Russia's capital, Moscow, was the largest city in the country with over 13.1 million residents as of January 1, 2024. Less than a half of Moscow's population resided in Saint Petersburg, the second-most populous city in the country. The third-largest city, Novosibirsk, was located in the Siberian Federal District, being the highest-populated city in the Asian part of Russia. Why is Moscow so populated? The Russian capital is the center of political, industrial, business, and cultural life in Russia. Despite being one of the most expensive cities worldwide, it continues to attract people from Russia and abroad, with its resident population following a generally upward trend over the past decade. Wages in Moscow are higher than in Russia on average, and more opportunities for employment and investment are available in the capital. Furthermore, the number of people living in Moscow was forecast to continue rising, exceeding 12.8 million by 2035. Urbanization in Russia In 2024, around 109 million Russian residents lived in cities. That was approximately three-quarters of the country’s population. The urbanization rate increased steadily over the 20th century, leading to a decline in the rural population. Among the country’s regions, the Northwestern Federal District had the highest share of residents in urban areas, measured at 85 percent. In the Central Federal District, the tendency was that more people moved to Moscow and cities in the Moscow Oblast.

Among Russian cities with more than one million inhabitants, the country's capital Moscow received the highest urban environmental quality index score of 304 out of 360 points in 2023, based on six criteria and six types of area. The second-leading city in this category was Saint Petersburg, Russia's second-largest city, while Nizhny Novgorod, ranked third.

On Sunday, September 12, 2021, the highest self-isolation index among Russian cities with over one million inhabitants was measured in Omsk at 2.8 points, indicating that there was a high number of people on the streets. In the capital Moscow, where most COVID-19 cases in Russia were recorded, the index reached two points. The non-working period in Russia ended on May 12, 2020.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This database provides a construction of Large Urban Regions (LUR) in Russia. A Large Urban Region (LUR) can be defined as an aggregation of continuous statistical units around a core that are economically dependent on this core and linked to it by economic and social strong interdependences. The main purpose of this delineation is to make cities comparable on the national and world scales and to make comparative social-economic urban studies. Aggregating different municipal districts around a core city, we construct a single large urban region, which allows to include all the area of economic influence of a core into one statistical unit (see Rogov & Rozenblat, 2019 for more details). In doing so we use four principal urban concepts (Pumain et al., 1992): political definition, morphological definition, functional definition and conurbation that we call Large Urban Region. We implemented LURs using criteria such as population distribution, road networks, access to an airport, distance from a core, presence of multinational firms. In this database we provide population data for LURs and their administrative units.

With a score of 23.62, Moscow was the leading city for startups in Russia in 2024. Saint Petersburg followed, having earned a score of 1.66 in the period observed. Furthermore, the Russia's capital ranked the major city for startups in Central and Eastern Europe (CEE). The score was based on several indicators, such as the number of startups in each city, the startups' qualitative results, and the cities' business and economic indicators.

As of January 1, 2024, 109.5 million inhabitants lived in Russian cities, opposed to 36.6 million people living in the countryside. The rural population of Russia saw a gradual decrease over the observed time period.

At the end of 2023, industrial vacancy rates in Moscow and St. Petersburg amounted to 0.4 percent, recovering from the previous two years. Among cities in Europe, Munich has the lowest warehouse vacancy rate.

Moscow had the highest public transportation quality index among Russian cities, at around 82.3 points in the third quarter of 2023. It was followed by Saint Petersburg and Perm with approximately 75.4 and 71.5 index points, respectively. The average trip price, comfort, convenience, and public transport network's efficiency were taken into account while comprising indexes for each city.

Russia Number of Employees: Foreign Capital Enterprises: BM: CF: City of Moscow data was reported at 545.700 Person th in Dec 2016. This records an increase from the previous number of 542.000 Person th for Sep 2016. Russia Number of Employees: Foreign Capital Enterprises: BM: CF: City of Moscow data is updated quarterly, averaging 514.800 Person th from Jun 2000 (Median) to Dec 2016, with 67 observations. The data reached an all-time high of 681.200 Person th in Dec 2012 and a record low of 146.000 Person th in Jun 2000. Russia Number of Employees: Foreign Capital Enterprises: BM: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OE002: Number of Employees in Foreign Capital Enterprises: Big and Medium: by Region (Discontinued).

