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3
Research Opportunities in the Demography of Aging
Melissa Hardy and Vegard Skirbekk
INTRODUCTION
Population aging has provoked a combination of acceptance and alarm as policy makers
search for a reasonable set of programmatic adjustments that will address both constrained
national budgets and the limited means of individuals and families to manage the risks of old age
on their own. In the 1960s, the major demographic concern was overpopulation and the need to
reduce fertility rates (Ehrlich, 1968). Initiatives to encourage zero population growth and
replacement rate fertility coincided with the development of family planning programs, improved
options for birth control, and the milestones in the women’s rights movement. The longer term
consequences of fewer births were magnified by unanticipated reductions in mortality at older
ages, and as fertility rates continued to fall and life expectancy increase, populations grew older.
The dilemma facing developed countries that relatively high and growing proportions now have
relatively high proportions of older people will not be an easy one to resolve: How can the
world’s largest economies continue to offer their citizens a high quality of life when projections
suggest many will experience a shrinking labor force, sluggish rates of economic growth, and an
increasing proportion of national spending encumbered by health care and wage replacement
commitments made to a segment of the population that continues to grow larger and live longer?
For the developing world, the challenge of accommodating a rapidly increasing proportion of
older people can be complicated by a combination of high levels of poverty, weak infrastructure,
corruption, hunger, infectious diseases, poor sanitation, limited job opportunities, or illiteracy.
The demography of aging is generally associated with a set of population statistics that
emphasize the compositional changes associated with the first demographic transition, a
theoretical model proposed to account for the observed declines in both birth and death rates as
countries shift from agricultural to industrial economies (Davis, 1945;Thompson, 1929). The
squaring of the age pyramid, increasing life expectancy, rising ratios of the older to the working-
age population, and declines in total fertility rates have become common statistical referents in
the media. These demographic developments coincided with changes in the gendered life course
and gender roles, an expanding base of scientific knowledge, and a growing role for government
in promoting public welfare. How these processes are connected in terms of cause and effect
remains a point of debate. What seems clear, however, is that fundamental demographic
processes such as birth and death, biological processes such as maturation and senescence, social
processes such as family formation and the division of labor, and economic processes such as
employment and investment are all intertwined. Change that occurs in any one process can ripple
through other domains in both expected and unexpected ways. The ambiguity in interpreting
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these standard aging measures significantly undermines their utility for social policy and
program development.
The macro-patterns of fertility or mortality rates result from the aggregation of births and
deaths within a specific time interval. Each birth and death marks an individual transition
preceded by a sequence of earlier behaviors, exposures, and social interactions. The macro-level
characteristics that predict lower or higher rates for national populations are unlikely to explain
the individual or subgroup behaviors that result in more or fewer children and in younger or
older ages of death. Addressing within- and between-population heterogeneity in behaviors,
states, and traits relative to the broader framework of compositional differences and changing
societal processes defines a research program that connects formal and social demography to the
dynamics of aging and social change.
The contributions of demography within this broad research agenda can occur at multiple
levels. Formal demography can provide projections based on population processes that assist
planners in designing and funding policies that smooth the transitions. Social demography can
provide a better understanding of the underlying behavioral patterns and micro-processes that
translate into these macro-patterns. But perhaps the greatest contribution will be for
demographers to begin to identify the translational mechanisms that connect the macro to the
micro, the interrelated processes that operate at the individual, group, and societal levels.
A GLOBAL PERSPECTIVE
Recently, the global nature of population aging has come into sharper focus. As
populations grow older, demographers are expanding their research into the processes and
consequences of aging. The types of societal changes that coincided with—some say fueled—the
decline in total fertility rates were not occurring in the developing world. Therefore, population
growth continued unevenly through the higher fertility in the developing world, but also through
the lowered later life mortality rates in developed countries. National populations became
distinguished by geometric shape—pyramids versus rectangles. Compared to most other
developed countries, the United States is in a more favorable position. For example, although the
total fertility rates in places such as Canada, China, the United Kingdom, Europe, Russia, and
Japan have fallen below replacement rates, the U.S. fertility rate continues to hover close to 2.
The list of the 25 oldest countries (measured by the proportion of people aged 65-plus) is topped
by Japan, Italy, Germany, Greece, and Sweden; the United States does not make the list.
Additional demographic features that distinguish the United States from many other developed
countries include a high level of immigration (which led to increases in the number of working-
aged adults as well as the total fertility rate); continuing growth in the overall population as well
as in the working-aged population (a trend that is expected to continue past mid-century);
relatively high rates of labor force participation for those 65 and older; and a rising average age
of retirement (although a relatively recent reversal of the post-war decline in retirement age).
In many developing countries, public health campaigns, which were often targeted at
stemming the AIDS epidemic, discouraged unprotected sex and encouraged the empowerment of
women. Imported medical technology, vaccines, antibiotics, and the like increased the odds of
surviving to adulthood as fertility rates began to decline in some developing countries. At the
same time, the gains in life expectancy at birth have been unequally distributed across richer and
poorer regions (see Figure 3-1). As can be seen, from Figure 3-1, the changes that occurred in
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mortality rates followed a very different tempo across countries. The approximate doubling of
life expectancies at birth that occurred over approximately 150 years in many European countries
took roughly half that time in countries such as Vietnam or Guatemala. The speed of change has
given them less time to adjust their fertility to a preferred number of surviving offspring, to
prepare for the impact of rapid population growth, or to prepare for the relatively rapid increase
in population aging taking place in these countries. One consequence of these trends is that the
proportion of young children in the global population will be surpassed by the proportion of
people age 65 and older within the next decade. These population dynamics have resulted in a
faster rate of aging among developing countries than was experienced by countries now counted
among the oldest.
Because of various international programs, the social dynamics of aging in developing
countries are influenced by the interventions of other nations. As in the developed countries, the
prevalence of chronic disease in the developing world is rising. At the same time, however,
developing countries must also deal with the relatively high incidence of infectious diseases,
inadequate access to drinking water, poor sanitation, a shortage of health professionals and
medicines, rudimentary shelters, and rising population density. The developing world is also
experiencing rapid urbanization without the infrastructure necessary to deal with the growing
population, in general, and the growing urban population, in particular.
China and India have the largest numbers of people aged 65 and older, and although the
United States, Japan, Germany, Italy and France are among the top 10 countries with the largest
older populations, Brazil, Pakistan, Mexico, Thailand, Bangladesh, Vietnam, and Nigeria are
also in the top 25 (U.S. Census Bureau, International Data Base1). The median age in Brazil is
expected to increase from the current 29 to 41 by 2040, in South Africa from 24 to 32, and in
Pakistan from 21 to 32. The time frame necessary for significant growth in the proportion of
people age 65 and older is also more compact in developing countries. For example, those 65-
plus were 7 percent of the U.S. population in 1944 and will be 14 percent by 2013. Brazil has
just reached the 7 percent mark, but will have 14 percent by 2032, a doubling within 21 years
compared to 69 in the United States (U.S. Census Bureau, International Data Base). That the
rate of change in population aging is much more rapid for developing countries than countries
that have already completed the first demographic transition may not be surprising since the
proportions of elderly in developing countries is now relatively small. Much of Sub-Saharan
Africa, for example, will continue to have single-digit proportions of people aged 65 and older
decades into the future; however by 2040, 76 percent of the world’s 65 and older population will
live in developing countries (Kinsella, 2000). Although the proportions of older people in the
population will be higher in developed countries, their absolute numbers will be swamped by the
elderly living in the developing world.
The scope of research into the demography of aging has expanded not only because
populations in both developed and developing countries are getting older, but also because
population aging is not occurring in isolation. Changes in family structure and living
arrangements, in the accumulation and transmission of capital (human, social, and financial), and
in work and retirement are changing the organization of the life course. In addition, trends in the
basic population processes of fertility, mortality, and migration are transforming populations at
different rates within different social, political, and economic contexts. Although statistics show
1
http://www.census.gov/population/international/data/idb/informationGateway.php Accessed July 27, 2012.
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evidence that population aging is a global phenomenon, the underlying processes behind
increased survival and longer lives, the quality of life, the sources of social support, and the
integration versus marginalization of older people are likely to vary considerably across time and
place.
The conceptual framework of the first demographic transition aptly describes the
trajectories of a subset of countries that are advanced industrial democracies. Those countries led
the world in developing greater economic capacity, extensive systems of education, publicly
funded research programs, rapid and reliable systems of communication and information
technology, and improved standards of living, which has included much improved odds of
survival to old age and longer lives. Should it be assumed that developing countries will evolve
in similar fashion? To what extent are patterns of aging and adult development conditioned on
the level of societal development?
These are some of the questions the “new” demography of aging will try to answer. But
in formulating answers, demography will need to work at disciplinary intersections with the
social, computational, and life sciences. The micro-processes behind the behaviors of social
actors who interact in and with physical, social, and temporal contexts somehow translate into
the macro-processes that operate at a different scale and pattern of complexity. In recent decades,
demographers operating at both the societal and individual levels have begun to look for
mechanisms that connect the two. These attempts rely on broadly interdisciplinary research
designs that embrace the heterogeneity of the aging process. Building a better understanding of
how biological and social processes interact within complex systems presents a challenge to the
conventional boundaries that have defined disciplines. But it is in the rethinking of conventional
approaches that space for innovation is created.
