works	0.152	0.169	0.100	-0.134	-0.248
	(0.142)	(0.144)	(0.200)	(0.182)	(0.163)
Total Assets 2013	0.723***
	(0.0176)
Total Assets 2014		0.704***		-0.593***
		(0.0187)		(0.0218)
Total Assets 2015			0.690***		-0.575***
			(0.0258)		(0.0236)
Constant	4.577***	5.061***	4.776***	-3.530***	-3.469***
	(0.271)	(0.274)	(0.382)	(0.314)	(0.337)
Observations	1,149	1,240	1,241	1,075	1,122
R-squared	0.781	0.717	0.662	0.686	0.673
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

a) If the amount of the state contract increases by 1 percent, we expect that revenue in 2013 will increase by 6 percent, considering all other things being equal.

b) If the amount of the state contract increases by 1 percent, we expect that revenue in 2014 will increase by 5 percent, considering all other things being equal.

c) If the amount of the state contract increases by 1 percent, we expect that revenue in 2015 will increase by 7 percent, considering all other things being equal.

d) If the amount of the state contract increases by 1 percent, we expect that revenue growth rate 2014/2013 will decrease by 9 percent, considering all other things being equal.

e) If the amount of the state contract increases by 1 percent, we expect that revenue growth rate 2015/2014 will decrease by 11 percent, considering all other things being equal.

Table 2

Descriptive statistics variables used in the regression

Variable	Obs	Mean	Std. Dev.	Min	Max
Amount of contracts (RUB)	965	14.49536	2.5202	.0009995	23.12901
Revenue 2013	965	1.93e+08	7.36e+08	134000	1.78e+10
Revenue 2014	965	2.06e+08	7.99e+08	145000	2.02e+10
Revenue 2015	965	1.88e+08	8.58e+08	34000	2.34e+10
Revenuу growth rate 2014/2013	965	-14.64319	1.663548	-20.43372	-9.223356
Revenue growth rate 2015/2014	965	-14.71358	1.689574	-20.55732	-9.176053

Table 3

Multicollinearity analysis of regression indicators with independent variable amount of contracts (VIF-test)

	VIF value
Revenue 2013	1.11
Revenue 2014	1.1
Revenue 2015	1
Revenue growth rate 2014/2013	1.11
Revenue growth rate 2015/2014	1

Result: Multicollinearity not detected. None of the values exceed 5.

Table 4

Multicollinearity analysis of regression indicators with independent variable amount of contracts (correlation)

	Amount of contracts (RUB)	Revenue 2013	Revenue 2014	Revenue 2015	Revenue growth rate 2014/2013	Revenue growth rate 2015/2014
Amount of contracts (RUB)	1.0000
Revenue 2013	0.5160	1.0000
Revenue 2014	0.4683	0.8381	1.0000
Revenue 2015	0.4528	0.7714	0.8332	1.0000
Revenue growth rate 2014/2013	-0.5180	0.9993	-0.8179	-0.7631	1.0000
Revenue growth rate 2015/2014	-0.5113	0.8622	-0.9993	-0.8118	0.8458	1.0

A multicollinearity regression analysis was performed. According to the results - all revenues and all revenue growth rates have an average dependence on the amount of state orders.

Table 5

Analysis of the impact of the number of state contracts on revenue and revenue growth rate

