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economic & COnsumer credit Analy tics
Merchandise Line Estimates, Forecasts and
ZIP Code Potential Methodology
Merchandise Line Estimates, Forecasts, and
ZIP Code Potential Methodology
Historical Merchandise
Line Estimates
(1997-2010)
Approach
Every five years (1992, 1997, 2002,
2007…) the U.S. Census Bureau undertakes
a census of retail trade that all retailers in
the country are legally required to complete.
One of the outputs of this census is a matrix
of merchandise line sales by store type. In
other words, sales by merchandise line are
provided for each store type. These data are
released about three years after the census
is taken. (That is, we received the 2007 data
late in 2010, although a preliminary version
was available a year earlier.) Between the
censuses, the Census Bureau provides survey-based estimates of retail sales by store
type (on a monthly basis) but no estimates
of merchandise line sales. Note that these
monthly data go back only until 1992, the
earliest we could begin our estimates.
The estimates of annual merchandise line
sales are developed by first creating annual
matrices of the share of sales by merchandise line for each store type. Data from the
census matrices are interpolated for the
years between 1997 and 2007 and trended
from 2008 to 2010. Then the sales by store
type data from the Census Bureau are run
through the matrices to get merchandise line
sales estimates for each year.
The shares of sales by merchandise line
change significantly from census to census,
so capturing this change is important in accurately measuring sales by merchandise
MOODY’S ANALYTICS / Copyright© 2011
line. For example, the share of drugs and
food sold discount stores has been growing,
at the expense of the share of many other
merchandise lines they sell. If this shift were
not accounted for, sales growth for food
would be understated and that for other
merchandise lines would be overstated.
Additional Issues
There were a number of additional issues that had to be addressed in producing
the estimates.
Incomplete merchandise line data
The Census Bureau has a set of aggregate
merchandise line categories that are asked
of almost all retailers. (Men’s apparel, kitchenware and home furnishings, and groceries
are examples.) However, the more detailed
merchandise lines are only asked for store
types where the line is thought to represent
a significant enough part of sales. Usually
this is sufficient, but sometimes, as in 1992
when there was no detail for apparel categories for discount department stores, there are
significant holes that must be estimated.
It is usually straightforward to estimate
the missing data. In many situations, including the discounter case noted above, the
missing shares can be interpolated using previous and following census data. Given the
relative weakness of the 1992 census, 1987
data are utilized frequently if history going
back that far is desired.
An additional area with missing data for
significant merchandise lines (200 men’s;
220 women’s; 240 children’s;, 260 footwear;
280 curtains, drapes, etc.; 340 furniture; 360
floor coverings; 380 kitchenware and home
furnishings) was nonstore retailers, including
electronic shopping and mail order retailers
and other direct selling establishments. After
consultation with the client and examination of data from NPD, it was determined to
allocate these sales to more detailed merchandise lines using the shares from general
merchandise stores.
Inconsistent merchandise line
detailed categories
For some major merchandise lines, the
number of detailed lines reported for it varies
by store type. This can limit the amount of
disaggregation that is feasible or force estimation of the distribution of the sales across
the more disaggregated lines. This situation
is especially prevalent in the home category,
although it can be problematic in other categories as well. In general, more aggregate
categories are used to avoid this problem.
1992 census store type definitions
The government switched all of their
data collection and reporting from an
SIC basis to a NAICS (North American
Industrial Classification System) basis. All
current reporting is on a NAICS basis including monthly retail sales by store type
data. However, 1992 and prior economic
censuses were done using SIC-based store
types. While many of the store type definitions are nearly or completely unchanged
between the two definitions (including apparel specialty stores, national and conventional department stores, and drug stores),
there were some significant definitional
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Merchandise Line Estimates, Forecasts, and ZIP Code Potential Methodology
changes. For example, supercenters were
part of discount department stores on an
SIC basis but are grouped with warehouse
clubs using NAICS. This changed the merchandise mix reported for discount department stores in a way that is very difficult
to adjust for. Other examples include significant redefining of store types within the
food store category. This makes estimates
prior to 1997 less reliable if provided.
The analysis was done on a NAICS basis
because that is how the store type data
are released. For estimates prior to 1997, it
would be assumed that the corresponding
SIC store type allocation was representative
of the NAICS allocation.
Changing merchandise line definitions
While not a major problem, there have
been a few cases where merchandise line
items were added, dropped or moved within
major categories. For example, in the 2007
census, new lines were created for some
forms of electronic media. They had been
counted within other lines previously.
Definitional drift
There are a few instances where share
movements seem extreme. The increase
in the share of children’s footwear sold at
warehouse clubs and supercenters in 1997
compared with both 1992 and 2002 is an
example. Issues with women’s undergarments may be another. While census did not
change the merchandise line definitions, it is
possible that the way retailers are reporting
has changed. While small judgmental adjustments have been considered, in general,
we recommend using reported data.
Trend vs. interpolation
To avoid extreme results, large shifts in
merchandise line sales between censuses
are muted somewhat as the shares are
extended beyond 2007. While there is the
benefit of avoiding extremes, this can result
in somewhat different growth rates in the
2007 forward period compared with earlier
years. Fortunately, this does not appear
nearly as significant as changes in growth
rates prior to 1997.
National Merchandise
Line Forecasts
(Beginning 2011)
Approach
The forecasts are basically econometrically estimated projections of the history
based on the economic outlook and the
Moody’s Analytics forecasts of corresponding personal consumption expenditure categories. More general drivers of consumer
spending—including income, employment,
interest rates and inflation, among others—
are also utilized since the PCE data are not
as detailed as the merchandise lines forecast
in the (WITHELD) project.
Additional Issues
As noted above, historic estimates are
more reliable beginning in 1997. If our examination of the data (or statistical tests)
suggested a structural change, we only utilized data beginning in 1997 in our estimation process even if earlier data are provided
to the client. Forecasts are adjusted to take
into account information the model may
not be able to incorporate. This would include recent monthly trends in store types
important to the merchandise line being
forecast, retailer or trade information not
incorporated into the model, and feedback
from the client analysts.
Zip Code Allocation of
Potential
Approach
The consumer expenditure survey (CEX)
is a survey of consumer (actually two separate surveys, a diary survey and an interview
www.economy.com
survey) designed to measure consumer
spending by demographic group. While the
surveys are conducted on an ongoing basis,
data are released annually, with a lag of
about a year. Data for 2009 were released in
late 2010, for example.
The interview survey is used for most
the client merchandise lines, as it is more
comprehensive. The diary survey focuses
on merchandise lines purchased at very
high frequency (such as weekly), which
includes only a few of the client merchandise lines.
From the survey, spending propensities (average spending per household) are
computed for households across seven
age groups (determined by the age of the
reference person in the household), seven
income groups, and the four census geographic regions. The exception to this is
that national propensities were collected
for high-income groups for very young and
old age groups because of the sparseness
of the data in these cells.
Since propensities were needed for years
after the latest CEX, the propensities were
forecast using time series techniques that
smooth out extreme values from the historic data to produce propensities for the
needed years.
The spending propensities by age and
income groups and by region were merged
with ZIP code-level counts of households
by age and income groups. The latter data
are provided by the client. The merging
produced an initial estimate of merchandise potential at the ZIP code level for both
years. However, because of concerns about
inconsistent underreporting of spending across lines in the CEX, the resulting
propensities were adjusted for each merchandise line. The adjustment was done
to ensure that the resulting propensities
summed to the national merchandise line
estimates calculated in the previous steps.
This produced the final propensities delivered to the client.
** Note: Separate documentation provided to the client provides detailed specifications including exact census of retail trade merchandise lines used, consumer expenditure survey
UCC codes used, and age and income groups used.
MOODY’S ANALYTICS / Copyright© 2011
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