The effects of an economic
downturn on consumer
attitudes and behaviors
Series 1
An Experian white paper
The effects of an economic downturn — Series 1
Introduction
The recent severe economic downturn has generated a whole new set of questions
for media companies, advertising agencies and advertisers. They want to know:
How will the economic downturn affect my sales? Who are the new, “most valuable”
customers I should be pursuing now? How should I update my old messaging
strategy to make it work in this new economy?
It is apparent from the feedback we are receiving from our clients every day that
they need market data more than ever to answer these questions, and they need it
quickly. The move by Experian Simmons to quarterly releases already has become
an invaluable improvement in dealing with this fast-moving consumer environment.
However, Experian Simmons is dedicated to continue doing even more for its clients.
®
SM
The latest three-month wave from the Simmons National Consumer Study (Fall
2008) holds a unique position in the analysis of consumer behavior in that its fielding
period straddles both the pre- and post-economic meltdown (subsequently referred
to as pre-event and postevent) that occurred during mid-September 2008. We take
advantage of an important property of the Experian Simmons sample design that
allows the breakdown of the data into smaller time periods to give the researcher the
ability to take an unprecedented look at consumer reaction to the economic crisis as
it unfolded.
Experian Simmons has undertaken the task of testing the ability to utilize “just-intime” data coming out of the field in order to meet this challenge. There are a number
of reasons for this effort, including the value added for our clients as well as our own
intellectual curiosity.
There also are significant technical and methodological barriers to producing “justin-time” data that include issues of weighting, data cleaning, in-tab resolution and
interpretation. The methodology section at the end of this document shares some of
the caveats that need to be taken into account when interpreting the data.
We are at a unique historical point in time for American consumers. What is clear
is that their reaction to the economic downturn will be complex and sometimes
unexpected. Understanding that reaction in a quick-moving environment is likely to
be a key factor in separating those companies that emerge from the crisis healthy
from those that do not.
An Experian white paper | Page 1
The effects of an economic downturn — Series 1
Financial situation and confidence in the U.S. economy
Two of the more powerful attitudinal factors in driving consumer behavior — especially
during tough economic times — are a person’s evaluation of his or her financial
situation and his or her confidence in the health of the American economy. These two
attitudes can have an important effect on consumer spending patterns and behaviors.
A person’s opinion of his or her past, recent and short-term future financial wellbeing is a reasonably good measure of that person’s perception of his or her financial
situation. In particular, the percentage of people who believe their economic fortunes
have improved in the past 12 months gives marketers an idea of the pool of the most
potentially valuable consumers.
25%
23%
21%
19%
17%
15%
13%
11%
9%
7%
5%
Au
g.
12
A
Au ug –18
g. . 19
26
–S –25
ep
Se t. 1
pt
Se . 2–
8
p
Se t. 9–
pt 15
.
Se 16–
Se pt. 22
2
pt
. 3 3–2
0–
9
Oc
Oc t. 6
t
Oc . 7–1
t. 1 3
O 4–
Oc ct. 20
t. 2 21–
8– 27
N
No ov. 3
v
No . 4–1
0
v.
No 11–1
7
No v.
18
v.
–
25
–D 24
ec
De . 1
c.
2–
8
Unweighted percentage
Figure 1 — Respondent now better off 1 than 12 months ago
1Am personally somewhat or significantly better off financially now than 12 months ago
Source: Simmons Wave 1608 preliminary unweighted daily data
As can be seen in Figure 1, the proportion of people who felt they were better off
financially than 12 months ago is fairly stable until around the week of Sept. 30
to Oct. 6, 2008, where there is the beginning of a steep decline. This decline lasts
until around the start of the Christmas shopping season, where the proportion of
respondents who think they are better off begins to recover.
An examination of pre-event and postevent statistics for the same question across
three income levels in Table 1 below also reveals differences between the two
time windows.
Table 1 — Current financial situation better off
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
16.3%
12.1%
$40,000–$99,000
20.2%
14.8%
More than $100,000
26.6%
19.8%
Page 2 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
All three household income categories show drops in the percentage of respondents
who perceive they are better off now than 12 months ago between the two time
windows. Those individuals with higher income show somewhat larger downward
shifts in unweighted percentages.
Individuals have perceptions about their short-term future financial situations
as well. These perceptions of financial well-being also can play a part in the
consumption behaviors of individuals in the present. In Figure 2 below, the weekly
chart of the percentage of respondents who expect to be better off financially in the
next 12 months is displayed.
Figure 2 — Respondent expects to be better off 2 in the next 12 months
Unweighted percentage
45%
40%
35%
30%
25%
20%
15%
10%
Au
g.
12
A
Au ug –18
g. . 19
26
–S –25
ep
Se t. 1
p
Se t. 2–
8
p
Se t. 9–
1
pt
5
.
Se 16–
Se pt. 22
2
pt
. 3 3–2
0–
9
Oc
t
Oc . 6
t
Oc . 7–1
t. 1 3
O 4–
Oc ct. 20
t. 2 21–
8– 27
N
No ov. 3
v
No . 4–1
0
v.
No 11–1
7
No v.
1
v.
25 8–24
–D
ec
De . 1
c.
2–
8
5%
2Expects to personally be somewhat or significantly better off financially 12 months from now
Source: Simmons Wave 1608 preliminary unweighted daily data
Here it can be seen that despite the past bad economic news, the unweighted
percentage of individuals who feel they will be better off financially in the next
12 months climbs and plateaus until just before the event boundary. There is then a
downward trend that persists until shortly before the election, when expectations of
a better financial future again appear to spread to more individuals. This recovery
finally plateaus in mid-November and remains stable until the end of the field period.
Is there any difference in the changes in future financial fortunes for respondents
across different household income levels?
Table 2 — Future financial situation better off
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
31.4%
34.4%
$40,000–$99,000
36.1%
29.9%
More than $100,000
36.2%
35.5%
An Experian white paper | Page 3
The effects of an economic downturn — Series 1
Interestingly, Table 2 reveals that respondents in the lowest-household-income
level postevent were somewhat more likely to feel that they would be better off in
the next 12 months than in the pre-event period. Perhaps they were less affected by
the investment losses that occurred around that time, or they may have been more
swayed by the government’s assurances that steps were being taken to soften the
economic downturn. The highest-income group had a small drop in the percentage of
people who felt that they would be better off in the next 12 months than they were at
this time. What is most interesting is that those in the middle-income group had the
largest drop in the percentage who felt they would be better off financially in the next
12 months. Given this difference, the McCain and Obama presidential campaigns
may have had the right strategy in targeting middle-class Americans.
Finally, confidence in the American economy is also likely related to consumer
spending patterns. The most obvious possible pattern is that when consumers feel
that the economy is going to be better off in the next year, they may be more likely to
make purchases that they might otherwise postpone or not make at all.
Figure 3 — Respondent expects the U.S. economy will be better off 3
in the next 12 months
Unweighted percentage
45%
40%
35%
30%
25%
20%
15%
10%
Au
g.
12
A
Au ug –18
g. . 19
26
–S –25
ep
Se t. 1
p
Se t. 2–
pt 8
Se . 9–
pt 15
.
Se 16–
Se pt. 22
2
pt
. 3 3–2
0–
9
O
Oc ct. 6
t.
Oc 7–1
t. 1 3
O 4–2
Oc ct. 2 0
t. 2 1–2
8–
7
N
No ov. 3
v.
No 4–1
0
v.
No 11–1
7
No v.
18
v.
–
25
–D 24
ec
De . 1
c.
2–
8
5%
3Expects the U.S. economy to be somewhat or significantly better off in the next 12 months
Source: Simmons Wave 1608 preliminary unweighted daily data
Looking at Figure 3 above, we can see that an event like a presidential election can
have an effect on consumer perceptions of the economy, even if that effect is fleeting.
For instance, the percentage of respondents who feel that the U.S. economy will be
better in the next 12 months is relatively stable until just after the election, where
there is a steep upswing in optimism. This postelection halo effect lasts a couple
of weeks, then begins an equally steep decline and levels out at the end.
Table 3 below shows the pre-event and postevent percentages for how respondents
feel the U.S. economy will fare in the next 12 months.
Page 4 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
Table 3 — U.S. economy better off
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
21.6%
28.9%
$40,000–$99,000
20.8%
22.6%
More than $100,000
25.1%
25.9%
Notice that the postelection halo had the effect of increasing the likelihood that
respondents feel the U.S. economy will improve, with the largest increase associated
with the low-income group. This increase in the percentage of people who are
optimistic about the U.S. economy would certainly be puzzling given the economic
environment without the knowledge of the week-to-week graph that plainly reveals
the short-term postelection halo period.
Short-term big- and medium-ticket purchase intentions
Another interesting question is the effect of the economic crisis on the purchase
intentions of medium-ticket items such as an appliance or big-ticket items such
as an automobile or a television in the next 30 days. Economic uncertainty logically
would suggest that the likelihood of making a medium- or big-ticket purchase would
decline as the economic crisis deepened.
