About this dataset

Context

The dataset tabulates the population of Russia town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Russia town. The dataset can be utilized to understand the population distribution of Russia town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Russia town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Russia town.

Key observations

Largest age group (population): Male # 65-69 years (154) | Female # 0-4 years (129). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Russia town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Russia town is shown in the following column.
• Population (Female): The female population in the Russia town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Russia town for each age group.

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/.

Recommended for further research

This dataset is a part of the main dataset for Russia town Population by Gender. You can refer the same here

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 population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Russia town. The dataset can be utilized to understand the population distribution of Russia town by age. For example, using this dataset, we can identify the largest age group in Russia town.

Key observations

The largest age group in Russia, New York was for the group of age 65-69 years with a population of 215 (8.76%), according to the 2021 American Community Survey. At the same time, the smallest age group in Russia, New York was the 85+ years with a population of 13 (0.53%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Russia town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Russia town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Russia town Population by Age. You can refer the same here

Context The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion in 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growing more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc.

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

Context

You found Russian Demography (1990-2017) Dataset. It contains demographic features like natural population growth, birth rate, urbanization, etc. Data was collected from various Internet resources.

Content

Dataset has 2380 rows and 7 columns. Keys for columns:

• "year" - year (1990-2017)
• "region" - name of a federal subject of Russia. It could be oblast, republic, krai, autonomous okrug, federal city and a single autonomous oblast
• "npg" - natural population growth by 1000 people. Calculating as the difference between birth rate and death rate
• "birth_rate" - number of births by 1000 people
• "death_rate" - number of deaths by 1000 people
• "gdw" - general demographic weight (how many people of non-woking age account for 100 people of working age). Working age for men 16-60 years, for women 16-55 years
• "urbanization" - % of urban population

Acknowledgements

ЕМИСС (UIISS) - Unified interdepartmental information and statistical system

Inspiration

You can analyze the relationships between various years, find best regions by each feature and compare them.

The total population in Russia was estimated at 146.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Russia Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Population: Female: VR: Republic of Chuvashia data was reported at 628,058.000 Person in 2023. This records a decrease from the previous number of 630,553.000 Person for 2022. Population: Female: VR: Republic of Chuvashia data is updated yearly, averaging 679,174.000 Person from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 727,230.000 Person in 1992 and a record low of 628,058.000 Person in 2023. Population: Female: VR: Republic of Chuvashia 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.

Russia RU: Total Population data was reported at 145,845.591 Person th in 2021. This records a decrease from the previous number of 146,459.802 Person th for 2020. Russia RU: Total Population data is updated yearly, averaging 145,976.470 Person th from Dec 1981 (Median) to 2021, with 41 observations. The data reached an all-time high of 148,538.190 Person th in 1992 and a record low of 139,221.500 Person th in 1981. Russia RU: Total Population data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Russian Federation – Table RU.OECD.MSTI: Population, Labour Force and Employment: Non OECD Member: Annual.

In response to Russia's large-scale aggression against Ukraine, the OECD Council decided on 8 March 2022 to immediately suspend the participation of Russia and Belarus in OECD bodies. In view of this decision, the OECD suspended its solicitation of official statistics on R&D from Russian authorities, leading to the absence of more recent R&D statistics for this country in the OECD database. Previously collected and compiled indicators are still available.

The business enterprise sector includes all organisations and enterprises whose main activity is connected with the production of goods and services for sale, including those owned by the state, and private non-profit institutions serving the above-mentioned organisations. In practice however, R&D performed in this sector is carried out mostly by industrial research institutes other than enterprises. This particularity reflects the traditional organisation of Russian R&D.

Headcount data include full-time personnel only, and hence are underestimated, while data in full-time equivalents (FTE) are calculated on the basis of both full-time and part-time personnel. This explains why the FTE data are greater than the headcount data.

New budgetary procedures introduced in 2005 have resulted in items previously classified as GBARD being attributed to other headings and have affected the coverage and breakdown by socio-economic objective.

