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Millennials, defined in this study as those born between 1979 and 2000, became the largest population segment in the United States in 2015. Compared to recent previous generations, they have been found to travel less, own fewer cars, have lower driver’s licensure rates, and use alternative modes more. But to what extent will these differences in behaviour persist as millennials move through various phases of the lifecycle? To address this question, this paper presents the results of a longitudinal analysis of the 2003–2013 American Time Use Survey data series. In early adulthood, younger millennials (born 1988–1994) are found to spend significantly more time in-home than older millennials (born 1979–1985), which indicates that there are substantial differences in activity-time use patterns across generations in early adulthood. Older millennials are, however, showing activity-time use patterns similar to their prior generation counterparts as they age, although some differences – particularly in time spent as a car driver – persist. Millennials appear to exhibit a lag in adopting the activity patterns of predecessor generations due to delayed lifecycle milestones (e.g. completing their education, getting jobs, marrying, and having children) and lingering effects of the economic recession, suggesting that travel demand will resume growth in the future.
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ACTIVITY PATTERNS, TIME USE, AND TRAVEL OF MILLENNIALS:
A GENERATION IN TRANSITION?
Venu M. Garikapati
Georgia Institute of Technology
School of Civil and Environmental Engineering
Mason Building, 790 Atlantic Drive, Atlanta, GA 30332-0355
Tel: 480-522-8067; Email: vgarikapati3@gatech.edu
Ram M. Pendyala (corresponding author)
Georgia Institute of Technology
School of Civil and Environmental Engineering
Mason Building, 790 Atlantic Drive, Atlanta, GA 30332-0355
Tel: 404-385-3754; Email: ram.pendyala@ce.gatech.edu
Eric A. Morris
Clemson University
Department of Planning, Development, and Preservation
2-317 Lee Hall, Clemson, SC 29634-0511
Tel: 818-625-3987; Email: emorri7@clemson.edu
Patricia L. Mokhtarian
Georgia Institute of Technology
School of Civil and Environmental Engineering
Mason Building, 790 Atlantic Drive, Atlanta, GA 30332-0355
Tel: 404-385-1443; Email: patmokh@gatech.edu
Noreen McDonald
University of North Carolina at Chapel Hill
Department of City and Regional Planning
317 New East Building, Chapel Hill, NC 27599-3140
Tel: 919-962-4781; Email: noreen@unc.edu
Submitted to Transport Reviews
ABSTRACT
Millennials, defined in this study as those born between 1979 and 2000, became the largest
population segment in the United States in 2015. Compared to recent previous generations, they
have been found to travel less, own fewer cars, have lower driver’s licensure rates, and use
alternative modes more. But to what extent will these differences in behavior persist as
millennials move through various phases of the lifecycle? To address this question, this paper
presents the results of a longitudinal analysis of the 2003-2013 American Time Use Survey data
series. In early adulthood, younger millennials (born 1988-1994) are found to spend
significantly more time in-home than older millennials (born 1979-1985), which indicates that
there are substantial differences in activity-time use patterns across generations in early
adulthood. Older millennials are, however, showing activity-time use patterns similar to their
prior generation counterparts as they age, although some differences particularly in time spent
as a car driver persist. Millennials appear to exhibit a lag in adopting the activity patterns of
predecessor generations due to delayed lifecycle milestones (e.g., completing their education,
getting jobs, marrying, and having children) and lingering effects of the economic recession,
suggesting that travel demand will resume growth in the future.
Keywords: millennials, activity-time use patterns, travel behavior, cohort analysis, age effects,
trend analysis, longitudinal analysis, lifecycle stages
1. INTRODUCTION
Many a headline posits that the millennial generation is redefining every aspect of the social,
political, demographic, technological, and economic fabric of the United States. In 2015, the
millennial generation (or Gen Y), which is defined in this paper as those born between 1979 and
2000, became the largest population segment in the United States, outnumbering each of the
other generations comprising the US population (Pew Research Center, 2015) namely, the
Silent Generation (born 1928-1945), the Baby Boomers (born 1946-1964), and Generation X
(born 1965-1978). With their increasing presence and clout in the marketplace and workplace, it
is not surprising that considerable attention is being paid to millennials’ priorities, lifestyle
preferences, environmental values, technology adoption, activity, travel, and housing choices.
Millennials are being touted as the frugal generation (O’Connell, 2015) with respect to their
spending habits, and the ‘go-nowhere’ generation (Buchholz and Buchholz, 2012) in their
activity and travel patterns.
In view of the unique traits exhibited by millennials thus far, transportation planning
professionals are grappling understanding how travel demand will evolve in the future as
millennials age, and the consequent implications for transport infrastructure investment and
policy formulation. There is therefore considerable interest in quantifying and tracking the
activity, travel, and time use patterns of millennials. It is well documented that millennials,
largely born and raised in an era of ubiquitous technology (“digital natives”), are exhibiting
mobility patterns that are different from those of their predecessor cohorts (Dutzik et al, 2014).
They have been found to exhibit lower rates of driver’s licensure (Delbosc and Currie, 2013;
Sivak and Schoettle, 2011, 2012), have lower rates of car ownership, and undertake fewer trips
and travel fewer miles and minutes on a daily basis (Polzin et al, 2014; McDonald, 2015).
The question of interest is whether these differences will persist or fade as millennials
age, and the resulting implications for the future of travel demand. Previous research has not
been able to shed light on the effects of aging on the activity and travel trends of millennials.
Although differences are observed when comparing young adults of today with the young adults
of past generations (McDonald, 2015), there is little evidence on whether the differences will
persist into the future. Lyons (2015) suggests that they will, with the advent of social media and
technology-based services contributing to lasting differences (Davis et al, 2012; van Wee, 2015).
But with myriad reports and surveys on the behavioral choices and lifestyle preferences of
millennials, it has become increasingly difficult to separate hype from reality and fully assess
how millennial activity and travel demand is likely to evolve in the future.
In light of the dearth of longitudinal studies on the activity and time use patterns of
millennials that effectively isolate cohort and aging effects, this paper aims to present an in-depth
analysis of trends in activity and time use for different age groups and generations. The intent of
the paper is to unravel the progression of activity and time use patterns for millennials as they
age, and compare their trends with those of the immediate preceding generation (Generation X).
Unlike previous research that used household travel survey data (e.g., McDonald, 2015), the
analysis in this paper is performed using the American Time Use Survey (ATUS) 2003-2013
data series with a view to deriving deeper insights about activity engagement and time use
differences across generational cohorts. As the 11 year span covered by the data includes the
period of the worst recession in recent memory, the longitudinal information in the multi-year
data set can not only be used to see how activity-travel patterns have changed over time for
specific age groups and cohorts, but also how the recession may have played a role in shaping
these trends. Several questions can be answered through such a trend analysis. How different are
the 18-24 year-old individuals of 2003-2004 (older millennials born 1979-1985) from the 18-24
year-old individuals of 2012-2013 (younger millennials born 1988-1994)? How have the travel
and activity patterns of older millennials changed as they have aged? How do older millennials
compare to the previous generation (Generation X) at the same age? Through a longitudinal
analysis that explicitly controls for aging effects and cohort effects, this paper aims to shed
considerable light on how activity and time use patterns differ across generations and differ in
the ways they have changed over time.
The scope of this paper is limited to the analysis of trends within the 11 year time span of
the American Time Use Survey (2003-2013) data series. As such, it is not possible to unravel
longer-term trends in activity and time use patterns; thus while the analysis is able to provide
initial insights on changes in millennial activity-time patterns over time, only future waves of the
ATUS data can truly provide confirmatory evidence on the patterns of change in millennial
activity-time use trends as they age into advanced lifecycle stages. The oldest millennials in the
data set (born in 1979) are 24 years of age in 2003 and 34 years of age in 2013; as a result,
activity-time use patterns of millennials are not observed into more mature lifecycle stages.
