This page summarizes Enterprise Surveys data for the Russian Federation. The graphs below provide an overview of the sample and highlight the biggest obstacles experienced by private sector firms in the Russian Federation. The 12 tables below the graphs summarize key factual indicators at the country and regional levels for each of the business environment topics. A few subjective indicators are also available.

 

NUMBER OF FIRMS SURVEYED

4,220

Business owners and top managers in 4,220 firms were interviewed from August 2011 through June 2012.

Characteristics of Firms Surveyed

Other Manufacturing: 260 Construction: 455 Wholesale: 1,268 Machinery & Equipment: 133 Fabricated Metal Products: 147 Non-Metallic Mineral Products: 107 Food: 130 Electronics & Precision Instruments: 193 Chemicals & Plastics & Rubber: 244 Supporting Transport Activities: 90 IT: 141 Wood & Furniture: 171 Other Services: 287 Hotels & Restaurants: 140 Retail: 454 Other Manufacturing: 260 Construction: 455 Wholesale: 1,268 Machinery & Equipment: 133 Fabricated Metal Products: 147 Non-Metallic Mineral Products: 107 Food: 130 Electronics & Precision Instruments: 193 Chemicals & Plastics & Rubber: 244 Food: 130 Food: 130 Non-Metallic Mineral Products: 107 Non-Metallic Mineral Products: 107 Fabricated Metal Products: 147 Fabricated Metal Products: 147 Machinery & Equipment: 133 Machinery & Equipment: 133 Other Manufacturing: 260 Other Manufacturing: 260 Construction: 455 Construction: 455 Wholesale: 1,268 Wholesale: 1,268 Retail: 454 Retail: 454 Hotels & Restaurants: 140 Hotels & Restaurants: 140 Other Services: 287 Other Services: 287 Wood & Furniture: 171 Wood & Furniture: 171 IT: 141 IT: 141 Supporting Transport Activities: 90 Supporting Transport Activities: 90 Chemicals & Plastics & Rubber: 244 Chemicals & Plastics & Rubber: 244 Electronics & Precision Instruments: 193 Electronics & Precision Instruments: 193 Small (5-19): 2,228 Large (100+): 505 Medium (20-99): 1,487 Small (5-19): 2,228 Large (100+): 505 Small (5-19): 2,228 Small (5-19): 2,228 Medium (20-99): 1,487 Medium (20-99): 1,487 Large (100+): 505 Large (100+): 505 Tomsk Region: 122 Saint Petersburg: 122 Kaliningrad Region: 122 Khabarovsk Territory: 122 Leningrad Region: 121 Kaluga Region: 121 Voronezh Region: 121 Sverdlovsk Region: 120 Belgorod Region: 120 Yaroslavl Region: 120 Ulyanovsk Region: 120 Volgograd Region: 120 Lipetsk Region: 120 Stavropol Territory: 120 Rostov Region: 120 Tver Region: 120 Samara Region: 120 Omsk Region: 120 Moscow City: 123 Novosibirsk Region: 123 Kemerovo Region: 124 Irkutsk Region: 131 Kirov Region: 134 Smolensk Region: 71 Chelyabinsk Region: 79 Nizhni Novgorod Region: 82 Kursk Region: 87 Krasnodar Territory: 88 Krasnoyarsk Territory: 89 Republic Of Sakha (Yakutia): 92 Republic Of Bashkortostan: 106 Republic Of Mordovia: 120 Murmansk Region: 120 Moscow Region: 120 Republic Of Tatarstan: 120 Primorsky Territory: 120 Perm Territory: 120 Tomsk Region: 122 Saint Petersburg: 122 Kaliningrad Region: 122 Khabarovsk Territory: 122 Leningrad Region: 121 Kaluga Region: 121 Voronezh Region: 121 Sverdlovsk Region: 120 Belgorod Region: 120 Yaroslavl Region: 120 Moscow City: 123 Novosibirsk Region: 123 Kemerovo Region: 124 Irkutsk Region: 131 Kirov Region: 134 Smolensk Region: 71 Chelyabinsk Region: 79 Nizhni Novgorod Region: 82 Kursk Region: 87 Krasnodar Territory: 88 Kirov Region: 134 Kirov Region: 134 Irkutsk Region: 131 Irkutsk Region: 131 Kemerovo Region: 124 Kemerovo Region: 124 Novosibirsk Region: 123 Novosibirsk Region: 123 Moscow City: 123 Moscow City: 123 Tomsk Region: 122 Tomsk Region: 122 Saint Petersburg: 122 Saint Petersburg: 122 Kaliningrad Region: 122 Kaliningrad Region: 122 Khabarovsk Territory: 122 Khabarovsk Territory: 122 Leningrad Region: 121 Leningrad Region: 121 Kaluga Region: 121 Kaluga Region: 121 Voronezh Region: 121 Voronezh Region: 121 Sverdlovsk Region: 120 Sverdlovsk Region: 120 Belgorod Region: 120 Belgorod Region: 120 Yaroslavl Region: 120 Yaroslavl Region: 120 Ulyanovsk Region: 120 Ulyanovsk Region: 120 Volgograd Region: 120 Volgograd Region: 120 Lipetsk Region: 120 Lipetsk Region: 120 Stavropol Territory: 120 Stavropol Territory: 120 Rostov Region: 120 Rostov Region: 120 Tver Region: 120 Tver Region: 120 Samara Region: 120 Samara Region: 120 Omsk Region: 120 Omsk Region: 120 Perm Territory: 120 Perm Territory: 120 Primorsky Territory: 120 Primorsky Territory: 120 Republic Of Tatarstan: 120 Republic Of Tatarstan: 120 Moscow Region: 120 Moscow Region: 120 Murmansk Region: 120 Murmansk Region: 120 Republic Of Mordovia: 120 Republic Of Mordovia: 120 Republic Of Bashkortostan: 106 Republic Of Bashkortostan: 106 Republic Of Sakha (Yakutia): 92 Republic Of Sakha (Yakutia): 92 Krasnoyarsk Territory: 89 Krasnoyarsk Territory: 89 Krasnodar Territory: 88 Krasnodar Territory: 88 Kursk Region: 87 Kursk Region: 87 Nizhni Novgorod Region: 82 Nizhni Novgorod Region: 82 Chelyabinsk Region: 79 Chelyabinsk Region: 79 Smolensk Region: 71 Smolensk Region: 71

