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378. 77427
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92- 20
Staff Pall_er
THE ECONOMICS OF CONSUMER RESPONSE TO
HEALTH RISK INFORMATION IN FOOD
by
Sedef Birkan, John P. Hoehn
a nd Eileen van Ravenswaay
February 1992
No. 92-20
WAITE MEMORfAL BOOK COLLECTION
DEPT. OF AG. AND APPLIED ECONOMICS
1994 BUFORD AVE. • 232 COB
UNIVERSITY OF MfNNESOTA
ST. PAUL. MN 66108 U.SA
.......... Department of Agricultural Economics
MICHIGAN STATE UNIVERSITY
East Lansing , Michigan
MSU is an Affirmative Action/Equal Opportunity Institution
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THE ECONOMICS OF CONSUMER
RESPONSE TO HEALTH RI~K
INFORMATION IN FOOD
by
Sedef Birkan, John P. Hoehn
and Eileen van Ravenswaay **
February 1992
*Financial support for this project was provided in part by the U.S. Environmental Protection
Agency Cooperative Agreement #CR-815424-01-2, the Michigan Agricultural Experiment
Station Project #3800, and the Department of Agricultural Economics, Michigan State
University.
**The names of the authors are presented .m alphabetical
. order. Birkan is a Graduate Student
and a Research Assistant, Hoehn is an Associate Professor and van Ravenswaay is a Professor
in the Department of Agricultural Economics, Michigan State University. They are very
grateful for the valuable contributions of Richard Baillie, Jeffrey Wooldridge and Lih-Chyun
Sun.
THE ECONOMICS OF CONSUMER RESPONSE TO HEALTH RISK INFORMATION
IN FOOD
ABSTRACT
G he manuscript examines the demand effects of health concerns
regarding
Alar
residues
in
apples.
An
econometric
model
is
estimated for the New York area apple market. Apple demand is found
to shift downward immediately following the initial announcements
of health risk. Demand recovers fully when Alar is withdrawn from
use. J
THE ECONOMICS OF CONSUMER RESPONSE TO HEALTH RISK INFORMATION
IN FOOD
Consumer
concerns
about
the
safety
of
the
food
supply,
especially pesticide residu es , has been high during the past decade
(Caswell). These concerns appear to be due to new information that
consumers
have
received
about
the
potential
health
risks
of
pesticide residues. These risks are conveyed by new information
about the toxicity and presence or absence of pesticide residues in
food .
An example of this is the Alar incident. Alar is a growth
regulator primarily used on apples. In 1984, EPA announced that it
would
reevaluate
evidence
that
its
Alar
risk assessment
and
its
for
derivative
Alar because of
UDMH
cause
cancer
the
in
laboratory animals. The toxicity of Alar was debated for five years
by the experts, the food industry, the government and the producer
of Alar, Uniroyal. This dispute was widely reported by the news
media and had a large impact on consumer purchases of apples (van
Ravenswaay and Hoehn).
This paper extends the previous research by van Ravenswaay and
Hoehn.
In that s tudy,
the authors found the demand for apples
declined after disclosure of health risk information associated
with lifetime consumption of apples treated with Alar . This study
looks at the long term effects of the Alar incident by extending
the observation period to the stage after the withdrawal of Alar
from the market.
This paper starts with describing a conceptual framework for
the
analysis
of
consumer
choice
under
uncertainty.
Then,
the
2
specification of the information variable is discussed and the
hypothesis on detecting long term effects of the Alar scare is
presented . The next section discusses the data and the econometric
model of the demand for fresh apples in a regional market. Then the
hypotheses on the coefficients for the information variables in the
econometric
model
are
stated.
The
next
section
is
on
model
specification . The econometric findings and the policy implications
are summarized in the final two sections.
Conceptual Framework
The model of consumption choice in this study is based on the
expected utility
framework.
safety context because
This framework is
useful
in food
consumption decisions are made in the
presence of uncertainty (Choi and Jensen, Eom).
Assume there is one health problem in the lifetime of the
representative consumer. The goods consumed are food x (apples) and
food
y
(all
other
foods).
The
apples
contain
residues
of
a
carcinogenic substance (Alar). Let n be the consumer 's perception
of the additional probability on the occurrence of the health
effect
due
to
the
Alar
residues
in
apples .
This
perceived
probability is associated with the lifetime consumption of apples
treated with Alar (perception of additional lifetime health risk).
The consumer ' s lifetime can be viewed as three periods. The
first period is the past . Since the consumption decisions in the
past period are already made,
the risk consequences due to the
consumption of x are taken as given .
present
period.
In
this
period,
The second period is the
the
consumer
chooses
the
3
consumption levels of x a nd y. The risk consequences of consuming
x are not realized until the third period. In the third period , the
c onsumer's utility is state dependent where in one state the health
problem i s experienced and in the other state the health problem is
not experien ced .
Each utility is multiplied by the consumer ' s
perception of the probability of the occurrence of each state.
In the current p e riod t, the perception of the probability of
the occurrence of the health problem in the third period, n r , is a
function of two factors.
One is the information in the current
period (dt ) on the toxicity of the substance (Alar) and the other
one
is
its presence or absence
in x
(apples) .
The consumer ' s
problem at t is to maximize the lifetime expected utility subject
to the budget constraint. Let p, r and m be the retail price of x,
retail
price
of
y
and
disposable
income
respectively.
The
maximization problem leads to the demand functions for x and y at
time t:
( 1)
xt
=
xt ( Pt
rt
dt
mt
(2)
Yt
=
Yt ( P t
rt
dt
mt
speci!ying the In!ormation variable
In d e termining the effect of the health risk information on
food
purchases ,
a
measure
of
information content needs
to
be
characterized. In this paper, the information content is identified
by two dummy variables . Both of the dummy variables measure the
presence or the absence of the reported risk
(S it and S 2 d.
The
4
potential long term effects of risk reports is also characterized
by a dummy variable (S 3 t> 1 •
In this specification , Sa represents the period that started
with the EPA' s
initial
announcement
that Alar was
a
possible
carcinogen. During the period of June 1984 to July 1989, EPA made
several announcements that Alar was a potential carcinogen.
S2t
represents the period during which the Natural Resource Defense
Council
(NRDC}
announced
a
greater
ri sk
estimate
June
1989,
(Sewell
and
Whyatt} 2 •
S3t
denotes
the
period
after
when
Alar was
withdrawn from the market . The coefficient in this variable should
be insignificant if there is no long term effect of the Alar
controversy on apple purchases.
Data
The demand for fresh apples was modeled and estimated for the
New York City-Newark metropolitan area during the period of July
1980 through July 1991.
1
The dummy variables are :
S1t
O if t < July 1984 and t > June 1989
1 otherwise
S 2t
0 if t < February 1 9 89 and t > June 1989
1 otherwise
S3t
1 if t > June 1989
o otherwise
2
The
EPA
NRDC
EPA
estimates of the lifetime cancer risks are :
(1985} : 1. 7
( 1989} : 4 . 1
( 1989} : 6. 0
*
*
*
10 5
10 5
10 6
5
Except for
the measures of the
income and national
apple
holdin gs and the length of the period studied , the data used in the
study are essentially the same as reported in van Ravenswaay and
Hoehn (1990). Monthly per capita purchases of apples (qr ) are the
fresh apple arrivals to the New York City- Newar k metropolitan area
reported
by
USDA
divided
by
monthly
population
size.
Monthly
deflated retail prices of apples (pr ) and bananas (br) are obtained
from
bi-weekly surveys conducted by the city of New York (New York
Department of Consumer Affairs) and divided by the consumer price
index in the New York City-Newark metropolitan area (U . S Department
of Labor) and expresses in 1893 dollars.
The income variable is constructed from the weekly earn ings of
the
employees
of
nonagricultural
payrolls
(State
of New
York
Department of Labor) . The national storage holdings of fresh apples
are
t he
amounts
in
cold
storage
and
controlled
atmosphere,
excluding the processor holdings (International Apple Industry).
The Econometric Model
The supply of apples to the New York region is assumed to be
perfectly elastic at the national price plus transportation costs .
The national price of apples is determined by national supply and
demand.
Let q , p, b, m, h
price
of
apples ,
and s be per capita apple purchases, retail
retail
price
of
bananas ,
d i sposable
income,
national apple holdings and health risk information, respectively .
The s u bscripts r , o and n shall denote New York Region, all other
regio n s and t h e Nat ion. The lett ers g and f are the proportionality
6
factors between the national apple price and the apple prices in
the New York Region and other reg'ions, respectively. The symbols,
u, w and z are random errors.
The econometric model
for
apples can be expressed in six
equations.
The demand equation for the New York region is,
(3)
pI=(l+g)pn
The demand equation for the other regions is,
(4)
Po= (l+f) pn
The demand equation for the nation is,
qn=p~+ p ~ o+p~b o+ P~m o+pf s o+p~+pfp r +p~b r +
( 5)
P5m r +p~s r +w
The supply equation for the nation is,
(6)
This
framework
suggests
that
the
national
price
may
be
affected by the information on Alar since the Alar incident was a
national event. Therefore, estimating consumer demand for apples in
the
New
York
region
by
a
single
equation
may
introduce
a
simultaneity bias because pr may be correlated with the error term
in the New York region apple demand equation.
7
Let the coefficient for the information variables (s" , s 0 and
sr)
for the reduced form for q r be y 11 y 2 and y 3 • Since we cannot
observe the risk information for the nation , other regions and New
York region separately, the coefficient for sr in the reduced form
equation
for
qr
is,
where
y=p 1y 1+p 2 y 2 +p 3 y 3 ,
correlation coefficients between s 0
,
p1 ,
p 21
p3
are
the
s 0 and s r with s r .
Similarly , let the coefficient for the information variables
in the reduced form for pr be a 1 , a 2 and a 3 • The coefficient for sr
in the reduced form for pr is a = p 1 al+p 2 a 2 + p 3 a 3 •
If
for
simplicity,
p 1=p 2 = p 3 =1,
then
the
information
coefficients of the reduced form equations and the demand equation
can be compared .
Let y , a and
p4
represent the coefficients for the information
variables for the reduced form equation for quantity , reduced form
equation for price and the demand equation, and l et
p1
be the price
elasticity of apples in t h e New York region . It is then possible to
compare the coefficient estimates with the following equality :
(7)
Hypothe s i s on the Information Coefficients i n t he Reduced form
Equations and the Demand Equation
The first hypothesis is that there is no change in apple sales
due t o information. This hypothesis implies that
y=a=P4=0 .
8
If the coefficient on the information variable in the reduced
form for price equation is statistically different than zero, then
the second and third hypotheses follow.
The second hypothesis is that the change in apple sales in the
New York region is only due to a change in the national price
induced by information. This hypothesis implies that
P4=0
and a+O.
The third hypothesis is that the change in apple sales in the
New York region is both due to a change in national price induced
by information and to a demand shift in the New York region. This
hypothesis implies that
P4 *0
and «*0.
The fourth hypothesis is that a change in apple sales in the
New York region is 2D.l.Y due to a demand shift in the New York
region. This hypothesis implies that a=O, y+o,
P4 +0.
Model Specification
The Box-Jenkins approach is used to detect seasonality and to
construct
a
stochastic
model
for
the
error
structure .
The
inspection of the autocorrelation functions for the per capita
apple consumption and the retail price of apples show evidence of
seasonality.
Seasonality occurs when there is a high degree of
correlation between the values observed during the same season in
consecutive years.
For example,
in apple consumption and retail
prices of apples in the New York region, the obse rvations twelve
months apart show correlation . It was found that a multiplicative
9
seasonal model was appropriate to model the non-random component of
the error structures for q r and pr . 1
After correcting for seasonality, the next step was to add the
exogenous variables to the model to determine the effect of the
information variables on apple purchases.
To test the hypothesis that the price at the New York region
is affected by information at the national level and thus to the
existence of the simultaneity bias, a model with an instrument for
the price variable was compared to a model without an instrument
for the price variable.
An instrument for price was formed by regressing the observed
price of apples on all the exogenous variables in the system (br,
mr, hn, sr). The demand equation for apples was estimated with an
instrument for price in a two stage process.
First the price is
regressed on all the exogenous variables in the system to obtain
the fitted values, pr' . These fitted values a re then used in the
demand
equation to
replace
the price variable.
The asymptotic
covariance matrix for the instrumental variables estimator with
seasonal ARJMA errors is derived and manually calculated following
Amemiya.
The Econometric Findings
The reduced
equation
for
form
price
equation
and
for
the demand
quantity,
equation
the
were
reduced
form
estimated
by
employing the seasonal error structure. Since the price of bananas
1
The error structure for q r = ARIMA(O,O,l)*SARIMA(0,1,1)
The error structure for pr= ARIMA(l,O,O)*SARIMA(0,1,1)
10
and the income variable were insignificant, they were excluded in
the demand equation . The demand equation was estimated both with
and without the instrumental variables . The coefficient estimates
are repo r ted in table 1.
The Hausman test
suggested that there
is
no evidence
of
simultaneity bias. The Hausman statistic is 2.46 and we fail to
reject the null hypothesis that there is no simultaneity bias .
The coefficients for the error structures of all the equations
are
highly
significant .
The
Q statistics
for
all
the
models
conclude that we fail to reject the existence of serial correlation
at the 10% significance level since the values for the Q-statistics
are lower than the critical value for chi-square(24)=33.20 .
The
inspection
of
the
coefficients
for
the
information
variables in the reduced form for price implies that they are not
significantly different than zero . Therefore, it is concluded that
the apple purchases in New York region are determined only due to
a demand shift in the New York region and
th~t
there is no change
in national price due to information. This result is consistent
with the results from the Hausman test.
The coefficients for the information variables in the reduced
form for quantity and the demand equation imply that there is no
long run effect on apple purchases due to the Alar scare . Had there
been
a
residual
effect,
statistically different
opposite is true.
from
the
coefficient
zero.
on
The results
S3
would
be
imply that the
11
Conclusions
The announcement on the presence of health risk associated
with the consumption of apples treated with Alar was found to have
a significant impact on apple sales. Moreover, the announcement of
a greater risk estimate reduced the apple sales even further.
The apple sales return to the pre-product warning levels after
Alar
was
withdrawn
from
the
market.
This
suggests
that
the
announcement of the elimination of health risk from the market was
effective in regaining consumer confidence and restoring apple
sales
to
pre-Alar
consumers
respond
scare
levels.
swiftly and
These
results
systematically
show
to
the
that
new
the
risk
information.
This study demonstrated that the price of apples were not
effected by information at the national level, at least in the new
York-Newark regional market.
simultaneity is not an
issue
Therefore,
the study suggests the
in examining the effect of risk
information in a regional market. One explanation to this is that
there may be an offsetting supply effect at the national level,
thus keeping the price constant . Furthe r resea rch in needed to t est
the
validity
of
such
hypothesis.
Research
is
also
n eeded
to
replicate the results obtained in this study to th e other markets
in the U. S .
Table 1 : Estimates fo r
Demand Equation
MODEL
CONSTANT
the Reduced Form Equations and the
Reduced
form for
price•
Reduced
form for
quantity
Demand
(with no
instrument)
Demand
(with
instrument)
-0.0 07
(-0.521)
- 0. 037***b
(-1.315)
-0.03 4***
(-1.43 8 )
- 0 . 039***
( -1. 354 )
- 0 . 652*
(2.273)
- 1 . 088**"
(-1. 308)
ln p r
ln br
-0 .002
(-0.022)
-0.299
(-0.951)
ln mr
0.347
(1.473)
0 . 076
(0.084)
ln h r
-0.089
(-1.370)
0.106
(0.470)
S1
-0.0 41
(-0.728)
-0.211***
(-1.618)
-0 . 192***
( -1. 557)
-o. 155**
( - 2 . 316)
S2
-0.0 2 1
(-0.355)
-0. 221 ...
(-1. 311)
- 0 . 295**
(-1.907)
- 0 .341***
(- 2 . 941)
S3
-0.142
(-1.450)
0 . 015
(0.066)
-0. 076
(-0.3 63 )
- 0 . 085
( - 0.548)
0. 546*
(6.550)
o. 521 *
(6 .444 )
0. 526*
(4.972)
AR(l)
0. 795•
(14.586)
MA(l)
(SEASONAL)
-0.7 96*
(-13.243)
-0. 73 1 *
(-9.931)
-0.7 46*
(-10. 038)
- 0 . 732*
( - 7 . 784)
adj. R2
0.724
0.606
0 . 600
0 . 584
Q Stat
19.535
3 1. 073
25 . 452
23.420
MA
a
The significance levels of the coefficients in the reduced
form for price are from a two tailed test. The significance
leve ls of the coefficients for the othe r equations are from a
one tailed test.
b
* Significant at the a ~ 0.01 level
** Significant at the a ~ 0.05 level
*** Significant at the a ~ 0.10 l evel
(Figures in parantheses are t values)
References
Amemiya,
Takeshi . Advanced
University Press , 1985 .
Box ,
Econometrics.
Cambridge :
Harvard
George E . P. and Gwilym M. Jenkins . Time Series Analysi s
Forecasting and Control. San Francisco : Holden- Day, 1976 .
Caswell, Julie A., ed . Economics of Food
Elseiver Science Publishing Co., 1 99 1 .
Safety .
New
York :
Choi, Kwan E . and Helen H. Jensen. "Modelling the Effect of Risk
on Food Demand." Economics of Food Safety, ed. Julie A.
Caswe ll, pp. 29-44, New York : Elseiver Science Publishing Co.,
1 991 .
Eom,
Young Sook. 11 Pesticide Residues and Averting Behavior. "
Division of Economics and Business , North Carolina State
University, February, 1991 .
Hausman, J .A. "Specification Tests in Econometrics . " Econometrica
6(1978):1251-1271.
Sewell, Bradford H. and Robin M. Whyatt . Intolerable Risk:
Pesticides in Our Children's Food . Washington D.C .: Natural
Resources Defense Council., 1989 .
van Ravenswaay, Eileen O. and John P . Hoehn. "The Impact of
Health Risk Information on Food Demand: A Case Study of Alar
and Apples." Economics of Food Safety . ed. Julie A. Caswell,
pp.155-174, New York : Elseiver Science Publishing Co . , 1991.