Nevertheless, the measurement of changes observed in the 11 year span of data can provide key
insights on the trajectory of activity-time use patterns. Another important caveat is that the
analysis in this paper effectively controls for aging effects and cohort effects, but there are likely
to be important period effects that are also at play in shaping activity-time use patterns. While
the 11 year time span of the ATUS data series is short enough that there are unlikely to be any
fundamental structural differences in societal form and function, the severe recession, the rapid
evolution of technology, and the growth in the sharing economy and social media platforms
experienced within this time span are likely to contribute to period effects. The analysis in this
paper is unable to isolate such period effects, but differences in activity-time use patterns and
trends are discussed in the context of potential period effects that may be at play. In order to
further mitigate the potential role of period effects, the analysis in this paper is limited to
comparing adjacent generational cohorts (Millennials and Generation X) as opposed to
comparing generations that are more temporally separated from one another (e.g., Millennials
and Baby Boomers).
The remainder of this paper is organized as follows. The next section provides an
overview of some of the conflicting literature on millennials. The third section describes the data
set used in this study and the overall trends that are present in the data. The fourth and fifth
sections of the paper offer descriptive trend analyses of the data with a focus on cohort effects,
aging effects, and differences across generations at specific age brackets. A discussion on the
findings and concluding remarks are in the sixth and final section of the paper.
2. CONFLICTING REPORTS ON THE MILLENNIALS
Millennials are held to be redefining the American dream, no longer valuing home ownership,
car ownership, and a steady job, but rather focusing on a purpose-driven life to impact society
and create a better tomorrow (Guay, 2015). Some suggest that millennials no longer value
ownership in an era that is seeing the burgeoning sharing economy (Lutz, 2014; O’Connell,
2015). Compared to previous generations, they are more likely to prefer on-demand mobility
services such as Uber and Lyft to traditional car ownership, rent accommodation through
AirBnB, and stream their music through Spotify. Millennials born and brought up in an era of
ubiquitous technology and connectivity may be substituting driving (and owning cars) and out-
of-home activity participation with in-home virtual activities. Indeed, recent research (e.g., Le
Vine et al, 2014) suggests that more time spent online is associated with less driving.
There is a steady stream of editorials and blogs discussing how and why millennials are
driving less (Jaffe, 2015), shunning cars (Badger, 2014), and utilizing alternative modes of
transportation in greater numbers than prior generations. They exhibit lower levels of car
ownership, driver’s licensure, and mobility (in terms of travel time expenditures, trip rates, and
vehicle miles of travel) when compared with previous generations (Polzin et al, 2014;
McDonald, 2015; Lyons, 2015). Stokes (2012) provides further evidence of falling licensure
rates among younger cohorts in Germany, France, and Britain. Similarly, Le Vine and Jones
(2012) report that individuals in their 20s (who largely constitute the millennial generation) are
driving significantly less than their earlier cohorts at the same age.
Some surveys indicate that millennials are redefining the urban landscape as they tend to
live in dense urban environments that are less car-dependent (Nielsen, 2014). Two-thirds of
millennials are renters, and they are more likely to be living with roommates or family members
than alone. Based on an exploratory analysis of the Great Britain National Travel Survey data,
Stokes (2013) reports that demographic shifts and changes in residential location preferences will
play a major role in shaping future levels of car access and use. Millennials are delaying
marriage and waiting to have children, perhaps in part due to financial woes brought about by the
great recession (Lamberti, 2015). Indeed, it has been found that millennials are more likely to be
in school (Thompson, 2012; Taylor et al, 2012) and less likely to be employed, married, or
parents (Furstenberg, 2010; Pew Research Center, 2014) than prior generations were at the same
ages. Others have written that millennials are adventurous, increasingly seeking overseas travel
experiences (Machado, 2014), possibly at the expense of local day-to-day travel.
While many of the headlines suggest the dawn of a new era with the emergence of the
millennial generation, several key questions remain unanswered. There are the beginnings of a
recognition that lifestyles and patterns of behavior may not necessarily be all that different in the
future as millennials age. The zipcar® annual millennial survey suggests that being a
‘millennial’ is related to where an individual lives (the contextual situation) as opposed to when
the individual was born (Wester, 2015; zipcar, 2015). It appears that millennials may not be all
that different from prior generations in terms of their motivations and desires in the workplace
(Biro, 2014). A consistent finding among studies of millennials is that only 12-14 percent live in
America’s downtowns (Leanne and Brett, 2015); while this fraction is larger than that of
previous generations, the difference is not substantial enough to transform the urban landscape.
Moreover, Walker (2015) notes that: “it turns out that many millennials were not ever planning
on settling in cities for good they were just putting off the move to the suburbs for a few more
years”. Rossenfeld (2015) suggests that millennials will demand “urban burbs” suburban
locations that offer the amenities and benefits of city living without the associated challenges. As
the economy improves and millennials move through various phases of their lives, their housing
preferences may mirror those of previous generations; the demand for new single-family homes
is likely to increase as millennials enter their 30s (Logan, 2014). A recent survey by the National
Association of Home Builders indicates that two-thirds of millennials want to live in the suburbs,
24 percent want to live in rural areas, and only 10 percent want to live in urban city centers
(Hudson, 2015).
A longitudinal analysis of activity, travel, and time use patterns has the potential to
provide considerable insights on how millennials are evolving with respect to their behavioral
choices. In the transport arena, such analyses have been largely limited to comparing behaviors
of specific age groups across cross-sections of national or other household travel surveys (e.g.,
Polzin et al, 2014; McDonald, 2015). While such comparisons offer interesting insights on
differences in mobility variables, particularly over longer time spans, they do not adequately
control for aging and cohort effects and do not provide detailed information about the
substitution of out-of-home activities with in-home activities. The American Time Use Survey
data set, although covering a shorter 11-year span and somewhat limited with respect to its
measurement of mobility variables, offers a rich basis to conduct age-period-cohort analysis of
activity-time use patterns for different generations. The detailed time use data offers information
about both out-of-home and in-home activity engagement, thus facilitating a determination of
how the millennial generation differs from Generation X in terms of its activity-travel behavior
and time use. An exploration of aging effects using the longitudinal data series will help reveal
whether activity-travel and time use patterns of millennials are remaining steady, or
converging/diverging in comparison to patterns observed for Generation X.
3. DATA DESCRIPTION AND OVERALL TRENDS IN TIME USE
The data for this study is derived from the 2003-2013 American Time Use Survey (ATUS) data
series. The survey is administered to a representative sample of individuals aged 15 years and
above to obtain detailed information about household and person-level socio-economic and
demographic characteristics, and out-of-home and in-home activity engagement and time use
patterns for a one-day study period. In this research effort, data were combined for three pairs of
neighboring years, namely, 2003-2004, 2007-2008, and 2012-2013, to perform a trend analysis.
These three snapshots were chosen for analysis to maximize the range in the period covered (the
entire 11-year period) and to capture the effects of the recession, if any, that should be reflected
in the 2007-2008 snapshot (with possible lingering effects into 2012-2013). Adjacent year data
were combined to ensure that adequate sample sizes were available for the age and cohort
subgroups of interest. Although the data includes information for individuals 15 years old and
above, the analysis in this paper is limited to adults (18 years and above) because minors are
largely dependent on adults for activity and travel engagement. For this reason, only millennials
born between 1979 and 1995 are included in the analysis sample for this paper (those born in
1995 are 18 years of age in 2013). In order to reduce the potential influence of period effects,
comparisons of activity-travel and time use patterns are performed between millennials and the
immediate prior generation, namely, Generation X. The sample is uniformly distributed across
all days of the week in each survey year, thus allowing comparisons across years without the
prevalence of day-of-week effects. All of the analysis was performed on weighted samples;
unweighted sample sizes are furnished in the tables for informational purposes.
