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Household Lifecycles and Life Styles in America
Rex Du Fuqua School of Business
Duke University Box 90120
Durham, NC 27708
Wagner A. Kamakura Fuqua School of Business
Duke University Box 90120
Durham, NC 27708 Tel: 919.660.7855 Fax: 919.684.2818
Email: [email protected]
April 13, 2004
Household Lifecycles and Life Styles in America
ABSTRACT
Family lifecycle has been widely used by marketers as a determinant of consumer behavior and as a basis for market segmentation. However, despite the prevalence of this concept in the literature, limited empirical evidence has been presented on the existence and nature of this lifecycle. All published studies use a priori definitions of the various stages in this lifecycle, which are tested on a cross-sectional survey of households collected at a single point in time, and therefore cannot reveal the real dynamics of the family lifecycle. Consequently, there is considerable disagreement among authors about how life-stages should be defined and how families progress through this lifecycle. Fortunately, the Panel Study of Income Dynamics provides longitudinal data on family composition in the U.S. for a period of 35 years, which can be used to identify the most typical stages and the most common paths followed by families since 1968. Taking advantage of this unique data set, we develop a Hidden Markov Model (HMM) where the stages of the family lifecycle are taken as latent, unobservable states, which are uncovered from the manifested demographic profiles of the households over the 35 years, assuming that they evolve through these latent stages following a first-order Markov process. We apply the HMM results to classify members of another panel (Consumer Expenditure Survey) into life-stages, which allows us to study the impact of the family lifecycle on the families’ budgetary allocations, providing a comprehensive analysis of lifestyles (through consumption expenditures) over the family lifecycle, after accounting for income and other household characteristics.
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Household Lifecycles and Life Styles in America
Introduction
Since it was introduced in the marketing literature by Clark (1955), the concept of
family or household lifecycle has been widely used by marketers as a determinant of
consumer behavior; nowadays, virtually every textbook on Marketing and Consumer
Behavior devotes some space to the family lifecycle, its use as a basis for market
segmentation and its impact on consumption and life styles. However, despite the
prevalence of this concept in the marketing literature, empirical evidence on the existence
and nature of the family lifecycle has been somewhat limited and incomplete. All
published studies of the family or household lifecycle use a priori definitions of the
various types of households, and utilize a cross-sectional survey of households collected
at a single point in time and therefore cannot reveal the real dynamics of the family
lifecycle. While the family lifecycle concept represents the evolution of family
composition over time, all studies in the Marketing literature provide snapshots of the
various types of families observed at a certain point in time, based on a pre-specified
typology. Consequently, there is considerable disagreement among authors (e.g., Wells &
Gubar, 1966; Murphy and Staples, 1979; Gilly & Enis 1982) about how the various life-
stages should be defined and how families progress through the lifecycle.
In this study, we argue that the study of lifecycles involves two inter-related steps:
a) the definition of life-stages, or the typology of families/households in a particular
population, and b) the analysis of the paths followed by individuals through these life-
stages, leading to the definition of family/household lifecycles. Rather than basing the
3
study of household life-stages and lifecycles on our own prior belief of how households
are formed and evolve over time, we utilize data from a panel of households spanning 35
years to empirically identify the typical types of households, and to determine how panel
members evolve through these various household types over time. More specifically, we
apply a Hidden Markov Model to the demographic information provided by each panel
member, identifying the typical life-stages occupied by these households at each year,
and the paths followed by these households over time, thereby obtaining an empirical
analysis of household life-stages and lifecycles in America in the past 35 years. This
approach differs from previous family or household lifecycle studies in two important
ways. First, instead of defining the life-stages a priori, our Hidden Markov Model
defines these stages based on the actual composition of the households, identifying the
most common demographic profiles observed over more than three decades. This way,
the definition of the life-stages is not biased by any pre-conception of family or
household composition at a given time, avoiding the earlier controversy over what the
composition of these life-stages should be. Second, previous studies have defined and
investigated the typology of households at one point in time, assuming that this cross-
sectional typology can represent a lifecycle that is longitudinal in nature. In contrast, our
definition of life-stages and lifecycles is obtained empirically by observing households
over more than three decades, providing us direct evidence on how households progress
over the various life-stages.
Family or Household Lifecycle
The concept of family or household lifecycle has its origins in sociology (Loomis
1936) and was first introduced to marketers by Clark (1955). Since then, household
4
lifecycle has been used as a predictor of various types of behavior, such as debt, home
ownership, purchase of consumer durables (Lansing & Kirsh 1957), entertainment
(Hirsch and Peters 1974), energy consumption (Fritzche 1981) and a broad range of
goods and services (Wells and Gubar 1966). Excellent reviews of the literature on
household lifecycle and its applications for market segmentation and for explaining
differences in consumption behavior across consumers can be found in Wilkes (1995),
Schaninger & Danko (1993), Gilly & Enis (1982). A review of this literature shows that
on one hand, there is a consensus among authors on the usefulness and value of the
household lifecycle concept in explaining consumption. On the other hand, despite
agreeing that household lifecycle is a useful tool for explaining differences in
consumption, researchers in this area seem to be at odds on how the lifecycle concept
should be operationalized.
Even though the concept of household lifecycle was first introduced to the
marketing literature by Lansing and Morgan (1955), the first systematic study is
attributed to Wells and Gubar (1966), who prescribed a family lifecycle of 10 stages,
based on age (<45, ≥45 years old), marital status and employment status
(employed/retired) of the head of household, and age of youngest child (<6, ≥6 years
old). This classification was criticized in subsequent studies proposing alternative
classifications, also based on ad-hoc definitions of family types (Derrick & Lehfeld,
1980; Wagner & Hanna, 1983), based on the argument that the original classification
ignored certain types of non-traditional families.
Another well-known life-stage classification is the one proposed by Murphy and
Staples (1979), containing 14 stages, based on marital status and presence of children
5
within each of three groups defined by age of the head of household (young, middle-age,
and older). An important distinction from previous classifications is that Murphy &
Staples prescribed multiple paths linking the various life-stages, rather than stipulating
sequential stages as in Wells & Gubar, allowing for non-traditional life paths.
A similar approach is taken by Gilly and Enis (1982), who also prescribe 14 life-
stages using the same three age groups, marital status and presence of children, but
treating co-habiting unmarried couples (of any sexual orientation) as married couples,
and defining the life-stages of married couples based on the age of the female, rather than
male head of household. Wilkes (1995) propose a hybrid of the Gilly & Enis and Wells
& Gubar typologies, containing 15 life-stages combining two age groups, marital status,
and presence of children (<6 or ≥6 years old).
A common characteristic of the various lifecycle typologies proposed in the
marketing literature is that they are pre-specified, based on the authors’ own
understanding about the most typical household compositions in the population, or on
their beliefs of how the prescribed typology might relate to consumption behavior. These
definitions are used to classify households into life-stages, and the performance of the
different classification schemes is evaluated by the amount of variance in consumption
behavior observed across the stages (Wilkes, 1995; Schaninger & Danko, 1993; Wagner
& Hanna, 1983). Even though each of the proposed lifecycle models prescribes specific
paths followed by households through their lives, we were not able to find any study that
actually provides evidence that households indeed move through the life-stages in the
prescribed sequences. All the studies we reviewed from the literature were based on
snapshot surveys that only presented a profile of the households at one particular point in
6
time, and therefore could not provide any empirical evidence regarding how households
move from one life-stage to another. Or, putting it differently, the extant household
lifecycle studies don’t provide us with a mechanism to quantify the probability of moving
from one life-stage to another, which makes it hard to empirically test the validity of the
prescribed life paths, or to predict future life-stages for a given household conditional on
its current stage. Another limitation of this a priori definition of life-stages is that in order
to produce a manageable number of segments, researchers must limit themselves to few
demographic markers (age of the head of household, marital status and presence of
children) on, at most, three levels each. This is necessary because the life-stages must
produce segments of substantial size that cover the entire population of households
(Wilkes, 1995).
The approach we propose next represents a major departure from the previous
ways of defining life-stages. Instead of using snapshot surveys, we take advantage of
detailed demographic data collected from a publicly-available longitudinal panel of
households; we then fit a Hidden Markov Model to the data, which allows us to 1)
identify the most typical life-stages observed over a three-decade period, and 2) verify the
paths followed by households through these life-stages over a relatively long sampling
period. Rather than prescribing what the life-stages should be, we use empirical evidence
to identify typical household compositions in a way that best explains the observed
demographic indicators. Because we define our life-stages on a post-hoc basis, we take
advantage of the correlation among the observed demographics, and therefore are not
limited to the three demographic markers typically used in the literature. Because our
model is applied to longitudinal demographic data, we let the data show us the lifecycles
7
followed by the sampled households over time, rather than prescribing what the paths
should be.
