This map layer shows the prevalent generations that make up the population of the United States using multiple scales. As of 2018, the most predominant generations in the U.S. are Baby Boomers (born 1946-1964), Millennials (born 1981-1998), and Generation Z (born 1999-2016). Currently, Millennials are the most predominant population in the U.S.A generation represents a group of people who are born around the same time and experience world events and trends during the same stage of life through similar mediums (for example, online, television, print, or radio). Because of this, people born in the same generation are expected to have been exposed to similar values and developmental experiences, which may cause them to exhibit similar traits or behaviors over their lifetimes. Generations provide scientists and government officials the opportunity to measure public attitudes on important issues by people’s current position in life and document those differences across demographic groups and geographic regions. Generational cohorts also give researchers the ability to understand how different developmental experiences, such as technological, political, economic, and social changes, influence people’s opinions and personalities. Studying people in generational groups is significant because an individual’s age is a conventional predictor for understanding cultural and political gaps within the U.S. population.Though there is no exact equation to determine generational cutoff points, it is understood that we designate generational spans based on a 15- to 20-year gap. The only generational period officially designated by the U.S. Census Bureau is based on the surge of births after World War II in 1946 and a significant decline in birth rates after 1964 (Baby Boomers). From that point, generational gaps have been determined by significant political, economic, and social changes that define one’s formative years (for example, Generation Z is considered to be marked by children who were directly affected by the al Qaeda attacks of September 11, 2001).In this map layer, we visualize six active generations in the U.S., each marked by significant changes in American history:The Greatest Generation (born 1901-1924): Tom Brokaw’s 1998 book, The Greatest Generation, coined the term ‘the Greatest Generation” to describe Americans who lived through the Great Depression and later fought in WWII. This generation had significant job and education opportunities as the war ended and the postwar economic booms impacted America.The Silent Generation (born 1925-1945): The title “Silent Generation” originated from a 1951 essay published in Time magazine that proposed the idea that people born during this period were more cautious than their parents. Conflict from the Cold War and the potential for nuclear war led to widespread levels of discomfort and uncertainty throughout the generation.Baby Boomers (born 1946-1964): Baby Boomers were named after a significant increase in births after World War II. During this 20-year span, life was dramatically different for those born at the beginning of the generation than those born at the tail end of the generation. The first 10 years of Baby Boomers (Baby Boomers I) grew up in an era defined by the civil rights movement and the Vietnam War, in which a lot of this generation either fought in or protested against the war. Baby Boomers I tended to have great economic opportunities and were optimistic about the future of America. In contrast, the last 10 years of Baby Boomers (Baby Boomers II) had fewer job opportunities and available housing than their Boomer I counterparts. The effects of the Vietnam War and the Watergate scandal led a lot of second-wave boomers to lose trust in the American government. Generation X (born 1965-1980): The label “Generation X” comes from Douglas Coupland’s 1991 book, Generation X: Tales for An Accelerated Culture. This generation was notoriously exposed to more hands-off parenting, out-of-home childcare, and higher rates of divorce than other generations. As a result, many Gen X parents today are concerned about avoiding broken homes with their own kids.Millennials (born 1981-1998): During the adolescence of Millennials, America underwent a technological revolution with the emergence of the internet. Because of this, Millennials are generally characterized by older generations to be technologically savvy.Generation Z (born 1999-2016): Generation Z or “Zoomers” represent a generation raised on the internet and social media. Gen Z makes up the most ethnically diverse and largest generation in American history. Like Millennials, Gen Z is recognized by older generations to be very familiar with and/or addicted to technology.Questions to ask when you look at this mapDo you notice any trends with the predominant generations located in big cities? Suburbs? Rural areas?Where do you see big clusters of the same generation living in the same area?Which areas do you see the most diversity in generations?Look on the map for where you, your parents, aunts, uncles, and grandparents live. Do they live in areas where their generation is the most predominant?

In 2024, Millennials were the largest generation group in the United States, making up about 21.81 percent of the population. However, Generation Z was not far behind, with Gen Z accounting for around 20.81 percent of the population in that year.