Table 1 summarizes the characteristics of the entire sample at the three snapshots
considered in this paper. As expected, the population is becoming increasingly diverse (albeit
slowly) with a slight drop in the White race share accompanied by a rise in share of Asian and
Hispanic subpopulations. Among individuals who have at least some college education, the
percentage of individuals without a college degree is falling while the percentage with a college
degree is modestly rising, signifying the growing influence of the millennials who are the most
educated generation ever (The Council of Economic Advisors, 2014). The percentage employed
shows a dip over time, possibly reflecting the tepid recovery from the recession and the aging of
the baby boomers who are increasingly joining the ranks of the retired. The percentage who
have spouses and children show steady declines, likely reflecting delayed marriage and child-
bearing among millennials, and the empty-nest lifestyles of baby boomer households.
The average total time (per day) that respondents spend alone shows a slight increase
over time, and this trend is accompanied by a decrease in time spent with household members.
The time spent with spouse shows a decreasing trend for the population as a whole; this trend is
explained by the lower share of households with spouses over time as millennials experience
lifecycle milestones (such as marriage) later in life than previous generations, and as they
constitute an increasing share of the adult population in the United States. However, when only
the subsample of households that includes a spouse is considered, the trend is reversed (albeit
modestly) with a gradual increase in time spent with spouse across the survey years. This
increase is consistent with the fact that the share of households with children present is
decreasing over time. This decreasing trend stems from two possible sources: first, increasing
numbers of baby boomers are joining the ranks of the retired and become empty nest households,
and second, millennials, who constitute an increasing share of the adult population over time, are
delaying having children.
TABLE 1. Descriptive Statistics for Adults 18 Years and Over (Weighted Sample)
Variable
Category
Survey Pool
2003-04
2007-08
2012-13
Race
White
83.8%
82.9%
81.6%
Black
11.3%
11.5%
12.0%
Asian
2.8%
3.2%
4.3%
All other
2.1%
2.4%
2.1%
Hispanic
Yes
12.3%
13.3%
14.7%
Highest Level of
Educationa
Some College No Degree
35.2%
33.2%
31.1%
Associate Degree
14.8%
15.5%
15.4%
Bachelor’s Degree
32.8%
33.4%
34.2%
Master’s Degree or Higher
17.2%
17.9%
19.3%
Labor Force Status
Employed
66.3%
67.7%
63.1%
Unemployed
4.4%
4.1%
5.5%
Not in Labor Force
29.3%
28.2%
31.4%
Incomeb,c
<$25,000
46.0%
39.9%
37.3%
>$50,000
19.7%
25.4%
29.7%
Spouse Presence
Yes
63.3%
61.3%
59.4%
Child Presence
Yes
39.2%
37.9%
35.9%
Home Ownership
Owned/bought by Household
75.7%
75.2%
71.6%
Time Spent…
(Minutes)
Alone
289
292
293
With Household Members
265
261
260
With Spouse Only
105
104
102
With Spouse Onlyd
166
169
171
With Spouse (Others Present)
163
160
157
With Friends
51
46
50
33,077
23,714
22,928
a Computed based on individuals with at least some college education
b Computed based on valid cases
c Not inflation-adjusted
d Computed only for individuals with a spouse
Table 2 presents a longitudinal exploration of mean time use patterns considering specific
age groups at different survey snapshots. The time use patterns of 18-24 year-olds, 25-34 year-
olds, and 35-54 year-olds are reported in three different cross-sections for each pair of years.
Although this table does not explicitly control for cohort or aging effects, the trends offer some
initial insights into how activity-time allocation patterns are changing over time for different age
groups. The table reports both in-home and out-of-home time use for various activity purposes.
Personal care and sleep activities do not have any location information recorded and are treated
as purely in-home; on the other hand, travel is treated purely as an out-of-home activity. All
other activities may have both in-home and out-of-home activity durations.
In viewing the trends shown in Table 2, it should be noted that different populations are
being compared over time, albeit in similar age brackets. For example, the 18-24 year-olds are
millennials in all three snapshots, but born in 1979-1985, 1983-1989, and 1988-1994
respectively. Compared to their same-age counterparts in prior survey years, individuals 18-24
years of age in 2012-2013 (who are younger millennials) slept longer, spent less time on
household activities, spent less time caring for non-household members, and spent more time
pursuing in-home (presumably online) education and less time pursuing out-of-home education.
This age group also spent less time at work in 2012-2013, but spent considerably more time
working or looking for work in 2007-2008 during the period of the recession.
Those who were 18-24 years of age in 2012-2013 also spent less time on consumer
purchases, presumably due to the effects of the severe recession. Socializing and relaxation
registered a dramatic drop among 18-24 year-olds during the recession (consistent with the spike
in time spent at work or looking for work), but recovered substantially for this age group in
2012-2013. There is some evidence of location substitution in this activity category, as in-home
socializing and relaxation shows a sharp increase in 2012-2013 for 18-24 year old individuals
while out-of-home socializing and relaxation shows a substantial decrease. These trends reflect
tighter monetary budgets and at least some substitution of in-home technology-enabled
socialization and relaxation. Of particular interest here, the 18-24 year-olds in 2012-2013
traveled 11 minutes less, on average, than 18-24 year-olds in 2003-2004, which is a large
proportional difference given that the total travel is in the neighborhood of 80 minutes per day.
Other activities held steady in duration, while activities that are unable to be coded registered a
dramatic increase for the 18-24 year-old age group over time (these include activities performed
during multitasking, and activities that respondents refused to disclose, did not know/remember,
or could not explain adequately).
The 25-34 year-old age group is comprised purely of (older) millennials in 2012-2013,
and purely of Generation X individuals in 2003-2004, with a mix of the two generations in 2007-
2008. Sleep duration, on average, increases for 25-34 year old individuals, suggesting that
millennials devote more time to sleep than Generation X individuals. The duration of household
activities drops over time among 25-34 year-olds, but (consistent with their more advanced
lifecycle stage) they still spend more time on these activities than 18-24 year-olds do. Unlike 18-
24 year-olds, the 25-34 year-olds register a drop over the course of the decade in time allocated
to caring for household members, presumably due to the postponement of marriage and child-
bearing among this age group (recall that 25-34 year-olds are exclusively millennials in 2012-
2013).