Figure 4 — Respondent likely to purchase big-ticket item in the next 30 days
Unweighted percentage
12%
10%
8%
6%
4%
2%
Au
g.
Au 12–1
Au
8
g
g. . 19
–2
26
–S 5
ep
Se t. 1
pt
Se . 2–8
p
Se t. 9–
pt 15
.
Se 16–
Se pt. 22
2
pt
. 3 3–2
0–
9
Oc
t
Oc . 6
t.
Oc 7–1
t. 1 3
Oc 4–2
0
Oc t.
t. 2 21–
8– 27
No
No v. 3
v.
No 4–1
0
v.
1
1
N
–
No ov. 17
18
v.
25 –24
–D
e
De c. 1
c.
2–
8
0%
Source: Simmons Wave 1608 preliminary unweighted daily data
In Figure 4 above, we see that expectations about a near-future purchase of a bigticket item were increasing in the weeks prior to the event boundary, in spite of
the obvious signs of economic slowdown before the meltdown. This increase is
likely being driven not by automotive sales, but rather by intentions to purchase
technology items such as large-screen televisions. It is clear from the chart that once
An Experian white paper | Page 5
The effects of an economic downturn — Series 1
the economic crisis came to a head, people’s expectations about a future big-ticket
purchase dropped dramatically for about four weeks. Then purchase intentions
began moving upward again, likely spurred again by electronic technology products.
The combination of the postpresidential election halo effect and the approaching
holiday sales season may have contributed to the postevent uptick.
Table 4 — Big-ticket purchase intentions
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
6.0%
4.9%
$40,000–$99,000
8.1%
5.6%
More than $100,000
11.2%
9.4%
When looking at the same question across income strata, as seen in Table 4,
all three income groups have pre-event to postevent drops in the likelihood of
purchasing a big-ticket item, with the middle-income group having the largest drop
of almost 31 percent in relative terms. This data corresponds to the media reports
of automakers and retailers suffering downturns in the sales of big-ticket items,
even in the face of the approaching holiday buying season. A somewhat different
picture appears for medium-ticket items, as can be seen in Figure 5 below. Purchase
intentions start out losing some ground in the beginning of the time period and then
recover for several weeks before again taking a four-week plunge that crosses the
event boundary and is likely associated with the emerging economic crisis.
Figure 5 — Respondent likely to purchase medium-ticket item in the next 30 days
Unweighted percentage
19%
17%
15%
13%
11%
9%
7%
Au
g.
Au 12–1
Au
8
g
g. . 19
–2
26
–S 5
ep
Se t. 1
p
Se t. 2–
pt 8
Se . 9–
pt 15
.
Se 16–
Se pt. 22
2
pt
. 3 3–29
0–
Oc
Oc t. 6
t.
Oc 7–1
t. 1 3
Oc 4–2
Oc t. 2 0
t. 2 1–2
8–
7
No
No v. 3
v.
No 4–1
0
v.
1
No 1–1
7
No v.
18
v.
–
25
–D 24
ec
De . 1
c.
2–
8
5%
Source: Simmons Wave 1608 preliminary unweighted daily data
Page 6 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
Medium-ticket purchase intentions begin a several-week recovery just prior to the
presidential election then plateau for three weeks. Finally, near the end of the field
period, the incidence of purchase intentions begins a steep recovery, likely due to
early holiday shopping.
Table 5 — Medium-ticket purchase intentions
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
10.0%
9.8%
$40,000–$99,000
9.7%
10.7%
More than $100,000
19.4%
15.3%
As can be seen in Table 5, the low-income group was nearly stable pre-event and
postevent, while the middle-income group actually showed an increase in mediumticket purchases, perhaps due to a surge in holiday shopping. The high-income group
showed a dramatic decline in medium-ticket items, in spite of the holiday season,
which may be a reaction to losses in the investment markets.
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The effects of an economic downturn — Series 1
Discount retailers
Discount retailers have a lot at stake in the current turbulent economy. There is a
significant amount of uncertainty about the magnitude of the effect of the economic
downturn on the shopping and purchasing patterns of traditional moderate- to
modest-household-income individuals. In addition, while large discount stores
already do have a nontrivial share of higher-income shoppers, there are reports being
circulated in the media that these discount stores are seeing additional affluent
shoppers visiting their stores. Figure 1 below reveals the weekly signatures for
higher-household-income consumers shopping at two major discount stores.