Millennials were the largest generation group in the United States in 2024, with an estimated population of ***** million. Born between 1981 and 1996, Millennials recently surpassed Baby Boomers as the biggest group, and they will continue to be a major part of the population for many years. The rise of Generation Alpha Generation Alpha is the most recent to have been named, and many group members will not be able to remember a time before smartphones and social media. As of 2024, the oldest Generation Alpha members were still only aging into adolescents. However, the group already makes up around ***** percent of the U.S. population, and they are said to be the most racially and ethnically diverse of all the generation groups. Boomers vs. Millennials The number of Baby Boomers, whose generation was defined by the boom in births following the Second World War, has fallen by around ***** million since 2010. However, they remain the second-largest generation group, and aging Boomers are contributing to steady increases in the median age of the population. Meanwhile, the Millennial generation continues to grow, and one reason for this is the increasing number of young immigrants arriving in the United States.

The China Multi-Generational Panel Dataset - Shuangcheng (CMGPD-SC) provides longitudinal individual, household, and community information on the demographic and socioeconomic characteristics of a resettled population living in Shuangcheng, a county in present-day Heilongjiang Province of Northeastern China, for the period from 1866 to 1913. The dataset includes some 1.3 million annual observations of over 100,000 unique individuals descended from families who were relocated to Shuangcheng in the early 19th century. These families were divided into 3 categories based on their place of origin: metropolitan bannermen, rural bannermen, and floating bannermen. The CMGPD-SC, like its Liaoning counterpart, the CMGPD-LN (ICPSR 27063), is a valuable data source for studying longitudinal as well as multi-generational social and demographic processes. The population categories had salient differences in social origins and land entitlements, and landholding data are available at a number of time periods, thus the CMGPD-SC is especially suitable to the study of stratification processes.

Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies. Methods The dataset and code stored here is for Case Studies 1 and 2 in the paper. Datsets were generated using simulations in R. Here we provide 1) the R code used for the simulations; 2) the simulation outputs (as .RDS files); and 3) the R code to analyse simulation outputs and generate the tables and figures in the paper.

Generation Z is a demographic cohort born from 2000 to 2010. As the oldest of the generation reaches adulthood and seeks to enter the workforce, Gen Z consumers will exercise growing economic and cultural clout over the next decade. Read More

The Generations and Gender Programme (GGP) is a Social Science Research Infrastructure that provides micro- and macro-level data which significantly improve the knowledge base for social science. The Infrastructure is run by institutes with strong traditions in academic and policy-related research on population and family change and on survey methodology.

The survey was conducted in two waves. Up till now, 19 countries have conducted at least one wave of data collection, with an average of 9,000 respondents per country. The Generations and Gender Programme is unique in its large coverage of Central and East European countries. Respondents are interviewed every 3 years and changes in the family life are recorded. GGP covers the whole adult life-course, between the age of 18 and 79. The Programme’s main goal is to provide data that can contribute to enhanced understanding of demographic and social developments and of the factors that influence these developments, with particular attention given to relationships between children and parents (generations) and those between partners (gender). A broad array of topics including fertility, partnership, the transition to adulthood, care duties and economic activities are covered by the survey.

Purpose:

The GGP aims to improve understanding of demographic and social changes and the factors influencing them. The GGP is particularly important for the study of relationships between children and parents (generations) and between couples (gender).

The study National Survey of American Life: Multi-Generational and Caribbean Cross-Section Studies also known as the Family Connections Across Generations and Nations is a follow-up to the National Survey of American Life (NSAL): Coping With Stress in the 21st Century, the baseline study which interviewed 6,200 adults and 1,200 adolescents in households of African Americans, non-Hispanic Whites, and Blacks of Caribbean descent. This study examines influences of family life on people's satisfaction with their lives and their health and general well-being. Specifically, it investigates family and inter-generational processes, with a special emphasis on contextual and structural influences on relationships as they affect individual and family health and well-being across, and within, ethnically and nationally diverse population samples. Categories of variables include sections on neighborhood, health, social support, depression, social support, mental health episodes (such as depression and mania), substance use, tobacco use, discrimination, and interviewer observations. Demographic variables include the race and ethnicity of the respondent and their spouse, racial background of parents, education, employment, volunteerism, and family income.