A Hidden Markov Model for Household Life-Stages in America
Our main objective is to utilize data from a panel of households to identify the
main types of households (which we call life-stages) and to undertand how households
progress through these stages over time (i.e., their lifecycles, or life paths). For this
purpose we use the Panel Study of Income Dynamics (PSID), a nationally representative
longitudinal panel of nearly 8,000 U.S. households tracked annualy since 1968 (Hill,
1991). For each year, we use the following information about a household’s
composition:
• Marital status of the head of the household (married, never married, widowed, divorced, separated)
• Age of the head of household (years) • Employment status of the head of household (working, unemployed, retired,
disabled, homemaker, student) • Employment status of the spouse, if any (working, unemployed, retired, disabled,
homemaker, student) • Number of other adults (none, 1, 2, 3, 4 or more) • Family size (1, 2, 3, 4, 5, 6, 7, 8, 9 or more) • Presence of kids younger than 7 years old • Presence of kids 7 to 14 years old • Presence of kids 15 to 18 years old • Presence of kids in college
These 10 demographic variables could result into at least 52x62x9x25 = 259,200
possible types of households. Fortunately, these demographic characteristics are not
entirely independent across the population of households, which allows us to identify
8
latent classes representing the most typical household compositions. Moreover, these
demographic characteristics are also inter-related over time, such that the household
composition is unlikely to change freely over the years. In order to capture these
interdependencies of the observed demographic characteristics across the sample and
over time, we utilize the Hidden Markov Model described next.
First let us define the demographic data for K variables across our sample of N
households over the sampling period of T+1 years as
(1) n=1,2,…,N, t=0,1,2,...,T. ),...,,,( 221 ntKntntntnt yyyyY =
These observed demographic characteristics will be used to classify each
household n into M latent states at each year t, through the indicator variable xnt
(2) , i=1,2,…,M ixnt =
We assume that households move among the M latent states from one year to the
next according to a first-order Markov process, defined by the conditional probabilities:
(3) ]|[ 1 ixjxPa ntntij === − , . 1,01
=≥ ∑=
M
jijij aa
The probability that the household enters the sample at stage i is given by
(4) ][ ixP noi ==π , 1,01
=≥ ∑=
M
iii ππ
The likelihood of the observed demographic characteristics for household n at year t,
conditional on the household being in state i, is represented by
(5) ]|[)( ixYPYb ntntnti == = ∏=
K
kknti yb
1
)( ,
where
[ ]∏=
=L
l
ylnkti
ntlk
ikyb
1
)( )()( θ , , if item k is multichotomous with L categories, or 10 )( ≤≤ lik
θ
9
−−= )2(
2)1(
2)(
exp21)(
ik
iknktnkti
yyb
θθ
π if item k is continuous.
Equations (1)-(5) define the multinomial Hidden Markov Model. In essence this
model assumes that the observed demographic data are indicators of the latent,
unobservable state occupied by the household at a given time. Our objective is to
estimate the transition probabilities (A), initial state probabilities (Π) and conditional
probabilities (B), from the demographic profiles observed in our sample of households
over time. These parameters are collected in the vector λ,
(6) },,{ Π= BAλ .
Since our main purpose is the identification of life-stages occupied by households
throughout their lives, we constrain the transition probabilities in A to an upper-diagonal
matrix. This implies that the life-stages are ordered in a particular sequence, and
households are not allowed to move back to a previous stage. At a first glance, this
assumption seems overly restrictive. For example, this assumption seems to prevent a
married person to get divorced and then married again. In reality the model allows for
this sequence of events, by identifying an earlier “married”, a “divorced” and a
subsequent “married after divorced” stage, if the data call for such a pattern. This
“sequential” HMM model is depicted in Figure 1.
FIGURE 1 ABOUT HERE
Model Estimation
Given the dynamic nature of the Hidden Markov Model, the multiple observations
for one household are not independent over time and therefore the likelihood function
must be computed over the whole sampling period of the household,
10
(7) . });,...,,{(});,...,,(|),...,,({});,...,,{( 111,...,,
11
λλλ ToToToxxx
To xxxPxxxYYYPYYYPTo
∑=
Following the formulation of our model in (1)-(6), the likelihood function for a
given household can be computed as
(8) [ ][ ]
TTooTo xxxxxxxX
TxxoxTo aaaYbYbYbYYY112111
...)()...()(});,...,,{( 11−
∑= πλP .
Since the latent class memberships in X,(the set of all the possible sequences of
hidden states) are not observed, they are treated as missing data, using the E-M
algorithm. We provide a brief description of the E-M algorithm below; readers interested
in more details are referred to the excellent tutorial by Rabiner (1989).
E-STEP In the E-step, we use the current parameter estimates to compute expectations for the missing data (xnt). Because the missing data follow a (hidden) first order Markov process, we must use the forward-backward Baum-Welsh algorithm to compute the likelihood of the observed data sequence.
Forward Let )(intα represent the likelihood for the observed data sequence up to time t, conditional on household n being in state i at t. This likelihood can be computed recursively as, (9) )()( noiino Ybi πα =
(10) t=0,1,2,…,T-1 , )()()( 1,1
1, +=
+
= ∑ tni
M
jjinttn Ybaji αα
up to the last observation at time T. At that point the likelihood of the observed sequence can be computed as,
(11) ∑=
=M
inTnTnno iYYYP
11 )(});,...,,{( αλ
Backward
11
In a similar way as in the forward sequence, we can compute )(intβ , the likelihood of the observed sequence backwards, starting from the last observation down to the current one at time t, conditional on household being at state i at the present time t, as (12) 1)( =inTβ
(13) t=T-1,T-2,…,2,1,0 ∑=
++=M
jtntnjijnt jybai
11,1, )()()( ββ
Once the forward and backward likelihoods are known, posterior probabilities for household n being at state i at time t can be computed as (Rabiner 1989, pg.263),
(14) ∑
=
= M
intnt
ntntnt
ii
iii
1')'()'(
)()()(βα
βαγ ,
and the posterior transition probabilities are obtained from
(15) ∑∑
= =++
++= M
i
M
jtntnjjint
tntnjijntnt
jYbai
jYbaiji
1' 1'1,1,'''
1,1,
)'()()'(
)()()(),(
βα
βαζ
M-STEP In the M-step we replace the missing data with their expected values and obtain new estimates for the parameters of the model via maximum likelihood estimation:
(16) ∑∑= =
=N
n
M
iino i
1 1ln)(maxargˆ πγπ
(17) ∑∑=
−
=
=N
n
T
Tntint ybiB
1
1
0)(ln)(maxargˆ γ
(18) ∑∑=
−
=
=N
n
T
tijto ajiA
1
1
0ln),(maxargˆ ζ
This maximization step leads to the following estimates,
(19) NiN
nnoi /)(ˆ
1∑
=
= γπ
12
(20a) ∑ ∑
∑ ∑
= ≤≤
= =≤≤= N
n Ttnt
N
n yTtnt
l
i
iknt
ik
1 0
1 ,0)(
)(
)(ˆ
γ
γθ l if item k is multichotomous with L categories
(20b) ∑∑
∑∑
= =
= == N
n
T
tnt
N
n
T
tntnt
ik
i
yi
1 0
1 0)1(
)(
)(ˆ
γ
γθ and 2)1(
1 0
1 0
2
)2( )()(
)(ˆ
ikN
n
T
tnt
N
n
T
tntnt
ik
i
yiθ
γ
γθ −=
∑∑
∑∑
= =
= = if item k is
continuous
(21) ∑∑
∑∑
=
−
=
=
−
== N
n
T
tnt
N
n
T
tnt
ij
i
jia
1
1
0
1
1
0
)(
),(ˆ
γ
ζ.
Household Life-stages and Lifecycles in America
We apply the Hidden Markov Model described earlier to a panel of 8,000
households observed from 1968 to 2001. However, because changes in household
composition tend to be minor from one year to the next, we skip every three years of data
for each household; otherwise, the Markov chain reflecting the moves between latent
states would be dominated by the diagonal due to the tendency to remain in the same
state from one year to the next, leading to a zero-order process. In other words, we
estimate a first-order Markov process that captures the transition probabilities among
different life-stages during a four-year period.
Because most of the 8,000 households have entered and left the panel during its
existence, we selected only those households for which we had at least 3 observations,
resulting in a final estimation sample of 6,887 households. However, we still use the
other households later, as a hold-out sample for predictive tests. We also applied the
13
demographic weights supplied by PSID in the estimation of our HMM model, so that its
results reflect the population of households in the U.S.
We estimated the model specifying up to 14 life-stages and, based on the
Bayesian Information Criterion, chose the solution with 13 life-stages. Table 1 displays
the parameter estimates ( ikθ ) from the HMM model. Based on these results, one can
roughly describe these life-stages as follows:
1. So/Co Singles and young couple with no children 2. C1 Small family (couple) with kids <7 years old 3. C2 Large family (couple) with kids<15 4. C3 Large family (couple) with older kids 5. C4 Small family (couples) with kids<15 6. S1 Single/Divorced with no kids 7. C5 Small family with older kids 8. C6 Empty nest couples 9. S2 Single/divorced with kids<15 10. S3 Divorced/Widow with older kids 11. S5 Widowed empty nest 12. S4 Divorced/Single empty nest 13. C7 Retired/Old Couples with adult dependents
TABLE 1 ABOUT HERE
The sequence of life-stages described above might seem counter-intuitive, given
that the estimated model assumed households to move through these stages in that
particular sequence. However, the hidden Markov model does not imply that every
household would go through each of these 13 stages. First, households enter the PSID
panel at different life-stages. For this reason, the initial probabilities πi represent the
likelihood that a household would enter the PSID panel at state i, and therefore serves
only as an adjustment factor for the number of households entering the panel after the
initial life-stage. Second, some households leave the panel before the most recent data
collection period. In fact, the estimated (conditional) switching probabilities (A) in Table
14
2a clearly show that transitions are only observed between relatively few pairs of stages,
suggesting that most households skip some of the 13 life-stages and different households
follow different paths through these stages. Table 1a also suggests multiple paths
followed by different households.