Figure 1 — Discount retailer A versus discount retailer B where respondent
shopped during last three months (household income of $75,000+)
65%
Percentage
55%
45%
Shop at discount
retailer A (household
income $75,000+)
35%
25%
Shop at discount
retailer B (household
income $75,000+)
15%
Au
g.
A
Au u 12–1
g. g. 1 8
26 9–
–S 25
e
Se pt.
1
p
Se t. 2
–
p
8
Se t. 9
pt –1
Se . 16– 5
Se p
2
pt t. 2 2
. 3 3–
0– 29
O
Oc ct.
t. 7 6
Oc –1
3
t
O .1
Oc ct. 4–20
t. 2 21–
8– 27
N
No ov.
3
v
No . 4–
v. 10
1
No Nov 1–17
.
v.
25 18–2
–D 4
e
De c. 1
c.
2–
8
5%
Source: Simmons Wave 1608 preliminary unweighted daily data
The data reveals that both retailers appear to have gained a modest number of
higher-income shoppers within the time period. Note that especially for retailer
A, there is an increase in the incidence of these shoppers around the time of the
economic event boundary. This would appear to support anecdotal reports in the
media of more-affluent shoppers adjusting their shopping patterns to embrace more
discounts and lower prices.
A finer-resolution picture can be obtained by looking at three household income
group levels and aggregating the data around the economic event boundary. Tables 1
and 2 show the results of that effort.
Table 1 — Retailer A (shopped last three months)
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
46.8%
47.1%
$40,000–$99,000
56.1%
53.3%
More than $100,000
44.5%
48.2%
Page 8 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
Table 2 — Retailer B (shopped last three months)
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
22.5%
25.8%
$40,000–$99,000
37.0%
37.6%
More than $100,000
41.6%
44.6%
There are probably several factors involved in the mix here. The pre-event to
postevent increase for the lowest-income group is likely due to the upcoming
holiday shopping season. These individuals normally shop at these stores, so as the
holidays approach, there is an increase in likelihood of shopping in retailer A and
retailer B stores for this income group. The middle-income group is mixed in terms
of incidence change for shopping at these two stores, which suggests there may be
some differences between the stores for these shoppers.
The high-household-income group shows marked increases across the economic
event boundary, suggesting that more of these higher-income people are shopping at
these discount stores, which corroborates some of the media reports about shifts in
shopper profiles for discount stores.
It also would be useful to see if there are associated attitudinal changes with these
shifts. In Figure 2 below, one can see the weekly incidence rate for those who agree
(top two boxes) with the statement “I am drawn to stores I normally don’t shop at by
sales” across the two income breaks.
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
Household
income more
than $75,000
Household
income less
than $75,000
Au
g.
Au Aug 12–1
g. . 1 8
26 9–
–S 25
Se ept.
1
Se pt. 2
pt –8
Se . 9
p –1
Se t. 16 5
Se pt –2
pt . 2 2
. 3 3–
0– 29
O
Oc ct.
6
Oc t. 7–
1
t. 1 3
Oc Oct 4–20
t. 2 . 21
8– –27
N
No ov.
3
No v. 4–
v. 10
No Nov 11–1
v. . 18 7
25 –
–D 24
De ec. 1
c.
2–
8
Unweighted percentage
Figure 2 — I am drawn to stores I normally don’t shop at by sales
(Top two boxes agree)
Source: Simmons Wave 1608 preliminary unweighted daily data
An Experian white paper | Page 9
The effects of an economic downturn — Series 1
Notice that the incidence rate for the lower-household-income group is relatively
flat across the event boundary. The higher-household-income group shows a slight
decrease as it crosses the event boundary. Also notice the bump in incidence for the
lower-income group around the time of the presidential election.
Splitting the respondents into finer household-income groups and aggregating
them across the event boundary produces a clearer picture. In Table 3 below, it can
be seen that the two lower-income groups have declines in going to different stores
for sales after the event boundary, suggesting that perhaps economic factors are
constraining their shopping trade area. Notice that the highest-income group shows
an increase across the event boundary for shopping at different stores for sales.
This data suggests that higher-income shoppers are being drawn to stores they
otherwise would not shop at because of sales and promotions. The economic crisis
appears to be changing shopping attitudes a bit among higher-income shoppers and
encouraging them to enlarge their repertoire of stores.
Table 3 — Drawn to different stores normally don’t shop at by sales
(Top two boxes agree)
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
38.6%
37.4%
$40,000–$99,000
36.1%
35.2%
More than $100,000
32.0%
33.4%
The idea that the economic downturn is constraining the shopping mobility of lowerincome groups and encouraging higher-income groups to enlarge their repertoire of
stores also can be found in Table 4 below.