VARIABLES	Revenue 2013	Revenue 2014	Revenue 2015	Revenue growth rate 2014/2013	Revenue growth rate 2015/2014
Number of government contracts	0.104***	0.0660**	0.00935	-0.157***	-0.115***
	(0.0299)	(0.0335)	(0.0359)	(0.0354)	(0.0315)
Saint Petersburg	-0.112	-0.106	-0.156*	0.140	0.173**
	(0.0694)	(0.0844)	(0.0849)	(0.0850)	(0.0845)
Republic of Tatarstan	-0.169	-0.121	0.0304	0.0895	0.0799
	(0.151)	(0.196)	(0.292)	(0.179)	(0.206)
Krasnodar region	-0.209**	-0.177	-0.0108	0.0115	-0.0300
	(0.0920)	(0.117)	(0.171)	(0.117)	(0.134)
Nizhniy Novgorod	0.0578	-0.0902	-0.107	-0.160	-0.00695
	(0.143)	(0.229)	(0.227)	(0.142)	(0.296)
Rostov on Don	-0.208**	-0.387***	-0.112	0.294***	0.246*
	(0.0982)	(0.116)	(0.138)	(0.112)	(0.131)
Sverdlovsk region	-0.674***	-0.425**	-0.205	0.823***	0.407*
	(0.221)	(0.199)	(0.165)	(0.247)	(0.214)
Volgograd	-0.309**	-0.246**	-0.0897	0.480***	0.491***
	(0.132)	(0.113)	(0.134)	(0.166)	(0.164)
Republic of Mordovia	-0.624**	-0.312*	0.0787	0.719**	0.339*
	(0.257)	(0.177)	(0.212)	(0.282)	(0.187)
Samara Region	-0.206	-0.0931	-0.136	0.125	-0.0491
	(0.129)	(0.125)	(0.176)	(0.125)	(0.113)
Kaliningrad	-0.641	-0.112	-0.493	0.761	0.191
	(0.526)	(0.204)	(0.420)	(0.518)	(0.200)
Construction works	0.00681	-0.0902	-0.0724	0.0745	0.0934
	(0.0577)	(0.0680)	(0.0784)	(0.0687)	(0.0719)
Architectural works	-0.255***	-0.325***	-0.236***	0.340***	0.242***
	(0.0737)	(0.0737)	(0.0856)	(0.0843)	(0.0804)
Engineering works	0.157*	0.112	0.200	-0.227**	-0.191
	(0.0882)	(0.106)	(0.136)	(0.1000)	(0.119)
Production works	0.0558	0.0837	0.0216	0.00509	-0.140
	(0.130)	(0.139)	(0.197)	(0.160)	(0.159)
Total Assets 2013	0.753***
	(0.0156)
Total Assets 2014		0.728***		-0.635***
		(0.0164)		(0.0188)
Total Assets 2015			0.722***		-0.624***
			(0.0231)		(0.0217)
Constant	4.937***	5.359***	5.197***	-4.036***	-4.208***
	(0.274)	(0.283)	(0.403)	(0.327)	(0.376)
Observations	1,149	1,240	1,241	1,075	1,122
R-squared	0.777	0.714	0.657	0.678	0.656
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

a) If the number of the state contract increases by 1 percent, we expect that revenue in 2013 will increase by 10 percent, considering all other things being equal.

b) If the number of the state contract increases by 1 percent, we expect that revenue in 2014 will increase by 6 percent, considering all other things being equal.

c) If the number of the state contract increases by 1 percent, we expect that revenue growth rate 2014/2013 will decrease by 16 percent, considering all other things being equal.

d) If the number of the state contract increases by 1 percent, we expect that revenue growth rate 2015/2014 will decrease by 12 percent, considering all other things being equal.

Table 6

Descriptive statistics variables used in the regression

Variable	Obs	Mean	Std. Dev.	Min	Max
Number of contracts	965	.6767848	.8489094	0	4.634729
Revenue 2013	965	1.93e+08	7.36e+08	134000	1.78e+10
Revenue 2014	965	2.06e+08	7.99e+08	145000	2.02e+10
Revenue 2015	965	1.88e+08	8.58e+08	34000	2.34e+10
Revenue growth rate 2014/2013	965	-14.64319	1.663548	-20.43372	-9.223356
Revenue growth rate 2015/2014	965	-14.71358	1.689574	-20.55732	-9.176053

Table 8

Multicollinearity analysis of regression indicators with independent variable number of contracts (VIF-test)

	VIF value
Revenue 2013	1.07
Revenue 2014	1.06
Revenue 2015	1.07
Revenue Growth rate 2014/2013	1.07
Revenue Growth rate 2014/2013	1.06

