Religiosity in Russia: Decomposition into Age, Cohort and Period Effects

This study identified recent changes in the prevalence of psychiatric disorders among Department of Veterans Affairs (VA) nursing home residents. Psychiatric diagnoses in administrative databases were summarized for nursing home residents in 1998, 2002, and 2006. Census prevalence rates were compared with findings from earlier VA nursing home surveys. Prevalence rates were compared for age groups and birth cohorts of VA nursing home admissions in 1998 (N=27,734) and 2006 (N=32,543). Among residents in the census samples, prevalence rates for dementia and schizophrenia fluctuated moderately from 1990 to 2006, depression prevalence increased sharply, alcohol use disorder prevalence declined, and drug use disorder prevalence increased. Among 1998 and 2006 admissions, dementia prevalence increased for most birth cohorts but declined for most age groups (35% to 32% overall). Depression prevalence increased for all age groups and birth cohorts (27% to 37% overall), as did posttraumatic stress disorder prevalence (5% to 12% overall). Serious mental illness prevalence increased among the oldest residents and birth cohorts (19% to 22% overall). Alcohol use disorder prevalence declined for all birth cohorts and most age groups (18% to 16% overall), but drug use disorder prevalence increased substantially for younger age groups (6% to 9% overall). Examining differences in prevalence between birth cohorts and age groups can clarify trends in nursing home resident characteristics and improve projections of their future needs.

Generational and Gender Perspectives on Career Flexibility: Ensuring the Faculty Workforce of the Future

Background : Information about aging used in gerontechnology has two main sources. Longitudinal, experimental, and to a limited extent cross-sectional research addresses technology-relevant differences and similarities in people as they age. Other research designs such as cross-sequential methods compare generational or cohort differences in people of different ages compared at the same time. Information from both sources are needed in gerontechnology, but frequently the age interpretation is dominant, though this may not be warranted. Goals : (i) to show how person-environment interactions that change over time are related to multiple age and birth cohort effects, and (ii) to identify how longitudinal and crosssectional information on aging can best be used in gerontechnology. Method : We review the dynamics of changes in person-environment interactions in relation to several cohort and age effects. We differentiate among: (i) nonspecific cohort effects, e.g., increasing longevity; (ii) specific cohort effects important in gerontechnology, e.g., increasing health and functioning in cohort flow; and (iii) technology-related cohort effects, e.g., changing attitudes toward technology in cohort flow of older adults. In addition, we suggest four areas to consider of age effects, i.e. (i) sensory and motor functioning; (ii) cognitive performance; (iii) personality and self; and (iv) motivation and emotion, and discuss the interplay of such cohort and age categories in gerontechnology in exemplary manner. Results and conclusions : It is important to simultaneously consider the impact of the various cohort and age effects to major domains of gerontechnology, i.e., housing, communication, personal mobility and transportation, health and functioning, work environments, and recreation and self-fulfilment. Applications : By identifying the best evidence about aging and cohort effects as related to technology, gerontechnology can improve the practical applications for technology use in old age.

Contributions of age-period-cohort effects to increases in BMI and overweight among Chinese adults must be resolved in order to design appropriate interventions. The objectives were to (i) describe the period effect on BMI and overweight among Chinese adults from 1991 to 2009 and assess modification of this effect by age (e.g. cohort effect) and gender, and (ii) quantify the influence of household income and community urbanicity on these effects. Data are from the China Health and Nutrition Survey, a prospective sample across nine provinces in China; 53,298 observations from 18,059 participants were collected over a 19-year period. A series of mixed effects models was used to explicitly assess differences in BMI within individuals over time (age effect) and population-wide differences in BMI over time (period effect), and implicitly assess differences in the experienced period effect across individuals of varying ages (cohort effect). Stronger period effects on BMI and overweight were observed among males compared with females; and younger cohorts had higher BMIs compared with older cohorts. Simulations predicted that increases in income and urbanicity in the order of magnitude of that observed from 1991 to 2009 would correspond to shifts in the BMIs of average individuals of 0.07 and 0.23 kg/m(2), respectively. Although period effects had a stronger influence on the BMI of males, interventions should not overlook younger female cohorts who are at increased risk compared with their older counterparts.

Understanding dynamics behind farm household income provides insights to foresee future income change among farm households. To better understand the change in farm household income, this study decomposes farm household income by age effects, cohort(generation) effects, and year effects. Constructing pseudo panel by birth year of household heads, this study investigates which factor affects farm household income. The results show that age effects are pronounced in farm household income, agricultural income, and non-agricultural income. Farm household income moves drawing inverse U shape. Moreover, cohort effects show that younger generations have earned more income than older generation, which has been resulted from economic growth. It is also noteworthy that transfer from public sector increases by age and younger generations receive more benefits from government than older generations while transfer from family and friends changes in different directions. This implies that the role of government is getting bigger in safety net among farm households and welfare expenditure is likely to explosively increase as farm population rapidly gets old.