TABLE 2 ABOUT HERE
A clearer sense of the multiple paths can be attained from Table 2b, which shows
the transitions between life-stages every four years, as a proportion of the population of
households, highlighting the transitions observed for at least 1% of the population.
These paths are depicted in Figure 2, where each arrow is drawn in proportion to the
percentage of households moving through the respective pair of life-stages. Notice that
the transitions depicted in Figure 2 are the actual transitions inferred from the data
observed in the PSID panel. Because most panel members enter or exit the panel during
its duration and because this may happen at any life-stage, the number of households
exiting a particular life-stage may be larger than the number entering that same stage.
For example, one can see in Table 2 and Figure 2 that 4.9% of all households moved
from stage S2 to S3, even though a smaller proportion entered state S2, because of the
entry and exit of households in the PSID panel at these stages.
FIGURE 2 ABOUT HERE
Differences in life paths across demographic groups
Figure 2 provides a full picture of household lifecycles in the U.S., projected to
the population from the PSID sample via demographic weighting. However, the model
also allows us to look at the paths followed by specific groups. The posterior state
probabilities )(intγ and transition probabilities ),( jintζ obtained from (14) and (15) can
15
be combined to compute the most likely path for each household n. These paths can then
be combined across households to produce the lifecycles for specific demographic
groups. Figure 3 compares lifecycles for households headed by whites and blacks.
Because most of the households have white heads of household, the lifecycles for this
subgroup is quite similar to the one for the population as a whole (Figure 2). The
lifecycles for households with black heads of household, on the other hand, are quite
distinct from the general population, with a higher incidence for stages involving single
or divorced heads of household, and a lower likelihood of moving from a divorced stage
to a married one.
Figure 4 contrasts the lifecycles of households headed by people with less and
more than a high-school education. Here we can clearly see that households headed by
people with more than a high-school education tend to follow the traditional family
lifecycle (Wells and Gubar 1966), while those with less than a high-school education are
more likely to have a single or divorced head of household. In fact, among households
headed by someone with more than a high-school education, one of the life-stages (S4:
Divorced/Single with no kids) becomes irrelevant, as few households in this group pass
through this stage.
FIGURES 3, AND 4 ABOUT HERE
Predictive Validity Tests
As discussed earlier, the concept of household lifecycles is useful because it
provides a basis for understanding consumption behavior at the household level and for
projecting this behavior on the long run, based on the expected life paths followed by the
household. Most studies of lifecycle income and consumption in the economics literature
16
utilize age of the household head (and family size occasionally) as a direct indicator of
life-stage changes (See Deaton, 1992 for an excellent review, and Gourinchas and Parker,
2002 for more recent developments.). In order to evaluate the predictive validity of our
household lifecycle model, we compare it with age as an indicator of lifecycle changes.
We limit our analysis to three variables, for which we have enough longitudinal data in
the PSID panel: income, food consumption inside the house, and home mortgages. We
first calibrate regression models to the first year of data for each household, explaining
each of the three dependent variables as a function of stable characteristics of the
household (birth cohort, gender, race and education of the head) and of the lifecycle
indicator (either our classification of 13 life-stages or a cubic function of age). We then
use the calibrated model and the projected lifecycles (either from our model or based on
age) to predict the dependent variable four and eight years ahead, to assess the predictive
validity of the proposed lifecycle model. Because the PSID panel focuses on income and
its related components, the dependent variables used in our test are not available for all
households for 8 years, reducing our sample to about 3,000 households.
Table 3 presents the calibrated regressions explaining income, food consumption
at home and mortgage balance as a function of stable demographics and life-stage
indicators, using a stepwise selection of predictors. These regressions, estimated on each
household’s first year of data, were used to make predictions 4 and 8 years ahead, based
on their stable demographics and lifecycles, projected on the basis of our model and on
age alone. Table 4 compares the goodness-of-fit and predictive fit of the two approaches.
These results indicate that life-stages are better predictors than age alone for income and
food consumption in the house, but are slightly dominated by age when predicting
17
mortgage balance. However, neither approach performs very well in predicting mortgage
balances, probably due to the fact that this particular dependent variable was truncated,
because not all households hold a mortgage.
TABLE 3 AND 4 ABOUT HERE
Household Lifecycles and Lifestyles in America
Our predictive validity tests in the previous section suggest that household
lifecycles might be useful for explaining differences in consumption patterns across
households, and to project future consumption based on expected shifts in life-stages.
The purpose of this next section is to “drill down” further on the relationship between
life-stages and consumption. Because patterns of consumption across the various
accounts of a household budget provide hard evidence of differences in lifestyles, we
hope that such analysis will show us the linkages between lifecycles and lifestyles in
America.
Unfortunately, the PSID panel is focused on the study of income and its
components, thereby providing limited data on consumption. For this reason, we take
advantage of another study, the Consumer Expenditure Survey (CEX), collected by the
Bureau of Labor Statistics (BLS), providing information on the buying habits of
American consumers, including data on their expenditures, income, and household
characteristics. While the survey is conducted quarterly, the National Economic
Research Bureau (http://www.nber.org/data/ces_cbo.html) provides annual extracts on
consumption for each survey respondent, from a sample that ranges between 1,300 and
3,300 respondents each year, making access to the data much more straightforward.
Unfortunately, the CEX panel is a tracking panel, in contrast to PSID’s static panel,
18
thereby precluding a longitudinal analysis at the household level. However, the CEX
survey provides all the information necessary to classify each household into our 13 life-
stages, allowing us to study the relationship between consumption (i.e., lifestyles) and
lifecycles over a period of 19 years from 1980 to 1998, using a total rotating sample of
52,000 households.
The first step in this analysis is the classification of the CEX sample into our life-
stages, based on household composition and the parameter estimates from the HMM
model (since the CEX sample is not longitudinal, households are classified into the life-
stages through a latent-class classification). Table 5 shows the classification of the CEX
sample into the 13 life-stages over the 19 available years, suggesting that the proportion
of households at each life-stage was fairly stable over time, except perhaps for the slight
increase in the proportion of households in stage C6 (empty nest couples) as a result of
aging baby boomers.
TABLE 5 ABOUT HERE
Once the households in the CEX sample are classified into the life-stages, we can
assess how the household lifecycle affects consumption, after accounting for other factors
such as income, gender, ethnicity, and birth cohort. Even though age (a component in
our life-stage classification) is collinear with birth cohorts, the fact that we have
expenditure data spanning almost two decades allows us to account for both life-stage
and cohort effects on consumption (although we had to reduce the number of cohorts to
two, due to the shorter sampling period, relative to the PSID panel). However, the
expenses and assets tracked by the CEX surveys are not observed for all households,
resulting in truncated data (for example, not all households own a home, hold a home
19
mortgage, or purchase a new car in a given year). Therefore, we resort to the Type-II
Tobit regression model described below, explaining the observed expenses or assets Yi as
a linear function of predictors Xi for each household i,
2iiy η= if 0>1iη or
0=iy otherwise
where
1111 iii X εµβη ++=
2222 iii X εµβη ++=
),0(~ Σφε , .
=Σ 2
1σρρ
Application of this Type-II Tobit regression to each of the 35 expenses and assets
obtained from the CEX survey lead to the parameter estimates reported in Table 6a and
6b (for the incidence component), and Table 6c and 6d (for the observable component).
Table 6a shows the average percentage of consumers in each life-stage that have positive
expenditure or ownership in a category, holding all the other covariates at the population
mean. More specifically, in Table 6a we see two basic types of categories: 1) those with
high incidence across life-stages, such as taxes, food in the house, clothing, magazine &
news, etc; and 2) those with incidence varying widely across life-stages, such as health
insurance, which are highlighted in Table 6a. For example, the average percentage of
households with positive expenditure on health insurance is lower in single and younger
life-stages: the lowest occurring in Co/So (singles and young couple with no children), S1
(single/divorced with no kids) and S2 (single/divorced with kids<15), versus the highest
among C6 (empty nester couple), C7 (retired/old couple with adult dependents) and S5
20
(widowed empty nest). A similar pattern exists for home asset ownership. In terms of
lower education expenditure, however, a different (nevertheless clear) lifecycle pattern in
incidence can be seen: the average percentage of households with positive expenditure on
lower education is the highest in C1 (young couple with kids<7), C2 (large family
withkids<15) and C4 (small family with kids<15), versus the lowest among Co/So
(single/couple with no kids), C6 (empty nest couple) and S5 (widowed empty nest). The
remaining estimates from the incidence model are reported in Table 6b.