TABLE 2. Activity-Time Use Trends for Different Age Groups (Average Minutes per Day)
Activity
Place
Age (18-24)
Age (25-34)
Age (35-54)
03-04
07-08
12-13
03-04
07-08
12-13
03-04
07-08
12-13
Personal Care In-home Only
Total
44
46
46
42
43
42
46
45
45
Sleep In-home Only
Total
543
540
556
510
513
522
494
498
509
Household Activities
In-Home
53
51
48
87
86
81
113
111
105
Out-of-Home
7
9
6
7
6
8
6
6
7
Total
60
60
54
94
93
89
120
117
112
Caring For & Helping Household
(HH) Members
In-Home
17
16
19
51
49
46
28
27
28
Out-of-Home
3
4
3
9
9
9
8
8
9
Total
21
20
22
60
58
55
36
36
37
Caring for & Helping Nonhousehold
(NonHH) Members
In-Home
3
1
1
1
1
1
3
3
2
Out-of-Home
8
8
5
8
4
4
7
5
5
Total
11
9
6
9
5
5
11
7
7
Work & Work-Related Activities
In-Home
6
6
12
16
18
17
21
26
27
Out-of-Home
187
218
169
252
257
238
248
249
238
Total
193
223
181
268
275
255
269
275
266
Education
In-Home
21
26
24
6
7
11
3
3
4
Out-of-Home
48
42
43
9
7
12
3
2
2
Total
69
68
67
15
14
23
6
5
6
Consumer Purchases
In-Home
1
1
0
0
1
1
1
1
1
Out-of-Home
23
22
18
25
22
22
24
23
22
Total
24
23
18
25
22
23
25
24
22
Eating and Drinking
In-Home
26
27
30
33
33
36
37
38
39
Out-of-Home
29
32
29
31
30
30
27
27
25
Total
54
59
60
63
63
66
65
65
64
Socializing, Relaxing, and Leisure
In-Home
190
171
206
176
173
180
195
196
201
Out-of-Home
87
81
75
52
53
50
40
39
39
Total
278
253
281
228
226
230
235
236
241
Sports, Exercise, & Recreation
In-Home
3
2
4
2
2
2
2
3
3
Out-of-Home
23
25
25
16
14
16
14
15
14
Total
25
28
29
19
17
18
17
17
16
Travel Out of Home Only
Total
87
79
76
81
78
77
82
77
77
Other
In-Home
10
9
10
6
7
6
9
9
8
Out-of-Home
15
14
15
15
15
14
20
19
17
Total
24
23
25
21
22
20
29
28
25
Unable to Code
In-Home
4
5
7
4
5
9
5
7
8
Out-of-Home
3
5
13
2
3
5
2
2
4
Total
7
10
19
6
9
14
6
10
12
Sample Size (Unweighted)
2,443
1,571
1,398
5,776
4,102
3,845
14,123
10,057
8,852
Note: No out-of-home durations were recorded for personal care and sleep. No in-home duration was recorded for travel.
Although not as pronounced as that seen for 18-24 year-olds, the other age groups also
register an increase in time allocated to work or looking for work in 2007-2008 (in the midst of
the recession) and a drop in 2012-2013. Time spent for education increases on average for 25-34
year-olds, suggesting that older millennials are pursuing education more than their equivalent
age counterparts in prior years. All three age groups exhibit increased time socializing and
relaxing in the more recent time period of 2012-2013, but the 18-24 year-olds show a substantial
decrease in this activity category in 2007-2008, suggesting that the youngest adults were most
affected by the recessionary forces. Travel time expenditures show a decreasing trend for all age
groups, with the 18-24 year old group showing the largest reduction in average daily travel time
expenditure. This is consistent with findings recently documented in the literature (Goodwin and
Van Dender, 2013; Kuhnimhof et al, 2012; Litman, 2006; MillardBall and Schipper, 2011;
Sivak, 2013). Although the literature alludes to a potential fundamental transformation in
attitudes toward travel that may be contributing to a “peak car/peak travel” phenomenon
underlying such trends, it is more likely that the economic recession contributed substantially to
this decrease. More recent data (Polzin, 2016) shows that, in the United States, aggregate
vehicle miles of travel reached new highs in 2015 and even per-capita vehicle miles of travel is
beginning to recover to pre-recessionary levels. Similar to the 18-24 year-olds, the other age
groups register substantial increases in activities that are unable to be coded, although the
magnitudes of increase are lower for 25-34 and 35-54 year-old individuals. It appears that
individuals of all ages, and 18-24 year olds in particular, are increasingly pursuing more complex
activity patterns characterized by multitasking and greater use of information and
communication technologies (ICT) that are difficult to code.
Overall, it can be seen that there are discernible trends in activity time allocation as the
decade progressed. However, barring a few exceptions, the trends are rather similar for the
different age groups, which suggests that differences seen among young adults over the course of
a decade are not that unusual or inconsistent with changes exhibited by adults in the older age
brackets. Of particular interest, time spent traveling dropped substantially for all age groups.
This suggests that, although the narrative of falling millennial travel is justified, this trend is
likely due in considerable measure to economic, technological, psychological, social, and
cultural factors experienced by all age groups. While the young adults show larger differences
over time, the general trends are consistent across age groups providing the first indication that
millennials may not be all that different from prior generations.
4. AN EXAMINATION OF AGING EFFECTS
The overview in the previous section offered a summary of trends in the data sets without
necessarily controlling for cohort or aging effects. In order to better isolate trends and identify
aging effects, this section presents a trend analysis for three distinct cohorts: Generation X born
1967-1972 (the older GenX, referred to as GenX1), Generation X born 1973-1978 (the younger
GenX, referred to as GenX2), and the Millennials born 1979-1985 (the older millennials). The
older GenX group (GenX1) would be in their early 30s in 2003-2004. GenX2 would largely be
in their late 20s in 2003-2004 and early 30s in 2012-2013. Millennials born 1979-1985 would be
roughly in their early 20s in 2003-2004, in their mid- to late-20s in 2007-2008, and in their late
20s to early 30s by 2012-2013. Thus, it is possible to compare the evolution of activity-time use
patterns of older millennials as they aged. No data is available for the GenX1 group in their
early 20s and mid/late 20s, and for the GenX2 group in their early 20s, because of the limited
span of the ATUS data series.
Before presenting trends in activity-time use patterns for various cohorts, trends in socio-
economic and demographic characteristics are examined first. Through an understanding of
differences in trends in socio-economic characteristics across generational cohorts, it is possible
to identify the socio-economic and demographic phenomena at play that may be contributing to
differences in activity-time use patterns. Table 3 presents socio-economic and demographic
trends for the three different cohorts considered in this paper.
TABLE 3. Progression of Demographic Trends for Different Cohorts
Variable
Category
Cohort (Year of Birth)
Age-Stage
Early 20s
Mid/Late
20s
Late 20s/
Early 30s
Household
Size
One
GenX1 (1967-1972)
--
--
9.10%
GenX2 (1973-1978)
--
10.00%
11.30%
Millennials (1979-1985)
5.40%
12.80%
12.00%
Child
Presence
Yes
GenX1 (1967-1972)
--
--
69.20%
GenX2 (1973-1978)
--
50.10%
62.50%
Millennials (1979-1985)
44.30%
42.00%
54.40%
Spouse
Presence
Yes
GenX1 (1967-1972)
--
--
73.10%
GenX2 (1973-1978)
--
62.80%
67.00%
Millennials (1979-1985)
20.20%
42.90%
61.60%
Race
White
GenX1 (1967-1972)
--
--
81.50%
GenX2 (1973-1978)
--
81.20%
80.30%
Millennials (1979-1985)
81.50%
81.20%
78.20%
Black
GenX1 (1967-1972)
--
--
11.90%
GenX2 (1973-1978)
--
11.30%
12.30%
Millennials (1979-1985)
12.10%
11.60%
12.90%
Asian
GenX1 (1967-1972)
--
--
4.10%
GenX2 (1973-1978)
--
4.80%
5.50%
Millennials (1979-1985)
3.70%
4.00%
6.30%
Currently
Enrolled
in College
Yes
GenX1 (1967-1972)
--
--
7.50%
GenX2 (1973-1978)
--
15.30%
9.60%
Millennials (1979-1985)
45.50%
17.50%
10.40%
Highest
Level of
Education
Some
College
or Higher
GenX1 (1967-1972)
--
--
60.30%
GenX2 (1973-1978)
--
57.80%
61.70%
Millennials (1979-1985)
45.50%
59.40%
66.80%
Labor
Force
Status
Employed
GenX1 (1967-1972)
--
--
81.60%
GenX2 (1973-1978)
--
80.40%
81.10%
Millennials (1979-1985)
69.10%
82.80%
79.40%
Sample Size
(Unweighted)
GenX1 (1967-1972)
--
--
4219
GenX2 (1973-1978)
--
2974
2711
Millennials (1979-1985)
2443
2102
2854
It can be seen that a greater proportion of millennials constitute single-person households
in their 20s and early 30s than is the case for Generation X. A smaller share of millennials have a
spouse or live in households with children. It is found that the GenX2 group also exhibits a lower
prevalence of spouse and children presence in comparison to the GenX1 group. In other words,
lifecycle milestones (e.g., marriage, having children) are progressively getting delayed from one
generation to the next (Lamberti, 2015; Martin et al, 2014), and these delayed milestone events
would naturally contribute to differences in activity-time use trends. The millennial generation is
also slightly more diverse than predecessor generations, leading to the presence of cultural
factors that contribute, at least in part, to differences in activity-time use trends, driver’s
licensure rates, vehicle ownership, and mode usage (Blumenberg, 2009). A larger percentage of
millennials are also enrolled in college or possess some college education when compared with
predecessor generations. The pattern in the table shows that each generation tends be more
educated than the preceding generation. As socio-economic and demographic characteristics are
important determinants of activity-travel and time use patterns, differences in such characteristics
among groups would be expected to contribute to differences in activity-time use trends.
Figure 1 presents the comparison of time use patterns for different cohorts as they age.
The objective here is to examine trends for similarities and differences over time. A one-way
analysis of variance (ANOVA) was conducted to test whether the average values are
significantly different across cohorts when they are in their late 20s/early 30s. It can be seen
from the figure that, barring a few exceptions, as the older millennials aged, their daily time
allocation is neatly converging to the corresponding values exhibited by GenX2 and GenX1
individuals in their late 20s/early 30s. A noteworthy observation is the very steady convergence
of total time spent in-home versus out-of-home (about 16 hours to 8 hours respectively). Work
and work related activities consume about 4.5 hours per day (note that the figures include
weekend and holiday days), and personal care activities for all three cohorts amount to just over
40 minutes in duration.
The amount of time spent caring for household members is settling at just over one hour
per day. Consistent with the finding that a smaller share of millennials reside in households with
a spouse or children in their late 20s/early 30s (as seen in Table 3), this cohort spends
comparatively less time caring for household members than GenX individuals; the difference is
not, however, statistically significant indicating a pattern of convergence in time allocation to
household member care. Similar convergent trends can be seen for time allocation to socializing
and relaxation, eating and drinking, and sports, exercise, and recreation activities. Millennials
spend less time caring for non-household members and engaging in other activities in their late
20s/early 30s when compared with GenX individuals; however, the differences are not
statistically significant, once again signifying a converging trend in activity-time use patterns.
A few notable exceptions can be observed wherein the average time use is significantly
different across cohorts. Millennials in their late 20s/early 30s are found to spend significantly
less time on household activities, and more time sleeping. These differences are consistent with
the notion that millennials have fewer household responsibilities than their GenX counterparts
because of the lower prevalence of spousal and child presence. Also, Table 3 showed that
millennials are enrolled in college to a greater degree than their predecessor GenX counterparts
(Stilwell, 2014), and this is reflected in their significantly higher time allocation to education.
FIGURE 1. Evolution of Time Use Patterns for Different Cohorts (in Minutes)
FIGURE 1 (continued). Evolution of Time Use Patterns for Different Cohorts (in Minutes)
Significant differences across cohorts are also observed in time spent for consumer
purchases and travel in the late 20s/early 30s age range. Although these differences are
statistically significant, it is worth noting that the daily time allocations for these purposes are
showing converging patterns; the trends clearly suggest that differences across cohorts,
particularly between millennials and the immediately preceding GenX2, decrease in magnitude
as individuals age into their late 20s/early 30s. The “unable to code” category is worthy of
additional research; millennials are engaging in such activities (multitasking, activities not well-
defined, and activities that individuals do not remember or wish to disclose) for a substantially
longer duration than GenX individuals at similar age ranges. Lacking a convincing narrative as
to why millennials might be more likely to forget activities, or be unable/unwilling to express
what their activities are, it is conjectured that most of the difference involves multitasking
activities that are difficult to code, which likely includes a large portion of technology use while
other activities are being undertaken. Along those lines, it is possible that millennials, having
grown up in an age of technology and multitasking (Silver, 2014), are prone to continuing and
leveraging established habits and patterns of multitasking into their older adulthood.
Unfortunately, it is not possible to compare the cohorts at the starting point (i.e., when all
generations were in their early 20s), due to the limitations of the timespan for which data is
available. Based on research reported in the literature (e.g., Le Vine and Jones, 2012; Polzin et
al, 2014; McDonald, 2015), it appears that the millennials (young adults of today) are behaving
quite differently than the young adults of prior generations. This implies that the millennials have
different starting points in life; when they are in their early 20s, they are not behaving like GenX
and Baby Boomers in their early 20s. However, despite having a different starting point, they
seem to be settling into a rather similar end point once they age into their late 20s/early 30s and
begin to experience the more advanced lifecycle milestones of marriage, child-bearing, and labor
force participation. Overall, the evidence shows that differences in activity-time use patterns
diminish with age and those that appear to persist may be explained by differences in socio-
economic and demographic characteristics as well as period-specific effects such as the severe
economic recession and rapid evolution of technology. These lingering differences may simply
be reflective of the well-documented delay or lag in the occurrence of lifecycle events for
millennials (Lamberti, 2015; Thompson, 2012; Taylor et al, 2012; Furstenberg, 2010; Pew
Research Center, 2014), as opposed to any fundamental structural shift in attitudes, values, and
perceptions relative to prior generations.
Table 4 presents a similar comparison for travel mode use patterns. The ATUS data set
does not provide travel-related information in as much detail as the National Household Travel
Survey (NHTS). However, it does provide basic mode choice information for any activity that is
classified as travel. The table shows time spent traveling by various modes and the trip rates by
mode for the three cohorts of interest. Once again, it can be seen that millennials in their late
20s/early 30s are largely behaving like GenX1 and GenX2 cohorts when it comes to mode use
patterns, especially in terms of travel as car passenger, by transit, or by non-motorized modes.
However, a lingering difference remains in the level of car driving. There is plenty of evidence,
as noted earlier in the paper, that millennials are somewhat more likely to shun cars and drive
less. The data here supports the literature; time spent traveling as a car driver held steady for
millennials at 53 minutes even as they aged into their late 20s/early 30s. This is somewhat
similar to that exhibited by GenX2 individuals who spent 55 minutes traveling as car drivers in
their late 20s/early 30s, but quite different from that exhibited by GenX1 (older generation X)
individuals who spent, on average, 61 minutes traveling as car drivers at that age.
TABLE 4. Evolution of Mode Use for Different Cohorts (Duration in Minutes/Day)
Mode
Cohort (Year of Birth)
Age Stage
Converging
With Age?
