Russia's capital, Moscow, was the largest city in the country with over **** 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 **** million by 2035. Urbanization in Russia In 2024, around *** 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 ** percent. In the Central Federal District, the tendency was that more people moved to Moscow and cities in the Moscow Oblast.

As of January 1, 2025, ***** million inhabitants lived in Russian cities, opposed to **** million people living in the countryside. The rural population of Russia saw a gradual decrease over the observed time period.

Dataset

This dataset was created by bluetrain

Released under CC0: Public Domain

Contents

Actual value and historical data chart for Russia Population In The Largest City Percent Of Urban Population

In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before 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 continent's 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.

In 2024, the total population of Russia was around 146.1 million people. Only a fraction of them live in the major Russian cities. With almost 12.5 million inhabitants, Moscow is the largest of them. In the upcoming years until 2030, the population was forecast to decline.Russia's economy Russia is one of the major economies in the world and is one of the wealthiest nations. Following the 1998 Russian financial crisis, Russia introduced several structural reforms that allowed for a fast economic recovery. Following these reforms, Russia experienced significant economic growth from the early 2000s and improved living standards in general for the country. A reason for the momentous economical boost was the rise in commodity prices as well as a boom in the total amount of consumer credit. Additionally, Russia is highly dependent on the mining and production of natural resources, primarily in the energy department, in order to promote economic growth in the country. Due to large energy reserves throughout the country, Russia has developed a stable economy capable of sustaining itself for many years into the future. The majority of Russian oil and energy reserves are located in the Western Siberian areas. These natural gas liquids, along with oil reserves that consist of crude oil, shale oil and oil sands are constantly used for the production of consumable oil, which is an annually growing industry in Russia. Oil products are one of Russia’s primary exports and the country is able to profit entirely off of sales due to high prices as well as high demand for such goods.

This database provides 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, 2020 for more details) thus, changing a city position in a global urban hierarchy. 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 constructed Russian 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.

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.

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

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.

The repeated cross-section design is far and away the simplest alternative for the RLMS. The sampling is cost efficient, easy to maintain, and easy to update when needed. The design supports both efficient cross-sectional and aggregate longitudinal analyses of change in the Russian household population. Updates to the sample, including a full replenishment of the probability sample of dwelling units, will not seriously disrupt the longitudinal data series.

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

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.

The repeated cross-section design is far and away the simplest alternative for the RLMS. The sampling is cost efficient, easy to maintain, and easy to update when needed. The design supports both efficient cross-sectional and aggregate longitudinal analyses of change in the Russian household population. Updates to the sample, including a full replenishment of the probability sample of dwelling units, will not seriously disrupt the longitudinal data series.

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

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

About this dataset

Context

The dataset tabulates the Russia town household income by gender. The dataset can be utilized to understand the gender-based income distribution of Russia town income.

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, New York annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Russia, New York annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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 town income distribution by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Russia median household income by race. The dataset can be utilized to understand the racial distribution of Russia income.

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 breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Russia, OH (2021, 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 by race. You can refer the same here

In all age groups until 29 years old, there were more men than women in Russia as of January 1, 2024. After that age, the female population outnumbered the male population in each category. The most represented age group in the country was from 35 to 39 years old, with approximately *** million women and *** million men. Male-to-female ratio in Russia The number of men in Russia was historically lower than the number of women, which was a result of population losses during World War I and World War II. In 1950, in the age category from 25 to 29 years, ** men were recorded per 100 women in the Soviet Union. In today’s Russia, the female-to-male ratio in the same age group reached *** women per 1,000 men. Russia has the highest life expectancy gender gap The World Health Organization estimated the average life expectancy of women across the world at over five years longer than men. In Russia, this gap between genders exceeded 10 years. According to the study “Burden of disease in Russia, 1980-2016: A systematic analysis for the Global Burden of Disease Study 2016,” Russia had the highest gender difference in life expectancy worldwide.

About this dataset

Context

The dataset tabulates the Russia household income by gender. The dataset can be utilized to understand the gender-based income distribution of Russia income.

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 annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Russia, OH annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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 income distribution by gender. You can refer the same here

In 2024, Russia had the largest population among European countries at ***** million people. The next largest countries in terms of their population size were Turkey at **** million, Germany at **** million, the United Kingdom at **** million, and France at **** million. Europe is also home to some of the world’s smallest countries, such as the microstates of Liechtenstein and San Marino, with populations of ****** and ****** respectively. Europe’s largest economies Germany was Europe’s largest economy in 2023, with a Gross Domestic Product of around *** trillion Euros, while the UK and France are the second and third largest economies, at *** trillion and *** trillion euros respectively. Prior to the mid-2000s, Europe’s fourth-largest economy, Italy, had an economy that was of a similar sized to France and the UK, before diverging growth patterns saw the UK and France become far larger economies than Italy. Moscow and Istanbul the megacities of Europe Two cities on the eastern borders of Europe were Europe’s largest in 2023. The Turkish city of Istanbul, with a population of 15.8 million, and the Russian capital, Moscow, with a population of 12.7 million. Istanbul is arguably the world’s most famous transcontinental city with territory in both Europe and Asia and has been an important center for commerce and culture for over 2,000 years. Paris was the third largest European city with a population of ** million, with London being the fourth largest at *** million.

