Population: Urban: NW: City of St Petersburg data was reported at 5,645,943.000 Person in 2024. This records an increase from the previous number of 5,597,763.000 Person for 2023. Population: Urban: NW: City of St Petersburg data is updated yearly, averaging 4,921,117.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 5,645,943.000 Person in 2024 and a record low of 4,656,474.000 Person in 2002. Population: Urban: NW: City of St Petersburg 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.GA011: Population: Urban: by Region.

Population: Female: NW: City of St Petersburg data was reported at 3,074,658.000 Person in 2023. This records an increase from the previous number of 3,074,589.000 Person for 2022. Population: Female: NW: City of St Petersburg data is updated yearly, averaging 2,701,170.000 Person from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 3,078,359.000 Person in 2021 and a record low of 2,563,704.000 Person in 2002. Population: Female: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA010: Population: Female: by Region.

Historical dataset of population level and growth rate for the Saint Petersburg, Russia metro area from 1950 to 2025.

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.

Population: Male: NW: City of St Petersburg data was reported at 2,523,105.000 Person in 2023. This records a decrease from the previous number of 2,525,455.000 Person for 2022. Population: Male: NW: City of St Petersburg data is updated yearly, averaging 2,207,668.000 Person from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 2,529,557.000 Person in 2021 and a record low of 2,091,874.000 Person in 2003. Population: Male: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA009: Population: Male: by Region.

Population: Working Age: Urban: NW: City of St Petersburg data was reported at 3,365,469.000 Person in 2022. This records an increase from the previous number of 3,292,937.000 Person for 2021. Population: Working Age: Urban: NW: City of St Petersburg data is updated yearly, averaging 2,999,320.000 Person from Dec 1989 (Median) to 2022, with 34 observations. The data reached an all-time high of 3,365,469.000 Person in 2022 and a record low of 2,824,544.000 Person in 1995. Population: Working Age: Urban: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA013: Population: Working Age: by Region.

Population: NW: St Petersburg City: Non Working Age: Age 1 to 6 data was reported at 393.200 Person th in 2019. This records a decrease from the previous number of 394.200 Person th for 2018. Population: NW: St Petersburg City: Non Working Age: Age 1 to 6 data is updated yearly, averaging 255.500 Person th from Dec 2003 (Median) to 2019, with 16 observations. The data reached an all-time high of 394.200 Person th in 2018 and a record low of 188.800 Person th in 2003. Population: NW: St Petersburg City: Non Working Age: Age 1 to 6 data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District.

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

Population: NW: St Petersburg City: Non Working Age: Age 0 to 15 data was reported at 864.800 Person th in 2019. This records an increase from the previous number of 846.200 Person th for 2018. Population: NW: St Petersburg City: Non Working Age: Age 0 to 15 data is updated yearly, averaging 627.600 Person th from Dec 2003 (Median) to 2019, with 16 observations. The data reached an all-time high of 864.800 Person th in 2019 and a record low of 563.000 Person th in 2007. Population: NW: St Petersburg City: Non Working Age: Age 0 to 15 data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District.

Research was conducted in two regions of the Russian Federation, Saint Petersburg and Krasnodar, where the PreventAIDS program is implemented. Research objectives were: 1) to assess the availability of medical services for most-at-risk groups of the population (with a focus on providing reproductive health services); 2) to evaluate the attitude of medical staff towards the target audience (with a focus on females involved in providing commercial sex); and 3) to compare behaviors of trusted doctors1 and regular district doctors providing services to those most vulnerable to HIV infection. The case study methodology was used to assess the availability of medical services that specialize in reproductive health services. Case study is an integrated research method involving different types of data collection. This method involves collecting information on the same issue from various sources, supplementing findings from one source with findings from another and evaluating the reliability of data by comparing the different types of information collected. In this case study, quantitative data was supplemented with data obtained from observations, field notes, and both express and in-depth interviews. Evaluated together, the data presents a complete picture of the availability of medical services pertaining to reproductive health. No priority is given to any separate source of information.

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: NW: St Petersburg City data was reported at 5,398.100 Person th in 2019. This records an increase from the previous number of 5,383.900 Person th for 2018. Population: NW: St Petersburg City data is updated yearly, averaging 4,757.400 Person th from Dec 1992 (Median) to 2019, with 28 observations. The data reached an all-time high of 5,398.100 Person th in 2019 and a record low of 4,568.100 Person th in 2007. Population: NW: St Petersburg City data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District.

Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg在2024达3.500%，相较于2023的4.400%有所下降。Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg数据按每年更新，1995至2024期间平均值为9.250%，共30份观测结果。该数据的历史最高值出现于1999，达33.100%，而历史最低值则出现于2024，为3.500%。CEIC提供的Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost。

Population: NW: St Petersburg City: Working Age data was reported at 3,108.700 Person th in 2019. This records an increase from the previous number of 3,070.200 Person th for 2018. Population: NW: St Petersburg City: Working Age data is updated yearly, averaging 3,070.300 Person th from Dec 2003 (Median) to 2019, with 16 observations. The data reached an all-time high of 3,135.100 Person th in 2013 and a record low of 2,836.400 Person th in 2009. Population: NW: St Petersburg City: Working Age data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA017: Population: by City: North Western Federal District. Labour Force population includes men aged 16-59 years old and women aged 16-54 years old Население трудоспособного возраста - мужчины 16-59 лет, женщины 16-54 года

Population: Internet Use: Orders of Goods & Services: % of Total: NW: City of St Petersburg data was reported at 73.600 % in 2024. This records an increase from the previous number of 70.300 % for 2023. Population: Internet Use: Orders of Goods & Services: % of Total: NW: City of St Petersburg data is updated yearly, averaging 47.300 % from Dec 2014 (Median) to 2024, with 11 observations. The data reached an all-time high of 73.600 % in 2024 and a record low of 25.600 % in 2015. Population: Internet Use: Orders of Goods & Services: % of Total: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TH003: Population: by Internet Use for Orders of Goods and Services: by Region.

Moscow had a relatively high amount of companies with a cryptocurrency ATM or in-store payment method in 2021 compared to Saint Petersburg. Russia's second-biggest city had a population that was roughly ********* that of the capital, but had roughly ** percent the figure of crypto-accepting businesses. The figures shown here come from an open-source website, where people can list (especially small) businesses with a crypto payment option. In the case of Russia, most of these firms are located in the most populated areas.

Paid Services Rendered to Population: NW: City of St Petersburg data was reported at 722,989,404.500 RUB th in 2022. This records an increase from the previous number of 634,912,742.500 RUB th for 2021. Paid Services Rendered to Population: NW: City of St Petersburg data is updated yearly, averaging 209,438,603.400 RUB th from Dec 1993 (Median) to 2022, with 30 observations. The data reached an all-time high of 722,989,404.500 RUB th in 2022 and a record low of 266,754.000 RUB th in 1993. Paid Services Rendered to Population: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HG006: Paid Services Rendered to Population: by Region: Annual.

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

Paid Services Rendered to Population: Prev Year=100: NW: City of St Petersburg data was reported at 106.400 Prev Year=100 in 2022. This records a decrease from the previous number of 124.500 Prev Year=100 for 2021. Paid Services Rendered to Population: Prev Year=100: NW: City of St Petersburg data is updated yearly, averaging 103.400 Prev Year=100 from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 124.500 Prev Year=100 in 2021 and a record low of 81.200 Prev Year=100 in 2020. Paid Services Rendered to Population: Prev Year=100: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HG009: Paid Services Rendered to Population: Volume Index: by Region: Previous Year=100.

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