Early 20s
Mid/Late 20s
Late 20s/
Early 30s
Car Driver
(Duration)
GenX1 (1967-1972)
--
--
60.7
No, Millens
Slightly
Lower
GenX2 (1973-1978)
--
57.8
55.1
Millennials (1979-1985)
52.8
53.1
53.1
Car Driver
(Trips/Day)
GenX1 (1967-1972)
--
--
3.44
No, Millens
Slightly
Lower
GenX2 (1973-1978)
--
3.21
3.17
Millennials (1979-1985)
3.18
3.05
2.94
Car Passenger
(Duration)
GenX1 (1967-1972)
--
--
11.4
GenX2 (1973-1978)
--
13.6
11.6
Yes
Millennials (1979-1985)
20.8
13.0
11.4
Car Passenger
(Trips/Day)
GenX1 (1967-1972)
--
--
0.55
GenX2 (1973-1978)
--
0.64
0.56
Yes
Millennials (1979-1985)
0.96
0.66
0.52
Non-Motorized
(Duration)
GenX1 (1967-1972)
--
--
3.0
GenX2 (1973-1978)
--
3.3
3.1
Yes
Millennials (1979-1985)
5.2
4.9
3.3
Non-Motorized
(Trips/Day)
GenX1 (1967-1972)
--
--
0.34
GenX2 (1973-1978)
--
0.38
0.36
Yes
Millennials (1979-1985)
0.58
0.43
0.35
Public Transit
(Duration)
GenX1 (1967-1972)
--
--
2.6
GenX2 (1973-1978)
--
2.9
2.9
Yes
Millennials (1979-1985)
4.8
4.3
3.4
Public Transit
(Trips/Day)
GenX1 (1967-1972)
--
--
0.09
GenX2 (1973-1978)
--
0.09
0.09
Yes
Millennials (1979-1985)
0.13
0.11
0.10
Other
(Duration)
GenX1 (1967-1972)
--
--
0.8
Small
Numbers
GenX2 (1973-1978)
--
0.9
0.6
Millennials (1979-1985)
1.1
1.6
1.1
Other
(Trips/Day)
GenX1 (1967-1972)
--
--
0.01
Small
Numbers
GenX2 (1973-1978)
--
0.02
0.01
Millennials (1979-1985)
0.02
0.02
0.02
Missing Mode
(Duration)
GenX1 (1967-1972)
--
--
3.1
No, Millens
Slightly
Higher
GenX2 (1973-1978)
--
2.7
2.8
Millennials (1979-1985)
2.5
2.6
3.6
Missing Mode
(Trips/Day)
GenX1 (1967-1972)
--
--
0.18
No, Millens
Slightly
Higher
GenX2 (1973-1978)
--
0.18
0.19
Millennials (1979-1985)
0.19
0.19
0.22
Thus, there is no clear evidence of convergence in the time allocation (or trip rate) for the
car driver mode; millennials are persistently lower than their prior generation counterparts,
although differences between younger GenX individuals (GenX2) and older millennials seem to
be fading with age. It was also found that the millennial sample exhibits a higher prevalence of
missing mode information, and the effects of this missing data on trends in the table are unclear.
The notion that millennials may increasingly mirror the travel patterns exhibited by prior
generations as they age is consistent with recent trends observed in vehicle miles of travel
(VMT) per capita. According to recent travel volume trends in the United States (covering the
period after the 2012-2013 ATUS data), VMT per capita is rising and beginning to inch closer to
the peak VMT per capita values seen in 2005 (Short, 2015; Polzin, 2016). This trend can be
attributed to an increase in travel in more recent years across all age cohorts and demographic
groups, including the millennials. While low fuel prices, disruptive transportation technologies
(autonomous vehicle technologies; ride hailing companies such as Uber and Lyft), and a
sustained economic recovery (i.e., period effects) may undoubtedly be contributing to the recent
increase in VMT per capita, it may also be conjectured that millennials who have now entered
their early/mid 30s (i.e., aged beyond the stages covered in Tables 3 and 4) are starting to exhibit
higher levels of travel similar to predecessor generations, thus leading to an increase in VMT per
capita. Whether rising VMT per capita figures will ever match or exceed the levels seen in the
United States at the peak in 2005 remains an open question and depends, at least in part, on how
millennial travel patterns continue to evolve over time. If a lingering difference in millennial car
travel persists, then it is unlikely that the peak levels of car travel seen in 2005 (in terms of VMT
per capita) will be realized in the foreseeable future. On the other hand, if millennials
increasingly mimic the behaviors of prior generations as they age and experience advanced
lifecycle milestones (albeit with a lag), then VMT per capita will continue to rise in the absence
of an economic shock or a transformative change in the built environment, rendering the
existence of a “peak car/peak travel” phenomenon increasingly suspect.
5. FOCUS ON THE YOUNGER MILLENNIALS
The analysis in the previous section provides insights into the aging effects while controlling for
cohort effects. The millennials considered in the previous section are the older millennials, those
born between 1979 and 1985. As the youngest GenX (GenX2) individuals were born during
1973-1978, it is not all that surprising that the older millennials and the younger GenX2 group
show similarities in activity-time use patterns. Although a number of surveys and studies define
the millennial generation as that born beginning in the late 1970s, there are others who define the
millennials as being born only after 1982 (Haughn, 2015). It may be argued that the
technological service-based applications (apps) revolution really started only in the 1990s, and
therefore it is the later millennials (those born in the 1990s and later) who would be truly
different in their patterns. In an attempt to control for age effects, and better understand the
activity-time use patterns of younger millennials relative to older millennials, an analysis was
undertaken to compare time allocation patterns across cohorts while controlling for age effects.
Table 5 presents a comparison of activity-time use patterns for:
A: Generation X individuals born 1970-1976: 27-33 years old in ATUS 2003-2004
B: Older millennials born 1979-1985: 27-33 years old in ATUS 2012-2013
C: Older millennials born 1979-1985: 18-24 years old in ATUS 2003-2004
D: Younger millennials born 1988-1994: 18-24 years old in ATUS 2012-2013
TABLE 5. Comparison of Activity-Time Use Patterns for Different Cohorts at the Same Age (Minutes per Day)
Activity
Born 1970-76
GenX at
Age 27-33
(Data 03-04)
(A)
Born 1979-85
Older Mill at
Age 27-33
(Data 12-13)
(B)
t-test
p-value
(A) vs (B)
Born 1979-85
Older Mill at
Age 18-24
(Data 03-04)
(C)
Born 1988-94
Younger Mill
at Age 18-24
(Data 12-13)
(D)
t-test
p-value
(C) vs (D)
t-test
p-value
(B) vs (C)
Personal Care (Except Sleep)
42
42
0.95
44
46
0.55
0.07
Sleep
506
519
0.01
543
556
0.19
0.00
Household Activities
94
91
0.10
60
54
0.18
0.00
Caring For Hhld Members
59
60
0.55
21
22
0.36
0.00
Caring for Non-HH Members
9
5
0.00
11
6
0.02
0.00
Work & Work-Related
272
266
0.15
193
181
0.83
0.00
Education
14
17
0.82
69
67
0.74
0.00
Consumer Purchases
26
24
0.00
24
18
0.00
0.35
Eating and Drinking
62
66
0.04
54
60
0.19
0.00
Social, Relaxing, and Leisure
228
223
0.03
278
281
0.25
0.00
Sports, Exercise, & Recreation
19
18
0.28
25
29
0.30
0.00
Unable to Code
6
14
0.00
7
19
0.00
0.00
Other
20
21
0.32
24
25
0.48
0.44
Total In-Home
932
953
0.17
920
963
0.00
0.00
Total Out-of-Home
508
487
0.17
520
477
0.00
0.00
Travel
82
76
0.00
87
76
0.00
0.00
Car Driver
59
53
0.00
53
42
0.00
0.47
Car Passenger
12
11
0.02
21
20
0.03
0.00
Public Transit
3
3
0.72
5
5
0.30
0.21
Non-Motorized
3
3
0.10
5
5
0.54
0.01
Other
1
1
0.64
1
1
0.71
0.92
Missing
3
4
0.15
2
3
0.67
0.11
Sample Size (Unweighted)
4198
2854
--
2443
1398
--
--
Null Hypothesis: H0 Means are Equal
Cannot Reject Null Hypothesis (p > 0.05)
Reject Null Hypothesis (p 0.05)
Comparing groups A and B, it can be seen that older millennials and GenX individuals
show both similarities and significant differences when they are 27-33 years of age. Equality of
means cannot be rejected for time spent on personal care, household activities, caring for
household members, work, education, sports and exercise, and travel by alternative modes. In
addition, equality of means cannot be rejected for total time spent in-home and out-of-home. The
takeaway is that older millennials show patterns of similarity with their GenX counterparts at 27-
33 years of age, but there are lingering and statistically significant differences that have
remained. Compared to GenX individuals at the same age, older millennials spend more time
sleeping, eating and drinking, and multitasking (which would be categorized as “unable to code”
in the ATUS data), and less time socializing/relaxing and driving/riding in a car somewhat
consistent with the stereotype that they are lazy (Linn, 2014) and go nowhere (McDonald, 2015).
