How Do Pre-Retirement Job Characteristics Shape One’s Post- Retirement Cognitive Performance? Dawn C. Carr, PhD 1 Stanford University Melissa Castora-Binkley, PhD University of South Florida Ben Lennox Kail, PhD Georgia State University Robert Willis, PhD University of Michigan Laura Carstensen, PhD Stanford University 1 Carr, corresponding author, can be reached at 579 Serra Mall, Stanford, CA, 94305; email: [email protected]; (650) 736-8643 (phone); (650) 723-1217 (fax). We are grateful to Michael Hurd for his comments on an earlier version of this paper at the conference on “Working Longer” at the Stanford Institute of Economic Policy Research, October 8-9, 2015. 1
73
Embed
siepr. Web viewHow Do Pre-Retirement Job Characteristics Shape One’s Post-Retirement Cognitive Performance? Dawn C. Carr, PhD. Carr, corresponding author, can be reached at 579
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
How Do Pre-Retirement Job Characteristics Shape One’s Post-Retirement Cognitive
Performance?
Dawn C. Carr, PhD1
Stanford University
Melissa Castora-Binkley, PhD
University of South Florida
Ben Lennox Kail, PhD
Georgia State University
Robert Willis, PhD
University of Michigan
Laura Carstensen, PhD
Stanford University
November 9, 2015
1 Carr, corresponding author, can be reached at 579 Serra Mall, Stanford, CA, 94305; email:
[email protected]; (650) 736-8643 (phone); (650) 723-1217 (fax). We are grateful to Michael
Hurd for his comments on an earlier version of this paper at the conference on “Working Longer” at
the Stanford Institute of Economic Policy Research, October 8-9, 2015.
neuronal development, and that accumulation of excess neuronal resources, or cognitive
reserve, may help people stave off the cognitive losses that come with aging (Fratiglioni &
Wang, 2007).
8
Regardless of the specific mechanisms at play, an individual’s cognitive aging
process appears to be influenced by a combination of mental stimulation across the life
span (i.e., the tendency of those with greater cognitive function to pursue more complex
jobs and activities leading to more significant accumulation of cognitive resources), and the
individual and environmental factors that impact one’s cognitive engagement in later life
(i.e., the extent to which an individual is capable and motivated to maintain cognitive
function in spite of changes to the environment) (Salthouse, 2012; Salthouse, 2006;
Salthouse, Atkinson, & Berish, 2003; Salthouse, Berish, & Miles, 2002). Thus, the potential
relation between retirement and cognitive decline might be thought of as a response to the
way pre-retirement cognitive engagement “habits” adapt to a non-work lifestyle and
environment.
To understand this process, we rely on the Scaffolding Theory of Cognitive
Engagement (STAC). According to STAC, brains respond to changes associated with aging
through utilization of “scaffolding,” or the development of effective adaptive responses
(Park & Reuter-Lorenz, 2009). They write:
Scaffolding is a normal process present across the lifespan that involves use and development of complementary, alternative neural circuits to achieve a particular cognitive goal. Scaffolding is protective of cognitive function in the aging brain, and available evidence suggests that the ability to use this mechanism is strengthened by cognitive engagement, exercise, and low levels of default network engagement.
It is plausible that certain job characteristics, particularly intellectual and
mechanical tasks, shape one’s ability to cognitively adapt to age- and environment-related
changes. The skills, abilities, and behaviors utilized while engaging in work-related tasks, or
during certain job-related training, skills, and education, can be thought of as a form of
“scaffolding” that can be honed during one’s career and tapped into during the post-work
period. So-called cognitive maintenance following retirement (despite disengagement from
9
work) could also be thought of as cognitive “resilience” because the effects of retirement on
cognition are less than expected (Mukherjee et al., 2014). Alternatively, in some cases, a job
may be cognitively stimulating enough to maintain cognitive function while working, but
not offer sufficient cognitive scaffolding to adapt to the deficit of work-related stimulation in
retirement, yielding significant cognitive loss. To that end, it is important to account for
level of complexity and the effect on retirement by occupational characteristics between
those who retire and those who continue to work when studying cognition as it relates to
the retirement process.
