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Lifting up ourselves by lifting up others: examining cognitive-affective states linking generativity to well-being

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Lifting up ourselves by lifting up others: examining cognitive-affective states linking generativity to well-being

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LIFTING UP OURSELVES BY LIFTING UP OTHERS: EXAMINING COGNITIVE-
AFFECTIVE STATES LINKING GENERATIVITY TO WELL-BEING

A dissertation presented to the Graduate School of the University of Southern California
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Gerontology

By

Molli R. Grossman
Bachelor of Arts, Georgetown University, 2012

University of Southern California
Los Angeles, California
(August, 2018)
ii

DEDICATION
In loving memory of my grandfather, Bernard Grossman, who highly valued education
and who inspired my interest in gerontology

iii
ACKNOWLEDGEMENTS
This dissertation is about generativity, and I need to recognize the many people
who have been giving towards me. First, I would like to thank Tara Gruenewald for her
mentorship and support over the last 5 years. Despite changes in circumstances, such as
being at different institutions, your continued time and devotion as a mentor have been
much appreciated and pivotal to the completion of this dissertation. I would also like to
thank Elizabeth Zelinski for serving as my co-mentor and always providing me with
honest feedback and guidance. I cannot thank you enough for your help, mentorship, and
invaluable feedback along the way. I would also like to thank Kate Wilber and Susan
Enguidanos for serving on my committee and providing unique insights that I know will
continue to strengthen my work, and to Cleopatra Abdou for her feedback in the earlier
stages of the dissertation.
I would like to thank all the teachers I’ve ever had who inspired me, from Mrs.
Violini in the 4
th
grade who first made learning fun, to Mrs. Hopkins in high school who
inspired me to strive for excellence and creativity, to Steven Sabat in college who first
opened my eyes to the misconceptions surrounding Alzheimer’s disease and aging in
such a thoughtful, inspirational way that has shaped my entire career trajectory. I also
owe significant thanks to my undergraduate research mentor, Abigail Marsh. Thank you
for providing my first research opportunities and allowing me to contribute to and learn
from the incredible work you are doing (which, incidentally, focuses on altruism).
I would like to thank my Healthy Aging Lab peers, especially Diana Wang and
Elizabeth Hagood, who have been there for me through every up and down of graduate
iv
school. Over the last few years, I learned with you and I learned from you, and I am so
grateful for your truly supportive friendships.
I want to thank Brad Rothseid for also being there for me every step of the way.
Thank you for your constant positivity, encouragement, and unwavering belief in me.
Through your example, you inspire me to work hard and be the best version of myself
everyday. I love you.
Finally, I need to thank my family for their support, emotionally and of course
financially, as I pursued this degree. Mom and Dad, you are the biggest role models in
my life – thank you for setting an amazing example for me, always investing in my
education, and instilling in me that I could achieve whatever I set my mind to. Thank you
and Sofie for always allowing me vent about my stress and frustrations and giving me
guidance and advice. I also want to thank my Nana, Beverly Grossman, for being such a
strong female role model and source of inspiration, and for always checking in on me.
And, I would also like to thank my grandparents, Marcy and Barry Iscovitz, because if I
don’t, I’ll never hear the end of it (but really, for their support and loving reminders not
to “work too hard”).
Lastly, I would like to acknowledge the funding I have received throughout
graduate school, including the USC Provost PhD Fellowship and the NIA
Multidisciplinary Training Grant (T32 AG000037). I would like to thank Eileen
Crimmins for making the training grant funding possible. I am very grateful to have
received this support.

v
TABLE OF CONTENTS
Dedication……………………………………………………………………………...…ii
Acknowledgements…………………………………………………………………...….iii
Abstract…………………...…………………………………………………………....…ix
Chapter 1: Background……………………………………………………………….…...1
1A. The Silver Tsunami: A Wave of Opportunity…………………………….…..1
1B. Generativity – “The What”…………………………………………………...2
1C. Generativity – “The Why”……………………………………………………4
1D. Generativity – “The Benefits”………………………………………….….....8
1E. Potential Mechanisms Linking Generativity to Health and Well-Being…….12
1F. Different Aspects of Generativity…………………………………………....14
1G. Dissertation Objectives…………………………………………………..….15
Chapter 2: Study 1 – Variations in Daily Cognitive Affective States as a Function of
Variations in Daily Generative Activity……………………..………………………..…18
2A: Introduction……………………………………………………………….....18
2B. Methods………………………………………………………………...……24
2C. Results………………………...…………………………………………..…30
2D. Discussion……………………………………………………………...……32
Chapter 3: Study 2 - Failure to Meet Generative Self-Expectations is Linked to Poorer
Cognitive-Affective Well Being……………………….…………………...............……42
3A: Introduction………………………………………………………………….42
3B: Methods…………………………………………………………………...…48
3C. Results……………………………………………….……………………....53
vi
3D. Discussion…………………………………………………………….……..55
Chapter 4: Study 3 - Generative Failure Linked to Poorer Health Through Cognitive-
Affective States…………………………………………………………………………..66
4A: Introduction………………………………………………………………….66
4B: Methods……………………………………………………………………...74
4C: Results……………………………………………………………………….78
4D: Discussion…………………………………………………………………...81
Chapter 5: Summary & General Discussion……………………………………..…....…94
5A. Study 1………………………………………………………………………94
5B. Study 2……………………………………………………………………....96
5C. Study 3………………………………………………………………………96
5D. General Discussion………………………………………………………….97
5E. Conclusions……………………………………………………………..….112
References........................................................................................................................114

vii
LIST OF FIGURES
Figure 1-1. Overview of Proposed Cognitive-Affective Pathways Linking Generativity to
Enhanced Health and Well-Being………………………………………….………..…...14
Figure 3-1. Unstandardized Indirect Effects of Generative Failure on Life Satisfaction
Through Cognitive-Affective States………………………………….………………….65
Figure 4-1. Overview of Proposed Model of Cognitive-Affective Pathways Linking
Generativity to Better Health…………………………………………...…………..……68
Figure 4-2. Unstandardized Indirect Effects of Generative Failure on Self-Rated Physical
Health Through Cognitive-Affective States………………………………..…………....90
Figure 4-3. Unstandardized Indirect Effects of Generative Failure on Self-Rated Mental
Health Through Cognitive-Affective States…………………………………………......91
Figure 4-4. Unstandardized Indirect Effects of Generative Failure on Chronic Conditions
Through Cognitive-Affective States……………………………………………..………92
Figure 4-5. Unstandardized Indirect Effects of Generative Failure on ADL Limitations
Through Cognitive-Affective States……………………………………………..………93

viii
LIST OF TABLES
Table 2-1. Characteristics of Analytic Sample (n = 1,747)……………………………...38
Table 2-2. Independent Variables: Generative Activities……………………………….39
Table 2-3. Dependent Variables: Cognitive-Affective States……………………...……40
Table 2-4. Results from Multilevel Regression Models Examining Effects of Daily
Generative Activities on Daily Cognitive-Affective States…………………………...…41
Table 3-1. Characteristics of the Analytic Sample (n = 2,252)…………………….……61
Table 3-2. Cognitive-Affective States and Generative Perceptions of Sample (n =
2,252)………………………………………………………………………………….…62
Table 3-3. Results from Regressions Examining the Relationship of Perceived
Generativity and Predicted Generativity to Cognitive-Affective States, Concurrently and
10 Years Later……………………………………………………………………………63
Table 3-4. Results from Regressions Examining the Association Between Generative
Failure and Cognitive-Affective States and Life Satisfaction/……………………….….64
Table 4-1. Characteristics of the Analytic Sample (n = 2,214)………………………….87
Table 4-2. Descriptive Statistics of Study Variables (n = 2,214)……………….……….88
Table 4-3. Results from Regressions Examining the Association Between Generative
Failure and Health, Controlling for Demographic Factors and Prior Generativity…...…89

ix
ABSTRACT
Generativity is defined as desire and activity dedicated to contributing to the
welfare of others. It was originally proposed by Erik Erikson as an important stage of
psychosocial development to be achieved during mid-life. The construct’s significance
has since extended to later life as well, when desire to leave behind a meaningful legacy
tends to grow with perceived finiteness of time. In addition to being an important concern
driving pro-social behavior, generativity seems to confer benefits such as improved
health and psychological well-being, supporting a successful aging trajectory. Though
accumulating research supports the positive links between generativity and well-being,
less is known about the underlying mechanisms through which this relationship occurs.
The following dissertation studies aimed to distinguish important cognitive-
affective pathways linking generativity to better health and well-being in middle and later
life. These studies sought to elucidate cognitive-emotional correlates of generativity to
improve our understanding of how generative goals and activities influence the well-
being of middle-aged and older adults. These aims were tested using the National Survey
of Midlife Development in the United States (MIDUS), and a MIDUS sub-study, the
National Study of Daily Experiences (NSDE). Specifically, three theoretically and
empirically derived cognitive-affective states were examined as correlates of generativity
in each study: feelings of self-worth or self-enhancement, positive affect, and social
connectedness. Study 1 used the NSDE to investigate within- and between-person
associations of daily generative activities and daily cognitive-affective states. After the
links between generativity and these cognitive-affective states were established, Studies 2
and 3 used data from Waves 2 (2004 - 2006) and 3 (2013-2016) of the MIDUS Survey to
x
examine whether perceived generative failure over time was tied to poorer life
satisfaction and health, respectively, and whether these links were mediated by the same
cognitive-affective states. Study 1 utilized multilevel regression models, and Studies 2
and 3 used regression and tests of multiple mediation to assess the hypothesized
cognitive-affective states as potential mechanistic pathways through which generativity
may be connected to better health and well-being.
The analyses revealed that generative activities, expectations, and self-perceptions
were related to higher levels of positive affect, social connectedness, and sense of self-
worth. Perceived failure to achieve generative goals over time was associated with poorer
cognitive-affective states, life satisfaction, and health. Mediation analyses suggested that
cognitive-affective states, most notably positive affect, seem to account for a substantial
proportion of the observed generativity/well-being associations.
In conclusion, the work presented in this dissertation provides preliminary
evidence for several cognitive-affective pathways (positive affect, social connectedness,
and sense of self-worth) that seem to play an important role in the relationship between
generativity and health and well-being in middle and later life. Generative activity seems
to affect individuals at the daily level, as well as more generally over time. This
dissertation supports prior researchers’ arguments that generativity may facilitate
successful aging and contributes important clarification on the mechanisms responsible
for these advantages. Findings suggest opportunities for intervention, as generativity may
provide a low-cost, broadly applicable mode of health promotion that benefits both
generative individuals and their communities. Given the cost of not giving to others,
efforts to recognize and address barriers are needed.
1
CHAPTER 1: BACKGROUND
1A: The Silver Tsunami: A Wave of Opportunity
“Aging is not ‘lost youth’ but a new stage of opportunity and strength.”
- Betty Friedan
As gerontologists are well aware, we are facing unprecedented population aging,
in the United States and across the globe. These demographic changes are due to a
combination of increased longevity and the Baby Boomer generation (born between 1946
and 1964) advancing into older age. Many view these changing tides negatively, calling
the phenomenon a “silver tsunami,” drawing a parallel between these demographic
changes and incomprehensible disaster. Those who hold these opinions likely fear the
growing burdens on our health care system and eventual insolvency of Social Security.
These are indeed significant challenges that accompany the growth of this segment of the
population, and they will require innovative solutions. However, our increase in life
expectancy, which has roughly tripled over the course of human history, has also been
called the “greatest accomplishment of the 20
th
century,” and rightfully so. Increased
lifespan is an astonishing achievement that has paved the way for individuals to continue
to contribute to society in meaningful ways in their later years. It can be argued,
therefore, that this “silver tsunami” should be reframed as an immense wave of
opportunity. As Hardy (2011) contends, the retiring cohort of Baby Boomers make up a
vast source of “untapped” potential. She discusses the notion of the “unretired retired,”
describing how many older adults have rejected the traditional notion of retirement as a
time for relaxation and instead desire to remain active and give back to their
communities. Indeed, older adults can revitalize communities in various ways, from
2
helping to staff under-funded programs to serving as mentors, among other activities,
creating a “win-win” situation that can benefit both older adults and their communities.
The Baby Boomer generation was once termed the “Me” generation, but as they
establish themselves as older adults, many will make an important transition, embracing a
“Beyond Me” focus (Kotre, 1984). Kotre (1984) argues that one effect of increasing
longevity may be to stimulate pro-social concern and involvement. For instance, he
explains, “never have so many generations in families been alive at the same time,”
suggesting growing opportunities for intergenerational engagement (Kotre, 1984, p.10).
In addition, unless individuals postpone their retirement, they will likely spend a growing
amount of time between retirement and the onset of disability. To put this in perspective,
the life expectancy for a 60 year-old U.S. woman today is another 24 years, a period that
is longer than the typical duration of child rearing. Kotre’s argument, though advanced
over 30 years ago, is still applicable as Baby Boomers find themselves confronting
increasing possibilities in their encore years, including opportunities to contribute to
society. Indeed, older adults often have much to contribute, drawing upon a lifetime of
work, social experiences, and accumulated wisdom. In North America, 40-50% of older
adults actively engage in formal volunteer roles. Moreover, older adults volunteer the
most of any segment of society (Gottlieb & Gillespie, 2008), and evidence suggests they
find opportunities to contribute to others across a wide range of domains (Warburton,
McLaughlin, & Pinsker, 2006).
1B: Generativity: “The What”
Generativity is defined as concern and activity dedicated to contributing to the
welfare of others, and has been the subject of an upsurge of interest to health and aging
3
researchers in recent years (Carlson, Seeman, & Fried, 2000; Schoklitsch & Baumann,
2012; Villar, 2012). Generativity was originally conceptualized by developmental
psychologist, Erik Erikson (1950), as an important stage of psychosocial development
that took on special significance during midlife (McAdams & de St. Aubin, 1992).
Erikson’s theory proposed eight central crises that individuals experience at different
stages leading to adaptive or maladaptive psychosocial development, and the midlife
stage was characterized by the conflict of generativity versus stagnation, or being stuck in
a pattern of self-absorption. Though proposed as a central focus of midlife, accumulating
evidence has suggested that generative desire and activity remain high into older age, as
well (McAdams, de St. Aubin, Logan, 1993; Kotre, 1984; McAdams, 2001; Schoklitsch
& Baumann, 2012; Villar, 2012). In her expansion of his life cycle theory, Joan Erikson
explained that the period of generativity could last 30 or more years, occupying the
duration of adulthood (Erikson, 1998). Though Erikson’s initial conceptual idea of
generativity focused on establishing and guiding future generations, emphasizing
parenthood as a key example, this definition has also evolved over time. The definition of
generativity is no longer limited by orientation towards the younger generation but rather
has extended to include any type of “activity or intention that may be beneficial to others
through the investment of one’s self” (Doyle, Rubenstein, & Medeiros, 2015, p. 410).
Generative desire or activity can therefore be targeted toward individuals in younger, the
same, or older generations – with the idea being that any contribution to others is a
contribution to the betterment of society. To that end, generativity can be expressed in a
variety of different ways, including through work and professional activities, volunteer
efforts, religious or political involvement, community activism and even in regular
4
expressions of friendship (McAdams & de St. Aubin, 1992). It can also be expressed
through a range of small activities, such as helping someone cross a street, to more
substantial commitments like caregiving, mentoring, or passing on life stories or values
(Doyle, Rubenstein, Medeiros, 2015).
Though Erikson originated the concept of generativity, McAdams and de St.
Aubin (1992) later expanded the construct into a more integrative theoretical framework.
According to their model, generativity originates from two motivational sources: cultural
demand (culture-specific normative expectations of how to behave) and inner desire (the
intrinsic need to behave in a certain manner). They argue that these sources engender a
concern for generativity in individuals, which in turn, leads to their conscious setting of
generative goals, or what they termed commitment. Individuals then strive towards
fulfilling these goals by setting out to engage in generative behavior or activities.
Ultimately, they argue, individuals attribute meaning to their generative actions in life
stories or narratives (Hofer et al., 2008; McAdams & de St. Aubin, 1992).
1C. Generativity: “The Why”
Though the McAdams & de St Aubin model touches upon sources of motivation
for generativity, neither their model nor the Erikson model truly probe individual
motivations for generative concern and activities in middle and later life. Generativity is
theorized to be a crucial part of healthy ego development and self-actualization, but why?
Interestingly, although generativity can sometimes imply willingness to risk harm/costs
for the benefits of others, generative engagement can reflect selfish as well as altruistic
motives, an important distinction between the two concepts, which are sometimes used
interchangeably (Jones & McAdams, 2013; Kruse & Schmitt, 2012). For example, Kotre
5
defined generativity as the “desire to invest one’s substance in forms of life and work that
will outlive the self” (1984, p. 10), a definition that reflects a more self-directed aim.
Others have since echoed the importance of living a meaningful life and leaving behind a
lasting legacy. Several theories offer insights and potential reasons as to why generativity
may become increasingly important in middle and later life. One potential answer may be
found in Terror Management Theory (Greenberg, Solomon, & Pyszczynski, 1997), which
proposes that the pursuit of symbolic immortality is a strategy individuals use to protect
themselves from death-related fear and anxiety. Terror Management Theory asserts that
the internal conflict between individuals’ desire for continued life and their certainty of
the inevitability of death often leads to severe anxiety. To cope with this anxiety, people
try to maintain the belief that they are beings of enduring value whose existence will
carry on in some form after their death (Maxfield et al., 2014). Support for this
hypothesis comes from research findings that older adults respond to reminders of death
by adopting more pro-social or generative orientations (Greenberg, Solomon, &
Pyszczynski, 1997; Major, Whelton, Schimel, Sharpe, 2016). Thus, fear of death may
inspire generativity in individuals who want to ensure that their legacies live on after their
physical lives have ceased.
Another theory that helps shed light on a growing focus on generativity with age
is Socioemotional Selectivity Theory. Socioemotional Selectivity Theory proposes that
aging signals a shift in the priorities guiding one’s goals and motivations, favoring goals
that are more emotionally meaningful over those that are more knowledge- or
achievement-focused (Carstensen, 1993). The idea underlying Socioemotional Selectivity
Theory is that when individuals perceive time to be finite or limited, they become more
6
present-oriented, striving for emotional fulfillment, for example through meaningful
social interactions and volunteerism (Carstensen, Isaacowitz, & Charles, 1999; Gottlieb
& Gillespie, 2008). Similar to Terror Management Theory, this theory suggests that
individuals may be influenced by the finiteness of life, but rather than focus on leaving
behind a legacy, individuals are driven to focus on the “here and now,” or positive
emotional and social experiences in the moment, to enjoy the remaining time in their
lives through meaningful interactions.
Others have proposed that being generative may be enticing to older adults
because it offers them an opportunity to transcend other personal difficulties they might
be experiencing with advancing age (Ehlman & Ligon, 2012). This idea of self-
transcendence aligns well with Baltes and Baltes’ (1990) theory of selection,
optimization, and compensation (SOC). SOC acknowledges that individuals’ resources
are limited and as people age, they become increasingly aware of the age-related gains
and losses they are experiencing. Baltes and Baltes (1990) argue that diminishing social,
cognitive, and functional reserves with age cause individuals to be more careful in terms
of how they allocate their resources and attention. To cope with these limitations, they
propose that people employ three strategies: selection, optimization, and compensation.
Individuals select goals that are important to them and can be realistically obtained. Then
they engage in behaviors that will optimize the likelihood of goal success. And if the
goals cannot be met by employing their usual strategies, they can engage in
compensatory activities such as enlisting the help of others. Thus, based on the theory of
SOC, individuals might be motivated to behave generatively because they are still
capable of contributing to others, despite growing limitations in other realms (cognitive,
7
physical) with age. Doing good for others may help them feel less regret over other things
they can no longer do and come to view their aging experience more positively (Ehlman
& Ligon, 2012).
Lastly, there are also likely evolutionary advantages to helping others that have
spurred generative behavior. Though it seems counter-intuitive to evolutionary
psychology’s emphasis on competition and survival of the fittest, group selection theory
suggests altruistic behavior within groups provides a competitive advantage against other
groups that would be selected for (Post, 2005; Sober & Wilson, 1998). According to this
theory, members of a successful group would likely have an innate orientation towards
cooperation and supporting others, the inhibition of which would not be healthful (Post,
2005). Similarly, Inagaki & Orehek (2017) argue that humans have a natural inclination
to care for, nurture, and protect others, particularly when others are in need. They posit
that the same processes that foster caring for offspring may also apply to others, such as
friends, other relatives, significant others, and the community. In this case, they argue, it
makes sense that biological mechanisms would exist to reinforce generativity and to
reduce the likelihood of social withdrawal and the strength of the stress response to allow
for helping those in need (Inagaki et al., 2016; Inagaki & Orehek, 2017). Thus, there may
be innate, evolutionary biological motivations driving generativity as well as cultural and
psychological influences. Most likely it is a confluence of the above factors contributing
to individuals’ generative concerns and driving their behavior, but it is important to
understand the motivating factors behind generativity to fully comprehend its value.