Comparing the older millennial cohort at age 18-24 years versus when they are age 27-33
years (i.e., columns B and C), it can be seen that they have gone through the expected significant
transformation with aging (consistent with results presented in the last section). Most activity
categories show statistically significant differences in durations, including total time spent in-
home and out-of-home. Their time spent driving a car remains steady at 53 minutes, but their
time spent as a car passenger drops (as expected with aging) from 21 minutes to 11 minutes.
Comparing these observations with those of the preceding paragraph, it appears that as older
millennials progress through life stages, they are becoming increasingly like their GenX
counterparts at the same age, albeit with a few lingering statistically significant differences.
Given that there are differences in socio-economic and demographic characteristics as shown in
Table 3, it may be conjectured that the older millennials are converging to the patterns of GenX,
but with a lag; in other words, older millennials are likely to mirror the patterns depicted by 27-
33 year-old GenX individuals when they reach the age range of mid/late 30s. Given that
millennials are choosing delayed marriage, child-bearing, and entry into the labor force, such a
lag is expected, and differences in activity-time use patterns may actually be due to a “stage of
life issue” (Linn, 2014) as opposed to any fundamental transformative shifts in attitudes and
values. This remains, however, an open question worthy of further research as additional cross-
sections of time use data become available: will millennials converge to patterns of prior
generations, albeit with a lag as they reach various lifecycle milestones, or will lingering
differences remain in millennial time use patterns (even after accounting for lifecycle stage) due
to the technology revolution, the plethora of service-based applications, concerns about the
economy, and fundamentally different lifestyle preferences and values (Sakaria and Stehfest,
2013)? The comparison between means in columns C and D suggests that older millennials and
younger millennials were not very different in their time use patterns when both groups were
young adults (18-24 years old). In comparing activity durations by purpose, it is found that the
means are largely not (statistically) significantly different, except for a few activity categories,
namely, caring for non-household members, consumer purchases, and activities unable to be
coded (multitasking involving technology use). However, what is noteworthy is that the total
durations spent in-home (and out-of-home) are significantly different, with younger millennials
spending more time in-home than their older counterparts in early adulthood. Time spent
traveling is significantly different as well, with younger millennials spending considerably less
time as a car driver. The similarity in total activity durations by purpose, but significant
differences in total in-home and out-of-home (and travel) durations, clearly points to a location
substitution effect where younger millennials are substituting out-of-home activity engagement
with in-home activity participation.
The pairwise differences in time use allocation between cohorts is depicted further in
Figure 2. In the figure, the light grey colored bars refer to differences in time use between older
millennials and GenX (at 27-33 years), while the dark grey colored bars refer to differences in
time use between younger and older millennials (at 18-24 years). It can be seen that the younger
millennials (at 18-24 years) are pursuing activities in-home significantly more than the older
millennials did when they were 18-24 years old. In other words, they are pursuing various
activities to a similar degree, but at a different location in-home which is associated with
reduced travel.
FIGURE 2. Differences in Activity Durations between Cohorts (in minutes)
Figure 3 provides further insights into the trends that contribute to the large in-home
duration difference between older and younger millennials (at 18-24 years). In the figure, the
light grey colored bars refer to differences in out-of-home durations by activity type while the
dark grey colored bars refer to differences in in-home durations. If the bars are in the positive
territory (↑), it means that younger millennials are spending more time in the respective category;
if the bars are in the negative territory (↓), then it means that older millennials are spending more
time in the respective category. In general, the additional time spent in-home for younger
millennials can be attributed to sleep, socializing-relaxation, work and looking-for-work,
eating/drinking, and “unable to code” categories. It would appear that younger millennials are
spending more leisure time at home, but they are also studying and working (or looking for
work) more at home than the older millennials did when they were 18-24 years of age. Why are
younger millennials spending more time at home than older millennials did in young adulthood?
Is it because of period effects characterized by a post-recessionary era of tighter budgets and
rapid evolution of technology and social media use, or is it because of fundamentally different
lifestyle preferences and attitudes? As the older millennials were 18-24 years of age in 2003-
0
13
-2
0
-4 -7
4
-3
4
-5 -1 -6
81
21
-21
2
13
-7
2
-5 -12
-2 -6
634
-11
13
0
43
-43
-50
-40
-30
-20
-10
0
10
20
30
40
50
Personal Care (Except
Sleep)
Sleep
Household Activities
Caring For & Helping
Household Members
Caring For & Helping
Non-household Members
Work & Work-Related
Activities
Education
Consumer Purchases
Eating and Drinking
Socializing, Relaxing,
and Leisure
Sports, Exercise, &
Recreation
Travel
Unable to Code
Other
Total In-Home
Total Out-of-Home
Difference in Time Use (minutes)
Difference in Time Use (Older Millennial - GenX) @ Age 27-33
Difference in Time Use (Younger Millennial - Older Millennial) @ Age 18-24
2004 when the US economy was at its peak performance while the younger millennials were 18-
24 years of age in 2012-2013 in a post-recessionary period, the reduced participation in out-of-
home activities and car travel for the latter group is not surprising. Nevertheless, these are
questions that remain subjects for future research as additional data on millennial behavior
becomes available. Regardless of the reasons underlying differences in time use patterns across
cohorts at early adulthood (18-24 years of age), it does appear that the differences fade with age.
This is demonstrated by the finding that differences in time use between older millennials and
GenX individuals at the relatively older age of 27-33 years are smaller in magnitude than the
differences between younger and older millennials in early adulthood (i.e., 18-24 years of age).
FIGURE 3. Differences in Activity Durations by Location for Millennial Groups
6. DISCUSSION AND CONCLUSIONS
This paper presents detailed comparisons of travel and time use trends between the millennial
generation and the preceding generation (Generation X) with a view to determine whether the
atypical travel patterns exhibited by the millennial generation (as documented in the literature)
persist or fade as they age. The findings in this paper suggest that the much-discussed and
written-about transformative changes that millennials may bring about in society are not likely to
occur, although additional cross-sections of data are needed to draw definitive conclusions. The
longitudinal trend analysis conducted using the American Time Use Survey (ATUS) in this
paper shows that, as millennials age into their 30s, they are increasingly exhibiting activity-time
use patterns that resemble those of Generation X individuals when they were in their early 30s.
This finding is corroborated by Mui (2015), who notes that as millennials reach ‘delayed’
milestones (marriage, parenthood) in their life, they are beginning to reevaluate their beliefs and
display behavioral trends similar to those of previous generations. Nevertheless, a few
differences in time use patterns remain even after controlling for age effects. Millennials are
found to spend about 20 additional minutes, on average, at home and show reduced levels of
driving compared to Generation X individuals at even the more advanced age of 27-33 years old.
The extent to which these differences may be attributed to differences in socio-economic and
demographic characteristics, the new reality brought on by the recession, fundamental shifts in
attitudes, preferences, and values, and rapid evolution of technology and social media use,
remains unclear. Unraveling the degree to which each of these factors contributes to differences
in activity-time use patterns across generations is an effort worthy of further research that calls
for the collection of additional data that specifically addresses “how and why” millennials
participate in activities and travel the way they do.