About this dataset

Context

The dataset tabulates the data for the Russia, New York population pyramid, which represents the Russia town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Russia, New York, is 27.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Russia, New York, is 25.5.
• Total dependency ratio for Russia, New York is 52.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Russia, New York is 3.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Russia town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Russia town for the selected age group is shown in the following column.
• Population (Female): The female population in the Russia town for the selected age group is shown in the following column.
• Total Population: The total population of the Russia town for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Russia town Population by Age. You can refer the same here

Population: North Western Federal District (NW) data was reported at 13,865,338.000 Person in 2024. This records an increase from the previous number of 13,840,352.000 Person for 2023. Population: North Western Federal District (NW) data is updated yearly, averaging 13,924,239.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 15,310,985.000 Person in 1990 and a record low of 13,604,203.000 Person in 2009. Population: North Western Federal District (NW) 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.GA002: Population: by Region.

Population: NW: Leningrad Region data was reported at 2,057,708.000 Person in 2024. This records an increase from the previous number of 2,035,762.000 Person for 2023. Population: NW: Leningrad Region data is updated yearly, averaging 1,693,136.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 2,057,708.000 Person in 2024 and a record low of 1,667,150.000 Person in 2002. Population: NW: Leningrad 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.GA002: Population: by Region.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Russian Federation is 2011.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

After a dramatic population decline, Steller sea lions have begun to recover throughout most of their range. However, Steller sea lions in the Western Aleutians and Commander Islands are continuing to decline. Comparing survival rates between regions with different population trends may provide insights into the factors driving the dynamics, but published data on vital rates have been extremely scarce, especially in regions where the populations are still declining. Fortunately, an unprecedented dataset of marked Steller sea lions at rookeries in the Russian Far East is available, allowing us to determine age and sex specific survival in sea lions up to 22 years old. We focused on survival rates in three areas in the Russian range with differing population trends: the Commander Islands (Medny Island rookery), Eastern Kamchatka (Kozlov Cape rookery) and the Kuril Islands (four rookeries). Survival rates differed between these three regions, though not necessarily as predicted by population trends. Pup survival was higher where the populations were declining (Medny Island) or not recovering (Kozlov Cape) than in all Kuril Island rookeries. The lowest adult (> 3 years old) female survival was found on Medny Island and this may be responsible for the continued population decline there. However, the highest adult survival was found at Kozlov Cape, not in the Kuril Islands where the population is increasing, so we suggest that differences in birth rates might be an important driver of these divergent population trends. High pup survival on the Commander Islands and Kamchatka Coast may be a consequence of less frequent (e.g. biennial) reproduction there, which may permit females that skip birth years to invest more in their offspring, leading to higher pup survival, but this hypothesis awaits measurement of birth rates in these areas.

this graph was created in R:

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driven primarily by high birth rates in developing countries and advancements in healthcare. According to the United Nations, the global population surpassed 8 billion in 2023, marking a critical milestone in human history. This growth, however, is unevenly distributed across continents and countries, leading to varied population densities and urban pressures.

Surface area and population density play vital roles in shaping the demographic and economic landscape of each country. For instance, countries with large land masses such as Russia, Canada, and Australia have low population densities despite their significant populations, as vast portions of their land are sparsely populated or uninhabitable. Conversely, nations like Bangladesh and South Korea exhibit extremely high population densities due to smaller land areas combined with large populations.

Population density, measured as the number of people per square kilometer, affects resource availability, environmental sustainability, and quality of life. High-density areas face greater challenges in housing, infrastructure, and environmental management, often experiencing increased pollution and resource strain. In contrast, low-density areas may struggle with underdeveloped infrastructure and limited access to services due to the dispersed population.

Urbanization trends are another important aspect of these dynamics. As people migrate to cities seeking better economic opportunities, urban areas grow more densely populated, amplifying the need for efficient land use and sustainable urban planning. The UN reports that over half of the world’s population currently resides in urban areas, with this figure expected to rise to nearly 70% by 2050. This shift requires nations to balance population growth and density with sustainable development strategies to ensure a higher quality of life and environmental stewardship for future generations.

Through an understanding of population size, surface area, and density, policymakers can better address challenges related to urban development, rural depopulation, and resource allocation, supporting a balanced approach to population management and economic development.