8
1D. Generativity: The Benefits
As there are multiple motivations that might facilitate generativity, there may also
be numerous benefits associated with generative feelings and activities. Though
definitions of successful aging are varied and somewhat controversial, there is a growing
consensus that generativity is a key component in the experience of successful aging,
supporting the idea that contributory activities can lead to better health and well-being in
later life (Depp and Jeste, 2006; Rowe and Kahn, 1998; Villar, 2012). These conceptions
are not only emphasized by researchers but by older adults themselves. For instance, in
qualitative interviews on interpretations of successful aging, 95% of older adults
identified engagement with life as an important factor, and within that domain, 73%
mentioned the importance of contributing to society (Reichstadt et al., 2010). Similarly,
from a philosophical point of view, the concept of generativity has been considered in
light of Aristotle’s teachings and beliefs on human flourishing. Snow (2015) argues that
one could not truly flourish without being generative, and that generativity is a way in
which individuals can actualize their potential as human beings.
Support for the idea that generativity is related to successful aging or flourishing
in later life emerges from evidence that higher self-perceptions of generativity, and actual
generative engagement, predict more favorable social, psychological, and physical well-
being in middle and later life (McAdams, de St. Aubin, Logan, 1993; Gruenewald, Liao,
& Seeman, 2012; Grand et al., 1988). In particular, higher perceptions of generativity
have been shown to predict lower mortality, lower risk of the development of disability,
and better subjective well-being in later life (Gruenewald et al., 2009; Gruenewald et al.,
2007). Older adults who feel more generative or socially useful have also been shown to
9
display better memory and executive function performance compared to those who
perceive themselves as less generative (Hagood & Gruenewald, 2013). On the other end
of the spectrum, studies have also shown that feelings of uselessness predict greater
mortality in those with disabilities three years later (Curzio, Bernacca, Bianchi, & Rossi,
2017). Importantly, the connections between generativity and health seem to remain even
when controlling for established sociodemographic, biobehavioral, and psychosocial risk
factors for poor health.
In addition to generative self-perceptions being beneficial, engagement in
generative or socially contributory behaviors is also associated with better overall
physical and mental health (Schwartz et al., 2002). Volunteering, for example, is
associated with myriad personal benefits, such as improved physical health, less
functional dependency in later life, lower levels of pain, fewer depressive symptoms,
greater life satisfaction, slower cognitive decline, and even lower mortality (Hong &
Morrow-Howell, 2010; Lum & Lightfoot, 2005; Musick & Wilson, 2003; O’Neill,
Morrow-Howell, and Wilson 2011; Van Willigen, 2000). Helping others is also linked to
higher levels of happiness and self-esteem (Ellison 1991; Weinstein and Ryan 2010). For
example, older adults taking part in Experience Corps, an intergenerational program in
which seniors volunteered as mentors for elementary school students, displayed higher
levels of social, cognitive, and physical activity compared to non-participants (Fried et al.
2004). After participating in the program for two years, volunteers experienced fewer
depressive symptoms and functional limitations (Hong and Morrow-Howell 2010).
Participation in the Experience Corps program has also been shown to contribute to
greater self-perceptions of generativity (Gruenewald et al. 2016) and a stronger sense of
10
purpose in life (Gruenewald et al. 2016). There has even been evidence suggesting short-
term neurocognitive plasticity as a result of older adults’ participation in the Experience
Corps program (Carlson et al. 2009; Carlson et al. 2015). Similar evidence can be found
in the social support literature, which has seen a shift in recent years from focusing
primarily on the benefits of receiving social support to devoting almost equal attention to
the benefits of giving support to others (Konrath & Brown, 2013). For instance, studies
have linked support giving to positive outcomes such as reduced depressive symptoms,
better psychological well-being, and reduced risk of mortality (Bangerter et al., 2014;
Brown et al., 2008; Eisenberger, 2013; Poulin et al., 2013; Schwartz, Bell Meisenhelder,
Ma, & Reed, 2003; Wang & Gruenewald, 2017). Support giving is also related to
physiological advantages, including lower ambulatory blood pressure and heart rate
(Eisenberger, 2013). Physiological benefits are evident across other expressions of
generativity as well, as engagement in contributory activities like volunteering has been
found to be associated with lower levels of C-reactive protein (CRP), a key biomarker of
inflammation in the body (S. Kim & Ferraro, 2013, Moieni, 2017).
Undoubtedly, the literature linking generativity to more favorable health and well-
being is extensive, but are expressions of generativity always healthful? This question
can arise when considering extremely giving acts such as caregiving. Caregiving may
certainly be categorized as a generative activity, in that it is an experience characterized
by social, physical, emotional, and often financial contributions to others, typically with
the primary goal of improving their health, functioning, and well-being (Grossman &
Gruenewald, 2017). Though the majority of caregiving research focuses on negative
consequences of providing care, many caregivers also report positive aspects of the
11
experience, often stemming from feelings of usefulness and being needed by a loved one
(Green, 2007; Schulz & Sherwood, 2008). Despite these reported gains, detrimental
consequences of caregiving have been widely documented, including greater levels of
depression, stress, and anxiety, as well as worsened physical health, compared to non-
caregivers (Cummins, 2001; Pinquart & Sörensen, 2003; Robison, Fortinsky, Kleppinger,
Shugrue, & Porter, 2009). However, it has been argued that some negative effects of
caregiving may actually stem from witnessing the decline and impending death of a loved
one, rather than from the caring or helping behavior itself, as these experiences have been
difficult for researchers to disentangle (Eisenberger, 2013). For instance, some research
has shown that caregivers who provide greater hours of care actually have lower
mortality, but caregivers with spouses in poorer health experienced greater mortality
(Brown et al, 2009). These findings support the idea that providing care may actually be
beneficial; however, the stress of a loved one’s illness and emotional turmoil of
confronting loss may be the more detrimental to caregivers’ health. Furthermore, research
suggests that deriving and focusing on positive aspects of caregiving, such as enhanced
feelings of generativity, instead of primarily on the demands and burden of the role, may
help mitigate some of the negative impacts of distress (Grossman & Gruenewald, 2017).
Additionally, studies have shown that generative women report less subjective burden in
caring for their aging parents (Peterson, 2002). However, though this research suggests
that generativity is typically beneficial, researchers emphasize that individuals are likely
to derive the most health benefits from helping others when the support they are
providing is not too overwhelming (Post, 2005).
12
Although an impressive body of research supports overall gains associated with
generativity, less is currently known about the mechanisms through which generativity
might be related to better health and well-being. If the more favorable health and well-
being associated with greater levels of generative engagement and more positive self-
perceptions of generativity reflects a causal effect of generativity on well-being, then it is
important to understand how it is that individuals who are more generative come to have
these better outcomes.
1E. Potential Mechanisms Linking Generativity to Health and Well Being
Theoretical and empirical formulations of pro-social behavior suggest several
benefits of generative engagement that may explain why more generative individuals
experience better health and well-being, including behavioral, physiological, and
cognitive-affective factors. As an example of a potential behavioral pathway,
volunteering has been found to be a precursor to increased levels of physical activity
(Librett, Yore, Buchner, & Schmid, 2005), suggesting that physical activity and its well-
documented health benefits represent one plausible behavioral mechanism linking
generative activity to improved health and well-being. Further support for this link comes
from Experience Corps findings that program volunteers showed a sustained increase in
physical activity levels compared to a control group, despite similar activity levels at
baseline (Tan et al., 2009). Many volunteer roles require some level of physical activity,
and as older adults are the most sedentary age group (Gardiner et al., 2011), even having
a reason to leave the house may provide health gains. In addition to physical activity,
volunteers have been found to be less likely to engage in risky health behaviors, such as
smoking (Musick, Herzog, & House, 1999). Besides potential behavioral mediators of
13
this association, physiological pathways may also play a role in the gains derived from
generativity. Giving social support to others, for instance, has been shown to be tied to
lower blood pressure, mean arterial pressure, and heart rate in the support giver (Piferi &
Lawler, 2006).
The following studies employ methodological innovations to better elucidate a
third set of pathways, cognitive-affective correlates, linking generativity to favorable
health and well-being indicators. Specifically, these studies focus on self-enhancement or
self-worth, positive affect, and social connectedness correlates of generativity (McAdams,
de St. Aubin, Logan, 1993; Post, 2005).

Several studies have suggested a positive
relationship between contributory activities and self-esteem, positive affect, and social
connectedness (Brown, Hoye, & Nickelson, 2012; Huta & Zuroff, 2008; Kahana, Bhatta,
Lovegreen, Kahana, & Midlarsky, 2013). For instance, studies contrasting pro-social acts
with more self-focused behaviors have found that spending money on others as opposed
to on oneself contributes to increased feelings of positive affect (Aknin et al., 2003), and
doing nice things for others instead of for oneself leads to a greater sense of social
belonging and increased happiness (Inagaki & Orehek, 2017; Nelson, Layous, Cole, &
Lyubomirsky, 2016). Other research findings also provide evidence for a reciprocal
relationship between generativity and positive affect and social connectedness
(Greenfield & Marks, 2004). Additionally, generativity or perceived contribution to
others has been found to be associated with greater perceptions of self-worth (Piferi &
Lawler, 2006). More annual volunteer hours enhance self-esteem (Thoits & Hewitt,
2001), and higher self-perceptions of generativity are linked to greater feelings of self-
efficacy and mastery (Gruenewald et al., 2007, 2009). Overall, helping others seems to
14
make people “feel good” (Musick & Wilson, 2003), and this pattern is reflected in the
favorable cognitive-affective states of giving for generative individuals.

Figure 1-1. Overview of proposed cognitive-affective pathways linking generativity
to more favorable health and well-being.

1F. Different Aspects of Generativity
Research has shown that higher self-perceptions of generativity and greater
generative engagement (e.g. volunteering, helping behaviors) are typically associated
with more favorable health and well-being over time (Grand et al., 1988; Gruenewald,
Liao, & Seeman, 2012; McAdams, de St. Aubin, Logan, 1993). The following studies
aim not only to gain a better understanding of the mechanisms underlying these
associations, but also to examine the influences of different aspects of generativity,
including generative activities, generative strivings, and generative perceptions. All three
components of generativity are hypothesized to have positive influences on health and
well-being; however, little empirical research has been conducted to begin to tease apart
or understand the differential benefits of these components of generativity (Gruenewald
et al., 2007; McAdams & de St. Aubin, 1992; O’Neill, Morrow-Howell, & Wilson,
2011).
Positive affect
Sense of self-worth
Social connectedness

Health &
Well-being

Generativity
15
Study 1 examined daily generative activities, and Studies 2 and 3 examined
generative strivings and perceptions over time, all in relation to cognitive-affective well-
being. Specifically, Studies 2 and 3 investigated a relatively unexamined element of the
generative experience in perceived failure to meet generative self-expectations over time.
Erikson’s theory focuses on psychosocial goals of different life stages and the effect that
an individual’s ability to satisfy these developmental goals may have on his/her self-
concept. Specifically, he felt that healthy adaptation depended on successful resolution of
the conflict at each life stage, including the stage of generativity versus stagnation. Self-
discrepancy theory suggests that an individual’s failure to meet his or her expectations
may result in worse affective and health outcomes, or greater emotional vulnerabilities
(Higgins, 1989; Kelly, Marshall, & Wood, 2015; Maio & Thomas, 2007). Psychiatrist
and theorist Victor Frankl similarly believed that mental health was based on the “tension
between what one has already achieved and what one still ought to accomplish, or the gap
between what one is and what one should become” (Frankl, 1956, p. 104). Thus, taking
these theories into account, failure to fulfill generative self-expectations should be
associated with poorer health and well-being. Together, these investigations of generative
activity and perceived generative failure are important in that they will promote a greater
understanding of how various expressions of generativity directly influence individuals’
thoughts and feelings to shape their health and well-being.
1G. Dissertation Objectives
The following dissertation studies aim to elucidate some of the mechanisms
through which generativity is related to better health and well-being. According to
Erikson’s theory of psychosocial development, generativity is an important
16
developmental goal of middle and later life. He theorized that without achieving
generativity, individuals would remain stagnant, a developmental barrier that could
ultimately prove detrimental to their health and psychological well-being. As aging is a
process typically accompanied by novel challenges, such as increased morbidity,
depleting energy resources, increased limitations with activities of daily living, and a
decline in one’s social network, it is increasingly important to understand the connections
between psychosocial variables and healthy/unhealthy trajectories of aging. Broadly, this
dissertation sought to gain a better understanding of cognitive-affective mechanisms
underlying the relationship between generativity and health and well-being. This goal
was achieved through a series of three studies with the following specific aims.
First, Study 1 examined the connections between several types of generative
activity and cognitive-affective states using daily experience data from the National
Study of Daily Experiences (NSDE), a sub-study of the National Survey of Midlife
Development (MIDUS) (Almedia, McGonagle, & King, 2009). This investigation
capitalized upon the opportunity to use daily data to investigate both within-person and
between person associations between generative activities (volunteering, informal help,
and emotional support) and the cognitive-affective states of positive affect, social
connectedness, or self-enhancement or self-worth. Building on the investigation of the
connections between generative activities and these cognitive-affective correlates, Study
2 utilized MIDUS 2 and 3 to examine the effects of perceived current and expected future
generative contributions on the same cognitive-affective states. Though the idea that
fulfilling generative goals is important for one’s psychosocial well-being is at the heart of
the Erikson model, empirical evaluation of this hypothesis is lacking, especially within
17
large-scale longitudinal investigations. Furthermore, since the effect of relative success in
achievement of generative goals is unknown, this study examined whether perceived
generative failure over time would be related to lower levels of these cognitive-affective
states.
Generativity has been highlighted as a key predictor of successful aging,
theoretically and empirically (Depp and Jeste, 2006; Rowe and Kahn, 1998; Villar,
2012). It has been argued that there are two predominant models when it comes to
defining successful aging, the psychosocial model and the biomedical model. The
psychosocial model emphasizes the importance of life satisfaction, the most commonly
proposed element of successful aging (Bowling & Dieppe, 2005). Therefore, Study 2
focused on life satisfaction as an outcome and examined whether the cognitive-affective
states mediated the relationship between generative failure and life satisfaction. The
biomedical model of successful aging emphasizes absence of disease and maintenance of
physical and mental functioning (Bowling & Dieppe, 2005). Therefore, Study 3 tested
whether the cognitive-affective states mediated the links between generative failure and
health indicators, including self-reported mental and physical health, number of chronic
conditions, and activities of daily living (ADL) limitations. Taken together, these studies
move the field a step forward in understanding the relationship between generativity and
a healthy or successful aging trajectory, with a focus on underlying cognitive-affective
pathways.

18
CHAPTER 2: STUDY 1
(Accepted author version, published in Journal of Happiness Studies)
Variations in Daily Cognitive Affective States as a Function of Variations in Daily
Generative Activity
Introduction

In the words of Booker T. Washington, “If you want to lift yourself up, lift up
someone else.” Indeed, research has supported the observation that in giving to others,
one may also benefit him or herself. One theoretical framework through which
researchers have investigated this occurrence is the construct of generativity. Generativity
is defined as concern and activity devoted to the promotion of the well-being of others,
including friends, family, and the community (Erikson 1950; Gruenewald, Liao, and
Seeman 2012). The developmental psychologist Erik Erikson proposed generativity as a
key milestone of psychosocial development in midlife. While the focus on caring for
others and guiding the next generation is postulated to attain greatest significance in
midlife, generative strivings have been found to remain important into older age as well
(Erikson 1963; McAdams, de St. Aubin, and Logan 1993; Villar, 2012). Individuals can
be generative in a variety of different ways, including through work and professional
activities, volunteer efforts, religious or political involvement, parenting, caregiving, and
even friendship (McAdams and de St. Aubin 1992). Generative engagement surfaces as a
key component across many definitions of successful aging, supporting the idea that
socially contributory activities lead to better health and well-being in later life (Depp and
Jeste 2006; Rowe and Kahn 1998; Villar 2012).