NUMBER OF FIRMS SURVEYED

4,220

After being presented with a list of 15 business environment obstacles, business owners and top managers in 4,220 firms were asked to choose the biggest obstacle to their business.

Top 10 Business Environment Constraints

Customs and trade regulations: 2.1 Access to land: 3.6 Transportation: 3.7 Business licenses and permits: 4.5 Inadequately educated workforce: 6.4 Practices of the informal sector: 7.0 Political instability: 7.8 Corruption: 8.2 Access to finance: 14.8 Tax rates: 36.1

ECONOMY OVERVIEW

Country Highlights summarize the key findings from the Enterprise Survey

211.1KB pdf file

Country Profiles provide key investment climate indicators for a country with benchmarks against their respective regional and income groups

the Russian Federation Country Profile

the Russian Federation Country Profile

455.6KB pdf file

REGION:
High income: nonOECD
INCOME CATEGORY:
High income
POPULATION:
143,533,000
GNI PER CAPITA (US $):
12,700

Resources

Custom Data Set

Custom Data Set

Generate a Custom Data Set for the Russian Federation including standard errors, indicator values by firm subgroups, historical data and comparable countries.

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Indicator Russian Federation High income: nonOECD All Countries 2  
Bribery incidence (percent of firms experiencing at least one bribe payment request) Percent of firms experiencing at least one bribe payment request during 6 transactions dealing with utilities access, permits, licences, and taxes.

