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Nonfoods,
and
the
Foods,
Demographic
Dacision
to
and
Socioeconomic
Characteristics
Purchase
Various
Meats
and
sumption,”
tribution
on
Seafoods
for
Home ConThe Journal
of Food DisResearch,
February
1980.
;: ;: A;t$c>kk*;k
APPLICATIONS
OF DEMANDRELATIONSIN THE
FRESHFRUITAND VEGETABLEINDUSTRY
by:
J.
E. Epperson,
H. L. Tyan
and
C. L. Huang
University
of Georgia
Athens,
Georgia
Problem
Statement
There
is a definite
need
in the
research
community
for
estimates
of demand
relationships
for
specific
fresh
produce
Such estimates
are
needed
in
i terns.order
to study
the
impacts
of different
phenomena
affecting
the
distribution
of
specific
fresh
fruits
and vegetables.
Examples
of potential
influences
include:
rising
energy
costs,
urban
encroachment
of production
areas,
production
restrictions
due to dwindling
water
resources,
and environmental
restraints.
A review
of the
literature
reveals
that
there
has not
been
a great
deal
accomplished
concerning
the
estimation
of demand
relations
for
particular
fresh
produce
items
in the
U.S.
The most
recent
work,
published
in January
198o
by
Smith,
pertains
only
to fresh
winter
and
spring
cucumbers
and green
peppers.
George
and King
in 1971
estimated
demand
relations
for
an array
of commodities.
However,
estimates
were
for
the
U+S.
as
a whole,
and not market
specific.
In
addition,
periods
of the
year
were
not
differentiated,
and with
few exceptions,
Journal
of
Food
Distribution
Research
demand
relation
estimates
for
fresh
Droduce
items
were
not
mnsnodity
specific.
The works
of Castro
and Simmons,
Bieri
and deJanvry,
and M ttelhammer
include
few fresh
produce
i ems and are
not
markets
(consuming
specific
to regiona
centers).
Aside
from
the
research
purview,
the
business
community
may also
have
a
desire
for
estimates
of demand
relationFor examships
for
planning
purposes.
ple,
produce
buyers
or brokers
may wish
to know which
produce
items
and markets
have
potential
for
expansion
by time
of
The orientation
of this
paper
year.
concerns
possible
applications
of demand
estimates
of particular
fresh
fruits
and
vegetables
by the
business
community.
Methodology
and
Data
Sources
Ordinary
least
squares
was used
to
estimate
demand
functions
for
16 fresh
fruits
and vegetables
originating
in the
Southeast.
The general
form
of the
relationship
for
a given
commodity,
consuming
center,
year,
and month
is (1)
P = f
(Q, i,~)where
P is price
per
hundred
cwt
February
81/page
135
(10,000 lbs.), Q is quantity in hundred
cwt,
I is income
per
capita,
and & is a
For statisvector
of dummy variables.
tical
estimation
dummy varibles
were
added
to equation
1 to allow
prices
to
vary
by consuming
center,
month,
and
year;
to allow
the
relationship
between
P and Q to vary
by consuming
center
and
month;
and to allow
the
relationship
between
P and
I to vary
by market,
The original
data
set
encompasses
‘:i~ervations
which
are
weekly
for
]8
m.-kets
from
January
1972
through
August
‘~5’?4
Price
data
(wholesale)
were
obr .
tsined
from
the Agricultural
Market
$e~vice
(AMS),
USDA.
Quantity
data
are
urlpubl
ished
records
of unload
shipments
September 1976)
obtained
[d~s~ontinued
f~om
the AMS.
Income
data
were
taken
from
the
Survey
of Current
Business
while
population
estimates
are
from
the
Departme:>t of Commerce.
The data
set
is pooled
crossThe pooled
apsectional
time-series.
proach
allows
more
degrees
of freedom
in
st.,.~istical
estimation
which
yields
more
efficient
results
than
estimating
separately
by time
period
or market.
Results
——
Two functional
forms
were
used
to
a linear
form
and a
generate
results:
Results
were
quite
similog-log
form.
lar
using
both
forms.
However,
results
reported
here
for
selected
commodities
and consuming
centers
are
from
the
loglog form
since
the
price
and
income
flexibil
ities
are
represented
directly
from
the
coefficients.
Results
to follow
which
are
depicted
via
tables
involve
only
7 commodities
rather
than
all
16 partially
bee.
cause
of space
limitations
and since
only
some of the
fresh
fruits
and vegetables
studied
are
needed
to demonstrate
the
potential
usefulness
of estimated
demand
coefficients
by commodity,
market,
This
and time
for
decision
making.
February
81/page
136
demonstration
cation
section
is attempted
to follow.
in
the
appli-
Tables
1 through
7 depict
the
variability
of price
flexibil
ities
by month
Each
table
is devoted
to
and market.
a particular
commodity:
eggplant,
watermelons,
tomatoes,
green
peppers,
cucumbers,
sweet
corn,
and squash,
reIn Table
1, a price
flexispectively.
bility
of -0.493
in the
Cleveland
market
in June
means
that
a 10 percent
increase
(decrease)
in shipments
of eggplant
to
Cleveland
results
in a 4.93
percent
decrease
(increase)
in the wholesale
price.
The meaning
of price
flexibility
as illustrated
may be interpreted
in
like
manner
throughout
this
paper.
As
can
be
seen
from
Tables
1 through
7, the
data
suggests
that
variability
of
price
flexibility
does
indeed
occur
by
market
and month
as well
as commodity.
Variability
of price
flexibility
by market
and month
appears
more
acute
for
some fresh
product
items
than
others.
For example,
variability
seems
greatest
for
eggplant
of the
commodities
presented,
Tables
1 through
7.
Price
flexibil
ities
of 0.000
which
appear
in tables
of this
paper
mean that
the
quantity
coefficients
were
not
significantly
different
from
zero
or that
the quantity
coefficients
were
positive.
A positive
relationship
between
price
and quantity
violates
the
general
theory
There
could
be several
reaof demand.
sons why the data
would
yield
such
unsatisfactory
results.
One plausible
reason
is perhaps
that
the
time
period
was misspecified
for
some commodities
and
associated
markets.
A month
might
not
be the
relevant
time
period
for
observing
market
interactions.
For example,
a
period
of a week
could
be necessary
to
show the
appropriate
price-quantity
relationship
in some markets
for
certain
fresh
produce
items.
Another
reason
for
zero
or positive
relationships
between
price
and quantity
might
be that
other
variables
such
as unmeasured
tastes
and
Journal
of
Food
Distribution
Research
Table 1. Price Flexibilitiesfor Eggplantby SelectedMonths andMarkets.
Market
June
July
August
Month
September
October
Novembeq
Chicago
Cleveland
Dallas
New Orleade
Philadelph!i,a
0,000
0.000
-0,068
-0.112
-0,052
-0.018
-0.513
-0.607
-0.547
-0.563
-0.493
-0.493
-0.116
-0.056
-0.022
-0.002
-0.002
-0.072
-0.422
-0,457
-0.517
-o.&73
-0.402
-0.402
-0.356
-0.390
-0.336
-0.406
-0.450
-0.336
+.l)~a
Othera
-0.142
-0.082
-0.048
-0.028
-0.099
ities: Atlanta,
a Other representsseveralmarketswhichhave identicalDrice flexibiI
Baltimore,Boston,Cincirinati,
Columbia,Detroit,‘KansasCity,Los Angeles,Louisville,
New York, Pittsburgh,and St. Louis.
.,
Modths and Markets.
Table 2. PriceFlexibilitiesfor Watermelonsby Selected”
,. ..-
.,
June
Market
‘
July
.
..
Septainber
Month
AuRust
,.
,- ,.
.
.,
OctObe~
Chicago‘
>
-0.174 “’ -0.174
-0.174
-0.174
-0.174
-0.2%2
-ai222
New Orl~ane
-0.222
-0.222
-0:222
0.900
0..000
Group Ab
0.000
0.000
Q..(XXJ
-0.035
; -0.035 ~
-0.035 L
Group B
-0.035 ‘
-0.035
a Group A represents3 markets,Dallas,Minnaipolis,snd St. Loud,~hich hav&:.
identicalpriceflexibilities.
b Group B representsseveralmarketswhichhave identicalprice flexibilities:
Atlanta,Baltimore,Bostcn, Cincinnati,Cleveland,Columbia,Detroit,Kansas
City, Loe Angeles,Louisville,New York, Philadelphiaand Pittsburgh.
.,.,
-.
.- ,,.
..
,.
~ June
.
...!
JUIY
.,, .,
.
..
.
.
..
.
.
TabIe 3. Pric&F~exjBilitiesfor.Tbmatoeaby selectedMonths and Markets.
Harket
.
..
Month
August
.
.“
,September
.$
Octibe~
,
;
Z*
7
.
‘~-o.534
-0.534
-0:534
-0,534
-0.534
,.
::-.0,616.,
~,
-0.616.
“!.-0.616,
~
-()
’;-136
-0.136
‘:::%
“ ::!;: ‘“
-o.i36 : :“’” “:”‘i’
Other
-0.0s3
-0.083 “ :-0’:
,
OU’3>”
-o;083
‘ -o.~~3
..,,~.;
:
a Other represen?.’a’
se$er’al”
marketstiichEai@ ia&fitical
pries
~”
‘‘ ~ ““ $.*
Atlanta,Baltimore,Boston,Chicago,Cincinnati,Cleveland,Columbia,DatrOit,
Semee City, Minneapolis,New Orleans,New York, PhiladelphiaPittsburghand
Dallas
Loe Angeies
Louia~i1le
St.
Journal
of
Imuis.
Food
Distribution
Research
February
81/page
137
Tabla4. Rice Flexibilitiee
for Green Peppers
by Selected
Monthe and Markata.
Month
Market
May
June
July
Auguet
Chicago
Loa,
Angeles
-0.126
-0.126
-0.126
-0.126
September
-0.153
octo~
-0.126.
-0.478
-0.450
-0.450
-0.450
-0.450
-0.450
-(jo~ol
Louievil
Ie
-0.201
-0.201
-0.227
-0.227
-0.201
Othera
-0.146
-0.146
-0.146
-0.146
-0.172
-0.146
tiae: Atlanta,
a Othar representssaveralmarketswhichhave identicalprice flexlbili
Baltimre,Boston,Cincinnati,Cleveland,Columbia,Dallae,Detroit,
KaoeasCity,Minneapoiia,
NewOrleana,New York,Philadelphia,
Pittsburgh,
and St. Louis.
for Cucumbers by Salectedllonthe
aad Markets.
Tab& S. PriceFlexibilitiea
Nmrket
May
Cincinnati
-0.496
n~polis
June
0.000
Other
-0.0?7
-0.470
O.oao
Month
JUIY
Aunuet
-0.556
0.000
sePcamber
-0.556
-0.556
4.s40
O.om
O.000
O.aoo
Octobqg
-0.0s1
-0.137
-0.137
-o.la
-0.137
●
eeveralxerketsvhichhava idaat
Ch● r repreeeote
icalpricef Iaxibilities:
Atlanta,
Ealtimere.Boston.
Chicaao.Cle?dend.Columbia.
Dallaa,Detroie,KaneaaCity, Leuiavillo.
W Orlea&, New York,Pii%iadalphia,
Pittsburgh* ●nd St.Louis.
for Smet Corn by SelectedMaotha end ?I@kata.
Table6. Prica Fla%ibilitiaa’
Market
May
O.000
-0.239
Chicago
Lea Angel&
NSoaeapeSi*
St.
0.000
U4iti.
Jima
O*WO
-0.23s
a.oao
nentll
JOY
-0.63s
-0.28s
o .Ooa.
see-
Atmmt
-0.037
4$87
0.600
-0..0s0
-0.329
a.000
‘
,,
February
81/page
138
Table
Price
7.
Flexibil
ities
for
Squash
by
Selected
Months
and
Markets.
Mont h
Market
April
May
June
July
August September October November
Baltimore
-0,355
-0.355
-0.302
-0.381
-0.248
-0,355
-0.329
-0.355
I@uisvi
1le
-0.263
-0.263
-0.289
-0.210
-0.157
-0.263
-0.238
-0.263
Phila~elphis
-0.485
-0.485
-0.512
-0.433
-0.379
-0.485
-0.485
-0.460
-oql~8
Other
-0.1s1
-0.181
-0.207
-0.075
-0.156
-0.181
-0.181
a Other representsseveralmarketswhich have identicalprice flesibilities:Atlanta.Boston.
Chicago,Cincinnati,Cleveland,Columbia,Dallas,Detroit;Loa Angeles,Minneapolis,
-N;w
‘
Orlesns,
New York, Pittsburgh,and St. LOuie.
ng factors
given
preferences
for
overrid
quantity,
the
ranges
in values
of price,
the
data
and per
capita
income
w thin
In addition
to
set
used
in this
study.
the
reasons
given,
there
is always
in
any study
the
issue
of validity
of the
data
used
in this
data. The unload
study
does
not account
for
total
quantity
consumed
in a given
market
and time
However,
every
effort
is made by
period.
Price
dependent
rather
the AMS to do so.
than
quantity
dependent
demand
functions
were
used
in this
study
to negate
the
possible
hazards
of
incomplete
quantity
For the
kind
of spatial
information.
and temporal
research
embodied
in this
study
there
is no other
source
of data
than
reported
unloads
by the AMS.
cent
rise
(fall)
in the wholesale
price
of watermelons
in most
markets,
Table
8.
Other
income
flexibil
ities
may be interpreted
in a similar
manner,
An attempt
will
be made to show the
value
of
income
flexibility
information
in the
application
section
of this
paper,
Conclusions
Results
are
supportive
of expected
differences
in demand
relations
by commodity,
consuming
center,
and month.
Results
also
support
the work
of Smith
regarding
price
flexibilitiesl
which
were
shown
to be inflexible
(absolute
value
less
than
1) for
spring
and winter
fresh
Since
cucumbers
and green
peppers.
price
flexibil
ities
for
all
commodities
in all
markets
in all
months
were
inflexible,
the
implication
is that
there
is
a great
deal
of substitution
among
produce
items
and perhaps
with
other
related
Research
is now underway
to
commodities.
determine
the
extent
of substitution
among
fresh
fruits
and vegetables
by consuming
center
and month.
Price
flexibil
ities
of 0.000
as
shown
for
June
and July
in the
Chicago
market
for
eggplant,
Table
1, do not
mean
that
the
Chicago
market
can be
fiooded
with
eggplant
during
June
and
July
without
serious
detriment
to price.
Rather,
it
indicates
that
based
on the
data,
the
Chicago
market
has been
unchallenged
in this
respect.
Of course,
the
same
logic
applies
regardless
of fresh
produce
item,
market,
or month.
Application
Food
Industry
To illustrate
the
existence
of variability
of
income
flexibility
by commodity
and market,
Table
8 is presented.
An income
flexibility
of
1.729
for
watermelons
means
that
a 1 percent
rise
(fall)
in per
capita
income
yields
a 1.729
per-
Planning
for
potential
change
is
Firms
reimportant
in any
industry.
sponsible
for
procuring
or channeling
fresh
produce
can use flexibility
estimates
from
demand
relations
to ascertain
the
impact
of changing
supplies,
consumer
Journal
of
Food
Distribution
Research
to
the
February
81/page
139
Table 8.
Income
Flexibili.ties
for
Selected
Fresh Vegetables
and Fruits by Market.
Commodity
Flarke t
Eggplant
Watermelons
Atlanta
().592
1.729
Baltimore
0.023
lloston
Chicago
C.i.ncinnati
Cleveland
0.592
0.592
0.592
0.716
(lolumbia
C%llas
0.592
0.592
I)etroit
Kansas City
Los Angeles
Louisville
Minneapolis
New Orleans
0.592
0.592
0.592
0.592
().592
0.743
1.709
1.729
1.729
1.729
1.729
1.709
1.729
1.729
1.729
1.729
1.729
1.729
1.729
York
Philadelphia
0.592
0.592
Pittsburgh
St. Louis
0.592
0.592
1.729
1.729
New
Tomatoes
0.419
0.407
Green
Peppers
squash
0.419
0.419
1.999
0.419
0.419
0.721
0.419
0.419
0.419
0.419
().419
0.419
1.091
1.091
1.091
1.091
1.358
1.091
1.091
1.091
1.091
1.091
-0.362
1.091
1.091
1.091
1.729
0.419
0.615
1.091
0.000
1.729
1.359
0.605
1.091
0.000
0.419
0.419
0.605
0.605
1.091
0.000
1.091
0.000
It should
be pointed
out
that
this
application
exercise
is based
on a micro
That
as opposed
to a macro
perspective.
is,
the
exercise
pertains
to possible
actions
of an individual
firm
rather
than
For example,
if
the
an entire
industry.
entire
industry
flooded
the
Dallas
market
with
watermelons
in June,
Table
9,
this
would
constitute
a change
far
beyond
the
limits
of the
data
used
in this
study
to estimate
demand
relations
and
would
obviously
have
a substantial
impact
on price.
income
flexibil
ities
for
all
in Table
9 are
greater
than
1
means
that
changes
in prices
in
markets
for
associated
comrnod! ties
0.000
0.000
0.000
0.000
0.023
0.000
0.000
0.009
0.022
0.000
0.205
0.013
0.000
0.000
~
1.307
1.343
1.307
1.307
1.307
1.307
1.307
1.307
1.307
1.307
0.428
1.307
1.307
1.307
1.318
1.410
0.743
1.307
tend
to be quite
responsive
to changes
in per
capita
income
based
on the
data
used
in this
study.
From Table
9,
watermelons,
cucumbers,
and squash
appear
to show the
greatest
proportionate
potential
for
most
markets
in June.
Also,
the
implication
is that
increased
shipments
of these
commodities
should
be considered
for
markets
anticipating
Table
10 is similar
to
income
growth.
Table
9 except
that
income
flexibiliThus
ties
for
entries
are
less
than
1.
the
long
term
possibilities
are
not
as
great
for
commodities
and associated
However,
markets
depicted
in Table
10.
the
ranking
short-run
wholesale
procedure
as it pertains
price.
applies
to
in the
effects
on
REFERENCES
Bieri,
entries
wh~ch
these
Sweet
Corn
0.605
0.605
0.605
0.605
0.605
0.605
0.605
0.618
0.605
0.591
0.605
0.605
0.619
0.605
i ncome,
and population
on the
price
of
For example,
specific
product
items.
Table
9 ranks
markets
and associated
commodities
proportionately
for
June
in an
effort
to identify
markets
that
can absorb
greater
supplies
without
serious
detriment
to wholesale
price.
The
Cucumhers
J. and
Analysis
Budgeting,
Monograph
Californiaj
A.
of
deJanvry,
Empirical
Demand
Under
Consumer
Giannini
Foundation
No. 30,
University
of
September
l~?:, ,
,
Table 9.
Ranking of Markets and Associated Commodities for June According
Price Flexibility with Income Flexibility Greater than 1.
Ranka
Market
to
Price
Flexibility
Commodity
Dallas
0.000
Watermelons
Minneapolis
Watermelons
0.000
0.000
St. Louis
Watermelons
:
Minneap lis
Cucumbers
0.000
E
5
Group A
-0.035
Watermelons
6
Group Bc
Cucumbers
-0.051
-0.083
7
Tomatoes
Cincinnati
Philadelphia
8
Tomatoes
-0.083
Watermelons
Chicagod
-0.174
9
-0.207
Squash
Group C
10
11
New Orleans
Watermelons
-0.222
Louisville
12
-0.289
Squash
13
Baltimore
Squash
-0.381
Philadelphia
14
Squash
-0.512
15
Cincinnati
Cucumbers
-0.560
greatest proportionate potential for expansion.
al=
b Group A represents:
Atlanta, Baltimore, Boston, Cincinnati, Cleveland,
Columbia, Detroit, Kansas City, Los Angeles, Louisville, New York., Philadelphia,
and Pittsburgh.
c
Atlanta, Baltimore, Boston, Chicago, Cleveland, Columbia, Dall,as, Detroit,
Kansas City, Los Angeles, Louisville, New Orleans, New York, Philadelphia,
Pittsburgh, and St. Louis.
d Atlanta, Boston, Chicago, Cincinnati, Cleveland, Columbia, Da],las, Detroit,
Los Angeles, Minneapolis, New Orleans, New York, Pittsburgh, and St. Louis.
1
2
Castro,
The Demand
R. and R. L. Simmons,
Cucumbers,
and
for
Green
Pep pers,
Cantaloupes
in the Winter
Season,
Econ.
Res.
Rept.
No. 27,
North
Carolina
State
University,
Raleigh,
April
1974.
Federal-State
~
Prices,
1972-1976.
George,
USDA,
News
AMS,
Service,
selected
cities,
of
R. C.,
Demand
Food
The
for
Distribution
A Priori
Information,
Washington
State
Ph.D.
University,
Smith,
The Demand
for
Fresh
Winter
E. B.,
Cucumbers
and Green
Peppers
in U.S.
Regional
Wholesale
Markets,
USDA,
ESCS,
January
198o.
Us.
Department
Estimates
P-25,
No.
Us.
Department
Current
Fresh
P. S. and G. A. King,
Consumer
Demand
for
Food Commodities
in the
United
States
with
Projections
for
1980,
Giannini
Foundation
Monograph
=26,
March
1971.
Mittelhammer,
Domestic
Journal
Market
Using
thesis,
1978.
of
and
704,
Commerce,
Population
Projections,
Series
July
1977.
of Commerce,
Business,
various
Survey
of
issues.
Estimation
of
Salad
Vegetables
Research
February
81/page
141
Table 10.
Ranking of Markets and Associated Commodities for June According
to Price Flexibility with Income Flexibility Less than 1.
Ranka
Market
~
Price
Flexibility,
Commodity
Eggplant
0.000
0.000
Sweet Corn
Sweet Corn
0.000
0.000
Minneapolis
4
Sweet Corn
Dallas
5
Eggplant
-0.002
6
Group B=
Eggplant
-0.028
7
-0.051
Los Ang$les
Cucumbers
-0.083
8
Tomatoes
Group C
9
New York
-0.095
Sweet Corn
-0.126
10
Chicago
Green Peppers
11
Louisvi&.le
Tomatoes
-0.136
12
Group D
Green Peppers
-0.145
-0.201
:[3
Green Peppers
Louisville
14
Pittsburgh
Squash
-0.207
-0.207
Squash
15
Los Angeles
16
Los Angeles
-0.239
Sweet Corn
-0.336
17
Philadelphia
Eggplant
18
New Orleans
Eggplant
-0.402
19
-0.450
Los Angeles
Green Peppers
20
Cleveland
Eggplant
-0.493
Dallas
-0.534
21
Tomatoes
22
Los Angeles
Tomatoes
-0.616
a 1 = greatest proportionate potential for larger quantities of unloads.
b Group A represents:
Atlanta, Baltimore, Boston, Cincinnati, Cleveland,
Columbia, Dallas, Detroit, Kansas City, Louisville, New Orleans, Philadelphia,
Pittsburgh, and St. Louis.
c Atlanta, Baltimore, Boston, Cincinnati, Columbia, Detroit, Kansas City, Los
Angeles, Louisville, New York, Pittsburgh, and St. Louis.
d Atlanta, Baltimore, Boston, Chicago, Cincinnati, Cleveland, Columbia, Detroit,
Kansas City, Minneapolis, New Orleans, New York, Philadelphia, Pittsburgh, and
St. T.ouis.
e Atlanta, Baltimore, Boston, Cincinnati, Cleveland, Columbia, Dallas, Detroit,
Kansas
City,
Minneapolis, New Orleans, New York, Philadelphia, Pittsburgh, and
St. Louis.
Chicagg
GroupA
Chicago
2
3
February
81/page
142
.@urnal
of
Food
Distribution
Research