Latin American fertility study conducted between 1964-66. The seven cities included in the study are Bogota, Buenos Aires, Mexico City, Caracas, Panama City, Rio de Janeiro, and San Jose (Costa Rica). The three largest Latin American cities included were Buenos Aires, Mexico and Rio de Janeiro. Medium sized cities were represented by Bogota and Caracas. The smallest cities included were Panama City and San Jose. Individuals surveyed were women, 20-50 years of age and all marital statuses. These city studies were conducted from 1964-66 in each country by national institutions with the design and supervision of the U.N. Demographic Training Center, CELADE, in Santiago. The Community and Family Study Center of the University of Chicago standardized the codes and the Population Council organized them into the present format. Topics included urbanization, levels and trends of fertility, attitudes and opinions toward desired family size and family planning, use of contraceptives, attitudes toward their use, and means of communicating about them. Additional demographic, economic, social and psychological details were also gathered.

According to a survey conducted in 2023, Gen Z teens were more likely than other generations to identify as independents in the United States, at 35 percent. A further 27 percent of Gen Z teens identified as Democratic while 22 percent identified as Republicans.

This paper is an attempt to review and integrate international and Turkish research on infant and child mortality. Recent research and multivariate analyses in African, Latin American and Asian countries have revealed that in many countries mother's education is a powerful predictor of child survival. The present review of research in Turkey has indicated that urban/rural and regional differentials in infant mortality have been clearly established as by-products of fertility, contraception, and health surveys covering nationally representative samples. However, there are only a few multivariate explanatory models of infant/child mortality in Turkey to isolate and measure the effects of mother's education in relation to other variables. Nevertheless, existing studies in Turkey seem to suggest that mother's and father's education might link socio-economic, psychocultural, and biomedical variables with each other at community, household, and individual levels, providing clues for the formulation of future research designs and policy decisions.

Anyone born between 1991 and 2005 is a member of Generation Hashtag. This demographic, which incorporates both younger Millennials and older members of Generation Z, cannot countenance a world without the internet or smartphones, and their priorities and preferences are reshaping the corporate world.
At a rough estimate, Generation Hashtag makes up one-quarter of the world's population and its influence will only increase over the next decade, as its members continue to enter the workforce. This new breed of consumers demands a personalized, convenient, omnichannel experience, and companies must adapt or risk becoming obsolete. Read More

Abstract copyright UK Data Service and data collection copyright owner.

This is a mixed methods data collection.

The Boomers and Beyond: Intergenerational Consumption and the Mature Imagination project focused on the consumption practices of the first wave 'baby boom' generation (born 1945-1954). This group, representing 17% of the UK population, began their life at a time of austerity but entered adulthood during a period of relative prosperity, experiencing major changes over their life course. Previous research has viewed 'boomers' as having experiences that set them apart from previous generations. This research project provided an account of the lives of the boomer generation, examining the role of consumption in changing traditional approaches to adult ageing. Phase One of the research comprised semi-structured interviews conducted with 150 respondents born between 1945 and 1954, resident in the Greater Manchester area (115 of these transcripts are included in this collection). For Phase Two, further in-depth interviews were conducted with 30 respondents from the phase one group, based on open-ended questions derived from initial analysis of the structured interviews. A quantitative data file covering respondents' demographic characteristics is also included. Further information about the study methodology may be found in the study user guide.

The study objectives were to:

• collect a new body of information on continuity/discontinuity in consumption patterns across the life course
• contribute to the development of research methods comparing social discourse/mid-life with personal experience/biographical narrative
• develop a particular theory around the material cultures for midlife and generational patterns of consumption
• contribute to national/international policy debates

Further information can be found on the ESRC project award page and the Cultures of Consumption: Boomers and Beyond project web page.


Main Topics:

Topics covered in the Phase One interviews included:

• background and demographic information, such as age, gender, employment status, occupation, marital status, household information and income, and parents' occupations and income
• interests and activities, group membership, charitable donations
• bodies and health, including physical wellbeing and care of appearance
• spending habits and finances, including household, leisure and clothing expenditure
• views about age, including life course patterns, changes in spending habits, differences between respondents and previous/next generations, advantages/disadvantages to age, middle age, and awareness of the term 'baby boomer'

Topics covered in the Phase One interviews included:

The quantitative data file includes demographic and household characteristics and derived variables covering occupation, social class and other details.