The 1997 the Kyrgyz Republic Demographic and Health Survey (KRDHS) is a nationally representative survey of 3,848 women age 15-49. Fieldwork was conducted from August to November 1997. The KRDHS was sponsored by the Ministry of Health (MOH), and was funded by the United States Agency for International Development. The Research Institute of Obstetrics and Pediatrics implemented the survey with technical assistance from the Demographic and Health Surveys (DHS) program. The purpose of the KRDHS was to provide data to the MOH on factors which determine the health status of women and children such as fertility, contraception, induced abortion, maternal care, infant mortality, nutritional status, and anemia. Some statistics presented in this report are currently available to the MOH from other sources. For example, the MOH collects and regularly publishes information on fertility, contraception, induced abortion and infant mortality. However, the survey presents information on these indices in a manner which is not currently available, i.e., by population subgroups such as those defined by age, marital duration, education, and ethnicity. Additionally, the survey provides statistics on some issues not previously available in the Kyrgyz Republic: for example, breastfeeding practices and anemia status of women and children. When considered together, existing MOH data and the KRDHS data provide a more complete picture of the health conditions in the Kyrgyz Republic than was previously available. A secondary objective of the survey was to enhance the capabilities of institutions in the Kyrgyz Republic to collect, process, and analyze population and health data. MAIN FINDINGS FERTILITY Fertility Rates. Survey results indicate a total fertility rate (TFR) for all of the Kyrgyz Republic of 3.4 children per woman. Fertility levels differ for different population groups. The TFR for women living in urban areas (2.3 children per woman) is substantially lower than for women living in rural areas (3.9). The TFR for Kyrgyz women (3.6 children per woman) is higher than for women of Russian ethnicity (1.5) but lower than Uzbek women (4.2). Among the regions of the Kyrgyz Republic, the TFR is lowest in Bishkek City (1.7 children per woman), and the highest in the East Region (4.3), and intermediate in the North and South Regions (3.1 and3.9, respectively). Time Trends. The KRDHS data show that fertility has declined in the Kyrgyz Republic in recent years. The decline in fertility from 5-9 to 0-4 years prior to the survey increases with age, from an 8 percent decline among 20-24 year olds to a 38 percent decline among 35-39 year olds. The declining trend in fertility can be seen by comparing the completed family size of women near the end of their childbearing years with the current TFR. Completed family size among women 40-49 is 4.6 children which is more than one child greater than the current TFR (3.4). Birth Intervals. Overall, 30 percent of births in the Kyrgyz Republic take place within 24 months of the previous birth. The median birth interval is 31.9 months. Age at Onset of Childbearing. The median age at which women in the Kyrgyz Republic begin childbearing has been holding steady over the past two decades at approximately 21.6 years. Most women have their first birth while in their early twenties, although about 20 percent of women give birth before age 20. Nearly half of married women in the Kyrgyz Republic (45 percent) do not want to have more children. Additional one-quarter of women (26 percent) want to delay their next birth by at least two years. These are the women who are potentially in need of some method of family planning. FAMILY PLANNING Ever Use. Among currently married women, 83 percent report having used a method of contraception at some time. The women most likely to have ever used a method of contraception are those age 30-44 (among both currently married and all women). Current Use. Overall, among currently married women, 60 percent report that they are currently using a contraceptive method. About half (49 percent) are using a modern method of contraception and another 11 percent are using a traditional method. The IUD is by far the most commonly used method; 38 percent of currently married women are using the IUD. Other modern methods of contraception account for only a small amount of use among currently married women: pills (2 percent), condoms (6 percent), and injectables and female sterilization (1 and 2 percent, respectively). Thus, the practice of family planning in the Kyrgyz Republic places high reliance on a single method, the IUD. Source of Methods. The vast majority of women obtain their contraceptives through the public sector (97 percent): 35 percent from a government hospital, and 36 percent from a women counseling center. The source of supply of the method depends on the method being used. For example, most women using IUDs obtain them at women counseling