Demand for Organic and Conventional Fruits.pdf

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Demand for Organic and Conventional Fresh Fruits 
 
 
 
Biing-Hwan Lin 
Economic Research Service 
US Department of Agriculture 
e-mail: blin@ers.usda.gov 
 
Steven T. Yen 
Department of Agricultural Economics 
The University of Tennessee 
e-mail: syen@utk.edu 
 
Chung L. Huang  
Department of Agricultural and Applied Economics 
University of Georgia 
e-mail: chuang@uga.edu 
  
 
 
 
 
Selected Paper prepared for presentation at the American Agricultural Economics 
Association Annual Meeting, Orlando, FL, July 27-29, 2008 
 
 
 
 
 
Research for this study was supported by USDA-ERS Cooperative Agreement No. 43-3AEM-5-
80043.  The views expressed in this study are those of the authors, and do not necessarily reflect 
those of the U.S. Department of Agriculture. 
Demand for Organic and Conventional Fresh Fruits 
Abstract 
We examine consumer demand for organic and conventional fruits by estimating a censored 
demand system, using Nielsen’s Homescan data. Sociodemographic characteristics and income 
are found to be significant factors of organic fruit consumption. Consumers are responsive to 
own-price changes in selected organic fruits, while the own-price elasticities for conventional 
fruits are much smaller. Asymmetric cross-price effects are found between organic and 
conventional fruits, suggesting that a change in relative prices will more likely cause consumers 
of conventional fruits to “cross-over” to organic fruits, while the reverse is less likely to happen 
such that organic consumers will “revert” to conventional fruits. 
Keywords: organic fruit, Homescan data, censored demand system, two-step estimation. 
 
Introduction 
The US market for organic foods has grown rapidly in the past decade. In 2000, conventional 
supermarkets for the first time sold more organic food than any other venues (Dimitri and 
Greene, 2002). Organic food sales rose from $3.6 billion in 1997 to $16.7 billion in 2006, 
growing at annual rates of 15-21 percent (OTA, 2007). According to OTA, the 2007 organic 
food sales would exceed $20 billion, and future growth would be at 18 percent a year between 
2007 and 2010.   
 
Among the organic food categories, fruit and vegetables by far comprise the largest retail 
sales, accounting for 40 percent of total organic food sales in 2006. The importance of fruits and 
vegetables in the organic food markets is also reflected in the production statistics, showing that 
only 0.2 percent of US corn and soybean acreage was certified organic in 2005, compared with 
2 
 
2.5 percent of fruits and 5 percent of vegetables (USDA-ERS, 2008). Dimitri and Greene (2002) 
estimated that between 1997 and 2001, US farmers and ranchers nearly doubled the acreage of 
certified organic land, totaling 2.3 million acres.    
Organic products are credence goods—consumers do not know whether a product is 
organic unless told (Giannakas, 2002). The US Department of Agriculture’s (USDA) standards 
for organic foods, implemented in October 2002, aim at boosting consumer confidence in the 
organic label and, hence, facilitating further growth in the organic food industry. Krystallis et al. 
(2006) demonstrated that the use of the organic label by farmers, agricultural firms and food 
companies can be an effective marketing strategy. Their study suggests that an organic label 
transforms its quality characteristics from credence to search and makes a product more easily 
accepted by consumers. 
Consumer preference for organic food based on perceived desirable attributes and 
characteristics has been widely documented (Yiridoe et al., 2005). Many studies (Gil et al., 2000; 
Magnusson et al., 2001; Roitner-Schobesberger et al., 2008; Tsakiridou et al., 2008) have shown 
that there is a widespread belief that organic food is substantially healthier and safer than 
conventional food, and those notions are fundamental for consumers’ purchasing of and their 
willingness to pay significant price premiums for organics. However, empirical analyses of 
consumer demand for organic foods are almost nonexistence and few studies have estimated and 
reported demand elasticities for organic foods (Glaser and Thompson, 1998, 2000). In his review 
of organic demand literature, Thompson (1998) concluded that “Attitudes, motives, and 
willingness to pay for organic products have been measured, but apparently no retail data have 
been available to estimate own-price, cross-price, and income elasticities.” The study fills this 
3 
 
research void by using the 2006 Nielsen Homescan data to estimate a system of 12 demand 
equations for 6 categories of organic and conventional fresh fruits.  
Even though organic food sector has experienced rapid growth and has made inroad into 
mainstream supermarkets, organic food sales were estimated to account for about 3 percent of 
total food sales in the US in 2006 (OTA, 2007). The majority of US consumers do not consume 
organic food, hence organic demand is characterized by a large portion of observations with zero 
consumption. This censoring in data is accommodated by using a two-step estimation procedure 
for consumer demand systems. 
Data and Sample 
The Nielsen Homescan panel consists of representative US households that provide food 
purchase data for at-home consumption. In 2006, the panel included 7,534 households, who 
reported purchases of food products sold as random weight or with the Uniform Product Code 
(UPC) at retail outlets. For UPC-coded food products, organic produce can be identified by the 
presence of the USDA organic seal or organic claims created by Nielsen. For random-weight 
items, Nielsen uses a coding system which identifies organic produce. Homescan panelists do 
not report prices paid for each food item; they report total quantity purchased and amount paid 
for that quantity. Therefore, the price is represented by unit value, which is derived by dividing 
total expenditure, net of any promotional and sale discounts, by the quantity purchased. The 
Homescan data also include product characteristics and promotion information, as well as 
detailed socio-demographic information of each household.  
 
For this study, household purchase records of fresh produce, in general reported weekly, 
were aggregated to the annual level. Among the 7,534 panelists, 7,237 participated at least 10 
months in 2006. After deleting observations with missing information on important variables and 
4 
 
observations with outliers such as those with extreme values in prices (i.e., 6 standard deviations 
from the sample mean of each price), a final sample of 6,696 observations was used in this study. 
Major conventional and organic fresh fruits were identified in this study. Specifically, the 
demand system includes equations for 12 fruit categories—5 major conventional and 5 major 
organic fruits (apples, bananas, grapes, oranges, and strawberry) and a catch-all category for 
other conventional fruits and for other organic fruits. As shown in Table 1, the sample contains 
large proportions of households who did not purchase organic produce. The proportions of 
consuming households are relatively small for organic fruits: oranges (2.8%), grapes (3.2%), 
strawberry (3.8%), apples (8.2%), bananas (11.1%), and other organic fruits (12.1%). The 
proportions of consuming households are much higher for conventional fruits, ranging from 
68.2% for oranges to 93.1% for other fruits. Note that only fresh fruits were included in this 
study.   
Demand System Specification and Econometric Procedure 
Our empirical analysis is based on the assumption that organic and conventional fruits are 
separable from all other consumer goods. We use the Translog demand system (Christensen et 
al., 1975) for n fruit products, in expenditure shares (si) form: 
 
,
,...,
1
,
/
)
log
(
1
n
i
D
v
s
j
ij
n
j
i
i
=
Σ
+
=
= β
α
 
(1) 
where v1, ..., vn are expenditure-normalized prices, and 
j
kj
n
j
n
k
j
n
j
v
D
log
1
1
1
β
α
=
=
=
Σ
Σ
+
Σ
=
 which is 
the sum of the numerator in equation (1), with the restriction that 
.
1
1
=
Σ =
j
n
j α
 This demand 
system is derived from the Translog indirect utility function which is second-order 
approximation to any functional forms. Homogeneity is implied in equation (1), and the 
symmetry restrictions 
 
j
i
ji
ij
,
∀
= β
β
 
(2) 
5 
 
are also imposed. Household characteristics are incorporated in equation (1) by specifying 
parameters 
iα  as functions of demographic variables 
)
,...,
1
(
L
h
=
l
l
 
 
,
1
,...,
1
,
1
0
−
=
Σ
+
=
=
n
i
h
i
L
i
i
l
l
l α
α
α
 
(3) 
where 
0
iα
  and 
l
iα
 are parameters to be estimated. Such demographic specifications for the n –1 
equations (only) are explained below. The linear demographic specification was also followed in 
other studies with the Translog demand system (e.g., Yen et al., 2003) and the linear 
approximate almost ideal demand system (e.g., Salvanes and DeVoretz, 1997).  
As noted above, the sample contains a large proportion of households who did not 
purchase certain fruit products during the sample period. Such censoring of the expenditures has 
to be accommodated to obtain consistent estimates of demand parameters and elasticities. While 
a number of maximum-likelihood (ML) estimators are available in the literature (e.g., Lee and 
Pitt, 1986; Wales and Woodland, 1983; Yen et al., 2003; Yen and Lin, 2006), the large demand 
system with many zeros makes ML estimation computationally difficult, with nearly 85% of the 
sample calling for 6 or higher-level integration of the normal probability density. The two-step 
censored system estimator (Shonkwiler and Yen, 1999), more formally motivated with a 
multivariate sample selection model (Yen and Lin, 2006), provides a practical solution to the 
problem.  
Let 
)
,...,
,
log
,...,
[log
1
1
′
=
L
n
h
h
v
v
x
 be a vector of explanatory variables and θ a vector 
containing all parameters (α’s and β’s), and consider an n-equation system in which each 
expenditure share wi is generated by a deterministic function fi(x; θ) constituting the RHS of the 
share equation (1), and an unobservable error term
i
e . Each equation is subject to the sample 
selection rule (cf. Heckman, 1979)  
 
,
,...,
1
],
)
;
(
[
n
i
e
x
f
d
w
i
i
i
i
=
+
=
θ
 
(4) 
6 
 
such that each indicator di is modeled with a binary probit 
 
,
,...,
1
),
0
(
1
n
i
u
z
d
i
i
i
=
>
+
′
=
γ
 
(5) 
where 1(A) denotes the indicator function, taking a value 1 if event A holds, and 0 otherwise, z is 
a vector of variables, γi is a vector of parameters, and ui is idiosyncratic error distributed as 
standard normal 
).
1
,
0
(N
 
 
The expenditure shares in equation (4) do not add up to unity unless d1 = … = dn = 1, that 
is, when none of the dependent variables are subject to sample selection. We follow the simple 
approach suggested in Yen and Lin (2006), by estimating the first n − 1 equations with the nth 
good treated as a residual category (cf. Pudney, 1989). The resulting estimates are not invariant 
with respect to the equation excluded. Yen and Lin (2006) however demonstrated in an 
application to food consumption in China that excluding alternative equations from the system 
did not cause discernable differences in the elasticity estimates. 
 