Russia Number of Foreign Capital Enterprises: BM: CF: City of Moscow data was reported at 4,047.000 Unit in Dec 2016. This records an increase from the previous number of 3,882.000 Unit for Sep 2016. Russia Number of Foreign Capital Enterprises: BM: CF: City of Moscow data is updated quarterly, averaging 3,473.000 Unit from Jun 2000 (Median) to Dec 2016, with 67 observations. The data reached an all-time high of 4,751.000 Unit in Dec 2012 and a record low of 2,046.000 Unit in Mar 2014. Russia Number of Foreign Capital Enterprises: BM: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OE001: Number of Foreign Capital Enterprises: Big and Medium: by Region (Discontinued).

The Russian Luxury Residential Real Estate Market Report is segmented by Type (Apartments and Condominiums and Villas and Landed Houses) and City (Moscow, St. Petersburg, Novosibirsk, and Other Cities). The market size and forecasts for the Russian luxury homes market are provided in terms of value (USD) for all the above segments.

Russia Number of Employees: Foreign Capital Enterprises: BM: NW: City of St Petersburg data was reported at 206.900 Person th in Dec 2016. This records an increase from the previous number of 205.700 Person th for Sep 2016. Russia Number of Employees: Foreign Capital Enterprises: BM: NW: City of St Petersburg data is updated quarterly, averaging 194.500 Person th from Jun 2000 (Median) to Dec 2016, with 67 observations. The data reached an all-time high of 229.200 Person th in Sep 2012 and a record low of 90.900 Person th in Mar 2001. Russia Number of Employees: Foreign Capital Enterprises: BM: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OE002: Number of Employees in Foreign Capital Enterprises: Big and Medium: by Region (Discontinued).

Moscow had nearly 17 CCTV cameras per 1,000 inhabitants in 2023. In total, 214,000 such devices were recorded in the Russian capital. The second-largest city of the country, Saint Petersburg, recorded a density of approximately 13.5 surveillance cameras per 1,000 population.

Large Towns of the Russian Empire, as depicted on the Geographical Atlas of the Russian Empire produced by the Military-Topographical Depot of His Imperial Majesty's General Staff, 1820-1827. Component of the Imperiia Project. Documentation and analysis available here (http://dighist.fas.harvard.edu/projects/imperiia/items/show/649)

Abstract

The Russia Longitudinal Monitoring Survey (RLMS) is a household-based survey designed to measure the effects of Russian reforms on the economic well-being of households and individuals. In particular, determining the impact of reforms on household consumption and individual health is essential, as most of the subsidies provided to protect food production and health care have been or will be reduced, eliminated, or at least dramatically changed. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data. Data have been collected since 1992.

Geographic coverage

National

Analysis unit

Households and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Phase II (Rounds V- XX) of the RLMS, a multi-stage probability sample was employed. Please refer to the March 1997 review of the Phase II sample. First, a list of 2,029 consolidated regions was created to serve as PSUs. These were allocated into 38 strata based largely on geographical factors and level of urbanization but also based on ethnicity where there was salient variability. As in many national surveys involving face-to-face interviews, some remote areas were eliminated to contain costs; also, Chechnya was eliminated because of armed conflict. From among the remaining 1,850 regions (containing 95.6 percent of the population), three very large population units were selected with certainty: Moscow city, Moscow Oblast, and St. Petersburg city constituted self-representing (SR) strata. The remaining non-self-representing regions (NSR) were allocated to 35 equal-sized strata. One region was then selected from each NSR stratum using the method "probability proportional to size" (PPS). That is, the probability that a region in a given NSR stratum was selected was directly proportional to its measure of population size.

The NSR strata were designed to have approximately equal sizes to improve the efficiency of estimates. The target population (omitting the deliberate exclusions described above) totaled over 140 million inhabitants. Ideally, one would use the population of eligible households, not the population of individuals. As is often the case, we were obliged to use figures on the population of individuals as a surrogate because of the unavailability of household figures in various regions.