In this chapter, we describe what we consider to be three promising areas of research in
the demography of aging: biodemography, the changing life course, and simulations and
projections. Each one can generate many research questions at different levels of social
organization, and each one requires a different set of collaborations. We rely on some common
themes in discussing these areas. We emphasize the importance of studying cohorts and
generations to better understand the underlying social processes. Both approaches classify people
relative to historical time and provide some comparability in exposure to trends or events, but
they differ significantly in the level and type of connectivity. Cohorts link people largely
unknown to each other whose experiences of aging are similarly framed by history and cultural
context. Generations link people through a common lineage and shared social and physical
environments, although family members of the same generation often are born in different years.
We stress the power of quantitative analysis techniques, large national samples, and innovative
measurement. We believe in the importance of comparative research and the need to study aging
at different levels of economic development and in different cultural contexts, which may require
initial qualitative investigations to better understand these contexts. The emphasis on the
comparative study of people born at different times, in different places, and in different social
circumstances underscores the need for creative measurement and data collection strategies that
allow such comparisons.
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COHORTS, GENERATIONS, AND THE LIFE COURSE
Three temporal concepts play a central role in demographic research by providing
different ways to organize interrelated behaviors and linked lives. At the population level,
fertility and mortality combine to define a metabolic process through which populations expand,
contract, and age. People born in the same time and place enter the population at once and create
sequential developmental pathways into adulthood, while at the same time, people born at earlier
times are moving into senescence and death. Research on the connection between the regular
replacement of “older” cohorts by “newer” cohorts and social change contributed to the
development of the life course perspective, which links life experiences to broader societal
features. Mannheim (1952) wrote of generations; Ryder’s essay (1965) shifted the focus to birth
cohorts; Cain (1964) and then Elder (1975) demonstrated how cohort similarities in the timing of
major life transitions were shaped by social structure. All three concepts—generation, cohort,
and life course—are useful for understanding the social production and transformation of
populations.
Cohorts introduce the temporal connection between life stage and historical time under
the argument that external events will impact people in different ways depending on their
developmental stage (the timing of exposure). Compositional differences in cohorts can be
created by internal or external processes: Whereas changing fertility rates can produce larger or
smaller cohorts as a reflection of shifts in reproductive choices, a health event that significantly
reduced the likelihood of reproduction would also result in smaller cohorts despite individual
preferences. The process of cohort formation and replacement is continuous, although
characteristics across cohorts can display discontinuities. Even so, compositional differences
among cohorts that signal a trend toward or away from a behavioral pattern will seldom occur in
only one birth year. More often, cohort-specific proportions will illustrate rates of change over
some time span. Examination of individual choices in cohort-specific samples may characterize
leaders (for example, women who used birth control pills as soon as they were available) or
laggards in the process under study.
Whereas birth cohorts organize regional populations relative to historical exposure,
generations perform a similar function in families. Alterations in the generational structure of
families reflect the transformation of society’s age structure and typical family size. For
example, with increasing longevity and fewer children per household, a “longer” multi-
generational family structure replaces a “wider” generational structure. On the other hand, as
family dissolution and reformation patterns become more complex, so does generational
structure, with several sets of grandparents and step-grandparents, parents and step-parents, and
the like. The span of a cohort is determined by some accounting convention—one-year birth
cohorts, five-year birth cohorts—whereas the temporal span of a generation is modified by
delays in the age at first birth. As age of first child is delayed, generations become longer
(Goldstein, Lutz, and Scherbov, 2003). Later-born cohorts inherit the institutional arrangements
put in place by earlier cohorts—periods of stability or volatility, opportunities and constraints,
levels of resources, degrees of distributional inequality, possibilities and capabilities (Sen, 2007).
In an analogous way, parents and grandparents endow their offspring with genetic, human,
social, and financial capital; intergenerational transfers can provide resilience in difficult times;
and economic inequality can be reproduced through the intergenerational transmission of wealth
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and resources. Finally, the life course describes the typical nature, timing, and sequencing of
transitions from one status to another (e.g., single to married), from a period of dependence to
independence (moving from parental to one’s own household), and from one stage to another
(schooling to employment) (Billari and Liefbroer, 2007; Hogan and Astone, 1986). The life
course experienced by the parent generation often varies from that of both their parents and their
children, and siblings may differ in their attempts to reproduce or deviate from family patterns.
Newer cohorts may change the nature, the timing, and the sequencing of life transitions. Further,
cohort differences can be apparent in “typical” timings and sequences as well as in the shape of
distributions in which the “typical” is embedded.
