Transcript Slides

Brooke Helppie McFall, Amanda Sonnega and Robert J. Willis
University of Michigan
Retirement Research Consortium Conference
August 6-7, 2015
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
Increased interest in learning about:
 Motivation to work longer or retire earlier,
including job characteristics
 Bridge job employment

What can we learn from more detailed
occupational information?
2

To what extent are occupational composition
and occupational characteristics related to
trends in retirement timing?
 Describe changes in occupational composition
over time
▪ Within cohorts
▪ Between cohorts
 Unpack role of occupation and occupational
characteristics in retirement trends
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Health and Retirement Study (HRS) Core Surveys,
1992-2012
 RAND HRS Data Version N
 Restricted HRS Occupation Data

 Aggregated from ~980 categories to 192, consistent over
time, larger cell counts allow sharing of more results
 In most years, over 98% of workers have occupation codes
(lower in 1994 and 1996)

Linked to Occupational Information Network job
characteristics (O*NET)
 Use Cobb-Douglas weighting to create measure including
both level and importance. Use CPS frequencies to weight
components of aggregated occupations
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

Results from HRS detailed occupation data
and linked characteristics
Examine:
 changes in occupational composition 1992-2012
 relationship between occupation and retirement
behavior
 relationship between job characteristics and
retirement behavior
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Figure 1. Occupation in the HRS over time: older workers (62+)
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1. Managerial specialty operation
2. Professional specialty operation and
technical support
3. Sales
4. Clerical, administrative support
Percent of workers (62+)
20
5. Service: private housefold, cleaning
and building services
6. Service: protection
7. Service: food preparation
15
8. Health services
9. Personal services
10. Farming, forestry, fishing
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11. Mechanics and repair
12. Construction trade and extractors
13. Precision production
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14. Operators: machine
15. Operators: transport, etc.
16. Operators: handlers, etc.
0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
17. Member of Armed Forces
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
We use the percentage change in this
measure,
# 𝑖𝑛 𝑜𝑐𝑐𝑗 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡 + 𝑛
# 𝑖𝑛 𝑜𝑐𝑐𝑗 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡
−
(# 𝑖𝑛 𝑎𝑙𝑙 𝑜𝑐𝑐𝑠 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡 + 𝑛)
(# 𝑖𝑛 𝑎𝑙𝑙 𝑜𝑐𝑐𝑠 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡)
× 100
# 𝑖𝑛 𝑜𝑐𝑐𝑗 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡
(# 𝑖𝑛 𝑎𝑙𝑙 𝑜𝑐𝑐𝑠 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡)
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Table 1. Top 10 early departure and long working occupations, all cohorts
Total these 20 occupations, 1992-2012
7,140
Total for 191 occupations with min 5, 1992-2012
28,624
Early departure occupations
Change '92- Change '92-'94
Occupation title
Obs '94 to 2010-12 to 2006-08
Other managers
2,549
-36%
-34%
Shipping and receiving clerks
181
-42%
-47%
Other mechanics and repairers
244
-46%
-54%
Precision metal working occupations
234
-82%
-75%
Farm occupations, except managerial
241
-43%
-26%
Other machine operators, assorted materials
576
-64%
-51%
Production inspectors, testers, samplers, and weighers
298
-44%
-50%
Construction equipment operators
161
-65%
-60%
Other freight, stock, and material handlers
370
-40%
-36%
Other personal service occupations
303
-39%
-46%
Long working occupations
Managers of medicine and health occupations
Other financial specialists
Management analysts
Lawyers and Judges
Health technologists and technicians
Cust. service reps, investigators and adjust., except
insurance
Teacher assistants
Farm operators and managers
Gardeners and groundskeepers
Taxi cab drivers and chauffeurs
194
252
149
141
157
216
242%
181%
282%
184%
129%
294%
168%
103%
256%
133%
164%
158%
254
124
276
220
133%
128%
126%
291%
121%
193%
101%
374%
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Summary statistics for job characteristic variables and analyses
Source Variable
HRS Early retirement
Description
Last occ observed before age 63? (Yes=1, No=0)
Mean
0.38
HRS
Late retirement
“Last” occ observed at age 66+? (Yes=1, No=0)
0.45
0
0.5
3781
HRS
More difficult (jdiff)
2.59
3
0.8
3456
2.45
3
0.82
3658
2.81
3
1.12
3642
0.36
0
0.48
3295
HRS
HRS
HRS
Job requires doing more difficult things than before
(1-4, strongly agree to strongly disagree)
Lots of stress (jstres)
Job involves a lot of stress (1-4, strongly agree to
strongly disagree)
Physical effort (jphys)
Job requires physical effort (1-4, all/almost all the
time to none/almost none)
Could reduce hours (credh) Could reduce hours if wanted to (Yes=1, No=0)
Median St. Dev. Obs.
0
0.49 3781
O*Net Activity 4
Analyzing data or information (0 - 1)
0.48
0.46
0.15
3780
O*Net Activity 5
Making decisions and solving problems (0 - 1)
0.62
0.61
0.13
3780
