About this dataset

Context

The dataset tabulates the population of Norway by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Norway. The dataset can be utilized to understand the population distribution of Norway by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Norway. 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 Norway.

Key observations

Largest age group (population): Male # 60-64 years (39) | Female # 25-29 years (28). 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 Norway population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Norway is shown in the following column.
• Population (Female): The female population in the Norway 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 Norway 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 Norway Population by Gender. You can refer the same here

Whereas roughly the same number of men and women were living in Norway in 2012, there were 2.77 million men and 2.72 million women living in the country at the beginning of 2023. The total population of Norway increased steadily during the past decade.

About this dataset

Context

The dataset tabulates the population of Norway town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Norway town. The dataset can be utilized to understand the population distribution of Norway town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Norway 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 Norway town.

Key observations

Largest age group (population): Male # 50-54 years (244) | Female # 60-64 years (405). 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 Norway town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Norway town is shown in the following column.
• Population (Female): The female population in the Norway 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 Norway 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 Norway town Population by Gender. You can refer the same here

This statistic shows the total population of Norway from 1769 until 2020. The data begins in 1769, when Norway was a part of the Kalmar Union with Denmark and was ruled from Copenhagen, however this data applies to the modern-day borders of Norway. From the beginning of the period, the numbers of men and women have grown at a similar rate throughout Norway's history. The number of women was always higher than the number of men, until the final entries in the graph where they are the same or the number of men exceeds women. The largest differences occur in the early twentieth century where there were up to 70 thousand more women than men between 1910 and 1930, however these numbers became closer throughout the rest of the 1900s.

In the Nordic countries, there were more women than men in Denmark and Finland, while there were more men than women in Iceland, Norway and Sweden. Sweden has the largest population of the five countries, while Iceland has the smallest. In 2024, there were **** million men and **** million women living in Sweden, compared to ******* men and ******* women in Iceland.

About this dataset

Context

The dataset tabulates the population of Norway town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Norway town. The dataset can be utilized to understand the population distribution of Norway town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Norway 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 Norway town.

Key observations

Largest age group (population): Male # 60-64 years (559) | Female # 55-59 years (419). 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 Norway town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Norway town is shown in the following column.
• Population (Female): The female population in the Norway 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 Norway 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 Norway town Population by Gender. You can refer the same here

Over the last two decades, the population of Denmark increased overall, reaching nearly six million at the beginning of 2024. The number of female inhabitants was constantly slightly higher than the number of men and amounted to about three million as of January 1, 2024, compared to 2.96 million male inhabitants.

Average age of the Danish population

Since 2005, the Danish population’s average age gradually increased. In this time period, the average age of women was constantly a little higher than that of men. As of January 2023, female inhabitants had an average age of 43.1 years, while male inhabitants on average were 41.4 years old.

 Population of Norway and Sweden

In the past decade, the Norwegian population also grew constantly. In 2023, the number of inhabitants reached nearly 5.5 million. In the neighboring country Sweden, the number of inhabitants increased as well, rising from 9.42 million in 2010 to nearly 10.55 million in 2023.

The most common type of household in Norway in 2023 was couples without resident children. Unsurprisingly, there were almost the same number of men and women living in this kind of household. Furthermore, a higher number of men than women lived in coupled households with children aged *** to 17 years. On the other hand, there were more single mothers with children of this age than single fathers with children of the same age.

Norway NO: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data was reported at 93.218 % in 2017. This records an increase from the previous number of 92.807 % for 2016. Norway NO: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data is updated yearly, averaging 87.633 % from Dec 1960 (Median) to 2017, with 40 observations. The data reached an all-time high of 94.056 % in 2014 and a record low of 28.838 % in 1960. Norway NO: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.; ; Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Norway, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

Norway NO: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data was reported at 89.994 % in 2017. This records an increase from the previous number of 89.761 % for 2016. Norway NO: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data is updated yearly, averaging 86.488 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 90.117 % in 2014 and a record low of 78.579 % in 1990. Norway NO: Labour Force Participation Rate: Modeled ILO Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.; ; Derived using data from International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

The data set is generated from the Norwegian Mapping Authority’s data set “Statistic units basic circuits” and is linked to statistics from Statistics Norway that show the population for base circuits. The data set we show the total population, number of men, number of women and population in five-year age groups from 0 to 90 years and over per base. This also shows the population change in total, for men and for women in relation to population as of January 1, five years ago.

Number of wild reindeer by year, including population structure (males, females and calves, some age classes)

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

The data set is generated from the Norwegian Mapping Authority’s data set “Statistic units basic circuits” and is linked to statistics from Statistics Norway that show the population for base circuits. The data set we show the total population, number of men, number of women and population in five-year age groups from 0 to 90 years and over per base. This also shows the population change in total, for men and for women in relation to population as of January 1, five years ago.