PROMISING AREAS
The three promising areas for the new demography of aging we discuss below include:
(1) biodemography and the study of micro-processes that attempts to link cells and physiological
systems (through genetics, biology and biochemistry) to cohorts and populations using the
individual (and individual behaviors, dispositions, and attitudes) as the nexus; (2) the
demography of the family, systems of stratification and inequality, and gender, which includes
the study of social change in the life course and explores the intersections of changing behavioral
patterns and variability in individual trajectories within changing environments and disparate
circumstances using the cohorts and generations as the organizing concepts; and (3) the
development of new approaches to projections and simulations that attempt to translate the
changing composition of successive cohorts and the changing micro-processes that transform
successive cohorts into macro-level features and population characteristics.
Starting Small: Biodemography, Chronological Age, Health, and Disability
Understanding the aging process requires learning more about health and mortality and
the connection between the two. Although it is tempting to assume that a population queue can
somehow form—especially of those in old age—from good to poorest health and expect that
those in the poorest health will be first to die, the association between health and mortality is not
that simple. Further, how increased longevity has changed the overall health distribution of the
older population remains a point of debate. In living longer lives, population health trajectories
can be reorganized in a number of possible ways (Crimmins et al., 2009; Zeng and Crimmins,
2006). People can enjoy more years of good health and a delayed onset of illness or disability;
can experience the same number of years of good health and a longer period illness and
disability; can spend the same proportion of their life span in good health, which thereafter
declines until death; or can alternate between intermittent spells of health, illness, and recovery,
with peaks and valleys becoming more or less pronounced over time. Researchers have
demonstrated that as life expectancy has increased, the fatal impact of a number of diseases has
been reduced. Have these changes made the population healthier or less healthy? How and why
do health trajectories differ across time and space, among people with the same diagnosed
conditions, between men and women or among people of different races? While good health is
being reported at older ages, recovery from health setbacks is occurring at older ages, and more
people are surviving to old age and living longer lifetimes, a clear explanation of the mechanisms
that have produced these successes remains elusive. One goal is to construct profiles of health
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risks, illness trajectories, the combinations of conditions that are amendable to intervention, and
the types of interventions with the greatest probability of success. Achieving that goal requires
finding a balance between the individual-level details and definable risk groups. This task will be
difficult to accomplish without knowledge of the contingencies of social and biochemical
responses, genetic and environmental factors, and gene-environment interactions—a task that
cannot be reasonably tackled by any single discipline, but one that does require a methodological
approach that translates conclusions into a broader population profile.
When comparing the demographic profiles of nations, measures such as median age,
percent of the population age 65 (or 85) and older, and average life expectancy are reported.
Chronological age seems a straightforward measure to calculate. But for countries without
systematic registries of births and deaths, measuring even age can be a challenge. In making
geographic or temporal comparisons or in establishing trends or regional differences, estimates
can be generated to document how populations have aged over time or how different regions of
the world are aging at different rates. However, expected increases in the number or proportion
of people in older age groups provide an incomplete and potentially misleading picture, since the
societal implications of population aging are often framed relative to functionality, health care
needs, medical expenses, disability, retirement income, and the like. In creating rules of
eligibility for social programs, societies have relied on age as a quick and easily implemented
criterion, but chronological age masks considerable population heterogeneity in capabilities. The
standard indicators of population aging mean little if the content of those chronologically based
measures, the health and functional status associated with any given age range, changes over
time and place.
The need for standardization in other measures is recognized when making comparisons
across time and place. For example, in reporting the number of U.S. households with income less
than $20,000 per year by decade, there is quick recognition of the need to adjust for inflation (to
compare “real” versus “nominal” dollars); for different tax rates and social welfare benefits
(since states or years with higher tax rates would yield lower after-tax income while more
generous in-kind benefits or subsidies would improve living standards, though these effects
would likely influence different portions of the income distribution in different ways ); perhaps
for differences in purchasing power (the cost of living is higher in California than Arkansas); or
even household (or family) size (to adjust for how many adults and children the income must
support). But speaking of population aging generally makes no comparable adjustments. Are
median chronological age and the proportion of people age 65 and older the best measures for
the questions asked?
As life expectancy increased, researchers began to focus not only on the number of years,
but also on the quality of life in the latter stages of the life span. Increased longevity could imply
a prolonged period of decline, or the longer time span could be accompanied by a delay in the
onset of health limitations. Policy makers’ concerns about population aging were grounded in the
historical connection between age, labor force withdrawal, and the onset of disability. But, if
people were living longer lives, perhaps they were also living more years in good health, with the
onset of disabling conditions being pushed to older and older ages. Assessing age structures
within the context of health and independent living required some adjustment to measurement.
