Among Central and Eastern European countries, young ********* were the latest to leave their family home in 2024 at nearly **** years old. Estonians and Lithuanians moved out of their parent's homes at an average age of **** and ****, respectively.

This graph shows the answers to a survey conducted in Italy in 2018. Individuals were asked how old they were when they permanently moved out of their parents' home. One in four respondents declared that they haven't yet moved out of their parents' home. On the other hand, ** percent of the respondents permanently moved out when they were 26 years of age or older and ** percent when they were 25 years of age or younger.

This statistic shows the median age at which young people leave the parental home in and outside Île-de-France in 1999, 2006 and 2011. In 2011, the median age for young adults to leave their parents' house was 24.7 years old in the Ile-de-France region. Outside Île-de-France, the median age was 22.1 years old.

In a survey conducted by Milieu Insight on the living situation of Singaporeans, ** percent of respondents who no longer lived with their parents stated that they moved out of their parents' home between 26 to 30 years. In comparison, ** percent moved out between 19 to 21 years.

In 2023, Croatia was the country where, on average, people left their parent's household the latest, at nearly 32 years old. Ranked second and third were Slovakia and Greece. Denmark, Sweden, and Finland were the countries where people became independent the youngest, with the average person in those countries leaving their parent's household before 22.

Total number of young adults aged 15 to 34 years and total number of young adults aged 20 to 34 years in the UK living with their parents.

In 2023, nearly 58 percent of 18-24-year old men in the United States lived with a parent, whereas approximately *** percent lived alone. In comparison, the share of women living with a parent was about **, compared to *** percent who lived alone.

Mean age and median age at divorce and at marriage, for persons who divorced in a given year, by sex or gender and place of occurrence, 1970 to most recent year.

's Average time of moving excl. commuting (Age 10 and over, male) is 29[minutes] which is the 8th highest in Japan (by Prefecture). Transition Graphs and Comparison chart between Chiba and Saitama(Saitama) and Hyogo(Hyogo)(Closest Prefecture in Population) are available. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

This dataset contains replication files for "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment" by Raj Chetty, Nathaniel Hendren, and Lawrence Katz. For more information, see https://opportunityinsights.org/paper/newmto/. A summary of the related publication follows. There are large differences in individuals’ economic, health, and educational outcomes across neighborhoods in the United States. Motivated by these disparities, the U.S. Department of Housing and Urban Development designed the Moving to Opportunity (MTO) experiment to determine whether providing low-income families assistance in moving to better neighborhoods could improve their economic and health outcomes. The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing). Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults. However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success. In this study, we present a new analysis of the effect of the MTO experiment on children’s long-term outcomes. Our re-analysis is motivated by new research showing that a neighborhood’s effect on children’s outcomes may depend critically on the duration of exposure to that environment. In particular, Chetty and Hendren (2015) use quasi-experimental methods to show that every year spent in a better area during childhood increases a child’s earnings in adulthood, implying that the gains from moving to a better area are larger for children who are younger at the time of the move. In light of this new evidence on childhood exposure effects, we study the long-term impacts of MTO on children who were young when their families moved to better neighborhoods. Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market. For older children (those between ages 13-18), we find that moving to a lower-poverty neighborhood has a statistically insignificant or slightly negative effect. More generally, the gains from moving to lower-poverty areas decline steadily with the age of the child at the time of the move. We do not find any clear evidence of a “critical age” below which children must move to benefit from a better neighborhood. Rather, every extra year of childhood spent in a low-poverty environment appears to be beneficial, consistent with the findings of Chetty and Hendren (2015). The MTO treatments also had little or no impact on adults’ economic outcomes, consistent with previous results. Together, these studies show that childhood exposure plays a critical role in neighborhoods’ effects on economic outcomes. The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls. Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains. Our estimates imply that moving a child out of public housing to a low-poverty area when young (at age 8 on average) using a subsidized voucher like the MTO experimental voucher will increase the child’s total lifetime earnings by about $302,000. This is equivalent to a gain of $99,000 per child moved in present value at age 8, discounting future earnings at a 3% interest rate. The additional tax revenue generated from these earnings increases would itself offset the incremental cost of the subsidized voucher relative to providing public housing. We conclude that offering low-income families housing vouchers and assistance in moving to lowerpoverty neighborhoods has substantial benefits for the families themselves and for taxpayers. It appears important to target such housing vouchers to families with young children – perhaps even at birth – to maximize the benefits. Our results provide less support for policies that seek to improve the economic outcomes of adults through residential relocation. More broadly, our findings suggest that efforts to integrate disadvant... Visit https://dataone.org/datasets/sha256%3Aa12b8c1f14eeabc92c1d91bd0311bc4aa3ddf6d7fb69ca798ca6926e7fa292c7 for complete metadata about this dataset.