Research Question and Hypotheses
Specifically, this research is designed to address the following research question:
How do pre-retirement occupational characteristics (i.e., intellectual and mechanical) impact
cognitive changes associated with retiring relative to staying engaged in full-time work?
Based on empirical evidence and the STAC, we propose three hypotheses. First, we
hypothesize that the relationship between retirement and cognitive decline is dependent on
the cognitive stimulation of the pre-retirement job. Specifically, those with jobs that require
more intellectual engagement will be more resilient and thus, experience less cognitive
decline relative to those with jobs that require less intellectual engagement. However, those
with jobs that require more mechanical engagement will be less resilient and thus,
experience more cognitive decline relative to those with jobs that require less mechanical
engagement. Second, we hypothesize that the effect of intellectual and mechanical complexity
of work will be less significant for those who continue to engage in full-time work than for
those who go on to retire. In other words, we expect that the work “lifestyle” will facilitate
maintenance of cognitive performance when people retire, but the absence of a work
10
lifestyle will increase the importance of non-work lifestyles in determining the impact of
retirement on cognitive performance.
DESIGN AND METHOD
Our study uses the Health and Retirement Study (HRS), a nationally representative
longitudinal survey of individuals over age 50 (and their spouses, regardless of the spousal
age). We use data from biennial waves of the HRS from 1992 to 2010. These data are ideal
for this study because they offer the most comprehensive nationally representative panel
data on US older adults available, including information about cognitive performance and
work behaviors (Lachman & Weaver, 1997; RAND Center for the Study of Aging, 2008;
Smith et al., 2012). For the current study, we include only individuals older than 50.
Selection of Full-Time Workers and Retirees
To test our hypotheses, we selected two samples: full-time workers and retirees.
First, to evaluate the effect of pre-retirement job complexity on change in cognitive
performance, we began by identifying full-time workers – i.e., those who worked 35 hours
or more and self-identified as not retired. From this group, we identified two samples –
those who transition from full-time work in wave t to retirement in wave t+1and those who
stay working full-time in both waves. We exclude retirees who engage in paid work for two
reasons. First, given the focus of this study on the lasting cognitive impact on departing
from paid work, those engaging in paid work in retirement are still participating in a “work
lifestyle.” While it may be helpful to assess the effect of variations in pathways to retirement
on the cognitive decline trajectory, individuals whose labor force status was recorded as
“retired” (even if they did engage in part-time paid work) were not consistently asked about
their occupation, preventing us from taking into consideration how work tasks changed
11
post-retirement. Additionally, recent research suggests that regardless of how retirement is
defined, the relative effect of characteristics of pre-retirement work on post-retirement
cognitive performance does not change (Kajitani et al., 2013). Thus, for this study, we
choose a conservative definition for retirement, limiting our retiree sample to those
individuals who transition from full-time work to complete retirement (i.e., for the first time
while participating in the HRS, self-identifying as being retired and working 0 hours per
week).
Second, in order to accurately measure cognitive changes in association with a
potential retirement transition, we selected specific pre- and post-retirement cognitive
performance measures. First, there is evidence that people may begin cognitively
disengaging from work in preparation for retirement, and this may result in cognitive
decline while working in the period leading up to retirement (Bonsang et al., 2012; Willis,
2013). Thus, to avoid this complication, our baseline cognitive performance occurs two
waves prior to retirement, limiting our sample only to those working full-time for two
consecutive waves prior to retirement. Second, the long-term adjustment to retirement,
with regard to a shift in the cognitive performance trajectory, does not occur until at least
one full year following retirement (Bonsang et al., 2012). Thus, to ensure that our post-
retirement cognitive performance is observed with an appropriate lag, our post-retirement
measure derives from cognitive status at the wave following reported retirement, limiting
our sample to only those retirees who continuously remain fully retired in the wave
following reported retirement.
Because persistent full time workers are not, by definition, observed making a
retirement transition, we use the most recent four-wave period of consistent full-time work
for the full-time working sample. For this group, baseline cognition is measured at Time 1,
compared with cognitive performance in Time 4 of continuous full-time work.
12
Third, in order to minimize the potential endogenous effect of declining cognitive
status accelerating the decision to retire and therefore, increasing the effect of retirement
on change in cognitive performance, we only considered individuals with normal pre-
retirement cognitive performance. Specifically, we excluded all individuals who had a
cognitive score indicating cognitive impairment during either of the two waves of full-time
work preceding potential retirement. Our final pooled sample of retirees included 721
individuals observed over four consecutive waves with complete data, two prior and two
following potential retirement. Our total sample of full-time workers included 1,296
individuals. Figure 1 provides the breakdown of our identification of the final samples
based on work-retirement patterns.
Measure of Cognitive Status
Cognitive performance is based on a 27-point test. This test derives from the
Telephone Interview for Cognitive Status (TICS) (Brandt, Spencer, & Folstein, 1988), which
has been validated for use as a screening instrument for cognitive performance (Plassman,
Newman, Welsh, & Breitner, 1994; Welsh, Breitner, & Mgruder-Habib, 1993). The TICS is
composed of measures of episodic memory (a 10-word immediate and delayed recall test (0
to 20 points)), working memory (a timed serial 7s test (0 to 5 points)), and processing
speed (backwards-counting test (0 to 2 points)). The total score ranges from 0 to 27, with
higher scores indicating better performance. These tests were administered every two
years.
Cognitive scores were standardized using the average score for HRS respondents
ages 51-55: a mean of 17.05, standard deviation corrected for measurement error, equal to
13
2.46.2 Our outcome variable is the difference in the standardized cognitive score from Time
1 to Time 4. A one-unit change in cognitive score is the equivalent of 4.57 points. A positive
score indicates improvement in cognitive performance from Time 1 to Time 4. Negative
scores indicate decline. The TICS has validated cut-points differentiating normal cognitive
functioning (≥12) from impairment (i.e., those with lower than 12 points) (Crimmins, Kim,
HRS respondents’ occupations at each wave of HRS (based on U.S. Census codes)
were linked to the O*NET database (via a crosswalk that links U.S. Census codes with the
Standard Occupation Classification (SOC) codes in the O*NET) to obtain external
occupational-level ratings of job characteristics pertaining to occupation at each wave.3 The
O*NET program is the primary source of occupational data in the United States. The O*NET
database contains information on standardized occupation-specific characteristics and is
publicly available. The O*NET-SOC taxonomy is a set of characteristics for a set of
standardized occupations that correspond to the U.S. Census. Each occupation characteristic
score is calculated based on a rating scale related to abilities (i.e., the expected abilities
required in order to engage in a given job), activities (i.e., the expectation of participation in
activities associated with a given job), and contexts (i.e., the situational aspects of day-to-
day working associated with a given job). For example, the degree to which a job involves
2 The corrected standard deviation of the change score is calculated using the formula
( / (2 ))s where
4.57s is the standard deviation of the raw scores in the 51-55 age
group and 0.45 is the test-retest correlation of the 27-point scale across four waves in the
analysis sample. Note that the test-retest correlation for the full sample is about 0.6; the smaller
value in the analysis sample reflects attenuation due to dropping those with scores below 12.3 We are grateful to Peter Hudomiet for sharing his cross-walk of HRS occupational codes and
O*NET’s SOC codes.
14
getting information is assessed, with a total score calculated on a range from 0 to 1 based on
how often that particular job typically requires getting information.
A total of 36 job-related abilities, activities, and contexts were available for the
standard occupations coded in the Health and Retirement Study. To identify meaningful job
factors, we used exploratory factor analysis (see Appendix A for the full list). Excluding all
items with an alpha score below 0.60, two factors emerged from the remaining 18 items.