19
Benefits of Generativity
Undoubtedly, individuals and the communities in which they live benefit
immensely from the generative contributions of their citizens, but an accumulating body
of research also suggests that generative individuals reap the benefits as well.
Researchers have examined both engagement in generative activities, such as
volunteering or caregiving, as well as individuals’ self-perceptions of generativity, in
relation to health and well-being. Individuals who have greater self-perceptions of
generativity have been found to experience more favorable social, psychological, and
physical well-being over time (Gruenewald, Karlamangla, Greendale, Singer, and
Seeman 2007, 2009; Grand, Grosclaude, Bocquet, Pous, and Albarede 1988). For
example, Gruenewald, Liao, and Seeman (2012) found that older adults with greater self-
perceptions of generativity had lower risk of the development of disability and lower
mortality than those who perceived themselves as less useful to others. Others have found
similar links between greater feelings of usefulness and reduced morbidity and mortality
(Grand et al. 1988; Okamoto and Tanaka 2004; Pitkala, Laakkonen, Strandberg, and
Tilvis 2004). Those with greater self-perceptions of generativity have also been found to
experience enhanced levels of subjective well-being in later life, including fewer
depressive symptoms, as compared to those who feel less generative (Gruenewald et al.
2007, 2009; McAdams et al. 1993).
Actual engagement in generative activities has also been found to be linked to
better health and well-being. Volunteering, for example, is associated with countless
personal benefits, including improved physical health and less functional dependency in
older adulthood, increased psychological well-being and fewer depressive symptoms,
20
greater quality of life, improved cognitive ability and slower cognitive decline, and lower
mortality (O’Neill, Morrow-Howell, and Wilson 2011). Helping others has also been
found to be associated with greater levels of happiness, life satisfaction, and self-esteem
(Ellison 1991; Weinstein and Ryan 2010). Importantly, there is support for the beneficial
effects of volunteerism and other types of helping behaviors across age groups. For
example, older adults taking part in Experience Corps, a program in which seniors
volunteer to help elementary school students with academic achievement and personal
development, reported higher levels of social, cognitive, and physical activity compared
to non-participants (Fried et al. 2004). Specifically, studies have found that after two
years of participation in the program, volunteers experienced fewer depressive symptoms
and functional limitations (Hong and Morrow-Howell 2010). Participation in the program
has also been found to lead to enhanced self-perceptions of generativity (Gruenewald et
al. 2016) and a greater sense of purpose in life (Gruenewald et al. 2016). There has also
been evidence suggesting short-term neurocognitive plasticity as a result of older adults’
participation in the Experience Corps program (Carlson et al. 2009; Carlson et al. 2015).
Yet, older adults are not the only individuals who have been shown to benefit from
engagement in contributory activities. Teenagers who help others have also been found to
derive benefits, such as more positive social relationships, greater feelings of purpose in
life, and increased self-acceptance (Schwartz, Keyl, Marcum, and Bode 2008),
suggesting that generative activities may be beneficial across age groups. To date,
however, little research has directly compared the strength of associations between
generative engagement and specific indicators of well-being. Additionally, although this
21
growing body of research suggests widespread benefits of generativity, less is known
about how generativity might lead to such positive outcomes.
Potential Mechanisms Underlying the Benefits of Generativity
Theoretical and empirical formulations of prosocial behavior suggest several
benefits of contributory behavior that may explain why more generative individuals
experience better psychological and physical well-being, including specific cognitive-
affective correlates of generative activity. The current study probes hypothesized
cognitive and affective states posited to be linked to engagement in generative behavior
including self-enhancement (self-esteem), positive affect, and social connectedness
cognitions and emotions (McAdams, de St. Aubin, and Logan 1993; Post 2005). One
theory that supports these empirical connections is role enhancement theory. An
adaptation of role theory, role enhancement is a theory that has often been used to explain
the positive link between volunteering and health. Role enhancement theory suggests that
by assuming a productive role, such as volunteering, individuals (especially older adults)
attain more resources, including larger social networks, as well as more power and
prestige, which lead to better mental and physical health (Lum and Lightfoot 2005;
Moen, Dempster-McClain, and Williams 1992; Morrow-Howell, Hinterlong, Rozario,
and Tang 2003). This theory supports the link between generative activities and stronger
social connections as well as greater self-esteem (“power and prestige”).
Empirical studies also suggest a positive relationship between contributory
activities and positive affect, self-esteem, and social connectedness (Brown, Hoye, and
Nicholson 2012; Huta and Zuroff 2007; Kahana, Bhatta, Lovegreen, Kahana, and
Midlarsky 2013). For example, generative perceptions and generative activities (i.e.
22
volunteering) have been shown to be associated with greater levels of positive affect (e.g.
Greenfield and Marks 2004), a link that is also supported by individuals’ reports that
helping others makes them “feel good” (Musick and Wilson 2003). Greater self-esteem
and social connectedness have been found to mediate the positive relationship between
volunteering, a typically generative activity, and well-being (Brown, Hoye, and
Nicholson 2012). Research also supports an association between formal volunteering and
greater positive affect (Greenfield and Marks 2004) and happiness (Dulin, Gavala,
Stephens, Kostick, and McDonald 2012). To date, however, there has been little
empirical examination of these hypothesized cognitive and affective correlates of
generativity and little to no direct investigation at the daily level. This study makes an
important contribution to the literature by employing daily diary surveys to test these
hypothesized associations and gain a more in-depth understanding of the relationship
between generativity and cognitive-affective well-being. Although collected data are
observational in nature, examining these links on a tighter temporal level than has been
achieved by prior research may also provide more support for a potential causal role of
generative activity on the three hypothesized cognitive-affective states.
Daily Experience Designs to Examine Correlates of Activity
Most of the observational studies examining the links between generative activity
and well-being have relied on reconstructive accounts, assuming accurate memory and
reporting among participants. Daily experience sampling methods, which obtain multiple
assessments of behavior and experiences over short periods of time, are designed to
capture experiences on a more proximal level (Hektner, Schmidt, and Csikszentmihalyi
2007). The use of daily reports of both activity and affect reduces recall biases and
23
increases reliability by providing more observations per subject. Daily experience
sampling methods also allow for the opportunity to gain a more focused understanding of
the relationship between generativity and cognitive-affective states by investigating these
links at both the day- and person-level. Researchers have employed similar experience
sampling methods to study other types of positive thoughts or activities, such as
gratitude, optimism, acts of kindness, or writing about life goals, and they have found that
engaging in positive or prosocial activities can have immediate and long term beneficial
effects on well-being. These methods also allowed them to better understand some of the
cognitive-affective mechanisms underlying the connections between their activity of
interest and well-being (Emmons and McCullough 2003; King 2001; Lyubomirsky,
Dickerhoof, Boehm, and Sheldon 2011). For example, in a study on counting blessings in
daily life, daily gratitude exercises were found to be associated with higher levels of
positive affect, potentially one of the underlying cognitive-affective pathways linking
feelings of gratitude to improved well-being (Emmons and McCullough 2003). Though
researchers have utilized these methods to investigate cognitive-affective states linked to
several different behaviors, to our knowledge, this methodology has not yet been
employed to study generative activity and its associated cognitive-affective correlates.
Examining these connections at the daily level can help promote a more focused
understanding of the aforementioned larger scale positive associations between
generativity and well-being.
Present Analysis
The primary aim of the current study was to capitalize upon the opportunity to
utilize daily data to better understand whether cognitive-affective correlates vary with
24
daily variations in generative activity. The current study provides an important addition
to this realm of research in its in-depth examination of potential cognitive-affective
correlates of generative activity at the daily level. The opportunity to examine these
associations in a repeated-measures dataset allows for this unique contribution to the
generativity literature. Limitations of prior studies have included the reliance on
reconstructive accounts of participants’ activities and feeling states, as well as wide-
ranging, non-specific time scales. For instance, participants are often asked to recall their
activities and feelings “over the past year” or “in general,” which is subject to bias and
inaccuracies. This study addresses these prior limitations by examining these associations
on a more proximal time scale, facilitating a tighter temporal coupling between activity
and feeling states. By providing a more in-depth understanding of the thoughts and
feelings that flow from engagement in specific generative activities, this study contributes
to a better understanding of how engagement in these activities might shape health and
well-being in a positive direction, as prior research overwhelmingly suggests. We expect
that on days when individuals have a greater level of generative engagement, they will
also experience greater feelings of self-worth, positive affect, and social connectedness.
Methods
Data and Participants
Data for this study come from the National Study of Daily Experiences (NSDE), a
sub-study of the National Survey of Midlife Development in the United States (MIDUS).
The MIDUS survey was designed with the goal of promoting the investigation of the role
of psychological, social, and behavioral factors in shaping health and well-being with
aging across the life course (www.midus.wisc.edu). The first wave of the MIDUS survey
25
collected data from 7,108 participants ages 25-74 and was administered in 1995/1996.
Subjects were recruited to participate in the study through national random digit dialing
and oversampling of 5 metropolitan cities in the United States. MIDUS II is the 10-year
follow-up to the original MIDUS study in 2004/2006 (n=4,963 initial phone survey and n
= 4,041 for subsequent mail survey; see www.midus.wisc.edu). The present study
utilized data from the NSDE II (from the second wave of the MIDUS Study) (2004-
2006), as it contained a more comprehensive measurement of the cognitive-affective
states of interest. The respondents were a sub-sample of 2,022 participants from the
larger MIDUS Study, ages 33-84. The NSDE involved collection of data via nightly
phone interviews over a period of 8 days to assess respondents’ daily experiences,
activities, and affect (www.midus.wisc.edu), yielding up to 8 total measurement
occasions.
Measures
Generative Activity. Each night, participants were asked about the activities in which
they engaged that day. Three types of generative activity were assessed in the NSDE that
are included as predictors in the present study, as they represent different ways
individuals tend to contribute to the well-being of others. These include whether
participants volunteered, gave informal help, or provided emotional support to others
each day. For each type of activity, respondents were provided specific examples of
activities that meet the criteria for each question.
Volunteering. Volunteering status was assessed with a Yes/No question asking
whether participants spent any time on formal volunteer work. Specifically, they were
asked, “Since (this time/we spoke) yesterday, did you spend any time doing formal
26
volunteer work at a church, hospital, senior center, or any other organization?” They were
informed that formal volunteering experiences could include working on behalf of
community organizations, local sports organizations, or any kind of voluntary work with
an organization (e.g. coaching a softball team).
Informal Help. Informal help was measured similarly with another Yes/No
question. Participants were asked, “Since (this time/we spoke) yesterday, did you spend
any time giving any unpaid assistance to people who do not live with you, such as free
baby-sitting or help with shopping?”
Emotional Support. Participants were also asked whether they provided
emotional support to others each day (Yes/No). Specifically, they were asked, “Not
counting work you might do as part of your job, did you spend any time giving emotional
support to anyone, like listening to their problems, giving advice, or comforting them,
since (this time/we spoke) yesterday?”
Dichotomous indicators were utilized to represent the continuous measures of
time spent engaged in volunteering and informal help, as the distribution of hours for
each variable was highly skewed, mostly due to a preponderance of individuals indicating
little to no volunteer and informal helping activity.
Cognitive-Affective States. Daily psychological well-being measures of positive affect,
self-enhancement, and social connectedness were examined as cognitive-affective
correlates of generative activities. The items comprising these scales were drawn from
individual thought and feeling items included in the repeated assessments each night. A
factor analysis was conducted to confirm the fit of specific items to each scale.
27
Positive affect. The positive affect scale asked participants for how much of the
day they felt “cheerful,” “satisfied,” “enthusiastic,” “full of life,” “extremely happy,”
“calm and peaceful,” and “in good spirits.” All of these items were rated on a 5-point
scale, ranging from 0 indicating “None of the time, to 4 indicating “All of the time.” The
item ratings were then averaged to compute a scale score for positive affect (α = 0.91)
(min: 0, max: 4).
Self-enhancement. The self-enhancement scale consisted of the two questions
assessing individuals’ feelings of self-worth, specifically querying how much of the time
each day they felt “confident” and “proud.” Again, respondents were asked to rate their
response to these questions on a 5-point scale measuring frequency of these feelings, and
their ratings were averaged to create a self-enhancement scale score (α = 0.72) (min: 0,
max: 4).
Social connectedness. The social connectedness scale was comprised of two
questions querying respondents’ feelings of social integration or connectedness. The
scale consisted of the questions, “How much of the time did you feel close to others?”
and “How much of the time did you feel like you belong?” Again, these items were rated
on a 5-point scale. Their ratings were then averaged to comprise a social connectedness
score (α = 0.81) (min: 0, max: 4).
Covariates. The analyses controlled for age, sex, race, and education. For race, a dummy
variable was created to represent white or non-white race/ethnicity. Educational
attainment was coded into a categorical variable with three categories, including “high
school or less,” “some college,” and “4 year college degree or greater.”
28
As well-being can differ based upon the day of the week (e.g. weekdays vs. weekends),
we also controlled for day of the week in the each of the models. Additionally, the
analysis controlled for marital status (dummy variable), work status (dummy variable),
functional limitations (1-4 scale, with a higher score indicating greater limitations), and
frequency of social contact (1-8 scale, from 1 representing “several times a day” to 8
representing “never or hardly ever”).
Analytic Strategy
All analyses were performed using STATA (Version 13.1). Before examining the
effects of daily generative activities on daily cognitive-affective correlates, descriptive
statistics were examined. The analytic model employed for the present study was a
multilevel regression model used to separate the between-person and within-person
variability to investigate the associations between daily generative activity and daily
cognitive-affective correlates over the 8-day period. Within-person analyses examined
whether daily cognitive-affective states varied alongside variations in generative activity
on a given day within people. Between-person variability encapsulated how individuals
who generally differ from one another in their average levels of generative activity vary,
on average, in their levels of examined cognitive-emotional states. Failure to explicitly
consider both between- and within- person sources of variation when modeling repeated
measures data (e.g. daily diary data) could lead to biased results and potentially false
conclusions regarding within-person relationships over time (Hoffman and Stawski
2009). Although longitudinal data is typically collected with the goal of measuring
within-person associations, it is important to recognize that within-person processes do
not occur “in a vacuum”, and the effects of more stable individual differences in the
29
longitudinal measures also need to be modeled explicitly, as is accomplished with
assessment of between-person associations (Hoffman and Stawski, 2009). In these
models, the coefficients account for the other association. For example, the within-person
parameter coefficient is derived from a model accounting for between-person association
and vice versa. For the technical details of implementing these models, see Hoffman
(2015).
An additional advantage of the multilevel approach to repeated measures data is
that it does not require equally spaced measurements. It allows individuals to vary in their
number of completed assessments, as sometimes occurs in multi-day investigations.
Assessment of the missing at random assumption was conducted to ensure that those
individuals with missing data did not differ in a meaningful way from those who were not
missing data. The equations below represent the association between a specific daily
cognitive-affective state and engagement in generative activity:
Level 1

𝐶𝑜𝑔𝑛𝑖𝑡𝑖𝑣𝑒 𝐴𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑆𝑡𝑎𝑡𝑒𝑠
01
= 𝛽
41
+ 𝛽
61
(𝐺𝑒𝑛 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦
01
− 𝐺𝑒𝑛 𝐴𝑐𝑡𝚤𝑣𝚤𝑡𝑦
<
= = = = = = = = = = = = = = = = = =
)+ 𝑒
01

Level 2

Intercept:
𝛽
41
= 𝑌
44
+ 𝑌
46
(𝑆𝑜𝑐𝑖𝑜𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠) + 𝑌
4E
(𝐺𝑒𝑛 𝐴𝑐𝑡𝚤𝑣𝚤𝑡𝑦
<
= = = = = = = = = = = = = = = = = =
– 𝐺𝑒𝑛 𝐴𝑐𝑡𝚤𝑣𝚤𝑡𝑦
GHIJK
= = = = = = = = = = = = = = = = = = = = = = =
)
+ 𝑈
41

Within-person generative activity:
𝛽
61
= 𝑌
64

Composite

𝐶𝑜𝑔𝑛𝑖𝑡𝑖𝑣𝑒 𝐴𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑆𝑡𝑎𝑡𝑒𝑠
01
= 𝑌
44
+ 𝑌
46
(𝑆𝑜𝑐𝑖𝑜𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠)
+𝑌
4E
(𝐺𝑒𝑛 𝐴𝑐𝑡𝚤𝑣𝚤𝑡𝑦
<
= = = = = = = = = = = = = = = = = =
– 𝐺𝑒𝑛 𝐴𝑐𝑡𝚤𝑣𝚤𝑡𝑦
GHIJK
= = = = = = = = = = = = = = = = = = = = = = =
)
+ 𝑌
64
(𝐺𝑒𝑛 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦
01
– 𝐺𝑒𝑛 𝐴𝑐𝑡𝚤𝑣𝚤𝑡𝑦
<
= = = = = = = = = = = = = = = = = =
) + 𝑈
41
+ 𝑒
01

30
Given dichotomous activity predictors, the person mean for generative activity in
the Level 1 equation represents the proportion of days in which the individual engaged in
that activity (e.g. volunteering). β0i represents the intercept. The individual within-person
effect of generative activity is defined by β1i for within-person Cognitive-Affective
Statesti. β1i is then defined by the Level 2 equation, which includes just the fixed effect,
Υ10. In the Level 1 equation, eti stands for the residual variance. In the Level 2 equation,
Υ00 represents the fixed intercept, or the expected value of the well-being outcome when
all predictors have a value of 0. Υ01 represents the main effects of the sociodemographic
controls, including age, sex, race, and education. Υ02 denotes the between-person main
effect of generative activity. The group mean, in the case of dichotomous predictors,
indicates the total proportion of days, or observations, that generative activity occurred
(across all individuals). And lastly, U0i represents the individual level residuals.
Results
Descriptive statistics were generated for all of the variables included in the
analysis (Table 1). Daily survey participation was high with respondents completing an
average of 7.2 out of the 8 daily surveys. The analytic sample contained 1,747
respondents, excluding those missing data on key variables. The average age of the
respondents was 57, with ages ranging from 33 to 83. The sample was comprised of 57%
female respondents and 43% male respondents. MIDUS contains a largely racially
homogeneous sample, and the majority of the study sample was white (92%). Individuals
in the sample had varied levels of educational attainment, with 41% of respondents
having attained a 4-year college degree or beyond, 30% having completed some college,
and 29% of the sample having a high school education or less.
31
An examination of the average frequency of experience of the examined
cognitive-affective states over the 8-day period also indicated variability across the
sample (Table 2). On the positive affect scale, the average score was 2.66 (SD = .84),
which represents an average frequency of feelings of positive affect falling between
“some of the time” and “most of the time.” Similarly, the average response on the social
connectedness scale was 2.94 (SD = .89), meaning that most participants reported feeling
connected to others “most of the time,” on average. Lastly, the average score on the self-
enhancement scale was 2.74 (SD = .94), meaning that on average, respondents reported
feeling proud and confident somewhere between “some of the time” and “most of the
time.” In terms of activity engagement, 30.9% of individuals reported engaging in
volunteering at least one day during the assessment period, 41.8% reported providing
informal help during at least one of the eight days, and a larger proportion of 78.7%
reported giving emotional support to others at least once during the study time frame
(Table 3).
Between-Person Associations of Generative Activity and Cognitive-Affective States
Results from the multilevel regression model analyses examining cognitive-
affective correlates of generative activity, including positive affect, social connectedness,
and self-enhancement states, are displayed in Table 4. As documented in Table 4,
multilevel model analyses revealed several significant between- and within-person
effects. The between-person effects of informal help on the cognitive-affective states
were not significant. However, those who volunteered more, on average, experienced
higher average levels of social connectedness (p < 0.01) and positive affect (p < 0.05)
compared to those who volunteered less or not at all. Conversely, those who reported
32
providing more emotional support to others reported lower levels of self-enhancement (p
< 0.01) and positive affect (p < 0.001), on average, compared to those who provided less
of this kind of assistance.
Daily Within-Person Associations of Generative Activity and Cognitive-Affective
States
On days when individuals provided informal help to others, they felt greater
levels of social connectedness (p < 0.001) and self-enhancement (p < 0.01), compared to
days when they did not provide such help. Similarly, on days when individuals engaged
in volunteer activity, they also reported experiencing greater feelings of social
connectedness (p < 0.05) and self-enhancement (p < 0.05) compared to their feelings on
non-volunteer days. The findings for emotional support were less positive. On days when
they provided emotional support, respondents reported lower levels of positive
emotionality or affect (p < 0.01), compared to days they did not give emotional support to
others (see Table 4). Though the within-person effect sizes are smaller than the between-
person effects, it is important to note that the within person associations control for the
between-person effects and represent the daily variation in each cognitive-affective state
from individuals’ own average levels of each state as a function of level of engagement in
generative activity. In other words, the within-person effects represent how individuals
vary from their own characteristic levels of a given cognitive-affective state as a function
of variations in generative activity engagement.

Discussion
These findings suggest several potential cognitive-affective states, including
social connectedness, positive affect, and self-enhancement, are influenced by engaging
33
in generative, or socially contributory, activities. Specifically, at the daily level, both
volunteering and giving informal help were found to be associated with greater feelings
of self-enhancement and social connectedness. In other words, on days when individuals
volunteered or provided informal help to others, they felt better about themselves
compared to days when they did not engage in these activities. On these days, they
additionally benefited by feeling more socially connected to others, compared to days
they did not volunteer or provide informal help to others. The data also provided the
unique opportunity to compare the average cognitive-affective states of individuals who
engaged in varying levels of these generative activities. Though the between-person
effects of informal help were not significant, findings did suggest that individuals who
volunteered more, on average, also experienced greater average feelings of positive affect
and felt more socially connected than those who volunteered less or not at all. In contrast,
the activity of giving emotional support to others seems to be associated with slightly
lower levels of these cognitive-affective correlates on a given day, and on average, which
fits within the mixed findings in the social support literature. While helping or giving
support to others has generally been regarded as advantageous for health and well-being
(Brown, Nesse, Vinokur, and Smith 2003; Liang, Krause, and Bennett 2001), providing
too much emotional support to others can be mentally draining and stressful, a pattern
seen throughout the vast caregiver burden literature (e.g. Adelman, Tmanova, Delgado,
Dion, and Lachs 2014; Green 2007).
These results regarding emotional support should be treated as equally important
and informative as the more positive findings in this study as they may begin to help
increase the understanding of why giving to others might be health-promoting in certain
34
circumstances and health-damaging in others. Some researchers have distinguished
between compassion and empathy, a distinction that may help explain why giving
emotional support shows an opposite pattern from the other generative activities in the
current study. Singer and Klimecki (2014) argue that empathy involves sharing the actual
feelings (e.g. suffering) of others, whereas compassion is characterized by warmth and
concern for others and the prosocial motivation to help. In other words, compassion is
“feeling for and not feeling with the other” (p. 875). They present evidence suggesting
that whereas compassion is often beneficial for health and well-being, empathy can
actually promote greater feelings of distress, as it is characterized by a greater sense of
attachment (Klimecki, Leiberg, Ricard, and Singer 2013; Singer and Klimecki 2014).
This distinction may provide one useful framework to help unravel the varying
associations between different types of generative activity and well-being. One
hypothesis is that emotional support provision may involve empathy, whereas
volunteering and informal help may be more compassionate in nature.
Another theoretical rationale through which one might understand the relationship
between daily generative activities and cognitive-affective states can be found in the
mental capital literature. Mental capital has been defined as an individual’s cognitive and
emotional resources, including cognitive capability, emotional intelligence, flexible and
efficient learning, and social resilience (Beddington et al., 2008; Kirkwood, Bond, May,
McKeith, & Teh, 2010; 2014). Mental capital is accumulated through habit formation,
during which activities repeatedly reinforce activated neural pathways. Greater mental
capital has been argued to spur the production of "mental goods," such as self-esteem,
social connectedness, and positive affect, which are hypothesized to contribute to higher
35
levels of well-being. In this light, the within-person findings in this study may provide
one mechanism through which generative engagement at the daily level might enhance
cognitive-affective states and reinforce the incentive to make continued social
contributions, strengthening one’s overall mental capital and well-being over time.
Importantly, the daily experience sampling methodology provided the valuable
opportunity to delve in and gain a more in-depth understanding of these relationships at a
more proximal level than prior research; Our findings allow us to understand how the
associations between generative activities and cognitive-affective states may operate in
individuals’ everyday lives. This study also promotes a greater understanding of how
generativity may be related to more favorable health and well-being outcomes. However,
there are several limitations to the current study and analysis that should be
acknowledged. Although the MIDUS survey is conducted with a national sample, survey
respondents were primarily white, suggesting these findings may not be generalizable to
other racial/ethnic groups in the United States. In addition, even though this study
contains tighter temporal coupling than has been achieved in past observational designs,
cognitive-affective states were still not assessed in direct relation to specific activities,
but instead respondents were asked to recount these feelings across each day. It has been
argued that techniques that query individuals’ cognitive-affective states in relation to
specific activities are likely to help researchers better elucidate the association between
the two (Reis, Sheldon, Gable, Roscoe, and Ryan 2000). Lastly, single end-of-day reports
did not allow for a careful parsing of the causal direction in the relationship between
activity and affect. Though we predict that engagement in generative activities influences
cognitive-affective states in a positive direction, the possibility of reverse causal ordering
36
cannot be ruled out in this analysis. In addition, it would have been preferable to examine
daily self-perceptions of generativity in addition to daily generative activities, however
this data was not collected in the current study but would be an important area for future
research. Lastly, it should be noted that some daily diary studies collect data over a
longer time period (e.g. 14 days). A longer data collection period may have provided
richer information.
Despite these limitations, this study also has notable strengths that support its
value and unique contribution to the literature. First, the current study utilized a repeated
measures study design to employ a daily experience sampling methodology, which
allowed the opportunity to examine within-person as well as between-person associations
between generative activity and affect. Again, this allowed for the examination of both
how individuals differed from each other on average as well as how they differed from
their own average tendencies each day. Although the data are observational in nature, the
daily within-person analyses provide greater confidence in the potential causal linkage of
generative behavior and the affective states that flow from such engagement. This unique
aspect of the data enabled us to gain a valuable understanding of how generative
activities like volunteering can affect individuals in their day-to-day lives. This analysis
also used a large, population-based sample to examine these connections and contained
fairly comprehensive assessment of cognitive-affective states, both of which can be
regarded as additional advantages of this study. Most importantly, this work furthered the
understanding of how generative activities seem to shape health and well-being in a
positive direction, elucidating some of the potential pathways underlying this widely-
supported connection.
37
There are several important future directions that arise from this research. As
findings suggest that instrumental generative activities may be more beneficial than
emotional generative activities, future research should continue to explore the distinction
between empathic distress and compassion to better understand the differential effects of
emotional support provision. Another important future direction will be to assess these
cognitive affective states in more direct relation to specific activities – facilitating an
even tighter temporal coupling of activities and affective states to further strengthen the
understanding of these complex associations. Studies that continue to probe these
associations are important in several ways, one being that they have the potential to
inform the future development and implementation of an intervention that might employ
generativity enhancement as a tool for health promotion. Similar interventions rooted in
positive psychology have been implemented with promising success. For example,
interventions aimed at cultivating positive feelings, behaviors, or cognitions have been
found to be effective at enhancing well-being and alleviating depression (Sin and
Lyubomirsky 2009). Similar efforts with a focus on generative activities would be
expected not only to improve the well-being of those who are generative, but also those
on the receiving end of their contributions. This study represents an early step toward
such innovative and promising possibilities for health and well-being promotion.