Various indicators with different sorts of data demands have been proposed for
comparisons across nations as well as across subpopulations within nations. Many are based on
making adjustments to overall life expectancy to produce health expectancy, where “health” is
defined in different ways. This family of measures can adjust for the disablement process,
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chronic morbidity, functional limitations, difficulties performing activities of daily living,
physical dependency, and cognitive functioning (Molla, Wagerner, and Madans, 2001). Active
life expectancy cordoned off years of functional ability, using activities of daily living to
accomplish the segmentation (Katz et al., 1983; Laditka and Wolf, 1998). Disability-free life
expectancy, disability-adjusted life expectancy, years of life lost to premature mortality, and
years of life lived with disabling conditions offer summary measures of the “burden” of
disability and mortality (Mathers, Lopez, and Murray, 2006; Murray and Lopez, 1997a,b).
Another approach is to compare the ages associated with a specific survival time, thereby
contextualizing chronological age relative to longevity (Lutz, Sanderson, and Scherbov, 2008).
Because policy entitlement is often defined by age, and demographic estimates (and
projections) are used to prepare budgets and to forecast program costs in the future,
chronological age distributions matter. But if the goal is to estimate how demand for long-term
care may change over the next 30 years, a measure that is more informative and better
incorporates related processes, such as the development of disabling health conditions or the
decline in cognitive functioning, is preferable. The measure should not only take into account
likely changes in the compositional characteristics of the elderly over the next 30 years, but also
allow for judging the level of uncertainty associated with these predictions.
Differences in life expectancy are one aspect of within-country inequality that also
change across time. Race and ethnicity, education, and income are among the factors that
structure mortality rates. For example, during the latter part of the 20th century, education-related
gaps in mortality rates grew substantially, particularly in the last decade (Cutler et al., 2010).
Relative access to medical technology, vulnerability to infectious diseases, and public health
investments in preventative care also can create longevity gaps within as well as between
countries. Therefore, variation in life expectancy within national populations but across
subgroups can be as important a feature as average longevity for nations as a whole.
Measuring Health
Better conceptualizations of health must precede the development of better measurement
of health. Self-reported health (an ordinal measure of perceived health status), disability status
(reporting a health condition that prevents working, being entitled to disability benefits, or being
unable to perform certain activities), health limitations (reporting a health condition that limits
the kind of amount of work one can do), number of ADLs (self-reported limitation in ability to
perform basic activities or an external assessment of whether someone needs help in performing
these activities), chronic disease (reporting a disease diagnosis, entry on a medical record of a
diagnosis, or experiencing symptoms associated with a disease), and comorbidity (reports of
more than one chronic disease) are common measures. These measures better address the
consequences of poor health—on any given day, a person may feel “fair” rather than “excellent,”
can no longer do certain tasks, or been diagnosed with certain illnesses or syndromes—rather
than the underlying processes that create these health-related problems. They most often rely on
thresholds (which vary), ordinal categories or counts (which produce limited variability), and
respondent reports (which are subject to error and varying levels of sensitivity and frames of
reference). Comparisons based on these measures often have an uncomfortable amount of
ambiguity in interpretation. For example, what one person defines as “good” health may be
deemed “excellent” health by another; how people make those distinctions may differ by cohort,
by culture, and by the information they have from various medical tests and family histories. A
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particular configuration of blood biomarkers that had been viewed as non-problematic 20 years
ago may now trigger a diagnosis of early-stage disease or high risk of developing that disease,
the same disease may produce symptoms or influence overall health differently across people,
and counts of chronic diseases mask variability in severity of disease. Further, the connection
between pathology and how societies sort people relative to levels of disablement may also be
changing (Freedman, 2008). How are the trajectories of disease pathology, disablement, and
mental health interrelated, and what are the key inflection points that effective interventions
might target?
In projecting the age and sex composition of future populations, the populations at risk
and their mortality hazards are recognized as heterogeneous. The trends in age at death may vary
across these subgroups. Projections that cannot incorporate information on both changing
composition and different social processes across cohorts and across regions may create a false
sense of stability or crisis. Cohort-specific health trajectories that incorporate multiple measures
may allow a better sense of interrelated processes, such as the interplay between biological and
behavioral processes that damage health or facilitate resilience, repair, or recovery and how these
processes are influenced by differential exposure to positive or negative social or physical
environments, advancements in medical treatment or adaptive technologies, or increases in social
support and interaction.