Breast cancer is the most diagnosed cancer among Australian women. Recently, incidence rates have risen but mortality rates have decreased. The extent to which these changes are the result of cumulative risk factor effects (age-effects), events affecting all women at specific points in time (period effects), or changes in generational risk factors (cohort effects) is unclear. This study investigates whether observed trends in breast cancer incidence and mortality are associated with age, period, or cohort effects. Annual Australian breast cancer incidence (1982-2020) and mortality data (1907-2022) were obtained from the Australian Institute of Health and Welfare. Age-Period-Cohort (APC) modelling with a drift in cohort function was used to estimate adjusted age, period and cohort effects. Age effects showed higher incidence and mortality rates with increasing age, peaking at 75-84 years. Cohort effects showed progressively increasing risk among women born after the 1940s, with higher incidence among younger cohorts. Incidence peaks corresponded with introduction of population-based mammography screening, and also changes in population level risk factors. Period effects (adjusting for cohort effects) were modest, demonstrating reductions in incidence over time, while mortality peaked in the 1990s before declining after 2000. Increasing age-related breast cancer incidence and mortality reinforce the importance of early prevention. Incidence has shifted due to cohort and period effects with younger generations showing the highest increases in incidence, suggesting generational shifts in breast cancer risk, likely attributable to mammography screening and increased prevalence of modifiable risk factors.

Distinguishing aging, period and cohort effects in longitudinal studies of elderly populations

This paper attempts to explain an unusual finding in previous studies about the life cycle of happiness in Japan. While it has become common to find happiness U-shaped over the life cycle, with the lowest point reached in the early 40s and a recovery up to the old age (Blanchflower and Oswald, 2004, 2008; Clark, 2007), a different pattern was reported for Japan (Cabinet Office, 2009; Tsutsui et al., 2010; Commission on Measuring Well-being, 2011). Specifically, Japan does not seem much different in the first half of the life cycle, with a declining happiness from young age to the early 40s. But a notable divergence appears in the second half of life cycle, when happiness in Japan does not increase after the 40s, but largely stays flat (Tsutsui et al., 2010). Evidently, the age-happiness profile in Japan is not U-shaped like in many other countries, but L-shaped, with a conspicuous lack of increased happiness in the old age. Overall, it is the elderly in Japan that appear to be the least happy across different age groups (Commission on Measuring Well-being, 2011, p. 16). As a possible explanation for this peculiar pattern of the age-happiness profile in Japan, Tsutsui et al. (2010) suggested a possible bias in estimated age effects due to omitted cohort effects (p. 51-53). This potential bias in age effects was previously emphasized by Clark (2007), who examined whether cohort effects may account for the U-shape in happiness in the United Kingdom, but found that U-shape remained largely unchanged even with included cohort effects. So far, no similar study has been done with Japanese data. Our goal in this paper is to verify whether cohort effects may explain the L-shape in age-happiness profile in Japan. In practice, when cohort effects are added to regression specifications, they are commonly used together with the effects of age and calendar year. This creates an identification problem among age, cohort and period effects, because these three effects are linearly dependent, with the individual’s age equal exactly to the current year minus the year of birth. The identification problem in age-period-cohort (A-P-C) models has long been known in economics (Deaton and Paxson, 1994), and it can be solved by imposing restrictions on parameter estimates of the A-P-C effects. In this paper, we consider two approaches that solve the identification problem. First, we deal with the exact linearity among A-P-C effects by specifying one of these effects with a non-linear function, as a part of a semiparametric regression model (Wunder et al., in press; Movshuk, 2011). We also consider an alternative restriction from Deaton and Paxson (1994), who solved the identification problem by restricting period effects to be orthogonal to a linear time trend1. The key assumption of the Deaton-Paxson (D-P) restriction is that period effects do not contain linear time trends. If linear trends are actually present in period effects, the D-P approach would attribute them to age and cohort effects, leading to biased estimates of these effects (Deaton and Paxson, 1997, p. 103). Fortunately, we could verify the validity of the D-P assumption with our semiparametric approach to solve the identification problem and we found no significant linear time trend in estimated period effects. This made either of our two approaches applicable to estimating A-P-C models of happiness in Japan.

