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.

Russia: Rural population, percent of total population: The latest value from 2024 is 24.45 percent, a decline from 24.67 percent in 2023. In comparison, the world average is 38.30 percent, based on data from 196 countries. Historically, the average for Russia from 1960 to 2024 is 30.18 percent. The minimum value, 24.45 percent, was reached in 2024 while the maximum of 46.27 percent was recorded in 1960.

Actual value and historical data chart for Russia Rural Population Percent Of Total Population

Population: Rural: NW: Novgorod Region data was reported at 150,824.000 Person in 2024. This records a decrease from the previous number of 153,053.000 Person for 2023. Population: Rural: NW: Novgorod Region data is updated yearly, averaging 189,251.000 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 228,448.000 Person in 1989 and a record low of 150,824.000 Person in 2024. Population: Rural: NW: Novgorod Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GA012: Population: Rural: by Region.

Population: Rural: CF: Yaroslavl Region data was reported at 227,383.000 Person in 2024. This records a decrease from the previous number of 227,421.000 Person for 2023. Population: Rural: CF: Yaroslavl Region data is updated yearly, averaging 235,939.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 283,525.000 Person in 1993 and a record low of 226,506.000 Person in 2010. Population: Rural: CF: Yaroslavl Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GA012: Population: Rural: by Region.

Historical dataset showing Russia rural population by year from 1960 to 2023.

The mortality rate decreased both in urban and rural areas of Russia in 2023. Nearly ** deaths per 1,000 population were recorded in cities in that year. To compare, in the countryside, the mortality rate reached ** deaths per 1,000 people.

Actual value and historical data chart for Russia People Using Basic Sanitation Services Rural Percent Of Rural Population

Population: Rural: SF: Republic of Adygea data was reported at 257,929.000 Person in 2024. This records an increase from the previous number of 256,981.000 Person for 2023. Population: Rural: SF: Republic of Adygea data is updated yearly, averaging 214,824.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 257,929.000 Person in 2024 and a record low of 206,609.000 Person in 1992. Population: Rural: SF: Republic of Adygea data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GA012: Population: Rural: by Region.

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

Population: Rural: Far East Federal District (FE) data was reported at 2,051,725.000 Person in 2024. This records a decrease from the previous number of 2,059,852.000 Person for 2023. Population: Rural: Far East Federal District (FE) data is updated yearly, averaging 1,900,062.000 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 2,326,872.000 Person in 2011 and a record low of 1,580,722.000 Person in 2003. Population: Rural: Far East Federal District (FE) data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GA012: Population: Rural: by Region.

The area of commissioned residential buildings per 1,000 population in rural Russia began to exceed that in urban locations in 2014. In 2023, almost 1,000 square meters of residential area per 1,000 Russian residents were built in rural locations, compared to 673 square meters in cities.

In 2021, the share of Russians aged 15 to 24 years residing in rural areas with minimum skills in information and communication technology (ICT) was over six percent lower than among urban inhabitants of the same age group. Approximately 95 percent of the youth living in Russian cities possessed ICT skills.

This data collection aggregates district- and province-level data from the All-Russian Agricultural and Land Census of 1917 to provide indicators of farm management, production, and consumption in revolutionary European Russia. Information is provided on two models of collective land management and agricultural organization that existed in European Russia between the revolutions of 1905 and 1917. The data allow for analysis of agricultural organization and resource allocation in both private and state enterprises. The study is available in two parts. Part 1, Province Level, contains data on 21 provinces only. Part 2, District Level, contains information on 332 districts and 37 provinces. Districts comprising the provinces in Part 1 are also contained in Part 2. Information is provided on the same 26 variables in both parts. The variables include the portion of privately-owned farms with unsown areas for crops, the portion of privately-owned farms without any livestock, the portion of privately-owned farms without working livestock, the portion of privately owned farms with land for rent, the number of hired workers on the farms with land for rent, the amount of arable land on one property (measured in desyatina, which is roughly equal to 2.7 acres), the amount of sown area on one property (in desyatina), the amount of ploughed field on one property, the quantity of hired labor on one property, the quantity of working livestock on one property, the quantity of productive livestock on one property, the number of ploughs per farm property, the number of farming-related working tools on one property, the number of hired workers per desyatin of sown land, the number of working livestock per desyatin of sown land, the number of productive livestock per desyatin of sown land, the number of ploughs per desyatin of sown land, the number of farming-related working tools per desyatin of sown land, the share of ploughs per privately-owned, arable landholdings, the portion of hayfields per privately-owned, arable landholdings, the portion of forested area per privately-owned, arable landholdings, the portion of sown area per privately-owned, arable landholdings, the portion of privately-owned crops in the entire sown area, the portion of privately-owned, arable land in the total area of arable land, the percentage of privately-owned sown area tilled at own expense, and the share of sown grassland in the entire sown area under private ownership.

Almost 89 percent of the Russian urban population between 15 and 74 years of age used internet almost every day in 2023. The share of the population having never used internet was greater in rural areas.

This dataset is about countries per year in Russia. It has 64 rows. It features 3 columns: country, and rural population.

The number of students per one school computer with internet access in Russia has been steadily decreasing since 2011 in both urban and rural areas. However, in urban areas, that number tended to be higher. In2019, there were ***** students per personal computer in public schools in Russian cities.

Approximately 22,000 public schools operated in rural areas of Russia at the beginning of the school year 2022/2023. The number of such educational establishments decreased over the period under consideration. The number of private rural schools dropped by six from the past year.

Since 2016, the fertility rate among the rural population has seen a decline in Russia. The country's inhabitants living outside cities had a fertility rate of **** in 2022. That was higher than the average number of children born per one woman in the urban area, which was measured at **** in the same year.

Maternal mortality sharply decreased in 2022 both in urban and rural areas. In cities, **** deaths of mothers per 100 thousand live births were recorded, down from **** deaths per 100 thousand live births in the previous year. Maternal mortality in rural areas of Russia was historically higher than in cities.