For categories where incidence is high across all households, life-stage
differences are largely manifested in the amount spent or owned, which will be reflected
in the estimates from the regression on the observable component. We have transformed
the Tobit regression coefficients for life-stages into deviations from the mean, as reported
in Table 6c, so that they reflect how each life-stage or birth cohort compares to the
population average, after accounting for income, gender and race. The remaining
coefficient estimates are reported in Table 6d.
TABLE 6 ABOUT HERE
Table 6c shows substantial differences in expenditures across the 13 life-stages,
even after accounting for the effects of income, birth cohorts and race/gender of the head
of the household. In general, the lowest levels of consumption are observed for older
households with single, divorced or widowed heads (S5 and S4), while the highest are
observed for large families with kids (C2 and C3). To make the results more accessible,
we focus our discussion on categories with average incidence about or above 80%. For
each of these categories, we rank the average level of expenditure by life-stages and
highlight on Table 6 the life-stages with the highest and lowest rankings in each category.
21
For example, the top two tax- and rent-paying life-stages are Co/So (single/young couple
with no children) and S1 (single/divorced with no kids), and the bottom two stages are S5
(widowed empty nest) and C7 (retired/old couple with adult dependents). Not
surprisingly, top spenders on health care and related products are households in C6
(empty nest couple) and C7 (retied couple with adult dependents), while Co/So and S1
spend the least. Households in the S1 (single/divorced with no kids) life-stage are the
biggest spenders on eating out, dry cleaning, alcohol and tobacco products; they don’t use
much domestic help, and the bills for personal care and utilities are relative small. In
contrast, young couples with kids<7 (C1) spend a lot more on domestic help, don’t
consume much alcohol or tobacco, and spend the least on eating out. The remaining
estimates from the regression on the observable component are reported in Table 6d.
In order to verify the contribution of household lifecycles in explaining
consumption over and above income and other demographics, we compare the goodness
of fit of the full Tobit model with two alternative models: one that excludes life-stages as
predictors, and one that excludes life-stages but includes a cubic polynomial on age as a
measure of lifecycle. The results reported in Table 7 show consistent improvement in
explaining both incidence and volume of expenditures when household lifecycles are
added to the other predictors. In general, the model with life-stages also dominates the
one using a polynomial on age, across the 52,000 observations in the CEX panel.
TABLE 7 ABOUT HERE
22
Conclusions and Directions for Future Research
The Hidden Markov Model we propose and apply to the Panel Study of Income
Dynamics provides marketers with a valuable tool for lifecycle segmentation. The life-
stages identified by the model are the most common types of households observed in a
large national sample, based on widely available demographic indicators, making it easy
to classify any other sample of households into the same typology of life-stages,
following the well-known latent-class model (Wedel and Kamakura 2000), as we outline
below:
1. Use the relative sizes of each life-stage in the U.S. reported in Table 5 as the prior probability that a household randomly drawn from the population belongs to each life-stage.
2. Compute the conditional likelihood of the household’s observed demographic profile, given that the household is currently at each of the 13 life-stages, by applying equation 5 to the observed demographic data and to the parameter estimates reported in Table 1.
3. Apply the Bayes rule to compute the posterior probability that the household belongs to each life-stage, given its observed demographic profile.
The simple latent-class classification outlined above allows a marketer to segment her
customer base into life-stages based on widely available demographic variables about
household composition, without any additional data collection or model estimation. If
the marketer also has data on household income and other widely available demographics
(ethnicity and gender of the head of household), she can use the results from our CEX
Tobit regressions (Table 6) to impute the incidence and expected annual consumption
requirements for the household on a wide range of consumption categories. Therefore,
using the limited demographic information available in rented mailing lists or in geo-
demographic databases, a marketer can classify prospects into life-stages and determine
23
the expected annual consumption of each prospect, to better target her customer
acquisition campaigns. Or, she can apply our results to her current customer database,
and determine the total consumption requirements for each customer, to estimate the
firm’s share of wallet.
As an illustration of this simple procedure, let us take one hypothetical consumer
John Doe, who, born after the 1950’s, is a 28 years old white male, married, with one kid
younger than seven. Both John and his wife are working. Together they earn an annual
income of $52,292. Given such a demographic profile, and following the three-step
latent-class classification procedure outlined above, we can calculate the posterior
probability of John’s family belonging to each of the 13 life-stages. As it turns out, the
chances of John’s family being in stage C1 and C4 are, respectively, 99.8% and 0.2%. In
other words, our model suggests that a family like John’s can be classified with near
certainty as in C1 -- the small family (couple) with young kids stage.
Now, suppose we are interested in predicting John’s family’s current annual
expenditure on durable goods (excluding cars). To do so, we need to use three sets of
predictors: stable demographics (birth cohort, gender and race of the head of household),
income measured in $1,000, and the classification of John Doe’s household into the life-
stages. Plugging these predictors into the Tobit regression model for durable goods that
has been calibrated on the CEX data Table 6), we get the following estimates for John’s
annual expenditure on durable goods: a) the likelihood of having any positive expenditure
is about 96.4%, and b) conditional on having any positive expenditure, the expected value
is about $1,254. In a similar fashion, we can estimate John Doe’s expenditures in other
24
categories covered by the CEX data, thereby forming a broad picture of the current
consumption profile, or lifestyle, for his household.
In contrast to previous approaches for lifecycle segmentation, our Hidden Markov
Model predicts how a household would move among the life-stages over time. Thus, our
model can be used not only to classify a customer into the lifecycle segments, but also to
project the consumption needs of a customer into the future. In other words, aside from
classifying John Doe into the life-stages based on his current household profile, we can
make projections about his household’s life-stages and the associated consumption
requirements in the future. For instance, we may be interested in getting some estimates
about his annual expenditure on durable goods four years1 into the future (or even 8, 12,
16 or 20 years ahead). To achieve that goal, we first need to determine the probability
that he will be in each of the 13 life-stages in four years. This can be done by multiplying
a) the vector of current probabilities of being in each life-stage, and b) the estimated
transition probability matrix A, as reported in Table 2a. Subsequently, we plug these
estimated life-stage probabilities into the income regression model that has been
calibrated on the PSID data, as reported in Table 3, which gives us estimates of John
Doe’s household income four years into the future. Finally, with the estimated life-stage
probabilities and income, we can estimate John’s annual expenditure on durables, using
the same method described in the previous paragraph. Applying the same procedure
recursively, we can predict life-stage probabilities, and the associated income and
expenditure for 8, 12, 16, or 20 years into the future. Table 8 reports the predictions about
John Doe’s future life-stage probabilities, income, probability of having positive
1 The projection is made every four years, because the estimated first-order Markov process operates at a four-year level.
25
expenditure on durables, and the conditional means of expenditure on durable goods,
based on his current demographic profile.
INSERT TABLE 8 ABOUT HERE
The results we report in this study reflect the life-stages and lifecycles observed in the
past 35 years. As new data becomes available from the PSID panel, the typology of life-
stages and lifecycles can be updated to reflect new patterns of household composition and
evolution.
The life-stages identified by our HMM model were based solely on the household
composition markers, observed over the 35 years of the PSID panel. The goal of the
HMM model was to identify the typical types of households, and the typical paths
followed by Americans over time. After identifying the life-stages on the basis of the
PSID panel, we classified the households from the CEX surveys into these stages and
then studied the consumption profiles of the 13 life-stages. One might argue, however,
that the main purpose of lifecycle analysis is to explain differences and changes in
consumption behavior as households evolve over time, and that it might be useful to
define the life-stages on the basis not only on the demographic markers, but also
simultaneously on the consumption patterns observed over time (i.e., “lifestyles”).
Unfortunately, the PSID panel does not provide enough longitudinal consumption
data to allow this type of hybrid lifecycle/lifestyle model. On the other hand, the CEX
survey provides complete data on each household’s consumption expenditures, but does
not track the same households from one year to the next, thereby precluding the hybrid
model. One possibility for this hybrid lifecycle/lifestyle segmentation approach (which
we leave for future research) would be to combine the demographic data from the PSID
26
panel with the demographic and consumption data from the CEX surveys in a joint HMM
(for the PSID data) and Latent-Class (for the CEX surveys) model, where the
identification of the latent states or classes (i.e., life-stages and lifestyles) at any point in
time would be based on all the available (demographic and consumption) data from both
sources, while the estimation of the hidden Markov process (i.e., lifecycles) would be
obtained from the longitudinal demographic data in the PSID panel. Such hybrid
approach might lead to life-stages that are maximally distinct not only in terms of
household composition only, but also in terms of consumption profiles, while still
providing valuable insights into how households move from stage to stage over time.
27
Table 1 – Demographic profile of the life-stages
Demographics Level Co/So C1 C2 C3 C4 S1 C5 C6 S2 S3 S5 S4 C7 married 53% 98% 99% 99% 98% 1% 98% 99% 2% 3% 1% 1% 92%
never married 38% 0% 0% 0% 0% 62% 0% 0% 30% 10% 0% 20% 0% widowed 0% 0% 0% 0% 0% 1% 0% 0% 7% 33% 98% 16% 6% divorced 6% 1% 0% 0% 1% 27% 1% 0% 41% 42% 0% 57% 1%
separated 3% 1% 0% 0% 0% 9% 0% 0% 21% 11% 1% 6% 1%mean 26 30 37 45 39 34 51 62 34 53 75 60 70
std dev 4 5 4 5 5 8 6 11 7 9 9 11 7 working now 90% 94% 95% 92% 96% 87% 89% 49% 59% 63% 9% 46% 14% unemployed 5% 4% 4% 4% 3% 8% 4% 3% 14% 6% 5% 6% 3%
retired 0% 0% 0% 1% 0% 0% 4% 45% 4% 13% 57% 35% 79% disabled 2% 0% 1% 3% 1% 3% 3% 3% 2% 7% 6% 8% 4%
homemaker 0% 0% 0% 0% 0% 0% 0% 0% 17% 11% 23% 4% 1% student 3% 1% 0% 0% 0% 1% 0% 0% 3% 0% 0% 0% 0%
working now 34% 50% 51% 61% 72% 1% 65% 37% 2% 1% 0% 0% 16% unemployed 1% 3% 3% 2% 3% 0% 2% 1% 0% 0% 0% 0% 1%
retired 0% 0% 0% 0% 0% 0% 1% 24% 0% 0% 0% 0% 27% disabled 0% 0% 0% 1% 0% 0% 2% 2% 0% 0% 0% 0% 5%
homemaker 4% 43% 45% 34% 23% 0% 29% 35% 1% 0% 0% 0% 45% student 1% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0%
not applicab 59% 2% 0% 1% 1% 98% 1% 0% 97% 99% 100% 99% 5% none 92% 98% 96% 45% 98% 96% 33% 100% 86% 22% 91% 99% 39%
1 7% 2% 3% 34% 2% 4% 52% 0% 12% 56% 8% 1% 49%2 0% 0% 0% 16% 0% 0% 13% 0% 2% 16% 1% 0% 9%3 0% 0% 0% 4% 0% 0% 3% 0% 1% 4% 0% 0% 2%
4+ 0% 0% 0% 2% 0% 0% 0% 0% 0% 1% 0% 0% 1%Kid in college 2% 0% 0% 17% 0% 0% 22% 0% 1% 13% 0% 0% 4%
1 37% 1% 0% 0% 0% 93% 0% 0% 2% 4% 90% 98% 2%2 60% 0% 0% 0% 1% 7% 2% 100% 29% 56% 9% 2% 29%3 2% 41% 0% 0% 21% 0% 51% 0% 34% 22% 1% 0% 50%4 0% 45% 1% 3% 75% 0% 37% 0% 18% 10% 0% 0% 12%5 0% 12% 58% 45% 2% 0% 9% 0% 10% 5% 0% 0% 3%6 0% 1% 27% 29% 0% 0% 1% 0% 4% 2% 0% 0% 2%7 0% 0% 9% 11% 0% 0% 0% 0% 2% 1% 0% 0% 2%8 0% 0% 3% 6% 0% 0% 0% 0% 1% 0% 0% 0% 0%
9+ 0% 0% 2% 7% 0% 0% 0% 0% 1% 0% 0% 0% 0%Kid <7 years old 1% 70% 51% 12% 11% 0% 3% 0% 32% 4% 0% 0% 4%Kid 7-14 years old 0% 11% 89% 65% 62% 0% 9% 0% 48% 10% 0% 0% 6%Kid 15-18 years old 0% 0% 11% 62% 15% 0% 24% 0% 18% 16% 0% 0% 4%
Other adults
Family Size
Marital Status
Age of the head of household
Employment status of head
Employment status of spouse
28
Table 2a – Initial and transition probabilities from the Hidden Markov Model
30% 12% 6% 5% 4% 10% 6% 6% 8% 6% 7% 2% 0%
Co/So C1 C2 C3 C4 S1 C5 C6 S2 S3 S5 S4 C7Co/So 46% 47% 0% 0% 1% 3% 0% 2% 0% 0% 0% 0% 0%
C1 0% 44% 19% 0% 32% 4% 0% 0% 1% 0% 0% 0% 0%C2 0% 0% 42% 51% 2% 3% 0% 0% 1% 0% 0% 0% 0%C3 0% 0% 0% 44% 2% 1% 49% 1% 0% 2% 0% 1% 0%C4 0% 0% 0% 0% 56% 3% 38% 1% 0% 1% 0% 0% 0%S1 0% 0% 0% 0% 0% 77% 6% 6% 6% 3% 0% 3% 0%C5 0% 0% 0% 0% 0% 0% 55% 37% 0% 1% 0% 1% 6%C6 0% 0% 0% 0% 0% 0% 0% 93% 0% 0% 4% 1% 2%S2 0% 0% 0% 0% 0% 0% 0% 0% 75% 22% 0% 3% 0%S3 0% 0% 0% 0% 0% 0% 0% 0% 0% 70% 10% 18% 1%S5 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 98% 0% 2%S4 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 98% 2%C7 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100%
iπ
Table 2b – Transitions as a proportion of all households in the sample
29
Co/So C1 C2 C3 C4 S1 C5 C6 S2 S3 S5 S4 C7Co/So 0.0% 13.0% 0.2% 0.0% 0.4% 1.1% 0.1% 0.5% 0.1% 0.0% 0.0% 0.0% 0.0%
C1 0.0% 0.0% 7.9% 0.0% 11.5% 2.5% 0.0% 0.1% 0.3% 0.0% 0.0% 0.0% 0.0%C2 0.0% 0.0% 0.0% 7.4% 0.5% 0.7% 0.0% 0.0% 0.2% 0.1% 0.0% 0.0% 0.0%C3 0.0% 0.0% 0.0% 0.0% 0.3% 0.2% 7.1% 0.1% 0.0% 0.5% 0.0% 0.2% 0.2%C4 0.0% 0.0% 0.0% 0.0% 0.0% 0.7% 6.8% 0.2% 0.0% 0.2% 0.0% 0.1% 0.0%S1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.0% 1.0% 2.0% 0.8% 0.0% 0.5% 0.0%C5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 8.5% 0.0% 0.4% 0.0% 0.3% 1.7%C6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 1.1% 0.5% 0.7%S2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 4.9% 0.0% 0.6% 0.0%S3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9% 3.3% 0.2%S5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.2%S4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.4%C7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Table 3 – Regression coefficients explaining income and consumption with stable demographic characteristics and lifecycles
Predictor Log-Income Food-in Mortgage(Constant) 9.73 $5,325.95 -$1,390.65Co/So -0.55 -$425.87C1 -0.47 $427.68C2 -0.81C3 -1.46 -$2,039.46 -$9,308.16C4 -0.46 $1,184.13 $14,182.59S1 $1,942.87 $14,427.53C5 -0.48 -$2,203.02 $19,258.29C6 -$1,685.87S2 $2,823.29 $26,824.62S3 -0.29 -$3,601.84S5 $930.59 $35,983.86S4 -$1,848.02 $25,547.02C7 -0.29 $8,570.77BORN30 Born in the 1930's -$584.30BORN40 Born in the 1940's 0.14 -$1,918.86BORN50 Born in the 1950's -$2,906.87 $2,225.01BORNA60 Born in the 1960's or later -0.19 -$3,177.37HS High school 0.29 $361.20MOREHS More than High school 0.59 $708.22 $7,643.43MALE Head is male 0.47 $972.30 $2,986.72WHITE Head is white 0.23 $433.76 $7,114.81
Regression Coefficients
Predictors Log-Income Food-in Mortgage(Constant) 6.47 $2,691.59 -$38,296.14AGE age of the head 0.14 $193.10 $1,669.08AGE2 squared age 0.00 -$4.03AGE3 cubic age 0.00 $0.02 -$0.21BORN20 Born in the 1920's $694.49 -$11,379.25BORN30 Born in the 1930's -$7,399.37BORN40 Born in the 1940's -$1,947.77BORN50 Born in the 1950's -$3,146.37BORNA60 Born in the 1960's or later -$3,573.97HS High school 0.28 $212.46MALE Head is male 0.60 $1,473.47 $3,799.63MOREHS More than High school 0.49 $395.80 $5,731.73WHITE Head is white 0.27 $282.86 $7,339.86
Regression Coefficients
30
Table 4 – Predictive Validity of Lifecycle Models
Dependent Variable Model 4-years 8-yearsLife-stages 0.57 0.49 0.42Age 0.30 0.11 -0.01LifeStages 0.62 0.50 0.41Age 0.28 0.27 0.25LifeStages 0.33 0.25 0.25Age 0.33 0.31 0.30
Prediction
Log-Income
Food consumed in the house
Mortgage balance
Fit
31
Table 5 – Classification of the CEX sample into life-stages
% within YEAR