Table 4 — For relatively expensive items, I’ll shop at different
stores to make certain I get the best price
(Top two boxes agree)
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
64.7%
63.9%
$40,000–$99,000
69.5%
72.3%
More than $100,000
72.4%
73.1%
Page 10 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
Notice that the incidence rate for the lowest-household-income group declines
slightly while it edges higher for both the middle- and high-income groups. This data
provides additional evidence that differential changes in shopping attitudes may be
at least partially responsible for changes in shopping repertoires.
Department stores — an example
It appears that the economic downturn has made consumers much more pricesensitive. What has this meant for the consumer attitudes prevailing in shoppers in
upscale department stores? One attitude that upscale department stores depend
upon is that their customers are willing to pay extra for quality merchandise or
services. Does the prevalence of this attitude change during tough economic times?
Figure 3 provides a weekly signature for this particular consumer attitude.
75%
73%
71%
69%
67%
65%
63%
61%
59%
57%
55%
Worth paying
extra
Au
g.
Au Au 12–1
g. g. 1 8
26 9–
–S 25
e
Se pt. 1
Se pt. 2
p –
Se t. 9– 8
p
15
S t.
Se ep 16–2
pt t. 2 2
. 3 3–
0– 29
O
Oc ct.
6
Oc t. 7–
1
t.
3
O 14
Oc ct. –20
t. 2 21–
8– 27
N
No ov.
v 3
No . 4–
v. 10
1
No Nov 1–17
.
v.
25 18–2
–D 4
e
De c. 1
c.
2–
8
Unweighted percentage
Figure 3 — It’s worth paying extra for quality goods
(Top two boxes agree)
Source: Simmons Wave 1608 preliminary unweighted daily data
Note that the incidence rate is fairly flat until just about the start of the economic
event boundary, where there then occurs a four-week steep decline in the number of
people who agree with this statement. Once this shock period is over, the incidence
of respondents agreeing with this statement begins to climb again.
What does this mean for a midscale department store? Let’s examine the incidence
of shopping at midscale retailer C in the last three months. Figure 4 below has the
week-by-week signature for this variable.
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The effects of an economic downturn — Series 1
30%
X
25%
X
20%
15%
X
X
X
X
X X
X
X
X
X X
X
X
X
X
X
Shop at
midscale
retailer C
in the last
3 months
10%
5%
Au
g.
Au Au 12–1
g. g. 1 8
26 9–
–S 25
e
Se pt.
1
p
Se t. 2
–
Se pt. 9 8
p –1
Se t. 16 5
Se p –2
pt t. 2 2
. 3 3–
0– 29
O
Oc ct.
6
t
Oc . 7–
1
t. 1 3
Oc Oct 4–20
t. 2 . 21
8– –27
N
No ov.
3
v
No . 4–
v. 10
1
No Nov 1–1
v. . 18 7
25 –2
–D 4
De ec. 1
c.
2–
8
Unweighted percentage
Figure 4 — Shopped at midscale retailer C during the last three months
Source: Simmons Wave 1608 preliminary unweighted daily data
Looking at the incidence signature, it is clear that starting from the economic
event boundary, there is a consistent increase in the incidence of respondents who
shopped at midscale retailer C. Why does this particular store buck the retail trend?
It is likely because midscale retailer C initiated a strategy that focused on sales
and price reduction promotions to counteract the worsening economic conditions.
While midscale retailer C still struggled in terms of sales compared with a year ago,
it actually emerged as one of the top-performing major retailers this year, having lost
fewer dollars than many of its competitors. If midscale retailer C had marketed a
more traditional upscale department store message such as the previous one —
i.e., “It’s worth paying extra for quality goods” — the outcome could have been
quite different.
Are there other shopping patterns that may be changing in relation to economic or
other national events? One area of interest is impulse shopping. What effect has
the economic downturn had on purchasing items “on the spur of the moment”? As
can be seen in Figure 5 below, purchasing items on the spur of the moment starts a
small but noticeable decline around the time of the economic event boundary until
just around the time of the presidential election, where it displays a sharp spike
and subsequent gradual decline again. This data suggests again that events of a
national scale, such as presidential elections, may have nontrivial effects on specific
shopping behaviors of the American consumer.
Page 12 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
40%
38%
36%
34%
32%
30%
28%
26%
24%
22%
20%
Au
g.
12
Au Aug –18
g. . 19
26
–S –25
ep
Se t. 1
pt
Se . 2–8
p
Se t. 9–
pt 15
.