Abstract (en): The China Multi-Generational Panel Dataset - Liaoning (CMGPD-LN) is drawn from the population registers compiled by the Imperial Household Agency (neiwufu) in Shengjing, currently the northeast Chinese province of Liaoning, between 1749 and 1909. It provides 1.5 million triennial observations of more than 260,000 residents from 698 communities. The population mainly consists of immigrants from North China who settled in rural Liaoning during the early eighteenth century, and their descendants. The data provide socioeconomic, demographic, and other characteristics for individuals, households, and communities, and record demographic outcomes such as marriage, fertility, and mortality. The data also record specific disabilities for a subset of adult males. Additionally, the collection includes monthly and annual grain price data, custom records for the city of Yingkou, as well as information regarding natural disasters, such as floods, droughts, and earthquakes. This dataset is unique among publicly available population databases because of its time span, volume, detail, and completeness of recording, and because it provides longitudinal data not just on individuals, but on their households, descent groups, and communities. Possible applications of the dataset include the study of relationships between demographic behavior, family organization, and socioeconomic status across the life course and across generations, the influence of region and community on demographic outcomes, and development and assessment of quantitative methods for the analysis of complex longitudinal datasets. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Smallest Geographic Unit: Chinese banners (8) The data are from 725 surviving triennial registers from 29 distinct populations. Each of the 29 register series corresponded to a specific rural population concentrated in a small number of neighboring villages. These populations were affiliated with the Eight Banner civil and military administration that the Qing state used to govern northeast China as well as some other parts of the country. 16 of the 29 populations are regular bannermen. In these populations adult males had generous allocations of land from the state, and in return paid an annual fixed tax to the Imperial Household Agency, and provided to the Imperial Household Agency such home products as homespun fabric and preserved meat, and/or such forest products as mushrooms. In addition, as regular bannermen they were liable for military service as artisans and soldiers which, while in theory an obligation, was actually an important source of personal revenue and therefore a political privilege. 8 of the 29 populations are special duty banner populations. As in the regular banner population, the adult males in the special duty banner populations also enjoyed state allocated land free of rent. These adult males were also assigned to provide special services, including collecting honey, raising bees, fishing, picking cotton, and tanning and dyeing. The remaining populations were a diverse mixture of estate banner and servile populations. The populations covered by the registers, like much of the population of rural Liaoning in the eighteenth and nineteenth centuries, were mostly descendants of Han Chinese settlers who came from Shandong and other nearby provinces in the late seventeenth and early eighteenth centuries in response to an effort by the Chinese state to repopulate the region. 2016-09-06 2016-09-06 The Training Guide has been updated to version 3.60. Additionally, the Principal Investigator affiliation has been corrected, and cover sheets for all PDF documents have been revised.2014-07-10 Releasing new study level documentation that contains the tables found in the appendix of the Analytic dataset codebook.2014-06-10 The data and documentation have been updated following re-evaluation.2014-01-29 Fixing variable format issues. Some variables that were supposed to be s...

Sibling and cousin correlations are empirically straightforward: they capture the degree to which siblings' or cousins' outcomes are similar. The meaning of these quantities, however, is complicated. A multitude of theoretical processes can produce any particular set of sibling and cousin correlations. Using multigenerational mobility as a substantive example, I show that sibling and cousin correlations in published research are equally consistent with several theoretical interpretations. While some prior authors have concluded that opportunity must skip parents to directly link the outcomes of grandparents and offspring, I show that this evidence is often consistent with alternative theories of latent transmission (measurement error) or of dynamic transmission (a parent-to-child transmission process that changes over generations). I clarify that point estimates which seem to contradict a given theory may also arise from estimation error. I develop a Bayesian procedure to estimate sibling and cousin correlations and quantify uncertainty about the statistic central to the argument. I conclude by outlining how future research might use sibling and cousin correlations as effective descriptive quantities while remaining cognizant that these quantities could arise from a variety of distinct theoretical processes.

Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (Ne) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward-time, individual-based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data. Methods All data for this publication were generated via evolutionary simulations in SLiM. Here, we archive all scripts necesarily to generate, analyze, and visualize the results presented in Clark et al. 2024. First, we performed simulations in SLiM using a perennial and annual model for a variety of average lifespans (for the perennial model), and bottleneck severities. The output of these simulations is (1) a .tree file contain the geneological history of the population, from which we will extract information about genetic diversity, (2) individual-based metadata for all individuls alive during the simulation sampling time: the generation number, individual pedigree id and the individual's age, (3) Census population size information about the population at each generation in the sampling period. Second, we used tskit, msprime, and pyslim to load and process .tree files as tree sequences. We then loop through focal sampling points in the tree sequence, and sampling individuals to perform age and temporal comparisons. Genetic diversity data from the sampled bins is exported as .txt files. Finally, genetic diversity data is loaded in R, permutation tests are performed to test for significant differences in genetic diversity between bins, and figures are created.

