- Email: [email protected]
Food safety concerns and consumer preferences for food safety attributes: Evidence from China
Journal Pre-proof Food safety concerns and consumer preferences for food safety attributes: Evidence from China Ruifeng Liu, Zhifeng Gao, Heather Arielle Snell, Hengyun Ma PII:
S0956-7135(20)30073-6
DOI:
https://doi.org/10.1016/j.foodcont.2020.107157
Reference:
JFCO 107157
To appear in:
Food Control
Received Date: 24 September 2019 Revised Date:
1 February 2020
Accepted Date: 4 February 2020
Please cite this article as: Liu R., Gao Z., Snell H.A. & Ma H., Food safety concerns and consumer preferences for food safety attributes: Evidence from China, Food Control (2020), doi: https:// doi.org/10.1016/j.foodcont.2020.107157. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
Ruifeng Liu: Conceptualization, Investigation, Methodology, Software, Data curation, Writing- Original draft preparation. Zhifeng Gao: Methodology, Software, WritingReviewing and Editing. Heather Arielle Snell: Software, Data curation, WritingReviewing and Editing. Hengyun Ma: Supervision, Writing- Reviewing and Editing.
Food safety concerns and consumer preferences for food safety attributes: Evidence from China
Ruifeng Liu College of Economics and Management, Henan Agricultural University, No.15, Longzi Lake College Park, Zhengzhou Eastern New District, Zhengzhou 450046, China (phone: (86)-371-56990018; fax: (86)-371-56990014;e-mail: [email protected]) Zhifeng Gao Food and Resource Economics Department, University of Florida, P.O. Box 110240, Gainesville, FL 32611-0240, USA (phone: (1)- 352-294-7672; fax: (1)- 352-846-0988; e-mail: [email protected]) Heather Arielle Snell Department of Agricultural Economics and Agribusiness, University of Arkansas, 217 Agriculture Building, Fayetteville, AR 72701, USA (phone: +1 (479) 575-6995; fax: (1)- 479-575-5306; e-mail: [email protected]) Hengyun Ma* College of Economics and Management, Henan Agricultural University, No.15, Longzi Lake College Park, Zhengzhou Eastern New District, Zhengzhou 450046, China (phone: (86)-371-56990018; fax: (86)-371-56990014; e-mail: [email protected])
*
Acknowledgement: This work was financially supported by the National Natural Science Foundation of China (Grant No: 71403082); Ministry of Education of Humanities and Social Science Research Project of China (Grant No: 14YCJ790080); National Social Science Foundation of China (Grant No: 14BGL093); 2017 Annual Scientific and Technological Innovation of Henan Province Talent (Humanities and Social Sciences) Support Program (Grant No: 2017-cxrc-002); Young Backbone Teachers Scheme of Henan Colleges and Universities (Grant No: 2015GGJS-085); Henan Province Philosophy and Social Science Planning Project (Grant No: 2017BJJ033); Henan Provincial Department of Education Humanities and Social Sciences Research Project (Grand No: 2019-ZZJH-327). Corresponding author: Tel: +86 371 56990018; fax: +86 371 56990014; email: [email protected] (H.Y. Ma)
1 2 3
Food safety concerns and consumer preferences for food safety attributes: Evidence from China
4 5
Abstract
6
Abstract: China has experienced a series of high-profile food safety scandals in the
7
past few years that seriously challenged public confidence in the domestic food
8
industry. Much attention has been paid to Chinese government’s food regulatory and
9
inspection systems. Scant research, however, has been devoted to analyzing Chinese
10
consumers’ food safety concerns. This study interviewed 2092 Chinese consumers in
11
Beijing, Shanghai, Guangzhou, Xi’an, Jinan, and Harbin and used the Conditional
12
Logit model, Mixed Logit model, and the Latent Class model to analyze consumer
13
preferences and marginal willingness to pay (WTP) for selected food safety attributes
14
of Fuji apple products. We identified three consumer segments: certification-oriented
15
(65.9%), price and origin-oriented (19.1%), and not interested (15.0%). Results reveal
16
that Chinese consumers, in general, are willing to pay a premium for selected food
17
safety
18
socio-demographic characteristics are used to determine the sources of preference
19
heterogeneity. Marginal analysis is also conducted to estimate the response of the
20
model and selection probability to potential policy levels, such as increasing the
21
perception of food safety and the trust on labeling and traceability information to
22
improve the evaluation of food safety.
attributes.
Consumers’
perceptual
and
attitudinal
factors
and
23 24
Keywords: Food safety; Choice experiment; marginal WTP; Latent Class model;
25
Chinese consumers.
26
JEL classifications: D12; Q18
1
27 28 29
Food safety concerns and consumer preferences for food safety attributes: Evidence from China
30
1. Introduction
31
Since the last decade, a series of high-profile food safety scandals have tarnished the
32
reputation of Chinese food products in international markets and have seriously
33
shaken Chinese consumers’ confidence in the domestic food industry (Cicia et al.,
34
2016; Lai et al., 2018; El Benni et al., 2019). One of the most notorious food safety
35
incidents involved melamine milk powder and infant formula in 2008, affecting
36
approximately 300,000 babies, sending 54,000 to hospitals, and killing at least six
37
(The Lancet, 2012, 2014). Other subsequent food safety incidents have made
38
headlines repeatedly in China. For instance, the clenbuterol was found in pork from
39
one of China’s largest meat producers in 2011. In 2013, a large number of dead pigs
40
were found floating in the Huangpu River of Shanghai and in 2015, “Zombie meat”
41
(referring to meat products that have been frozen for years) was sold in many cities
42
across the country.
43
The difficulty of regulatory oversight seems insurmountable because of numerous
44
issues, including the complexity and scale of the contemporary food supply chain, the
45
geographic dispersion of food-related public health threats, the intricate intersections
46
of food safety, and the broad policy agenda (Kang, 2019). Most important of all, the
47
sheer size, diversity, and complexity of China’s domestic food producers and
48
processors make regulation through China’s single regulatory structure extremely
49
difficult and costly (Fewsmith & Gao, 2014; Zhou et al., 2015; Roberts & Lin, 2016;
50
Guo et al., 2019). Although the China State Administration for Market Regulation
51
raised the level of supervision after consolidating several departments in 2018, serious
52
coordination problems continue to undermine the government’s regulatory capacity
2
53
(Berti & Semprebon, 2018; Kang, 2019).
54
At the same time, in the food industry globally, there is growing pressure to track
55
and trace food products through the production, processing and distribution stages
56
(Liu et al., 2019). As a result, food traceability systems were installed in a number of
57
countries including Japan, Australia, and many European countries. The European
58
General Food Law (European Commission, 2002) defines traceability as “the ability
59
to trace and follow a food, feed, food-producing animal or substance through all
60
stages of production and distribution”, while the International Organization for
61
Standardization (ISO) defines traceability as “the ability to trace the history,
62
application and location of that which is under consideration”, and notes that “when
63
considering a product, traceability can relate to the origin of materials and parts, the
64
process history, and the distribution and location of the product after delivery” (ISO,
65
2007). Traceability systems make it possible to track product history from top to
66
bottom in the food supply chain, including raw materials, processing, packaging,
67
storage, transportation, and marketing to identify problem sectors and recall high-risk
68
products from the market (Asioli et al., 2014; Duan et al., 2017). Therefore,
69
traceability systems are considered an important tool to prevent food safety incidents
70
(Hobbs et al., 2005; van Rijswijk et al., 2008; Berti & Semprebon, 2018).
71
Given the grim food safety situation, it is important for Chinese food industrialists
72
and policymakers to understand Chinese consumers’ awareness of food safety and
73
their preferences for food safety information attributes (Yu et al., 2014; Cicia et al.,
74
2016; Lai et al., 2018). Many researchers have investigated the major determinants of
75
food safety issues (Grunert, 2005; Lin et al., 2010; Lam et al., 2013) and China’s food
76
safety policies (Broughton & Walker, 2010; Yu et al., 2014; Duan et al., 2017; Kang,
77
2019). However, there is scant literature on Chinese consumers’ food safety
3
78
preferences and behaviors. Specifically, few studies have focused on Chinese
79
consumers’ concerns and evaluation of food safety issues, and on Chinese consumers’
80
preferences and willingness to pay (WTP) for food safety attributes (Wu et al., 2015;
81
El Benni et al., 2019).
82
The aim of this study is to evaluate Chinese consumers’ preferences and marginal
83
WTP for food safety attributes of apples (i.e., Fuji apple products). Given that a
84
number of market segments exist in China, in addition to the mixed logit model that
85
assumes continuous heterogeneous preference among consumers, we use the Latent
86
Class model (LC model) to estimate consumer preferences and consumer profiles of
87
each segment. The LC model also links preference heterogeneity to consumer
88
characteristics, such as socio-demographics (Boxall & Adamowicz, 2002). We take
89
Chinese consumers’ perception and attitude of food safety issues into account to
90
investigate how these factors help form heterogeneous preferences in the LC model
91
(Rao, 2014; Thiene et al., 2018). Finally, we perform a marginal analysis to assess the
92
effect of a set of scenarios to provide useful advice for policy makers. Since most
93
previous studies have focused on meat and milk in China (e.g., Wang et al., 2008;
94
Ortega et al., 2011a, 2011b; Bai et al., 2013; Wu et al., 2015, 2017; Wu et al., 2019),
95
here we use Fuji apple as the product of interest to examine consumers’ preferences
96
for traceable fruit products.
97
The remainder of this paper goes on as follows: Section 2 reviews the literature.
98
Section 3 describes the survey and data. Section 4 specifies the econometric models.
99
Section 5 estimates and discusses the results. Section 6 concludes with policy
100
implications.
101
2. Literature review
102
There are a number of studies that have examined consumers’ preferences and WTP
4
103
for food safety attributes.1 Many of these studies found evidence that consumers are
104
willing to pay a premium but they have different preferences and WTP for these food
105
safety attributes (e.g., Ubilava & Foster, 2009; Lee et al., 2011; Wu et al., 2015;
106
Thiene et al., 2018; Wu et al., 2019). A number of studies have also discussed the food
107
safety aspects of various attributes including organic or green food labeling, country
108
of origin/local, quality certification, and traceability (e.g., Liu et al., 2013; Meas et al.,
109
2015; Wu et al., 2015, 2017; Lusk et al., 2018; Gao et al., 2019; Liu et al., 2019).
110
There is also a burgeoning literature 2 on consumers’ preferences for food
111
traceability (e.g., Jin & Zhou, 2014; Dandage et al., 2017; Jin et al., 2017; Liu et al.,
112
2018). Studies have found that traceability information has a significant influence on
113
consumer preferences, and that consumers are willing to pay a higher price for this
114
attribute. However, most of these studies only consider traceability information as one
115
of the food safety attributes; i.e., they tend to ignore the heterogeneity of traceability
116
information, in which consumers are highly interested. In addition, to the best of our
117
knowledge, there has been no large-scale investigation on consumers’ valuation for
118
different types of traceability information on fresh fruit in China.
119
Region of origin claim (ROO) or country of origin (COO) may affect consumer
120
preferences because consumers use it to infer product quality based on their shopping
121
experience (Claret et al., 2012; Eng et al., 2016). This finding has been repeatedly
122
confirmed by researchers (e.g., Skreli & Imami, 2012; Gao et al., 2019). ROO claims
123
provide consumers’ information about where the food was produced (Xie et al., 2016).
124
When ROO comes in the form of a label, it is not only a credence attribute, but also
125
an experience attribute (Tsakiridou et al., 2011). Other studies also indicate that ROO
1 Earlier studies include Lusk (2003), Loureiro & Umberger (2007), Wang et al. (2008), Lin et al. (2010), and Ortega et al. (2011a, 2011b). 2 Earlier studies include Hobbs et al. (2005), Verbeke & Ward (2006), Loureiro & Umberger (2007), van Rijswijk et al. (2008), and Ubilava & Foster (2009).
5
126
has a symbolic and emotional meaning (Ehmke et al., 2008; d’Astous & Ahmed,
127
1999).
128
Consumers’ perception of food safety certification can also have a significant
129
impact on consumers’ WTP (Zhang et al., 2012). Certification of the authenticity of
130
traceability information provides important assurance for consumers (Wu et al., 2015).
131
Previous studies have suggested that Chinese consumers’ preference for traceable
132
food is influenced by certification bodies (Bai, et al., 2013; Liu et al., 2019).
133
Furthermore, researchers have compared consumers’ preferences for the ROO/COO
134
labeling, food traceability attribute, and the certification type (e.g., Wu et al., 2017;
135
Liu et al., 2019). Consumers’ preferences and emphasis on these attributes could vary,
136
however, depending on location or country.
137
In addition, both quantitative and qualitative methods have been applied to study
138
consumer preferences for food safety. Quantitative methods based on consumer
139
surveys have been used to identify the interaction between sociodemographic
140
characteristics and consumers’ choices for food products and WTP (e.g.,
141
Wongprawmas and Canavari, 2017; Lusk et al., 2018; Liu et al., 2019), while
142
qualitative methods have been applied to study consumers’ perceptions of food safety
143
attributes (e.g., Sirieix et al., 2011; Cui et al., 2016; Hasimu et al., 2017; Ha et al.,
144
2019). The results from these studies tend to show that age, gender, education levels,
145
and households or individual monthly income significantly affect consumers’
146
perception and WTP for different attributes. In addition, research shows that the
147
findings could vary across methods employed (Lusk et al., 2005; Dannenberg, 2009).
148
These methodological approaches usually include structural equation modeling,
149
discrete choice experiments (DCE), and experimental auctions, among others.
150
DCEs are widely used in research about food safety information (Akaichi et al.,
6
151
2013; Johnson et al., 2013; Rao, 2014). DCEs simulate consumers’ actual purchasing
152
decisions by evaluating the utility of attributes in various combinations. When using
153
the DCE method, many researchers3 employ a multinomial logit model (MNL) or
154
Mixed Logit model/Random Parameter Logit model (ML/RPL) to estimate consumer
155
preferences for food safety attributes (Wongprawmas & Canavari, 2017; Thiene et al.,
156
2018; Gao et al., 2019). However, the ML/RPL model cannot directly explain the
157
source of heterogeneity (Boxall & Adamowicz, 2002; Thiene et al., 2018). In reality,
158
to implement customized marketing strategies, we often need to understand consumer
159
segments and their profiles (Wu et al., 2019). Therefore, some researchers use the LC
160
model to explain the differences in consumer preferences across different market
161
segments (e.g., Skreli & Imami, 2012; Zhllima et al., 2015; Peschel et al., 2016; Skreli
162
et al., 2017; Banovic et al., 2019; Pishbahar et al., 2019).
163
3. Survey and Data
164
3.1 Attributes specification
165
We chose Fuji apple as the product of interest in this study for the following reasons.
166
First, with the improvement of Chinese consumers’ living standards and health
167
awareness, consumer demand for fruits has increased rapidly, with per capita
168
consumption of fresh fruits reaching 45.6 kg in 2017.4 However, negative reports
169
about fruit quality and safety are frequently reported in the media. According to the
170
big data research report on food safety incidents reported by mainstream online public
171
opinion in 2016, fruit and fruit products are among the top five food categories with
172
the largest number of safety incidents.5 Second, apple is the main fruit consumed by
173
Chinese consumers, and Fuji apple is the most popular apple cultivar. Furthermore,
3 Earlier studies include Verbeke & Ward (2006), Managi et al. (2008), Ortega et al. (2011a, 2011b), Bai et al. (2013), Van Wezemael et al. (2014), Yu et al. (2014), Meas et al. (2015), and Wu et al. (2015). 4 China statistical yearbook (2018). 5 http://paper.cfsn.cn/content/2017-12/21/content_57634.htm.
7
174
apple is one of the first agricultural products in China to be traceable.6 In 2017, the
175
country’s first quality traceability platform for featured agricultural products was for
176
apples.7
177
Selecting the appropriate attributes to describe the Fuji apple alternatives within
178
the DCE design is important. DCE studies dealing with apples are many. For example,
179
Costanigro et al. (2011), Durham et al. (2012), Denver & Jensen (2014), Mascarello et
180
al. (2015) and Ceschi et al. (2017) have justified the choice of the relevant attributes
181
of apples, such as local and organic production, certification, traceability information,
182
brand, price, and organoleptic sphere (seasonality, freshness, taste, and appearance),
183
etc. However, we cannot include too many attributes in the DCE since this could
184
cause undue cognitive burden on respondents (Powe et al., 2005). In this study,
185
attribute selection was based on expert consultations, literature reviews, and the
186
findings from our focus groups (e.g., Wu et al., 2017; Thiene et al., 2018). In addition
187
to the price attribute, we included three other attributes in our DCE: traceability
188
information, certification type, and region of origin claim.8 Table 1 displays the
189
attributes and their levels.
190
These attributes are included in the survey for following reasons:
191
Firstly, food traceability is the primary concern of this investigation. Food
192
traceability system provides consumers with food quality and safety information,
193
which can reduce consumer information asymmetry and concern about food safety
194
risks (Hobbs et al., 2005; van Rijswijk et al., 2008). Moreover, traceability typically
195
has a strong impact on consumer’s food choice (Verbeke & Ward, 2006; Lee et al.,
196
2011; Bai et al., 2013; Wu et al., 2017; Dandage et al., 2017). Based on the supply 6
Opinions of the Ministry of Agriculture on Accelerating the Construction of Traceability System for Quality and Safety of Agricultural Products (Agricultural Quality and Development [2016] No. 8). 7 http://www.zyczs.gov.cn/html/syncp/2017/11/1510713547509.html. 8 However, the study did not include other attributes that may affect consumer preferences and marginal WTP, such as organic, size, color and brand. This is one of the limitations of this study. 8
197
chain process, traceability information is represented in our DCE using four levels
198
(See Table 1). Notrace refers to no traceability information; Lotrace refers to
199
traceability information that includes only the production part of the value chain;
200
Mitrace refers to traceability information that includes the production and processing
201
parts of the value chain; Hitrace refers to traceability information that includes the
202
production, processing and distribution parts of the value chain.
203
Secondly, the authentication/certification type is an essential attribute. Even
204
though certification has a great influence on consumers’ purchasing behavior, the
205
traceability certification of apple products is very rare in the Chinese market (Yu et al.,
206
2014; Carter & Cachelin, 2018; Lusk et al., 2018). Authenticity verification of
207
traceability information will provide important assurances for consumers. Following
208
Durham et al. (2012), Abhijit et al. (2016), Wu et al. (2017) and Liu et al. (2019), we
209
added the authentication/certification type attribute in our DCE to indicate the
210
existence of traceability information as verified by certain organizations. These
211
certification types include no certification (Nothcert), government certification
212
(Govcert), domestic third-party certification (Dothcert) and international third-party
213
certification (Inthcert).
214
As previously discussed, region of origin is an additional important attribute. Food
215
quality is closely related to the natural environment of the region, such as soil, water,
216
and air. For instance, state/provincial boundaries are widely employed as a proxy to
217
identify local foods (Lusk et al., 2006; Chamorro et al., 2015; Bazzani et al., 2017).
218
Furthermore, the local origin attribute has been identified in many consumer studies
219
as a valued attribute (Loureiro & Umberger, 2007; Aprile et al., 2012; Hu et al., 2012;
220
Gracia et al., 2014; Meas et al., 2015). Therefore, we included region of origin
221
attribute in the choice experiment with the following levels: no region of origin claim
9
222
(Noclaim), produced in Shandong (Shandong), produced in Xinjiang (Xinjiang) and
223
produced in Shaanxi (Shaanxi). These three regions are the main production regions
224
of Fuji apple in China. In 2017, the area of apple orchards in Shandong, Shaanxi, and
225
Xinjiang accounted for 13.63%, 30.11% and 3.65%, respectively of the total apple
226
acreage of China. Apple production in these three provinces accounted for 22.7%,
227
26.39% and 3.48% of the country’s total apple production, respectively.9
228
Finally, four levels of prices are included in this study. The range of price levels
229
were determined from the prevailing prices at supermarkets, local grocery stores, fruit
230
stores, and farmers’ markets, as well as discussions with Chinese manufacturers and
231
food marketing researchers.
232
3.2 Experimental design
233
Based on the selected Fuji apple products attributes and their levels, a full factorial
234
design generate a total of 256 (4 traceability information × 4 certification type × 4
235
region of origin claim × 4 price levels) possible product profiles. Each choice set
236
includes two product profiles. As a result, the number of choice sets can grow
237
exponentially. To reduce the cognitive burden of participants, we used SSIWeb 7.0
238
software and adopted a randomized design to establish a choice experiment with 120
239
choice sets. The 120 selection sets were then divided into 10 blocks, and respondents
240
were randomly assigned to one of these blocks. A “no-buy” option was included in
241
each choice set (see Fig. 1). Not only does the inclusion of the “no-buy” option helps
242
make choices more realistic, it can also help consumers choose not to pick a product
243
in the choice set if they are not satisfied with them (Gao et al., 2016, Bazzani et al.,
244
2017). It can also prohibit mandatory choice of unacceptable alternatives, thereby
245
improving the quality of the data (Johnson & Orme, 2003).
9
China rural statistical yearbook (2018). 10
246
Highly efficient experimental designs have been emerging in recent years. A
247
balanced overlap method10 minimizing-error for the simulated MNL model was used
248
to generate the choice sets in this study. The final choice experiment efficiency
249
(Kuhfeld et al., 1994) statistics are summarized in Appendix A. Table A1, generated
250
by SSIWeb 7.0 software, shows the frequency of all attribute levels and the efficiency
251
of the standard error compared to the ideal model. As shown in Table A1, the
252
frequency of the levels of all attributes is generally balanced and the bias between
253
actual and ideal standard deviation is less than 10%, except for the level “HITRACE”
254
of traceability information and the level “SD” of region of origin claim. Table A2
255
exhibits the efficiency analysis post-experiment. We observe that the design has
256
achieved 87% efficiency with regard to the D-error, but only 74% in A-error.11 For
257
the parameter standard errors, efficiency ranges from 83% to 88%. Therefore, our
258
experimental design seems to have performed adequately.
259
3.3 Data collection
260
Data for this study are collected via face-to-face interviews of Chinese consumers in
261
Beijing, Shanghai, Guangzhou, Xi’an, Harbin and Jinan from July to October of 2017.
262
These cities are the pilot cities of the Chinese traceability system program for meat
263
and vegetable. 12 The sample taken from each city is proportional to the total
264
population of that city. Moreover, four administrative districts are sampled in each
265
city based on their geographical characteristics. In this study, 408 respondents were
266
randomly selected in Beijing, 413 in Shanghai, 383 in Guangzhou, 324 in Xi’an, 269
10 This method is an intermediate between the random strategy and the complete enumeration strategy. Overlap provides a method to simplify multiple-choice questions, reducing the number of attribute differences that respondents must evaluate (Johnson et al., 2013), and thus is statistically valid (Trenz, 2015). 11 The same model specification was applied for the collected data, and the ratios of standard errors, D-errors and A-errors were computed as suggested in Scarpa & Rose (2008). 12 Guangzhou is also included in this study, although Guangzhou is not a national pilot city for China’s meat and vegetable circulation traceability system construction program. Because the development of meat and vegetable traceability system construction in Guangzhou has been walking at the forefront of China is chosen as a sample city.
11
267
in Harbin and 295 in Jinan, respectively.
268
The survey is composed of the Fuji apple choice experiment, and questions related
269
to food safety concern and socio-demographics. Only those consumers who met the
270
recruitment criteria and have purchased Fuji apple products in the past six months
271
were invited to participate in this study. Interviews were conducted by professionally
272
trained and experienced investigators. A sample of 2,092 consumers, aged 18 and over
273
were surveyed on their fruit purchasing behavior and preferences for Fuji apple
274
products.
275
Furthermore, we used a “cheap talk” script (see Appendix B) before the choice
276
tasks to potentially reduce hypothetical bias (Farrell and Rabin, 1996; Lusk, 2003;
277
Murphy et al., 2005; Silva et al., 2011). Specifically, respondents are reminded of
278
their budget constraints, and are asked to choose an alternative as they would in a
279
real-world scenario. The product options in each choice set were randomly sorted
280
following Loureiro & Umberger (2007) and Savage & Waldman (2008).
281
4. Econometric models
282
Following Lancaster’s (1966) theory of consumer demand and random utility theory
283
(McFadden, 1974), consumer
’s utility from choosing alternative
284
this study) in choice situation
can be expressed as: =
285
=
286
where
.
287
Fuji apple’s attributes
288
the utility.
289
When
+
(e.g. apple in
(1)
is a vector of unknown part-worth utilities associated with , and
is the stochastic and unobserved component of
follows a Type I Extreme Value distribution assuming independently
290
and identically distributed (iid), and the assumption of IIA is valid, a Conditional
291
Logit model (CL model) can be employed to estimate the probability of the th option 12
292
being chosen as: (
=∑
293
) (
(2)
)
294
If the assumption of homogeneous preferences is relaxed, then the probability that
295
consumer
296
written as:
chooses alternative
in choice situation
(
= # ∏()*+ )* ∑
297
%)
(
%)
(for all
= 1, … , ") can be
&( )'
(3)
298
where &(. ) is the probability distribution function of the random preference
299
parameter (Train, 2009). If the parameters are fixed at
300
distribution will collapse, i.e., β =
301
individual’s likelihood within an ML model or RPL model. The RPL is an appropriate
302
approach to capturing the heterogeneity in the decision making of consumers
303
(Brownstone & Train, 1999; McFadden & Train, 2000; Hu et al., 2005; Ortega et al.,
304
2011a, 2011b). If &( ) is discrete, Eq. (3) can be incorporated into an LC model.
305
The probability for consumer
306
choice situation
-,
then &( - ) = 1. Eq. (3) can represent an
falling into class / and choosing alternative
(
()*+ = ∑2 1)* ∏ )* ∑
%)
(
%)
Ψ
in
(4)
1
is the parameter vector of the consumer group in class /, and Ψ
308
where
309
probability for consumer
310
corresponding probability can be estimated by:
311
(non-random), the
can be expressed as:
307
1
-
1
is the
falling into class /. Following Ouma et al. (2007), the
Ψ
1
= ∑7
( 3 4%5 )
( 36 465 )
6
312
where 8 is a range of observed values influencing consumer
313
and 91: denotes the parameter vector of consumer in class /.
13
(5) in a certain class,
Because dummy variables are used for the non-price attributes,13 the marginal
314 315
WTP for product attributes is calculated by ;WTP = −
@
, where
is the
316
coefficient of non-price attribute , and
317
marginal WTP can be interpreted as consumer WTP for an attribute level relative to
318
that for the baseline level (e.g. mWTP for Lotrace, Mitrace or Hitrace, relative that
319
that of Notrace).
320
5. Results and discussion
321
5.1 Descriptive statistics
322
Table 2 presents the socio-demographic characteristics and food safety concern of
323
respondents in the six cities of China. On average, about half of the respondents are
324
male, accounting for 50.72%. About 36.33% of the pooled sample are between 25-34
325
years old, and about 57.41% of the pooled sample have been educated for 13-16 years.
326
In our sample, most of the participants’ monthly household income is between
327
¥10000−19999. Table 2 also reports that respondents in the pooled sample have paid a
328
lot of attention to food safety issues. About 43.12% of respondents are very concerned
329
about food safety issues. About 40.44% of respondents report that the current level of
330
food safety in China is very good. However, in general, consumers have a low
331
evaluation of the government’s food safety supervision. About 43.79% of the
332
respondents think that the government supervision of food safety is bad. Regarding
333
“Can labeling improve food safety”, about 66.30% of respondents think labeling
334
might improve food safety. In terms of “Can traceability improve food safety”, about
335
47.04% of respondents believe that traceability might improve food safety.
336
5.2 Choosing numbers of classes
A
13
is the estimated price coefficient. The
Dummy coding results in the omitted levels being estimated within one constant, the omitted levels represent products with no origin, no traceability, and no certification. Since we include an alternative specific constant for the no-choice option, the constant associate with the plain product with no origin, no traceability, and no certification is constrained to zero. 14
337
To take into account taste heterogeneity, we benchmark the model specification search
338
for the conditional logit specification with a fixed utility coefficient, in which all
339
respondents are restricted under the assumption of “preference cloning” (e.g., Thiene
340
et al., 2018; Banovic et al., 2019; Wu et al., 2019). We then run a canonical search to
341
explore the preference heterogeneity dimensions between 2–10 preference classes
342
(see in Table A3, Appendix A). Following Kamakura & Russell (1989), Gupta &
343
Chintagupta (1994), Swait (1994), Bhat (1999), and Boxall & Adamowicz (2002), the
344
minimum Akaike information criterion (AIC) and the minimum Bayesian information
345
criterion (BIC) (Allenby, 1990) are used to identify the optimal number of latent
346
preference classes/segments to fit the data (McLachlan & Peel, 2000; Thacher et al.,
347
2005; Morey & Thiene, 2012, 2017). In our case, both the AIC and BIC values
348
decrease as the number of classes increases throughout. Therefore, the best model is
349
selected according to the two comprehensive criteria: 1) the credibility of the
350
parameter estimation and 2) the level of the marginal improvement of the AIC and
351
BIC values as a new class is added (Boxall & Adamowicz, 2002; Thiene et al., 2018).
352
The results from this combined method suggest that the three preference-class model
353
is best.
354
5.3 Choice models
355
5.3.1 Heterogeneous preference
356
We first look at the results of the fixed-coefficient CL model and ML model. In ML
357
model, upon testing various distributions, we specify normally distributed parameters
358
for the non-price attributes,14 while the coefficients of ASC (Choose no variable) and
359
the price are assumed to be fixed as suggested by Ubilava & Foster (2009)15 and
14
Base on model performance and past studies, such as Lin et al. (2019), Wu et al. (2017) and Bazzani et al. (2017), we specified normally distributed parameters for Fuji apple attributes in our data set. 15 There are at least two advantages to having the price parameter as fixed (Ubilava & Foster, 2009). First, in the case of fixed price parameters, the WTP distribution of consumers is consistent with the distribution of associated 15
360
Bazzani et al. (2017). As shown in Table 3, the coefficient of no-buy option
361
(Chooseno) is negative and significant in both models, implying that the utility of not
362
choosing either option is less than that of choosing any of the proposed product
363
alternatives in the choice set. The price coefficient is negative and significant in both
364
models as expected, indicating that price increases reduce utility. The estimated
365
coefficients for all the Fuji apple attributes are positive and significant at the 1% level
366
in both models. This means that all of these attributes have a significant effect on the
367
preferences of respondents for Fuji apple. Furthermore, results from the ML model
368
indicate strong heterogeneity in respondents’ preference for Fuji apple attributes as the
369
estimated standard deviations for all attributes (except for Lotrace variable) differ
370
significantly from zero.
371
5.3.2 Class preference and marginal WTP
372
Table 4 displays the estimated parameters of the three-class model. With regard to
373
membership probabilities of preference classes, results suggest a 65.9% probability of
374
a respondent belonging to Class 1, 19.1% belonging to Class 2, and 15.0% to Class 3,
375
respectively. The respondents’ age, education level, family income, evaluation of food
376
safety, evaluation of government’s supervision of food safety, responses to “Can
377
labeling improve food safety”, and “Can traceability (information) improve food
378
safety” are found to be significant in determining class membership. In contrast,
379
respondent’s food safety concerns are not helpful in predicting class membership.
380
Respondents belonging to Class 1 are found to be significantly and positively
381
influenced by Fuji apple’s traceability information, certification type, and ROO.
382
When compared to traceability information and ROO, the certification type is found
attribute parameters, rather than the ratio of two distributions, thus avoiding the difficult estimation of WTP distribution. Second, because the frame of demand theory restricts the price coefficient to be negative, it is difficult to select the distribution of price parameter. However, Revelt & Train (1999) stated that it is not guaranteed if a normal distribution is assumed. 16
383
to be of highest importance. Moreover, the government certification is rated as the
384
most important of the certification types. Based on this, Class 1 is named as
385
“Certification-oriented”. This result indicates that trust of product in Class 1 is
386
communicated by the external validation represented by the authentication process,
387
not the attributes being certified. This finding is in agreement with the results of El
388
Benni et al. (2019). Respondents in Class 1 are more likely to choose lower priced
389
over higher priced Fuji apple. In addition, respondents in Class 1 are likely to have a
390
higher education level, have a higher evaluation of food safety and government’s
391
supervision of food safety, as well as have higher perceptions of “Can labeling
392
improve food safety” and “Can traceability (information) improve food safety” than
393
those in Class 3. There are no significant differences between respondents in Class 1
394
and Class 3 regarding age and family monthly income.
395
Respondents in Class 2 also take Fuji apple’s traceability information, certification
396
type and ROO into consideration when making choices. In contrast to Class 1,
397
respondents in Class 2 preferred ROO to other attributes in this study. Furthermore,
398
Shaanxi is rated as the most important of the ROO. It suggests that Fuji apples
399
associated with ROO have a significantly higher probability of being chosen by
400
respondents in Class 2. Moreover, one important characteristic of Class 2 is the high
401
value of the price parameter, indicating a high price-sensitivity compared to the other
402
classes. Thus, we name Class 2 “Price and Origin- oriented”. A system that
403
convincingly guarantees the ROO of Fuji apple could become an important tool for
404
Chinese fresh fruit producers to influence consumer decisions. This finding aligns
405
with most studies such as Meas et al. (2015), Xie et al. (2016), Wu et al. (2015, 2017)
406
and Gao et al. (2019). Respondents in Class 2 are older, with less family monthly
407
income, and have a higher evaluation of food safety for Fuji apples than those in Class
17
408
3.
409
The coefficient of “Chooseno” variable in Class 3 is positive and significant. Its
410
value is greater than the coefficient estimates of other Fuji apple attributes, implying
411
that respondents in Class 3 tend not to choose any product. Therefore, we name Class
412
3 “Not interested”. Even if the coefficients for traceability information, certification
413
type and ROO are different, a significance test does not show significant differences,
414
which indicates that respondents in this class have no obvious preference for one or
415
the other. This finding is consistent with El Benni et al. (2019).
416
The marginal WTP estimates for each latent class are reported in Table 5. It shows
417
that marginal WTP differs considerably between Fuji apples food safety attributes and
418
between consumer classes. For example, respondents in Class 1 are willing to pay a
419
premium of 16.02 yuan for 500 g Fuji apples certified by the government. By contrast,
420
the additional marginal WTP of respondents in Class 2 and Class 3 is 0.88 yuan and
421
10.20 yuan for 500 g Fuji apples certified by the government, respectively. In addition,
422
other studies have considered different products and found that consumers have
423
different levels of WTP for different products (e.g., Wu et al., 2016; Lu et al., 2016;
424
Yin et al., 2018; Xu et al., 2019; Liu et al., 2019).
425
5.4 Marginal analysis and determinants of membership probabilities
426
Following Thiene et al. (2018), we apply marginal analysis to investigate how class
427
membership changes across different respondent profiles. Fig. 2 shows predicted class
428
probabilities by age. Higher ages indicate a higher probability of Class 2 membership
429
and a corresponding lower probability of Class 1 membership. From a policy
430
perspective, this suggests a policy aimed at the elderly, or the educated middle-aged
431
needs paying more attention. Assuming that the change is caused by age, rather than
432
characteristics associated with a particular age group, one might conclude that if
18
433
customized action is not taken, there is a 12.96% chance for young consumers in
434
Class 2 to fall into this category; for elder consumers in their 60’s, the probability
435
could rise to almost 26.45%. Fig.3 presents predicted class probabilities by
436
respondents’ family monthly income. Results suggest that, as expected, respondents
437
with higher monthly family income are more likely to belong to Class 1, and thus less
438
likely to fall into Class 2. It suggests that food enterprises should conduct market
439
segmentation and provide more food certification products for consumer groups with
440
higher monthly family income.
441
Turning to the food safety concerns and evaluation of food safety, Fig. 4 indicates
442
that the food safety concerns of respondents do not have a strong impact on
443
membership probability in Class 1 and Class 2. Nevertheless, the effect is negative for
444
Class 2 but positive for Class 1, which indicates that Class 2 decreases from 19.55%
445
to 16.25%, while Class 1 increases from 67.23% to 68.38%. From the comparisons
446
holding all else being equal, an increase in food safety concerns of respondents
447
redistributes a member from Class 2 to Class 1. With regard to respondents’
448
evaluation of food safety, we have noted an increase in membership probability from
449
66.54% to 68.40% for Class 1, and from 14.10% to 19.14% for Class 2, but a
450
relatively large drop from 19.36% to 12.46% in Class 3.
451
Fig. 5 describes the effect of respondents’ evaluation of the government’s
452
supervision of food safety on class membership probabilities. There is a drop from
453
16.57% to 11.30% in membership probability in Class 3, while an increase from 66.23%
454
to 71.55% in Class 1. Hence, there is clear evidence for the desire to improve the
455
government’s supervision of food safety.
456
Fig. 6 displays the effects of labeling and traceability information. Through the
457
comparison, we can see clearly: if other conditions are the same, increasing
19
458
respondents’ evaluation of food labeling will reassign members from Class 2 to Class
459
1. That is, Class 2 decreases from 21.13% to 14.75%, while Class 1 increases from
460
59.05% to 73.48%. Similar results are also observed for the evaluation of traceability
461
information. As can be seen, the supply of labeling and traceability information is
462
potentially strongly associated with classes containing different attribute preferences.
463
6. Conclusion and implications
464
We use a discrete choice experiment and a Latent Class model to analyze Chinese
465
consumer preferences and marginal WTP for food safety attributes of Fuji apple
466
products. We identify three latent consumer segments and determine the source of
467
heterogeneity using socio-demographic variables and individual food safety concern
468
and evaluation factors.
469
We have three major findings. First, our results suggest that consumers’ valuation
470
of certification differs depending on the type of certification. Specifically, the results
471
suggest that Chinese consumers place the highest value on government certification.
472
For the traceability information, they place the least value on traceability that includes
473
only the production part of the process. Second, we identify three consumer segments
474
in Fuji apple products market; i.e., certification-oriented consumers (Class 1, 65.9% in
475
market share), price and origin-oriented consumers (Class 2, 19.1% in market share),
476
and not interested consumers (Class 3, 15.0% in market share). Third, we found that
477
each consumer class has its own perceptual and attitudinal characteristics in relation
478
to food safety. Specifically, certification-oriented consumers have a high evaluation of
479
food safety and government’s food safety supervision. They believe that labeling and
480
traceability can improve food safety. On the other hand, price and origin-oriented
481
consumers only place a high value on their evaluation of local food safety.
482
In addition, we found that socio-demographic variables and individual food safety
20
483
concerns and evaluation factors are important determinants of consumer preferences.
484
Results indicate that older or more educated middle-aged people, or those with higher
485
household income, are more concerned about Fuji apple’s choices. We also have
486
found that food safety concerns tend not to have a strong impact on membership
487
probability for certification-oriented consumers or on price and origin-oriented
488
consumers. Moreover, the provision of labeling and traceability information may be
489
closely related to products with different attribute choices.
490
These findings provide important managerial implications for the fresh fruit
491
industry. The positive values of marginal WTP for each of the Fuji apple safety
492
attributes indicates that Chinese consumers are willing to pay a premium for food
493
safety attributes. The results provide valuable information for the fruit industry to
494
develop food safety strategies. Furthermore, given that Chinese consumers are highly
495
responsive to certified Fuji apple, establishing a trusted certification system is likely
496
to improve Chinese consumers’ evaluation of food safety and increase the demand for
497
high-quality and safe fruits in the Chinese domestic market.
498
In addition to the existing forms of food labeling in China, there are alternative
499
labeling applications, as well as specific information programs for specific consumer
500
segments, which are a form of personalized labeling. While much of the focus and
501
previous research has focused on the overall design of food safety policies, the
502
broader policy picture seems to require broader multi-dimensional interventions
503
aimed at targeting specific market segments based on the characteristics of Chinese
504
consumers and their perceptions and evaluations of food safety issues.
505
Future research can improve this study from at least two aspects. Firstly, future
506
studies can include other food safety attributes such as organic certification, and other
507
attributes such as freshness, size, and color. Secondly, the sample is from first-tier
21
508
cities and provincial capitals with higher socio-economic development and higher
509
purchasing power. In the future, more representative samples that include cities at
510
different tiers and even rural consumers could result in a more comprehensive
511
conclusion regarding Chinese consumers’ preferences for food safety attributes.
22
512
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Table1. Fuji apple products attributes in the choice experiment Attribute Levels Description Traceability 4 No traceability information (Notrace) information Traceability information that includes only the production part of the value chain (Lotrace) Traceability information that includes the production and processing parts of the value chain (Mitrace) Traceability information that includes the production, processing and distribution parts of the value chain (Hitrace) Certification type 4 No certification (Nothcert) Government certification (Govcert) Domestic third-party certification (Dothcert) International third-party certification (Inthcert) Region of origin claim 4 No region of origin claim (Noclaim) Produced in Shandong (Shandong) Produced in Xinjiang (Xinjiang) Produced in Shaanxi (Shaanxi) Price 4 6 yuan/500 g 8 yuan/500 g 10 yuan/500 g 12 yuan/500 g Note: In July 2017, 1 US dollar = 6.77 yuan.
Table 2. Socio-demographic characteristics and consumers’ food safety consciousness between cities in % of the sample Variables Beijing Shanghai Guangzhou Xi’an Jinan Harbin Pooled (N = 408) (N = 413) (N = 383) (N = 324) (N = 295) (N = 269) sample (N = 2092) Gender: Male 47.55 58.84 48.04 47.84 51.86 49.07 50.72 Female 52.45 41.16 51.96 52.16 48.14 50.93 49.28 Age (years): ≤ 24 29.41 20.58 29.24 25.00 25.42 22.30 25.48 25–34 39.95 42.13 37.34 30.25 32.20 32.34 36.33 35–44 15.44 15.74 16.19 21.91 21.02 19.33 17.93 45–54 10.29 7.26 8.62 12.35 11.86 13.38 10.33 55–64 3.68 9.20 5.22 7.10 6.78 8.18 6.60 ≥ 65 1.23 5.08 3.39 3.40 2.71 4.46 3.35 Education level (years): ≤ 9 4.66 11.86 12.53 18.83 11.86 11.52 11.62 10–12 9.80 14.53 20.63 13.89 20.68 17.84 15.92 13–16 52.70 57.87 59.27 60.80 59.32 55.02 57.41 >16 32.84 15.74 7.57 6.48 8.14 15.61 15.06 Household monthly income (CNY/month): < 5000 7.11 4.12 3.66 8.64 12.20 12.27 7.50 5000–9999 18.87 13.80 16.19 35.49 41.69 37.55 25.57 10000–19999 37.50 30.51 35.51 41.98 39.32 39.41 36.95 20000–29999 20.83 22.52 24.54 8.02 6.44 7.81 16.16 30000–39999 4.66 13.80 12.53 3.40 0.34 0.74 6.60 40000–49999 4.41 3.87 1.57 0.93 0 0.37 2.10 50000–59999 2.70 4.84 2.61 0.93 0 1.12 2.25 60000–99999 2.70 2.66 1.83 0.31 0 0 1.43 ≥100000 1.23 3.87 1.57 0.31 0 0.74 1.43
Food safety concerns: Not A little Very Evaluation of food safety: Bad Good Very good Evaluation of government’s supervision of food safety: Bad Good Very good Can labeling improve food safety? No, it can not Perhaps Yes, definitely Can traceability (information) improve food safety? No, it can not Perhaps Yes, definitely
5.15 50.00 44.85
5.33 47.70 46.97
6.27 56.92 36.81
4.32 51.85 43.83
7.80 52.20 40.00
8.55 45.35 46.10
6.07 50.81 43.12
11.52 43.14 45.34
7.99 41.89 50.12
7.83 46.48 45.69
13.58 53.70 32.72
11.86 51.53 36.61
23.42 52.42 24.16
12.05 47.51 40.44
47.06 44.85 8.09
45.04 48.91 6.05
35.77 51.96 12.27
49.69 44.75 5.56
37.29 48.81 13.90
48.33 45.72 5.95
43.79 47.61 8.60
7.35 62.99 29.66
6.54 72.15 21.31
4.96 74.15 20.89
11.11 63.27 25.62
4.75 59.32 35.93
7.43 62.45 30.11
6.98 66.30 26.72
6.62 49.26 44.12
9.93 50.61 39.47
9.14 53.79 37.08
8.95 38.58 52.47
5.76 43.05 51.19
9.67 43.12 47.21
8.37 47.04 44.60
Notes: We captured the consumers’ food safety conception derived from five questions: on 3-point Likert scale (1-3 scale), How much do you concern about food safety? What is your evaluation of food safety in China? What is your evaluation of government’s supervision of food safety in China? Can labeling improve food safety? Can food traceability (information) improve food safety?
Table 3. Estimates from Conditional Logit Model (CL model) and Mixed Logit Model (ML model) Variables CL model ML model Mean Mean Standard deviation *** *** Chooseno −0.379 −0.487 − (0.052) (0.097) *** Price −0.165 −0.223*** − 0.004 (0.008) Lotrace 0.407*** 0.534*** −0.031 (0.026) (0.035) (0.423) *** *** Mitrace 0.632 0.819 0.736*** (0.027) (0.042) (0.063) *** *** Hitrace 0.825 1.041 0.790*** (0.029) (0.045) (0.072) *** *** Govcert 1.165 1.424 1.170*** (0.028) (0.049) (0.057) *** *** 1.164 0.705*** Dothccert 0.937 (0.028) (0.043) (0.075) *** *** Inthcert 1.058 1.288 1.032*** (0.028) (0.047) (0.061) *** *** Xinjiang 0.898 1.104 0.894*** (0.028) (0.046) (0.080) *** *** 1.147 0.903*** Shandong 0.943 (0.029) (0.046) (0.063) *** *** Shaanxi 0.932 1.150 0.861*** (0.028) (0.044) (0.066) Log likelihood −22307.07 −21091.47 LR chi2 10544.99 − Wald chi2 − 2931.04 Pseudo R2 0.1912 − Prob > chi2 − 0.0000 Observations 75,312 75,312 Notes: Standard errors in parentheses. *, ** and *** indicate significance at 10%, 5% and 1% significance levels, respectively.
Table 4. Estimates from Latent Class model (LC model) Variable Class1 (Certification Class 2 -oriented) (Price and origin-oriented) 0.659 0.191 Class size Attributes: Price −0.088*** (0.007) −0.714*** (0.037) Chooseno −0.971*** (0.098) −4.962*** (0.381) Lotrace 0.513*** (0.033) 0.248** (0.099) 0.216** (0.104) Mitrace 0.822*** (0.036) Hitrace 1.022*** (0.039) 0.389*** (0.109) Govcert 1.401*** (0.039) 0.626*** (0.113) Dothccert 1.135*** (0.037) 0.523*** (0.100) 0.744*** (0.096) Inthcert 1.293*** (0.038) Xinjiang 1.061*** (0.037) 1.063*** (0.105) Shandong 1.099*** (0.038) 0.989*** (0.104) 1.447*** (0.107) Shaanxi 1.008*** (0.037) Membership Equations: Age −0.000 (0.005) 0.021*** (0.006) −0.016 (0.044) Education 0.052** (0.022) Family income 0.000 (0.000) −0.000*** (0.000) Food_cons −0.067 (0.101) −0.168 (0.157) Food_qual 0.234*** (0.090) 0.373*** (0.129) Gov_sup 0.230** (0.104) 0.190 (0.144) *** Label_safe 0.370 (0.120) 0.081 (0.190) *** Trace_safe 0.328 (0.100) 0.112 (0.152) Constant −1.601*** (0.383) −1.173 (0.884) Observations 75,312 75,312 No. of groups 25,104 25,104
Class 3 (Not interested) 0.150 −0.119*** (0.020) 2.392*** (0.294) 0.322*** (0.108) 0.592*** (0.108) 0.917*** (0.110) 1.216*** (0.124) 0.798*** (0.124) 0.893*** (0.120) 1.116*** (0.133) 1.306*** (0.128) 1.052*** (0.137) − − − − − − − − − 75,312 25,104
Notes: Standard errors in parentheses. *, ** and *** indicate significance at 10%, 5% and 1% significance levels, respectively.
Table 5. Marginal WTP estimates for each latent class Variable Class1 Class 2 (Certification (Price and -oriented) origin-oriented ) Lotrace 5.86 0.35 [4.71, 7.02] [0.07, 0.63] Mitrace 9.40 0.30 [7.70, 11.09] [0.00, 0.60] Hitrace 11.69 0.54 [9.68, 13.69] [0.22, 0.87] Govcert 16.02 0.88 [13.37, 18.88] [0.53, 1.23] Dothccert 12.97 0.73 [10.78,15.17] [0.43, 1.03] Inthcert 14.78 1.04 [12.29,17.27] [0.75, 1.34] Xinjiang 12.13 1.49 [10.04, 14.22] [1.15, 1.82] Shandong 12.57 1.39 [10.43, 14.71] [1.06, 1.72] Shaanxi 11.52 2.03 [9.57, 13.48] [1.66, 2.40]
Class 3 (Not interested) 2.70 [0.66, 4.74] 4.96 [2.42, 7.50] 7.69 [4.51, 10.87] 10.20 [6.23, 14.18] 6.70 [3.73, 9.66] 7.49 [4.37, 10.61] 9.36 [5.32, 13.41] 10.95 [6.45, 15.46] 8.83 [4.99, 12.66]
Notes: Numbers in brackets represent 95% confidence intervals, which are estimated by using the parametric bootstrapping procedure of Krinsky and Robb (1986).
Appendix A Table A1. A priori estimates of standard errors for attribute levels provided by SSIWeb 7.0 Attribute Level Frequency Actual Ideal Efficiency S.D. S.D. Traceability LOTRACE 60 0.2270 0.2346 1.0679 information MITRACE 60 0.2293 0.2346 1.0468 HITRACE 60 0.2496 0.2346 0.8839 NOTRACE 60 Certification type GOVCERT 61 0.2277 0.2325 1.0426 DOTHCERT 59 0.2391 0.2325 0.9458 INTHCERT 60 0.2341 0.2325 0.9865 NOCERT 60 Region of origin SD 61 0.2456 0.2325 0.8958 claim XJ 60 0.2386 0.2325 0.9496 SHX 60 0.2341 0.2325 0.9862 NOORIGIN 59 Price 12 yuan 59 0.2224 0.2294 1.0645 10 yuan 60 0.2411 0.2294 0.9055 8 yuan 60 0.2229 0.2294 1.0593 6 yuan 61 Notes: Task generation method is “Balanced Overlap” using a seed of 1. The efficiencies reported above for this design assume an equal number of respondents complete each version. Ideal standard deviation is the standard deviation that meets the orthogonality condition. The “strength” of design for this model is 2697.15. The ratio of “strengths” of design for two designs reflects the D-efficiency of one design relative to the other. Please refer to Table 1 for the definitions of different levels.
Table A2. Evaluation of design efficacy Level ASC-Buy Traceability information
Certification type
Region of origin claim
Price D-error A-error
LOTRACE MITRACE HITRACE NOTRACE GOVCERT DOTHCERT INTHCERT NOCERT SD XJ SHX NOORIGIN PRICE
Simulated S.E. 0.0454 0.0232 0.0236 0.0252 0.0232 0.0240 0.0236 0.0247 0.0241 0.0237 0.0036 0.00031 0.0072
Actual S.E. 0.0517 0.0264 0.0273 0.0290 0.0279 0.0281 0.0282 0.0287 0.0281 0.0279 0.0042 0.00039 0.0097
Ratio 0.877 0.877 0.865 0.869 0.832 0.854 0.837 0.859 0.857 0.848 0.878 0.800 0.742
Notes: Simulated standard errors resulting from an MNL model with simulated choices using the same number of responses per block as in the final data set. It is assumed the probability of “no-buy” is 15% and the probabilities of the two purchase options are equal. The design was generated under the same assumptions.
Table A3. A comparison of LCM with different number of classes a Classes Number of LLb LL(0)b ρ2c AICd BICe parameters 2 23 −19555.8 −22300.8 0.12 39310.5 39287.5 3 35 −19037.3 −22262.1 0.14 38377.2 38342.2 4 47 −18593.2 −22235.6 0.16 37592.7 37545.7 5 59 −18399.0 −22211.8 0.17 37308.2 37249.2 6 71 −18227.5 −22203.7 0.18 37068.8 36997.8 7 83 −18071.5 −22214.2 0.19 36860.7 36777.7 8 95 −17754.7 −22194.5 0.20 36330.7 36235.7 9 107 −17640.3 −22161.2 0.20 36205.7 36098.7 10 119 −17481.3 −22167.2 0.21 35991.4 35872.4 a b Notes: Sample size is 25104 choices from 2092 respondents (N). LL means Log likelihood at convergence. LL(0) means Log likelihood evaluated at 0. c ρ2 is calculated by 1−LL/LL(0). d AIC is calculated using −2(LL−P). e BIC is calculated by (P/2)*ln(N)–LL.
Appendix B Cheap talk script “We know from past studies that people often respond in one way but behave differently. Several people state a higher WTP than what one really was willing to pay for the product in a grocery store. No one really has to pay to show a particular preference. A possible reason for this is that people do not really think about the finite amount of money we have. When you really do not need to pay, generosity is easy. But when we’re really in the grocery store, if we decide to buy this good, we had to spend money. In any case, we ask you to answer the preferences and WTP of each of these questions, just like you have to pay for your choice in a real grocery. Please keep in mind when answering the last few questions.”
Option A Traceability information that includes production, processing and distribution parts of the value chain International third-party certification Shaanxi Price：12 yuan per 500 g
Option B
Option C
Traceability information that includes production
Neither A or B
No certification Xinjiang Price：8 yuan per 500 g
I choose… Fig.1. Sample choice task
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
age 18
age 20
age 30 Class 1
Class 2
age 40
age 50
Class 3
Fig. 2. Class membership probabilities by age increase
age 60
90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
inc 5000
inc inc inc inc inc inc inc inc inc 10000 15000 20000 25000 30000 40000 50000 60000 100000 Class 1
Class 2
Class 3
Fig. 3. Class membership probabilities by family monthly income increase
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
food_cons1
food_cons2
food_cons3 Class 1
food_qual1
Class 2
food_qual2
food_qual3
Class 3
Fig. 4. Class membership probabilities by food safety concerns and evaluation of food safety
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
gov_sup1
gov_sup2 Class 1
Class 2
gov_sup3 Class 3
Fig. 5. Class membership probabilities by evaluation of government’s supervision of food safety
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
label_safe1
label_safe2
label_safe3
Class 1
trace_safe1
Class 2
trace_safe2
trace_safe3
Class 3
Fig. 6. Class membership probabilities by labeling and traceability information
Research Highlights
Conduct a discrete choice experiment in five cities across China; Identify three consumer segments but most consumers are certification-oriented; Find consumers valuate the government certification most; Conclude region of origin and price are major attributes of consumer choices; Suggest simplifying labeling application and specific information program.
Conflict of interest form
There is not any conflict of interest for this study.
S0956-7135(20)30073-6
DOI:
https://doi.org/10.1016/j.foodcont.2020.107157
Reference:
JFCO 107157
To appear in:
Food Control
Received Date: 24 September 2019 Revised Date:
1 February 2020
Accepted Date: 4 February 2020
Please cite this article as: Liu R., Gao Z., Snell H.A. & Ma H., Food safety concerns and consumer preferences for food safety attributes: Evidence from China, Food Control (2020), doi: https:// doi.org/10.1016/j.foodcont.2020.107157. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
Ruifeng Liu: Conceptualization, Investigation, Methodology, Software, Data curation, Writing- Original draft preparation. Zhifeng Gao: Methodology, Software, WritingReviewing and Editing. Heather Arielle Snell: Software, Data curation, WritingReviewing and Editing. Hengyun Ma: Supervision, Writing- Reviewing and Editing.
Food safety concerns and consumer preferences for food safety attributes: Evidence from China
Ruifeng Liu College of Economics and Management, Henan Agricultural University, No.15, Longzi Lake College Park, Zhengzhou Eastern New District, Zhengzhou 450046, China (phone: (86)-371-56990018; fax: (86)-371-56990014;e-mail: [email protected]) Zhifeng Gao Food and Resource Economics Department, University of Florida, P.O. Box 110240, Gainesville, FL 32611-0240, USA (phone: (1)- 352-294-7672; fax: (1)- 352-846-0988; e-mail: [email protected]) Heather Arielle Snell Department of Agricultural Economics and Agribusiness, University of Arkansas, 217 Agriculture Building, Fayetteville, AR 72701, USA (phone: +1 (479) 575-6995; fax: (1)- 479-575-5306; e-mail: [email protected]) Hengyun Ma* College of Economics and Management, Henan Agricultural University, No.15, Longzi Lake College Park, Zhengzhou Eastern New District, Zhengzhou 450046, China (phone: (86)-371-56990018; fax: (86)-371-56990014; e-mail: [email protected])
*
Acknowledgement: This work was financially supported by the National Natural Science Foundation of China (Grant No: 71403082); Ministry of Education of Humanities and Social Science Research Project of China (Grant No: 14YCJ790080); National Social Science Foundation of China (Grant No: 14BGL093); 2017 Annual Scientific and Technological Innovation of Henan Province Talent (Humanities and Social Sciences) Support Program (Grant No: 2017-cxrc-002); Young Backbone Teachers Scheme of Henan Colleges and Universities (Grant No: 2015GGJS-085); Henan Province Philosophy and Social Science Planning Project (Grant No: 2017BJJ033); Henan Provincial Department of Education Humanities and Social Sciences Research Project (Grand No: 2019-ZZJH-327). Corresponding author: Tel: +86 371 56990018; fax: +86 371 56990014; email: [email protected] (H.Y. Ma)
1 2 3
Food safety concerns and consumer preferences for food safety attributes: Evidence from China
4 5
Abstract
6
Abstract: China has experienced a series of high-profile food safety scandals in the
7
past few years that seriously challenged public confidence in the domestic food
8
industry. Much attention has been paid to Chinese government’s food regulatory and
9
inspection systems. Scant research, however, has been devoted to analyzing Chinese
10
consumers’ food safety concerns. This study interviewed 2092 Chinese consumers in
11
Beijing, Shanghai, Guangzhou, Xi’an, Jinan, and Harbin and used the Conditional
12
Logit model, Mixed Logit model, and the Latent Class model to analyze consumer
13
preferences and marginal willingness to pay (WTP) for selected food safety attributes
14
of Fuji apple products. We identified three consumer segments: certification-oriented
15
(65.9%), price and origin-oriented (19.1%), and not interested (15.0%). Results reveal
16
that Chinese consumers, in general, are willing to pay a premium for selected food
17
safety
18
socio-demographic characteristics are used to determine the sources of preference
19
heterogeneity. Marginal analysis is also conducted to estimate the response of the
20
model and selection probability to potential policy levels, such as increasing the
21
perception of food safety and the trust on labeling and traceability information to
22
improve the evaluation of food safety.
attributes.
Consumers’
perceptual
and
attitudinal
factors
and
23 24
Keywords: Food safety; Choice experiment; marginal WTP; Latent Class model;
25
Chinese consumers.
26
JEL classifications: D12; Q18
1
27 28 29
Food safety concerns and consumer preferences for food safety attributes: Evidence from China
30
1. Introduction
31
Since the last decade, a series of high-profile food safety scandals have tarnished the
32
reputation of Chinese food products in international markets and have seriously
33
shaken Chinese consumers’ confidence in the domestic food industry (Cicia et al.,
34
2016; Lai et al., 2018; El Benni et al., 2019). One of the most notorious food safety
35
incidents involved melamine milk powder and infant formula in 2008, affecting
36
approximately 300,000 babies, sending 54,000 to hospitals, and killing at least six
37
(The Lancet, 2012, 2014). Other subsequent food safety incidents have made
38
headlines repeatedly in China. For instance, the clenbuterol was found in pork from
39
one of China’s largest meat producers in 2011. In 2013, a large number of dead pigs
40
were found floating in the Huangpu River of Shanghai and in 2015, “Zombie meat”
41
(referring to meat products that have been frozen for years) was sold in many cities
42
across the country.
43
The difficulty of regulatory oversight seems insurmountable because of numerous
44
issues, including the complexity and scale of the contemporary food supply chain, the
45
geographic dispersion of food-related public health threats, the intricate intersections
46
of food safety, and the broad policy agenda (Kang, 2019). Most important of all, the
47
sheer size, diversity, and complexity of China’s domestic food producers and
48
processors make regulation through China’s single regulatory structure extremely
49
difficult and costly (Fewsmith & Gao, 2014; Zhou et al., 2015; Roberts & Lin, 2016;
50
Guo et al., 2019). Although the China State Administration for Market Regulation
51
raised the level of supervision after consolidating several departments in 2018, serious
52
coordination problems continue to undermine the government’s regulatory capacity
2
53
(Berti & Semprebon, 2018; Kang, 2019).
54
At the same time, in the food industry globally, there is growing pressure to track
55
and trace food products through the production, processing and distribution stages
56
(Liu et al., 2019). As a result, food traceability systems were installed in a number of
57
countries including Japan, Australia, and many European countries. The European
58
General Food Law (European Commission, 2002) defines traceability as “the ability
59
to trace and follow a food, feed, food-producing animal or substance through all
60
stages of production and distribution”, while the International Organization for
61
Standardization (ISO) defines traceability as “the ability to trace the history,
62
application and location of that which is under consideration”, and notes that “when
63
considering a product, traceability can relate to the origin of materials and parts, the
64
process history, and the distribution and location of the product after delivery” (ISO,
65
2007). Traceability systems make it possible to track product history from top to
66
bottom in the food supply chain, including raw materials, processing, packaging,
67
storage, transportation, and marketing to identify problem sectors and recall high-risk
68
products from the market (Asioli et al., 2014; Duan et al., 2017). Therefore,
69
traceability systems are considered an important tool to prevent food safety incidents
70
(Hobbs et al., 2005; van Rijswijk et al., 2008; Berti & Semprebon, 2018).
71
Given the grim food safety situation, it is important for Chinese food industrialists
72
and policymakers to understand Chinese consumers’ awareness of food safety and
73
their preferences for food safety information attributes (Yu et al., 2014; Cicia et al.,
74
2016; Lai et al., 2018). Many researchers have investigated the major determinants of
75
food safety issues (Grunert, 2005; Lin et al., 2010; Lam et al., 2013) and China’s food
76
safety policies (Broughton & Walker, 2010; Yu et al., 2014; Duan et al., 2017; Kang,
77
2019). However, there is scant literature on Chinese consumers’ food safety
3
78
preferences and behaviors. Specifically, few studies have focused on Chinese
79
consumers’ concerns and evaluation of food safety issues, and on Chinese consumers’
80
preferences and willingness to pay (WTP) for food safety attributes (Wu et al., 2015;
81
El Benni et al., 2019).
82
The aim of this study is to evaluate Chinese consumers’ preferences and marginal
83
WTP for food safety attributes of apples (i.e., Fuji apple products). Given that a
84
number of market segments exist in China, in addition to the mixed logit model that
85
assumes continuous heterogeneous preference among consumers, we use the Latent
86
Class model (LC model) to estimate consumer preferences and consumer profiles of
87
each segment. The LC model also links preference heterogeneity to consumer
88
characteristics, such as socio-demographics (Boxall & Adamowicz, 2002). We take
89
Chinese consumers’ perception and attitude of food safety issues into account to
90
investigate how these factors help form heterogeneous preferences in the LC model
91
(Rao, 2014; Thiene et al., 2018). Finally, we perform a marginal analysis to assess the
92
effect of a set of scenarios to provide useful advice for policy makers. Since most
93
previous studies have focused on meat and milk in China (e.g., Wang et al., 2008;
94
Ortega et al., 2011a, 2011b; Bai et al., 2013; Wu et al., 2015, 2017; Wu et al., 2019),
95
here we use Fuji apple as the product of interest to examine consumers’ preferences
96
for traceable fruit products.
97
The remainder of this paper goes on as follows: Section 2 reviews the literature.
98
Section 3 describes the survey and data. Section 4 specifies the econometric models.
99
Section 5 estimates and discusses the results. Section 6 concludes with policy
100
implications.
101
2. Literature review
102
There are a number of studies that have examined consumers’ preferences and WTP
4
103
for food safety attributes.1 Many of these studies found evidence that consumers are
104
willing to pay a premium but they have different preferences and WTP for these food
105
safety attributes (e.g., Ubilava & Foster, 2009; Lee et al., 2011; Wu et al., 2015;
106
Thiene et al., 2018; Wu et al., 2019). A number of studies have also discussed the food
107
safety aspects of various attributes including organic or green food labeling, country
108
of origin/local, quality certification, and traceability (e.g., Liu et al., 2013; Meas et al.,
109
2015; Wu et al., 2015, 2017; Lusk et al., 2018; Gao et al., 2019; Liu et al., 2019).
110
There is also a burgeoning literature 2 on consumers’ preferences for food
111
traceability (e.g., Jin & Zhou, 2014; Dandage et al., 2017; Jin et al., 2017; Liu et al.,
112
2018). Studies have found that traceability information has a significant influence on
113
consumer preferences, and that consumers are willing to pay a higher price for this
114
attribute. However, most of these studies only consider traceability information as one
115
of the food safety attributes; i.e., they tend to ignore the heterogeneity of traceability
116
information, in which consumers are highly interested. In addition, to the best of our
117
knowledge, there has been no large-scale investigation on consumers’ valuation for
118
different types of traceability information on fresh fruit in China.
119
Region of origin claim (ROO) or country of origin (COO) may affect consumer
120
preferences because consumers use it to infer product quality based on their shopping
121
experience (Claret et al., 2012; Eng et al., 2016). This finding has been repeatedly
122
confirmed by researchers (e.g., Skreli & Imami, 2012; Gao et al., 2019). ROO claims
123
provide consumers’ information about where the food was produced (Xie et al., 2016).
124
When ROO comes in the form of a label, it is not only a credence attribute, but also
125
an experience attribute (Tsakiridou et al., 2011). Other studies also indicate that ROO
1 Earlier studies include Lusk (2003), Loureiro & Umberger (2007), Wang et al. (2008), Lin et al. (2010), and Ortega et al. (2011a, 2011b). 2 Earlier studies include Hobbs et al. (2005), Verbeke & Ward (2006), Loureiro & Umberger (2007), van Rijswijk et al. (2008), and Ubilava & Foster (2009).
5
126
has a symbolic and emotional meaning (Ehmke et al., 2008; d’Astous & Ahmed,
127
1999).
128
Consumers’ perception of food safety certification can also have a significant
129
impact on consumers’ WTP (Zhang et al., 2012). Certification of the authenticity of
130
traceability information provides important assurance for consumers (Wu et al., 2015).
131
Previous studies have suggested that Chinese consumers’ preference for traceable
132
food is influenced by certification bodies (Bai, et al., 2013; Liu et al., 2019).
133
Furthermore, researchers have compared consumers’ preferences for the ROO/COO
134
labeling, food traceability attribute, and the certification type (e.g., Wu et al., 2017;
135
Liu et al., 2019). Consumers’ preferences and emphasis on these attributes could vary,
136
however, depending on location or country.
137
In addition, both quantitative and qualitative methods have been applied to study
138
consumer preferences for food safety. Quantitative methods based on consumer
139
surveys have been used to identify the interaction between sociodemographic
140
characteristics and consumers’ choices for food products and WTP (e.g.,
141
Wongprawmas and Canavari, 2017; Lusk et al., 2018; Liu et al., 2019), while
142
qualitative methods have been applied to study consumers’ perceptions of food safety
143
attributes (e.g., Sirieix et al., 2011; Cui et al., 2016; Hasimu et al., 2017; Ha et al.,
144
2019). The results from these studies tend to show that age, gender, education levels,
145
and households or individual monthly income significantly affect consumers’
146
perception and WTP for different attributes. In addition, research shows that the
147
findings could vary across methods employed (Lusk et al., 2005; Dannenberg, 2009).
148
These methodological approaches usually include structural equation modeling,
149
discrete choice experiments (DCE), and experimental auctions, among others.
150
DCEs are widely used in research about food safety information (Akaichi et al.,
6
151
2013; Johnson et al., 2013; Rao, 2014). DCEs simulate consumers’ actual purchasing
152
decisions by evaluating the utility of attributes in various combinations. When using
153
the DCE method, many researchers3 employ a multinomial logit model (MNL) or
154
Mixed Logit model/Random Parameter Logit model (ML/RPL) to estimate consumer
155
preferences for food safety attributes (Wongprawmas & Canavari, 2017; Thiene et al.,
156
2018; Gao et al., 2019). However, the ML/RPL model cannot directly explain the
157
source of heterogeneity (Boxall & Adamowicz, 2002; Thiene et al., 2018). In reality,
158
to implement customized marketing strategies, we often need to understand consumer
159
segments and their profiles (Wu et al., 2019). Therefore, some researchers use the LC
160
model to explain the differences in consumer preferences across different market
161
segments (e.g., Skreli & Imami, 2012; Zhllima et al., 2015; Peschel et al., 2016; Skreli
162
et al., 2017; Banovic et al., 2019; Pishbahar et al., 2019).
163
3. Survey and Data
164
3.1 Attributes specification
165
We chose Fuji apple as the product of interest in this study for the following reasons.
166
First, with the improvement of Chinese consumers’ living standards and health
167
awareness, consumer demand for fruits has increased rapidly, with per capita
168
consumption of fresh fruits reaching 45.6 kg in 2017.4 However, negative reports
169
about fruit quality and safety are frequently reported in the media. According to the
170
big data research report on food safety incidents reported by mainstream online public
171
opinion in 2016, fruit and fruit products are among the top five food categories with
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the largest number of safety incidents.5 Second, apple is the main fruit consumed by
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Chinese consumers, and Fuji apple is the most popular apple cultivar. Furthermore,
3 Earlier studies include Verbeke & Ward (2006), Managi et al. (2008), Ortega et al. (2011a, 2011b), Bai et al. (2013), Van Wezemael et al. (2014), Yu et al. (2014), Meas et al. (2015), and Wu et al. (2015). 4 China statistical yearbook (2018). 5 http://paper.cfsn.cn/content/2017-12/21/content_57634.htm.
7
174
apple is one of the first agricultural products in China to be traceable.6 In 2017, the
175
country’s first quality traceability platform for featured agricultural products was for
176
apples.7
177
Selecting the appropriate attributes to describe the Fuji apple alternatives within
178
the DCE design is important. DCE studies dealing with apples are many. For example,
179
Costanigro et al. (2011), Durham et al. (2012), Denver & Jensen (2014), Mascarello et
180
al. (2015) and Ceschi et al. (2017) have justified the choice of the relevant attributes
181
of apples, such as local and organic production, certification, traceability information,
182
brand, price, and organoleptic sphere (seasonality, freshness, taste, and appearance),
183
etc. However, we cannot include too many attributes in the DCE since this could
184
cause undue cognitive burden on respondents (Powe et al., 2005). In this study,
185
attribute selection was based on expert consultations, literature reviews, and the
186
findings from our focus groups (e.g., Wu et al., 2017; Thiene et al., 2018). In addition
187
to the price attribute, we included three other attributes in our DCE: traceability
188
information, certification type, and region of origin claim.8 Table 1 displays the
189
attributes and their levels.
190
These attributes are included in the survey for following reasons:
191
Firstly, food traceability is the primary concern of this investigation. Food
192
traceability system provides consumers with food quality and safety information,
193
which can reduce consumer information asymmetry and concern about food safety
194
risks (Hobbs et al., 2005; van Rijswijk et al., 2008). Moreover, traceability typically
195
has a strong impact on consumer’s food choice (Verbeke & Ward, 2006; Lee et al.,
196
2011; Bai et al., 2013; Wu et al., 2017; Dandage et al., 2017). Based on the supply 6
Opinions of the Ministry of Agriculture on Accelerating the Construction of Traceability System for Quality and Safety of Agricultural Products (Agricultural Quality and Development [2016] No. 8). 7 http://www.zyczs.gov.cn/html/syncp/2017/11/1510713547509.html. 8 However, the study did not include other attributes that may affect consumer preferences and marginal WTP, such as organic, size, color and brand. This is one of the limitations of this study. 8
197
chain process, traceability information is represented in our DCE using four levels
198
(See Table 1). Notrace refers to no traceability information; Lotrace refers to
199
traceability information that includes only the production part of the value chain;
200
Mitrace refers to traceability information that includes the production and processing
201
parts of the value chain; Hitrace refers to traceability information that includes the
202
production, processing and distribution parts of the value chain.
203
Secondly, the authentication/certification type is an essential attribute. Even
204
though certification has a great influence on consumers’ purchasing behavior, the
205
traceability certification of apple products is very rare in the Chinese market (Yu et al.,
206
2014; Carter & Cachelin, 2018; Lusk et al., 2018). Authenticity verification of
207
traceability information will provide important assurances for consumers. Following
208
Durham et al. (2012), Abhijit et al. (2016), Wu et al. (2017) and Liu et al. (2019), we
209
added the authentication/certification type attribute in our DCE to indicate the
210
existence of traceability information as verified by certain organizations. These
211
certification types include no certification (Nothcert), government certification
212
(Govcert), domestic third-party certification (Dothcert) and international third-party
213
certification (Inthcert).
214
As previously discussed, region of origin is an additional important attribute. Food
215
quality is closely related to the natural environment of the region, such as soil, water,
216
and air. For instance, state/provincial boundaries are widely employed as a proxy to
217
identify local foods (Lusk et al., 2006; Chamorro et al., 2015; Bazzani et al., 2017).
218
Furthermore, the local origin attribute has been identified in many consumer studies
219
as a valued attribute (Loureiro & Umberger, 2007; Aprile et al., 2012; Hu et al., 2012;
220
Gracia et al., 2014; Meas et al., 2015). Therefore, we included region of origin
221
attribute in the choice experiment with the following levels: no region of origin claim
9
222
(Noclaim), produced in Shandong (Shandong), produced in Xinjiang (Xinjiang) and
223
produced in Shaanxi (Shaanxi). These three regions are the main production regions
224
of Fuji apple in China. In 2017, the area of apple orchards in Shandong, Shaanxi, and
225
Xinjiang accounted for 13.63%, 30.11% and 3.65%, respectively of the total apple
226
acreage of China. Apple production in these three provinces accounted for 22.7%,
227
26.39% and 3.48% of the country’s total apple production, respectively.9
228
Finally, four levels of prices are included in this study. The range of price levels
229
were determined from the prevailing prices at supermarkets, local grocery stores, fruit
230
stores, and farmers’ markets, as well as discussions with Chinese manufacturers and
231
food marketing researchers.
232
3.2 Experimental design
233
Based on the selected Fuji apple products attributes and their levels, a full factorial
234
design generate a total of 256 (4 traceability information × 4 certification type × 4
235
region of origin claim × 4 price levels) possible product profiles. Each choice set
236
includes two product profiles. As a result, the number of choice sets can grow
237
exponentially. To reduce the cognitive burden of participants, we used SSIWeb 7.0
238
software and adopted a randomized design to establish a choice experiment with 120
239
choice sets. The 120 selection sets were then divided into 10 blocks, and respondents
240
were randomly assigned to one of these blocks. A “no-buy” option was included in
241
each choice set (see Fig. 1). Not only does the inclusion of the “no-buy” option helps
242
make choices more realistic, it can also help consumers choose not to pick a product
243
in the choice set if they are not satisfied with them (Gao et al., 2016, Bazzani et al.,
244
2017). It can also prohibit mandatory choice of unacceptable alternatives, thereby
245
improving the quality of the data (Johnson & Orme, 2003).
9
China rural statistical yearbook (2018). 10
246
Highly efficient experimental designs have been emerging in recent years. A
247
balanced overlap method10 minimizing-error for the simulated MNL model was used
248
to generate the choice sets in this study. The final choice experiment efficiency
249
(Kuhfeld et al., 1994) statistics are summarized in Appendix A. Table A1, generated
250
by SSIWeb 7.0 software, shows the frequency of all attribute levels and the efficiency
251
of the standard error compared to the ideal model. As shown in Table A1, the
252
frequency of the levels of all attributes is generally balanced and the bias between
253
actual and ideal standard deviation is less than 10%, except for the level “HITRACE”
254
of traceability information and the level “SD” of region of origin claim. Table A2
255
exhibits the efficiency analysis post-experiment. We observe that the design has
256
achieved 87% efficiency with regard to the D-error, but only 74% in A-error.11 For
257
the parameter standard errors, efficiency ranges from 83% to 88%. Therefore, our
258
experimental design seems to have performed adequately.
259
3.3 Data collection
260
Data for this study are collected via face-to-face interviews of Chinese consumers in
261
Beijing, Shanghai, Guangzhou, Xi’an, Harbin and Jinan from July to October of 2017.
262
These cities are the pilot cities of the Chinese traceability system program for meat
263
and vegetable. 12 The sample taken from each city is proportional to the total
264
population of that city. Moreover, four administrative districts are sampled in each
265
city based on their geographical characteristics. In this study, 408 respondents were
266
randomly selected in Beijing, 413 in Shanghai, 383 in Guangzhou, 324 in Xi’an, 269
10 This method is an intermediate between the random strategy and the complete enumeration strategy. Overlap provides a method to simplify multiple-choice questions, reducing the number of attribute differences that respondents must evaluate (Johnson et al., 2013), and thus is statistically valid (Trenz, 2015). 11 The same model specification was applied for the collected data, and the ratios of standard errors, D-errors and A-errors were computed as suggested in Scarpa & Rose (2008). 12 Guangzhou is also included in this study, although Guangzhou is not a national pilot city for China’s meat and vegetable circulation traceability system construction program. Because the development of meat and vegetable traceability system construction in Guangzhou has been walking at the forefront of China is chosen as a sample city.
11
267
in Harbin and 295 in Jinan, respectively.
268
The survey is composed of the Fuji apple choice experiment, and questions related
269
to food safety concern and socio-demographics. Only those consumers who met the
270
recruitment criteria and have purchased Fuji apple products in the past six months
271
were invited to participate in this study. Interviews were conducted by professionally
272
trained and experienced investigators. A sample of 2,092 consumers, aged 18 and over
273
were surveyed on their fruit purchasing behavior and preferences for Fuji apple
274
products.
275
Furthermore, we used a “cheap talk” script (see Appendix B) before the choice
276
tasks to potentially reduce hypothetical bias (Farrell and Rabin, 1996; Lusk, 2003;
277
Murphy et al., 2005; Silva et al., 2011). Specifically, respondents are reminded of
278
their budget constraints, and are asked to choose an alternative as they would in a
279
real-world scenario. The product options in each choice set were randomly sorted
280
following Loureiro & Umberger (2007) and Savage & Waldman (2008).
281
4. Econometric models
282
Following Lancaster’s (1966) theory of consumer demand and random utility theory
283
(McFadden, 1974), consumer
’s utility from choosing alternative
284
this study) in choice situation
can be expressed as: =
285
=
286
where
.
287
Fuji apple’s attributes
288
the utility.
289
When
+
(e.g. apple in
(1)
is a vector of unknown part-worth utilities associated with , and
is the stochastic and unobserved component of
follows a Type I Extreme Value distribution assuming independently
290
and identically distributed (iid), and the assumption of IIA is valid, a Conditional
291
Logit model (CL model) can be employed to estimate the probability of the th option 12
292
being chosen as: (
=∑
293
) (
(2)
)
294
If the assumption of homogeneous preferences is relaxed, then the probability that
295
consumer
296
written as:
chooses alternative
in choice situation
(
= # ∏()*+ )* ∑
297
%)
(
%)
(for all
= 1, … , ") can be
&( )'
(3)
298
where &(. ) is the probability distribution function of the random preference
299
parameter (Train, 2009). If the parameters are fixed at
300
distribution will collapse, i.e., β =
301
individual’s likelihood within an ML model or RPL model. The RPL is an appropriate
302
approach to capturing the heterogeneity in the decision making of consumers
303
(Brownstone & Train, 1999; McFadden & Train, 2000; Hu et al., 2005; Ortega et al.,
304
2011a, 2011b). If &( ) is discrete, Eq. (3) can be incorporated into an LC model.
305
The probability for consumer
306
choice situation
-,
then &( - ) = 1. Eq. (3) can represent an
falling into class / and choosing alternative
(
()*+ = ∑2 1)* ∏ )* ∑
%)
(
%)
Ψ
in
(4)
1
is the parameter vector of the consumer group in class /, and Ψ
308
where
309
probability for consumer
310
corresponding probability can be estimated by:
311
(non-random), the
can be expressed as:
307
1
-
1
is the
falling into class /. Following Ouma et al. (2007), the
Ψ
1
= ∑7
( 3 4%5 )
( 36 465 )
6
312
where 8 is a range of observed values influencing consumer
313
and 91: denotes the parameter vector of consumer in class /.
13
(5) in a certain class,
Because dummy variables are used for the non-price attributes,13 the marginal
314 315
WTP for product attributes is calculated by ;WTP = −
@
, where
is the
316
coefficient of non-price attribute , and
317
marginal WTP can be interpreted as consumer WTP for an attribute level relative to
318
that for the baseline level (e.g. mWTP for Lotrace, Mitrace or Hitrace, relative that
319
that of Notrace).
320
5. Results and discussion
321
5.1 Descriptive statistics
322
Table 2 presents the socio-demographic characteristics and food safety concern of
323
respondents in the six cities of China. On average, about half of the respondents are
324
male, accounting for 50.72%. About 36.33% of the pooled sample are between 25-34
325
years old, and about 57.41% of the pooled sample have been educated for 13-16 years.
326
In our sample, most of the participants’ monthly household income is between
327
¥10000−19999. Table 2 also reports that respondents in the pooled sample have paid a
328
lot of attention to food safety issues. About 43.12% of respondents are very concerned
329
about food safety issues. About 40.44% of respondents report that the current level of
330
food safety in China is very good. However, in general, consumers have a low
331
evaluation of the government’s food safety supervision. About 43.79% of the
332
respondents think that the government supervision of food safety is bad. Regarding
333
“Can labeling improve food safety”, about 66.30% of respondents think labeling
334
might improve food safety. In terms of “Can traceability improve food safety”, about
335
47.04% of respondents believe that traceability might improve food safety.
336
5.2 Choosing numbers of classes
A
13
is the estimated price coefficient. The
Dummy coding results in the omitted levels being estimated within one constant, the omitted levels represent products with no origin, no traceability, and no certification. Since we include an alternative specific constant for the no-choice option, the constant associate with the plain product with no origin, no traceability, and no certification is constrained to zero. 14
337
To take into account taste heterogeneity, we benchmark the model specification search
338
for the conditional logit specification with a fixed utility coefficient, in which all
339
respondents are restricted under the assumption of “preference cloning” (e.g., Thiene
340
et al., 2018; Banovic et al., 2019; Wu et al., 2019). We then run a canonical search to
341
explore the preference heterogeneity dimensions between 2–10 preference classes
342
(see in Table A3, Appendix A). Following Kamakura & Russell (1989), Gupta &
343
Chintagupta (1994), Swait (1994), Bhat (1999), and Boxall & Adamowicz (2002), the
344
minimum Akaike information criterion (AIC) and the minimum Bayesian information
345
criterion (BIC) (Allenby, 1990) are used to identify the optimal number of latent
346
preference classes/segments to fit the data (McLachlan & Peel, 2000; Thacher et al.,
347
2005; Morey & Thiene, 2012, 2017). In our case, both the AIC and BIC values
348
decrease as the number of classes increases throughout. Therefore, the best model is
349
selected according to the two comprehensive criteria: 1) the credibility of the
350
parameter estimation and 2) the level of the marginal improvement of the AIC and
351
BIC values as a new class is added (Boxall & Adamowicz, 2002; Thiene et al., 2018).
352
The results from this combined method suggest that the three preference-class model
353
is best.
354
5.3 Choice models
355
5.3.1 Heterogeneous preference
356
We first look at the results of the fixed-coefficient CL model and ML model. In ML
357
model, upon testing various distributions, we specify normally distributed parameters
358
for the non-price attributes,14 while the coefficients of ASC (Choose no variable) and
359
the price are assumed to be fixed as suggested by Ubilava & Foster (2009)15 and
14
Base on model performance and past studies, such as Lin et al. (2019), Wu et al. (2017) and Bazzani et al. (2017), we specified normally distributed parameters for Fuji apple attributes in our data set. 15 There are at least two advantages to having the price parameter as fixed (Ubilava & Foster, 2009). First, in the case of fixed price parameters, the WTP distribution of consumers is consistent with the distribution of associated 15
360
Bazzani et al. (2017). As shown in Table 3, the coefficient of no-buy option
361
(Chooseno) is negative and significant in both models, implying that the utility of not
362
choosing either option is less than that of choosing any of the proposed product
363
alternatives in the choice set. The price coefficient is negative and significant in both
364
models as expected, indicating that price increases reduce utility. The estimated
365
coefficients for all the Fuji apple attributes are positive and significant at the 1% level
366
in both models. This means that all of these attributes have a significant effect on the
367
preferences of respondents for Fuji apple. Furthermore, results from the ML model
368
indicate strong heterogeneity in respondents’ preference for Fuji apple attributes as the
369
estimated standard deviations for all attributes (except for Lotrace variable) differ
370
significantly from zero.
371
5.3.2 Class preference and marginal WTP
372
Table 4 displays the estimated parameters of the three-class model. With regard to
373
membership probabilities of preference classes, results suggest a 65.9% probability of
374
a respondent belonging to Class 1, 19.1% belonging to Class 2, and 15.0% to Class 3,
375
respectively. The respondents’ age, education level, family income, evaluation of food
376
safety, evaluation of government’s supervision of food safety, responses to “Can
377
labeling improve food safety”, and “Can traceability (information) improve food
378
safety” are found to be significant in determining class membership. In contrast,
379
respondent’s food safety concerns are not helpful in predicting class membership.
380
Respondents belonging to Class 1 are found to be significantly and positively
381
influenced by Fuji apple’s traceability information, certification type, and ROO.
382
When compared to traceability information and ROO, the certification type is found
attribute parameters, rather than the ratio of two distributions, thus avoiding the difficult estimation of WTP distribution. Second, because the frame of demand theory restricts the price coefficient to be negative, it is difficult to select the distribution of price parameter. However, Revelt & Train (1999) stated that it is not guaranteed if a normal distribution is assumed. 16
383
to be of highest importance. Moreover, the government certification is rated as the
384
most important of the certification types. Based on this, Class 1 is named as
385
“Certification-oriented”. This result indicates that trust of product in Class 1 is
386
communicated by the external validation represented by the authentication process,
387
not the attributes being certified. This finding is in agreement with the results of El
388
Benni et al. (2019). Respondents in Class 1 are more likely to choose lower priced
389
over higher priced Fuji apple. In addition, respondents in Class 1 are likely to have a
390
higher education level, have a higher evaluation of food safety and government’s
391
supervision of food safety, as well as have higher perceptions of “Can labeling
392
improve food safety” and “Can traceability (information) improve food safety” than
393
those in Class 3. There are no significant differences between respondents in Class 1
394
and Class 3 regarding age and family monthly income.
395
Respondents in Class 2 also take Fuji apple’s traceability information, certification
396
type and ROO into consideration when making choices. In contrast to Class 1,
397
respondents in Class 2 preferred ROO to other attributes in this study. Furthermore,
398
Shaanxi is rated as the most important of the ROO. It suggests that Fuji apples
399
associated with ROO have a significantly higher probability of being chosen by
400
respondents in Class 2. Moreover, one important characteristic of Class 2 is the high
401
value of the price parameter, indicating a high price-sensitivity compared to the other
402
classes. Thus, we name Class 2 “Price and Origin- oriented”. A system that
403
convincingly guarantees the ROO of Fuji apple could become an important tool for
404
Chinese fresh fruit producers to influence consumer decisions. This finding aligns
405
with most studies such as Meas et al. (2015), Xie et al. (2016), Wu et al. (2015, 2017)
406
and Gao et al. (2019). Respondents in Class 2 are older, with less family monthly
407
income, and have a higher evaluation of food safety for Fuji apples than those in Class
17
408
3.
409
The coefficient of “Chooseno” variable in Class 3 is positive and significant. Its
410
value is greater than the coefficient estimates of other Fuji apple attributes, implying
411
that respondents in Class 3 tend not to choose any product. Therefore, we name Class
412
3 “Not interested”. Even if the coefficients for traceability information, certification
413
type and ROO are different, a significance test does not show significant differences,
414
which indicates that respondents in this class have no obvious preference for one or
415
the other. This finding is consistent with El Benni et al. (2019).
416
The marginal WTP estimates for each latent class are reported in Table 5. It shows
417
that marginal WTP differs considerably between Fuji apples food safety attributes and
418
between consumer classes. For example, respondents in Class 1 are willing to pay a
419
premium of 16.02 yuan for 500 g Fuji apples certified by the government. By contrast,
420
the additional marginal WTP of respondents in Class 2 and Class 3 is 0.88 yuan and
421
10.20 yuan for 500 g Fuji apples certified by the government, respectively. In addition,
422
other studies have considered different products and found that consumers have
423
different levels of WTP for different products (e.g., Wu et al., 2016; Lu et al., 2016;
424
Yin et al., 2018; Xu et al., 2019; Liu et al., 2019).
425
5.4 Marginal analysis and determinants of membership probabilities
426
Following Thiene et al. (2018), we apply marginal analysis to investigate how class
427
membership changes across different respondent profiles. Fig. 2 shows predicted class
428
probabilities by age. Higher ages indicate a higher probability of Class 2 membership
429
and a corresponding lower probability of Class 1 membership. From a policy
430
perspective, this suggests a policy aimed at the elderly, or the educated middle-aged
431
needs paying more attention. Assuming that the change is caused by age, rather than
432
characteristics associated with a particular age group, one might conclude that if
18
433
customized action is not taken, there is a 12.96% chance for young consumers in
434
Class 2 to fall into this category; for elder consumers in their 60’s, the probability
435
could rise to almost 26.45%. Fig.3 presents predicted class probabilities by
436
respondents’ family monthly income. Results suggest that, as expected, respondents
437
with higher monthly family income are more likely to belong to Class 1, and thus less
438
likely to fall into Class 2. It suggests that food enterprises should conduct market
439
segmentation and provide more food certification products for consumer groups with
440
higher monthly family income.
441
Turning to the food safety concerns and evaluation of food safety, Fig. 4 indicates
442
that the food safety concerns of respondents do not have a strong impact on
443
membership probability in Class 1 and Class 2. Nevertheless, the effect is negative for
444
Class 2 but positive for Class 1, which indicates that Class 2 decreases from 19.55%
445
to 16.25%, while Class 1 increases from 67.23% to 68.38%. From the comparisons
446
holding all else being equal, an increase in food safety concerns of respondents
447
redistributes a member from Class 2 to Class 1. With regard to respondents’
448
evaluation of food safety, we have noted an increase in membership probability from
449
66.54% to 68.40% for Class 1, and from 14.10% to 19.14% for Class 2, but a
450
relatively large drop from 19.36% to 12.46% in Class 3.
451
Fig. 5 describes the effect of respondents’ evaluation of the government’s
452
supervision of food safety on class membership probabilities. There is a drop from
453
16.57% to 11.30% in membership probability in Class 3, while an increase from 66.23%
454
to 71.55% in Class 1. Hence, there is clear evidence for the desire to improve the
455
government’s supervision of food safety.
456
Fig. 6 displays the effects of labeling and traceability information. Through the
457
comparison, we can see clearly: if other conditions are the same, increasing
19
458
respondents’ evaluation of food labeling will reassign members from Class 2 to Class
459
1. That is, Class 2 decreases from 21.13% to 14.75%, while Class 1 increases from
460
59.05% to 73.48%. Similar results are also observed for the evaluation of traceability
461
information. As can be seen, the supply of labeling and traceability information is
462
potentially strongly associated with classes containing different attribute preferences.
463
6. Conclusion and implications
464
We use a discrete choice experiment and a Latent Class model to analyze Chinese
465
consumer preferences and marginal WTP for food safety attributes of Fuji apple
466
products. We identify three latent consumer segments and determine the source of
467
heterogeneity using socio-demographic variables and individual food safety concern
468
and evaluation factors.
469
We have three major findings. First, our results suggest that consumers’ valuation
470
of certification differs depending on the type of certification. Specifically, the results
471
suggest that Chinese consumers place the highest value on government certification.
472
For the traceability information, they place the least value on traceability that includes
473
only the production part of the process. Second, we identify three consumer segments
474
in Fuji apple products market; i.e., certification-oriented consumers (Class 1, 65.9% in
475
market share), price and origin-oriented consumers (Class 2, 19.1% in market share),
476
and not interested consumers (Class 3, 15.0% in market share). Third, we found that
477
each consumer class has its own perceptual and attitudinal characteristics in relation
478
to food safety. Specifically, certification-oriented consumers have a high evaluation of
479
food safety and government’s food safety supervision. They believe that labeling and
480
traceability can improve food safety. On the other hand, price and origin-oriented
481
consumers only place a high value on their evaluation of local food safety.
482
In addition, we found that socio-demographic variables and individual food safety
20
483
concerns and evaluation factors are important determinants of consumer preferences.
484
Results indicate that older or more educated middle-aged people, or those with higher
485
household income, are more concerned about Fuji apple’s choices. We also have
486
found that food safety concerns tend not to have a strong impact on membership
487
probability for certification-oriented consumers or on price and origin-oriented
488
consumers. Moreover, the provision of labeling and traceability information may be
489
closely related to products with different attribute choices.
490
These findings provide important managerial implications for the fresh fruit
491
industry. The positive values of marginal WTP for each of the Fuji apple safety
492
attributes indicates that Chinese consumers are willing to pay a premium for food
493
safety attributes. The results provide valuable information for the fruit industry to
494
develop food safety strategies. Furthermore, given that Chinese consumers are highly
495
responsive to certified Fuji apple, establishing a trusted certification system is likely
496
to improve Chinese consumers’ evaluation of food safety and increase the demand for
497
high-quality and safe fruits in the Chinese domestic market.
498
In addition to the existing forms of food labeling in China, there are alternative
499
labeling applications, as well as specific information programs for specific consumer
500
segments, which are a form of personalized labeling. While much of the focus and
501
previous research has focused on the overall design of food safety policies, the
502
broader policy picture seems to require broader multi-dimensional interventions
503
aimed at targeting specific market segments based on the characteristics of Chinese
504
consumers and their perceptions and evaluations of food safety issues.
505
Future research can improve this study from at least two aspects. Firstly, future
506
studies can include other food safety attributes such as organic certification, and other
507
attributes such as freshness, size, and color. Secondly, the sample is from first-tier
21
508
cities and provincial capitals with higher socio-economic development and higher
509
purchasing power. In the future, more representative samples that include cities at
510
different tiers and even rural consumers could result in a more comprehensive
511
conclusion regarding Chinese consumers’ preferences for food safety attributes.
22
512
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Table1. Fuji apple products attributes in the choice experiment Attribute Levels Description Traceability 4 No traceability information (Notrace) information Traceability information that includes only the production part of the value chain (Lotrace) Traceability information that includes the production and processing parts of the value chain (Mitrace) Traceability information that includes the production, processing and distribution parts of the value chain (Hitrace) Certification type 4 No certification (Nothcert) Government certification (Govcert) Domestic third-party certification (Dothcert) International third-party certification (Inthcert) Region of origin claim 4 No region of origin claim (Noclaim) Produced in Shandong (Shandong) Produced in Xinjiang (Xinjiang) Produced in Shaanxi (Shaanxi) Price 4 6 yuan/500 g 8 yuan/500 g 10 yuan/500 g 12 yuan/500 g Note: In July 2017, 1 US dollar = 6.77 yuan.