Approaching health measurement with biological data has linked demography with
biology, medicine, biochemistry, and genetics. Assaying levels of various hormones, markers of
inflammation, the presence of specific alleles, and the like have linked the collection of
biologically enhanced survey data to the routine blood work ordered by physicians for diagnostic
purposes. Rather than analyzing them on a case-by-case basis, however, their use in national
samples provides another method for assessing risk factors in the population. Connecting
biomarker profiles to risks of death or illness or more complex health trajectories requires an
understanding of the mechanisms that link the two, as well as a formulation of how other
personal characteristics or exposures may moderate these relationships.
Heterogeneity of Risk
Demographers have identified a number of factors predictive of health risk, such as
gender, race, marital status, level of education, and socioeconomic status. Associated with many
of these factors are “puzzles” or “paradoxes.” For example, although men appear healthier and
less prone to chronic disease, women survive longer, but the reasons why are not fully
understood (Luy, 2009). Married people tend to be in better health, but this marriage benefit is
stronger for men than it is for women; although selection into marriage may be part of the story
and the quality of the marriage also plays a role, the underlying mechanisms continue to be
debated. If the benefit to women is economic, will an increase in gender parity in earnings reduce
this effect? If the benefit to men is carried through women’s caring activities, will parity in
gender roles eliminate this gap? And as marriage rates decline, will health in old age decline as
well? In the United States, African American men have the shortest life expectancy among
various demographic groups, but can this longevity gap be attributed to lifestyle and
socioeconomic status (SES), or is the story more complicated? Without a better understanding of
mechanisms, these questions have no answers.
Studies of health have tended to focus on different life stages—infancy, childhood,
adolescence, young adulthood, midlife, and old age. Studies of health in older age have
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emphasized conditions associated with the major causes of death or a need for long-term care,
such as stroke, cardiovascular disease, dementia, or Alzheimer’s. Some explanations for
increased life expectancy point to advances in early childhood health for part of the story.
Vaccinations against common childhood diseases and better treatment for infections may have
reduced levels of inflammation at younger ages. Combined with improvements in nutrition, these
factors may have been important in reducing health risks at older ages (Crimmins and Finch,
2006). However, since mortality and late-life health research is necessarily cohort-specific, and
the cohorts studied were born and came of age as medical technology was being transformed, the
variability and correlates of exposures, medical treatments, and prophylaxis also may have been
reconfigured.
Certain risk factors can be reduced through healthy behaviors and lifestyle changes,
although adopting and maintaining these changes has proven difficult. Both gender and
education have been linked to health behaviors, with women and those with more education
more likely to make healthy choices. While access to quality health care, nutritional foods, social
support, and healthy behaviors may be reducing health risks across cohorts, the amount of stress
that people routinely manage as more responsibilities fall into their laps may be working in the
opposite direction. All of these observations point to possible connections between larger
environments, cognitive styles, physiological reactions, coping techniques, and other types of
resources on which people may draw. Understanding health trajectories will require identifying
the complex connections among these dispersed but interrelated processes.
The Changing Life Course: Family, Socioeconomic Status, and Inequality
The processes of fertility and mortality jointly create a population metabolism through
cohort succession. Cohort differences in the life course have been a strong focus of demographic
research, which has documented changes in educational attainment, delay in entry into full-time
employment, the early- and mid-20th century decline and late-20th century increases in average
retirement age, changes in family structure and family formation, and changes in home
ownership, household income, debts, and assets. Pension wealth (the expected value of future
benefits from defined benefit and defined contribution plans) has become a standard component
of any financial profile and, as in other sources of income or wealth, provides a clear advantage
for married couples versus singles, whites versus nonwhites, the more educated versus less
educated, and men relative to women. Individuals and families are expected to absorb more of
the risks of economic downturns, the need for expensive health care, and the financing of
retirement, leading some to observe that the era of early retirement has passed. What these trends
will mean for the process of aging is difficult to predict. Both family and education are regarded
as core institutions by the social sciences. The family household is viewed as the basic unit of
consumption, decision making, socialization, and social cohesion. Education serves as a primary
element of socioeconomic status, the key to upward mobility, entry into a favorable career, and
the ability to master the technology necessary to navigate an increasingly complex environment.
Both have been linked to improvements in health and the standard of living at the societal level
and to better health and a higher standard of living at the individual and household levels.
Exactly what roles do they play in shaping the underlying processes that drive health
maintenance, survival, and longevity?
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Education
Cohort and generational differences in educational attainment reflect the adoption of
compulsory schooling and the elaboration of systems of public education. At the individual level,
those with more schooling enjoy a competitive advantage in launching and sustaining stable and
rewarding careers, higher levels of compensation and accumulated wealth, and longer and
healthier lives. At the same time, more highly educated regions attract business and industry, and
more highly educated countries boast higher levels of worker productivity, higher standards of
living, and healthier populations along with lower total fertility rates, longer life expectancy, and
a national infrastructure to promote social welfare.