Harvesting and culling are methods used to monitor and manage wildlife diseases. An important consequence of these practices is a change in the genetic dynamics of affected populations that may threaten their long-term viability. The effective population size (Ne) is a fundamental parameter for describing such changes as it determines the amount of genetic drift in a population. Here, we estimate Ne of a harvested wild reindeer population in Norway. Then we use simulations to investigate the genetic consequences of management efforts for handling a recent spread of chronic wasting disease, including increased adult male harvest and population decimation. The Ne/N ratio in this population was found to be 0.124 at the end of the study period, compared to 0.239 in the preceding 14-year period. The difference was caused by increased harvest rates with a high proportion of adult males (older than 2.5 years) being shot (15.2 % in 2005-2018 and 44.8 % in 2021). Increased harvest rates decreased Ne in the simulations, but less sex-biased harvest strategies had a lower negative impact. For harvest strategies that yield stable population dynamics, shifting the harvest from calves to adult males and females increased Ne. Population decimation always resulted in decreased genetic variation in the population, with higher loss of heterozygosity and rare alleles with more severe decimation or longer periods of low population size. A very high proportion of males in the harvest had the most severe consequences for the loss of genetic variation. This study clearly shows how the effects of harvest strategies and changes in population size interact to determine the genetic drift of a managed population. The long-term genetic viability of wildlife populations subject to disease will also depend on the population impacts of the disease and how these interact with management actions. Methods Data collectionThe data was collected from the wild reindeer population at Hardangervidda in Southern Norway (60°09’55’’ N, 07°27’58’’ E). The Hardangervidda population is subject to annual harvest before the rut in late summer or the beginning of autumn (August-September). Generally, hunters do not differentiate between female and male calves, and it is also difficult to determine the sex of yearlings (1.5 years old) during hunting. Thus, harvest quotas generally separate between calves (0.5 years old), females (2.5 years and older), yearlings (females and males 1.5 years old), and free licenses (animals of any age and sex). The latter category is typically used to shoot adult males (2.5 years and older), as their size and status as trophy is considered attractive by hunters. Data on the number of harvested animals in each of the six categories (calves, yearlings, and adults of both sexes) were collected as reported by hunters. Four different annual surveys are performed throughout the year to monitor the population size and structure. First, a minimum estimate for the population size is made using flight transects during mid-winter (January-March), where all observed groups of reindeer are photographed and counted. Second, the annual calf production is estimated using flight transects during summer (late June to mid-July), where a subset of groups with females, calves, and yearling males are photographed and the ratio of calves to adult females and yearlings of both sexes are calculated. Adult males generally aggregate in separate groups in other areas at this time of the year. Third, data is recorded on the number of calves, yearlings, and adults of both sexes that are shot during the harvest (August-September). Finally, the population age and sex structure are estimated using ground surveys just after the harvest (September-October). At this time of the year the reindeer aggregate in groups with both sexes and can be classified into age and sex classes (calves, females, yearling males, and adult males). Data on population sizes in the years 2005-2021 were collected from an established Bayesian integrated population model which uses data from these four surveys for this population (Viljugrein et al. 2023). Additional dataAdditional data on fertility for females, average summer survival for calves, and survival for adult animals in the Hardangervidda population were collected from Mysterud et al. (2020), data on mating skew for male reindeer were collected from Røed et al. (2005), data on primary sex ratio was collected from Loison and Strand (2005) and data on the distribution of age-specific fertilities were collected from Skogland (1985, 1989). These additional data are provided in the main text of the publication. References

Loison, A., Strand, O. 2005. Allometry and variability of resource allocation to reproduction in a wild reindeer population. Behavioural Ecology, 16: 624-633. Mysterud, A., Hopp, P., Alvseike, K.R., Benestad, S.L., Nilsen, E.B., Rolandsen, C.M., Strand, O., Våge, J., Viljugrein, H. 2020. Hunting strategies to increase detection of chronic wasting disease in cervids. Nature Communications, 11: 4392. Røed, K.H., Holand, Ø., Gjøstein, H., Hansen, H. 2005. Variation in male reproductive success in a wild population of reindeer. Journal of Wildlife Management, 69: 1163-1170. Skogstad, T. 1985. The effects of density-dependent resource limitations on the demography of wild reindeer. Journal of Animal Ecology, 54: 359-374. Skogstad, T. 1989. Natural selection of wild reindeer life history traits by food limitation and predation. Oikos, 55: 101-110. Viljugrein, H. 2023. Data and Figure-Scripts for the Paper ‘An Infectious Disease Outbreak and Increased Mortality in Wild Alpine Reindeer’. Zenodo. doi: 10.5281/zenodo.7624490

In Norway, the share of people between 15 and 64 years of age with a tertiary education was higher among women than among men. More than half of all women within this age group in Norway had a tertiary education in 2023, whereas below ** percent of men had the same. Furthermore, ** percent of men had an upper secondary education, whereas ** percent of women had the same. The share was almost equal for people with a primary or lower secondary education.

As of the fourth quarter of 2023, ** percent of women aged between 18 and 29 years in Norway were using social media app Snapchat. Overall, ** percent of men belonging to this age group were also using the platform. Snapchat was less popular amongst those aged 60 years and older, with an average usage of ** percent in this demographic. Additionally, the photo-based app proved more successful with women across all age categories.  Who uses social media the most?  A 2022 survey found that ** percent of Norwegian women were Facebook users, as were ** percent were men. Similarly, ** percent of women and ** percent of men in Norway used Snapchat users. In contrast, ** percent of LinkedIn and ** percent of men were Twitter users.  Snapchat in the Nordics  As of January 2024, Norway had the largest Snapchat reach among the Nordic countries, with more than ** percent of its digital population reporting engaging with the video sharing platform. As of the fourth quarter of 2022, five out of every 10 Norwegian Snapchat users reported using it on a daily basis. Only **** percent of the respondents claimed they used the messaging app monthly or less often.

No：出生时性别比例：新生儿男女比例在12-01-2016达1.057Ratio，相较于12-01-2015的1.056Ratio有所增长。No：出生时性别比例：新生儿男女比例数据按年更新，12-01-1962至12-01-2016期间平均值为1.056Ratio，共20份观测结果。该数据的历史最高值出现于12-01-1967，达1.064Ratio，而历史最低值则出现于12-01-2002，为1.051Ratio。CEIC提供的No：出生时性别比例：新生儿男女比例数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的挪威 – 表 NO.世界银行：人口和城市化进程统计。

In Denmark and Finland, death rates among men were higher than among women in 2023. They were higher among women in Iceland and Norway, whereas it was equal for the two genders in Sweden. That year, Finland had the highest death rate of the Nordic countries.