O*Net Activity 9
Controlling machines and processes (0 - 1)
0.38
0.33
0.19
3780
O*Net Activity 11
Interacting with computers (0 - 1)
0.48
0.51
0.21
3780
O*Net Activity 13
Repairing and maintaining electronic equipment
0.22
0.19
0.12
3780
O*Net Activity 14
Documenting/recording information (0 - 1)
0.53
0.53
0.15
3780
O*Net Activity 16
Assisting and caring for others (0 - 1)
0.46
0.42
0.15
3780
O*Net Activity 17
O*Net Activity 18
Performing for or working directly with the public
Coaching and developing others (0 - 1)
0.49
0.47
0.5
0.44
0.18
0.15
3780
3780
O*Net Ability 3
Mathematical reasoning (0 - 1)
0.34
0.34
0.13
3780
O*Net Ability 4
Arm-hand steadiness (0 - 1)
0.35
0.38
0.17
3780
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Which occupations predict “late” retirement?
Variable
coef.
Financial Managers (excl. cat.)
-Managers of properties and real estate 0.26*
Management analysts
0.34*
Purchasing managers, agents and
-0.24
buyers; business and promotion agents
Postsecondary teachers
0.25**
0.15
0.18
Variable
General office clerks
Teacher assistants
Industrial machinery repairers
0.16
Production supervisors or foremen
0.13
Primary school teachers
-0.14
0.12
Social workers
Clergy and religious workers
Lawyers and Judges
Writers, authors, technical writers
Designers
0.27*
0.36**
0.52**
0.40**
0.40**
0.16
0.15
0.24
0.2
0.2
Musician or composer
0.40**
0.2
Precision metal working occupations
Other precision work, assorted
materials
Farm operators and managers
Textile sewing machine operators
Other machine ops, assorted materials
Bus drivers
Taxi cab drivers and chauffeurs
Other freight, stock, & material
handlers
0.38*
0.21
Athletes, sports instructors, officials
and announcers
Licensed practical nurses
Real estate sales occupations
Other sales and sales related
Messengers
-0.25
0.32**
0.20*
0.45***
se
Guards, watchmen, doorkeepers
0.17
Other protective services
0.16 Constant
0.11 R-squared
0.17 Adjusted R-squared
Observations
coef.
0.25*
0.25*
-0.1
se
0.15
0.14
0.16
-0.29**
0.14
-0.13
0.15
-0.37**
0.15
0.37**
-0.14
-0.32**
0.25*
0.42***
0.15
0.15
0.13
0.14
0.14
-0.1
0.13
0.22*
0.12
0.41** 0.19
0.48*** 0.1
0.15
0.09
2842
Linear probability models (OLS) with "late" retirement (0/1) as dependent variables and occupation indicators as regressors. All data from
2010. Includes respondents who were 51-61, working full-time, and not self-employed at their baseline interview, and over age 66 in 2010.
"Late" retirement equals 1 if the last recorded occupation was at age 66 or later, or if the respondent was over 66 and still had a listed
occupation in 2010. Excluded occupation is "Financial Managers." Only occupations which were statistically significant in one of the two
regressions are included. Significance levels denoted as * for p<0.1, ** for p<0.05, *** for p<0.01.
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Which job characteristics predict “late” retirement?
Covariate source:
Variable
Physical effort (jphys)
Lots of stress (jstres)
More difficult (jdiff)
Could reduce hours (credh)
Analyzing data or information
Making decisions and solving problems
Controlling machines and processes
Interacting with computers
Repairing and maintaining electronic equipment
Documenting/recording information
Assisting and caring for others
Performing for or working directly with the public
Coaching and developing others
Mathematical reasoning
Arm-hand steadiness
Constant
R-squared
Adjusted R-squared
Observations
HRS only
coef
se
0.04***
0.09***
0.06***
0.2***
O*Net only
coef
se
0.01
0.01
0.01
0.02
-0.15***
0.04
0.11
0.11
3051
0.29*
0.07
-0.46***
-0.33***
-0.03
-0.11
0.05
0.39***
-0.18*
-0.08
0.17
0.48***
0.05
0.05
3780
0.17
0.16
0.1
0.08
0.11
0.12
0.09
0.05
0.09
0.11
0.12
0.07
Both
coef
0.04***
0.09***
0.05***
0.17***
0.27
-0.19
-0.33***
-0.22**
0.02
-0.02
0.17*
0.3***
-0.04
-0.17
0.09
-0.07
0.15
0.14
3051
se
0.01
0.01
0.01
0.02
0.18
0.16
0.1
0.09
0.12
0.12
0.1
0.06
0.1
0.11
0.12
0.08
Regressions are linear probability models. Dependent variable is "late" retirement indicator. Same sample restrictions
as in regressions of retirement on occupation, except these use characteristics of last occupation observed, max one
observation per respondent, for those who are observed past age 66. "Late" retirement is equal to 1 if last observed
occupation in HRS data was at age 66 or later, and zero otherwise. Significance levels denoted as * for p<0.1, ** for
p<0.05, *** for p<0.01.
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

Some interesting compositional change in
detailed occupational information
Some occupations are associated with later
work
 Role of job characteristics
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Use hazard model to include time-varying
factors in prediction of retirement timing and
probability of full-time work past age 65.
 Examine relationship between retirement and
individual O*Net variables
 Case studies of occupations associated with
working longer

 Which are bridge jobs, which are late-working career-
type jobs?
 Especially jobs for which many older workers may be
qualified
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

Support from SSA
Research assistance and code from Peter
Hudomiet and Seth Koch
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