Assuming the concatenated error vector 
]
,...,
,
,...,
[
1
1
1
1
′
−
−
n
n
e
e
u
u
 is distributed as (2n–2)-
dimensioned normal distribution with zero means and a finite covariance matrix with elements 
),
2
2
,...,
1
,
(
−
=
n
j
i
ij
σ
 the sample selection model can be estimated with the ML procedure (Yen 
and Lin, 2006). However, with a large system of n = 12 equations, the ML procedure would 
require estimation of a much larger number of parameters than the two-step procedure and 
evaluations of 11-level probability integrals for all sample observations which is not feasible 
(even with a simulation estimation procedure) with the large sample size for the current 
7 
 
application.1 A practical alternative is to estimate the system with a two-step procedure, 
motivated by the unconditional mean of the expenditure shares 
 
,
1
,...,
1
),
(
)
;
(
)
(
)
(
),
1
(
−
=
+
′
Φ
=
+
−
n
i
z
x
f
z
w
E
i
i
i
i
n
i
i
i
i
γ
φ
σ
θ
γ
 
(6) 
where φ(⋅) and Φ(⋅) are univariate standard normal probability density and cumulative 
distribution functions, respectively, and 
i
i
n
),
1
(
+
−
σ
 is the covariance between the error terms (ui, ei) 
of the ith selection and level equations. The unconditional means (6) follow from the bivariate 
normality of the error terms (ui, ei) for i = 1,..., n–1, and suggest a two-step estimation procedure 
which, as initially suggested in Shonkwiler and Yen (1999) for a linear system, consists of two 
steps: (i) a probit estimation based on a binary outcome for di = 1(wi > 0) to obtain ML estimates 
i
γ̂  for each i; and (ii) estimation of the augmented nonlinear system 
 
1
,...,
1
,
)
ˆ
(
)
;
(
)
ˆ
(
),
1
(
−
=
+
′
+
′
Φ
=
+
−
n
i
z
x
f
z
w
i
i
i
i
i
n
i
i
i
i
ξ
γ
φ
σ
θ
γ
 
(7) 
with ML or a method of moments procedure such as the iterated seemingly unrelated regression, 
where 
iξ is a composite and heteroscedastic error term, and 
i
i
n
),
1
(
+
−
σ
 are additional parameters to 
estimate (in addition to θ). This two-step procedure is less efficient than the ML procedure in 
Yen and Lin (2006) but produces statistically consistent estimates for θ and 
i
i
n
),
1
(
+
−
σ
. Demand 
elasticities for the first (n – 1) goods can be derived by differentiating the unconditional means 
(6), and elasticities for the nth good by using the adding-up restriction (Yen et al., 2003, 
Footnote 9). 
                                                 
1In the current application we estimate n – 1 = 11 equations, all of which subject to sample 
selection, which requires estimation of a 22 × 22 covariance matrix with 253 elements. 
8 
 
Results 
The Translog demand system of 11 equations was estimated with the two-step procedure 
described above. With such a large demand system, we needed to pay special attention to the 
inclusion of demographic variables because the number of parameters is an exponential function 
of explanatory variables. Therefore, we included most of the socio-demographic variables in the 
first step (probit) estimation, and minimized the number of parameters by using only two 
demographic variables in the second step. The definitions and sample statistics of demographic 
variables included in the empirical analysis are presented in Table 2. Variables used in the first 
step included income, household size, and dummy variables indicating presence of children, race 
and ethnicity (white, black, Hispanic, oriental, and other race), region (East, Central, South, and 
West), urbanization (urban and rural), marriage status of household head (married or not), 
employment status of the female head (employed or not), and education (no college, some 
college, and college degree). The demand system incorporated only two dummy variables 
indicating age of the household head (age 40–64 and age ≥ 65). Note that the Translog demand 
system includes expenditure on the 12 categories of fresh fruits. The expenditure is expected to 
increase with income, even though we did not examine the relationship between income and 
expenditure on fresh fruits.  
Table 3 presents the probit results obtained from the first-step estimation. As typical with 
cross sectional data, goodness-of-fit measures are low for the probit analysis, with McFadden’s 
R2 ranging from 1% for organic grapes to 3.8% for conventional apples. However, percentage of 
correct predictions, at a probability cut-off of 0.5 (Wooldridge, 2002: 465), are all greater than 
65%, ranging from 68.2% for conventional oranges to 97.2% for organic oranges. 
9 
 
Many of the socio-demographic variables did not have any significant impacts on the 
probabilities of purchasing fresh fruits in general. However, household income, education, 
household size, region, marital, and employment status are important variables that influence 
significantly the probabilities of purchasing fresh fruits. Specifically, household income is found 
to have significant positive impacts on the purchases of fresh fruits, particularly on both organic 
and conventional apples and strawberry, and other organic fruits. With respect to education, 
households with at least some college education (of the household head) are more likely to 
purchase organic fruits than those households with only high school or less than high school 
education. Household size also shows a significant positive effect on the likelihood of purchasing 
conventional grapes, oranges and strawberry. Married households are more likely to purchase 
organic bananas and all conventional fruits than their counterparts. Similarly, households with 
unemployed female household head are found more likely to purchase conventional fruits and 
some organic fruits, including apples, bananas, strawberry and other organic fruits. Households 
residing in the Western region of the US are more likely to purchase fresh fruits than households 
in other regions. This is especially true with respect to purchases of organic fruits, except for 
households in the Central region which appear to be more likely to purchase conventional apples, 
grapes, oranges and strawberry than households residing in other regions.  
 