Se 16–
p
Se
t 22
pt . 23–
.3
0– 29
O
Oc ct. 6
t.
Oc 7–1
t. 1 3
Oc 4–20
Oc t.
t. 2 21–
8– 27
No
No v. 3
v.
No 4–1
0
v.
No 11–1
7
No v.
1
v.
25 8–24
–D
ec
De . 1
c.
2–
8
Unweighted percentage
Figure 5 — When in the store, I often buy an item on the spur of the moment
(Top two boxes agree)
Source: Simmons Wave 1608 preliminary unweighted daily data
A breakout of the data pre-event and postevent by income reveals some differential
results by income level. In Table 5, it can be seen that the low-income group suffers
the largest decrease in agreeing that they purchase items on the spur of the moment
across the event boundary.
Table 5 — When in a store, I often buy an item on the spur of the moment
(Top two boxes agree)
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
30.2%
27.2%
$40,000–$99,000
27.9%
27.1%
More than $100,000
28.4%
31.3%
The middle-income group shows only a slight decrease in agreement with the
statement. The high-income group actually sees some increase in spur-of-themoment purchases from pre-event to postevent. This data supports anecdotal
evidence in the marketplace that some affluent shoppers are making impulse
purchases. This phenomenon is perhaps related to some type of compensation
mechanism or behavior in relation to losses in the financial marketplace experienced
by higher-income individuals.
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The effects of an economic downturn — Series 1
Fast-food, family restaurants and food attitudes
Sudden economic change can have far-reaching effects on consumer behavior.
Food is one American household expense where family members can shift
significant dollars between dining out and cooking at home. Fast-food outlets serve
the consumer as a place where they can purchase an inexpensive meal. It is likely
that tough economic times will have an effect on the distribution of food dollars for
the household.
As can be seen in Figure 1, those in the lower-income group pursue a relatively
random walk around a reasonably stable base line throughout the time period.
65%
60%
55%
Household
income more
than $75,000
50%
45%
Household
income less
than $75,000
40%
35%
30%
Au
g.
Au Au 12–1
g. g. 1 8
26 9–
–S 25
e
Se pt. 1
pt
Se . 2–
p
Se t. 9 8
pt –15
S .1
Se ept 6–2
pt . 23 2
.3
0– –29
O
Oc ct.
t. 7 6
Oc –1
3
t.
Oc 14–
2
Oc t.
0
t. 2 21–
8– 27
N
No ov.
v. 3
No 4–
v 10
No . 11–
No v. 17
1
v.
25 8–2
–D 4
De ec. 1
c.
2–
8
Unweighted percentage
Figure 1 — Visited fast-food outlet A in the last 30 days (by income breaks)
Source: Simmons Wave 1608 preliminary unweighted daily data
What is interesting is the higher-income group. Ignoring the initial spike, the higherincome group has a steadily increasing incidence rate until just around the election,
where it takes a quick dip before continuing its climb. This data suggests that the
economic meltdown has not dramatically changed lower-income fast-food visit
patterns. It also suggests that more-affluent consumers may be shifting some
of their food dollars from more upscale sit-down restaurants to fast-food outlets.
Table 1 — Visiting fast-food outlet A in the last 30 days
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
47.8%
46.5%
$40,000–$99,000
53.5%
55.5%
More than $100,000
50.1%
57.3%
Page 14 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
When you examine this trend with a little more resolution at the household income
level and divide the temporal window into pre-event and postevent periods, it appears
that the low-income group incidence rate at fast-food outlet A has dropped just
slightly, while the middle-income group has a modest increase and the uppermost
income group has a reasonably large pre-event to postevent increase.
Why might this be the case for more-affluent consumers? Besides the obvious
economic explanations, is this shift in fast-food behaviors by higher-income
individuals perhaps the result of some attitudinal shifts toward food and eating
patterns? An examination of the weekly signature for two important food indicators
may shed some light on this situation. Figure 2 below shows the response to two food
attitudes for adults in households with household incomes of $75,000 or greater.
40%
35%
30%
I try to eat
gourmet food
whenever I can.
25%
20%
15%
I like the trend
toward healthier
fast-foods.
10%
5%
0%
Au
g.
Au Aug 12–1
g. . 1 8
26 9–2
–S 5
e
Se pt.
1
p
Se t. 2
–
p
8
Se t. 9
p –1
S t. 1 5
Se ep 6–2
pt t. 2 2
. 3 3–
0– 29
O
Oc ct.
6
Oc t. 7–
1
t.
3
O 14
Oc ct. –20
t. 2 21–
8– 27
N
No ov.
3
v
No . 4–
v. 10
1
No Nov 1–17
v. . 18
25 –2
–D 4
De ec. 1
c.
2–
8
Unweighted percentage
Figure 2 — Gourmet food versus fast-food among higher-income adults
Agree a lot (top box) with statement
Source: Simmons Wave 1608 preliminary unweighted daily data
The first noticeable trend is that there is an overall modest increase in the incidence
of respondents across the weeks who agree a lot with the statement “I like the trend
toward healthier fast food.” During the same time period, there is a much more
pronounced decrease in the percentage of these higher-income individuals who
agree a lot with the statement “I try to eat gourmet food whenever I can.” The first
trend may be just a reflection of the gentle market movement toward more healthconscious eating behaviors and may not be related to the tough economic times.
The trend that measures gourmet food consumption is more likely to be the result
of the economic downturn. That is, gourmet food is generally almost always
significantly more expensive than nongourmet types of food — and, in reaction to
the economic crisis, people may be rethinking their attitudes toward these moreexpensive foods.
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The effects of an economic downturn — Series 1
Are all fast-food outlets benefiting from this shift in the attitudes of more-affluent
consumers? This next fast-food purveyor shown in Figure 3 is the most direct
competitor to fast food outlet A.
Figure 3 — Visited fast-food outlet B in the past 30 days (by income breaks)
45%
40%
35%
30%
Household
income more
than $75,000
25%
Household
income less
than $75,000
20%
15%
10%
Au
g.
A
Au u 12–
g
g. . 1 18
26 9–
–S 25
e
Se pt.
1
p
Se t. 2
–
p
Se t. 9 8
pt –15
S .1
Se ep 6–2
pt t. 2 2
. 3 3–
0– 29
O
Oc ct.
6
Oc t. 7–
t. 1 13
Oc Oct 4–20
t. 2 . 21
8– –27
N
No ov.
v 3
No . 4–
v. 10
1
No Nov 1–1
.
7
v.
25 18–2
–D 4
De ec. 1
c.
2–
8
Unweighted percentage
50%
Source: Simmons Wave 1608 preliminary unweighted daily data
Note that the more than $75,000 group has a noticeable upward tick. The less-affluent
group in the chart appears to have a flat or slightly downward trend line. What
happens when we redraw the analysis into more discrete income groups and look for
a pre-event and postevent change? Table 2 below reveals a somewhat similar pattern,
with the exception that the middle-income group shows a slight decline in visits
similar to the lowest-income group. The most-affluent group with more than $100,000
in household income shows an increase in visits similar to fast-food outlet A.
Table 2 — Visiting fast-food outlet B in the last 30 days
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
31.2%
30.1%
$40,000–$99,000
33.3%
31.9%
More than $100,000
25.7%
31.3%
Page 16 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
Fast-food outlets A and B both are known for serving hamburgers as their main
fare. What happens when we change the “main attraction” to chicken? Figure
4 below shows the week-by-week signature for major fast-food outlet C, which
features chicken.
Figure 4 — Visited fast-food outlet C in the past 30 days (by income breaks)
Percentage
(Base line Aug. 12–18, 2008)
30%
25%
20%
Household
income more
than $75,000
15%
10%
Household
income less
than $75,000
5%
Au
g.
Au Au 12–1
g. g. 1 8
26 9–
–S 25
e
Se pt. 1
Se pt. 2
p –
Se t. 9 8
pt –15
Se . 16–
Se p
2
pt t. 2 2
. 3 3–
0– 29
O
Oc ct.
6
Oc t. 7–
t. 13
O 14
Oc ct. –20
t. 2 21–
8– 27
N
No ov.
v 3
No . 4–
v. 10
1
No Nov 1–17
.
v.
25 18–2
–D 4
e
De c. 1
c.
2–
8
0%
Source: Simmons Wave 1608 preliminary unweighted daily data
Note that the weekly trend signatures in Figure 4 for both income groups look fairly
flat, ignoring week-to-week sample variance noise. If we split the income groups
further and look for a pre-event and postevent effect, as shown in Table 3 below, it
can be seen that there is a slight decline in visits in the lowest-income group across
the event boundary, a nearly flat pre-event and postevent result for the middle-income
group and a modest decline for the highest-income group.
Table 3: Visiting fast-food outlet C in the last 30 days
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
19.8%
18.9%
$40,000–$99,000
20.0%
20.4%
More than $100,000
17.8%
15.7%
An Experian white paper | Page 17
The effects of an economic downturn — Series 1
In contrast to fast-food outlets, how are family restaurants doing in the economic
downturn? Figure 5 below shows the week-by-week signature for a national family
restaurant chain.