centers (42 percent) or hospitals (39 percent). Government pharmacies supply 46 percent of pill users and 75 percent of condom users. Pill users also obtain supplies from women counseling centers or (33 percent). Fertility Preferences. A majority of women in the Kyrgyz Republic (45 percent) indicated that they desire no more children. By age 25-29, 20 percent want no more children, and by age 30-34, nearly half (46 percent) want no more children. Thus, many women come to the preference to stop childbearing at relatively young ages-when they have 20 or more potential years of childbearing ahead of them. For some of these women, the most appropriate method of contraception may be a long-acting method such as female sterilization. However, there is a deficiency of use of this method in the Kyrgyz Republic. In the interests of providing a broad range of safe and effective methods, information about and access to sterilization should be increased so that individual women can make informed decisions about using this method. INDUCED ABORTION Abortion Rates. From the KRDHS data, the total abortion rate (TAR)-the number of abortions a woman will have in her lifetime based on the currently prevailing abortion rates-was calculated. For the Kyrgyz Republic, the TAR for the period from mid-1994 to mid-1997 is 1.6 abortions per woman. The TAR for the Kyrgyz Republic is lower than recent estimates of the TAR for other areas of the former Soviet Union such as Kazakhstan (1.8), and Yekaterinburg and Perm in Russia (2.3 and 2.8, respectively), but higher than for Uzbekistan (0.7). The TAR is higher in urban areas (2.1 abortions per woman) than in rural areas (1.3). The TAR in Bishkek City is 2.0 which is two times higher than in other regions of the Kyrgyz Republic. Additionally the TAR is substantially lower among ethnic Kyrgyz women (1.3) than among women of Uzbek and Russian ethnicities (1.9 and 2.2 percent, respectively). INFANT MORTALITY In the KRDHS, infant mortality data were collected based on the international definition of a live birth which, irrespective of the duration of pregnancy, is a birth that breathes or shows any sign of life (United Nations, 1992). Mortality Rates. For the five-year period before the survey (i.e., approximately mid-1992 to mid1997), infant mortality in the Kyrgyz Republic is estimated at 61 infant deaths per 1,000 births. The estimates of neonatal and postneonatal mortality are 32 and 30 per 1,000. The MOH publishes infant mortality rates annually but the definition of a live birth used by the MOH differs from that used in the survey. As is the case in most of the republics of the former Soviet Union, a pregnancy that terminates at less than 28 weeks of gestation is considered premature and is classified as a late miscarriage even if signs of life are present at the time of delivery. Thus, some events classified as late miscarriages in the MOH system would be classified as live births and infant deaths according to the definitions used in the KRDHS. Infant mortality rates based on the MOH data for the years 1983 through 1996 show a persistent declining trend throughout the period, starting at about 40 per 1,000 in the early 1980s and declining to 26 per 1,000 in 1996. This time trend is similar to that displayed by the rates estimated from the KRDHS. Thus, the estimates from both the KRDHS and the Ministry document a substantial decline in infant mortality; 25 percent over the period from 1982-87 to 1992-97 according to the KRDHS and 28 percent over the period from 1983-87 to 1993-96 according to the MOH estimates. This is strong evidence of improvements in infant survivorship in recent years in the Kyrgyz Republic. It should be noted that the rates from the survey are much higher than the MOH rates. For example, the KRDHS estimate of 61 per 1,000 for the period 1992-97 is twice the MOH estimate of 29 per 1,000 for 1993-96. Certainly, one factor leading to this difference are the differences in the definitions of a live birth and infant death in the KRDHS survey and in the MOH protocols. A thorough assessment of the difference between the two estimates would need to take into consideration the sampling variability of the survey's estimate. However, given the magnitude of the difference, it is likely that it arises from a combination of definitional and methodological differences between the survey and MOH registration system. MATERNAL AND CHILD HEALTH The Kyrgyz Republic has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. This system includes special delivery hospitals, the obstetrics and gynecology departments of general hospitals, women counseling centers, and doctor's assistant/midwife posts (FAPs). There is an extensive network of FAPs throughout the rural areas. Delivery. Virtually all births in the Kyrgyz Republic (96 percent) are delivered at health facilities: 95 percent in delivery hospitals and another 1 percent in either general hospitals