Using an iterative selection process, we excluded all variables that loaded on both factors,
and then systematically removed items until we identified the fewest number of items with
the highest alpha score. As shown in Table 1A, the first factor, which we describe as the
“intellectual” factor, includes five items: (1) making decisions and solving problems; (2)
thinking creatively; (3) coaching and developing others; (4) frequency of decision-making;
and (5) freedom to make decisions. The second factor, which we describe as the
“mechanical” factor, includes four items: (1) inspecting equipment, structures or material;
(2) handling and moving objects; (3) controlling machines and processes; and (4) operating
vehicles, mechanized devices or equipment (see Table 1B). The Chronbach’s alpha scores
for these factors are 0.952 and 0.958 respectively. The scores for the intellectual measure
ranged from 2.417 to 3.724, and the mechanical measure ranged from 0.916 to 2.773.
To get a general sense of how the intellectual and mechanical tertials relate to
broader occupational categories, we identified all major occupation categories (an HRS
variable that reflects the broad Census categorization for major occupation types) that fell
into each tertial. Table 2 shows the breakdown of occupational types, demonstrating that
the highest level of the intellectual variable includes primarily individuals in managerial
positions (e.g., legislators, CEOs, marketing managers, administrators and officials in the
public administration sector, and accountants and auditors). The middle group is composed
primarily of individuals with professional specialty positions (e.g., social workers,
15
statisticians, dentists, dieticians and teachers), and secondarily in personal services jobs
(e.g., supervisors of welfare service aides, hairdressers, or child care workers),
mechanics/repair work, construction, and precision production jobs (e.g., machinists). The
lowest cognitive grouping is composed primarily of individuals in sales (e.g., insurance sales
occupations and apparel sales clerks) and clerical jobs (e.g., secretaries and typists), and
secondarily personal services and operator jobs (e.g., printing machine operators, textile
sewing machine operators).
Regarding the mechanical variable, the highest mechanical group is composed
primarily of individuals who work in mechanical, construction, precision production, and
operator jobs. The moderate mechanical group is composed of individuals primarily in sales
positions and health and personal services jobs. The lowest mechanical group is composed
of individuals who are in managerial, professional specialty, and clerical positions.
Covariates
Demographic covariates included gender, race (an indicator of whether an
individual is non-Hispanic white (reference group), non-Hispanic black, Hispanic, or
another race), and age (a continuous measure at Time 3 because our selection required
individuals to be at least 50 at potential retirement), education (in years). Because changes
in health status could initiate a retirement transition or a change in cognitive status, we
include measures that adjust for potential pre-retirement health decline: (a) raw cognitive
score at Time 2, a continuous measure of frailty at Time 2; and (b) to adjust for the potential
causal effect of declining health as an impetus for the retirement transition, we also include
a measure for change in self-rated health observed at Time 3 (relative to Time 2). Frailty (at
Time 2) is measured following Yang and Lee (2010), as an index based on 30-items : 8
chronic illnesses, 5 activities of daily living limitations, 7 instrumental activities of daily
16
living limitations, 8 depressive symptoms (Radloff, 1977), obesity (i.e., body mass index of
30 or greater), and self-rated health (a five point likert item with higher values indicating
better health.
Given the significant relationship between physical engagement behaviors and
Table A2. Propensity Model for Selection into Occupation and Retirement Treatment(Retired, Low is omitted)
Intellectual ComplexityMechanical Complexity
Coef.Robust
SECoef.