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 database contains the survey on the changes of gender time allocation during two waves of the coronavirus lockdown (self-isolative restrictions) in Russia. Self-isolation included shift to remote work and study, the closure of childcare facilities, restrictions of mobility, etc.

Sample information

The survey was conducted on Yandex.Survey platform. The first wave was conducted on 22-23 th of May, 2020, after 2 months of the beginning of first lockdown. The second wave took place on 17-19th of November, 2020 after 1 month of the second lockdown’start.

Data was collected via online service Yandex.Survey. The platform offers a service for conducting an online survey among 50 million users of the Yandex advertising network with the ability to make a random sample, including a sample by demographic, geographic and some socio-economic characteristics.

The respondents were women of predominantly working/reproductive age (15-55) from Russia. 1411 women took part in the first wave and 1408 in the second. After cleaning data and removing outliers 2795 respondents left.

The coincidence of the distributions with the general population in terms of the main parameters (age, size of the settlement, employment, household composition) is satisfactory. The observed (insignificant) deviations are as follows: the proportion of women aged 30-43, living in cities with a population over one million has increased; decreased - at the age of 50-54 years, living in settlements with a population of less than 100 thousand people working in agriculture.

The female respondents were asked if they spend more or less time household chores and care, including: cleaning, cooking, laundry, shopping, management, child care, other care or nothing. If a woman marked, that she is living with a partner during the lockdown, she was also asked if her partner spends more or less time on each chore.

The survey also includes questions concerning the occupation type (work, work and study, study, child care leave, doesn’t work), if a woman works (or works and studies), how the lockdown effected on her job: shift to remote work, fired, paid leave, unpaid leave, no income on restrictions, continues in-person work, and if a woman lives with a partner the same question was asked considering his work on the lockdown. Further, occupational features were divided into three: income (or husband’s income) means that a woman (or her partner) has her income on the lockdown which includes remote work, in person work, paid leave; gotowork means a woman (in her partner’s case – husb_gotowork) continues in person work; and distant if a woman is working online (husb_distant for her partner). Further, we asked whether a woman has an experience of remote work: no, and it is impossible, no, but it is possible, yes. We also asked about the size and type of her employer (small, medium, large firm or state firm).

The next set of questions considers who a woman is living with on self isolation: alone, children, partner, parents, parents-in-law, others. At last, we asked respondents age, number of children and the age of the youngest child (if the number of children >0).

The database’ structure

Survey's wave variables

Social and demographic variables

age of female respondent

size of the city

number of children

the age of the youngest child

age at last birth

woman lives with her husband

woman lives with children

woman lives with children over 18 years old

woman lives with her parents

woman lives with her husband's parents

woman lives alone

woman lives with someone else

type of activity

how the lockdown effected female occupation

field of employment

type of enterprise where woman works (or does not)

there is wife's income in household

how the lockdown effected her husband's occupation

there is husband's income in household

woman's work experience at a remote location

woman has remote work in the period of lockdown

her husband has remote work in the period of lockdown

her husband has out of home work in the period of lockdown

woman has out of home work in the period of lockdown

her husband is fired or doesn't have income temporarily because of the lockdown

her husband was fired because of the lockdown

Time use variables: the changes in lockdown

WOMAN MORE

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

WOMAN LESS

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

HER HUSBAND MORE

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

HER HUSBAND LESS

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

TOGETHER MORE

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

TOGETHER LESS

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

INSTEAD MORE

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

INSTEAD LESS

childcare

care

cleaning

cooking

laundry

shopping

management

nothing

There are English and Russian versions of variables’ description.

During exploratory data analysis we introduced features instead or together. These new features are restricted to answers of women who live with partners. Whether a woman marks that she spends less(more) time on the chore and her husband spends more(less) time on that exact type of chore, that means he does it instead of his wife. Whether both a woman and her partner spend more (less) time one the chore, it means they do it together.

The variable “type of enterprise” was built on the criteria of credibility and stability during the corona-crisis from a small to a state firm (small, medium, large, state firm). Small and medium enterprises were hit the most by the pandemic (http://doklad.ombudsmanbiz.ru/2020/7.pdf), whether large and especially state firms had more resources to maintain employment and payments.

London was by far the largest urban agglomeration in the United Kingdom in 2025, with an estimated population of *** million people, more than three times as large as Manchester, the UK’s second-biggest urban agglomeration. The agglomerations of Birmingham and Leeds / Bradford had the third and fourth-largest populations, respectively, while the biggest city in Scotland, Glasgow, was the fifth largest. Largest cities in Europe Two cities in Europe had larger urban areas than London, with Istanbul having a population of around **** million and the Russian capital Moscow having a population of over **** million. The city of Paris, located just over 200 miles away from London, was the second-largest city in Europe, with a population of more than **** million people. Paris was followed by London in terms of population size, and then by the Spanish cities of Madrid and Barcelona, at *** million and *** million people, respectively. The Italian capital, Rome, was the next largest city at *** million, followed by Berlin at *** million. London’s population growth Throughout the 1980s, the population of London fluctuated from a high of **** million people in 1981 to a low of **** million inhabitants in 1988. During the 1990s, the population of London increased once again, growing from ****million at the start of the decade to **** million by 1999. London's population has continued to grow since the turn of the century, and despite declining between 2019 and 2021, it reached *** million people in 2023 and is forecast to reach almost *** million by 2047.