Abstract

The sample survey on households' budgets is the method of state statistical observation on living standards of population. The frame of the study and dissemination of budget survey data is defined by the following goals: to collect data on distribution of population by levels of prosperity; to get weighting indicators to calculate a consumer price index; to get data to compile accounts of household sector for the system of national accounts.

The survey on households budgets is conducted in all regions of the Russian Federation and covers 47,800 households. The returns of the survey are compiled quarterly and for a year as a whole.

Starting from 1997 two-stage random sampling built up by territorial principle has been used to form sample frame of households. A final unit of the selection is a household. Collective households consisting of persons who are for a long period in hospitals, in special retirement houses for the elders, boarding schools and other institutional organizations are not included in the sample survey.

Data is collected through face-to-face interviews and households expenditure diaries.

Geographic coverage

Survey covers the entire territory of the Russian Federation, with the exception of the Chechen Republic.

Analysis unit

• Households,
• Individuals.

Universe

All private households and population in them living on the territory of the Russian Federation, with the exception of the Chechen Republic.

Kind of data

Sample survey data [ssd]

Sampling procedure

Two-stage probability sampling with stratification and random sampling on each stage was considered the most adequate for household sample totality. The base for sampling is:

On the first stage: aggregation of folders (enumeration districts) formed on the base of 1994 Microcensus dataset. Aggregation of folders is composed on regional level, for urban and rural population separately. Each folder (enumeration district) has a number assigned, where its belonging to certain administrative region (by region code) and locality (by locality code) is indicated.

On the second stage: totality of microcensus forms for a separate household within the enumeration district selected on the first stage.

Stratification is aimed at creating a representative household sample, reflecting territorial peculiarities of population distribution, its demographic and socio-economic structure.

During actual sampling for each subject of the Russian Federation were created tables, containing numbers of microcensus enumeration districts and microcensus forms included into the sampling. The selection of enumeration districts and forms was conducted on Federal level.

Four variants of selection were created within each enumeration district: the first is aimed at primary sampling per se; the second is aimed at replacing inaccessible households on this stage; third and fourth variants are aimed at replacing households withdrawed in the course of survey. The list of households addresses was created on regional level basing on information from the above mentioned tables.

Mode of data collection

Face-to-face [f2f]

Research instrument

HBS program represents a set of the following kinds of questionnaires differing by data collection period:

Household Diary is designated to record household's daily expenditures by certain types of expenditures and consumption within two consequent weeks of a quarter. Households are keeping diaries in accordance with a special rotation scheme. The procedure of households data collection using two-week diary records is organized on a rotation basis within one sample area. For this purpose the household sample totality surveyed by each interviewer is divided into 12 strata. The interviewer compiles rotation groups by lot. Once the households have been divided into rotation groups, the interviewer enters households numbers into the household quarterly rotation scheme developed for these purposes. The rotation scheme is developed in such a way that each group is updated with 2 to 3 households weekly (depending on the sample area: urban or rural territory).

Household Register (Log Book) is designated to record household's expenditures on those days of the quarter when the household does not keep the Diary. Diary and Register is kept by the person administering all or part of total money, who is engaged in housekeeping most of all and is informed about other household's members expenditures, i.e. responsible person.

Questionnaire for Household Budget Survey (quarter) contains questions focused on collecting information for three months of the quarter prior to data collection.

Questionnaire for Household Budget Survey (annual) records information as at the end of forth quarter of the last (reporting) year. This information relates only to households surveyed within the fourth quarter.

BackgroundIn migration and health research, the healthy migrant effect has been a common finding, but it usually pertains to specific contexts only. Existing findings are inconsistent and inconclusive regarding the cognitive functioning of the (aging) foreign-origin population relative to the populations of their host and sending countries. Moreover, this comparison is an understudied design setting.ObjectiveWe analyze the outcomes and associations of cognitive functioning outcomes of the non-institutionalized middle-aged and older population, comparing the Russian-origin population in Estonia with Estonians in Estonia and Russians in Russia in a cross-sectional design. We aim to estimate the (long-term) effects of migration on cognitive functioning in later life, contextualizing the findings in previous research on the healthy migrant effect.Data and methodsWe use data from face-to-face interviews conducted within the SHARE Estonia (2010–2011) and SAGE Russia (2007–2010) surveys. Respondents aged 50+ living in urban areas were grouped by self-identified ethnicity, including 2,365 Estonians, 1,373 Russians in Estonia, and 2,339 Russians in Russia (total N = 6,077). Cognitive functioning was measured using a 25-percentile cut-off threshold for the results of two cognition outcomes - immediate recall and verbal fluency - and the odds of impairment were estimated using binary logistic regression.ResultsRussian men and women living in Estonia have significantly higher odds of impairment in immediate recall than Estonian men and women, though they do not differ from Russians in Russia in the final adjusted models. The differences between all groups are non-significant if age at migration is considered. There are no significant differences between the groups in verbal fluency.ConclusionContrary to the commonly found healthy migrant effect, the middle-aged and older foreign-origin population in Estonia fares initially worse than the native population in the immediate recall outcome, but does not differ from their sending country population, possibly due to Russia’s higher mortality rate and therefore the selective survival of healthier people. Different results depending on the cognitive functioning outcome suggest that migration may affect temporary memory more than crystallized knowledge. However, there are no differences between the groups if defined based on age at migration, which suggests that the age profile differences explain most of the groups’ differences in cognitive functioning.