Robust SE
Retired, Medium
Non-Hispanic Black -0.0541 0.2684 0.6044+ 0.3236Cognitive performance (time 2) -0.0166 0.0301 -0.1207** 0.0447Education (years) 0.2443*** 0.0435 -0.3827*** 0.0596Age (time 1) 0.0136 0.0285 -0.0231 0.0386Age 62 or Higher (time 2) -0.0531 0.3122 0.4486 0.4224Time Span (Months between time 1 and time 4)
-0.0048 0.0207 -0.0106 0.0262
Female -0.6977*** 0.1827 -1.0485*** 0.2558Wealth (in quintiles, time 2) 0.0202 0.0308 -0.0683+ 0.0410Income (in quintiles, time 2) 0.1099*** 0.0316 -0.2750*** 0.0472Frailty Score (time 2) 0.4628 0.9091 -1.6778 1.1175Change in Self-Rated Health (time 3 relative to time 2)
Non-Hispanic Black -0.8321+ 0.4421 0.1059 0.3312Cognitive performance (time 2) 0.0235 0.0358 -0.1163*** 0.0347Education (years) 0.2478*** 0.0500 -0.5235*** 0.0473Age (time 1) 0.0300 0.0349 -0.0042 0.0335Age 62 or Higher (time 2) -0.1895 0.3718 -0.5445 0.3592Time Span (Months between time 1 and time 4)
0.0288 0.0231 -0.0176 0.0231
Female -0.4257 0.2244 -2.7988*** 0.2499Wealth (in quintiles, time 2) 0.0863 0.0399 -0.0448 0.0360Income (in quintiles, time 2) 0.1620 0.0465 -0.1401*** 0.0373Frailty Score (time 2) 0.9360 1.1119 -0.6445 0.9726Change in Self-Rated Health (time 3 relative to time 2)
Education (years) 0.0928** 0.0331 0.1727*** 0.0360Age (time 1) -0.0932** 0.0301 -0.0942*** 0.0245Age 62 or Higher (time 2) -0.5881* 0.2849 -0.7339** 0.2396Time Span (Months between time 1 and time 4)
0.0784*** 0.0191 0.0852*** 0.0149
Female -0.0582 0.1632 -0.1862 0.1440Wealth (in quintiles, time 2) -0.0334 0.0281 -0.0132 0.0236Income (in quintiles, time 2) -0.0206 0.0284 -0.0823*** 0.0257Frailty Score (time 2) 3.4858*** 0.7976 2.6317*** 0.7336Change in Self-Rated Health (time 3 relative to time 2)
Table A2. Propensity Model for Selection into Occupation and Retirement Treatment (cont.)
Intellectual ComplexityMechanical Complexity
Coef.Robust
SECoef.
Robust SE
Working, Meidum
Non-Hispanic Black -0.0474 0.2460 0.9130*** 0.2457Cognitive performance (time 2) 0.0216 0.0270 -0.0131 0.0272Education (years) 0.3417*** 0.0419 -0.2185*** 0.0449Age (time 1) -0.0623* 0.0312 -0.0710* 0.0319Age 62 or Higher (time 2) -0.8965** 0.2934 -0.8351** 0.3046Time Span (Months between time 1 and time 4)
0.0784*** 0.0200 0.0630** 0.0201
Female -0.9128*** 0.1661 -1.1840*** 0.1897Wealth (in quintiles, time 2) 0.0010 0.0286 -0.0284 0.0324Income (in quintiles, time 2) -0.0330 0.0285 -0.2625*** 0.0337Frailty Score (time 2) 4.0690*** 0.8356 4.0456*** 0.9287Change in Self-Rated Health (time 3 relative to time 2)
Non-Hispanic Black -0.1397 0.2804 0.2069 0.3016Cognitive performance( time 2) 0.0024 0.0299 -0.0449 0.0298Education (years) 0.3889*** 0.0422 -0.4795*** 0.0481Age (time 1) -0.0474 0.0318 -0.0889* 0.0378Age 62 or Higher (time 2) -0.7179* 0.3192 -0.7033* 0.3315Time Span (Months between time 1 and time 4)
0.0975*** 0.0217 0.0508* 0.0213
Female -0.7066*** 0.1809 -2.9036*** 0.2179Wealth (in quintiles, time 2) 0.1364*** 0.0340 -0.0571+ 0.0335Income (in quintiles, time 2) 0.0176 0.0323 -0.2698*** 0.0327Frailty Score (time 2) 3.8207*** 0.9297 2.6107** 1.0167Change in Self-Rated Health (time 3 relative to time 2)