38
Table 2-1. Characteristics of Analytic Sample (n = 1,747).
n % M (SD) Possible Range
Age

1,747 56.58 (12.1) 33 - 83
Female 987 56.5
White 1,604 91.8
Education
< High school 514 29.4
Some college 520 29.8
> 4 year college 709 40.7
Married 1,260 72.1
Working currently 873 50.0
Functional limitations
(ADL) 1.31 (0.63) 1 - 4
Social contact frequency 5.67 (1.69) 1 - 8

39
Table 2-2. Independent Variables: Generative Activities.

n % M (SD) Range
Volunteering (any) 625 30.9
Informal Help (any) 846 41.8
Emotional Support (any)
Volunteering (days)
Informal Help (days)
Emotional Support (days)
1,591

78.7

0.66 (1.3)
0.85 (2.0)
2.33 (1.3)
0-8
0-8
0-8

40
Table 2-3. Dependent Variables: Cognitive-Affective States.
n M (SD) Range
Positive Affect 1,747 2.66 (.84) 0 - 4
Social Connectedness 1,747 2.94 (.89) 0 - 4
Self-Enhancement 1,747 2.74 (.94) 0 - 4
Note: Items are rated on a scale of 0 to 4, with 0 indicating “None of the time”
and 4 indicating “Most of the time.”

41
Table 2-4. Results from Multilevel Regression Models Examining Effects of Daily Generative
Activities on Daily Cognitive-Affective States.
Positive Affect Self-Enhancement Social Connectedness
Informal Help B B B
Within-Person (day-to-day) 0.025 0.049 ** 0.059 ***
Between-Person -0.059 0.014 0.056
Age (centered) 0.014 *** 0.012 *** 0.011 ***
Female 0.011 -0.024 0.155 ***
Nonwhite 0.068 0.140 * 0.037
< High school 0.084 * 0.141 ** 0.067
Married 0.071 0.132 ** 0.265 ***
Currently working 0.023 0.062 0.008
Functional limitations -0.175 *** -0.161 *** -0.115 ***
Social contact frequency 0.040 *** 0.047 *** 0.063 ***
Volunteering B B B
Within-Person (day-to-day) 0.020 0.046 * 0.043 *
Between-Person 0.233 * 0.133 0.270 **
Age (centered) 0.014 *** 0.012 *** 0.011 ***
Female 0.004 -0.026 0.152 ***
Nonwhite 0.071 0.141 * 0.038
< High school 0.090 * 0.145 ** 0.076
Married 0.066 0.130 ** 0.260 ***
Currently working 0.031 0.065 0.014
Functional limitations -0.170 *** -0.158 *** -0.109 ***
Social contact frequency 0.037 *** 0.046 *** 0.060 ***
Emotional Support B B B
Within-Person (day-to-day) -0.028 ** -0.015 0.002
Between-Person -0.287 *** -0.234 ** 0.059
Age (centered) 0.014 *** 0.012 *** 0.012 ***
Female 0.045 0.006 0.151 ***
Nonwhite 0.067 0.137 0.037
< High school 0.060 0.122 ** 0.072
Married 0.071 0.133 ** 0.266 ***
Currently working 0.025 0.061 0.006
Functional limitations -0.176 *** -0.161 *** -0.115 ***
Social contact frequency 0.045 *** 0.052 *** 0.063 ***
*p < .05 ** p < .01 *** p < .001
Note: All well-being outcomes are scored on 0-4 scales.
Both STATA and SPSS only generate the unstandardized estimates for these models.

42
CHAPTER 3: STUDY 2
(Accepted author version, published in Journals of Gerontology, Psychological Sciences)
Failure to Meet Generative Self-Expectations is Linked to Poorer Cognitive-
Affective Well-Being
Introduction

Psychiatrist and Holocaust survivor Viktor Frankl said, “Life is never made unbearable
by circumstances, but only by lack of meaning and purpose.” Indeed, a sense of purpose
seems to be a crucial component of leading a fulfilling life. One way many individuals
find meaning in their lives is through giving to others or leaving a lasting legacy behind.
Researchers have investigated how giving and feeling useful to others may in fact benefit
the giver, a phenomenon sometimes understood in the context of generativity.
Generativity is defined as concern and activity focused on supporting the welfare and
well-being of others, often including friends, family, and the community (Erikson, 1963;
Gruenewald, Liao, & Seeman, 2012). There are many ways to be generative, ranging
from volunteering, mentoring, religious or political or community involvement,
parenting, and contributions within friendship networks (McAdams & de St. Aubin,
1992). Generative activity is common element across many definitions of successful
aging, underscoring the relationship between contributory activities and more positive
well-being outcomes (Rowe & Kahn, 1998; Villar, 2012). Indeed, empirical research has
shown that both perceptions and behavioral manifestations of generativity are linked to
improved well-being, including not only better psychological health but also better
physical health, reduced disability, and lower mortality (Grand et al., 1988; Gruenewald,
Liao, & Seeman, 2012; McAdams, de St. Aubin, Logan, 1993).
43
Developmental Significance of Generativity
The developmental psychologist Erik Erikson proposed an influential model of
psychosocial development across the lifespan, consisting of eight stages each of which is
characterized by a specific crisis or challenge (1950). The seventh of the eight major
stages in this trajectory consists of overcoming the conflict of generativity versus
stagnation, as generativity was initially conceptualized as a key goal of psychosocial
development to be achieved during midlife. While the focus on caring for others and
supporting the next generation was initially postulated to attain greatest significance in
midlife, generative strivings have been found to play a key role in older age as well
(Erikson, 1950; McAdams, de St. Aubin, & Logan, 1993; Villar, 2012). The definition of
generativity has also evolved, shifting from a narrow focus on guiding future generations
to include any type of “activity or intention that may be beneficial to others through the
investment of one’s self” (Doyle, Rubenstein, & Medeiros, 2015, p. 410). This pro-social
conceptualization of generativity, in which recipients may be younger, older, or peers, is
the definition utilized by the present study. Viktor Frankl (1959) postulated that humans
are motivated primarily by a “will to meaning,” or a need or desire to find meaning in
life. Generativity encapsulates one way of deriving such meaning, specifically through
providing valuable social contributions before the end of one’s life.
Erikson’s theory suggested that adults who contribute to the welfare of others are
likely to experience more positive mental health as a result of these actions. His theory
also suggested that those who repeatedly fail to be generative, or who fail to strive
towards generative goals, are likely to experience stagnation, self pre-occupation, and as
a result, relatively poor psychological adjustment (McAdams & Guo, 2015). In general,
44
research suggests that generative desire typically plays a prominent role in guiding
individuals’ goals and activities as they age. For example, when prompted to write about
their personal strivings, middle-aged and older adults tend to describe many of their goals
or strivings in generative terms (e.g. be a positive role model for those who are younger,
provide counsel to others, help as a volunteer) (McAdams, de St. Aubin, & Logan, 1993).
Indeed, McAdams & de St. Aubin’s (1992) widely accepted model of generativity is built
around the concept of generative concern, or a general disposition toward generativity.
According to their model, generativity originates from two motivational sources: cultural
expectations and inner desire. These motivational sources feed generative concern which
in turn leads to the formation of conscious generative goals that motivate and direct
individuals’ behavior (Hofer, Busch, Chasiotis, Kartner, Campos, 2008).
Benefits of Generativity and Potential Underlying Mechanisms
Greater levels of perceived generativity have been tied to more favorable well-
being outcomes over time. Specifically, higher perceptions of generativity or usefulness
have been shown to be tied to lower mortality, lower risk of the development of
disability, and better subjective well-being in later life (Gruenewald et al., 2007, 2009;
Okamoto & Tanaka, 2004; Pitkala & Laakkonen, 2004). Older adults who feel more
generative have also been shown to have better memory and executive function
performance compared to those who perceive themselves as less generative (Hagood &
Gruenewald, 2016), as well as greater levels of optimism and life satisfaction (Kruse &
Schmitt, 2012). Enhanced perceptions of generativity may also serve to buffer against
some experiences of adversity, as they have been suggested to dampen some of the
deleterious effects of family caregiving (Grossman & Gruenewald, 2017). A review study
45
conducted by Adams, Liebbrandt, & Moon (2011) provides additional support for these
benefits of generativity, positing productive engagement as a key correlate of well-being
in later life. Importantly, the links between generativity and health seem to remain when
controlling for established sociodemographic, biobehavioral, and psychosocial risk
factors for poor health. Though existing research clearly provides support for a positive
relationship between generativity and well-being, less is known about the mechanisms
through which they are connected as well as the role of generative goal achievement.
One potential path to gaining a better understanding of how generativity might be
linked to better health and well-being is to elucidate the thoughts and feelings that flow
from generative self-perceptions. Theoretical and empirical interpretations of generative
behavior suggest several cognitive-affective benefits of pro-social activity (McAdams, de
St. Aubin, Logan, 1993; Post, 2005).

Specifically, several studies have suggested a
positive relationship between contributory activities and positive affect, self-esteem, and
social relationships (Brown, Hoye, & Nickelson, 2012; Grossman, Wang, & Gruenewald,
2017; Huta & Zuroff, 2008; Kahana, Bhatta, Lovegreen, Kahana, & Midlarsky, 2013).
These empirical connections are supported by role enhancement theory, often used to
explain the positive link between volunteering and health. Role theory posits that by
assuming productive roles, like volunteering or mentoring, individuals accumulate
greater resources, including expanded social networks, as well as greater power and
prestige, which positively influence mental and physical health (Lum & Lightfoot, 2005;
Moen, Dempster-McClain, and Williams 1992). This theory supports the relationship
between generative activities and enhanced social connections as well as a greater sense
of self-worth. Additionally, generative perceptions and generative activities (i.e.
46
volunteering) have been shown to be associated with greater levels of positive affect (e.g.
Greenfield & Marks, 2004), a link that is also supported by individuals’ reports that
helping others makes them “feel good” (Musick & Wilson, 2003). To date, however,
there has been little empirical examination of these hypothesized cognitive and affective
pathways linking generativity to improved well-being.
Goal Striving and Self-Discrepancy Theory
Another mechanism through which generativity may be linked to cognitive-
affective states is through individuals’ abilities to fulfill their generative strivings.
Erikson’s theory focuses on psychosocial goals at different stages of the life course and
the effect that achieving or failing to achieve such goals has on an individual’s self-
concept. Generally it is hypothesized that it feels good to achieve personal goals.
Research supports the association between successful goal attainment and more positive
emotional experiences as well as greater life satisfaction (Brunstein, 1993; Emmons,
1986; Klug & Maier, 2014). However, what happens when individuals are unable to
follow through with their goals? Self-discrepancy theory suggests that an individual’s
failure to meet his or her internal expectations may result in worse affective and health
outcomes, or greater emotional vulnerabilities (Higgins, 1989; Maio & Thomas, 2007).
For example, it has been found that individuals who fail to attain goals that are
intrinsically important to them experience a decrement in well-being (Sheldon & Elliot,
1999). Generativity is generally linked to enhanced well-being, however a high intrinsic
desire for generativity may lead to frustration if for some reason this desire cannot be
fulfilled (Hofer et al., 2008). Thus, individuals’ failure to meet their generative goals may
47
influence their well-being and life satisfaction, one way being through the
aforementioned cognitive-affective pathways.
The Potential Role of Age
As generativity is hypothesized to take on greatest developmental salience in
middle and later life, it is also important to understand how generative goals shape well-
being at different stages of life. While Erikson’s construct of generativity was initially
hypothesized to attain peak significance during midlife, generative strivings have been
found to remain and continue to be important into older age (Erikson, 1950; McAdams,
de St. Aubin, & Logan, 1993; Villar, 2012). Indeed, older adults often seek out
opportunities for generative activities and report that these actions contribute to a more
positive experience of aging and well-being (Warburton, McLaughlin, & Pinsker, 2006).
Despite these patterns, research on the relationship between age and generativity has
yielded mixed results. On one hand, research suggests that goals become more prosocial
or generative with age, whereas younger adults tend to prioritze more self-focused goals
(Hoppman & Blanchard-Fields, 2010; Maxfield et al., 2014). This pattern is theorized to
be related to perceptions of time as limited as well as the symbolic immortality that
generativity can provide (Maxfield et al., 2014). On the other hand, other researchers
have found no differences in generative concern between younger and older adults
(Bellizzi, 2004; McAdams, de St. Aubin, & Logan, 2003, Pratt, Norris, & Arnold, &
Filyer, 1999); thus, this uncertainty requires further scrutiny to gain a better
understanding of how these associations vary as a function of age.

48
Present Analysis
One aim of the current study was to examine how generative self-perceptions and
expectations for future generative contributions are related to cognitive-affective states,
concurrently and 10 years in the future. Another aim was to determine whether failure to
meet one’s expected level of generative contribution over time would be related to poorer
cognitive-affective states in addition to lower life satisfaction. Additionally, we examined
whether the cognitive affective states mediated the relationship between generative
failure and decreased life satisfaction. Though the importance of fulfilling generative
goals for one’s psychosocial well-being is at the heart of the Erikson model, empirical
evaluation of this hypothesis is lacking, especially within large-scale longitudinal
investigations. This study will examine these questions using two waves of a large-scale
population based survey, filling an important gap in the generative strivings literature.
Additionally, to better understand how age may play a role in the manifestation of
generativity and its benefits, another goal of the current study was to examine how the
association between generative failure and these well-being outcomes might vary as a
function of age.
Methods
Data and Participants
Data for this study come from the National Survey of Midlife Development in the U.S.
(MIDUS), a longitudinal survey containing three waves of data collection (1995/1996,
2004-2006, 2013-2016). The MIDUS survey was designed with the goal of promoting the
investigation of the role of psychological, social, and behavioral factors in shaping health
and well-being with aging across the life course (http://midus.wisc.edu/). The first wave
49
of the MIDUS survey collected data from 7,108 participants ages 25-74 and was
administered in 1995/1996. Subjects were recruited to participate in the study through
national random digit dialing and oversampling of 5 metropolitan cities in the United
States. MIDUS 2 is the 10-year follow-up to the original MIDUS study in 2004/2006
(n=4,963 initial phone survey and n = 4,041 for subsequent mail survey). Most recently,
the MIDUS 3 follow-up was conducted, with data collection beginning in 2013. The
current study will include data from the latter two waves of the MIDUS survey, as the
first wave did not contain complete assessment of the targeted cognitive-affective states.
MIDUS 3 researchers were able to contact 4,686 individuals from the prior survey waves,
of whom 3,294 completed the phone interview, reflecting a 77% response rate after
adjusting for deaths or ineligibility at follow-up. Consistent with prior research, higher
rates of retention occurred among Whites, females, higher educated, and healthier
individuals. Analyses utilized the maximum number of cases available from MIDUS 2
and 3 who provided data on the focal variables of interest, resulting in a final analytic
sample of 2,252 adults.
Measures
Perceived Generative Contributions. Perceived generative contributions are measured
in MIDUS with the question, “Using a scale from 0 to 10 where 0 means “the worst
possible contribution to the welfare and well-being of other people” and 10 means “the
best possible contribution to the welfare and well-being of other people,” how would you
rate your contribution to the welfare and well-being of other people these days?”
Additional instructions specify to “take into account all that you do, in terms of time,
money, or concern, on your job, and for your family, friends, and the community.”
50
Expected Generative Contributions. At each time point, respondents were also asked,
“Looking ahead ten years into the future, what do you expect your contribution to the
welfare and well-being of other people will be like at that time?” Again, respondents rate
their response on an 11-point scale ranging from 0 to 10, 10 being the best rating. This
study focuses on individuals’ expected generative contributions assessed during MIDUS
2 predicting their level of contribution 10 years later, approximately the time of the
MIDUS 3 re-assessment.
Generative Failure. Generative failure was determined by subtracting individuals’
expected contributions score reported at MIDUS 2 (0-10) from their perceived
contributions score reported at MIDUS 3 (0-10). If individuals failed to meet or exceed
their generative expectations, measured by a negative score after the subtraction, they
were placed in the generative failure group; otherwise they were placed in the met or
exceeded expectations group.
Cognitive-Affective States. Psychological well-being measures of positive affect, sense
of self worth, and social connectedness were examined as cognitive-affective correlates
of generative activities. The questions comprising these scales were included in the Self-
Administered Questionnaire, and a factor analysis was conducted to confirm the fit of
specific items to scales for positive affect, sense of self-worth, and social connectedness.
Positive Affect. The positive affect scale asked participants for how much of the
past 30 days they felt “cheerful,” “satisfied,” “enthusiastic,” “full of life,” “extremely
happy,” “calm and peaceful,” and “in good spirits.” All of these items were rated on a 5-
point scale, ranging from 1 indicating “All the time, to 5 indicating “None of the time.”
51
The item ratings were then averaged to compute a scale score for positive affect (α=.91)
(min: 1, max: 5).
Self Worth. The self-worth scale consisted of the two questions assessing
individuals’ sense of self worth, specifically querying how much of the time over the past
30 days they felt “confident” and “proud.” Again, respondents were asked to rate their
response to these questions on a 5-point scale measuring frequency of these feelings, and
their ratings were averaged to create a self-worth scale score (α=.72) (min: 1, max: 5).
Social connectedness. The social connectedness scale was comprised of two
questions querying respondents’ feelings of social integration or connectedness. The
scale consisted of questions asking how much of the time over the past 30 days
respondents felt “close to others” and “like you belong.” Again, these items were rated on
a 5-point scale. Their ratings were then averaged to comprise a social connectedness
score (α=.81) (min: 1, max: 5).
Life Satisfaction. Life satisfaction, which refers to overall assessments of one’s quality
of life, was assessed with a 5-item measure (Diener, 1984; Prenda and Lachman, 2001).
The items assessed satisfaction across the domains of work, finances, health, relationship
with child(ren) and/or relationship with spouse/partner, and overall satisfaction with life.
Each item was measured on a scale ranging from 0-10, with 0 indicating “worst possible”
and 10 indicating “best possible” satisfaction. The scores for relationship with child(ren)
and relationship with spouse/partner were averaged to create one score representing
satisfaction with family. Then this score was averaged with the scores for the other
domains to compute the overall score. Scale scores were computed for those who
provided a response to at least one item on the scale, and higher scores reflect higher
52
overall levels of life satisfaction. Thus, if an individual did not have children or a spouse,
the score was made up of the average of the ratings across the other domains.
Sociodemographic Variables and Controls. Age (centered), sex, race, and education
were included as covariates in the analyses. For race, a dummy variable was created to
represent white or non-white race/ethnicity. Educational attainment was coded into a
categorical variable with three categories, including “high school or less,” “some
college,” and “4 year college degree or greater.” Generative failure analyses also
controlled for major health conditions, measured on a scale from 0-9, assessing the sum
of serious health conditions experienced in the last 12 months (e.g. heart disease,
diabetes, and cancer).
Analytic Strategy
Regression analyses were conducted using SPSS to examine the effects of perceived and
expected generative contributions on the cognitive-affective states of positive-affect,
feelings of self-worth, and social connectedness (hypothesis 1). Then, regression analyses
were used to determine whether failure to meet one’s generative expectations was related
to poorer cognitive-affective states (hypothesis 2). Multiple mediation analyses were run
using the PROCESS macro in SPSS (Hayes, 2012) to test whether positive affect, self-
worth, and social connectedness mediated the link between generative failure and
decreased life satisfaction. In addition, Johnson-Neyman analyses were conducted to
determine whether these associations varied as a function of age and if so, at which ages
they were strongest (Hayes, 2013). All of theses analyses controlled for age, sex, race,
and education. Additionally, the analyses assessing generative failure also controlled for
perceived generativity in MIDUS 2 as well as health status.
53
Results
Descriptive statistics were generated for all of the variables included in the
analysis. The sample contained 2,252 individuals who participated in both Waves 2 and 3
of the MIDUS survey and provided data on the key variables of interest. The average age
of the respondents at time 1 (Wave 2) was 54.86, ranging from 30 to 84. The average age
of respondents at time 2 (Wave 3) was 63.96, with ages ranging from 39 to 92. The
sample contained slightly more females than males (55%) and the majority of
respondents were White (92%) (see Table 1). 46% of the sample failed to meet their
generative expectations (n=1,038), and 54% met or exceeded their expected level of
contribution (n=1,214) (Table 2).
` The analysis revealed that both greater self-perceptions of generative
contributions and more positive expectations regarding future generative contributions
were associated with higher levels of each examined cognitive-affective state,
concurrently and ten years later (all p’s < 0.001). Specifically, greater perceived
generative contributions predicted greater positive affect concurrently and ten years into
the future (see Table 3). Similarly, greater self-perceptions of generative contributions
were associated with a greater sense of self-worth concurrently and in the future.
Individuals with higher levels of perceived contributions also reported higher levels of
social connectedness at the time of assessment and ten years later. Greater expectations
regarding future levels of contribution were also associated with greater feelings of
positive affect, sense of self-worth, and social connectedness concurrently and at the ten-
year follow up (Table 3). Furthermore, supplementary analyses indicated that
associations with time 3 cognitive-affective states remained significant, though reduced
54
in magnitude, when also including the time 2 measure of each cognitive-affective state in
the model (M2 perceived contributions standardized coefficients for association with M3
positive affect = .04*, with social connectedness = .05*, and with self-worth = .05*; M2
expected generative contributions 10-years into the future standardized coefficients for
association with M3 positive affect = .04*, with social connectedness = .05*, and for self-
worth = .04*) (Compare with Table 3).
Examination of the discrepancy between self-reported generative contributions at
follow-up and the level of generativity participants had predicted for themselves ten years
prior indicated that failure to meet one’s generative expectations was associated with
poorer levels of the three cognitive-affective states at follow-up, compared to meeting or
exceeding one’s expectations (p < 0.001) (see Table 4). Additionally, generative failure
was also significantly associated with lower levels of life satisfaction. These associations
remained even after controlling for prior perceived generative contribution and health
status at MIDUS 2 as well as age, sex, race, and education. Again, supplementary
analyses showed that when also including the MIDUS 2 measure of each cognitive-
affective state in the model, associations with MIDUS 3 outcomes, including life
satisfaction, remained significant but were slightly reduced in magnitude (Generative
failure standardized coefficients for association with M3 positive affect = -.07***, sense
of self worth = -.08***, social connectedness = -.07***, and life satisfaction = -.10***).
Furthermore, multiple mediation analyses revealed that positive affect, sense of
self-worth, and social connectedness mediated the relationship between generative failure
and poorer life satisfaction. Use of the PROCESS macro in SPSS revealed significant
indirect paths from generative failure to life satisfaction for positive affect, social
55
connectedness, and sense of self worth, indicating that these may be routes through which
generative failure is linked to life satisfaction (See Figure 1 for summary of mediation
results).
Lastly, a Johnson-Neyman analysis was conducted to examine whether this
association varied in strength by age. There was a significant interaction of generative
failure and age (F(1, 2244) = 6.22, p = .013, with relatively stronger associations between
generative failure and lower life satisfaction at younger ages (1 SD below mean age (age
53) B = -.43 (95% CI (-.58, -.29)), mean age (age 64) B = -.30 (95% CI(-.41, -.20), and 1
SD above mean age (age 75) B = -.17 (95% CI(-.32, -.02)). Inspection of coefficients in
the Johnson-Neyman analysis for the range of ages indicated that generative failure
significantly predicted lower life satisfaction up through age 75 and then associations
became weaker and non-significant at older ages. Although the interactions of generative
failure and age in predicting levels of each cognitive-affective state were not statistically
significant, a similar pattern of stronger associations at younger ages was observed for
positive affect and self-enhancement, but not social connectedness.
Discussion