5.3% 12.7% 3.5% 7.5% 6.9% 5.8% 10.3% 16.9% 5.8% 7.9% 9.1% 5.6% 2.9% 100.0%5.4% 12.2% 4.7% 6.1% 6.9% 8.1% 10.8% 15.7% 6.1% 7.7% 8.7% 5.6% 2.2% 100.0%5.7% 11.0% 3.8% 5.8% 6.4% 8.2% 10.5% 16.9% 7.3% 7.6% 8.1% 6.2% 2.6% 100.0%5.6% 10.6% 3.9% 5.4% 6.7% 8.1% 9.6% 17.0% 7.1% 8.9% 8.9% 5.7% 2.5% 100.0%4.7% 10.7% 4.0% 5.7% 6.8% 8.0% 10.6% 16.6% 6.7% 7.8% 9.5% 5.6% 3.4% 100.0%5.1% 10.3% 4.0% 5.3% 8.4% 7.5% 12.5% 16.5% 5.9% 7.1% 8.2% 6.2% 3.1% 100.0%4.9% 9.7% 4.5% 5.0% 7.1% 8.1% 10.8% 17.2% 7.4% 7.5% 9.5% 5.2% 3.3% 100.0%5.1% 9.9% 4.0% 4.6% 7.6% 7.2% 10.0% 17.7% 6.4% 7.6% 10.4% 6.5% 2.9% 100.0%4.8% 9.9% 3.7% 4.1% 6.3% 7.7% 11.0% 18.9% 7.1% 8.5% 8.8% 6.5% 2.7% 100.0%5.0% 10.2% 4.0% 5.3% 7.0% 7.2% 10.3% 18.5% 6.3% 9.1% 8.6% 5.6% 2.7% 100.0%4.8% 10.5% 3.8% 4.2% 6.8% 8.0% 10.2% 18.2% 6.5% 8.3% 9.3% 6.2% 3.2% 100.0%4.4% 10.6% 3.8% 4.4% 6.8% 8.1% 10.0% 18.3% 6.8% 8.1% 9.6% 5.9% 3.0% 100.0%4.1% 9.5% 3.5% 3.3% 7.4% 8.2% 10.1% 18.3% 7.7% 8.7% 8.9% 7.5% 2.9% 100.0%4.6% 9.6% 3.9% 4.0% 7.2% 8.5% 10.0% 17.3% 7.8% 8.6% 9.6% 6.5% 2.5% 100.0%4.0% 8.3% 3.9% 4.3% 7.7% 7.9% 10.2% 18.4% 7.1% 9.7% 9.3% 7.0% 2.2% 100.0%3.6% 8.2% 2.7% 3.8% 8.6% 8.4% 10.7% 18.4% 7.0% 9.1% 8.4% 7.8% 3.3% 100.0%4.0% 9.0% 3.7% 4.8% 7.1% 8.5% 9.0% 17.8% 8.1% 9.0% 9.9% 6.3% 2.9% 100.0%3.6% 8.0% 3.8% 4.1% 7.1% 8.0% 9.2% 19.3% 7.2% 9.6% 10.4% 7.1% 2.5% 100.0%5.0% 8.0% 3.3% 4.0% 7.7% 8.2% 9.0% 20.1% 6.9% 7.9% 9.2% 7.7% 3.1% 100.0%4.7% 10.0% 3.9% 4.8% 7.1% 7.9% 10.2% 17.8% 6.9% 8.4% 9.3% 6.3% 2.8% 100.0%
1980198119821983198419851986198719881989199019911992199319941995199619971998
YEAR
Total
Co/So C1 C2 C3 C4 S1 C5 C6 S2 S3 S5 S4 C7Life Stage
Total
32
Table 6a – Average Percentage of Consumers with Positive Expenditure/Ownership by Life-stage
Expenditures/Assets Co/So C1 C2 C3 C4 S1 C5 C6 S2 S3 S5 S4 C7taxes 99% 96% 95% 96% 96% 98% 97% 96% 89% 96% 90% 90% 95%rent 87% 70% 72% 70% 73% 86% 73% 67% 85% 66% 57% 73% 54%food in the house 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%food out 100% 99% 99% 98% 100% 100% 99% 97% 99% 98% 95% 97% 97%alcohol & tobacco 93% 86% 81% 84% 86% 93% 85% 79% 88% 86% 61% 76% 78%clothing 100% 100% 100% 100% 100% 99% 100% 99% 100% 98% 94% 96% 99%dry cleaning 91% 81% 78% 77% 78% 90% 78% 75% 82% 74% 67% 77% 72%personal care 93% 91% 89% 90% 92% 92% 94% 95% 90% 93% 89% 88% 96%durables 95% 96% 96% 95% 95% 90% 93% 92% 90% 86% 77% 81% 91%utilities 95% 97% 97% 98% 98% 93% 98% 98% 92% 97% 95% 92% 99%phone 99% 99% 99% 99% 100% 99% 100% 100% 98% 99% 99% 98% 100%domestic help 78% 91% 87% 81% 87% 81% 83% 90% 81% 80% 88% 82% 87%health 91% 97% 94% 95% 95% 82% 95% 96% 82% 89% 92% 85% 96%health insurance 52% 58% 58% 64% 61% 45% 69% 84% 39% 72% 98% 74% 98%life insurance 60% 72% 73% 66% 78% 47% 73% 66% 50% 59% 45% 46% 68%new car 33% 33% 33% 47% 30% 20% 43% 21% 27% 32% 7% 13% 25%car maintenance 99% 99% 100% 99% 100% 98% 100% 99% 97% 98% 93% 95% 99%mass transport 63% 50% 52% 57% 53% 64% 55% 53% 59% 55% 48% 54% 50%auto insurance 85% 84% 80% 82% 86% 77% 88% 85% 67% 82% 63% 71% 87%airfare 32% 20% 19% 25% 22% 42% 29% 34% 21% 30% 31% 33% 30%books & maps 74% 75% 82% 77% 85% 71% 69% 56% 71% 59% 33% 48% 55%magzines & news 99% 100% 99% 98% 99% 98% 98% 98% 98% 96% 94% 94% 98%recreation 100% 99% 99% 99% 100% 99% 99% 98% 99% 98% 94% 97% 98%high education 37% 28% 44% 58% 51% 27% 48% 15% 34% 30% 6% 13% 21%low education 1% 47% 40% 21% 29% 2% 8% 1% 28% 6% 1% 1% 5%charity 62% 66% 67% 62% 66% 60% 62% 68% 46% 53% 65% 57% 62%car payment 60% 54% 50% 55% 52% 35% 57% 28% 35% 45% 8% 19% 33%interest payment 67% 66% 65% 64% 67% 59% 58% 34% 45% 50% 16% 31% 36%mortgage payment 49% 74% 77% 79% 84% 49% 78% 80% 49% 69% 69% 59% 81%home maintenance 45% 67% 68% 67% 73% 42% 67% 76% 42% 61% 66% 54% 79%pension contribution 23% 24% 23% 20% 31% 34% 32% 23% 18% 25% 3% 26% 10%asset_home 33% 61% 70% 71% 77% 40% 80% 86% 40% 74% 79% 66% 90%asset_checking 79% 74% 66% 61% 74% 78% 65% 69% 58% 64% 69% 68% 63%asset_savings 71% 66% 57% 52% 63% 67% 53% 56% 48% 52% 52% 53% 51%asset_stocks&bonds 25% 34% 26% 19% 30% 30% 24% 33% 19% 23% 28% 30% 23%
Lifestages
33
Table 6b – Type II Tobit Coefficients for the Incidence Component
Categories Intercept IncomeBorn after
50's Female WhitePopulation Average
Incidencetaxes 1.697 0.017 0.055 -0.096 0.391 95.5%rent 0.605 0.005 0.106 0.048 0.050 72.8%food in the house 51.771 0.009 0.429 0.552 -0.007 100.0%food out 2.207 0.022 0.146 -0.053 0.494 98.6%alcohol & tobacco 0.980 0.007 -0.002 -0.198 0.269 83.7%clothing 2.493 0.015 0.081 0.326 0.334 99.4%dry cleaning 0.816 0.010 -0.188 0.037 -0.245 79.3%personal care 1.402 0.009 0.002 -0.145 0.239 92.0%durables 1.381 0.010 -0.014 0.236 0.446 91.6%utilities 1.841 0.007 0.044 0.058 0.266 96.7%phone 2.432 0.014 -0.022 0.302 0.257 99.3%domestic help 1.026 0.011 -0.178 0.084 0.351 84.8%health 1.437 0.007 -0.183 0.215 0.491 92.5%health insurance 0.583 0.001 0.086 0.026 0.080 72.0%life insurance 0.313 0.007 -0.234 -0.037 -0.221 62.3%new car -0.625 0.002 0.122 -0.172 0.327 26.6%car maintenance 2.226 0.022 0.059 -0.247 0.572 98.7%mass transport 0.126 0.007 -0.152 0.087 -0.217 55.0%auto insurance 0.866 0.008 0.063 -0.115 0.501 80.7%airfare -0.585 0.011 0.095 0.116 0.113 27.9%books & maps 0.436 0.011 -0.055 0.227 0.401 66.8%magzines & news 2.096 0.018 -0.180 0.180 0.515 98.2%recreation 2.259 0.020 0.319 0.056 0.613 98.8%high education -0.536 0.007 0.000 0.108 0.162 29.6%low education -1.412 0.006 -0.073 0.074 -0.046 7.9%charity 0.291 0.008 -0.291 0.087 0.088 61.4%car payment -0.268 0.004 0.118 -0.061 0.106 39.5%interest payment 0.004 0.004 -0.053 0.067 0.064 50.2%mortgage payment 0.524 0.013 -0.277 -0.004 0.429 70.0%home maintenance 0.320 0.011 -0.299 -0.022 0.511 62.6%pension contribution -0.813 0.012 0.100 0.032 0.202 20.8%asset_home 0.469 0.011 0.042 -0.021 0.361 68.1%asset_checking 0.482 0.009 -0.019 0.013 0.515 68.5%asset_savings 0.179 0.008 -0.094 -0.009 0.330 57.1%asset_stockbond -0.635 0.011 -0.065 -0.023 0.476 26.3%
34
35
Table 6c – Deviation from Population Average by Life-stages
