Understanding
Demand Shifts
for
Grain-Based Foods
Consumer Demand
and Market Trends:
What do the Data Tell Us
and Where are the
Knowledge Gaps?
Part II
Discussion
Oral Capps, Jr.
Texas A&M University
September 28, 2004
Background
 The availability, accessibility, and choice of foods to
meet an adequate and safe diet and to promote health
and nutrition are fundamental challenges facing the
US food distribution system.
 Understanding factors influencing food choices is
needed to better understand the mechanisms by
which individuals select and consume foods.
Discussion of Presentations
Goergen
 Provides trends and insights from grain-based
categories using weekly scanner data from ACNielsen.
 RTE cereal, bread and bakery goods, pasta, and
crackers have experienced declines in grain-based
categories.
Discussion of Presentations (cont)
Eales
 Examines RTE cereal consumption trends in the 1990s
by region and by nutritional content (protein, fat,
fiber, sugar, sodium.)
 Use of grocery marketing data (SAMI) for 1990.
 Use of ACNielsen HomeScan data for 1999.
Commentary
 The trends described by Goergen and Eales, although
useful, are not sufficient for marketing strategies.
 Several questions are begged in the discourse of the
aforementioned trends associated with the
consumption of grain-based foods.
(1) Which demographic segments of the U.S. population
are most likely to consume grain-based foods?
(1) Which demographic segments of the U.S. population
are most likely to consume grain-based foods?
(2) Do differences exist between low-income and nonpoverty segments of the U.S. population in the
consumption of grain-based foods? What about
differences among race; region; ethnicity; and age?
(1) Which demographic segments of the U.S. population
are most likely to consume grain-based foods?
(2) Do differences exist between low-income and nonpoverty segments of the U.S. population in the
consumption of grain-based foods? What about
differences among race; region; ethnicity; and age?
(3) What are the driving forces behind the demand for
grain-based foods? Specifically, what roles do
traditional economic factors such as prices and
income play?
(1) Which demographic segments of the U.S. population
are most likely to consume grain-based foods?
(2) Do differences exist between low-income and nonpoverty segments of the U.S. population in the
consumption of grain-based foods? What about
differences among race; region; ethnicity; and age?
(3) What are the driving forces behind the demand for
grain-based foods? Specifically, what roles do
traditional economic factors such as prices and
income play?
(4) To what degree have health and nutrition issues (e.g.
low-carb diets) influenced the demand for grainbased foods?
Commentary
 To address these issues, it is necessary to develop
econometric (structural) models with appropriate
data.
 Currently, ACNielsen HomeScan Panel data are
available from the ERS for 1998, 1999, 2000, and
2001; presumably these data also are available for
2002, 2003, and 2004.
Commentary (Con’t)
 The use of the HomeScan Panel data from 1998
to present permits a perspective by household,
fine-tuning the trends previously discussed.
 Obtain a micro-perspective in lieu of a macro
perspective.
 Marketing strategists require this micro
orientation.
Commentary (Con’t)
 Consumers today are offered an ever-increasing
number of choices within the category of grainbased foods.
 ACNielsen HomeScan Panel data allow for
detailed analyses not only by household, but
also by type of grain-based foods.
Figure 1. Taxonomy of Grain-Based Foods
Breads and Bakery Products
RTE Cereals
White
Individual specification by brand
or
Wheat
Low-fat Cereals
Pumpernickel
Low-sodium Cereals
Rye
High-fiber Cereals
Oat/Oat Bran
Low-sugar Cereals
A/O Non-White
High-sugar Cereals
Pasta
Rice
Spaghetti & other long pasta
White/Instant
Lasagna
Brown/Wild
Elbows
Dry Pasta
Regular Pasta
Wheat Pasta
Commentary
 Probit models/analysis
 Demand models/analysis
Who is most likely to purchase (or not to
purchase) grain-based foods? By addressing
this issue, market strategists may target
population groups to increase consumption of
grain-based foods.
Single-equation Heckman or Double-Hurdle
models; multi-equation demand system models.
Commentary (Con’t)
 Provide an understanding of the demographic
factors associated with the level of consumption
of grain-based foods.
 Obtain own-price, cross-price, and income
elasticities of demand for grain-based products;
measures of sensitivity on the part of consumers
to changes in prices and to changes in income.
Commentary (Con’t)
 A by-product of demand systems analysis-ascertain whether goods are complements,
substitutes, or independent
 Develop alternative measures for ranking
substitutes.
 Use of diversion ratios
 Allows market analysis to determine if, for
example, sales of white bread decrease, which
sales of other products are positively impacted?
Alternative Measures for Ranking Substitutes, j, of Base Product i.
Cross Elasticity
of Demand
 ji
Unit Diversion
 ji q j
Sales Diversion
 ji p jq j
Unit Diversion
Ratio
 ji q j
Dollar
Diversion Ratio
Relative Unit
Diversion Ratio
ii q i
 ji p jq j
ii p i q i
 ji q jsi
ii q i s j
Percentage increase in quantity of substitute j,
relative to percentage change in price of product i
Absolute increase in unit sales of substitute j
Absolute increase in dollar sales of substitute j
Increase in unit sales of substitute j,
relative to decrease in unit sales of product i
Increase in dollar sales of substitute j,
relative to decrease in dollar sales of product i
Increase in unit sales of substitute j,
relative to decrease in unit sales of product i;
relative to that with substitution proportionate to
share
We use the unit diversion ratio which is tantamount to
 Qj
 Qi
Commentary
 Given the widespread attention on health and
nutrition issues from the news media, food product
labels, and from medical personnel, it is important for
market analysis to identify and assess the impacts of
this information on the demand for grain-based
foods.
 As one illustration, using ACNielsen HomeScan
panel data from 1998 to present, we are in position to
examine consumption patterns of grain-based foods
before the low-carb diet phenomenon; during the
height of the low-carb diet phenomenon; and in the
twilight of the phenomenon.
Data Gaps
 Current data available to the USDA/Economic
Research Service
 Time-series data: (1) consumption of flour and cereal
products, by type of grain, on a pounds per capita
basis from 1967 to 2002; (2) per capita consumption of
breakfast cereals from 1970.
Data Gaps (Con’t)
 Positive feature of time-series data
Disappearance data, both at-home and away-fromhome markets.
 Negative features of time-series data
Typically not specific enough to address market
issues
Do not reflect current market conditions.
Frequency is annual.
Table 2: Per capita consumption of Ready-to-Eat and Ready-to-Cook Breakfast Cereals
Year
Ready-to-eat
Ready-to-cook
Total 2/
1970
1971
1972
1973
1974
8.6
8.6
8.6
8.7
8.9
1.7
1.9
2.0
2.2
2.4
10.3
10.5
10.6
10.9
11.3
1975
1976
1977
1978
1979
9.0
9.2
9.4
9.5
9.6
2.6
2.8
2.9
2.7
2.5
11.6
12.0
12.3
12.2
12.1
1980
1981
1982
1983
1984
9.7
9.8
9.9
10.1
10.3
2.3
2.2
2.0
2.1
2.2
12.0
12.0
11.9
12.2
12.5
1985
1986
1987
1988
1989
10.5
10.7
10.7
11.2
11.8
2.3
2.4
2.6
3.0
3.2
12.8
13.1
13.3
14.2
14.9
1990
1991
1992
1993
1994
12.6
13.4
13.9
14.6
14.8
2.9
2.7
2.6
2.7
2.6
15.4
16.1
16.6
17.3
17.4
1995
1996
1997
14.6
14.3
14.3
2.5
2.5
2.6
17.1
16.9
16.9
1/ Based on Census of Manufactures. Estimates interpolated between noncensus years.
2/ Computed from unrounded data
Source USDA/Economic Research Service
Data Gaps
 Current data available to the
USDA/Economic Research Service
 Cross-sectional data: (1) ACNielsen
HomeScan Panel; (2) Continuing Survey of
Food Intakes for Individuals (CSFII.)
 But these data say nothing, however, about
purchases/consumption in the away-fromhome market, institutional, or conveniencestore channels.
Data Gaps (Con’t)
 One may employ National Panel Diary data
to say something about the away-from-home
market.
 What about getting information from mass
merchandisers like Wal-Mart?
 To be sure, then, data gaps exist.
Concluding Remarks
 In order to better understand
demands for grain-based foods,
it is necessary to develop and
estimate formal structural
models with appropriate data.
 Currently we have the ability to
assess consumption patterns in
the at-home market with
scanner data.
Concluding Remarks (Con’t)
 However, data from mass
merchandisers typically are not
available so at-home
consumption of grain-baesd
foods is likely to be understated.
 To assess away-from-home
consumption or consumption
from the convenience channel,
data typically are lacking.