Findings suggest that greater feelings of generativity, and more positive
expectations for future generative contributions, are associated with better cognitive-
affective states, cross-sectionally and over a 10 year period. Results also indicate that
generative failure, or failing to meet one’s expected level of contribution over time, is
predictive of poorer cognitive-affective states and life satisfaction. These associations
remained even after controlling for prior generative contributions and health status. These
findings support previous research findings linking greater perceptions of generativity to
56
more favorable health and well-being over time (e.g. Gruenewald et al., 2007, 2009,
2012; Grand et al., 1988; McAdams et al., 1993). However, this study adds to the
generativity literature with the finding that not only are individuals’ self-perceptions of
generativity important for psychosocial development and well-being, as theory and
research suggest, but the perceived attainment of one’s generative goals over time may
also play a significant role. While some research has considered the importance of
generative strivings for well-being, no prior studies to our knowledge have tested this
theory in a large-scale national dataset.
Another important contribution of the present investigation is its elucidation of
several potential cognitive-affective pathways through which making generative, or
socially useful, contributions may influence well-being, including social connectedness,
positive affect, and feelings of self-worth. This study showed that not only is failing to
live up to generative self-expectations important for individuals’ thoughts and feelings,
but that these links seem to affect satisfaction with life more broadly. Life satisfaction is
an important outcome, as it has been shown to have beneficial health effects ranging from
better mental health to lower risk of disease and mortality (Collins, Glei, & Goldman,
2009; Siahpush, Spittal, & Singh, 2008). Therefore, this study’s findings provide
convincing support for Erikson’s theory on the importance of achieving generativity in
middle and later life, especially as it relates to psychosocial functioning and flourishing.
A Johnson-Neyman analyses indicated that failure to achieve generative goals
was more strongly linked to lower life satisfaction in the middle and young-old age
groups. A similar, but non-significant, age-related pattern was observed for positive
affect and self-enhancement states. This finding also supports Erikson’s theory of
57
psychosocial development, in which he proposes generativity as a key goal of mid-life.
Other researchers have argued that generative desire remains important into older age,
and this study supports the salience of these connections, especially for younger older
adults (< 75 years old). Socioemotional selectivity theory postulates that time plays a
fundamental role in the selection and pursuit of social goals. As time becomes more finite
(such as with age), the focus of individuals’ goals switches from being knowledge-related
to more emotion-focused, social goals (Carstensen, 1993). The possibility that the
younger-old may be more affected by generative failure than the older-old may be
reflective of a number of different circumstances, one being increased physical
limitations. Self-discrepancy literature suggests that people will accept discrepancies that
they do not have the power to change (Maio & Thomas, 2007). Furthermore, research has
shown that desire to develop or strengthen social ties is a primary motive of volunteer
participation in older adults (Okun & Schultz, 2003). However, though this motivation
was unique to those over age 60, researchers found that these motivations were much
stronger in those from 60-70 than those over the age of 80. Moreover, the years
immediately following retirement present an individual with unparalleled free time, often
accompanied by boredom and depression, making these years a prime time for volunteer
engagement. Therefore, it may be that the young-old are more sensitive than the oldest-
old to their degree of success in meeting their generative goals, as they may have greater
motivations and opportunities to contribute to others.
While this study provides important contributions to the generativity literature,
there are some limitations that should be acknowledged. Even though the national
MIDUS sample was derived from random digit dialing sampling, the survey participants
58
are predominantly white (90%), raising the concern that these findings may not be
generalizable to other racial/ethnic groups in the U.S. Though the goal of this study was
to elucidate cognitive-affective correlates of generativity, there are other potential
pathways that may also link generativity to health and well-being, including behavioral or
physiological mechanisms. For example, volunteering has been found to lead to increased
physical activity, which may represent one plausible behavioral pathway linking
generativity to improved well-being (Librett, Yore, Buchner, & Schmid, 2005).
Physiological pathways may also play a role in these associations, as giving support to
others has been linked to lower blood pressure and heart rate (Piferi & Lawler, 2006).
Additionally, the MIDUS surveys do not specifically assess for individuals’ personal
interpretations of generative contributions and the types of activities and behaviors they
are considering when they rate their current and expected contribution levels. There could
potentially be a wide range of activities and behaviors that constitute contributions to the
welfare of others in the minds of different individuals. Future studies would benefit from
collecting more specific data on the types of activities individuals are referencing when
answering questions about their contributions. Similarly, this study focuses solely on self-
perceptions of generativity, and future studies might benefit from examining indicators of
generativity that could be more objectively reported. Prior research has shown similar
cognitive-affective benefits of specific generative activities, thus joint assessment of both
activities and perceptions could be especially informative (Grossman & Gruenewald,
2017). Finally, the positive affect variable used in this study contains both high and low
arousal items. Future experimental work should probe different components of positive
affect to better elucidate the mechanisms at play.
59
Despite these limitations, the study was also characterized by several notable
strengths. The MIDUS survey provided access to two waves of data to analyze and
examine changes in respondents over time. Because of this longitudinal nature of the
data, this study lends some strength to the directionality of this link, suggesting that
greater generative perceptions may lead to better cognitive-affective states, and as a
result, increased life satisfaction. As shown by the supplementary analyses controlling for
baseline cognitive-affective associations, generative perceptions and future predictions of
generativity maintain a unique connection with future cognitive-affective states even
when accounting for levels of well-being when these perceptions are assessed, adding
support to this causal hypothesis. This analysis also utilized a large, population-based
sample to examine these connections, which to our knowledge has not yet been done in
the context of generativity. Most importantly, this work furthered the existing
understanding of how pivotal generative contributions and goals may be for health and
well-being, elucidating some of the potential pathways underlying this connection.
Studies that continue to examine these associations are important in several ways,
including their potential to inform and encourage programs and policy to facilitate
generative goal attainment as a tool for health promotion. Research suggests that high
levels of generativity are predicted by early life positive socializing influences, through
the family, teachers, mentors, the education system, and other societal institutions (Jones
& McAdams, 2013). Thus, as with many aspects of health, early intervention may be key,
as well as the modeling of generative behaviors in individuals’ early lives.
In summary, the current study suggests that more positive self-perceptions of
generative contributions and expectations of future contributions are both linked to
60
greater feelings of social connectedness, sense of self-worth, and positive affect. Whether
individuals meet their personal generative goals can also significantly influence their
cognitive-affective states, with perceived failure of generative goals linked to worse
mental states, which in turn lessen life satisfaction. These findings suggest that it is not
only the process of generative goal striving that is important for well-being, but also
individuals’ perceived degree of success in accomplishing their contributory goals. This
study also highlights the future value in examining and promoting factors that may
enhance individuals’ abilities to follow through with their generative goals to support
improvements in health and well-being, both at the personal and societal level.

61
Table 3-1. Characteristics of the Analytic Sample (n = 2,252).
n % M (SD) Possible Range
Age at MIDUS 2
2,252
54.86 (11) 30 – 83
Age at MIDUS 3 2,252 63.96 (11) 39 – 92
Female 1,238 55.0
White 2,099 93.2
Education
< High school 647 28.7
Some college 619 27.5
> 4 year college
Major health conditions 2,252 .79 (.96) 0 – 9

62
Table 3-2. Cognitive-Affective States and Generative Perceptions of Sample (n = 2,252).
%
M (SD) Range
Positive affect (M2) 3.47 (.68) 1 – 5
Social connectedness (M2)
3.70 (.85) 1 – 5
Sense of self-worth (M2)
3.69 (.81) 1 – 5
Perceived current contributions (M2)
6.64 (2.1) 0 – 10
Expected contributions in 10 years (M2)
6.86 (2.2) 0 – 10
Positive affect (M3)
3.44 (.72) 1 – 5
Social connectedness (M3)
3.65 (.89) 1 – 5
Sense of self-worth (M3)
3.61 (.86) 1 – 5
Perceived current contributions (M3)
6.49 (2.2) 0 – 10
Failed to meet expectations (M3) 46.1

Met/exceeded expectations (M3)
Life satisfaction (M3)
53.9
7.84 (1.27) 1 – 10

63
Table 3-3. Results from Regressions Examining the Relationship of Perceived Generativity and
Predicted Generativity With Cognitive-Affective States, Concurrently and 10 Years Later.
MIDUS 2 outcome MIDUS 3 outcome
Positive
affect
Self
worth
Social
connectedness
Positive
affect
Self
worth
Social
connectedness
M2 perceived
contributions
(centered)

.158***

.170***

.223***

.134***

.140***

.161***
Age
(centered)

.171***

.144***

.160***

.110***

.089***

.121***
Female -.064** -.111*** .013 -.013 -.072*** .014
Nonwhite .043* .062** .012 .028 .021 -.005
< High school -.027 -.004 -.025 -.011 .002 -.014

M2 predicted
contributions
(centered)

.181***

.178***

.222***

.143***

.141***

.160***
Age
(centered)

.212***

.184***

.212***

.143***

.122***

.159***
Female -.059** -.104*** .023 -.008 -.066** .021
Nonwhite .035 .055** .003 .022 .015 -.011
< High school -.019 .002 -.018 -.006 .006 -.009
*p < .05 ** p < .01 *** p < .001; Note: Table contains standardized (β) values.

64
Table 3-4. Results from Regressions Examining the Association Between Generative Failure and
Cognitive-Affective States and Life Satisfaction, Controlling for Demographic Factors.
MIDUS 3 outcomes
Positive
affect
Self
worth
Social
connectedness
Life
satisfaction
Generative failure -.104*** -.099*** -.113*** -.122***
Age (centered) .151*** .115*** .140*** .218***
Female -.017 -.077*** .007 -.012
Nonwhite .037 .028 .001 .000
< High school .001 .011 -.005 -.046*
Generative
contributions (M2)
(centered)
Major health
conditions (M3)
.155***

-.168***
.161***

-.119***
.185***

-.104***
.150***

-.230***
*p < .05 ** p < .01 *** p < .001; Note: Table contains standardized (β) values.

65
Figure 3-1. Unstandardized Indirect Effects of Generative Failure on Life Satisfaction
Through Cognitive-Affective States (Model includes M2 generative contributions, age,
race, sex, education as covariates).

Indirect effects:
Unstandardized (95% bootstrapped CI)
TOTAL -.1582 (-.2248, -.1020)
Positive Affect -.0959 (-.1436, -.0576)
Self-Worth -.0182 (-.0434, -.0048)
Social Connectedness -.0441 (-.0701, -.0226)

M2 - M3
Generative
Failure
M3
Positive
Affect
M3
Social
Connectedness
M3
Life
Satisfaction
-.15***
-.17***
-.20***
-.15** (-.31***)
.65***
.22**
.11***
M3
Self
Worth
66
CHAPTER 4: STUDY 3
Generative Failure Linked to Poorer Health Through Cognitive-Affective States
“Doing nothing for others is the undoing of ourselves.”
– Horace Mann
Introduction

With the United States facing unprecedented population aging and increases in
life expectancy, individuals will likely live a growing proportion of their lives post-
retirement. On the one hand, retirement can be a challenging milestone for many older
adults, a time often marked by role loss and identity crises, social attrition, and significant
financial adjustments (Moen, 1995; Musick & Wilson, 2003). On the other hand,
retirement can also be a time of reinvention, renewal, and opportunity, especially for the
Baby Boomers who tend to envision a more active retirement than their predecessors. As
Hardy (2011) argues, the retiring cohort of Baby Boomers make up a vast source of
“untapped” potential. She discusses the notion of the “unretired retired,” describing how
many older adults have rejected the traditional view of retirement as a time for relaxation
and instead desire to remain active and give back to their communities. However, to
maximize older adults’ opportunities and quality of life during this period, it is critical to
support health promotion efforts so that living longer also means living healthier. Thus
far, there has been mixed evidence cautioning that while individuals in the U.S. are living
longer, they are not necessarily living well (Beltrán-Sánchez, Razak, & Subramanian,
2014; Crimmins & Beltran-Sanchez, 2011). With increased longevity, there remains a
high prevalence of disease and disability in our aging population (Eggleston & Fuchs,
2012; Kaye, 2013). Advances in medicine have simply improved our capacity to manage
67
and live longer with these diseases. For example, four out of five adults over the age of
50 have one chronic condition, and more than half have more than one (AARP, 2012).
Therefore, it is increasingly important to understand psychosocial factors that may
influence the health trajectory in later life. One such factor may be generativity, or
concern and activity focused on making positive contributions to the well-being of others.
Proposed to be an important psychosocial developmental milestone of middle and
later life, generativity has since been highlighted as a key predictor of successful aging
(Erikson, 1950; Fisher, 1995; Villar, 2012; Warburton, McLaughlin, & Pinsker, 2006).
Successful aging has typically been defined as consisting of three main components: low
probability of disease and disease-related disability, high cognitive and physical
functional capacity, and active engagement with life (Rowe & Kahn, 1997). Active
engagement with life is often defined in terms of productive or generative activities, and
the theory emphasizes that it is not just physical heath and overall functioning that are
important but rather these factors in combination with continued contribution to society
in active and meaningful ways. Indeed, researchers have examined both engagement in
generative activities and individuals’ self-perceptions of generativity in relation to health
and found that the benefits of generativity extend far beyond those on the receiving end
of these contributions. Individuals who have greater self-perceptions of generativity
experience more favorable psychological and physical health profiles over time
(Gruenewald, Karlamangla, Greendale, Singer, & Seeman, 2007, 2009; Okamoto &
Tanaka, 2004). For example, Gruenewald, Liao, and Seeman (2012) discovered that older
adults who felt more useful toward others had lower odds of disability, impairment in
activities of daily living, and lower mortality than those with lower self-perceptions of
68
generativity. Other research findings have revealed similar links between greater feelings
of usefulness and reduced morbidity and mortality, as well as lower risk of placement in
institutional care facilities (Grand et al. 1988, 1990; Gruenewald et al., 2007, 2009;
Okamoto & Tanaka 2004; Pitkala, Laakkonen, Strandberg, & Tilvis, 2004). Benefits of
perceived generativity extend to the mental health domain as well, as those with greater
self-perceptions of generativity report fewer depressive symptoms in comparison to those
who feel less useful (Gruenewald et al. 2007, 2009; McAdams et al. 1993).
Physiologically, contributing to others seems to reduce the sympathetic nervous system-
related response to stress (Inagaki & Eisenberger, 2015). For example, individuals in an
intervention who were directed to give money to others had lower resting blood pressure
post-intervention than those who were instructed to take the money for themselves
(Whillans et al., 2016; Inagaki & Orekeh, 2017). A writing-based generativity
intervention study also revealed that those in the generative writing group showed
reductions in pro-inflammatory gene expression (Moieni, 2017). Thus, studies support
the idea that those who feel more generative experience more favorable biomarkers of
health.

Figure 4-1. Overview of Proposed Model of Cognitive-Affective Pathways Linking
Generativity to Better Health.