Expenditures/Assets Co/So C1 C2 C3 C4 S1 C5 C6 S2 S3 S5 S4 C7taxes $2,204 $415 -$683 -$507 $1,647 $3,683 $857 $7 -$1,572 $193 -$2,309 -$895 -$3,040rent $2,724 -$198 -$381 -$503 -$864 $2,884 -$520 -$1,319 $2,163 -$158 -$1,553 $642 -$2,917food in the house -$1,183 $394 $2,197 $3,074 $1,266 -$2,058 $908 -$649 $88 -$426 -$2,158 -$2,231 $778food out $282 -$435 -$189 $264 $101 $388 $367 -$17 $103 $2 -$423 -$159 -$282alcohol & tobacco $83 -$144 -$131 $24 -$81 $173 $108 -$62 $110 $214 -$211 -$31 -$52clothing $280 $247 $570 $965 $521 -$190 $278 -$329 $191 -$313 -$998 -$825 -$397dry cleaning $108 $0 -$36 -$19 -$30 $128 -$11 -$36 $70 -$7 -$87 -$8 -$72personal care -$54 -$35 $21 $108 $49 -$130 $84 $47 -$50 -$19 -$8 -$71 $57durables $521 $435 $344 $139 $298 -$158 $105 $93 -$133 -$310 -$628 -$621 -$87utilities -$528 $128 $510 $761 $357 -$818 $380 $10 -$124 $5 -$396 -$705 $418phone -$33 -$14 $24 $287 -$31 -$74 $199 -$39 -$38 $108 -$231 -$188 $29domestic help -$751 $982 $466 -$266 $200 -$521 -$297 $364 -$141 -$421 $402 -$247 $229health -$524 $40 $182 $233 $17 -$681 $151 $490 -$523 -$125 $190 -$487 $1,036health insurance -$665 -$115 -$19 $57 $49 -$683 $236 $652 -$426 $4 $179 -$93 $823life insurance -$245 $721 $819 $454 $1,134 -$1,190 $772 $462 -$928 -$201 -$1,340 -$1,126 $667new car -$3,237 -$4,545 -$5,386 -$12,159 -$2,841 $4,087 -$8,851 $6,092 -$2,353 -$4,509 $19,666 $12,014 $2,022car maintenance $330 $131 $279 $1,350 $318 -$465 $966 -$458 -$223 -$60 -$1,113 -$885 -$171mass transport $27 -$127 -$130 $53 -$92 $87 $11 $52 $50 $54 -$57 -$1 $73auto insurance -$197 -$247 -$237 $287 -$226 -$202 $402 -$89 $34 $143 $201 $22 $110airfare $224 -$109 -$128 $22 -$54 $134 -$90 $230 -$105 -$136 -$14 -$56 $81books & maps $44 -$46 $9 $94 -$3 $18 $81 -$50 -$22 $32 -$82 -$47 -$28magzines & news -$53 $221 $231 $3 $105 -$46 -$62 -$26 $18 -$90 -$136 -$125 -$38recreation $235 -$263 $411 $525 $816 $153 $104 -$190 $28 -$126 -$886 -$528 -$278high education $1,329 -$599 $743 $3,006 $1,335 -$113 $2,965 -$1,735 $207 $364 -$3,880 -$2,492 -$1,130low education $963 -$1,558 -$1,396 -$550 -$927 $897 -$132 $735 -$628 $44 $1,983 $665 -$97charity -$444 $175 $729 $425 $198 -$429 $134 $788 -$1,417 -$732 $426 -$163 $309car payment -$2,963 -$2,238 -$1,789 -$1,946 -$1,979 -$11 -$1,883 $1,855 -$67 -$1,012 $7,163 $3,654 $1,217interest payment $2,102 $2,026 $1,881 $1,668 $1,968 $1,153 $1,136 -$1,916 -$381 $38 -$5,361 -$2,418 -$1,895mortgage payment -$2,902 $142 $1,503 $1,247 $1,775 -$1,984 $919 $1,305 -$2,427 $170 -$341 -$779 $1,371home maintenance -$1,311 $506 $1,130 $329 $685 -$1,487 $44 $1,221 -$2,098 -$98 $211 -$617 $1,485pension contribution -$568 -$359 -$591 -$679 -$22 $780 $720 $1,318 -$447 $284 -$1,024 $1,252 -$665asset_home $5,380 $183 $264 -$5,130 -$4,829 $17,999 -$15,303 -$4,406 $14,317 -$7,569 -$3,494 $3,056 -$469asset_checking -$253 -$940 -$2,763 -$5,130 -$288 $2,973 -$2,917 $3,216 -$3,850 -$461 $4,948 $3,873 $1,593asset_savings -$9,506 -$8,431 -$9,853 -$13,216 -$8,453 -$2,797 -$6,003 $14,528 -$5,603 $615 $21,005 $10,841 $16,872asset_stocks&bonds -$34,735 -$13,474 -$14,566 -$31,561 -$12,686 -$2,300 -$10,377 $33,835 -$26,688 $5,734 $40,369 $34,671 $31,779
Lifestages
Two stages with the highest volume in the category Two stages with the lowest volume in the category
36
Table 6d Estimates from the Regression on the Observable Component Categories Intercept Income ($1000) Born after 50's Female White taxes $7,397.8 $237.4 -$145.8 -$447.3 $1,553.4rent -$215.0 $10.5 $857.7 $292.1 -$food in the house $4,759.5 $15.1 -$590.7 $8.9 $216.9food out $1,601.7 $23.3 -$148.8 -$286.5 $498.1alcohol & tobacco $1,045.7 $2.6 -$35.6 -$236.4 $186.8clothing $1,778.8 $24.4 -$323.9 $327.9 $132.4dry cleaning $123.1 $2.9 -$16.3 $1.9 -$75.6personal care $440.2 $2.6 -$22.1 $89.5 -$12durables $944.9 $18.9 -$279.3 $113.4 $344.9utilities $2,140.1 $8.2 -$341.4 $60.9 $121.9phone $847.4 $3.6 $77.0 $74.8 -$13domestic help -$373.4 $22.5 -$376.0 $200.6 $773.5health $1,098.5 $8.3 -$303.4 $70.7 $551.6health insurance $1,020.8 $2.3 $209.7 $35.3 $248.3life insurance -$1,864.4 $21.6 -$691.3 -$145.9 -$57new car $43,067.9 $54.1 -$2,057.6 $3,870.0 -$6,05car maintenance $2,961.1 $16.0 -$380.2 -$209.3 $159.2mass transport -$18.6 $3.8 -$33.6 $62.2 -$19auto insurance $1,460.1 $3.5 $114.4 $72.1 -$31airfare $105.7 $11.3 -$229.4 $5.1 $5.7books & maps $185.7 $1.7 -$8.0 $13.2 $29.3magzines & news $459.6 $4.3 -$87.9 -$8.0 $145.6recreation $1,759.1 $32.1 $187.7 -$138.0 $781.7high education -$5,308.3 $32.0 -$69.9 $490.1 $593.7low education $4,868.3 $11.2 $73.8 $82.1 -$51.3charity -$1,727.5 $29.3 -$858.5 $184.2 $290.4car payment $12,696.0 $1.9 -$364.8 $181.9 -$63interest payment -$5,145.6 $23.3 -$350.8 $278.6 $199.1mortgage payment -$1,235.6 $66.9 -$588.7 $17.2 $1,809.7home maintenance -$993.9 $51.3 -$1,064.4 $50.6 $1,998.7pension contribution $624.4 $51.2 $72.9 $5.5 $643.7asset_home $148,305.7 $847.2 -$11,771.1 $5,724.3 -$11,2asset_checking -$6,332.0 $142.5 $53.1 -$780.5 $5,947.9asset_savings $12,965.5 $219.6 -$4,345.2 -$2,369.8 $6,088.2asset_stockbond -$50,399.2 $868.9 $2,406.4 -$2,211.9 $34,50
412.3
4.7
5.1
6.31.3
4.24.2
9.4
10.4
7.2
37
Table 7 – Comparison
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itho stag Fu l vs. Cu AgeExpenditures/Assets Hit ratio ati Hit r Hi ion Hit r Cor tiontaxes 90.9% .8% 90 0 0. 0rent 71.0% 0 70.9% 24 71. -0.2% 4food in the house 100.0% 0.66 100.0% 100.0% 0 0.0% 0.21 0.0%food out 94.3% 0.51 94.3% 0.49 94.4% 0.50 0.0% 0.01 0.0% 0.01alcohol & tobacco 81.5% 0.25 80.5% 0.22 81.6% 0.25 1.0% 0.04 -0.1% 0.00clothing 97.2% 0.5 97.2% 0.51 97.2% 0.53 0.0% 0.03 0.0% 0.01dry cleaning 76.4% 76.4% 0.34 0.0 .02 0.0% 1personal care 90.3% .3%durables 88.2% .2%utilities 95.5% .5%phone 98.5% 0.36 98.5% 0.31 98.5% 0.34 0.0% 0.05 0.0% 0.02domestic help 83.0% 0.40 82.9% 0.36 82.9% 0.37 0.0% 0.04 0.0% 0.03health 90.4% 0.22 90.4% 0.19 90.4% 0.22 0.0% 0.03 0.0% 0.00health insurance 71.0% 0.27 69.5% 0.16 71.2% 0.25 1.5% 0.11 -0.2% 0.02life insurance 67.5% .3% 23 0.23 1.2 0 7%new car 72.6% .0%car maintenance 93.4% 93.2%mass transport 59.4% 0.18 59.2% 0.16 59.3% 0.17 0.2% 0.02 0.1% 0.01auto insurance 80.1% 0.42 79.5% 0.36 80.3% 0.36 0.6% 0.06 -0.1% 0.06airfare 73.0% 0.3 72.5% 0.29 72.5% 0.29 0.4% 0.01 0.4% 0.00books & maps 71.0% 0.2 70.5% .7% 6magzines & news 95.1% .1%recreation 94.7% .6%high education 73.8% 70.9%low education 87.4% 0.30 86.3% 0.30 86.5% 0.30 1.0% 0.00 0.9% 0.00charity 65.7% 0.28 65.1% 0.26 65.3% 0.28 0.5% 0.01 0.4% 0.00car payment 67.7% 0.2 64.8% 0.24 67.0% 0.24 2.9% 0.01 0.6% 0.01interest payment 65.4% 62.6% 0.1 0.0mortgage payment 74.1% .9%home maintenance 71.0% .2%pension contribution 76.0% 0.35 75.9% 0.33 76.3% 0.35 0.2% 0.01 -0.3% 0.00asset_home 74.3% 0.45 72.3% 0.45 73.8% 0.46 1.9% 0.00 0.5% 0.00asset_checking 70.8% 0.2 70.1% 0.21 70.1% 0.25 0.7% 0.04 0.7% 0.00asset_savings 65.0% 64.7% 0. 64.5% 0.40 % 0.09 0. -0.0asset_stocks&bonds 72.7% .5%