Expanding educational opportunities was a primary goal of the 20th century and was seen
as the pathway to a better and more fulfilling life. Persistent inequities by gender and race were
often discounted on the basis of differences in education and experience, which were proposed as
the “true” sources of the inequality. Gender and race differences in these qualifications provided
a partial explanation for the disparities in the types of jobs and levels of compensation. The
historical educational advantage of men relative to women, however, is disappearing. Among
recent cohorts, the proportion of U.S. women with at least a high school education now exceeds
that of men (KC et al., 2010). Societal trends toward more highly educated populations and the
expected growth in the proportion of educated women are expected to continue. Educating
women in the developing world is a major initiative in slowing population growth, since more
educated women not only desire fewer children, but are better able to realize their fertility
aspirations by having fewer children than less educated women. Research on women in Africa
also points to the importance of empowering women to make decisions about their own lives.
The social and political environment that values education, improves women’s rights, promotes
the importance of schooling for girls, and expands employment opportunities for women may
also be the environment that encourages a reduction in family size and the utilization of
contraception at the discretion of women rather than men.
Countries with more educated populations also boast more productive workers, and the
higher rates of productivity have been attributed to the enhanced skills—both general and job-
specific—learned by those with more schooling. Recent research has demonstrated that cognitive
ability also predicts worker productivity better than any other observed personal characteristic
(Neisser 1997; Schmidt and Hunter 2004; Spitz-Oener 2006), a relationship that is found in both
high- and low-income countries (Behrman, Ross, and Sabot, 2008; Tansel, 1994). In place of
using life expectancy to adjust indicators of population aging (Lutz et al., 2008), the old age
dependency ratio could also be adjusted for cognitive functioning (Skirbekk, Loichinger and
Weber, 2012). Countries with more educated populations also demonstrate higher levels of
cognitive functioning among those aged 50 to 85, using immediate recall of ten words as the
cognitive measure. Comparing Figure 3-2 with Figure 3-3, the ordering of countries is similar.
Northern and Central Europe and the United States have more than 90 percent of their
populations completing lower secondary education or more, with rates above 70 percent even at
older ages. Southern Europe, China, and Mexico show sharp educational differences between
their older and younger cohorts, with the majority of younger cohorts completing lower
secondary schooling, but completion rates less than 30 percent at older ages. Word recall is also
highest in Northern and Central Europe and the United States. For example, a 53-year-old
respondent in Mexico functions at the same level as a 78-year-old in the United States, on
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variation, techniques that allow disaggregated rates into their components, and the ability to
construct conditional projections that allow answering important “what if” questions would
significantly enhance the utility of projections. However, each extension will require pushing the
boundaries of the science.
Simulations and Agent-Based Models
As useful as accurate forecasts can be, they remain extensions of a methodology of
extrapolation. Based on certain assumptions about the trends in transition rates, some idea is
gained of how these processes will “play forward.” The dynamic micro-to-macro articulation
between changes in cohort composition, changes in social structure, and the nature, timing, and
sequencing of transitions remains a black box. Simulation techniques can be applied to large
national samples or to “manufactured” data to demonstrate how social processes may unfold
over time. When based on statistical models, the judicious use of counterfactuals, and Monte
Carlo algorithms to adjust parameters of the time trend, simulations can extend the methodology
of cohort component projections in useful directions (Raftery and Bao, 2010).
Microsimulations can incorporate a larger number of covariates, interactions among
covariates, and a wider range of link functions in specifying social processes and tracing their
effects. Static versions of these models, which begin with baseline data and a set of accounting
rules, have been eclipsed by dynamic alternatives that do a better job of incorporating time into
the calculations. These data-based approaches draw heavily on techniques of event history
analysis and differ in how they model transitions and whether they rely on discrete or continuous
time approaches. Building these models requires a substantial initial investment, and the level of
detail and reliability of the estimates depends on the quality of data used to generate the
parameter estimates of the statistical models that drive them. Their strengths include a capacity
to assess the effects of policy changes on both the decision-making process and on the
consequences of the decisions taken; flexibility in aggregation, which makes distributional
outcomes measurable; and the stochastic basis of the modeling exercise, which incorporates
elements of uncertainty and precision into the results. Although they are individually based, the
“actors” in these models do not “interact” in social space. Further elaboration of event history
techniques, such as estimation of diffusion models or a melding of event history with network
analysis, may create more flexibility; however, even those developments would not change the
fundamental data-driven, equation-defined nature of these approaches. Because of their high
level of detail and large number of calibrations, equations, and interactions, these models invoke
“black box” approaches at intervening steps, thereby exchanging an intuitive understanding of
the simulated process for computational feasibility.