Result: Multicollinearity not detected. None of the values exceed 5



Table 7

Multicollinearity analysis of regression indicators with independent variable number of contracts (correlation)

	Number of contracts	Revenue 2013	Revenue 2014	Revenue 2015	Revenue growth rate 2014/2013	Revenue growth rate 2015/2014
Number of contracts	1.0000
Revenue 2013	0.2465	1.0000
Revenue 2014	0.1957	0.8381	1.0000
Revenue 2015	0.1556	0.7714	0.8332	1.0000
Revenue growth rate 2014/2013	-0.2439	-0.9993	-0.8179	-0.7631	1.0000
Revenue growth rate 2015/2014	-0.2118	-0.8622	-0.9993	-0.8118	0.8458	1.0000

A multicollinearity regression analysis was performed. According to the results - all revenues and all revenue growth rates have a weak dependence on the number of state orders.

Government contracts for 2014

At this stage, data are collected for all construction small and medium enterprises in Russia for 2014. Regressions were constructed, as dependent variables - revenue of companies for 2013, 2014, 2015 and the revenue growth rate for 2014/2013 and 2015/2014. As independent variables - the amount of state contracts (RUB) and the number of state contracts.



Table 9

Analysis of the impact of the amount of state contracts on revenue and revenue growth rate

VARIABLES	Revenue 2013	Revenue 2014	Revenue 2015	Revenue growth rate 2014/2013	Revenue growth rate 2015/2014
Amount of government contracts	0.0480**	0.120***	0.0990***	-0.0439*	-0.127***
	(0.0207)	(0.0182)	(0.0295)	(0.0249)	(0.0159)
Saint Petersburg	-0.161	0.0940	0.0332	-0.0973	-0.0363
	(0.182)	(0.137)	(0.414)	(0.245)	(0.118)
Republic of Tatarstan	0.317	0.212	0.358	-0.750***	-0.143
	(0.234)	(0.198)	(0.435)	(0.279)	(0.163)
Krasnodar region	-0.239	0.187	0.763*	-0.205	-0.197
	(0.316)	(0.361)	(0.424)	(0.329)	(0.288)
Nizhniy Novgorod	-0.344	-0.0639	0.101	0.138	0.0126
	(0.228)	(0.157)	(0.423)	(0.289)	(0.135)
Rostov on Don	-0.277	-0.236	0.125	0.149	0.247
	(0.227)	(0.173)	(0.434)	(0.286)	(0.168)
Sverdlovsk region	-0.0167	0.0451	0.151	-0.188	-0.0828
	(0.198)	(0.144)	(0.420)	(0.264)	(0.131)
Volgograd	0.0704	-0.142	0.154	-0.124	0.0846
	(0.221)	(0.167)	(0.442)	(0.281)	(0.156)
Republic of Mordovia	-0.368	0.0638	0.125	0.257	-0.0595
	(0.269)	(0.164)	(0.444)	(0.347)	(0.152)
Samara Region	-0.215	-0.139	-0.0165	-0.212	0.170
	(0.195)	(0.150)	(0.420)	(0.262)	(0.140)
Kaliningrad	-0.337	-0.302	-0.0399	0.360	0.263
	(0.222)	(0.235)	(0.483)	(0.307)	(0.218)
Construction works	-0.0306	-0.0528	0.0792	0.0805	0.0980
	(0.0950)	(0.0766)	(0.0923)	(0.119)	(0.0790)
Architectural works	-0.288**	0.00403	0.0440	0.290*	0.0785
	(0.125)	(0.0968)	(0.118)	(0.161)	(0.102)
Engineering works	0.214*	0.203**	0.230*	-0.391***	-0.273***
	(0.113)	(0.0990)	(0.123)	(0.130)	(0.104)
Production works	0.323**	0.215**	0.197	-0.383**	-0.148
	(0.149)	(0.108)	(0.202)	(0.159)	(0.134)
Total Assets 2013	0.759***
	(0.0335)
Total Assets 2014		0.649***		-0.676***
		(0.0267)		(0.0365)
Total Assets 2015			0.696***		-0.606***
			(0.0292)		(0.0226)
Constant	4.075***	4.700***	3.871***	-2.202***	-2.448***
	(0.442)	(0.375)	(0.629)	(0.535)	(0.353)
Observations	602	726	758	574	682
R-squared	0.778	0.774	0.690	0.647	0.760
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

a) If the amount of the state contract increases by 1 percent, we expect that revenue in 2013 will increase by 4 percent, considering all other things being equal.