Positive affect
Sense of self-worth
Social connectedness

Health &
Functioning

Generativity
69
Although there is growing evidence supporting the health advantages of
generativity, less is currently known about the mechanisms underlying these benefits.
One strategy to clarify the link between generativity and health is to investigate
cognitive-affective states, or the thoughts and feelings that are tied to generative activities
and perceptions. Theoretical and empirical examinations of generative behavior point to
several important cognitive-affective correlates of contributory activity (McAdams, de St.
Aubin, Logan, 1993; Post, 2005). For example, studies looking at generative or
contributory acts in contrast to more self-focused acts have found that spending money
on others versus spending it on oneself leads to greater positive affect (Aknin et al.,
2003), and doing nice things directed at others versus oneself leads to a greater sense of
belonging to a social group and increased happiness (Inagaki & Orehek, 2017; Nelson,
Layous, Cole, & Lyubomirsky, 2016). Other studies have also confirmed the association
between generativity and greater levels of positive affect and social connectedness
(Greenfield & Marks, 2004; Inagaki & Orehek, 2017; Musick & Wilson, 2003; Nelson,
Layous, Cole, & Lyubomirsky, 2016; Whiteley, 2004). Additionally, generativity or
perceived usefulness to others has been linked to feelings of self-worth (Gruenewald et
al., 2007; Piferi & Lawler, 2006). Overall, helping others makes the helper “feel good”
(Musick & Wilson, 2003), and this pattern is evident in individuals’ cognitive-affective
states. Likewise, feeling less useful toward others is associated with lower levels of these
states (Gruenewald et al., 2007). Despite this literature, there has been little empirical
examination of these cognitive-affective states as potential pathways connecting
generativity to health.
70
Additional support for the mediation potential of these cognitive-affective states is
the burgeoning evidence highlighting the relationship between affective states and health.
Negative affective states such as depression are widely known to be associated with a
plethora of negative health outcomes, including increased risk of coronary heart disease,
type 2 diabetes, and disability (Steptoe, Wardle, & Marmot, 2005). On the opposite end
of the spectrum, research has also begun to identify positive effects of psychological well
being on health. This study focused on the cognitive-affective states of social
connectedness, sense of self-worth, and positive affect, each of which are theorized to
improve health. Perhaps most thoroughly documented are the health benefits of social
connectedness and social networks. In 1979, a landmark study by Berkman & Syme
showed that across age groups, having social connections was associated with a lower
risk of mortality over time, as was having a greater number of connections. In 1988, a
review study by House et al. established low levels of social connectedness and low
quality social relationships as important risk factors for poor health, a finding which has
since been widely corroborated (Berkman, Glass, Brissette, & Seeman, 2000; Hawton et
al., 2011; Shankar, McMunn, Banks, & Steptoe, 2011; Steptoe, Shankar, Demakakos, &
Wardle, 2013). In fact, social connection has been found to have a stronger relationship to
odds of survival than standard mortality risk factors like smoking (House et al., 1988).
Social integration has been found to protect against coronary heart disease and is also
associated with lower incidence of myocardial infarction, better post-stroke recovery, and
lower risk of dementia (Seeman & Crimmins, 2001). Lacking social connections, in the
form of social isolation, can be detrimental to health, serving as a risk factor for
71
morbidity and mortality (Cacioppo & Cacioppo, 2014; Cacioppo & Hawkley, 2003; Holt-
Lunstad, Smith, Baker, Harris, & Stephenson, 2015).
While the gains from social connectedness have been most extensively
documented, a growing body of research has also emphasized the beneficial influence of
positive affect on health. Researchers have shown that positive affect is tied to enhanced
longevity, delay of the onset of illness, lower levels of inflammation, and reduced reports
of symptoms and pain (Cross & Pressman, 2015; Pressman & Cross, 2005; Stellar et al.,
2015). For example, in a famous study, researchers examined essays written by nuns in
the 1930’s and their health outcomes later in life and found that the nuns who expressed
the most positive emotions in their writing lived approximately 10 years longer than
those who expressed the fewest positive emotions. They also seemed to experience
cognitive protection in the form of delayed onset of dementia (Danner, 2001; Post, 2005).
As a result of these studies, many positive psychology interventions have been employed
to increase positive emotions with the goal of improving health or slowing disease in a
particular population (e.g. those diagnosed with HIV or various types of cancer) (e.g.
Diener & Chan, 2011; Taylor et al., 2000).
Less research has been conducted on the relationship between sense of self-worth
and health. However, conceptually related constructs such as pride and self-esteem have
been found to generate mental and physical health benefits. For these reasons, some
researchers have argued that self-esteem should be regarded as an important focus for
health promotion, highlighting it as an important factor contributing to health and quality
of life (Evans, 1997). Low self-esteem during adolescence has been shown to predict
poorer mental and physical health, as well as other negative outcomes, later in adulthood
72
(Trzesniewski et al., 2006). Furthermore, levels of self-esteem predict symptom severity
in the daily lives of individuals with chronic illness, with those who have higher self-
esteem faring better (Juth et al., 2008). Similarly, pride has been found to predict lower
levels of interleukin-6 (IL-6), a pro-inflammatory cytokine, which the study’s authors
suggested might be one pathway linking positive emotions to health (Stellar et al., 2015).
Several studies have suggested a positive relationship between generative activities and
social connectedness, positive affect, and sense of self-worth (Brown, Hoye, &
Nickelson, 2012; Grossman, Wang, & Gruenewald, 2017; Huta & Zuroff, 2008; Kahana,
Bhatta, Lovegreen, Kahana, & Midlarsky, 2013). Thus, as evidence suggests social
connectedness, positive affect, and sense of self-worth are connected to both generativity
and health outcomes, they might be important cognitive-affective pathways contributing
to this relationship.
The majority of research on generativity thus far has focused on how generative
activities (volunteering, caregiving, support provision) or generative self-perceptions are
related to health and well-being. Erik Erikson initially proposed generativity as a critical
stage of psychosocial development in midlife and beyond, theorizing that individuals who
failed to achieve generativity would experience stagnation, a developmental barrier that
he theorized could have adverse consequences on their health and well-being (Erikson,
1950, 1964). However, to our knowledge, no research has examined whether individuals
who fail to meet their generative expectations actually experience poorer health outcomes
than those who accomplish successful resolution of the generativity vs. stagnation crisis.
Empirical testing of this hypothesis is critical to evaluate the validity of Erikson’s theory
73
and determine the relative importance of generativity in the “successful aging” of middle
aged and older adults today.
Present Study
Researchers examining generative activities have found strong evidence
suggesting that engagement in contributory behavior positively shapes health and well-
being. However, researchers have also emphasized the need to gain a better
understanding of the pathways through which generativity may be linked to such
benefits. Some research has begun to investigate cognitive-affective correlates of
generative activity in an effort to discover potential important pathways underlying the
health and well-being benefits (Grossman, Wang, & Gruenewald, 2017). Additionally, it
has been shown that perceived generative failure over time is linked to decreased life
satisfaction, mainly through these same cognitive-affective pathways (Grossman &
Gruenewald, submitted). The present study builds upon these investigations by exploring
how generative failure is related to health outcomes, including self-rated mental and
physical health, number of chronic conditions, and ADL limitations. These outcomes
were selected as they represent differential health indicators, ranging from subjective
reports, to objective diagnoses, to functional limitations, respectively. If generative
failure is associated with poorer health as hypothesized, mediation tests will then
determine whether the cognitive-affective states (positive affect, social connectedness,
and sense of self-worth) mediate these links. As it has been argued that generativity is
related to better health, these studies will test the applicability of this assertion for failure
to achieve generative goals over time and will provide a clearer understanding of the
cognitive-affective mechanisms at play in this relationship.
74
Methods
Data and Participants
The data come from the National Survey of Midlife Development in the U.S.
(MIDUS), a longitudinal survey containing three waves of data collection (1995/1996,
2004-2006, 2013-2016). The MIDUS survey was designed with the goal of promoting the
investigation of the role of psychological, social, and behavioral factors in shaping health
and well-being with aging across the life course (www.midus.wisc.edu). The first wave
of the MIDUS survey collected data from 7,108 participants ages 25-74 and was
administered in 1995/1996. Subjects were recruited to participate in the study through
national random digit dialing and oversampling of 5 metropolitan cities in the United
States. MIDUS II is the 10-year follow-up to the original MIDUS study in 2004/2006
(n=4,963 initial phone survey and n = 4,041 for subsequent mail survey). Most recently,
the MIDUS III follow-up was conducted, with data collection beginning in 2013. The
current study will include data from the latter two waves of the MIDUS survey, as the
first wave of the MIDUS study did not collect as complete information on cognitive-
affective states.
Measures
Perceived Generative Contributions. Perceived generative contributions are
measured in MIDUS with the question, “Using a scale from 0 to 10 where 0 means “the
worst possible contribution to the welfare and of other people” and 10 means “the best
possible contribution to the welfare and well-being of other people,” how would you rate
your contribution to the welfare and well- being of other people these days?” Additional
instructions specify to “take into account all that you do, in terms of time, money, or
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concern, on your job, and for your family, friends, and the community.” Respondents
then rate their response, which can range from 0, indicating worst possible contribution to
10 indicating best possible contribution.
Expected Generative Contributions. At each time point, respondents were also
asked, “Looking ahead ten years into the future, what do you expect your contribution to
the welfare and well-being of other people will be like at that time?” Again, respondents
rate their response on an 11-point scale ranging from 0 to 10, 10 being the best rating.
This study focuses on individuals’ expected generative contributions assessed during
MIDUS 2 predicting their level of contribution 10 years later, approximately the time of
the MIDUS 3 re-assessment.
Generative Failure. Generative failure was determined by subtracting
individuals’ expected contributions reported at Wave 2 of the MIDUS Study from their
perceived contributions reported at Wave 3. If individuals failed to meet or exceed their
generative expectations, they were placed in the generative failure group; otherwise they
were placed in the met or exceeded expectations group.
Cognitive-Affective States. Psychological well-being measures of positive affect,
sense of self-worth, and social connectedness were examined as cognitive-affective
correlates of generative activities. The questions comprising these scales were included in
the Self-Administered Questionnaire, and a factor analysis was conducted to confirm the
fit of specific items to scales for positive affect, sense of self-worth, and social
connectedness.
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Positive Affect. The positive affect scale asked participants for how much of the
past 30 days they felt “cheerful,” “satisfied,” “enthusiastic,” “full of life,” “extremely
happy,” “calm and peaceful,” and “in good spirits.” All of these items were rated on a 5-
point scale, ranging from 1 indicating “All the time, to 5 indicating “None of the time.”
Items were reverse coded so that 5 reflected the maximum score, and item ratings were
then averaged to compute a scale score for positive affect (α=.91) (min: 1, max: 5).
Sense of Self-Worth. The self-worth scale consisted of the two questions
assessing individuals’ sense of self-worth, specifically querying how much of the time
over the past 30 days they felt “confident” and “proud.” Again, respondents were asked
to rate their response to these questions on a 5-point scale measuring frequency of these
feelings, and their ratings were averaged to create a self-worth scale score (α=.72) (min:
1, max: 5).
Social Connectedness. The social connectedness scale was comprised of two
questions querying respondents’ feelings of social integration or connectedness. The
scale consisted of the questions, “How much of the time did you feel close to others?”
and “How much of the time did you feel like you belong?” Again, these items were rated
on a 5-point scale. Their ratings were then averaged to comprise a social connectedness
score (α=.81) (min: 1, max: 5).
Self-rated Health. Self-rated physical health was assessed with the question, “In
general, would you say your physical health is: excellent, very good, good, fair, or poor?”
Respondents were then asked the same question regarding their mental health. Responses
to both questions were rated on a 5-point scale, ranging from 1 indicating excellent health
77
to 5 indicating poor health. The variables were reverse coded so that a higher score
indicated better health for clearer interpretation.
Chronic Conditions. This variable was measured by the total number of serious
health conditions the respondent has experienced in the past 12 months. It is constructed
by summing the total number of “Yes” responses to a range of major chronic health
conditions, ranging from 0 to 9. Major conditions included cancer, heart trouble,
Diabetes, lung conditions, lupus or autoimmune disorders, AIDS, hypertension, stroke, or
neurological disorders.
ADL Limitations. Activities of daily living impairment was measured as the total
number of six ADLs that participants reported “a lot” of difficulty in performing: lifting
or carrying groceries, bathing or dressing, climbing several flights of stairs, walking
several blocks, bending, kneeling or stooping, and moderate physical activity (e.g.,
vacuuming). Thus, the minimum participants could report was 0, reflecting no
limitations, and maximum was 6, reflecting a lot of difficulty with all six of the ADLs
assessed.
Sociodemographic Variables. Age (centered), sex, race, and education were
included as covariates in the analyses. For race, a dummy variable was created to
represent white or non-white race/ethnicity. Educational attainment was coded into a
categorical variable with three categories, including “high school or less,” “some
college,” and “4 year college degree or greater.”
Analytic Strategy
All analyses were performed using SPSS (Version 22). After descriptive statistics
were examined, regression analyses were conducted to examine the effects of generative
78
failure on health outcomes, including self-reported mental and physical health, number of
chronic conditions, and ADL limitations. Multiple mediation analyses were run using the
PROCESS macro in SPSS (Hayes, 2012) to test whether positive affect, sense of self-
worth, and social connectedness mediated the link between generative failure and the
selected health outcomes. In addition, Johnson-Neyman analyses were conducted to
determine whether the associations between generative failure and health varied as a
function of age and if so, at which ages they were strongest (Hayes, 2013). Tests of
moderation were also conducted to determine if generativity/health associations varied by
gender. All of these analyses controlled for age, sex, race, and education. Additionally,
the analyses also controlled for prior perceived generative contributions and the selected
health indicators at Wave 2 of the MIDUS Study.
Results
Descriptive statistics were generated for all of the variables included in the current
analysis and are presented in Table 4-1. The analytic sample contained 2,214 individuals
who participated in both Waves II and III of the MIDUS survey and provided data on the
variables in the analysis. The average age of the respondents at time 1 (Wave 2) was
approximately 55, ranging from 30 to 84. The average age of respondents at time 2
(Wave 3) was 64, with ages ranging from 39 to 92. The sample contained slightly more
females than males (55%) and the majority of respondents were White (93%) (see Table
4-1). In terms of education, 43.8% of the sample had graduated from a 4-year college or
beyond, 27.6% had some college, and 28.6% had high school or less.
With regards to the cognitive-affective states, most participants reported relatively
high levels of positive affect, self-worth, and social connectedness (on average,
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experiencing these feelings somewhere between “some of the time” and “most of the
time” in the past 30 days). Self-reported generative contributions to others at MIDUS 2
were relatively high on the 0-10 scale (M = 6.66; SD = 2.1), and only slightly lower at
MIDUS 3 (M = 6.50; SD = 2.2). When asked in MIDUS 2 about their expected level of
generative contribution 10 years in the future, the average response was similarly high (M
= 6.87; SD = 2.2). When the discrepancy between expected and actual reported
generative contribution over the 10 year period was assessed, it was found that 46% of
the sample failed to meet their generative expectations over time (n=1,020), and 54% met
or exceeded their expected level of contribution (n=1,194) (Table 4-2).
Hypothesis 1: Failing to meet generative self-expectations over the 10 year
period will be associated with worse health outcomes at Time 2. As illustrated in
Table 4-3, generative failure over time was significantly associated with worse health
outcomes across all domains assessed, providing support for Hypothesis 1. Specifically,
perceived generative failure from MIDUS 2 to MIDUS 3 was significantly predictive of
worse self-rated physical and mental health at MIDUS 3 (physical health: β = -0.076, B =
-0.154; p < 0.001; mental health: β = -0.061, B = -0.116; p < 0.01). Similarly, generative
failure was associated with a greater number of chronic conditions (β = 0.057, B = 0.354;
p < 0.01) and ADL limitations (β = 0.054, B = 0.140; p < 0.01). These regressions
accounted for prior levels of physical health, mental health, chronic conditions, and ADL
limitations, respectively.
Hypothesis 2: The cognitive affective-states of positive affect, sense of self-
worth, and social connectedness will mediate the association between generative
failure and poorer health outcomes. The multiple mediation analyses revealed that
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positive affect, in particular, at least partially mediated the relationship between
generative failure and the measured health outcomes. Use of the PROCESS macro in
SPSS revealed a significant indirect path from generative failure to self-rated physical
and mental health for positive affect (both p’s < 0.001). Analyses also revealed
significant indirect paths from generative failure to the number of chronic conditions for
positive affect (p < 0.001). And finally, there was a significant indirect path from
generative failure to ADL limitations through positive affect as well (p < 0.001).
In preliminary efforts to probe the specific dimensions of positive affect that seem
to be most salubrious in the context of generativity, we broke down our positive affect
variable into several dimensions (low arousal, high arousal, and high engagement with
life) and re-ran our analyses. We found that the least predictive positive affect indicators
were those that made up the high arousal category, defined as feeling “extremely happy”
and “cheerful.” The dimension reflecting higher engagement with life (defined by feeling
“enthusiastic” and “full of life”) was the most significantly associated with generative
failure, and both high engagement with life and low arousal positive affect (“satisfied”
and “calm/peaceful”) were significant mediators of the link between generative failure
and life satisfaction and health outcomes. Interestingly, low arousal positive affect played
the most significant role with life satisfaction and mental health outcomes, and high
engagement with life played the biggest role with physical health indicators. These
analyses suggest that different dimensions of positive affect may be important for
psychological well-being as opposed to physical health, but more thorough research
investigations are needed to gain a better understanding of these processes and the
positive affect correlates of generativity that are most beneficial.
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Moderation Analyses. A Johnson-Neyman analysis was conducted to examine
whether the association between perceived generative failure and poorer health varied as
a function of age. The Johnson-Neyman procedure identifies the points along a
continuous moderator where the relationship between the independent variable and the
outcome variable is statistically significant or non-significant. The test revealed that the
associations between generative failure and the health outcomes, including self-rated
mental and physical health, ADL limitations, and chronic conditions, varied by age.
There were significant interactions between age and generative failure for self-rated
mental and physical health. Specifically, these associations were significant for age 70
and below, and age 72 and below, respectively. While the regressions did not show
significant overall age interactions for the chronic conditions and ADL limitations
outcomes, the Johnson Neyman analyses revealed that they were similarly only
significant for those age 72 and below for chronic conditions. The only outcome for
which these associations became significant with advancing age was ADL limitations,
significant only among those who were age 54 and above. Gender interactions were also
run and not significant for any of the health outcomes.
Discussion
Findings suggest that generative failure, or failing to meet one’s expected level of
contribution over time, is associated with poorer health outcomes, including lower self-
rated mental and physical health, increased ADL limitations, and a greater number of
chronic conditions. These findings support previous research findings linking greater
perceptions of generativity to more favorable health outcomes, such as reduced disability
and lower rates of mortality (e.g. Grand et al., 1988; Gruenewald et al., 2007, 2009,
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2012). However, no study yet to our knowledge has assessed the effects of perceived
generative failure over time on individuals’ health and functioning. By examining this
unique aspect of generativity, we were able to determine that it is not simply individuals
with higher levels of generative concern who have better outcomes, but those who
actually follow through and fulfill their goals through commitment.
Results also suggest that cognitive-affective states partially mediate the link
between generative failure and poorer health outcomes. Specifically, positive affect,
social connectedness, and sense of self-worth were examined as potential mediators in
the present analysis. Interestingly, of the three cognitive-affective states included in the
mediation analyses, positive affect seemed to play the most significant role in linking
generative failure to health. In the multiple mediation models for each of the four health
outcomes, positive affect was the only cognitive-affective state with a significant indirect
effect, supporting the importance of this particular correlate of generativity. These results
beg the question of why positive affect might play such an essential role. A famous
proverb states, “A cheerful heart is good medicine.” Certainly, many have described the
sort of “warm glow” or “helper’s high” that individuals generate by helping others
(Andreoni, 1989, 1990; Hartmann, Eisend, Apaolaza, & D’Souza, 2017; Luks, 1988). A
sizable body of research has focused on negative cognitive-affective states and their
detrimental effect on health. However, studies have also revealed that a lack of positive
affect, as opposed to greater negative affect, predicts mortality, stroke, and the
development of disability in older adults (Blazer & Hybels, 2004; Ostir, Markides, Black,
& Goodwin, 2000; Ostir, Markides, Peek, & Goodwin, 2001; Steptoe, Wardle, &
Marmot, 2005). Therefore, in the case of generative failure, positive affect may be
83
strongly linked with the health outcomes because of the lower levels of positive affect or
lack thereof. Researchers have also found that positive affective states are linked to more
favorable biological profiles, including reduced levels of cortisol, a key stress hormone
related to a range of pathologies, and increased immune function (Davidson et al., 2003;
Steptoe, Wardle, & Marmot, 2005). These biological pathways might in part explain how
positive affect mediates the relationship between generativity and health, but additional
research is needed to explore this further. Despite these findings, sense of self-worth,
social connectedness, and other cognitive-affective states should not be overlooked in
their importance as correlates of contributory behaviors and self-perceptions. It may be,
rather, that different cognitive-affective states are responsible for mediating different
health and well-being outcomes, a distinction which should be probed by future research.
Age and gender were also examined as potential moderators of the connection
between generative failure and health. A Johnson-Neyman analysis determined that
failure to meet generative expectations generally had a more significant influence on
health in those in the middle and young-old age groups. The young-old may be more
influenced by perceived generative failure for a number of reasons, one being the decline
in physical abilities with age. Research suggests that individuals will accept discrepancies
(between their expectations and actualities) if they do not have the power to change them
(Maio & Thomas, 2007). Additionally, studies have found that building social ties is a
key motivator driving volunteer participation in older adults (Okun & Schultz, 2003).
However, these motivations are strongest in those from age 60-70, suggesting that young-
old may be more motivated to engage in generative behaviors and more adversely
affected by their failure to do so. Indeed, in the analytic sample, those over 70 had lower
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generative self-expectations on average than those age 70 and below, suggesting health
may factor into generative self-expectations. The exception for the age moderation
findings trend was ADL limitations, in which the associations were only significant for
individuals over the age of 54. This may be due to the fact that ADL limitations typically
arise later in life, with a drastic increase in those over the age of 75 (CDC, 2016). There
were no significant moderation effects of gender in these analyses, indicating that being
male or female did not influence the degree of detrimental effects of generative failure on
health.
Even though the MIDUS survey contains a national sample that was derived from
random digit dialing sampling, the representation of those at the extremes of the
socioeconomic spectrum and of racial and ethnic minorities is lower than that of the
general U.S. population. Though these characteristics can be common in national
telephone and mail surveys, it is possible that findings may not be generalizable to the
larger population. Additionally, the MIDUS surveys did not take into account
individuals’ personal interpretations of generative contributions and the types of activities
and behaviors they are considering when they rate their current and future predicted
contribution levels. Different individuals could potentially consider very different
activities and behaviors to constitute contributions to the well being of others. Future
studies would benefit from collecting more specific data on the types of activities
individuals are referencing when answering questions regarding their social
contributions. Similarly, the positive affect measure utilized in this study contains both
high and low arousal items. Future experimental work should further probe the
85
differential components of positive affect to better elucidate the mechanisms underlying
associations between generativity and health indicators.
Lastly, it is important to acknowledge the potential for reverse causality in these
investigations. While our hypothesis follows that generativity may give rise to better
health outcomes, it is also possible that individuals in worse health are less able to
contribute to the welfare of others, and thus feel as if they have failed to meet their
expectations. In an effort to address this possibility, analyses controlled for each health
variable from time 1 to account for prior health status. Although the magnitude of the
effect size was reduced slightly, the associations remained significant, suggesting an
effect above and beyond that of prior health status. As Post (2005) acknowledges in his
argument for a causal relationship between altruism and well-being, there seems to be
some self-selection of the healthy into altruism and generativity, but that only seems to
partially explain the better health of generative individuals. Studies using biological
markers provide strong support for the idea that altruistic emotions and behavior
contribute to better mental and physical health (e.g. Field et al., 1998; Inagaki & Orekeh,
2017). Although it is true that those who are generative typically must have some
baseline level of health and functioning, this factor does not negate the possibility that
generativity also contributes to better health and well-being (Post, 2005). Still, future
longitudinal investigations should examine the associations between generativity and
health to facilitate a deeper understanding of these links and the temporal relationship.
Despite the study’s limitations, this analysis makes a substantial contribution to
the literature in (1) its examination of a relatively unexamined aspect of generativity,
focusing on perceived generative failure, and (2) its use of a large, population based
86
survey to investigate the connections between generative failure and health. Furthermore,
this study sheds light on the developmental salience of generativity throughout the
lifespan, examining the ages at which generative failure seems to be most predictive of
negative health outcomes.
In summary, the present study indicates that perceived generative failure over
time is associated with worse health outcomes, as measured by self-reported mental and
physical health, ADL limitations, and number of chronic conditions. Less positive levels
of cognitive-affective states, especially positive-affect, seem to play an important role in
the connection between failure to meet generative expectations and experiencing poorer
health. Overall, the findings suggest that health promotion researchers and society at
large should focus on understanding factors that might support or deter individuals from
pursuing the generative opportunities they seek. Public policy and programs should be
developed to maximize the contributory capacity of community members, with a special
focus on the young-old as social contributions might influence their aging trajectory.