The third wave of social simulation research is represented by agent-based models
(ABMs), which provide a different strategy for modeling emergent properties of social systems
(Macy and Willer, 2002). Social science has traditionally had to weight the design trade-off
between experimental and survey methodologies. On the one hand, experiments carried the loss
of external validity, limitations in the number of factors that could be explicitly considered at any
one time, and ethical concerns for the protection of participants while they allowed
manipulations, treatments, a tight focus supported by random assignment, and the assumed
controls for unobserved heterogeneity it afforded. On the other hand, surveys were compromised
by the selective sorting of people into social categories, participant refusals and attrition,
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reporting error, and the inability to dismiss the confounding effects of unobserved population
heterogeneity. ABMs combine advances in artificial intelligence, theories of social process and
social behavior, and “agents” that are autonomous, interdependent, adaptive rule followers to
create computerized thought experiments. Rather than emphasizing the forecasting role of
microsimulations, ABMs allow a test of possible mechanisms that, through their operation in
social space, produce patterns of outcomes. These models thereby provide leverage in
illuminating fundamental processes, such as the emergence of social structure, unintended
consequences of policy “treatments,” or the societal ramifications of rapid technological change.
By design, ABMs fit well with demographic research on the life course, since the agents
in ABMs are “located” in social time and space, socially integrated through networks of
interaction, capable of strategic goal-oriented actions integrated across time, and able to adapt by
adjusting the timing or sequencing of key transitions, for example. Consistent with their
experimental nature, ABMS are “artificial” in design and thereby subject to questions of validity.
Even so, they provide demographers with another tool to gain a better understanding of how the
micro level links to the macro level in ways more complex than aggregation.
CONCLUSIONS
During the mid-20th century, the scientific study of aging and the aged was organized into
the discipline of gerontology. Proposed as an interdisciplinary study of the experiences,
characteristics, and behaviors of a marginalized subpopulation, this area of scholarship remained
on the margins of traditional scientific disciplines. The interdisciplinary nature of gerontology
was more a juxtaposition of the various “systems” that needed to be addressed when studying
aging and the elderly. Understanding the biological and psychological changes that accompanied
aging allowed societies to better confront the social problems of elder poverty, unaddressed
needs for long-term care, and increased vulnerability. The claim was that the biology of aging
put years into lives, and the sociology of aging put life into the years. That division is not as
clear-cut as previously thought.
The demography of aging provides a valuable counterpoint to person-centered studies of
the aging process. As more is learned about how environments shape the way people change
over time, demography can contribute to an understanding of how changes in the aging process
at the person-level translate into population changes in salient compositional characteristics. But
as appreciation grows about the variability in the aging process across time and space, better
ways to translate key dimensions of that variation to the macro-level must be developed. The
traditional measures of population aging implicitly assume homogeneity in the aging process,
and temporal comparisons based on these traditional measures assume that the societal
implications associated with skills, abilities, functionalities, and behaviors linked to
chronological age operate in the same way across time. There is ample evidence that neither of
these assumptions is correct. Those over age 65 are not uniformly dependent, retired, on fixed
income, or unhealthy, nor are those in their 20s uniformly independent, employed, and healthy.
Nevertheless, policy discussions continue to rely on measures that have increasingly ambiguous,
and arguably erroneous, interpretation.
Demographic techniques of standardization, event-history analysis, cohort projections,
and simulations are the foundations of quantitative techniques that will help build the bridges
across levels of aggregation. But the task is about more than statistical estimators with desirable
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properties; also central to this enterprise is the development of comparative criteria that allow us
to extend our measures across time and space, across cohorts and cultures. This task requires
strong conceptual grounding in cross-level linkages, careful consideration of the way these
measures can be, will be, and should be used, and a sensitivity to the vectors of social change
that will inevitably shift the terrain on which our measurement is based.
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FIGURE 3-1 Gains in life expectancy for selective countries.
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FIGURE 3-2 Population share with at least lower secondary education, 2005.
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FIGURE 3-3 Mean age-group–specific immediate recall scores.
NOTE: Immediate recall score (values between 0 and 1, where, e.g., a score of 0.4 means being
able to recall 40% of the given words). Curves are smoothed by using spline interpolations.
Logistic regression to test for significant age-related decline, significance levels P <0.001.
Analysis of variance to test for differences between countries, significance levels P < 0.01.
SOURCE: Skirbekk et al. (2012). Reproduced with permission.
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FIGURE 3-4 Multistate cohort component model, stylized diagram.
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