Original provider: Washington Department of Fish & Wildlife

Dataset credits: Data provider: WA Dept of Fish & Wildlife Waterfowl; Originating data center: Satellite Tracking and Analysis Tool (STAT); Project sponsor or sponsor description: Washington Department of Fish & Wildlife

Abstract: The Northern Puget Sound (NPS) wintering population of lesser snow geese occurs in the Skagit and Fraser Deltas along the western border between the United States and Canada. This population of snow geese have traditionally used very discrete estuary and agricultural habitats associated with Skagit and Port Susan Bays.The breeding grounds of this population are on Wrangel Island, Russia. Because Wrangel Island snow geese represent the last major snow goose population breeding in Asia, and the primary Russian goose population that winters in North America, it is a high priority for the Pacific Flyway and the subject of long standing international cooperative management and conservation. Data collected since the early 1970s on Wrangel Island indicates that the population has grown in abundance, become younger, and changed its behavior relative to traditional habitat and resources. These population changes have become more apparent since the early 1990s and appear to be in response to warmer spring conditions, earlier snowmelt, and changes in the predator community on Wrangel Island. Some of these changes are also evident in the NPS wintering population where the total overwintering population size has increased.The objectives of this project are to examine the current relationship of the NPS population to other Pacific flyway use areas. This will include documentation of migration routes, phenology, staging areas, and stopover locations throughout the flyway. Particular questions that we hope to answer include: - When do geese depart and return to NPS during spring and fall migration? - Where are important flyway use areas during migration? - When and how long do geese use areas along migration routes? - Document inter and/or intra-year interchange among NPS and other wintering areas. - Do some geese that use NPS move to other locations within the flyway during the same winter or among different years? - If NPS geese are moving to other locations, what is the timing of emigration and potential return to NPS?The transmitters are programed to transmit for three years.Acknowledgments:WDFW Biologist Roozen and Technicians Anderson, Deyo, and Otto were instrumental in the successful snow goose captures - without their untiring efforts and perseverance through poor weather conditions, deployment of the full sample of transmitters would not have been possible. We are especially grateful to Dr. Scott Ford of Avian Specialty Veterinary Services for his expertise and exceptional work with the transmitter implant procedures, and to WDFW Technician Deyo and Vet-Tech Yana Podobedova who assisted Dr. Ford with many of the procedures. We are also indebted to WDFW Waterfowl Section Manager Kraege for his support for this project; it is because of his efforts that project was able to take flight. We are grateful to the WDFW staff at the Skagit Wildlife Area for their continued support during our capture efforts. We would like to thank M. Axelson for caring for one of the geese that was unable to fly immediately after the capture - this goose quickly recovered and was able to take flight. Vasiliy Baranyuk provided flock sighting information which assisted us in determining where to focus capture efforts. We are also extremely grateful to the many landowners who were gracious in granting access to their lands.Project PI's-Joe Evenson - WDFW Waterfowl Survey and Sea Duck SpecialistChris Danilson - WDFW District Biologist This dataset is a summarized representation of the telemetry locations aggregated per species per 1-degree cell.

Little is known about the population structure of pinnipeds in the Bering Sea region between the 1950’s and the late 1980’s. This period is of great interest to scientists because populations of NFS, Steller sea lions, harbor seals and sea otters began to precipitously decline during this time. Our project addresses this critically important information gap for northern fur seals. More than 313,000 northern fur seal pups were flipper-tagged on the four fur seal rookeries on the Commander Islands from 1961 to 2007. From the early 1960’s to the end of the 1980’s, we made more than 28,000 resightings of fur seals with flipper tags on these rookeries. Our database also includes a complete dataset of pup counts (both alive and dead), fur seal commercial harvest information, rookery plans, and copies of original reports (in Russian). These datasets were archived as part of the North Pacific Research Board legacy project recovery effort undertaken by Axiom Data Science and NPRB in 2025. The goal of the recovery effort was to assess the NPRB-funded data projects from 2002 to 2014 and archive final data packages that were ready for publication to increase long-term accessibility and discoverability. Data packages were archived as is given limited funding and resources.