The second-step estimates for the Translog demand system suggest that age of household 
head plays a role in buying fresh conventional fruits, but does not affect the purchase of organic 
fruits. The second-step estimates are not presented due to space consideration but are available 
upon request. Nearly one-half (38) of the 78 coefficients for the quadratic price terms (βij) are 
statistically significant at the 5% level or lower, and three more are significant at the 10% 
significance level. The selectivity terms are not significant for organic oranges, organic bananas, 
10 
 
and conventional apples but significant for all other equations (at the 10% significance level or 
lower), suggesting the importance of accommodating zero observations. In what follows, we 
focus primarily on the demand elasticities derived from the estimation of the Translog system 
instead of the parameter estimates. 
Demand Elasticities 
The uncompensated price and expenditure elasticities computed from the estimated Translog 
demand system are presented in Table 4. All expenditure elasticities are positive and significant 
at the 1% probability level, ranging from 0.81 for organic bananas to 1.03 for organic oranges 
and from 0.98 for conventional bananas to 1.01 for other major conventional fruits. The results 
show that the expenditure elasticities for organic and conventional fruits tend to be unitary, 
implying that given an increase in the spending on the selected fresh fruits, consumers would 
allocate approximately the same proportion of increase in their purchase of conventional and 
organic fresh fruits. Our expenditure estimates are quite similar to those reported by Glaser and 
Thompson (1998), who reported elasticities ranging from 0.778 for organic frozen corn to 1.489 
for organic green peas and from 0.892 for conventional frozen green peas to 1.158 for 
conventional frozen corn.  
 
There appears to be a widely held belief in the organic trade circle that household income 
is not correlated with expenditures on organic food. Thus, some popular presses have suggested 
that lower income families may choose to buy organic when possible as a means of preventative 
medicine, and they are at least as likely to purchase organic as other income groups (Hartman 
Group, 2003; OTA, 2004). A simple cross-tabular analysis of spending on organic produce by 
household income class also seems to support the commonly held belief (Stevens-Garmon et al., 
2007). Thus, income has not been tracked in monitoring organic trade (Fromartz, 2006). Our 
11 
 
finding of positive and statistically significant expenditure elasticities strongly suggest that 
demands for fresh organic fruits rise with income—a result that seems at odds with the 
conventional wisdom in the organic trade. 
 
All own-price elasticities are negative and statistically significant at the 1% significance 
level, except for organic oranges, organic strawberry, and other organic fruits. Among those with 
significant own-price elasticities, demands for organic fruits are found to be price elastic, 
whereas demands for conventional fruits are price inelastic. Our estimates of organic own-price 
elasticities range from –1.06 for apples to –3.19 for bananas and –3.54 for grapes. The finding of 
a highly elastic demand for organic fruits is to be expected because organic produce typically 
commands a price premium (Lin et al., 2008) with a small market share. The result implies that 
the demands for organic fruits are price sensitive, suggesting that a 1% change in the prices will 
elicit more than 1% change in quantities demanded. Empirical studies that report price 
elasticities for organic foods are far and between. Glaser and Thompson (1998) reported own-
price elasticities for four organic frozen vegetables (broccoli, corn, green peas, and green beans) 
ranging from –1.630 to –2.268. Demands for organic milk were found to be highly responsive to 
own-price changes with elasticities ranging from –3.637 for whole fat milk to –9.733 for 1% fat 
milk (Glaser and Thompson, 2000). 
Our estimates of own-price elasticity for conventional fresh fruits are –0.49 (grapes), –
0.50 (strawberry), –0.57 (oranges), –0.70 (bananas), –0.83 (apples), and –0.85 (other fruits). In a 
recent study of demands for conventional fresh fruits, Brown and Lee (2002) reported similar 
elasticities of –0.52 for apples, –0.54 for bananas, –0.56 for grapes, and –0.67 for oranges. In an 
early study of demands for fresh fruits, George and King (1971) reported own-price elasticities 
of –0.72 for apples, –0.61 for bananas, –0.65 for oranges, and –0.60 for other fruits. 
12 
 
 
Among the 66 pairs of cross-price elasticities estimated for organic and conventional 
fruits, we found about half of them, or 34 pairs, are statistically significant at the 10% 
significance level or lower. Complementary relationships are also found to be dominant between 
most organic and conventional fruits. Among the six organic fruits, there are 8 significant 
positive and 10 significant negative cross-price elasticities. In contrast, the results show that 
almost all conventional fruits are gross complements to each other; only conventional strawberry 
is found to have a significant substitution relationship with other conventional fruits. As with the 
own-price elasticities, all conventional fruits are shown to have inelastic cross-price elasticities, 
while organic cross-price elasticities are larger in magnitudes and more responsive to price 
changes of other organic fruits.  
As shown in Table 4, among the 10 significant cross-price elasticities between organic 
and conventional fruits, 6 cross-price elasticities are positive and 4 negative. The result shows 
that consumers tend to increase their purchase of organic fruits, if there is an increase in the 
prices of conventional fruits. On the other hand, there are 7 positive and 7 negative significant 
cross-price elasticities between conventional and organic fruits. The results seem to suggest that 
consumers are more likely to substitute organic fruits for conventional fruits than the other way 
around. It is noted that among the significant cross-price elasticities, most organic fruits are gross 
substitutes for conventional fruits whereas organic fruits could be either gross substitutes or 
complements for conventional fruits. The finding is perhaps to be expected given that organic 
fruits are priced higher than conventional fruits, making it easier to switch from conventional to 
organic fruits, if organic fruits become relatively inexpensive when the prices of conventional 
fruits increase.  
13 
 
In addition, the magnitudes of the cross-price elasticities between conventional and 
organic fruits in general tend to be larger than those between organic and conventional fruits, 
suggesting that changes in the prices of conventional fruits will generally induce proportionally 
larger responses to organic fruits than to conventional fruits as organic prices change. In other 
words, purchases of organic fruits are more responsive to changes in prices of conventional fruits 
than changes in purchase of conventional fruits as organic prices change. Although they found 
only two pairs of significant cross-price elasticities for corn and between organic and 
conventional broccoli, Glaser and Thompson (1998) also observed a similar asymmetry in cross-
price responses. They suggest that this asymmetry would imply that a change in relative prices 
will more likely cause consumers of conventional fruits to “cross-over” to buy organic fruits, 
while the reverse is less likely to happen such that organic consumers will “revert” to buy 
conventional fruits. 
Conclusions 
The study fills a critical empirical void in the extant literatures pertaining to demands for organic 
foods. Most previous studies of organic demands have focused on consumer attitudes and 
willingness to pay for organic foods primarily due to the lack of available retail purchase data. 
Although the demand for organic foods has grown rapidly, the organic market share at retail 
level remains relatively small. According to the Nielsen Homescan panel data, the proportion of 
households that purchase organic fruits in 2006 varies from 2.81% for oranges to 11.14% for 
bananas (12.08% for the catch-all other fruits). In this study, we examine consumer demand for 
selected major organic and conventional fruits by estimating a censored demand system. By 
investigating the interrelationship between consumption of organic and conventional fresh fruits, 
14 
 
this study presents the much needed price elasticities for organic fruits as well as those cross-
price elasticities between organic and conventional fruits. 
Results obtained from this study provide several important market implications to assist 
producers, retailers and policy-makers in planning future development and growth of organic 
foods in the US. Our study shows that many socio-demographic characteristics are significant 
factors in affecting the probability of purchasing organic fruits. Specifically, households in the 
Western region, married households, and households with unemployed female head, college 
education and higher income were found to be more likely to buy some kinds of organic fresh 
fruits than their counterparts.  
 
In addition to the positive effect of income on the likelihood of buying organic fresh 
fruits, expenditure elasticities for organic fruits are found to be positive and statistically 
significant. Even though the scope of our study is limited to fresh organic fruits and not the 
comprehensive organic food sector, the finding of a positive relationship between income and 
organic demand suggests that simple correlation analysis of the relationship could be misleading. 
Future study on the issue should be encouraged.     
As to be expected, our elasticities measures suggest that consumers are quite sensitive to 
own-price changes in fresh organic fruits. The organic own-price elasticities are found to be 
highly elastic ranging from –1.06 to –3.54 as compared to the range of –0.49 to –0.85 for 
conventional fresh fruits. Our finding that consumers are more responsive to changes in prices of 
organic fruits than that of conventional fruits is consistent with a previous study which found 
own-price elasticities for organic frozen vegetables are generally two to three times larger than 
their conventional counterparts (Glaser and Thompson, 1998).  
15 
 
 
There are some strong statistical evidences indicating that cross-price effects between 
organic and conventional fruits are asymmetric. This asymmetry in cross-price elasticities would 
imply that a change in relative prices more likely will cause consumers of conventional fruits to 
“cross-over” to buy organic fruits, while the reverse is less likely to happen such that organic 
consumers will “revert” to buy conventional fruits. Given that organic own-price elasticities are 
highly elastic and the cross-price elasticities are asymmetric, we would expect that as the price 
premium on organic fruits drops the demand for organic fruits would continue to grow and 
expand as more consumers would participate and purchase organic foods. 
 
A few caveats pertain. First, due to the small proportions of consuming households 
demands were aggregated to the annual level and therefore, some information (such as seasonal 
variation) is lost during such aggregation. Further studies might consider estimation of the 
demand system with monthly or weekly data as organic food products become more popular. We 
also note that, by focusing on organic and conventional fruits the system estimated is a partial 
demand system. Last but not least, future studies might consider estimation of the demands for a 
broader group of conventional and organic food products. 
16 
 
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20 
 
Table 1. Sample statistics: expenditures, expenditure shares, and prices 
Variable 
% Consuming 
Mean 
S.D. 
Expenditures ($ / year) 
 
 
 
Organic fruits 
 
 
 
Apples (o) 
 
0.78 
7.70 
Consuming households 
8.17 
9.58 
25.35 
Bananas (o) 
 
0.50 
3.86 
Consuming households 
11.14 
4.45 
10.77 
Grapes (o) 
 
0.21 
1.93 
Consuming households 
3.15 
6.76 
8.65 
Oranges (o) 
 
0.16 
1.69 
Consuming households 
2.81 
5.84 
8.34 
Strawberry (o) 
 
0.24 
2.23 
Consuming households 
3.79 
6.33 
9.65 
Other fruits (o) 
 
1.20 
9.14 
Consuming households 
12.08 
9.92 
24.60 
 
 
 
 
Conventional fruits 
 
 
 
Apples (c) 
 
19.34 
30.59 
Consuming households 
82.69 
23.39 
32.20 
Bananas (c) 
 
16.59 
20.70 
Consuming households 
88.50 
18.74 
21.07 
Grapes (c) 
 
14.70 
24.17 
Consuming households 
74.09 
19.84 
26.20 
Oranges (c) 
 
10.42 
19.34 
Consuming households 
68.18 
15.29 
21.78 
Strawberry (c) 
 
12.31 
19.43 
Consuming households 
69.25 
17.78 
21.17 
Other fruits (c) 
 
47.21 
62.97 
Consuming households 
93.10 
50.71 
63.89 
 
 
 
 
Expenditure shares 
 
 
 
Apple (o) 
 
0.004 
0.028 
Bananas (o) 
 
0.005 
0.036 
Grapes (o) 
 
0.002 
0.015 
21 
 
Oranges (o) 
 
0.001 
0.019 
Strawberry (o) 
 
0.002 
0.016 
Other fruits (o) 
 
0.008 
0.046 
Apples (c) 
 
0.154 
0.165 
Bananas (c) 
 
0.162 
0.176 
Grapes (c) 
 
0.116 
0.137 
Oranges (c) 
 
0.083 
0.119 
Strawberry (c) 
 
0.104 
0.140 
Other fruits (c) 
 
0.359 
0.224 
 
 
 
 
Prices ($ / lb.) 
 
 
 
Apple (o) 
 
1.44 
0.22 
Bananas (o) 
 
0.61 
0.08 
Grapes (o) 
 
1.95 
0.19 
Oranges (o) 
 
0.95 
0.13 
Strawberry (o) 
 
2.51 
0.75 
Other fruits (o) 
 
1.41 
0.72 
Apples (c) 
 
1.08 
0.31 
Bananas (c) 
 
0.48 
0.12 
Grapes (c) 
 
1.55 
0.47 
Oranges (c) 
 
0.93 
0.23 
Strawberry (c) 
 
2.08 
0.58 
Other fruits (c) 
 
1.21 
0.68 
Sample size 
6,696 
Source: Compiled from Nielsen’s Homescan panel, 2006. Abbreviations “o” and “c” in 
parentheses indicate organic and conventional fruits, respectively. 
 
 
22 
 
 
Table 2. Definitions and sample statistics of demographic variables 
Variable 
Definition 
Mean 
S.D. 
Continuous variables 
 
 
 
Household size 
Number of members in households 
2.33 
1.29 
Income 
Household income as a % of Federal poverty level 
421.60 
275.59 
Binary variable (yes = 1; 0 = no) 
Child 
Presence of a child(ren) 
0.22 
 
White 
Race is white 
0.74 
 
Black 
Race is black 
0.13 
 
Hispanic 
Race is Hispanic 
0.07 
 
Oriental 
Race is Asian 
0.04 
 
Other race 
Race is others (ref.) 
0.02 
 
East 
Resides in East region of the country 
0.22 
 
Central 
Resides in central region 
0.17 
 
South 
Resides in South region 
0.38 
 
West 
Resides in South region (ref.) 
0.23 
 
Urban 
Resides in an urban area 
0.86 
 
Married 
Dual-headed household 
0.58 
 
Unemployed (F) Female household head unemployed 
0.38 
 
≤ High school 
Max. education of head is HS or lower (ref.) 
0.18 
 
Some college 
Max. education of head is some college 
0.30 
 
≥ college 
Max. education of head is college grad. or higher 
0.51 
 
Age < 40 
Oldest head age ≤ 40 (reference) 
0.10 
 
Age 40−64 
Oldest head age 41–64 
0.61 
 
Age ≥ 65 
Oldest head age ≥ 65 
0.29 
 
Sample size 
 
6,696 
 
Source: Compiled from Nielsen’s Homescan panel, 2006. 
 
23 
 
Table 3. First-step probit estimates of fruit demands 
Variable 
Apples (o) Bananas (o) Grapes (o) Oranges (o) 
Straw- 
berry (o) 
Other 
fruits (o) 
Apples (c) 
Bananas (c) 
Constant 
–1.530*** 
–1.406*** 
–2.198*** 
–2.109*** 
–2.152*** 
–1.308*** 
0.599*** 
1.104*** 
 
(0.187) 
(0.185) 
(0.321) 
(0.289) 
(0.269) 
(0.170) 
(0.166) 
(0.183) 
Child 
0.084 
–0.029 
–0.036 
–0.093 
–0.007 
0.011 
0.207*** 
0.013 
 
(0.079) 
(0.073) 
(0.105) 
(0.111) 
(0.107) 
(0.071) 
(0.068) 
(0.075) 
White 
–0.198 
0.037 
0.244 
0.009 
0.070 
–0.172 
–0.120 
–0.059 
 
(0.157) 
(0.162) 
(0.290) 
(0.253) 
(0.230) 
(0.143) 
(0.146) 
(0.159) 
Black 
–0.133 
0.238 
0.384 
0.233 
0.082 
–0.130 
–0.280* 
–0.214 
 
(0.167) 
(0.169) 
(0.298) 
(0.263) 
(0.241) 
(0.152) 
(0.152) 
(0.165) 
Hispanic 
–0.048 
0.172 
0.462 
0.195 
0.056 
–0.052 
–0.185 
–0.165 
 
(0.173) 
(0.175) 
(0.303) 
(0.270) 
(0.251) 
(0.158) 
(0.160) 
(0.174) 
Oriental 
–0.136 
–0.066 
0.458 
0.036 
0.149 
–0.190 
–0.249 
–0.229 
 
(0.187) 
(0.191) 
(0.317) 
(0.291) 
(0.259) 
(0.171) 
(0.173) 
(0.188) 
East 
–0.135** 
–0.183*** 
–0.064 
–0.058 
–0.122 
–0.211*** 
–0.046 
–0.168*** 
 
(0.066) 
(0.061) 
(0.092) 
(0.09) 
(0.083) 
(0.059) 
(0.056) 
(0.062) 
Central 
–0.235*** 
–0.205*** 
–0.127 
–0.191* 
–0.185** 
–0.282*** 
0.221*** 
0.038 
 
(0.075) 
(0.067) 
(0.104) 
(0.107) 
(0.096) 
(0.067) 
(0.064) 
(0.070) 
South 
–0.181*** 
–0.203*** 
–0.049 
–0.185** 
–0.238*** 
–0.244*** 
0.006 
–0.078 
 
(0.059) 
(0.054) 
(0.081) 
(0.084) 
(0.076) 
(0.052) 
(0.050) 
(0.057) 
Urban 
0.099 
0.042 
0.096 
0.026 
0.216** 
0.111* 
–0.042 
–0.001 
 
(0.073) 
(0.063) 
(0.100) 
(0.102) 
(0.103) 
(0.065) 
(0.058) 
(0.064) 
Married 
0.093 
0.088* 
–0.050 
–0.036 
–0.049 
–0.076 
0.353*** 
0.355*** 
 
(0.057) 
(0.052) 
(0.075) 
(0.079) 
(0.074) 
(0.051) 
(0.046) 
(0.052) 
Unemployed (F) 
0.110** 
0.085** 
0.021 
0.103 
0.115* 
0.130*** 
0.225*** 
0.228*** 
 
(0.048) 
(0.044) 
(0.066) 
(0.069) 
(0.063) 
(0.044) 
(0.041) 
(0.046) 
 
24 
 
Some college 
0.165** 
0.093 
–0.081 
–0.092 
–0.033 
0.148** 
0.141*** 
–0.084 
 
(0.074) 
(0.065) 
(0.093) 
(0.099) 
(0.09) 
(0.065) 
(0.053) 
(0.062) 
≥ College 
0.282*** 
0.232*** 
0.041 
0.051 
0.181** 
0.250*** 
0.178*** 
–0.092 
 
(0.071) 
(0.062) 
(0.088) 
(0.093) 
(0.090) 
(0.063) 
(0.052) 
(0.060) 
Income × 10–2 
0.019** 
0.003 
–0.009 
0.019 
0.040*** 
0.032*** 
0.015** 
0.009 
 
(0.009) 
(0.008) 
(0.013) 
(0.012) 
(0.010) 
(0.008) 
(0.008) 
(0.008) 
Household size 
–0.022 
–0.008 
0.036 
0.060 
–0.019 
0.019 
0.001 
0.012 
 
(0.029) 
(0.027) 
(0.037) 
(0.038) 
(0.040) 
(0.026) 
(0.024) 
(0.027) 
Log likelihood 
−1,858.423 −2,310.027 −927.847 
−843.817 
−1,047.225 −2,415.848 −2,966.842 −2,312.651 
McFadden’s R2 
0.019 
0.013 
0.010 
0.015 
0.031 
0.021 
0.038 
0.032 
% correct predict 
91.80 
88.90 
96.80 
97.20 
96.20 
87.90 
82.70 
88.50 
Note: Asymptotic standard errors in parentheses. Levels of statistical significance: *** = 1%, ** = 5%, * = 10%.
 
25 
 
Table 3 continued.  
Variable 
Grapes (c) 
Oranges (c) 
Straw- 
berry (c) 
Constant 
0.052 
0.209 
–0.196 
 
(0.145) 
(0.145) 
(0.144) 
Child 
0.084 
0.013 
0.046 
 
(0.061) 
(0.058) 
(0.060) 
White 
0.089 
–0.156 
0.008 
 
(0.125) 
(0.126) 
(0.125) 
Black 
0.156 
–0.107 
–0.236* 
 
(0.132) 
(0.132) 
(0.131) 
Hispanic 
0.088 
–0.064 
–0.080 
 
(0.138) 
(0.139) 
(0.138) 
Oriental 
–0.054 
–0.047 
–0.095 
 
(0.150) 
(0.152) 
(0.150) 
East 
0.040 
–0.032 
–0.120*** 
 
(0.051) 
(0.049) 
(0.049) 
Central 
0.199*** 
0.145*** 
0.209*** 
 
(0.056) 
(0.054) 
(0.055) 
South 
0.026 
–0.099** 
0.057 
 
(0.045) 
(0.044) 
(0.044) 
Urban 
0.026 
0.008 
0.180*** 
 
(0.051) 
(0.049) 
(0.049) 
Married 
0.265*** 
0.188*** 
0.229*** 
 
(0.042) 
(0.041) 
(0.041) 
Unemployed (F) 
0.170*** 
0.188*** 
0.149*** 
 
(0.037) 
(0.035) 
(0.036) 
 
26 
 
 
Some college 
0.055 
0.057 
0.077 
 
(0.050) 
(0.048) 
(0.048) 
≥ College 
0.068 
0.136*** 
0.167*** 
 
(0.048) 
(0.047) 
(0.047) 
Income 
0.010 
0.007 
0.017*** 
 
(0.007) 
(0.007) 
(0.007) 
Household size 
0.049** 
0.053*** 
0.079*** 
 
(0.023) 
(0.021) 
(0.022) 
Log likelihood 
−3,740.826 −4,107.903 −3,996.544 
McFadden’s R2 
0.024 
0.019 
0.033 
% correct predict 
74.10 
68.20 
69.50 
Note: Asymptotic standard errors in parentheses. Levels of 
statistical significance: *** = 1%, ** = 5%, * = 10%. 
 
27 
 
Table 4. Uncompensated price and total expenditure elasticities 
Product 
Apples (o) Bananas (o) Grapes (o) Oranges (o) 
Straw- 
berry (o) 
Other 
fruits (o) 
Apples (c) 
Bananas (c) 
Apples (o) 
–1.06*** 
0.65*** 
–0.55* 
0.07 
0.05 
0.41*** 
0.10 
–0.46*** 
Bananas (o) 
1.05*** 
–3.19*** 
2.72*** 
–1.51*** 
0.10 
–0.16 
–0.20 
1.13*** 
Grapes (o) 
–0.65* 
1.97*** 
–3.54*** 
1.78*** 
–0.49 
–1.13*** 
0.73*** 
–0.10 
Oranges (o) 
0.08 
–0.98*** 
1.66*** 
–0.92 
–0.82* 
0.11 
–0.32 
0.48* 
Strawberry (o) 
0.06 
0.08 
–0.50 
–0.90** 
–0.36 
–0.61*** 
0.69*** 
0.19 
Other fruits (o) 
0.32** 
–0.07 
–0.75*** 
0.07 
–0.39*** 
–0.01 
–0.06 
0.04 
Apples (c) 
0.03 
–0.05 
0.19*** 
–0.09 
0.18*** 
–0.02 
–0.83*** 
–0.08** 
Bananas (c) 
–0.16*** 
0.23*** 
–0.03 
0.14* 
0.05 
0.02 
–0.08** 
–0.70*** 
Grapes (c) 
0.02 
–0.13*** 
0.05 
0.05 
0.04 
–0.06 
–0.10*** 
–0.12*** 
Oranges (c) 
–0.19*** 
–0.03 
0.03 
0.03 
0.18*** 
–0.02 
–0.12*** 
–0.10** 
Strawberry (c) 
0.00 
–0.05 
0.01 
–0.15* 
–0.02 
–0.11*** 
–0.13*** 
–0.09** 
Other fruits (c) 
0.08*** 
0.00 
–0.10*** 
0.01 
–0.14*** 
0.04* 
0.06*** 
–0.02 
 
 
28 
 
Table 4 continued. 
Product 
Grapes (c) 
Oranges (c) 
Straw- 
berry (c) 
Other 
fruits (c) 
Total 
Expend. 
Apples (o) 
0.05 
–0.40*** 
0.00 
0.16 
0.99*** 
Bananas (o) 
–0.53*** 
–0.10 
–0.20 
0.08 
0.81*** 
Grapes (o) 
0.15 
0.08 
0.04 
0.21 
0.97*** 
Oranges (o) 
0.12 
0.07 
–0.44 
–0.07 
1.03*** 
Strawberry (o) 
0.13 
0.45*** 
–0.06 
–0.18 
0.99*** 
Other fruits (o) 
–0.13 
–0.03 
–0.22*** 
0.23** 
0.99*** 
Apples (c) 
–0.08*** 
–0.08*** 
–0.11*** 
–0.08*** 
1.01*** 
Bananas (c) 
–0.10*** 
–0.07** 
–0.07** 
–0.21*** 
0.98*** 
Grapes (c) 
–0.49*** 
–0.10*** 
–0.08** 
–0.09*** 
1.01*** 
Oranges (c) 
–0.12*** 
–0.57*** 
0.00 
–0.10*** 
1.01*** 
Strawberry (c) 
–0.08** 
0.00 
–0.50*** 
0.11*** 
1.01*** 
Other fruits (c) 
–0.03 
0.00 
–0.04** 
–0.85*** 
1.00*** 
Note: Asymptotic standard errors in parentheses. Levels of statistical significance: *** = 1%, 
** = 5%, * = 10%.