Peru Average Monthly Income: 3 Months Moving Average: Lima Metropolitan: Age 14 to 24 data was reported at 1,041.100 PEN in Oct 2018. This records an increase from the previous number of 1,033.400 PEN for Sep 2018. Peru Average Monthly Income: 3 Months Moving Average: Lima Metropolitan: Age 14 to 24 data is updated monthly, averaging 864.900 PEN from Jun 2007 (Median) to Oct 2018, with 137 observations. The data reached an all-time high of 1,069.400 PEN in May 2017 and a record low of 546.400 PEN in Jun 2007. Peru Average Monthly Income: 3 Months Moving Average: Lima Metropolitan: Age 14 to 24 data remains active status in CEIC and is reported by National Institute of Statistics and Information Science. The data is categorized under Global Database’s Peru – Table PE.G008: Monthly Income .

's Average time of moving excl. commuting (Age 10 and over, female) is 26[minutes] which is the 38th highest in Japan (by Prefecture). Transition Graphs and Comparison chart between Toyama and Yamagata(Yamagata) and Akita(Akita)(Closest Prefecture in Population) are available. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

This table contains 49005 series, with data for years 1987 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Nova Scotia; Prince Edward Island ...), Days lost (5 items: Total days lost (including maternity leave);Own illness or disability; Total days lost (excluding maternity leave);Other personal (including maternity leave) ...), National Occupational Classification for Statistics (NOC-S) (33 items: Total; all occupations; Other management occupations; Senior management occupations; Management occupations ...), Sex (3 items: Both sexes; Males; Females ...), Age group (9 items: 15 years and over;15 to 24 years;25 to 44 years;25 years and over ...).

Monaco is the country with the highest median age in the world. The population has a median age of around 57 years, which is around six years more than in Japan and Saint Pierre and Miquelon – the other countries that make up the top three. Southern European countries make up a large part of the top 20, with Italy, Slovenia, Greece, San Marino, Andorra, and Croatia all making the list. Low infant mortality means higher life expectancy Monaco and Japan also have the lowest infant mortality rates in the world, which contributes to the calculation of a higher life expectancy because fewer people are dying in the first years of life. Indeed, many of the nations with a high median age also feature on the list of countries with the highest average life expectancy, such as San Marino, Japan, Italy, and Lichtenstein. Demographics of islands and small countries Many smaller countries and island nations have populations with a high median age, such as Guernsey and the Isle of Man, which are both island territories within the British Isles. An explanation for this could be that younger people leave to seek work or education opportunities, while others choose to relocate there for retirement.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..The number of people moving out of Alaska to a different state has been overestimated in previous years due to collection issues. See .Errata Notes.. for details..This table provides geographical mobility for persons relative to their residence at the time they were surveyed. The characteristics crossed by geographical mobility reflect the current survey year..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available...Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013-2017 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .Accuracy of the Data..). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by census metropolitan area, sex and age group, last 5 months.

Peru Average Monthly Income: 3 Months Moving Average: Lima Metropolitan: Age 45 & Over data was reported at 1,984.600 PEN in Oct 2018. This records a decrease from the previous number of 2,032.100 PEN for Sep 2018. Peru Average Monthly Income: 3 Months Moving Average: Lima Metropolitan: Age 45 & Over data is updated monthly, averaging 1,560.400 PEN from Jun 2007 (Median) to Oct 2018, with 137 observations. The data reached an all-time high of 2,032.100 PEN in Sep 2018 and a record low of 982.500 PEN in Oct 2007. Peru Average Monthly Income: 3 Months Moving Average: Lima Metropolitan: Age 45 & Over data remains active status in CEIC and is reported by National Institute of Statistics and Information Science. The data is categorized under Global Database’s Peru – Table PE.G008: Monthly Income .

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..This table provides geographical mobility for persons relative to their residence at the time they were surveyed. The characteristics crossed by geographical mobility reflect the current survey year..The number of people moving out of Alaska to a different state has been overestimated in previous years due to collection issues. See Errata Notes for details..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by gender, age group and immigrant status, three-month moving average, unadjusted for seasonality.