In the first quarter of 2024, 51.8 percent of the total wealth in the United States was owned by members of the baby boomer generation. In comparison, millennials own around 9.4 percent of total wealth in the U.S. In terms of population distribution, there is almost an equal share of millennials and baby boomers in the United States.

In 2024, Generation Z represented 24.6 percent of the global population, making them the largest generation group in the world, according to the source. In 2030, Millennials were forecast to represent 21.6 percent of the population worldwide.

The Multilinks project explores how demographic changes shape intergenerational solidarity, well-being and social integration. The project examines a) multiple linkages in families (e.g. transfers up and down family lineages, interdependencies between older and younger family members); b) multiple linkages across time (measures at different points in time, at different points in the individual and family life course); c) multiple linkages between, on the one hand, national and regional contexts (e.g. policy regimes, economic circumstances, normative climate, religiosity) and, on the other hand, individual behaviour, well-being and values.



The conceptual approach builds on three key premises. First, ageing affects all age groups: the young, the middle-aged and the old. Second, there are critical interdependencies between family generations as well as between men and women. Third, we must recognize and distinguish analytical levels: the individual, the dyad (parent-child, partners), family, region, historical generation and country.



The database aims to map how the state, in form of public policies and legal norms, defines and regulates intergenerational obligations within the family. What is the contribution of public authorities to support and secure financial and care needs for the young and the elderly in the family? In what ways the state assumes that intergenerational responsibilities are a family matter? In order to answer these questions the database includes a dual intergenerational perspective: upwards generations; from children to parents; and downwards; from parents to children. It looks across a variety of social policies and also includes legal obligations to support. It entails over 70 indicators on social policy rights, legal obligations to support, and care service usage. It offers a structured access to the public support for families with children and for elderly people within 30 European countries for 2004 and 2009.





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The research project MULTILINKS (How demographic changes shape intergenerational solidarity, well-being, and social integration: A Multilinks framework) existed from 2009 to 2011. It has received funding from the European Union's Seventh Framework Programme (FP7/2007-2011) under grant agreement n° 217523.



After the end of the project the results were made available as a web application and as individual datasets together with the documentation files by the WZB (http://multilinks-database.wzb.eu). Since 2020, this website no longer exists. The single datasets and reports are available here unchanged.



However, the web application, together with the documents, is still available through the "Gender & Generations Programme (GGP)" and the French Institute for Demographic Research (INED). There you will find further information, additional descriptive variables and full possibilities to explore and navigate through the database. For more details see: https://www.ggp-i.org/data/multilinks-database/

If not actively managed, small and isolated populations lose their genetic variability and the inbreeding rate increases. Combined, these factors limit the ability of populations to adapt to environmental changes, increasing their risk of extinction. The effective population size (Ne) is proportional to the loss of genetic diversity and therefore of considerable conservation relevance. However, estimators of Ne that account for demographic parameters in species with overlapping generations require sampling of populations across generations, which is often not feasible in long-lived species. We created an individual-based model that allows calculation of Ne based on demographic parameters that can be obtained in a time period much shorter than a generation. It can be adapted to every life-history parameter combination. The model is freely available as an r-package NEff. The model was first used in a simulation experiment observing changes in Ne in response to different degrees of generational overlap. Results showed that increased generational overlap slowed annual rates of heterozygosity loss, resulting in higher annual effective sizes (Ny) but decreased Ne per generation. Adding the effect of different recruitment rates only affected Ne for populations with low generational overlap. The model was further tested using real population data of the Australian arboreal gecko Gehyra variegata. Simulation results were compared to genetic analyses and matched estimates of the real population very well. Unlike other estimation methods of Ne, NEff neither requires long time series of population monitoring nor genetic analyses of changes in gene frequencies. Thus, it seems to be the first method for calculating Ne within short time periods and comparably low costs facilitating the use of Ne in applied conservation and management.