14.2 7.6 17.0  
Bribery depth (% of public transactions where a gift or informal payment was requested) Bribery depth is the percentage of transactions (out of 6 transactions dealing with utilities access, permits, licences, and taxes) where a gift or informal payment was requested.

9.7 5.6 13.0  
Percent of firms expected to give gifts in meetings with tax officials Percent of firms expected to give gifts or an informal payment in meetings with tax officials.

7.3 4.5 12.3  
Percent of firms expected to give gifts to secure government contract Percent of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to secure government contract.

30.9 10.7 26.4  
Value of gift expected to secure a government contract (% of contract value) Percentage of the contract value expected as a gift to secure a government contract. Only firms that have confirmed that they have secured or attempted to secure a government contract in the last 12 months were required to answer this question.

2.5 0.5 1.8  
Percent of firms expected to give gifts to get an operating license Percent of firms expected to give gifts or an informal payment to get an operating license.

12.6 9.6 13.9  
Percent of firms expected to give gifts to get an import license Percent of firms expected to give gifts or an informal payment to get an import license.

27.5 6.4 12.6  
Percent of firms expected to give gifts to get a construction permit Percent of firms expected to give gifts or an informal payment to get a construction permit.

26.8 12.8 21.7  
Percent of firms expected to give gifts to get an electrical connection Percent of firms expected to give gifts or an informal payment to get an electrical connection.

25.8 7.7 15.7  
Percent of firms expected to give gifts to get a water connection Percent of firms expected to give gifts or an informal payment to get a water connection.

12.7 6.8 15.3  
Percent of firms expected to give gifts to public officials "to get things done" Percent of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to "get things done” with regard to customs, taxes, licenses, regulations, services, etc.

20.5 11.6 19.6  
Percent of firms identifying corruption as a major constraint Percent of firms identifying corruption as a major constraint. The computation of the indicator is based on the rating of the obstacle as a potential constraint to the current operations of the establishment.

33.1 22.6 34.0  
Percent of firms identifying the courts system as a major constraint Percent of firms identifying the courts system as a major constraint. The computation of the indicator is based on the rating of the obstacle as a potential constraint to the current operations of the establishment.

7.4 9.3 15.4  
  • Notes
    * This indicator is computed using data from manufacturing firms only.

    Additional Notes

    1. Most surveys were administered using the Enterprise Surveys Global Methodology as outlined in the Methodology page, while some others did not strictly adhere to the Enterprise Surveys Global Methodology. For example, for surveys which do not follow the Global Methodology, the Universe under consideration may have consisted of only manufacturing firms or the questionnaire used may have been different from the standard global questionnaire. Data users should exercise caution when comparing raw data and point estimates between surveys that did and did not adhere to the Enterprise Surveys Global Methodology. For surveys which did not adhere to the Global Methodology plus Afghanistan 2008, any inference from one of these surveys is representative only for the data sample itself.
    2. Regional and "all countries" averages of indicators are computed by taking a simple average of country-level point estimates. For each economy, only the latest available year of survey data is used in this computation. Only surveys, posted during the years 2009-2014, and adhering to the Enterprise Surveys Global Methodology are used to compute these regional and "all countries" averages.
    3. Descriptions of firm subgroup levels, e.g. how the ex post groupings are constructed, are provided in the Indicator Descriptions (PDF, 710KB) document.
    4. Statistics derived from less than or equal to five firms are displayed with an "n.a." to maintain confidentiality and should be distinguished from ".." which indicates missing values. Also note for three growth-related indicators under the "Performance" topic, these indicators are not computed when they are derived from less than 30 firms.
    5. Standard errors are labeled "n.c.", meaning not computed, for the following:
           1) indicators for all surveys that were not conducted using the Enterprise Surveys Global Methodology and
           2) for indicator breakdowns by ex post groupings: exporter or ownership type, and gender of the top manager.
    6. Please cite our data as follows:
      Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank.