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.

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.

1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate.

How to use

from pathlib import Path
import requests
import pandas as pd

url = ("https://raw.githubusercontent.com/"
   "epogrebnyak/ru-cities/main/assets/towns.csv")

# save file locally
p = Path("towns.csv")
if not p.exists():
  content = requests.get(url).text
  p.write_text(content, encoding="utf-8")

# read as dataframe
df = pd.read_csv("towns.csv")
print(df.sample(5))

Files:

• towns.csv - city information
• regions.csv - list of Russian Federation regions
• alt_city_names.json - alternative city names

Сolumns (towns.csv):

Basic info:

• city - city name (several cities have alternative names marked in alt_city_names.json)
• population - city population, thousand people, Rosstat estimate as of 1.1.2020
• lat,lon - city geographic coordinates

Region:

• region_name - subnational region (oblast, republic, krai or AO)
• region_iso_code - ISO 3166 code, eg RU-VLD
• federal_district, eg Центральный

City codes:

• okato
• oktmo
• fias_id
• kladr_id

Data sources

• City list and city population collected from Rosstat publication Регионы России. Основные социально-экономические показатели городов and parsed from publication Microsoft Word files.
• City list corresponds to this Wikipedia article.
• Alternative dataset is wiki-based Dadata city dataset (no population data).

Comments

City groups

• Ханты-Мансийский and Ямало-Ненецкий autonomous regions excluded to avoid duplication as parts of Тюменская область.

• Several notable towns are classified as administrative part of larger cities (Сестрорецк is a municpality at Saint-Petersburg, Щербинка part of Moscow). They are not and not reported in this dataset.

By individual city

• Белоозерский not found in Rosstat publication, but should be considered a city as of 1.1.2020

Alternative city names

• We suppressed letter "ё" city columns in towns.csv - we have Орел, but not Орёл. This affected:

  • Белоозёрский
  • Королёв
  • Ликино-Дулёво
  • Озёры
  • Щёлково
  • Орёл

• Дмитриев and Дмитриев-Льговский are the same city.

assets/alt_city_names.json contains these names.

Tests

poetry install
poetry run python -m pytest

How to replicate dataset

1. Base dataset

Run:

• download data stro rar/get.sh
• convert Саратовская область.doc to docx
• run make.py

Creates:

• _towns.csv
• assets/regions.csv

2. API calls

Note: do not attempt if you do not have to - this runs a while and loads third-party API access.

You have the resulting files in repo, so probably does not need to these scripts.

Run:

• cd geocoding
• run coord_dadata.py (needs token)
• run coord_osm.py

Creates:

• coord_dadata.csv
• coord_osm.csv

3. Merge data

Run:

• run merge.py

Creates:

• assets/towns.csv

This horizontal bar chart displays female population (people) by capital city and is filtered where the country is Russia. The data is about countries per year.

Despite that Moscow accounted for the largest sporting goods online sales share, the highest consumption index of sporting goods in Russia was measured in Krasnodar. To compare, Moscow listed in the fourth place.

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.

Among Russian cities with 100,000 to 250,000 inhabitants, Reutov, located in the Moscow Oblast, received the highest urban environmental quality index score of 273 out of 360 points in 2023, based on six criteria and six types of area. The second-leading city in this category was Krasnogorsk.

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.

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.

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.

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

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.

About this dataset

Context

The dataset tabulates the median household income in Russia. It can be utilized to understand the trend in median household income and to analyze the income distribution in Russia by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Russia, OH Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Russia, OH: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Russia, OH
• Russia, OH households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Russia median household income. You can refer the same here

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.

Krasnoyarsk had the highest PM2.5 concentration among cities in Russia, with an average of 26 micrograms per cubic meter of air in 2023. Moscow's pollution levels were measured at over 10 micrograms per cubic meter of air.

One third of all telemedicine services users on SberHealth in Russia resided in the capital Moscow in 2019. Clients from Ufa, the capital of the Republic of Bashkortostan, accounted for 11 percent of the total. The largest share of telemedicine users on the platform were aged from 25 to 40 years.

Among Russian cities with 250,000 to one million inhabitants, Yaroslavl, Khimki, and Mytishchi received the highest urban environmental quality index score of 251 out of 360 points in 2023, based on six criteria and six types of area. Tyumen and Belgorod followed with 250 points.

In 2023 Moscow was the largest city in Europe with an estimated urban agglomeration of 12.68 million people. The French capital, Paris was the second largest city in 2023 at 11.2 million, followed by the capitals of the United Kingdom and Spain, with London at 9.6 million and Madrid at 6.75 million people. Istanbul, which would otherwise be the largest city in Europe in 2023, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in this seventy-year period. Prior to the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950, to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continents largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

Kyiv is the largest city of Ukraine, with approximately 2.95 million inhabitants as of January 1, 2022. Kharkiv had the second-largest population of around 1.42 million, followed by Odesa and Dnipro. Economic situation in Ukraine Ukraine has a population of around 42 million inhabitants - close to 70 percent of which live in urban areas, with almost three million living in Ukraine’s largest city and capital, Kyiv. The city is located in the north central part of the country on the Dnieper River and is one of the largest in Europe. The country’s second-largest city, Kharkiv, is about half of Kyiv's size and located in the northeast. Kharkiv was the first city to be occupied by the Soviet Union in 1917 until the collapse of the Soviet Union in 1991. Since the collapse, Ukraine has been largely divided between east and west. Many inhabitants speak Ukrainian to the west, whereas Russian is dominant in parts of the east and south. Like Kharkiv, many of Ukraine’s other biggest cities which have fewer than one million inhabitants are located to the east of the country – a region which has uprooted and displaced many of its inhabitants because of the military actions that started in 2014. In 2015, Ukrainians across the country were affected by a huge spike in inflation, which reached near 49 percent. In the following years, it marked a decrease, measuring below three percent in 2020. The country’s GDP has also been significantly impacted by the crisis, which has left approximately 1.5 million Ukrainians internally displaced since 2014, according to the United Nations High Commissioner for Refugees (UNHCR). The unemployment rate was above nine percent in 2020.