The generation that depicts remarkably different patterns in activity location is the
younger millennial cohort born between 1988 and 1994. In total, younger millennials 18-24
years old are spending 40 more minutes at home per day than the older millennials did when they
were 18-24 years old. It is unclear whether younger millennials will also begin to converge to
the activity-time use patterns of prior generations as they age, as the older millennials are. The
statistics suggest that millennials have a different starting point at young adulthood, 18-24 years,
in terms of their activity and travel choices, and these differences slowly fade as they age (as
seen in Figure 1, and Tables 4 and 5). However, during the period that differences do exist,
millennials drive less; this period of lower car ownership and vehicle use yields tangible benefits
in terms of reduced vehicle miles of travel, energy consumption, and emissions. These benefits
are likely to be substantial and are worthy of explicit recognition in transportation planning
processes through a specific accounting of the activity location and travel choices of the
millennial generation. The differences do fade (as shown in the aging effects section of this
paper) as the millennials enter their early 30s, and any differences that persist are closely aligned
with differences in socio-economic and demographic characteristics associated with delayed
lifecycle milestones experienced by millennials (delayed marriage, child-bearing, and entry into
labor force). In other words, after accounting for differences in socio-economic and
demographic characteristics, and period-specific effects (state of the economy, fuel prices,
technology and social media, and disruptive mobility services), there do not appear to be many
cohort-specific effects (lifestyle preferences, attitudes, and values) contributing to differences in
activity-time use patterns; if there were such effects, then differences in activity-time use patterns
would not fade to the degree that they do.
The contribution of information and communications technologies (ICT) to reduced
levels of mobility remains unclear. Unfortunately, the ATUS data does not provide information
on ICT use at sufficient level of detail, and there may be missing ICT use when such use is
secondary to a primary activity. Enhancements in the recording of ICT-related activities and
secondary/tertiary activities in the ATUS data would help unravel the patterns of
complementarity and substitution that may exist between out-of-home activity-travel
engagement and ICT use. A preliminary analysis of ICT-related time expenditures for select
activity codes in the ATUS data shows that older millennials significantly reduce ICT time use
as they age and become quite similar to Generation X in their early 30s. Younger millennials,
however, are found to devote significantly more time to ICT use than older millennials in early
adulthood (18-24 years of age), and additional cross-sections of data are needed to determine
whether such differences will persist or fade with age. There is, however, little evidence to
suggest that technology is substituting for travel in any significant way. In a study that is now
somewhat dated, Robinson et al (2000) found no significant or consistent evidence of time
displacement from mass media use or social activities as a result of internet or computer use.
Blumenberg et al (2012) found that web use does not substitute for travel; further, they report
that a higher level of web use is associated with increased person miles of travel across all age
categories, presumably because web use, auto access, and personal travel are all positively
associated with education and income. Similarly, Mokhtarian (2009) discusses a number of
mechanisms accounting for what, so far, has overall been a complementary relationship between
telecommunications and travel. While there is clear evidence that younger millennials use
technology more than prior generations (Pew Research Center, 2014), there is a lack of
confirmatory evidence on the evolving relationship between travel and ICT use.
Overall, the analysis in this paper shows that millennials, as they age, are exhibiting
behaviors that mimic the activity-time use patterns of prior generations, and therefore
transportation planning professionals should not assume that travel demand or vehicle miles of
travel will cease to grow into the future. Indeed, recent evidence shows that, in the United States,
trends in vehicle miles of travel depict an upward trajectory both in the aggregate and on a per-
capita basis (Polzin, 2016), challenging the notion of “peak car/peak travel” that has been the
subject of recent research (Goodwin and Van Dender, 2013; Stokes, 2013; van Wee, 2015).
Aggregate vehicle miles of travel reached new highs in 2015, and vehicle miles of travel per
capita inched closer to the peak values seen in 2005-2006, clearly indicating that the nosedive in
vehicle miles of travel between 2007 and 2013 may be largely attributed to the effects of the
recession as opposed to any fundamental shifts in behaviors, attitudes, and lifestyles.
Based on the trends reported in this paper, transportation planning professionals should
not expect a fundamental shift in travel demand in the future. Millennials are often touted as the
generation that will bring about transformative changes in the transport sector. Their adoption of
technology-based services, the sharing economy, the internet of things, and alternative modes of
transportation is seen as the harbinger of a turning point in transportation that is characterized by
lower levels of personal car ownership and use. The longitudinal analysis in this paper suggests
that, as young adults, millennials are behaving differently, but the differences dampen with age,
and are likely to fade further as millennials experience advanced lifecycle milestones. Given
these findings, transportation planning efforts should aim to design urban spaces and modal
alternatives that leverage and sustain the differences seen in young adulthood into the latter
stages of life as much as possible. This involves a thoughtful design of urban spaces where
young adults experience and enjoy a sense of place consistent with the needs of a maturing
household. Green spaces for children to play, urban gardens, safe neighborhoods to walk and
bicycle, a variety of affordable housing options that meet the demands of a growing family, high
quality educational institutions, and retail and recreational opportunities that are easily accessible
are likely to constitute a built environment that retains millennials and sustains their less car-
oriented activity-travel patterns over longer periods of time, even after they have married and
had children. In addition, even though millennials may age to be increasingly similar to their
predecessor generations, there will always be new young adults in subsequent generations
(Generation Z and beyond) that may exhibit traits similar to the millennials of today, and/or
distinctive new traits of their own. Continuously leveraging the unique behavioral characteristics
of young adults through a targeted set of strategies may offer substantial gains in advancing
sustainable transportation patterns, and bring about the fundamental shifts in behaviors and
attitudes needed to avoid the proverbial inevitability of the repeat of history dominated by sprawl
and auto-oriented mobility.
In the transport modeling arena, activity-travel demand forecasting models supporting
transportation planning processes often use income and vehicle ownership as segmentation
variables (to recognize differences in behavioral patterns across socio-economic segments).
Given the unique traits of millennials in their early adult years, and the desire to sustain their
traits over an extended period of time, it may behoove the profession to consider using
generational cohort as a segmentation variable instead of or in addition to existing segmentation
variables. By isolating and modeling the travel patterns of each generation separately over time,
it will be possible to plan transport infrastructure investments, design modal options, and
formulate land use policies that cater to the needs of the disparate generations.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the valuable comments and suggestions of four anonymous
referees that greatly contributed to improving the paper. The authors thank Dr. Daehyun You for
assistance with data preparation. The authors are responsible, however, for any errors or
omissions.
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... • Diverse passenger profiles: Different groups such as young (Garikapati et al. 2016) or elderly passengers Haustein 2013, 2015) or tech-savvy 3 Number of revenue passengers multiplied with total distance traveled. 4 Kluge is the maiden name, and the author is now called Schmalz. ...
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Thesis
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Background The paper examines whether the widespread assumption holds that younger birth cohorts (referred to as Millennials or Generation Y) act as pioneers of changing everyday mobility. Methodology Based on the time-series dataset "Mobility in Germany" (Mobilität in Deutschland), cohort-specific changes in everyday bicycle and car use that have occurred between 2002 and 2017 are analyzed. The empirical analyses are differentiated by age-group and settlement type. Additionally, socio-structural factors are taken into account. Results The results show a decline in the predominant everyday use of cars in metropolitan cities, especially among Generation Y. However, the Millennials do not emerge as pioneers of the trend toward predominant bicycle use. The results challenge the assumption that changes in everyday mobility are essentially driven by generational change.
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