Michael Munley

William A. Strauss

English

Research Papers in Economics

Chicago Fed Letter
Analyzing an economic indicator: The Michigan Retailers Survey
by Michael Munley, associate economist, and William A. Strauss, senior economist and economic advisor
While national retail sales information is readily available, it is difficult to obtain state
or regional data. To address this need for local information, the Michigan Retailers
Association and the Federal Reserve Bank of Chicago began a monthly survey of retailers
in July 1994. The survey compares sales, inventories, prices, promotions, and hiring plans
versus the prior year and analyzes retailers’ expectations. Seasonally adjusting the
index will make it an even better tool for researchers and business analysts in the future.
SPECIAL ISSUE THE FEDERAL RESERVE BANK APRIL 2002
OF CHICAGO NUMBER 176a
Given that consumer spending repre-
sents two-thirds of the U.S. economy,
understanding retailing is an important
part of analyzing overall economic ac-
tivity. While retail sales information is
readily available nationally, it is more
difficult to obtain on a state-by-state or
regional basis. The Census
Bureau published monthly
retail sales data for large
states from the mid-1980s
to the mid-1990s but then
discontinued the series.
In an effort to better un-
derstand retail activity in
Michigan, the Michigan
Retailers Association and
the Federal Reserve Bank
of Chicago began a month-
ly survey of retailers in July
1994.1 This article discuss-
es the survey, how seasonal
adjustment improves the
results, and how the results
compare with published
economic data.
The Michigan Retailers (MR) Survey is
mailed to members of the Michigan Re-
tailers Association who do business in
Michigan—around 3,900 firms—in the
fourth week of each month. The dead-
line for responses is usually during the
second week of the following month.
The response rate averages 7%, in line
with the typical response rate for mail
surveys. The respondents appear to rep-
resent a geographic sampling of many
different firms throughout the state,
while the sampling across retail sectors
is diverse but not entirely representa-
tive of their distribution in Michigan.
The survey covers five areas: sales, in-
ventories, prices, promotions, and hiring
plans. Respondents indicate whether,
based on their own business a year ago,
the current month’s experience or their
expectations for the next three months
represent an increase, decrease, or no
change. The results are then calculated
into a diffusion index, which is the sum
of the percentage indicating an increase
plus half the percentage indicating no
change, assumed to be positive. The
Michigan Retailers Association publish-
es the results on the fourth Wednesday
of the following month (i.e., the January
data are released on the fourth Wednes-
day in February). The results provide
insights into the Michigan retailing en-
vironment. However, a persistent sea-
sonal pattern in the raw data has made
month-to-month comparisons difficult.
Seasonal adjustment
It is well known that retailers experience
significant seasonal effects in their
1. Inventories index
Chicago
City
net percent reporting increase
SOURCES: Michigan Retailers Association and the Federal Reserve Bank
of Chicago.
35
45
55
65
75
1994 '95 '96 '97 '98 '99 '00 '01
seasonally
adjusted
not seasonally
adjustedbusiness. While we ask respondents to
keep these seasonal effects in mind—
by asking them to compare their current
activity with a year ago—there is still a
significant seasonal pattern in the sur-
vey results. Every index we develop from
the survey, except for sales in the cur-
rent month,2 tests positive for a signifi-
cant seasonal pattern.
Generating a seasonally adjusted series
requires many observations of the data.
Now that we have more than seven years
worth of data, we can use the X-11 sea-
sonal adjustment procedure developed
by the Census Bureau to eliminate the
seasonal patterns in the survey data.
We began releasing seasonally adjust-
ed data with the January 2002 survey re-
sults.3 This change allows researchers to
make better use of the survey.
To illustrate the benefits of seasonal ad-
justment, figure 1 shows the unadjusted
(black line) and seasonally adjusted
(red line) inventory index. Before the
adjustment, the data show a substantial
build-up of inventories during the sum-
mer and early fall and a significant
downsizing at the start of the year. This
pattern is due to businesses stocking
up for the holiday selling period. It is
not unusual for inventories to rise dur-
ing the summer and early fall; what we
are really interested in is whether inven-
tories are rising more or less than usual
for this time of year. Seasonally adjust-
ing the data provides an answer to this
question. For example, the seasonally
adjusted data reveal that during fall
2000 inventories were not increasing
as rapidly as one would have thought.
How do the indexes perform?
Using survey data as an indicator of
economic activity is not new. A popular
survey, and an inspiration for our work,
is the Institute for Supply Management’s
(ISM) monthly manufacturing Purchas-
ing Managers Survey. The ISM, former-
ly the National Association of Purchasing
Management (NAPM), has been gath-
ering information in one form or an-
other since 1930 on various aspects of
manufacturing, including new orders,
production, prices, employment, and
inventories.
There is a significant amount
of literature about the use-
fulness of the ISM indexes
for economic analysis and
forecasting.4 Much of the
research focuses on how well
the indexes relate to pub-
lished data. For example,
Robert Bretz, former chair-
man of the Business Survey
Committee of NAPM, re-
ports that there is a strong
correlation (an R2 of .71)
between the quarterly aver-
age of the ISM production
index and the quarterly
growth rates of the Federal
Reserve Board’s Industrial
Production Index. Addi-
tional research has shown that when
the ISM production index falls below
49.5, generally industrial production
will be declining.5 Additionally, much
of the research reports that because of
the nature of the ISM indexes—they
are also diffusion indexes—they tend
to lead turning points in comparable
economic data.
The popularity and applicability of the
ISM indexes raises two questions for
our survey. First, how well does the MR
survey compare with published eco-
nomic data? Second, does our survey
have any leading indicator properties?
There are three main problems in as-
sessing the performance of our survey.
The first problem is the dearth of ap-
propriate data to measure against the
survey. Second, monthly data for both
our survey indexes and the published
data that we are using for comparison
tend to be very volatile. To get a good
picture of how well our index performs,
we should use quarterly, or even annual
data, as was done for much of the ISM
analysis. However, this would mean
basing our analysis on an even smaller
number of observations. Instead, we
have made efforts to smooth out the
volatility wherever appropriate. The
third problem we run into is that the
ISM survey sample reflects the distribu-
tion of industries in the manufacturing
sector, whereas our survey sample is
largely random, which might skew our
results. Below, we look at how four of
our five survey indexes measure against
comparable published data. In the case
of inventories, we have no comparable
regional data, so we measure our index
against national data. We have no com-
parable regional or national data for the
promotions index.
Sales
An obvious choice of comparison for our
sales data is the retail sales data published
by the Census Bureau. Unfortunately,
the Census Bureau stopped publishing
monthly sales data after 1996, so we have
only 30 observations to measure our sur-
vey against. The correlation coefficient6
of our monthly sales survey against the
Census Bureau’s data on Michigan’s
monthly total retail sales is 0.53. One of
the problems of this comparison, how-
ever, is that the Census Bureau’s data in-
clude sales of motor vehicles—a very
volatile portion of retail activity—and our
sample contains few, if any, auto deal-
ers.7 The Census Bureau does not pro-
vide motor vehicle retail sales data on
a monthly basis at the state level, but it
does break out retail sales of nondurable
and durable goods. When we compare
the MR sales survey against nondurable
good retail sales, we get a better corre-
lation coefficient of 0.64. Neither the
sales index nor the expected sales in-
dex of the MR survey has any leading
indicator properties for these data.
Another option for data comparison is
monthly sales tax revenue, but these
data can be somewhat problematic.
2. Sales index vs. real retail gross state product
Chicago
City
annual weighted average
SOURCES: Sales index—Michigan Retailers Association and Federal
Reserve Bank of Chicago; gross state product—Bureau of Economic
Analysis.
1994 ’95 ’96 ’97 ’98 ’99 ’00 ’01
40
50
60
70
0
5
10
15
Sales
(left)
Real retail gross
state product
(right)
percent changeThe timing of sales tax payments may
not correspond to the exact volume of
sales. To overcome this challenge, we
compared a three-month moving aver-
age of sales tax revenue with a three-
month moving average of the MR sales
index. The correlation coefficient was
.43, and there was a very slight tendency
for the MR sales index to lead the sales
tax data. The correlation coefficient
between the expected sales index and
the sales tax revenue was .49, and the
leading tendency of the expected sales
index is also very slight—a somewhat
disappointing conclusion since we would
hope that the expected sales index
would have strong leading indicator
properties. But, similar to the problem
with the retail sales data, the sales tax
revenue data also include motor vehicle
sales tax revenue.
We also plotted our sales survey against
real retail trade gross state product,
which is available on an annual basis.
To get an annual figure for our sales
index, we took a weighted average of our
sales index in each year; the weights were
each month’s historical share of annu-
al retail sales. We found that in five of
the six years for which we have data,
the MR sales index moves in the same
direction as the growth rate of retail
gross state product (see figure 2).
Prices
Comparing the MR price indexes with
various inflation measures exhibits the
best correlations, but curiously they
are not where we expected. We looked
at the price and expected price index
against the Midwest and Detroit Con-
sumer Price Indexes (CPI). We elimi-
nated food and energy prices, which
tend to be more volatile than other
prices, from our analysis by using the
so-called core price index. Additionally,
we looked at total and nondurable com-
modity prices—in order to exclude
services prices—both including and
excluding food and beverage.
While there were significant correlations
looking at unsmoothed data, the cor-
relation was even stronger when we used
three-month moving averages. The cor-
relation coefficient between the Midwest
core price index and the MR price in-
dex was .68, and there was some ten-
dency for the MR price index to lead
changes in the Midwest core inflation
rate. There was an even stronger ten-
dency for the expected price index to
lead changes in inflation: The coeffi-
cient for a three-month lead in the ex-
pected price index was .80.
However, the correlations with the
Detroit CPI were not as strong as the
correlations for the Midwest inflation
rates. When we compared various
Detroit inflation rates—for the core,
commodities, and commodities exclud-
ing food and beverage—with both the
price and expected price indexes, only
one comparison yielded a correlation
coefficient better than .50, and there
were no leading indicator properties.
Smoothing the data im-
proved the correlation
coefficients but they were
still not as high as those
for the Midwest inflation
rates. We had expected that
the inflation measure more
closely related to our sur-
vey sample would result in
higher correlations. One
reason for this might be
that the Detroit CPI data
are only available every
other month, which de-
creases the number of ob-
servations and raises issues
about the best way to ag-
gregate the survey indexes
into bimonthly data—whether to take
a two-month average or to use every
other month.
Employment
The final index for which we have com-
parable regional data is the hiring plans
index. The Bureau of Labor Statistics
releases monthly data on industry pay-
roll employment for each state. For
Michigan, we looked at total retail trade
employment. We smoothed the survey
index by taking a three-month moving
average and compared it with the un-
smoothed employment data. Still, the
correlation coefficient for the hiring
plans index was only .39, and there were
no leading tendencies associated with it.
The correlation for the expected hiring
plans also was not very significant, but
there was a strong tendency for the ex-
pected hiring plans index to lead em-
ployment. The correlation coefficient
for the expected hiring plans index in
the third month before the employment
data was .51.
Though the level of the correlation is
not enormous, there is probably a sim-
ple explanation for the deficiency. The
survey asks respondents about hiring
plans not employment levels. It could
be that when respondents increase their
hiring plans, they are unable to fill the
job because of tight labor markets.
We have some evidence to back up
this conclusion. As figure 3 shows, the
Michael H. Moskow, President; William C. Hunter,
Senior Vice President and Director of Research;  Douglas
Evanoff, Vice President, financial studies; Charles
Evans, Vice President, macroeconomic policy research;
Daniel Sullivan, Vice President, microeconomic policy
research;  William Testa, Vice President, regional
programs and Economics Editor; Helen O’D. Koshy,
Editor; Kathryn Moran, Associate Editor.
Chicago Fed Letter is published monthly by the
Research Department of the Federal Reserve
Bank of Chicago. The views expressed are the
authors’ and are not necessarily those of the
Federal Reserve Bank of Chicago or the Federal
Reserve System. Articles may be reprinted if the
source is credited and the Research Department
is provided with copies of the reprints.
Chicago Fed Letter is available without charge from
the Public Information Center, Federal Reserve
Bank of Chicago, P.O. Box 834, Chicago, Illinois
60690-0834, tel. 312-322-5111 or fax 312-322-5515.
Chicago Fed Letter and other Bank publications
are available on the World Wide Web at http://
www.chicagofed.org.
ISSN 0895-0164
3. Hiring plans index vs. retail employment growth
46
48
50
52
54
56
58
-6
-4
-2
0
2
4
6
1994 '95 '96 '97 '98 '99 '00 '01
three-month moving average percent change
Employment
(right)
Hiring plans
(left)
SOURCES: Hiring plans index—Michigan Retailers Association and Federal
Reserve Bank of Chicago; employment data—Bureau of Labor Statistics.P
R
E
S
O
R
T
E
D
F
I
R
S
T
 
C
L
A
S
S
 
M
A
I
L
U
S
 
P
O
S
T
A
G
E
 
P
A
I
D
C
H
I
C
A
G
O
,
 
I
L
P
E
R
M
I
T
 
1
9
4
2
P
u
b
l
i
c
 
I
n
f
o
r
m
a
t
i
o
n
 
C
e
n
t
e
r
P
.
O
.
 
B
o
x
 
8
3
4
C
h
i
c
a
g
o
,
 
I
l
l
i
n
o
i
s
 
 
6
0
6
9
0
-
0
8
3
4
(
3
1
2
)
 
3
2
2
-
5
1
1
1
R
e
t
u
r
n
 
s
e
r
v
i
c
e
 
r
e
q
u
e
s
t
e
d
F
E
D
E
R
A
L
 
R
E
S
E
R
V
E
 
B
A
N
K
 
O
F
 
C
H
I
C
A
G
O
C
h
i
c
a
g
o
 
F
e
d
 
L
e
t
t
e
r
major break in the relationship between
the three-month moving average hir-
ing plans index and the retail employ-
ment growth data occurs between July
1997 and July 1999. During this time,
the unemployment rate in Michigan
fell to a near record low of 3.7%, a sign
of tight labor market conditions. Ex-
cluding this period, the correlation co-
efficient between the smoothed hiring
plans index and retail employment
growth improves to .65 and the expect-
ed hiring plans index in the third month
before improves to .73.
Inventories
For the inventories index, there are no
appropriate regional data available for
comparison. However, there are com-
parable national data. The correlation
coefficient for the MR inventory index
against national retail inventories exclud-
ing motor vehicles is .65, and there is a
slight tendency for the MR index to lead
the national data. This could be a sign
that regional inventory trends do not
wander too far from national trends.
Conclusion
Our research shows that the MR survey
is relevant in signaling economic chang-
es: It generally correlates well with pub-
lished data and occasionally has leading
indicator properties. Seasonally adjust-
ing the index improves these relation-
ships and makes the index easier to
understand. We will continue to track
the relevance of the survey as we add
more years of data, as well as exploring
additional ways to improve its usefulness
to researchers and business analysts.
1 The authors would like to thank the
Michigan Retailers Association, particu-
larly Tom Scott, for their continued
partnership on the survey. They would
also like to thank Scott Walster for his
research assistance.
2 Although the current sales index does
not exhibit a statistically significant sea-
sonal pattern, we found that seasonally
adjusting the index resulted in a modest
improvement in its correlation with
published data.
3 Historical data and press releases are
available on the Michigan Retailers As-
sociation website at www.retailers.com/
news/aboutri.html.
4 Among the most recent works summariz-
ing this research is Ralph G. Kauffman,
1999, “Indicator qualities of the NAPM
report on business,” Journal of Supply
Chain Management, Vol. 53, No. 2, Spring,
pp. 29–43. But Robert, J. Bretz, 1990,
“Behind the economic indicators of the
NAPM report on business,” Business
Economics, July, pp. 42–48, is slightly
more comprehensive.
5 Most recently reported in ISM, 2002,
“January manufacturing ISM report on
business,” February 1.
6 Our measure for correlation is the Pearson
Product-Moment Correlation, which
ranges from –1 to 1. A result close to 1
indicates that the two series tend to
move in the same direction and magni-
tude, while results close to –1 indicate that
the two series move in the opposite direc-
tion but at the same magnitude. Results
close to 0 mean that there is no correla-
tion between the series.
7 Since our survey is completely anonymous,
we cannot know this for sure. While we do
have a space for respondents to indicate
their type of business, we do not have a box
for automotive dealers. But, an examina-
tion of the business titles on our mailing
list reveals that we mail less than 1% of the
surveys to businesses that probably sell
vehicles. According to the U.S. Census
Bureau’s 1992 Economic Census for the State
of Michigan, 6% of all retail establishments
in Michigan are automotive dealers.

Analyzing an economic indicator: the Michigan retailers survey

http://www.chicagofed.org/digital_assets/publications/chicago_fed_letter/2002/cflapr2002_176a.pdf

Analyzing an economic indicator: the Michigan retailers survey

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics