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MINISTRY OF EDUCATION AND TRAING
UNIVERSITY OF ECONOMICS HCMC

--------------

LE THI TUYET THANH

INFORMATIONAL ASYMMETRIES
AND THE DEMAND FOR
VEGETABLE IN HCMC
MAJOR: DEVELOPMENT ECONOMICS
CODE: 9310105

PH.D. DISSERTATION
EXECUTIVE SUMMARY

HO CHI MINH CITY 2019


INTRODUCTION
This study is motivated by the asymmetric information problem

on HCMC vegetable market, in which a large proportion of
vegetable does not comply with safety and hygiene standards. In
most cases, consumers can not recognize the safety attributes of
vegetable, and thus can not distinguish the conventional
vegetable and safe vegetable. This results in two problems. One
is that producers may apply unsafe production techniques to
maximize their own profits (moral hazards). The other is safe
vegetable can be driven out of the market being unable to
compete with conventional vegetable (adverse selection). These
two problems result in the failures of safe vegetable market in
HCMC. Both buyers and distributors have taken measures to
overcome these problems.
Lacking information, consumers try to seek information about
safety and hygiene standards of vegetable to be able to buy safe
vegetable. The prevalent information channels in HCMC are
internet, television, and newspaper, which can affect consumer
behavior and thus influence the development of safe vegetable
market. Understanding the effect of these channels on the
demand for safe vegetable, the preferences for the safety
attributes, and vegetable store choice are therefore important to
the safe vegetable distributors in expanding their market share.
The results of this study also help policymakers in providing
information and selecting the appropriate channels to develop the
market for safe vegetable.
The safe vegetable distributors, especially those of modern
distribution channels, confronted many difficulties in competing
with conventional vegetable in the context that buyers can not
distinguish the two types of vegetable. The first problem is how
to set vegetable prices in such a way that can compensate for the
high production and distribution costs and at the same time,
accepted by consumers. The second one is which safety
standards are important to consumers in order to have the
relevant development strategy. The last one is what store
attributes are critical to vegetable buyers in order to attract more
shoppers.
RESEARCH OBJECTIVES AND METHODOLOGY


Stemming from the above problems, this study proposes the
following research objectives.
Objective 1: Analyze the impact of information and informationseeking behavior of safety and hygiene standards on the demand
for safe vegetable. The research results help sellers realize the
response of consumer to the safe vegetable prices in order to develop
reasonable pricing strategy. The results also show the effect of
information on the demand for safe vegetable, thereby assisting
policymakers in providing information and selecting appropriate
information channels to promote the safe vegetable market. This
goal is solved by estimating the demand equation system for both
conventional and safe vegetables, with Linear Approximation
Almost Ideal Demand System (LA-AIDS), controlling for
endogeneity of prices and zero demand.
Objective 2: This ojective analyses the effect of information and
information-seeking behavior of safety and hygiene standards on
the willingness to pay for vegetable attributes. This is to have
the suitable policy in improving quality and safety attributes to
attract more buyers. The findings provide information for
vegetable distributors to develop price strategy in conjunction
with appropriate marketing procedures to maximize profits. This
objective applies Choice Experiment method to estimate the
willingness to pay for safe vegetable attributes, using
Conditional Logit (CL) and Mixed Logit (MX) models.
Objective 3: Investigate the influence of information and
information-seeking behavior of safety and hygiene standards on
the vegetable store choice. Other factors under consideration
include the distribution channel attributes and consumer
characteristics. The Multinomial Logit Model (MNL) is used to
analyze the impact of buyer characteristics on the store choice
decision of buying vegetable, while the Conditional Logit model
is used to explore the effect of distribution channel attributes on
the store choice decision. This study help sellers to perceive
factors that need to be improved in order to attract more
consumer. This study also helps to examine the target customer
group of each vegetable distribution channel, as well as find out
which information channel are mostly effective to appeal target
customer.
The conceptual framework of this study is summarized in Figure 1.


BUYER
CHARACTERISTICS

▪ Individual
characteristics:
age,
gender,
education,
occupation, bargaining
habit, number of vegan
days
▪ Household
characteristics: income,
number
of
meals,
household size, number
of childrens, elders.
▪ Market
price
(endogenous).

LA-AIDS
6 vegetable groups

▪ Frequency of accessing information
via TV, newspapers and internet
▪ Number of poisonings cases watched
▪ Number of poisoning cases
experienced by family members in the
past 12 months

MNL
models

CL/MX
models

Certification
Safety guarantee
Store format
Packaging & information
Price

Objective 3
Store
choice

Interaction

SAFE ATTRIBUTES
OF VEGETABLE






Demand for
vegetable

INFORMATION

Interaction

DISTRIBUTION
CHANNEL
CHARACTERISTICS
Price
Distance
Freshness
Diversity
Input control
Preprocessing
Providing information
Safety level

Objective 1

Objective 2
CE with CL/MX
models

Figure 1: Conceptual framework.

WTP for
safety
attributes


The study uses survey data of 320 people who bought vegetables
in HCMC in 2018. The information factors and informationseeking behavior of buyers is the main explanatory variable in all
the three research objectives. Specifically, the information of
poisoning and safety and hygiene standard violation, as well as
the frequency of monitoring safety and hygiene standards
through communication channels, were included to analyze the
impact on safe vegetable demand, WTP for safe vegetable, and
store choice behavior.
OBJECTIVE 1: INFORMATION AND THE DEMAND
FOR VEGETABLE
Identification of vegetable groups
The study classifies vegetables on the market into 3 groups:
leafty vegetables (including flower and stem groups), root
vegetables and fruits (vegetable family). Thus, the estimated
equation system will comprise 6 equations: 3 equations for the
conventional vegetables of leaves, roots and fruits, and other 3
equations for the safe vegetables that eat leaves, roots and fruits.
Then measuring the average weekly quantity demanded of
vegetable groups in the past 3 months.
Prices and the issues of missing prices and endogeneity
Prices in this objective has an endogeneity problem caused by
the zero demand that would result in no information of prices,
and the heterogeneity in quality of vegetable, which results in the
simultaneity between expenditure share and prices.
To solve the endogeneity, prices are initially regressed on the
variables representing the survey location and household
characteristics. Households are surveyed in the same period
(several days) and location (these households supposedly under
the same cluster) have identical price level for each type of
vegetable, and the diffference of price among the households (if
any) because of variations in the quality and measurement errors.
The predicted price from sub-regression model would eliminate
these differences. The auxiliary regression is
ln 𝑝𝑟𝑖𝑐𝑒𝑖𝑘 = 𝛼0 + 𝛼1 𝐶𝑘 + 𝛼2 𝐷𝑘
(1)
where 𝑝𝑟𝑖𝑐𝑒𝑖𝑘 be the price of vegetables 𝑖 (there are 6 categories,
𝑖 = 1,2, … ,6, and 6 sub-regression equations respectively) for
household 𝑘, 𝐷𝑘 be the characteristics of buyers and households


𝑘, 𝐶𝑘 be the dummy variables describe the cluster. The cluster is
determined by ward/commune. The households surveyed in the
same ward/commune will be considered in the same cluster. The
variables of characteristics of buyers and households are shown
in Table 1.
Bảng 1: Characteristics of buyers and their families
Variable
Definition
Number of meals/week The average number of home-cooked
meals per week
Age
Age of vegetable buyer
Household
size Number of members who regularly eat at
(persons)
home in the household
Number of childrens
The number of childrens under 6 in the
household
Number of elders
The number of peoples over 60 in the
household
Household
income Total income of household members
(mil. VND/month)
Number of vegan The average number of vegan days per
days/month
month of buyer
Gender (1 = Male)
Dummy variable, the gender of vegetable
buyer, 1 = Male
Bargain (1 = Yes)
Dummy variable, 1 = Having bargain
habit while purchasing
Occupation (*)
Unskilled worker (baseline)
Office worker
Manager
Skilled worker
Homaker
Student
Others
Education (*)

Finished primary or less (baseline)
Finished secondary
Finished high school
Finished college
Finished university or higher


The adjusted price would be:
̂ 𝑖𝑘 = 𝛼
̅
ln 𝑝𝑟𝑖𝑐𝑒
̂0 + 𝛼
̂𝐶
̂𝐷
(2)
1 𝑘+𝛼
2 𝑘
̅𝑘 be the household characteristics, taking the average
where 𝐷
value of sample. The category variables are assigned by the most
frequent values in the sample, whereby the dummy variables of
education and occupation in the model are made for high school
and homemaker. The predicted price ln 𝑝𝑟𝑖𝑐𝑒
̂ 𝑖𝑘 would be used
into the model.
Zero demand and sample selection bias
The safe vegetable groups might have a significant number of
households with zero demand. As a result, the quantity demanded
or the expenditure ratio which is censored from 0 and the normal
estimation method could be bias. This goal employs two-step
estimation method of Heckman (1976) to deal with this problem.
According to this method, in step 1, the consumer is assumed to
decide whether consume each type of vegetable or not:
𝑑𝑖𝑘 = 𝛼𝐷𝑘 + 𝛾𝐼𝑘 + 𝑢𝑖𝑘
(3)
where 𝑑𝑖𝑘 be a dummy variable indicates whether household 𝑘
consumes vegetable 𝑖 or not, 𝐷𝑘 and 𝐼𝑘 be the exogenous
variable explains for the choice of consuming vegetable, in
which 𝐷𝑘 be the characteristics of individual and household, and
𝐼𝑘 be the information variables are shown in Table 2.
Bảng 2: The frequency of tracking safety and hygiene standards
information and poisonings
Variable
Tracking safety and
hygiene
standards
information via TV

Tracking safety and
hygiene
standards

Definition
The frequency of tracking safety and
hygiene standards information via TV (*):
Under once/month (baseline)
More than once/month
More than once/week
Per day
The frequency of tracking safety and
hygiene standards information
via
newspaper(*):
Under once/month (baseline)


information
newspaper

via

Tracking safety and
hygiene
standards
information
via
internet

Number of poisoning
cases watched per
month
Number of violations
of safety and hygiene
standards watched per
month
Number of poisonings
cases experienced in
12 months

More than once/month
More than once/week
Per day
The frequency of tracking safety and
hygiene standards information
via
internet(*):
Under once/month (baseline)
More than once/month
More than once/week
Per day
The number of food poisoning cases
watched per month, on average in the past 6
months
The number of violations of safety and
hygiene standards watched per month, on
average in the past 6 months
The number of poisoning symptoms of
family members experienced in the past 12
months

Then the demand (or expenditure) function would be defined as
𝑞𝑖𝑘 = 𝛽𝑋𝑖𝑘 + 𝜀𝑖𝑘
if 𝑑𝑖 > 0
𝑞𝑖𝑘 = 0
if 𝑑𝑖 ≤ 0
where 𝑋𝑖𝑘 is a vector of explanatory variables for demand
function would be described in the following section. The
expected quantity demanded of demand (or expenditure)
function with distributed assumption would be
𝐸(𝑞𝑖𝑘 |𝑋𝑖𝑘 , 𝑑𝑖 > 0) = 𝛽𝑋𝑖𝑘 + 𝐸(𝜀𝑖𝑘 |𝑢𝑖𝑘 > −𝛼𝐷𝑘 − 𝛾𝐼𝑘 )
(4)
and:
𝐸(𝜀𝑖𝑘 |𝑢𝑖𝑘 > −𝛼𝐷𝑘 − 𝛾𝐼𝑘 ) =

−𝛼𝐷𝑘 −𝛾𝐼𝑘⁄
𝜎2)
𝜎2 1−𝛷(−𝛼𝐷𝑘 −𝛾𝐼𝑘⁄𝜎 )
2

𝜎12 𝜙(

The demand function becomes

(5)


𝐸(𝑞𝑖𝑘 |𝑋𝑖𝑘 , 𝑑𝑖 > 0) = 𝛽𝑋𝑖𝑘 +

−𝛼𝐷𝑘 −𝛾𝐼𝑘⁄
𝜎2)
−𝛼𝐷
−𝛾𝐼
𝑘
𝑘⁄ )
𝜎2 1−𝛷(
𝜎2

𝜎12 𝜙(

(6)

where 𝜎2 is the standard deviation of 𝜀𝑖𝑘 and 𝜎12 is the
covariance between 𝜀𝑖𝑘 and 𝑢𝑖𝑘 .
Thus, this method ogriginally estimates the Probit model
explaining the choice of whether consume a type of vegetable or
not (i.e. estimating the coefficient 𝛼 in 𝑑𝑖 = 𝛼𝑍𝑖 + 𝑢𝑖 , then
calculating the Inverse Mills Ratio (IMR):
−𝛼𝐷 −𝛾𝐼

𝐼𝑀𝑅𝑖 = 𝜆 (

𝑘
𝑘⁄ )
𝜙(
𝜎2
−𝛼𝐷𝑘 − 𝛾𝐼𝑘⁄
𝜎2 ) = 1−𝛷(−𝛼𝐷𝑘−𝛾𝐼𝑘⁄ )

(7)

𝜎2

Then estimating the demand (or expenditure) function with IMR
𝑞𝑖𝑘 = 𝛽𝑋𝑖𝑘 + 𝜑𝐼𝑀𝑅𝑖 + 𝜀𝑖𝑘
(8)
where 𝜑 =

𝜎12
𝜎2

.

In this study, the equation system has 6 demand functions for
three types of vegetable that eat leaves, tubers and fruits, each of
these with conventional and safe type. Consequently, 6 Probit
models would be estimated correspondingly to 6 types of
vegetable. Following the calculation for 6 IMR variables for each
type of vegetable and using 6 estimated IMR variables for
measuring the LA-AIDS demand equation system.
Estimation methods
Demand function indicates quantity demanded that maximizes
utility and as a result of utility maximization problem. In addition
to estimating single demand equation, it is possible to estimate
the demand equation system. Estimating the single demand
equation has the critical limitation that is the separating goods
from related ones. This is an unreasonable assumption.
Moreover, the single demand equation will not help to show the
correlation among the goods, for example, cross-price elasticity
of demand can not be calculated. Thus, the demand equation
system with related goods is more preferred.
The dominant models are used to estimate demand equation
system including Linear expenditure system, Rotterdam System,
and Almost Ideal Demand System.


The AIDS model is the most regularly used, with two variants
LA-AIDS and QUAIDS. However, QUAIDS is more difficult to
estimate, especially the demand equation system has a censoring
problem with many cases of quantity demanded equals to 0.
While LA-AIDS is easier to estimate, despite the important
disadvantage is that the log of expenditure ratio is assumed to be
a linear function of the log of total expenditure. However, this is
not significant indeed, because of log-log formulation, the
expenditure ratio fulfills nonlinear function with respect to the
total expenditure. Solely the expenditure ratio is compelled to be
a square function of total expenditure – i.e. expenditure ratio
could be deviated when total expenditure reaches a certain level
- then LA-AIDS is virtually inappropriate. Consequently, the
study uses LA-AIDS to estimate the demand equation system of
vegetables.
LA-AIDSis developed by Deaton and Muellbauer (1980) from
the expenditure function:
1

ln 𝑒(𝑢, 𝑝) = 𝛼0 + ∑𝑖 𝛼𝑖 ln 𝑝𝑖 + 2 ∑𝑖 𝛾𝑖𝑗 ln 𝑝𝑖 ln 𝑝𝑗 + 𝑢𝛽0 ∏ 𝑝𝑖 𝛽𝑖

(9)

The estimated equation system would be:
𝑤𝑖 = 𝛼𝑖 + ∑𝑗 𝛾𝑖𝑗 ln 𝑝𝑗 + 𝛽𝑖 ln

𝑦
𝑃∗

+ 𝜑𝑖 𝐼𝑀𝑅𝑖 + 𝜖𝑖

(10)

where ln 𝑃 ∗ = ∑𝑗 𝑤𝑗 ln 𝑝𝑗
Note that the price variable used in this model is the predicted
price from the OLS models, 𝐼𝑀𝑅𝑖 is the Inverse Mills Ratio
estimated from the regression results of Probit models.
The demand function system in this objective has 6 equations,
however only 5 equations are estimated. The coefficient of the
6th equation would be computed by the theoretical constraints:

Additivity: ∑𝑖 𝛼𝑖 = 1, ∑𝑖 𝛽𝑖 = 0, ∑𝑖 𝛾𝑖𝑗 = 0

Homogeneity: ∑𝑗 𝛾𝑖𝑗 = 0
(with all i)

Symmetry: 𝛾𝑖𝑗 = 𝛾𝑗𝑖
Therefore, these constraints in the fact that are imposed on this
objective. Some studies also examined these constraints, but this


is not actually essential, because after testing if the constraints
are not satisfied, these constraints also are applied to this goal.
Elasticities
The price elasticity of demand or cross-price elasticity of demand
for commodity could be calculated by the estimated coefficients
from the demand equation system. There are 2 elasticities: noncompensated elasticity (referred to as “elasticity”, which is the
Marshallian demand) and compensated elasticity (Hicksian
elasticity). Marshallian elasticity is computed by the assumption
that total expenditure for vegatables in the demand equation
system be unchanged, while compensated elasticity is calculated
by the assumption that the utility is constant (and thus total
expenditure might be varied). Practically, the vegetable prices
change the consumer can adjust expenditure level for vegetables.
Therefore, compensated elasticity might have the greater
practical implications.
There are many procedures to calculate elasticity, but according
to Green and Alston (2008), the following calculation (applied to
LA-AIDS) is the most basically precise for Marshallian elasticity
𝜂𝑖𝑗 = −𝛿𝑖𝑗 +

𝛾𝑖𝑗
𝑤𝑖

− 𝛽𝑖

𝑤𝑗
𝑤𝑖

(11)

where 𝛿𝑖𝑗 is the Kronecker indicator, in which 𝛿𝑖𝑗 = 1 if 𝑖 = 𝑗
and 𝛿𝑖𝑗 = 0 if 𝑖 ≠ 𝑗, 𝛾𝑖𝑗 and 𝛽𝑖 is the estimated coefficients from
(10), 𝑤𝑖 is the expenditure ratio for vegetable 𝑖. The compensated
elasticity (Hicksian) be

𝜂𝑖𝑗
= 𝜂𝑖𝑗 + 𝑤𝑗 (

1+𝛽𝑖
𝑤𝑖

)

(12)

OBJECTIVE 2: INFORMATION AND WTP FOR SAFE VEGETABLE

In this objective, the study evaluates the WTP for safety
attributes and investigating the effect of information on WTP
based on the Choice Experiment (CE). This research chooses two
vegetables, water spinach and carrots, to conduct the experiment
because the characteristics and storage time of two vegetables are
very different, thus the safety attributes might differ.
The experiment uses 5 attributes. The definition and attribute
levels are shown in Table 3.


Table 3: Attributes and their levels
Attribute

Definition

Price

% higher than
price in Choice 1
Vegetables are
sold in different
stores

Store format

Safety
certification

Vegetables are
granted by safety
standards
certificates

Safety
guagrantee

The
seller
commits
to
compensate
VND300 million
if the toxin
content exceeds
the prescribed
threshold
Packaging,
manufacturer
information and
traceability
stamps

Packaging
and
information

Level
of Level
of
Alternative 1 Alternative 2
0%
50%, 150%,
300%, 500%
Formal
Formal market
market
Supermarket
Safe vegetable
stores
No
No
certification
certification
VietGAP
certification
Organic
certification
No
safety No
safety
guagrantee
guagrantee
Safety
guagrantee

No
packaging, no
information,
no
traceability
stamps

Packaging,
manufacturer
information
Packaging,
manufacturer
information
and
traceability
stamps

With two choices (in which Choice 1 be fixed), 5 attributes and
levels in Table 3, it would be possible generate 4×3×3×2×2=144
alternatives. The study utilizes the interaction effect orthogonal
fractional factorial design to select 60 alternatives for
interviewing the buyers. 3 alternatives are grouped into 1 block,
there are 20 blocks respectively. Multiplying these 20 blocks


with 16 cases, the total is 320 blocks. Each respondent should be
answered 1 block for water spinach and 1 block for carrots, thus
each respondent answers a total of 6 choice tasks.
Model and estimation methods
With the database pointing out the preferences from the
hypothetical choice scenario mentioned before of two choices
𝑗 = 1,2, the model can estimate utility function:
𝑈𝑗 = 𝛽𝑋𝑗 + 𝜀𝑗
(13)
where 𝑋𝑗 is a set of attributes of choice 𝑗 described in Table 3 and
𝑈𝑗 is the utility of choice 𝑗 and 𝜀𝑗 is the residuals.
Phương trình ước lượng
This study initially estimates the basic utility function as shown
in (13)
𝑈𝑖𝑗 = 𝛽1 𝑝𝑟𝑖𝑐𝑒𝑖𝑗 + 𝛽2 𝑝𝑟𝑖𝑐𝑒𝑖𝑗 × 𝑖𝑛𝑐𝑜𝑚𝑒𝑖 + 𝛽2 𝑠𝑢𝑝𝑒𝑟𝑖𝑗 +
𝛽3 𝑠𝑝𝑒𝑐𝑖𝑎𝑙𝑡𝑦𝑖𝑗 + 𝛽4 𝑣𝑖𝑒𝑡𝑔𝑎𝑝𝑖𝑗 + 𝛽5 𝑜𝑟𝑔𝑎𝑛𝑖𝑐𝑖𝑗 +
𝛽6 𝑔𝑢𝑎𝑟𝑎𝑛𝑡𝑒𝑒𝑖𝑗 + 𝛽7 𝑖𝑛𝑓𝑜𝑖𝑗 + 𝛽8 𝑞𝑟𝑐𝑜𝑑𝑒𝑖𝑗 + 𝜀𝑖𝑗
(14)
where 𝑈𝑖𝑗 is the utility of buyer 𝑖 obtained from the choice 𝑗 (𝑗 =
1,2), 𝑝𝑟𝑖𝑐𝑒𝑖𝑗 is the prices of choice 𝑗 in the choice set of buyer 𝑖.
Note that 𝑝𝑟𝑖𝑐𝑒𝑖𝑗 is the specific amount with the unit of thousand
VND/kg, not the percent (%). 𝑠𝑢𝑝𝑒𝑟𝑖𝑗 and 𝑠𝑝𝑒𝑐𝑖𝑎𝑙𝑡𝑦𝑖𝑗 is dummy
variables indicating the store be the supermarket and safe
vegetable store (baseline: formal market). 𝑣𝑖𝑒𝑡𝑔𝑎𝑝𝑖𝑗 and
𝑜𝑟𝑔𝑎𝑛𝑖𝑐𝑖𝑗 is dummy variables denoting safety certificate of
vegetables of choice 𝑗 (baseline: no certification). 𝑔𝑢𝑎𝑟𝑎𝑛𝑡𝑒𝑒𝑖𝑗
is dummy variable equals to 1 if the seller commits to
compensate VND300 million in the case that the toxin content
exceeds the prescribed threshold. 𝑖𝑛𝑓𝑜𝑖𝑗 vand 𝑞𝑟𝑐𝑜𝑑𝑒𝑖𝑗 is
dummy variables presenting the having packaging and
manufacturer information 𝑖𝑛𝑓𝑜𝑖𝑗 , and having packaging,
manufacturer information and traceability stamps 𝑞𝑟𝑐𝑜𝑑𝑒𝑖𝑗 .
Assumme that these two attributes be prefferred to the consumer,
then 𝛽7 > 0, 𝛽8 > 0 and 𝛽8 > 𝛽7 , because the utility with
traceability stamps (𝛽8 ) must be higher than the utility without
traceability stamps.
With the expectation that those with higher income are less
sensitive to prices, the interaction between price and income is


introduced into the utility function. If the expectation is correct,
then 𝛽1 < 0 and 𝛽2 > 0.
Among the attributes, this study is particularly interested in the
safety attributes, including VietGAP certification and organic
certification. Additionally, the compensated guarantee of sellers
also is a kind of certificate (the self-certification of sellers),
because the sellers have to comply with safety standards of
vegetables even though hazards for compensation. To examine
the impact of information on WTP for safety attributes, the
information variables (Table 2) would be interacted with these
safety attributes.
If the information variables interact with 3 safety attributes are
simultaneously put into (14) which can cause the
multicollinearity. Therefore, each model can only include the
interaction variables of an attribute. As a result, 4 models are
estimated:
• The basic model: solely the attributes and the interaction
between price and income (14).
• VietGAP model: the basic model with the interaction
variables between VietGAP certification and information
variables.
• Organic model: the basic model with the interaction variables
between organic certification and information variables.
• Guarantee model: the basic model with the interaction
variables between safety guarantee and information variables.
These models are estimated by Maximum Likelihood. The
parameters 𝛽 are estimated by maximizing the log-likelihood
function:
𝐿𝐿 = ∑𝑖 ∑𝑗 𝑦𝑖𝑗 𝑝𝑖𝑗
(15)
𝑒

where 𝑝𝑖𝑗 = ∑

𝛽𝑋𝑗

𝑙𝑒

𝛽𝑋𝑙

is the probability of choosing 𝑗 by buyer 𝑖,

and 𝑦𝑖𝑗 is dummy variable indicating the observed choice from
the buyer, 𝑦𝑖𝑗 = 1 if the buyer 𝑖 selects choice 𝑗.
MX model is more complicated. In this model, log-likelihood
function is identical as CL, but the probability of choosing 𝑗 by
the buyer 𝑖 becomes:


𝑝𝑖𝑗 = ∫ (


𝑒

𝑙

𝛽𝑋𝑖𝑗

𝑒 𝛽𝑍𝑖𝑙

) 𝑓(𝛽)𝑑(𝛽)

(16)

where 𝑓 (𝛽) be the probability distributed function of 𝛽, because
𝛽 is a random variable instead of fixed variable as in CL. To
estimate 𝛽, the model will choose randomly 𝑅 values from 𝑓 (𝛽)
and calculate the average probability from 𝑅. 𝑅 be the number of
draws, the larger 𝑅 the more accurate estimated parameters. The
research of Bierlaire (2003) shows that 𝑅 = 500 is the most
effective value, thus this study uses 𝑅 = 500.
Estimation of WTP
The parameter 𝛽 describes the marginal utility of attributes. For
example, the parameter 𝛽𝑣𝑖𝑒𝑡𝐺𝐴𝑃 of 𝑉𝑖𝑒𝑡𝐺𝐴𝑃 variable shows the
utility of VietGAP certificate. Comparing this utility to the
marginal utility of prices help to calculate the willingness to pay
for VietGAP certificate attribute:
𝛽

𝑊𝑇𝑃𝑉𝑖𝑒𝑡𝐺𝐴𝑃 = − 𝛽4

𝑝

(17)

where 𝛽𝑝 is the marginal utility of price (in thousand VND). In the
basic model, the marginal utility of 1 thousand VND to the buyer 𝑖
be:
𝛽𝑝 = 𝛽1 + 𝛽2 𝑖𝑛𝑐𝑜𝑚𝑒𝑖
(18)
𝑖𝑛𝑐𝑜𝑚𝑒𝑖 is the income of buyer 𝑖. If calculating at the mean value
of income, the marginal utility of price becomes
𝛽𝑝 = 𝛽1 + 𝛽2 𝑖𝑛𝑐𝑜𝑚𝑒
̅̅̅̅̅̅̅̅̅̅
(19)
where 𝑖𝑛𝑐𝑜𝑚𝑒
̅̅̅̅̅̅̅̅̅̅ is the average income of the sample.
OBJECTIVE 3: INFORMATION AND VEGETABLE STORE
CHOICE

This objective has two specific research objectives: (1) analyzing
the impact of buyer characteristics on the vegetable store choice
and (2) investigating the effect of distribution channel
characteristics on the vegetable store choice.
This study uses MNL and RUM models to identify the factors
influence store choice of buying vegetable. The collected data


shows each trip of buying vegetable in the last 7 days in detail
and thus each trip of buying vegetable is an observation – i.e. one
time of decision-making. Each trip of buying vegetable is
supposed to be exclusively travel to a unique store and therefore
the alternatives are mutually exclusive.
From the results of initial surveys and pilot surveys, the store of
buying vegetable is chosen to study as follows:
1. Hypermarket/Supermarket
2. Mini supermarket
3. Specialty store
4. Traditional market (legally established)
5. Street vendor (illegally established)
6. Local green grocery.
MNL Model
The assumption that the utility of consumer 𝑖 of choosing the
store 𝑚 from the shopping trip 𝑗 be a linear function as
𝑚
𝑈𝑖𝑗𝑚 = 𝛼𝑚 𝐷𝑖 + 𝛽𝑚 𝐼𝑖 + 𝜀𝑖𝑗
(20)
where 𝑚 = 1,2, … ,6 indicating the distribution channels
mentioned above, 𝐷𝑖 be a vector of the individual and household
characteristics, and household 𝑖 consists of variables shown in
Table 1 (The characteristics of individual and household), 𝐼𝑖 be
the information variables including the frequency of tracking
safety and hygiene, the number of safety and hygiene standards
violations and the number of poisoning cases experienced
depicted in Table 2.
The parameters 𝛼𝑚 and 𝛽𝑚 presenting the influence of
determinants on the utility of store choice 𝑚. Note that each of
store would have a vector of parameters. Each of 𝐷𝑖 variable
would have 𝛼1 defining the utility of extra supermarket to
individual with 𝐷𝑖 characteristics, 𝛼2 be the utility of minimart,
and similar to other stores.
Let the probability of consumer 𝑖 from the trip 𝑗 of choosing the
store 𝑚 be:
𝑝𝑖𝑗𝑚 = Pr(𝑦𝑖𝑗𝑚 = 1)
(21)


𝑚
where 𝑦𝑖𝑗
be the dummy variable defines the store choice 𝑚 of

the buyer 𝑖 from the trip 𝑗. Seting formal market is the base
category, the log-odds would be:
log

𝑝𝑖𝑗𝑚
𝑝𝑖𝑗4

= 𝛼𝑚 𝐷𝑖 + 𝛽𝑚 𝐼𝑖

∀𝑚 ≠ 1

(22)

𝑝1

𝑚
Note that log 𝑝𝑖𝑗1 = 0 and 𝜀𝑖𝑗
is assumed to be the logistic
𝑖𝑗

distribution. The probability of choosing the store 𝑘 would
become:
𝑘

𝑘
𝑝𝑖𝑗
=

𝑉
𝑒 𝑖𝑗

(23)

𝑚

𝑉
∑6𝑚=1 𝑒 𝑖𝑗

where 𝑉𝑖𝑗𝑚 = 𝛼𝑚 𝐷𝑖 + 𝛽𝑚 𝐼𝑖 . The MNL model will estimate the
parameters 𝛼𝑚 and 𝛽𝑚 for each of store (exclude 𝑚 = 4) by
Maximum Likelihood method:
log 𝐿 = ∑𝑖 ∑𝑗 ∑𝑚 𝑦𝑖𝑗𝑚 ln 𝑝𝑖𝑗𝑚
(24)
This model helps to analyze the influence of individual and
household characteristics, and the frequency of tracking safety
and hygiene information on the store choice of buying vegetable.
RUM
RUM model is applied to determine the impact of distribution
channel attributes on the store choice of buying vegetable.
Table 4: Attributes of vegetable stores
Variable

Definition

Price

The price indicator of the closest
store of each channel compared to
the closest formal market (the
price of the closest formal market
= 0%, the negative value in this
variable means that the price of
the closest store lower than the
formal market)

Measurement
units
%


Distance

Freshness

Diversity

Input control

Preprocessing

Providing
information

Safety level

Distance from the house to the
closest store (outlet) of each
distribution channel
Vegetables at each shopping
channel have different levels of
freshness
There are many types of
vegetables, the diversity of spicies
are sold at each distribution
channel
Whether vegetable stores have
strict input control or not
Vegetables at each channel before
being sold are preprocess:
cleaning, trimming, washing with
clean water, and packaging
The
level
of
providing
information on vegetable quality
(Expired date, the origin of
vegetables, processing, safety
standards, etc.) of each shopping
channel
Personal evaluation of buyers on
the probability of vegetables
fulfilling safety standards at each
channel

Minutes

1 = Freshness;
0
=
No
freshness
1 = Diversity;
0
=
No
diversity
1 = Strictly;
0
=
Not
strictly
1 = Carefully;
0
=
Not
carefully
1
=
Completely;
0
=
Not
completely

%

Model specification
This model will estimate the utility function
𝑈𝑖𝑗𝑚 = 𝐴𝑆𝐶𝑚 + 𝜌𝑋𝑚 + 𝜀𝑖𝑗𝑚
(25)
where 𝑋𝑚 is a vector of store attributes 𝑚. The attributes include
distance, diversity, preprocessing, freshness, providing
information and price. The definition of attribues are shown in
Table 4. These variables are the personal evaluation of buyers for
the store attributes of buying vegetable.
Interaction terms
Those with higher income might have different responses to the
price, thus income is interacted with price, is set into the model.


In some cases of buying vegetable, the buyer does not travel from
home to store but in such a convenient way with other works and
visit the store, therefore the parameter of distance in these cases
might have the difference compared to the cases of traveling to
stores from home. Hence, the interaction between distance and
dummy variable be convenient, is set into the model.
Consequently, the basic model will include the store attributes
and two interaction variables. Additionally, to investigate the
influence of information on the store choice of buying vegetable,
RUM model is set by the interaction variables between
information and “safety levels” attributes. This is to examine that
those with different frequency of tracking information might
have different responses to the safety levels of vegetables in the
stores. There would be 4 models are estimated:
• Model 1: Estimate equation (25) in which X includes the
attributes in Table 4 and two interaction variables
(income x price and distance x convenient)
• Model 2: This model comprises the variables in Table 1,
and the interaction variables between safety level and
frequency of tracking safety and hygiene standards
information via TV, newspaper and internet, is set into
the model.
• Model 3: The model consists of the variables in Model 1
together with the interaction variables between safety
level and the number of violations safety and hygiene
standards cases watched, the number of poisoning cases
watched, and the number of poisoning cases experienced
by family members.
• Model 4: This model contains the variables in Model 1
and all interaction variables used in Model 2 and 3.
The reason of developing Model 2 and 3 that is the interaction
variables might be correlated, resulting in the biasedness of
parameters.
MAIN FINDINGS
The demand for vegetable
This study categories vegetables into 3 groups: leafty vegetables,
root vegetable and fruits, each group have 2 kinds, the
conventional and safe vegetable. The safe vegetable is


considered by vegetables that are certificated or are sold in
supermarkets under strict input control procedures.
The results of survey show that the percentage of household
consumes safe vegetable is about 50%. The results of Probit model
explaining the choice of consuming 6 vegetable types indicate that
the households with small size, high income and having more
childrens under 6 tend to have higher likelihood of choosing safe
vegetable. Those who tend to buy higher safe vegetable including
male, elders, office workers, student, homemaker, vegetarian, and
those without bargain habit. The frequency of tracking safety and
hygiene standards information via TV, newspaper, and internet
generally has no impact on the choice of safe and conventional
vegetables. However, the information of poisoning and safety and
hygiene standards violations cases which induces the consumer
less purchase conventional vegetable and buying safe vegetable
instead. While the number of poisoning cases experienced by
family members in the past 12 months insignificantly effects the
choice of safe vegetable.
The results of demand equation system using LA-AIDS
describing the expenditure and quantity demanded of safe
vegetable, those who spend higher for safe vegetable consisting
the households with more children, male, office workers, student,
and homemaker. Meanwhile, the level of education insignificantly
affects the demand for safe vegetable. The frequency of tracking
safety and hygiene standards information in all stores has no
impact the demand for safe vegetable. The information of
violations cases watched has the influence in a manner of buying
less conventional leafty vegetables and buying more the safe
leafty and root vegetables, probably being the leafty vegetables
have hazards of safety violations higher than root vegetables and
fruits. While, the information of poisoning cases watched and the
number of poisoning cases experienced by family members does
not affect the demand for safe vegetable.
The estimated Hicksian and Marshallian elasticities of demand
are low, which shows that a weakness in the responses of
consumer to vegetable price. The conventional and safe
vegetables neither complements nor substitutes. In general, the
price of a conventional vegetable has the effect of quantity
demanded of other conventional vegetables, but has no impact


the quantity demanded of safe vegetable, and vice versa. This
implies that the price of conventional vegetable in the current
levels does not influence the demand for safe vegetable, the
consumer only responses to the safe vegetable as the price of
other safe vegetables be changed. Especially, the safe leafty
vegetable elastics with the price of conventional root vegetables
and fruits (substitution effects) and the price of conventional fruit
vegetables (income effects).
WTP for safe vegetable
This objective uses the choice experiments with hypothetical
scenario. The Conditional Logit and Mixed Logit model is
utilized to estimate the utility function for the attributes of two
vegetables. The regression results of two models are generally
identical. The result of Conditional Logit model is used to
calculate the willingness to pay for vegetable attributes.
The WTP estimation results show that consumers are not willing
to pay higher for VietGAP water spinach and VietGAP carrots
than conventional ones. The organic certification is more
preferred because buyers are willing to pay more than VND
12,000 /kg to buy organic water spinach and more than VND
22,000/kg to buy organic carrots. The organic certification is paid
higher price than VietGAP certification which is understandable,
but even the safety guarantee of sellers is more appreciated than
VietGAP. Water spinach which is guaranteed by sellers with
compensated amount of VND 300 million if the toxic contents
exceeding the safety threshold, is paid more than VND 11,000/kg
compared to the unguaranteed ones, and more than VND
22,000/kg of carrots.
The attribute of packaging and manufacturer information should
be used depending on vegetables. In this objective, the results
indicate that these attributes are important for water spinach,
while is not important for carrots. The buyers are willing to pay
more than VND 15,000/kg to buy water spinach with packaging
and manufacturer information, although they are not willing to
pay higher price for carrots. The buyers ask to have the origin
stamps for carrots instead, and are willing to pay more than VND
15,000/kg for carrots with traceability stamps.


While the Conditional Logit model shows that consumers are not
willing to pay for VietGAP, the result of Mixed Logit model
reveals more information. The standard deviation of VietGAP
coefficient is different from 0 and the value is high, which
implies that a half of buyers prefer to VietGAP water spinach and
are willing to pay higher for VietGAP ones, but the other half of
buyers does not prefer to VietGAP water spinach. Although the
average WTP equals to 0, there is a heterogeneity in preferences
for VietGAP water spinach. However, there is a homogeneity in
preferences for VietGAP carrots: most of the buyers are willing
to pay higher price for VietGAP carrots.
The MX model also shows that the willingness to pay higher for
water spinach and carrots with organic certification, the
preferences for this attribute is also heterogenous in both
vegetables. This implies that although most consumers prefer
organic certification and are willing to pay higher price, many
consumers are not considered in this certification.
The frequency of tracking safety and hygiene standards
information generally less affects the WTP for VietGAP
certification, organic certification and safety guarantee. The
trend of general influence that is those with moderate frequency
of tracking information are willing to pay higher than those with
irregular tracking information, while those with everyday
tracking information are not willing to pay higher than those with
irregular tracking information. This is probably because those
who follow the information at the frequent and moderate level,
are more sensitive about the safety and hygiene standards,
therefore are willing to pay higher price for safety attributes.
However, those who follow the information at very frequent
level might lose their trust in the certifications and guarantees
from the sellers, thus are not willing to pay for these attributes.
This indicates that, in a manner of speaking, the information of
safety and hygiene standards in the social media is failing to
encourage the development of safe vegetable.
Meanwhile, the information of safety and hygiene standards
violation cases, the number of poisoning cases, and the number
of poisoning cases experienced by family members does not
influence the preferences for VietGAP, organic certification, and


safety guarantee, therefore the willingness to pay for these
attributes is fixed.
Vegetable store choice
The third objective of study investigates the drivers affecting
vegetable store choice, including the factors of buyer
characteristics and store attributes, with the frequency of tracking
information which are the key variables. The study observes the
buyer choice of buying vegetable for all trip in the last 1 week,
using the MNL model to analyze the impact of buyer
characteristics, and the CL/MX model to analyze the effect of
store attributes.
The results of survey show that on average, each buyer purchases
vegetables 5 times per week, each time value approximately
VND 42,000.
Those who tend to purchase vegetables in the modern channels
consisting the households with less members, less elders, and
those who are male, high income, older people, without
bargaining habit, vegetarian, office worker, student and
homemaker. Education and the number of children in the
household has no effect.
The analysis of store attributes points out that the distance
variable is very important. The farther the store is, the less likely
store is chosen. This preference takes advantages for the
traditional channels that are closer to residential areas. Other
important attributes comprise the freshness, safety level and
providing information, while the diversity, input control and
preprocessing does not affect the vegetable store choice.
Regarding the impact of information, MNL model shows that the
effect of frequency of tracking safety and hygiene standards
information on the vegetable store choice is very limited in
general. The Internet has absolutely no effect on the store choice.
The frequency of tracking safety and hygiene information at the
moderate level is more likely to choose the traditional channel,
contrary to the frequency tracking information at everyday. The
information of safety and hygiene standards violation cases and
the number of poisoning cases show the general influence that a
decrease in the probability of choosing traditional channels and
an increase in the probability of choosing the modern channels.


Households have the higher number of poisoning cases
experienced by family members are more likely to choose the
minimart.
CL model with the interaction variables between information and
safety level shows that those with the higher frequency of
tracking safety and hygiene standards information via TV are less
sensitive to the safety level, while those with the higher
frequency of tracking information via internet and newspaper are
more sensitive to the safety level. The number of poisoning and
violation cases watched that induces the consumer’s respond
more strongly for the safety level and thus they tend to choose
the safer stores. However, the number of poisoning cases
experienced by family members in the past induces the
consumers to be familiar with and fewer responses to the safety
level.
POLICY IMPLICATIONS
The conventional vegetable does not substitute for safe
vegetable
Although the asymmetric information theory states that the
conventional vegetable has the lower production costs which
tend to push the safe vegetable out of the market, but the analysis
result of demand equation system in this study shows that the
conventional vegetable does not substitute for the safe vegetable.
In particular, the decrease in the price of conventional vegetable
will not significantly reduce the demand for safe vegetable. This
is a good signal, showing the asymmetric information will be
difficult to drive the safe vegetable out of the market. This is
probably being the difference in the product of the two groups.
Even though the traditional markets have the advantage of being
close to home and an ancient culture, but the modern markets
have its own advantages such as clean store’s space and modern
shopping way. These modern distribution channels therefore
should be maintained and promote its own advantages against the
traditional channels.
Low elasticities
The safe vegetable has the low price elasticity of demand that
also be a good signal. The safe vegetable buyers do not strongly
respond to the change in price. This implies that the safe


vegetable distributors, in the case of necessity, can raise the price
without considering about a significant decrease in quantity
demanded.
In fact, the price of vegetables in traditional markets are quite
fluctuating, depending on origin, quality, freshness and store
format. For example, the price of vegetables in the peak hour and
off-peak hour greatly differs. In many cases, the price of
vegetables in traditional markets is as high as or higher than the
price of the same vegetables in supermarket. This induces buyers
in the modern channels easily accept higher prices, resulting in
the demand for safe vegetable be inelastic. Moreover, higher
prices can be a signal of quality. Because vegetables sold at the
lower prices might be considered unsafe.
VietGAP is not really a signal of safety
While current retail channels, both traditional and modern
markets, use VietGAP term to send the signal of safe vegetable
to consumer, this research result shows that consumers are not
truely willing to pay higher for VietGAP vegetables, at least for
water spinach and carrots. There is high heterogeneity in the
preferences of buyers for VietGAP certification. Some buyers
appreciate VietGAP certification, the others do not. These buyers
might not believe in the compliance of manufacturer for
VietGAP production process. For the average calculated of the
sample, the buyers are not willing to pay any additional amount
for VietGAP vegetables. This implies that retailers need to have
another evidence to send the signal of quality and safety and
hygiene standards, at the same time VietGAP certification
organization need to reconsider the certification process and
supervise the compliance with safety regulatory standards.
Organic certification is a symbol of safety and hygiene
While VietGAP certification is not appreciated, this research
result reveals that the buyers are willing to pay more than VND
12,000/kg for organic water spinach against conventional water
spinach, and VND 22,000/kg for organic carrots against
conventional carrots. This is a high willingness to pay, close to
the market prices of these two vegetables (VND 16,000 for
conventional water spinach and VND 23,000 for conventional
carrots). This states that the buyers appreciate organic


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