Table 2. Socio-demographic characteristics and consumers’ food safety consciousness between cities in % of the sample Variables Beijing Shanghai Guangzhou Xi’an Jinan Harbin Pooled (N = 408) (N = 413) (N = 383) (N = 324) (N = 295) (N = 269) sample (N = 2092) Gender: Male 47.55 58.84 48.04 47.84 51.86 49.07 50.72 Female 52.45 41.16 51.96 52.16 48.14 50.93 49.28 Age (years): ≤ 24 29.41 20.58 29.24 25.00 25.42 22.30 25.48 25–34 39.95 42.13 37.34 30.25 32.20 32.34 36.33 35–44 15.44 15.74 16.19 21.91 21.02 19.33 17.93 45–54 10.29 7.26 8.62 12.35 11.86 13.38 10.33 55–64 3.68 9.20 5.22 7.10 6.78 8.18 6.60 ≥ 65 1.23 5.08 3.39 3.40 2.71 4.46 3.35 Education level (years): ≤ 9 4.66 11.86 12.53 18.83 11.86 11.52 11.62 10–12 9.80 14.53 20.63 13.89 20.68 17.84 15.92 13–16 52.70 57.87 59.27 60.80 59.32 55.02 57.41 >16 32.84 15.74 7.57 6.48 8.14 15.61 15.06 Household monthly income (CNY/month): < 5000 7.11 4.12 3.66 8.64 12.20 12.27 7.50 5000–9999 18.87 13.80 16.19 35.49 41.69 37.55 25.57 10000–19999 37.50 30.51 35.51 41.98 39.32 39.41 36.95 20000–29999 20.83 22.52 24.54 8.02 6.44 7.81 16.16 30000–39999 4.66 13.80 12.53 3.40 0.34 0.74 6.60 40000–49999 4.41 3.87 1.57 0.93 0 0.37 2.10 50000–59999 2.70 4.84 2.61 0.93 0 1.12 2.25 60000–99999 2.70 2.66 1.83 0.31 0 0 1.43 ≥100000 1.23 3.87 1.57 0.31 0 0.74 1.43
Food safety concerns: Not A little Very Evaluation of food safety: Bad Good Very good Evaluation of government’s supervision of food safety: Bad Good Very good Can labeling improve food safety? No, it can not Perhaps Yes, definitely Can traceability (information) improve food safety? No, it can not Perhaps Yes, definitely
5.15 50.00 44.85
5.33 47.70 46.97
6.27 56.92 36.81
4.32 51.85 43.83
7.80 52.20 40.00
8.55 45.35 46.10
6.07 50.81 43.12
11.52 43.14 45.34
7.99 41.89 50.12
7.83 46.48 45.69
13.58 53.70 32.72
11.86 51.53 36.61
23.42 52.42 24.16
12.05 47.51 40.44
47.06 44.85 8.09
45.04 48.91 6.05
35.77 51.96 12.27
49.69 44.75 5.56
37.29 48.81 13.90
48.33 45.72 5.95
43.79 47.61 8.60
7.35 62.99 29.66
6.54 72.15 21.31
4.96 74.15 20.89
11.11 63.27 25.62
4.75 59.32 35.93
7.43 62.45 30.11
6.98 66.30 26.72
6.62 49.26 44.12
9.93 50.61 39.47
9.14 53.79 37.08
8.95 38.58 52.47
5.76 43.05 51.19
9.67 43.12 47.21
8.37 47.04 44.60
Notes: We captured the consumers’ food safety conception derived from five questions: on 3-point Likert scale (1-3 scale), How much do you concern about food safety? What is your evaluation of food safety in China? What is your evaluation of government’s supervision of food safety in China? Can labeling improve food safety? Can food traceability (information) improve food safety?
Table 3. Estimates from Conditional Logit Model (CL model) and Mixed Logit Model (ML model) Variables CL model ML model Mean Mean Standard deviation *** *** Chooseno −0.379 −0.487 − (0.052) (0.097) *** Price −0.165 −0.223*** − 0.004 (0.008) Lotrace 0.407*** 0.534*** −0.031 (0.026) (0.035) (0.423) *** *** Mitrace 0.632 0.819 0.736*** (0.027) (0.042) (0.063) *** *** Hitrace 0.825 1.041 0.790*** (0.029) (0.045) (0.072) *** *** Govcert 1.165 1.424 1.170*** (0.028) (0.049) (0.057) *** *** 1.164 0.705*** Dothccert 0.937 (0.028) (0.043) (0.075) *** *** Inthcert 1.058 1.288 1.032*** (0.028) (0.047) (0.061) *** *** Xinjiang 0.898 1.104 0.894*** (0.028) (0.046) (0.080) *** *** 1.147 0.903*** Shandong 0.943 (0.029) (0.046) (0.063) *** *** Shaanxi 0.932 1.150 0.861*** (0.028) (0.044) (0.066) Log likelihood −22307.07 −21091.47 LR chi2 10544.99 − Wald chi2 − 2931.04 Pseudo R2 0.1912 − Prob > chi2 − 0.0000 Observations 75,312 75,312 Notes: Standard errors in parentheses. *, ** and *** indicate significance at 10%, 5% and 1% significance levels, respectively.
Table 4. Estimates from Latent Class model (LC model) Variable Class1 (Certification Class 2 -oriented) (Price and origin-oriented) 0.659 0.191 Class size Attributes: Price −0.088*** (0.007) −0.714*** (0.037) Chooseno −0.971*** (0.098) −4.962*** (0.381) Lotrace 0.513*** (0.033) 0.248** (0.099) 0.216** (0.104) Mitrace 0.822*** (0.036) Hitrace 1.022*** (0.039) 0.389*** (0.109) Govcert 1.401*** (0.039) 0.626*** (0.113) Dothccert 1.135*** (0.037) 0.523*** (0.100) 0.744*** (0.096) Inthcert 1.293*** (0.038) Xinjiang 1.061*** (0.037) 1.063*** (0.105) Shandong 1.099*** (0.038) 0.989*** (0.104) 1.447*** (0.107) Shaanxi 1.008*** (0.037) Membership Equations: Age −0.000 (0.005) 0.021*** (0.006) −0.016 (0.044) Education 0.052** (0.022) Family income 0.000 (0.000) −0.000*** (0.000) Food_cons −0.067 (0.101) −0.168 (0.157) Food_qual 0.234*** (0.090) 0.373*** (0.129) Gov_sup 0.230** (0.104) 0.190 (0.144) *** Label_safe 0.370 (0.120) 0.081 (0.190) *** Trace_safe 0.328 (0.100) 0.112 (0.152) Constant −1.601*** (0.383) −1.173 (0.884) Observations 75,312 75,312 No. of groups 25,104 25,104
Class 3 (Not interested) 0.150 −0.119*** (0.020) 2.392*** (0.294) 0.322*** (0.108) 0.592*** (0.108) 0.917*** (0.110) 1.216*** (0.124) 0.798*** (0.124) 0.893*** (0.120) 1.116*** (0.133) 1.306*** (0.128) 1.052*** (0.137) − − − − − − − − − 75,312 25,104
Notes: Standard errors in parentheses. *, ** and *** indicate significance at 10%, 5% and 1% significance levels, respectively.
Table 5. Marginal WTP estimates for each latent class Variable Class1 Class 2 (Certification (Price and -oriented) origin-oriented ) Lotrace 5.86 0.35 [4.71, 7.02] [0.07, 0.63] Mitrace 9.40 0.30 [7.70, 11.09] [0.00, 0.60] Hitrace 11.69 0.54 [9.68, 13.69] [0.22, 0.87] Govcert 16.02 0.88 [13.37, 18.88] [0.53, 1.23] Dothccert 12.97 0.73 [10.78,15.17] [0.43, 1.03] Inthcert 14.78 1.04 [12.29,17.27] [0.75, 1.34] Xinjiang 12.13 1.49 [10.04, 14.22] [1.15, 1.82] Shandong 12.57 1.39 [10.43, 14.71] [1.06, 1.72] Shaanxi 11.52 2.03 [9.57, 13.48] [1.66, 2.40]
Class 3 (Not interested) 2.70 [0.66, 4.74] 4.96 [2.42, 7.50] 7.69 [4.51, 10.87] 10.20 [6.23, 14.18] 6.70 [3.73, 9.66] 7.49 [4.37, 10.61] 9.36 [5.32, 13.41] 10.95 [6.45, 15.46] 8.83 [4.99, 12.66]
Notes: Numbers in brackets represent 95% confidence intervals, which are estimated by using the parametric bootstrapping procedure of Krinsky and Robb (1986).
Appendix A Table A1. A priori estimates of standard errors for attribute levels provided by SSIWeb 7.0 Attribute Level Frequency Actual Ideal Efficiency S.D. S.D. Traceability LOTRACE 60 0.2270 0.2346 1.0679 information MITRACE 60 0.2293 0.2346 1.0468 HITRACE 60 0.2496 0.2346 0.8839 NOTRACE 60 Certification type GOVCERT 61 0.2277 0.2325 1.0426 DOTHCERT 59 0.2391 0.2325 0.9458 INTHCERT 60 0.2341 0.2325 0.9865 NOCERT 60 Region of origin SD 61 0.2456 0.2325 0.8958 claim XJ 60 0.2386 0.2325 0.9496 SHX 60 0.2341 0.2325 0.9862 NOORIGIN 59 Price 12 yuan 59 0.2224 0.2294 1.0645 10 yuan 60 0.2411 0.2294 0.9055 8 yuan 60 0.2229 0.2294 1.0593 6 yuan 61 Notes: Task generation method is “Balanced Overlap” using a seed of 1. The efficiencies reported above for this design assume an equal number of respondents complete each version. Ideal standard deviation is the standard deviation that meets the orthogonality condition. The “strength” of design for this model is 2697.15. The ratio of “strengths” of design for two designs reflects the D-efficiency of one design relative to the other. Please refer to Table 1 for the definitions of different levels.
Table A2. Evaluation of design efficacy Level ASC-Buy Traceability information
Certification type
Region of origin claim
Price D-error A-error
LOTRACE MITRACE HITRACE NOTRACE GOVCERT DOTHCERT INTHCERT NOCERT SD XJ SHX NOORIGIN PRICE
Simulated S.E. 0.0454 0.0232 0.0236 0.0252 0.0232 0.0240 0.0236 0.0247 0.0241 0.0237 0.0036 0.00031 0.0072
Actual S.E. 0.0517 0.0264 0.0273 0.0290 0.0279 0.0281 0.0282 0.0287 0.0281 0.0279 0.0042 0.00039 0.0097
Ratio 0.877 0.877 0.865 0.869 0.832 0.854 0.837 0.859 0.857 0.848 0.878 0.800 0.742
Notes: Simulated standard errors resulting from an MNL model with simulated choices using the same number of responses per block as in the final data set. It is assumed the probability of “no-buy” is 15% and the probabilities of the two purchase options are equal. The design was generated under the same assumptions.
Table A3. A comparison of LCM with different number of classes a Classes Number of LLb LL(0)b ρ2c AICd BICe parameters 2 23 −19555.8 −22300.8 0.12 39310.5 39287.5 3 35 −19037.3 −22262.1 0.14 38377.2 38342.2 4 47 −18593.2 −22235.6 0.16 37592.7 37545.7 5 59 −18399.0 −22211.8 0.17 37308.2 37249.2 6 71 −18227.5 −22203.7 0.18 37068.8 36997.8 7 83 −18071.5 −22214.2 0.19 36860.7 36777.7 8 95 −17754.7 −22194.5 0.20 36330.7 36235.7 9 107 −17640.3 −22161.2 0.20 36205.7 36098.7 10 119 −17481.3 −22167.2 0.21 35991.4 35872.4 a b Notes: Sample size is 25104 choices from 2092 respondents (N). LL means Log likelihood at convergence. LL(0) means Log likelihood evaluated at 0. c ρ2 is calculated by 1−LL/LL(0). d AIC is calculated using −2(LL−P). e BIC is calculated by (P/2)*ln(N)–LL.
Appendix B Cheap talk script “We know from past studies that people often respond in one way but behave differently. Several people state a higher WTP than what one really was willing to pay for the product in a grocery store. No one really has to pay to show a particular preference. A possible reason for this is that people do not really think about the finite amount of money we have. When you really do not need to pay, generosity is easy. But when we’re really in the grocery store, if we decide to buy this good, we had to spend money. In any case, we ask you to answer the preferences and WTP of each of these questions, just like you have to pay for your choice in a real grocery. Please keep in mind when answering the last few questions.”
Option A Traceability information that includes production, processing and distribution parts of the value chain International third-party certification Shaanxi Price：12 yuan per 500 g
Option B
Option C
Traceability information that includes production
Neither A or B
No certification Xinjiang Price：8 yuan per 500 g
I choose… Fig.1. Sample choice task
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
age 18
age 20
age 30 Class 1
Class 2
age 40
age 50
Class 3
Fig. 2. Class membership probabilities by age increase
age 60
90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
inc 5000
inc inc inc inc inc inc inc inc inc 10000 15000 20000 25000 30000 40000 50000 60000 100000 Class 1
Class 2
Class 3
Fig. 3. Class membership probabilities by family monthly income increase
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
food_cons1
food_cons2
food_cons3 Class 1
food_qual1
Class 2
food_qual2
food_qual3
Class 3
Fig. 4. Class membership probabilities by food safety concerns and evaluation of food safety
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
gov_sup1
gov_sup2 Class 1
Class 2
gov_sup3 Class 3
Fig. 5. Class membership probabilities by evaluation of government’s supervision of food safety
80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
label_safe1
label_safe2
label_safe3
Class 1
trace_safe1
Class 2
trace_safe2
trace_safe3
Class 3
Fig. 6. Class membership probabilities by labeling and traceability information
Research Highlights
Conduct a discrete choice experiment in five cities across China; Identify three consumer segments but most consumers are certification-oriented; Find consumers valuate the government certification most; Conclude region of origin and price are major attributes of consumer choices; Suggest simplifying labeling application and specific information program.
Conflict of interest form
There is not any conflict of interest for this study.