35%
30%
25%
Household
income more
than $75,000
20%
15%
Household
income less
than $75,000
10%
5%
Au
g.
Au Au 12–1
g. g. 1 8
26 9–
–S 25
e
Se pt.
1
p
Se t. 2
p –
Se t. 9 8
pt –15
S .1
Se ept 6–2
pt . 23 2
.3
0– –29
O
Oc ct.
6
Oc t. 7–
t. 1 13
Oc Oct 4–20
t. 2 . 21
8– –27
N
No ov.
3
No v. 4–
v. 10
1
No Nov 1–17
v. . 18
25 –2
–D 4
e
De c. 1
c.
2–
8
Unweighted percentage
Figure 5 — Visited family restaurant A in the past 30 days (by income breaks)
Source: Simmons Wave 1608 preliminary unweighted daily data
Note here that for both income groups, visits to the family restaurant appear to
decline slightly, especially after the economic event boundary. Unlike the fast-food
outlets previously examined, this family restaurant chain appears to show declines
for both income groups.
This trend line is further confirmed by looking at a finer-resolution income break
pre-event and postevent. Table 4 below shows a decline in the lowest-income
group — a pattern similar to that of the fast-food outlet. The middle-income group
trend line is basically flat, and there is a modest decline in the highest-income group,
unlike the fast food examples.
Table 4 — Visiting family restaurant A in the last 30 days
Household
income
Pre-event
unweighted %
Postevent
unweighted %
Less than $40,000
17.3%
13.4%
$40,000–$99,000
22.6%
22.4%
More than $100,000
25.6%
22.4%
One thing that distinguishes family restaurants from fast-food outlets is that
while fast-food outlets have similar pricing structures, family restaurants exhibit
significantly more variation in pricing and quality. This variation may be confounding
our analysis a bit. Let’s examine the pre-event to postevent data on visits for four
other family restaurants (B through E). Ordering these restaurants by historical
median income levels for diners taken from an earlier, pre-event meltdown Simmons
National Consumer Study, we have Table 5 below for our most-affluent dining group:
Page 18 | The effects of an economic downturn on consumer attitudes and behaviors
The effects of an economic downturn — Series 1
Table 5 — Visiting family restaurants B through E in the last 30 days
Family restaurant
Historical median
household income
(000s) for visitor
Pre-event and postevent
delta for household
income more than
$100,000 (unweighted %)
B
105
-0.4%
C
82
-0.6%
D
76
+1.3%
E
61
+3.2%
This pattern is one that we have observed to some extent in the retail sector as
well. As one progresses away from upscale establishments and toward more
downscale establishments, there is an incremental increase in visits by moreaffluent consumers in the postevent. More and more consumers begin to aggregate
at merchant levels as you progress down the product/service price continuum. This
is reminiscent of the physical-world phenomenon of scree behavior, where large
boulders roll down a mountain and form a scree field of rocks and boulders that gets
deeper and deeper as one progresses downward.
In summary, it is clear that the economic downturn is having effects — both good and
bad — on dining establishments. There is also evidence that the economic downturn
is changing opinions and attitudes about food. Finally, there is some evidence
that more-affluent consumers are beginning to aggregate at less-upscale dining
establishments, a trend that mirrors some of the movement of affluent customers
that we have seen in the retail sector.
For any questions, please contact Ellen Romer, Senior Vice President of
Strategic Planning and Marketing, at [email protected] or 1 212 471 2870.
The date for the economic event boundary (i.e., “the economic meltdown”) as indicated
by the blue (“pre-event”) and yellow (“postevent”) segments was determined by a
combination of the Dow Jones Industrial Average for the period, a timeline of selected
significant financial events and several proprietary economic indicators.
Data is sourced directly from a national probability sample. Because the objective
of these reports is to provide information as quickly as possible (i.e., “just-in-time
data”), the data has not been fully projected to the U.S. population. However, the
sampling strategy utilizes sample replicate techniques designed for continuous
measurement. This means that while the absolute percentages in the charts and
tables should be interpreted with caution because they are not weighted, they are
indices suitable for tracking changes in behavior across time. The relative percentage
changes across the economic event boundary as well as the week-to-week changes
upon which the narrative is based can be interpreted as indicators of change with
reasonable confidence. A full description of the nature of the “just-in-time” data can
be found at http://smrb.com/aspx/content.aspx?pid=5&sid=258&page=Methodolog
y_Methodology_-_Just-in-time.
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