87
Table 4-1. Characteristics of the Analytic Sample (n = 2,214).
n % M (SD) Possible Range
Age at Time 1
2,214
54.90 (11) 30 – 83
Age at Time 2 2,214 64.00 (11) 39 – 92
Female 1,224 55.3
White 2,065 93.3
Education
< High school 634 28.6
Some college 610 27.6
> 4 year college 970 43.8

88
Table 4-2. Descriptive Statistics of Study Variables (n = 2,214).
%
M (SD) Range
Positive affect (M2) 3.47 (.68) 1 – 5
Social connectedness (M2)
3.71 (.85) 1 – 5
Sense of self-worth (M2)
3.69 (.81) 1 – 5
Perceived current contributions (M2)
6.66 (2.1) 0 – 10
Expected contributions in 10 years (M2)
6.87 (2.2) 0 – 10
Self-rated mental health (M2)
3.93 (.89) 1 – 5
Self-rated physical health (M2)
3.72 (.92) 1 – 5
Number of chronic conditions (M2)
0.79 (.96) 0 – 7
ADL limitations (M2)
0.34 (.97) 0 – 6
Positive affect (M3)
3.44 (.72) 1 – 5
Social connectedness (M3)
3.66 (.89) 1 – 5
Sense of self-worth (M3)
3.61 (.85) 1 – 5
Perceived current contributions (M3)
6.50 (2.2) 0 – 10
Failed to meet expectations (M3) 46.1

Met/exceeded expectations (M3)
Self-rated mental health (M3)
53.9
3.70 (.94) 1 – 5
Self-rated physical health (M3)
3.49 (1.0) 1 – 5
Number of chronic conditions (M3)
1.15 (1.1) 0 – 9
ADL limitations (M3)
0.59 (1.3) 0 – 6
Note: M2 indicates measurements from MIDUS 2. M3 indicates measurements from
MIDUS 3.

89
Table 4-3. Results from Regressions Examining the Association Between Generative Failure
and Health, Controlling for Demographic Factors, Prior Generativity, and Prior Health Status.
MIDUS 3 outcomes
Physical
Health
Mental
Health
Chronic
Conditions
ADL
Limitations
Generative failure -.076*** -.061** .057** .054**
Age (centered) -.059** -.007 .067*** .135***
Female .000 -.036 .069*** .075***
Nonwhite -.066*** -.045* .028 .010
< High school -.066*** -.101*** .055** .095***
Generative contributions (M2)
(centered)
.042* .044* -.035* -.031
Physical health (M2) .517*** ----- ------ -----
Mental health (M2) ----- .452*** ------ -----
Chronic conditions (M2) cent ----- ----- .553*** -----
ADL limitations (M2) ----- ----- ------ .485***
*p < .05 ** p < .01 *** p < .001; Note: Table contains standardized (β) values.

90
Figure 4-2. Unstandardized Indirect Effects of Generative Failure on Self-Rated Physical
Health Through Cognitive-Affective States (model includes M2 generative contributions,
M2 physical health, age, race, sex, education as covariates).

Indirect effects:
Unstandardized (95% bootstrapped CI)
TOTAL -.0428 (-.0664, -.0261)
Positive Affect -.0362 (-.0583, -.0213)
Self-Worth -.0011 (-.0114, .0090)
Social Connectedness -.0055 (-.0189, .0049)

M2 - M3
Generative
Failure
M3
Positive
Affect
M3
Social
Connectedness
M3
Physical
Health
-.13***
-.15***
-.19***
-.11** (.56***)
.27***
.03
.01
M3
Self
Worth
91
Figure 4-3. Unstandardized Indirect Effects of Generative Failure on Self-Rated Mental
Health Through Cognitive-Affective States (model includes M2 generative contributions,
M2 mental health, age, race, sex, education as covariates).

Indirect effects:
Unstandardized (95% bootstrapped CI)
TOTAL -.0631 (-.0929, -.0350)
Positive Affect -.0512 (-.0788, -.0281)
Self-Worth -.0033 (-.0128, .0050)
Social Connectedness -.0085 (-.0203, .0007)

M2 - M3
Generative
Failure
M3
Positive
Affect
M3
Social
Connectedness
M3
Mental
Health
-.12***
-.14***
-.17***
-.05 (-.12**)
.41***
.05
.02
M3
Self
Worth
92
Figure 4-4. Unstandardized Indirect Effects of Generative Failure on Chronic Conditions
Through Cognitive-Affective States (model includes M2 generative contributions, M2
chronic conditions, age, race, sex, education as covariates).

Indirect effects:
Unstandardized (95% bootstrapped CI)
TOTAL .0163 (.0054, .0315)
Positive Affect .0237 (.0098, .0461)
Self-Worth .0022 (-.0102, .0141)
Social Connectedness -.0096 (-.0241, .0013)

M2 - M3
Generative
Failure
M3
Positive
Affect
M3
Social
Connectedness
M3
Chronic
Conditions
-.15***
-.16***
-.20***
.09* (.11**)
-.16***
.05
-.01
M3
Self
Worth
93
Figure 4-5. Unstandardized Indirect Effects of Generative Failure on ADL Limitations
Through Cognitive-Affective States (model includes M2 generative contributions, M2
ADL limitations, age, race, sex, education as covariates).

Indirect effects:
Unstandardized (95% bootstrapped CI)
TOTAL .0371 (.0164, .0621)
Positive Affect .0464 (.0244, .0787)
Self-Worth .0027 (-.0120, .0186)
Social Connectedness -.0120 (-.0317, .0036)

M2 - M3
Generative
Failure
M3
Positive
Affect
M3
Social
Connectedness
M3
ADL
Limitations
-.14***
-.15***
-.19***
.11* (.14**)
-.34***
.06
-.02
M3
Self
Worth
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CHAPTER 5: SUMMARY & GENERAL DISCUSSION
The body of work presented in this dissertation sought to clarify the relationship
between generativity and health and well-being. Though the benefits of generativity have
been increasingly documented, empirical research illuminating the potential reasons
behind these benefits is scarce. Thus, this dissertation sought to identify some of the
cognitive-affective pathways that might play important roles in facilitating the gains
associated with generativity. The studies examined a range of aspects of generativity,
from different types of generative activities in Study 1 to generative self-perceptions and
expectations in Studies 2 and 3, and their relationship to cognitive-affective well-being.
To examine these connections, these investigations utilized a large nationally
representative dataset (the National Survey of Midlife Development, or MIDUS). Below,
the results from each study are summarized, followed by an overall discussion of the
findings and how, together, they inform a larger picture regarding the relationship
between generativity and well-being.
5A. Study 1: The Daily Flux of Generative Activity and Cognitive-Affective States
The primary aim of Study 1 was to capitalize upon the opportunity to utilize daily
data to better understand whether cognitive–affective correlates vary with daily variations
in generative activity. The analyses utilized data from the National Study of Daily
Experiences (NSDE), a sub-study of the National Survey of Midlife Development
(MIDUS) to investigate the within and between-person associations of generative
activities and cognitive-affective states. Multilevel regression models assessed person-
level and day-level indicators of three forms of generative activity (volunteering,
emotional support, informal help) as predictors of daily self-enhancement, social
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connectedness, and positive affect states over an 8-day period, controlling for
sociodemographic factors. Although theoretical formulations of pro-social behavior
suggested that self-enhancement, social connectedness, and positive affect pathways
might underlie such links, little empirical examination has been conducted. It was
predicted that daily engagement in generative activities would be related to better daily
cognitive-affective well-being. Multilevel regression analyses revealed several significant
between- and within-person effects. Specifically, those who volunteered more
experienced greater feelings of positive affect and social connectedness compared to
those who volunteered less or not at all. Those who provided emotional support to others
actually experienced a lower sense of self-worth and less positive affect compared to
those who provided less emotional help. At the daily level, on days when individuals
provided informal help to others, they reported greater levels of social connectedness and
self-worth than on the days when they did not engage in this kind of behavior. Similarly,
on days when individuals volunteered, they felt a greater sense of social connectedness
and self-worth compared to their non-volunteering days. Again, the findings showed
emotional support was less beneficial in that on days when individuals provided
emotional support they had lower positive affect than on days when they did not provide
this kind of support to others. This study suggests that these cognitive-affective states are
important correlates of generative activity that may be responsible for how generativity
affects our health and well-being. Interestingly, the findings also suggest that
instrumental forms of helping may be more positively linked to well-being than
emotional support provision. This may be due in part to a distinction between empathy
and compassion, in that compassion can be beneficial but empathy can be detrimental
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through the internalization of others’ negative emotions.
5B. Study 2: Perceived Generative Failure Associated With Lower Life Satisfaction
The first objective of Study 2 was to investigate the relationship between
generative self-perceptions, generative self-expectations, and perceived generative failure
and the cognitive-affective states examined in Study 1. An additional aim was to then
examine whether positive affect, social connectedness, and sense of self-worth mediated
the association between perceived generative failure and level of life satisfaction.
Findings revealed that greater feelings of generativity, and more positive expectations for
future generative contributions ten years into the future, were associated with better
cognitive-affective well-being, cross-sectionally and over a 10 year period. Results also
showed that generative failure, or failing to meet one’s expected level of contribution
over the 10 year period, was associated with poorer cognitive-affective well-being, in
terms of positive affect, social connectedness, and sense of self-worth. Additionally, the
study showed that not only is failing to meet generative self-expectations important for
individuals’ cognitive-affective states, but that these relationships seem to be linked to
satisfaction with life more broadly. An important indicator of successful aging and
quality of life, life satisfaction has also been linked to positive health outcomes, ranging
from better mental health to lower risk of disease and mortality. Therefore, these findings
provide important preliminary support for the value of generativity-based interventions.
5C. Study 3: Perceived Generative Failure Linked to Poorer Health
Lastly, Study 3 builds upon the prior investigations by exploring how perceived
generative failure is related to several health indicators, including self-rated mental and
physical health, number of chronic conditions, and ADL limitations. These outcomes
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were selected as they represented differential health indicators, ranging from subjective
reports, to objective diagnoses, to functional limitations, respectively. Regression
analyses revealed that generative failure, or failing to meet one’s expected level of
contribution over time, was associated with poorer health outcomes, including lower self-
rated mental and physical health, increased ADL limitations, and a greater number of
chronic conditions. These findings support previous research demonstrating links
between greater self-perceptions of generativity and more favorable health outcomes,
such as reduced disability and lower rates of mortality (e.g. Grand et al., 1988;
Gruenewald et al., 2007, 2009, 2012). However, in addition, this study makes a unique
contribution to the existing literature in its empirical investigation of Erikson’s theory
that healthy development and adaptation depends on successful resolution of the
generativity-stagnation conflict. Though this theory is widely accepted, few have tested
the real-life implications of failure to be generative with regards to health and
functioning. This study not only revealed significant relationships between perceived
generative failure and dimensions of health, but also provided greater clarity on
cognitive-affective states contributing to these associations. Interestingly, of the
examined cognitive-affective states, positive affect seemed to play the most significant
role in connecting generative failure to health. The results of this study, as well as those
of Studies 1 and 2, will be examined and expanded upon in the context of the broader
literature below.
5D. General Discussion
The studies contained within this dissertation make substantial contributions to
the generativity literature, a realm of research that has been gaining interest in the fields
98
of gerontology and developmental psychology in recent years. One of the reasons for the
resurgence of generativity research is the widely demonstrated association between
contributing to the welfare of others and contributing to the health and well-being of
oneself (e.g., Gruenewald et al., 2012). As the population faces a growing proportion of
older adults and heightened longevity, it is critical that public health and research efforts
be directed at supporting healthy or successful aging. Enhancements in generativity may
represent one route toward experiencing healthier aging trajectories. However, the
mechanisms underlying these generativity-associated gains have remained relatively
unknown, as have the consequences of failing to meet generative self-expectations with
advancing age.
5D(a). Generativity & Cognitive-Affective States.
This dissertation focused on elucidating potential cognitive-affective states which
might serve as pathways connecting generativity to health and well-being. Each study
focused on the cognitive-affective states of positive affect, social connectedness, and
sense of self-worth (termed “self-enhancement” in Study 1). Study 1 established these
states as important correlates of generative activity, in general and at the daily level, and
Studies 2 and 3 provided additional support for these relationships and built on these
analyses to test the potential of the cognitive-affective states as pathways mediating the
link between generativity and health and well-being. Of these cognitive affective states,
positive affect seemed to play the most significant role in linking generativity to life
satisfaction and various health indicators, giving rise to the question of what it is about
positive affect that seems to be so beneficial. There are several ideas about why positive
affect may be related to positive outcomes, including through physiological, behavioral,
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and other mechanisms. For instance, researchers have demonstrated a link between
positive affect and reduced neuroendocrine, inflammatory, and cardiovascular activity in
middle-aged adults (Steptoe, Wardle, & Marmot, 2005). Positive emotions also seem to
buffer against dangerous negative emotions, accelerating physiological recovery
processes (Tugade & Fredrickson, 2004). Another possibility is that positive affect may
be associated with more favorable health habits and less risky lifestyles. Support for this
hypothesis comes from findings that cigarette smoking is linked to psychological distress
and lower levels of physical activity are related to depression and anxiety (Biddle &
Mutrie, 2001; Jarvis, 2002; Steptoe, Wardle, Marmot, 2005). Despite the evidence
supporting the importance of positive affect for health and well-being, existing research
on positive affect is complicated by the fact that researchers define and measure the
construct in many different ways. The positive affect measure utilized in studies 1-3
included both high (e.g., extremely happy, cheerful) and low (e.g., satisfied,
calm/peaceful) arousal emotions, as well as assessments of engagement with life (e.g.,
enthusiastic, full of life).
In preliminary efforts to probe the characteristics of positive affect that seem to be
most beneficial in the context of generativity, supplementary analyses were conducted in
Study 3 to explore different dimensions of positive affect. The least predictive positive
affect indicators were those that comprised the high arousal category. The dimension
reflecting enthusiasm and engagement with life was the most significantly related to
generative failure, and both engagement with life and low arousal positive affect
mediated the links between generative failure and life satisfaction and health outcomes.
Interestingly, the low arousal dimensions of positive affect played the most significant
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role in mediating life satisfaction and mental health outcomes, and engagement with life
stood out as the most significant pathway to physical health indicators. These analyses
suggest that different components of positive affect may be associated with psychological
well-being as opposed to physical health, but more thorough research investigations are
needed to gain a better understanding of these associations and the positive affect
correlates of generativity that are most beneficial.
It is also important to note that although positive affect seemed to play the most
vital role as a mediator in these analyses, evidence suggests that social connectedness and
sense of self-worth can also be important indicators of health and well-being.
Importantly, all three states were revealed to be significant mediators in Study 2’s
analysis of the relationship between perceived generative failure and life satisfaction.
These findings suggest that positive affect, social connectedness, and sense of self-worth
all play a role in the connection between failing to meet one’s generative self-
expectations and lower life satisfaction. Though in Study 3, only positive affect was
revealed to be a significant mediator linking generative failure to worse health, other
research has provided support for the importance of social connectedness and sense of
self-worth for mental and physical health. For instance, Juth et al (2008) found that low
self-esteem predicted greater stress and symptom severity in the daily lives of individuals
with asthma and rheumatoid arthritis. Researchers have also found that individuals with
lower self-esteem as adolescents had worse physical and mental health in later life
(Trzesniewski et al., 2006), and greater feelings of pride are associated with reduced
inflammation (Stellar et al., 2015). Additionally, the literature on the health benefits of
social support is well-known and extensive, demonstrating that a greater number of social
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connections and stronger social integration are related to lower mortality and decreased
morbidity and/or severity across a number of conditions, such as heart disease, stroke,
and dementia (Berkman, Glass, Brissette, & Seeman, 2000; House et al., 1988; Seeman
& Crimmins, 2001). The inverse to social connectedness, social isolation, has also been
found to be related to adverse health consequences, such as impaired executive
functioning, sleep, and mental and physical well-being (Cacioppo & Cacioppo, 2014). As
more positive levels of these cognitive-affective states have been associated with more
desirable health outcomes, sense of self-worth and social connectedness should not be
overlooked in their importance as correlates of generative activities and generative self-
perceptions. It may be that different cognitive-affective states mediate the relationship
between different manifestations of generativity and specific health and well-being
outcomes, distinctions that should be explored by future empirical investigations.
5D(b). Generativity & Successful Aging.
Generativity has been proposed to be an important facilitator, even a cornerstone,
of successful or healthy aging (Baltes & Baltes, 1990; Villar, 2012; Fisher, 1995). There
are debates over the value of defining “successful aging,” as it may embody different
qualities for everyone, but it may offer a useful framework through which to interpret
these findings. It should be noted that successful aging need not be viewed as black and
white, or success as opposed to failure, but rather should be regarded as an “ideal state to
be aimed for,” or a multi-dimensional idea that exists on a continuum (Bowling &
Dieppe, 2005, p. 1550). In this lens, successful aging can be influenced by health
promotion and preventative efforts, perhaps even by psychosocial factors such as
generativity. Again, it has been argued that there are two predominant models when it
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comes to defining successful aging, the psychosocial model and the biomedical model.
The psychosocial model emphasizes the importance of life satisfaction, the most
commonly proposed element of successful aging (Bowling & Dieppe, 2005). The
biomedical model emphasizes the importance of absence of disease and maintenance of
physical and mental functioning (Bowling & Dieppe, 2005; Bowling, 2007). This
dissertation suggests that, by either definition, successful aging is associated with, if not
shaped by, generative achievement. Study 2 revealed that perceived failure to achieve
generative expectations over time was related to lower life satisfaction, and Study 3
showed that generative failure was also linked to poorer health and functioning, including
worse self-reported mental and physical health and increased numbers of chronic
conditions and ADL limitations. Thus, whether successful aging is defined primarily
from a psychosocial or biomedical standpoint, this dissertation suggests a positive
influence of generativity on the aging process.
Prior research has also laid a strong foundation for the association between
generativity and successful aging, particularly in its demonstration of the links between
greater self-perceptions of generativity and engagement in generative behaviors and
enhanced social, psychological, and physical well-being in middle and later life
(McAdams, de St. Aubin, Logan, 1993; Gruenewald, Liao, & Seeman, 2012; Grand et al.,
1988). As described in earlier chapters, studies have demonstrated that greater self-
perceptions of usefulness or generativity are associated with lower mortality, reduced
likelihood of disability, and better subjective well-being in later life (Gruenewald et al.,
2009; Gruenewald et al., 2007). These associations seem to hold true for opposite
patterns as well, as studies have also found links between perceived uselessness and
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higher rates of mortality in those with disabilities at follow-up (Curzio, Bernacca,
Bianchi, & Rossi, 2017). Older adults who feel more useful towards others have also
demonstrated better memory and executive function performance compared to those who
feel less generative (Hagood & Gruenewald, 2016). The findings on the value of
generative self-perceptions are also supported by the sizable literature on the health and
well-being benefits of contributory activities, such as volunteering in the community and
giving social support to others (e.g. Inagaki & Orehek, 2017; Morrow-Howell et al.,
2003). Moreover, it has been argued that promoting productive and social engagement
should enhance successful aging, as staying engaged and contributing to others are both
important themes that emerge when older adults are asked their opinions about what
constitutes successful aging (Reichstadt et al., 2010).
It should be reiterated that cognitive-affective states make up only one proposed
mechanism linking generativity to enhancements in health and well-being. Empirical and
theoretical evidence suggests other important pathways that may play a significant role in
these associations as well, such as behavioral and physiological pathways. For instance,
volunteering has been found to be a precursor to increased levels of physical activity
(Librett, Yore, Buchner, & Schmid, 2005), suggesting that physical activity and its health
benefits may represent one important behavioral mechanism linking generative activity
to improved health and well-being. In addition to behavioral mediators of this
association, there may also be physiological pathways that play a role. Giving social
support to others, for instance, has been shown to be tied to lower blood pressure, mean
arterial pressure, and heart rate in the support giver (Piferi & Lawler, 2006). Studies have
also shown associations between giving or caring behaviors and positive immune
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functioning. Thus, it must be cautioned that although certain cognitive-affective states
have emerged as important correlates of generativity and mediators of the links between
generative contribution and health and well-being, there may be confounding factors also
contributing to these connections.
5D(c). Age.
Studies 2 and 3 examined age moderation to examine whether the associations
between generative failure and poorer health and well-being varied as a function of age.
For the majority of the analyses, the only exception being ADL limitations, these
connections were most significant (or only significant) in those in middle aged and
young-old age groups. Thus, despite theoretical arguments that generativity may increase
in salience with age, these studies do not contradict Erikson’s proposal that generativity
may become most important during midlife. However, midlife is likely occurring later
and for a longer duration than he would have imagined. Results may be even better
aligned with Stewart & Vandewater’s later model of generativity (1998), in which they
suggested that generative desires are formed in early adulthood, confidence and capacity
for generativity peak in midlife, and actualization or achievement of generativity
continues to increase in middle and later years of life (Schoklitsch & Baumann, 2012).
According to their model, older adults are generally high in generativity and
accomplishment, but they feel lower capacity and less desire to be generative than in
midlife (Schoklitsch & Baumann, 2012). In our sample, older adults did report lower
generative expectations on average, compared to middle aged adults. Other positive
dimensions of aging show a similar trajectory, such as the positivity effect, or the
tendency of older adults to focus more on positive information and suppress the negative
105
(Mather & Carstensen, 2005). This pattern in which individuals tend to become happier
and more positive emotionally as they get older has been called the “paradox of well-
being” (Mroczek & Kolarz, 1998). However, an important limitation of the positivity
effect is that it is most likely to be displayed by older adults with superior cognitive
function (Mather & Knight, 2005). Indeed, some contend that the very end of life is
associated with a decrease in emotional well-being, despite overall positive patterns with
age. This downturn is hypothesized to be due to the onset of disease and disability in later
life, which may similarly contribute to lower perceptions of generative capacity and thus
less of an impact from generative failure. These studies revealed interesting age-related
patterns suggesting that future research should continue to probe how generativity
influences health and well-being at different ages and stages of life. It will be important
for determining which groups stand to gain the most from generative opportunities and
interventions.
5D(d). Limitations & Future Directions
All studies utilized the data collected in the MIDUS study. Even though the
MIDUS survey consists of a national sample acquired by random digit dialing, the
representation of those at the socioeconomic extremes and of racial and ethnic minorities
is lower than that of the general U.S. population. Though these characteristics can be
common in national telephone and mail surveys, it is possible that findings may not be
generalizable to all racial/ethnic groups in the larger population. Furthermore, the
MIDUS surveys did not take into account individuals’ personal interpretations of
generative acts and behaviors they are considering when rating their current and future
expected contribution levels. Different activities and behaviors could plausibly constitute
106
contributions to the well being of others in the minds of different people. Thus, future
studies could benefit from collecting more specific data on the types of activities and
behaviors individuals are considering when asked about their social contributions.
Researchers should consider employing quantitative as well as qualitative designs to shed
more light on individuals’ unique generative contributions and self-perceptions.
Another important limitation of these research studies is the potential for reverse
causality. While the hypothesis underlying this dissertation follows that generatively
shapes health and well-being in a positive direction, it is important to acknowledge the
possibility that healthy individuals or those with more positive affective states may be
more likely to help others. If someone is depressed or has physical limitations, he or she
may be less likely to engage in helping behaviors or feel as generative towards others.
These studies attempted to address this possibility by (1) examining the associations
between generative activities and cognitive-affective states using tighter temporal
coupling at the daily level in Study 1, and (2) controlling for prior health and well-being
states in Study 2 and 3. All associations remained significant despite adjusting for these
variables, suggesting an effect of generativity on health and well-being above and beyond
that which is explained by potential self-selection into generative behaviors. As Post
(2005) acknowledges in his argument for a causal relationship between altruism and well-
being, there seems to be some self-selection of the healthy into altruistic and generative
behaviors, but that only seems to partially explain the better health of giving individuals.
Studies using biological markers have provided strong support for the idea that altruistic
emotions and behavior contribute to better mental and physical health. For instance,
researchers have examined individuals before and after engaging in generative moods
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and behaviors and found positive physiological changes in the immune systems of these
individuals. Additional support comes from the stress-buffering literature, which has
revealed that positive emotional states (e.g. love, kindness) can help displace negative
emotional states (e.g. rage, fear), which can otherwise cause stress and stress-related
illness through processes of immunosuppression (Fredrickson, 2003; Lawler et al., 2003;
Post, 2005; Sternberg, 2001). Thus, pro-social concern and behavior can help alleviate
negative emotional states that can have detrimental effects on health. Although it is true
that those who are generative typically must have some baseline level of health and
functioning, this pattern does not invalidate the argument that generativity also
contributes to better health and well-being (Morrow-Howell, Hinterlong, Rozario, &
Tang, 2003; Post, 2005, Thoits & Hewitt, 2001).
There are many directions for future research that stem from this dissertation. For
instance, to address the potential for reverse causality in the relationship between
generativity and health and well-being, rigorous longitudinal and experimental research
should be conducted to supplement the findings obtained from observational
investigations. Additional research should also be conducted to investigate why certain
individuals may derive greater or lesser benefit from their generative contributions. Some
have examined motivations for pro-social behavior and distinguished between pleasure-
based or pressure-based motivations (Gebauer, Riketta, Broemer, & Maio, 2007). They
found that pleasure-based pro-social motivation is related to greater self-esteem, positive
affect, life satisfaction, and other health outcomes. However, pressure-based motivation
(arising out of feelings of duty) was linked to none of these benefits, and was actually
related to greater levels of negative affect (Gebauer, Riketta, Broemer, & Maio, 2007).
108
Thus, those who are intrinsically motivated to contribute to others may have more to gain
from their contributions than those who simply feel pressured to go good as a result of
societal expectations, and future research should assess these motivations and distinguish
between these individuals as they continue to probe these questions.
5D(e). Implications.
The findings presented in this dissertation support prior research suggesting that
generativity may contribute to healthy or successful aging, in that generative activities
and self-perceptions seem to be tied to health and well-being. The studies also revealed
that failing to meet one’s generative self-expectations over time is associated with worse
life satisfaction and poorer health, and that these connections operate largely through the
related cognitive-affective states of social connectedness, sense of self-worth and positive
affect. These studies suggest that researchers should continue to investigate the construct
of generativity with regards to health promotion, especially by employing thorough
longitudinal and experimental designs to provide further clarification on the direction of
these associations. Studies should also further investigate occurrences of perceived
generative failure and how individuals’ varying self-concepts regarding their levels of
social contribution influence their health and well-being.
One potential promising opportunity to arise out of this line of research may be
generativity-based interventions rooted in the style of those influenced by positive
psychology. Similar interventions aimed at cultivating positive feelings, behaviors, or
cognitions have been found to be effective at enhancing well-being and alleviating
depression (Sin and Lyubomirsky 2009). Moieni (2017) has conducted preliminary
generativity-focused intervention studies with older women and shown that it is possible
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to manipulate feelings of generativity in older adults and that experimentally enhanced
feelings of generativity might contribute to health benefits, such as decreases in pro-
inflammatory activity. While expanded opportunities for social contribution should be
valuable for anyone, increasing generative feelings via intervention might be especially
beneficial both for a) individuals who are already engaged in generativity but in a
stressful context, such as caregiving, and b) individuals for whom generative
opportunities are limited but contributory potential remains, such as those diagnosed with
Alzheimer’s disease. Though, in general, generative engagement seems to be
advantageous to health and well-being, some over-expressions of generativity can lead to
negative consequences. For instance, research has overwhelmingly shown that family
caregiving is associated with compromised mental and physical health as a result of the
high-burden, time-consuming nature of providing care. However, co-occurring alongside
the experience of distress are many gratifying aspects of caring for loved ones (Green,
2007; Schulz & Sherwood, 2008; Zarit, 2012). Evidence suggests that deriving and
focusing on the positive aspects of caregiving, such as heightened feelings of generativity
and usefulness, instead of solely on care-related demands, may help mitigate some of the
negative impacts of distress (Grossman & Gruenewald, 2017). In high stress populations,
the benefits of generativity may have the potential to have even greater impact, a
possibility supported by post-traumatic growth literature and gains associated with
benefit-finding (e.g. Bower, Moskowitz, & Epel, 2009).
Another group that may have much to gain from generativity-focused
interventions is that of cognitively impaired older adults, including those diagnosed with
dementia (e.g. early stage Alzheimer’s disease). Individuals with cognitive impairment in
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particular often face social isolation and, with it, a decline in opportunities to continue to
be productive, generative members of society (Ayalon, Shiovitz-Ezra, & Roziner, 2016;
Sabat, 2001). However, observational and clinical research suggests they might have
much to gain from these types of opportunities (Scales, Zimmerman, & Miller, 2018).
For example, one study found that nursing home residents who provided help to other
residents, cared for roommates, and taught others, among other generative behaviors,
were thought to have skills that helped them transcend other personal difficulties
(Bickerstaff et al., 2003; Ehlman & Ligon, 2012). Additionally, Stuckey (2006) argues
that “meaningful aging” is more inclusive than the idea of “successful aging,” and
individuals with dementia are capable of achieving this state (Harris & Keady, 2008).
Though Alzheimer’s is typically characterized by decline, individuals with Alzheimer’s
disease may maintain several “indicators of relative wellbeing,” including self-respect,
humor, social sensitivity, helpfulness, creativity, and more into later stages of their
disease. However, researchers emphasize that all of these indicators of well-being require
social interaction and opportunities to facilitate them (Sabat, 2001). Unfortunately, even
the most well-intentioned caregivers remove opportunities to contribute or be helpful to
others for those with dementia, often out of love and fear for their safety. However, rather
than removing these opportunities, caregivers and communities should be striving to
provide more ways for persons with dementia to remain active, engaged, and contributing
members of society. Similarly to age, greater involvement of those with dementia in their
communities in a positive way will only serve to educate the public, rid them of their
misconceptions, and reduce or eliminate harmful stereotypes about dementia. Generative
opportunities will also help reinforce for the person with dementia that they are not their
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disease, and that despite all that the disease may have taken, they still have something to
give. Thus, given this dissertation’s findings linking generative activities to more positive
cognitive-affective states and showing the poorer health and well-being indicators
associated with failure to be generative, this should be an important area of focus of
future research and clinical efforts.
A growing body of research focusing on the benefits of meaningful contributions
to others could inspire a public health movement focused on promoting civic engagement
and helping behaviors across individuals and communities. It has been argued that a large
gap exists between older adults’ desire to contribute to others and feel useful and
available outlets through which they can channel these pro-social desires (Carlson,
Seeman, & Fried, 2000). Thus, increased efforts are needed to develop social programs
and policies that maximize meaningful opportunities for older adults to engage in a
contributory capacity, such as through volunteering and mentoring roles. These efforts
may help alleviate some of the rising burdens on our health care system, and they can
improve the quality of life for many older adults. Studies have shown that even small
amounts of volunteering (e.g. as little as three hours per month) seem to have favorable
health outcomes in older adults (Morrow Howell et al., 2003). Though there have been
many health interventions targeted toward older adults, evidence suggests the success of
these interventions might be improved if they motivate older adults in ways that are
relevant to their daily lives, especially in ways that promote a positive image of aging and
inspire confidence in their abilities (Carr, Lennox Kail, & Rowe, 2017; Bardach,
Schoenberg, & Howell, 2016). There are growing efforts by gerontology researchers and
advocates to eliminate ageism, “the last remaining acceptable form of prejudice,” from
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our society. Witnessing older adults engaged and contributing to society in meaningful
ways can help combat age stereotypes and alter perspectives – shifting views from older
adults as a growing burden to a valuable resource that can strengthen and support our
communities. Indeed, research has demonstrated how negative aging stereotypes can
hamper mental and physical performance (Hagood & Gruenewald, 2016; Levy, 2003)
and more positive views of aging are associated with increased longevity, among other
benefits (Levy, Slade, Kunkel, & Kasl, 2002). As aging stereotypes are the only
stereotypes for which the “outgroup” eventually becomes a part of the “ingroup,” should
they live long enough, a positive change in views regarding aging has the potential to
improve the lives of current older adults as well as anyone who will one day grow old
(Levy, 2003; Snyder & Miene, 1994).
5E. Conclusion
In conclusion, the studies presented in this dissertation represent an important step
forward in the generativity literature in their elucidation of cognitive-affective pathways
linking generativity to health and well-being in middle and later life. These studies also
suggest potential costs to health and well-being when individuals fail to achieve their
generative self-expectations. Erikson had theorized that failure to successfully resolve the
generativity-stagnation conflict would result in negative outcomes, but most of the
research carried out thus far has been focused on positive expressions or self-perceptions
of generativity. Thus, the insights provided by this dissertation underscore the importance
of contributing to others, not simply because it is the ethical thing to do, but also because
from a selfish standpoint, not helping others may be costlier than the act of contribution.
As individuals are living longer and older adults are the fastest growing segment of the
113
population, it is critical to continue to identify and understand psychosocial factors, such
as generativity, that might shape more favorable trajectories of health and aging. There
are many health promotion efforts currently in existence; however, such programs are
often not free of charge or available in all communities or across all levels of the
socioeconomic spectrum. Generativity may provide one means of supplementing health
and well-being that transcends these traditional barriers, as expressions of generativity
can be woven into the fabric of everyday lives, reflecting individual desires.
Caring for others seems to be a natural human inclination. There is evidence of
generativity and social contribution even in the face of adversity and disaster.
Encouraging individuals to harness this natural inclination toward generativity might
confer health and well-being benefits that extend far beyond those receiving their
contributions. Through expressions of generativity, individuals may be able to both
“give” and “get,” as their contributions to others may not only strengthen friendships and
communities, but also support their own health and well-being. Aristotle believed that
helping others was the path to higher individual well-being or flourishing (Snow, 2015).
It may very well help us flourish as a society as well, as increased giving may lead to
better living for generations to come.

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Abstract (if available)

Abstract Generativity is defined as desire and activity dedicated to contributing to the welfare of others. It was originally proposed by Erik Erikson as an important stage of psychosocial development to be achieved during mid-life. The construct’s significance has since extended to later life as well, when desire to leave behind a meaningful legacy tends to grow with perceived finiteness of time. In addition to being an important concern driving pro-social behavior, generativity seems to confer benefits such as improved health and psychological well-being, supporting a successful aging trajectory. Though accumulating research supports the positive links between generativity and well-being, less is known about the underlying mechanisms through which this relationship occurs. ❧ The following dissertation studies aimed to distinguish important cognitive-affective pathways linking generativity to better health and well-being in middle and later life. These studies sought to elucidate cognitive-emotional correlates of generativity to improve our understanding of how generative goals and activities influence the well-being of middle-aged and older adults. These aims were tested using the National Survey of Midlife Development in the United States (MIDUS), and a MIDUS sub-study, the National Study of Daily Experiences (NSDE). Specifically, three theoretically and empirically derived cognitive-affective states were examined as correlates of generativity in each study: feelings of self-worth or self-enhancement, positive affect, and social connectedness. Study 1 used the NSDE to investigate within- and between-person associations of daily generative activities and daily cognitive-affective states. After the links between generativity and these cognitive-affective states were established, Studies 2 and 3 used data from Waves 2 (2004 - 2006) and 3 (2013-2016) of the MIDUS Survey to examine whether perceived generative failure over time was tied to poorer life satisfaction and health, respectively, and whether these links were mediated by the same cognitive-affective states. Study 1 utilized multilevel regression models, and Studies 2 and 3 used regression and tests of multiple mediation to assess the hypothesized cognitive-affective states as potential mechanistic pathways through which generativity may be connected to better health and well-being. ❧ The analyses revealed that generative activities, expectations, and self-perceptions were related to higher levels of positive affect, social connectedness, and sense of self-worth. Perceived failure to achieve generative goals over time was associated with poorer cognitive-affective states, life satisfaction, and health. Mediation analyses suggested that cognitive-affective states, most notably positive affect, seem to account for a substantial proportion of the observed generativity/well-being associations. ❧ In conclusion, the work presented in this dissertation provides preliminary evidence for several cognitive-affective pathways (positive affect, social connectedness, and sense of self-worth) that seem to play an important role in the relationship between generativity and health and well-being in middle and later life. Generative activity seems to affect individuals at the daily level, as well as more generally over time. This dissertation supports prior researchers’ arguments that generativity may facilitate successful aging and contributes important clarification on the mechanisms responsible for these advantages. Findings suggest opportunities for intervention, as generativity may provide a low-cost, broadly applicable mode of health promotion that benefits both generative individuals and their communities. Given the cost of not giving to others, efforts to recognize and address barriers are needed.

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Asset Metadata

Creator Grossman, Molli R. (author)

Core Title Lifting up ourselves by lifting up others: examining cognitive-affective states linking generativity to well-being

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Gerontology

Publication Date 01/23/2020

Defense Date 03/19/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag aging,generativity,OAI-PMH Harvest,positive affect,usefulness,well-being

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Zelinski, Elizabeth (committee chair), Enguidanos, Susan (committee member), Gruenewald, Tara (committee member), Wilber, Kate (committee member)

Creator Email grossman89@gmail.com,molliegr@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-25810

Unique identifier UC11670957

Identifier etd-GrossmanMo-6446.pdf (filename),usctheses-c89-25810 (legacy record id)

Legacy Identifier etd-GrossmanMo-6446.pdf

Dmrecord 25810

Document Type Dissertation

Format application/pdf (imt)

Rights Grossman, Molli R.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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