Fu
Table 8 – Projections about John Doe’s Future Life-stages, Income and Expenditures on Durables
Year Co/So C1 C2 C3 C4 S1 C5 C6 S2 S3 S5 S4 C7 Income Incidence ExpenditureCurrent 0.0% 99.8% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% $52,292 96.4% $1,254
+4 0.0% 44.0% 19.3% 0.1% 31.8% 4.0% 0.1% 0.2% 0.6% 0.0% 0.0% 0.0% 0.0% $50,193 95.4% $1,126+8 0.0% 19.4% 16.6% 9.9% 32.3% 6.4% 12.5% 0.9% 1.1% 0.5% 0.0% 0.2% 0.0% $46,756 94.3% $966
+12 0.0% 8.5% 10.8% 12.9% 24.9% 7.4% 24.6% 6.3% 1.4% 1.3% 0.1% 0.9% 0.8% $48,006 93.6% $912+16 0.0% 3.8% 6.2% 11.2% 17.2% 7.3% 30.0% 15.7% 1.6% 2.1% 0.5% 2.0% 2.4% $52,377 93.3% $934+20 0.0% 1.7% 3.3% 8.1% 11.2% 6.6% 29.1% 26.3% 1.7% 2.7% 1.4% 3.3% 4.5% $58,006 93.3% $992
DurablesLife-stages
38
39 39
Figure 1 – Hidden Markov Model
4040
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large family
Older kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
S5Widowed
Empty NestAge 66-84
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large family
Older kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
S5Widowed
Empty NestAge 66-84
Figure 2 – Household Lifecycles in America
Figure 3 – Household Lif
ecycles in America by Race
Households headed by Blacks
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
S5dowed ty Nest 66-84
S5dowed ty Nest 66-84
WiEmpAge
C7Couple
dult dependentsAge 63-77
A
C6Coup
Empty NAge 51-
leest73
S5
C7Couple
dult dependentsAge 63-77
A
C6Coup
Empty NAge 51-
leest73
S5Widowed
Empty NestAge 66-84
Households headed by Whites
Co/Soe/Married
Kidse 22-30
WiEmpAge
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
SinglNo
Ag
SinglNo
Ag
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
Widowed Empty NestAge 66-84
Co/Soe/Married
Kidse 22-30
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
41
Figure 4 – Household Lifecycles in America by Education
Households heade School education
d by people with less than High-
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
S5Widowed
Empty NestAge 66-84
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
S5Widowed
Empty NestAge 66-84
Households headed -School education
by people with more than High
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
S5Widowed
Empty NestAge 66-84
Co/SoSingle/Married
No KidsAge 22-30
C1Small family
Kids <7Age 25-35
C4Small family
Kids <15Age 34-44
C5Small familyOlder KidsAge 45-57
C2Large family
Kids <15Age 33-41
C3Large familyOlder kidsAge 40-50
C7Couple
Adult dependentsAge 63-77
C6Couple
Empty NestAge 51-73
S1Single/Divorced
No KidsAge 26-42
S2Divorced/Single
Kids<15Age 27-41
S3Divorced/Widow
Older KidsAge 44-62
S4Divorced/Single
Empty NestAge 49-71
S5Widowed
Empty NestAge 66-84
42
REFERENCES
Clark, Lin niversity Press).
Deaton, Angus (1992) Understanding Consumption, (Oxford: Oxford University Press). Derrick, Frederick and Alane K. Lehfeld (1980), “The family life cycle: An alternative
approach,” Journal of Consumer Research, 7 (September), 214-17. Fritzche, David J. (1981) “An analysis of energy consumption patterns by stage of family
lifecycle,” Journal of Marketing Research, 18 (May), 227-32. Gourinchas, Pierre O. and Jonathan A. Parker (2002) “Consumption over the life cycle,”
Econometrica, 70 (1), 47-89. Gilly, Mary C. and Ben M. Enis (1982), “Recycling the family life cycle: A proposal for
re-definition,” in Advances in Consumer Research, Vol. 9 ed. Andrew A. Mitchell, Ann Arbor, MI: Association for Consumer Research, 271-6.
Hill, Martha S. (1991) The Panel Study of Income Dynamics: A User’s Guide, Vol. II,
(California: Sage Publications). Hisrich, Robert D. and Micahel P. Peters (1974) “Selecting the superior segmentation
correlate,” Journal of Marketing, 38, 60-63.
Lansing, J ycle,” in Consumer Behavior, Vol.2, ed. Lincoln H. Clark, New york: New York University Press, 36-51.
Lansing , John B. and Leslie Kish (1957) “Family life cycle as an independent variable,”
American Sociological Review, 22, 512-9. Loomis, Charles P. (1936) “The study of the life cycle of families,” Rural Sociology, 1,
180-99. Murphy, Patrick E. and William A. Staples (1979) “A modernized family life cycle”,
Journal of Consumer Research, 6(June) 1979. Rabiner, Lawrence R. (1989), “A tutorial on Hidden Markov Models and selected
applications in speech recognition,” Proceedings of the IEEE, 77(2), 257-86. Schaninger, Charles M. and William D. Danko (1993) “A conceptual and empirical
comparison of alternative household lifecycle models,” Journal of Consumer Research, 19(March) 580-94.
conl H. (1955) Consumer Behavior, Vol. II, (New York: New York U
ohn B. and James N. Morgan (1955), Consumerfinances over the life c
43
Wagner, Janet and Sherman Hanna ( iveness of family life cycle variables in consumer expenditure research,” Journal of Consumer Research,
edel, Michel and Wagner A. Kamakura (2000) Market Segmentation: Conceptual
1983) “The effect
10(September) 281-91.
WMethodological Foundations, Second Edition. Boston: Kluwer Academic Publishers.
Wells, Research,” Journal of Marketing Research, 3(November), 355-63.
Wilkes product expenditures,” Journal of Consumer Research, 22 (June) 27-42.
William and George Gubar (1966), “Life cycle concept in Marketing
, Robert E. (1995) “Household lifecycle stages, transitions and
44