b) If the amount of the state contract increases by 1 percent, we expect that revenue in 2014 will increase by 12 percent, considering all other things being equal.

c) If the amount of the state contract increases by 1 percent, we expect that revenue in 2013 will increase by 10 percent, considering all other things being equal.

d) If the amount of the state contract increases by 1 percent, we expect that revenue growth rate 2014/2013 will decrease by 4 percent, considering all other things being equal.

e) If the amount of the state contract increases by 1 percent, we expect that revenue growth rate 2015/2014 will decrease by 13 percent, considering all other things being equal.

Table 10

Descriptive statistics variables used in the regression

Variable	Obs	Mean	Std. Dev.	Min	Max
Amount of contracts (RUB)	543	14.6262	3.109957	-6.907755	22.20569
Revenue 2013	543	1.43e+08	3.85e+08	14000	4.51e+09
Revenue 2014	543	1.49e+08	3.54e+08	154000	3.45e+09
Revenue 2015	543	1.34e+08	2.89e+08	44000	2.32e+09
Revenue growth rate 2014/2013	543	-14.32998	1.805016	-19.14129	-7.066525
Revenue growth rate 2015/2014	543	-14.62406	1.554606	-18.90248	-9.099525

Table 12

Multicollinearity analysis of regression indicators with independent variable amount of contracts (VIF-test)

	VIF value
Revenue 2013	3.15
Revenue 2014	2.99
Revenue 2015	3.33
Revenue growth rate 2014/2013	3.25
Revenue growth rate 2015/2014	3.26

Result: Multicollinearity not detected. None of the values exceed 5

Table 11

Multicollinearity analysis of regression indicators with independent variable amount of contracts (correlation)

	Amount of contracts (RUB)	Revenue 2013	Revenue 2014	Revenue 2015	Revenue growth rate 2014/2013	Revenue growth rate 2015/2014
Amount of contracts (RUB)	1.0000
Revenue 2013	0.3404	1.0000
Revenue 2014	0.4390	0.8535	1.0000
Revenue 2015	0.3785	0.7294	0.8397	1.0000
Revenue growth rate 2014/2013	-0.3523	-0.9996	-0.8387	-0.7282	1.0000
Revenue growth rate 2015/2013	-0.4403	-0.8572	-0.9992	-0.8174	0.8431	1.0000

A multicollinearity regression analysis was performed. According to the results - all revenues and all revenue growth rates have a weak dependence on the amount of state orders.