This research examined the development of materialism across the life span. Two initial studies revealed that (a) lay beliefs were that materialism declines with age and (b) previous research findings also implied a modest, negative relationship between age and materialism. Yet, previous research has considered age only as a linear control variable, thereby precluding the possibility of more intricate relationships between age and materialism. Moreover, prior studies have relied on cross-sectional data and thus confound age and cohort effects. To improve on this, the main study used longitudinal data from 8 waves spanning 9 years of over 4,200 individuals (16 to 90 years) to examine age effects on materialism while controlling for cohort and period effects. Using a multivariate multilevel latent growth model, it found that materialism followed a curvilinear trajectory across the life span, with the lowest levels at middle age and higher levels before and after that. Thus, in contrast to lay beliefs, materialism increased in older age. Moreover, age effects on materialism differed markedly between 3 core themes of materialism: acquisition centrality, possession-defined success, and acquisition as the pursuit of happiness. In particular, acquisition centrality and possession-defined success were higher at younger and older age. Independent of these age effects, older birth cohorts were oriented more toward possession-defined success, whereas younger birth cohorts were oriented more toward acquisition centrality. The economic downturn since 2008 led to a decrease in acquisition as the pursuit of happiness and in desires for personal growth, but to an increase in desires for achievement. (PsycINFO Database Record

Firm data are accumulated on a yearly basis. In view of the linear relationship of firm age + foundation year = survey year, the fluctuations of firm data classified by age and period cannot be decomposed into age, period and cohort (foundation year) effects. Three decomposition methods are briefly reviewed and applied to Japanese data on new ventures founded since 1995. Regarding sales and employment growth, the age effect is the largest with a downward trend, and the cohort effect is negligible. Regarding labour productivity, the age effect indicates upward movements, and the cohort effect is negligible. The reason of the negligible cohort effect is discussed.

The present study is devoted to the definition of the role of religion in the formation of reproductive behavior and attitudes of the population of the Russian Federation. Within the study the authors found out that the issue of religiosity of the population and its impact on reproductive behavior are widely discussed both in the scientific community and among the spiritual leaders of various faiths. The significance of the religious factor is greatly exaggerated. In this regard, the authors tried to find objective, statistically significant and comparable parameters to assess the level of religiosity of Russian society and its impact on the reproductive behavior of the population. First of all, the authors assessed dynamics of the number of religious organizations. This is an indirect indicator of the level of spread of different religions in the country. The analysis showed an increase in the number of religious organizations, which may indirectly indicate an increase in the religiosity of the population. In addition, to assess the impact of religiosity on the reproductive behavior of the Russian population, an original approach was used, which is an analysis "from the opposite". On the basis of statistical data on the level of the total fertility rate in the regions of the country, two groups of subjects were formed – with the highest and the lowest levels of this indicator. Further, in the selected regions, the authors made an attempt to assess the level of religiosity. However, the study was conducted not within a group of believers, but a group of atheists. The results of the analysis showed that the level of religiosity of the younger generation of Russians has very little effect on their reproductive attitudes. Moreover, on the basis of indicators of the number of religious organizations in the subjects of Russia, the proportion of persons professing a particular religion in the total population of the regions and the total fertility rate for 2011-2016 were carried out correlation and regression analyses, which confirmed the weak relationship between the level of religiosity and fertility in modern Russia.

This paper is a response to Firebaugh and Harley (1991), as well as an attempt to enhance our understanding of age-related patterns in Protestant church attendance. Firebaugh's and Harley's arguments in favor of an effects only model of Protestant church attendance are not compelling. Most importantly, age effects that are attributable to family formation sociologically imply cohort effects since there are substantial differences among cohorts in their propensity to form traditional nuclear families. In a new data analysis, I have empirically grounded age and cohort effects in the process of family formation. This analysis of 1972-90 General Social Survey data establishes that family structure variation partially explains both cohort effects and age effects on Protestant church attendance. These results, in addition to their intrinsic interest, considerably increase the plausibility of the age, period, and cohort interpretation versus the effects only interpretation of Protestant church attendance.

In this study, the expenditures of food away from home of households are examined using the Household Consumption Expenditure Survey, which was conducted by the Turkish Institute of Statistics from 2002 to 2018. We adopt the Dependent Double Hurdle model with the incorporation of age, period, and cohort effects and the impact of socio-demographic variables. Analysis results reveal that the cohort and age effects are significant in both participation and consumption on food expenditure away from home of household. The findings suggest that the expenditure of food away from home for younger cohorts rises faster for older cohorts. The elasticity of food expenditures for cohorts and age groups is increasing from the youngest cohort and from the youngest age group to the oldest cohort which is the largest age group. The results show that participation and consumption decisions are significantly affected by income and demographic characteristics in Turkey.

How the entrepreneurship rate changes with age and the business cycle has been theoretically and empirically studied. On the other hand, cohort effects have been mostly neglected in the literature. Cohort effects are intrinsic generation characteristics that follow individuals over their lifetime and the business cycles. We propose a methodology to differentiate between age, time (business cycle) and cohort effects in entrepreneurship. We find that time effects are highly correlated with GDP growth. Age effects show and inverse U shaped with maximum between 40 and 50 years. In Brazil, Mexico and Uruguay we find decreasing entrepreneurship in younger cohorts, and almost no change in Peru and Chile.

Are there gender differences in levels of heavy, binge and problem drinking? Evidence from three generations in the west of Scotland