Although the target sample size was set at 4,000, the number of households drawn into the sample was inflated to 4,718 to allow for a nonresponse rate of approximately 15 percent. The number of households drawn from each of the NSR strata was approximately equal (averaging 108), since the strata were of approximately equal size and PPS was employed to draw the PSUs in each one. However, because response rates were expected to be higher in urban areas than in rural areas, the extent of over-sampling varied. This variation accounted for the differences in households drawn across the NSR PSUs. It also accounted for the fact that 940 households were drawn in the three SR strata--more than the 14.6 percent (i.e. 689) that would have been allotted based on strict proportionality.

Since there was no consolidated list of households or dwellings in any of the 38 selected PSUs, an intermediate stage of selection was then introduced, as usual. Professional samplers will recognize that this is actually the first stage of selection in the three SR strata, since those units were selected with certainty. That is, technically, in Moscow, St. Petersburg, and Moscow oblast, the census enumeration districts were the PSUs. However, it was cumbersome to keep making this distinction throughout the description, and researchers followed the normal practice of using the terms "PSU" and "SSU" loosely. Needless to say, in the calculation of design effects, where the distinction is critical, the proper distinction was maintained. The selection of second-stage units (SSUs) differed depending on whether the population was urban (located in cities and "villages of the city type," known as "PGTs") or rural (located in villages). That is, within each selected PSU the population was stratified into urban and rural substrata, and the target sample size was allocated proportionately to the two substrata. For example, if 40 percent of the population in a given region was rural, 40 of the 100 households allotted to the stratum were drawn from villages.

In rural areas of the selected PSUs, a list of all villages was compiled to serve as SSUs. The list was ordered by size and (where salient) by ethnic composition. PPS was employed to select one village for each 10 households allocated to the rural substratum. Again, under the standard principles of PPS, once the required number of villages was selected, an equal number of households in the sample (10) were allocated to each village. Since villages maintain very reliable lists of households, in each selected village the 10 households were selected systematically from the household list. In a few cases, villages were judged to be too small to sustain independent interviews with 10 households; in such cases, three or four tiny villages were treated as a single SSU for sampling purposes.

In urban areas, SSUs were defined by the boundaries of 1989 census enumeration districts, if possible. If the necessary information was not available, 1994 microcensus enumeration districts, voting districts, or residential postal zones were employed--in decreasing order of preference. Since census enumeration districts were originally designed to be roughly equal in population size, one district was selected systematically without using PPS for each 10 households required in the sample. In the few cases where postal zones were used, one zone was likewise selected systematically for each 10 households. However, where voting districts were used, to compensate for the marked variation in population size, PPS was employed to select one voting district for each 10 households required in the urban sub-stratum.

In both urban and rural substrata, interviewers were required to visit each selected dwelling up to three times to secure the interviews. They were not allowed to make substitutions of any sort. The interviewers' first task was to identify households at the designated dwellings. "Household" was defined as a group of people who live together in a given domicile, and who share common income and expenditures. Households were also defined to include unmarried children, 18 years of age or younger, who were temporarily residing outside the domicile at the time of the survey. If perchance the interviewer identified more than one household in the dwelling, he or she was obliged to select one using a procedure outlined in the technical report. The interviewer then administered a household questionnaire to the most knowledgeable and willing member of the household.

The interviewer then conducted interviews with as many adults as possible, acquiring data about their individual activities and health. Data for the children's questionnaires were obtained from adults in the household. By virtue of the fact that an attempt was made to obtain individual questionnaires for all members of households, the sample constitutes a proper probability sample of individuals as well as of households, without any special weighting. Actually, the fact that we did not interview unmarried minors living temporarily outside the domicile slightly diminished the representativeness of the sample of individuals in that age group.

The multivariate distribution of the sample by sex, age, and urban-rural location compared quite well with the corresponding multivariate distribution of the 1989 census. Of course, because of random sampling error and changes in the distribution since the 1989 census, we did not expect perfect correspondence. Nevertheless, there was usually a difference of only one percentage point or less between the two distributions.