Table 13

Analysis of the impact of the number of state contracts on revenue and revenue

VARIABLES	Revenue 2013	Revenue 2014	Revenue 2015	Growth rate 2014/2013	Growth rate 2015/2014
Number of government contracts	0.0196	0.0344**	0.0384**	-0.0160	-0.0355***
	(0.0134)	(0.0136)	(0.0155)	(0.0162)	(0.0128)
Saint Petersburg	-0.202	0.0496	-0.00813	-0.0712	-0.0323
	(0.189)	(0.131)	(0.415)	(0.245)	(0.125)
Republic of Tatarstan	0.271	0.197	0.344	-0.718**	-0.169
	(0.239)	(0.194)	(0.437)	(0.279)	(0.174)
Krasnodar region	-0.273	0.175	0.766	-0.184	-0.237
	(0.296)	(0.398)	(0.482)	(0.302)	(0.445)
Nizhniy Novgorod	-0.431*	-0.191	0.000428	0.208	0.0975
	(0.234)	(0.155)	(0.424)	(0.289)	(0.143)
Rostov on Don	-0.298	-0.154	0.173	0.168	0.114
	(0.237)	(0.189)	(0.438)	(0.286)	(0.187)
Sverdlovsk region	-0.0614	0.0185	0.130	-0.159	-0.113
	(0.202)	(0.140)	(0.421)	(0.263)	(0.138)
Volgograd	-0.0243	-0.271	0.0479	-0.0473	0.170
	(0.229)	(0.165)	(0.442)	(0.283)	(0.157)
Republic of Mordovia	-0.447*	-0.0816	-0.00464	0.337	0.0417
	(0.263)	(0.166)	(0.451)	(0.340)	(0.165)
Samara Region	-0.231	-0.0864	0.0282	-0.214	0.0667
	(0.201)	(0.146)	(0.420)	(0.262)	(0.148)
Kaliningrad	-0.367	-0.336	-0.0520	0.385	0.252
	(0.225)	(0.232)	(0.480)	(0.303)	(0.221)
Construction works	-0.0700	-0.153*	-0.000876	0.117	0.211**
	(0.0939)	(0.0793)	(0.0966)	(0.117)	(0.0818)
Architectural works	-0.332***	-0.135	-0.0666	0.322**	0.232**
	(0.120)	(0.101)	(0.116)	(0.157)	(0.104)
Engineering works	0.212*	0.185*	0.225*	-0.390***	-0.265**
	(0.113)	(0.103)	(0.125)	(0.129)	(0.110)
Production works	0.267*	0.120	0.124	-0.333**	-0.0521
	(0.144)	(0.101)	(0.203)	(0.152)	(0.122)
Total Assets 2013	0.783***
	(0.0271)
Total Assets 2014		0.715***		-0.701***
		(0.0227)		(0.0289)
Total Assets 2015			0.750***		-0.677***
			(0.0220)		(0.0200)
Constant	4.438***	5.424***	4.460***	-2.468***	-3.141***
	(0.504)	(0.429)	(0.630)	(0.574)	(0.382)
Observations	607	730	764	578	686
R-squared	0.777	0.757	0.681	0.647	0.736
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

a) If the number of the state contract increases by 1 percent, we expect that revenue in 2014 will increase by 3 percent, considering all other things being equal.

b) If the number of the state contract increases by 1 percent, we expect that revenue in 2015 will increase by 4 percent, considering all other things being equal.

c) If the number of the state contract increases by 1 percent, we expect that revenue growth rate 2015/2014 will decrease by 4 percent, considering all other things being equal.



Table 14

Descriptive statistics variables used in the regression

Variable	Obs	Mean	Std. Dev.	Min	Max
Number of contracts	543	.8415014	2.364124	-6.907755	14.46263
Revenue 2013	543	1.43e+08	3.85e+08	14000	4.51e+09
Revenue 2014	543	1.49e+08	3.54e+08	154000	3.45e+09
Revenue 2015	543	1.34e+08	2.89e+08	44000	2.32e+09
Revenue growth rate 2014/2013	543	-14.32998	1.805016	-19.14129	-7.066525
Revenue growth rate 2015/2014	543	-14.62406	1.554606	-18.90248	-9.099525

Table 16

Multicollinearity analysis of regression indicators with independent variable number of contracts (VIF-test)

	VIF value
Revenue 2013	3.23
Revenue 2014	2.96
Revenue 2015	3.24
Revenue growth rate 2014/2013	3.22
Revenue growth rate 2015/2014	3.23

Result: Multicollinearity not detected. None of the values exceed 10

Table 15

Multicollinearity analysis of regression indicators with independent variable number of contracts (correlation)

	Number of contracts	Receipts 2013	Receipts 2014	Receipts 2015	Growth rate 2014/2013	Growth rate 2015/2014
Number of contracts	1.0000
Receipts 2013	0.0648	1.0000
Receipts 2014	0.0837	0.8535	1.0000
Receipts 2015	0.0745	0.7294	0.8397	1.0000

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