Another way to evaluate the adequacy (or efficiency) of the sample was to examine design effects. An important factor in determining the precision of estimates in multi-stage samples was the mean ultimate cluster (PSU) size. All else being equal, the larger the size the less precise the measure is. In Rounds I through IV of the RLMS, the average cluster size approached 360--a large number dictated by constraints imposed by our collaborators. Thus, although the sample size covered around 6,000 households, precision was less than we would have liked for a sample of that size. In Rounds I and III of the RLMS, the 95 percent confidence interval for household income was about ?±13 percent.

In the Phase II (Rounds V - XX) sample, the situation was considerably better. Although there were only 4,000 households, the mean size of clusters was much smaller than in Phase I. There were 35 PSUs with about 100 households each; even this result was an improvement over the average of 360 in the design of the RLMS Rounds I through IV. However, in the three self-representing areas, the respondents were drawn from 61 PSUs. Recall that Moscow city and oblast, as well as St. Petersburg city, were not sampled but were chosen with certainty. Therefore, the first stage of selection in them was the selection of census enumeration districts. Thus the mean cluster size in the entire sample was about 42, i.e., 4,000/(35+61). Given these much smaller cluster sizes, researchers had reason to expect that precision in this survey would be as good as it was in Rounds I through IV despite the smaller sample size, and this expectation, in fact, turned out

As of January 1, 2023, over 13.1 million persons resided in Moscow, the largest city in Russia and Europe. The population of the Russian capital increased slightly from the previous year. The number of Moscow residents crossed the 13-million mark in 2021. Starting from 2012, the city’s population grew by roughly 1.5 million. Moscow is one of the world’s megacities with the largest land area, which exceeds 6,600 square kilometers. Cost of living in Moscow While prices in Moscow are higher than in most other cities of Russia, they are lower than in many other megacities around the world, such as Singapore, New York, and Paris. In 2023, Moscow recorded the largest drop in the rank in the list of the most expensive cities worldwide, at 105 positions. Moscow residents earned an average net salary of 128,300 Russian rubles per month in 2022. Immigration to Moscow Due to the presence of various companies, job opportunities, higher salaries than in most other regions of the country, acclaimed universities, and highly developed infrastructure, Moscow is an attractive destination for both internal and international immigrants. In 2022, more than 940,000 Russian residents migrated to the Central Federal District of the country, where Moscow is located. From the international immigrants, the largest share comes from Central Asian countries.

Russia Trade Turnover: Foreign Capital Enterprises: BM: Year to Date: CF: City of Moscow data was reported at 8,783.400 RUB bn in Dec 2016. This records an increase from the previous number of 6,199.000 RUB bn for Sep 2016. Russia Trade Turnover: Foreign Capital Enterprises: BM: Year to Date: CF: City of Moscow data is updated quarterly, averaging 3,919.231 RUB bn from Mar 2005 (Median) to Dec 2016, with 48 observations. The data reached an all-time high of 10,293.700 RUB bn in Dec 2011 and a record low of 863.613 RUB bn in Mar 2005. Russia Trade Turnover: Foreign Capital Enterprises: BM: Year to Date: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OE003: Foreign Capital Enterprises Trade Turnover: Big and Medium: by Region: ytd (Discontinued).

Loss Amount: Big & Medium Enterprises: Year to Date: CF: City of Moscow data was reported at 1,312,888.000 RUB mn in Jul 2023. This records an increase from the previous number of 1,135,790.000 RUB mn for Jun 2023. Loss Amount: Big & Medium Enterprises: Year to Date: CF: City of Moscow data is updated monthly, averaging 211,985.124 RUB mn from May 2000 (Median) to Jul 2023, with 279 observations. The data reached an all-time high of 2,238,059.000 RUB mn in Dec 2022 and a record low of 2,481.000 RUB mn in Jan 2001. Loss Amount: Big & Medium Enterprises: Year to Date: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OD008: Enterprises Balance (Profit less Loss): Big and Medium: ytd: Loss: by Region.

As of September 2020, Moscow had the biggest amount of public routes, among which the largest number was accounted for bus routes and tram lines. The Russian northern capital Saint Petersburg, followed next with a total number of 838 routes of public transport.

In Moscow, approximately 77 CCTV cameras were installed per square kilometer as of November 2020. That was nearly twice as many as in Russia's second largest city, Saint Petersburg. The capital also had a higher density of surveillance cameras by thousand inhabitants.