Chinese urban consumers' preferences for white shrimp: Interactions between organic labels and traceable information

Aquaculture 521 (2020) 735047

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Chinese urban consumers' preferences for white shrimp: Interactions between organic labels and traceable information

T

⁎

Shijiu Yin , Fei Han, Mo Chen, Kai Li, Qi Li Research Center for Food Safety and Green Agricultural Development, Qufu Normal University, Rizhao, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Aquatic product safety Willingness to pay Choice experiment Generalised mixed logit model White shrimp

Organic certification and traceable information systems are important ways to alleviate information asymmetry and to improve food safety levels. In this study, 996 consumers in China's Shandong Province were surveyed about their preference for white shrimp (slang for Litopenaeus vannamei) in a choice experiment that varied three product attributes: organic labels, traceable information and price. Using a generalised mixed logit model, the results revealed that, compared to conventional white shrimp, consumers preferred white shrimp with organic labels and/or traceable information. Provision of organic and traceable knowledge generally improves consumer willingness to pay (WTP) but weakens the complementary relationship between an organic label and traceable information. In fact, it can turn the complementary relationship between European Union (EU) organic labels and traceable information into the substitution relationship. In the Chinese market, introducing EU organic certification and providing knowledge introduction increase average total consumer WTP by 84.06% and 120.16%, on an average, respectively. These conclusions provide a reference for relevant organic certification policies and traceable food system management by the Chinese government, in addition to providing a reference for business decision-making by aquatic product suppliers and certification service providers.

1. Introduction With the rapid development of China's economy and the continuous improvement of its per-capita income, the Chinese population is eating more aquatic products including seafood, such as shrimp and fish (FAO, 2018; Qiao, 2019). However, the quality and safety of these products have been tainted by water pollution, drug residue, poor production management, improper addition of prohibited substances and even outright fraud (Fang et al., 2014; Yin et al., 2018). These aquatic product safety incidents, such as the malachite green incident in Beijing in 2016, have caused concerns among Chinese consumers about the safety of aquatic products (Li et al., 2016).1 From an economic perspective, market failure caused by information asymmetry is an important cause of food safety risk (Darby and Karni, 1973). Suppliers who provide quality assurance to consumers by means of third-party certification and traceable information (collected and recorded in main or even all

stages of the food chain, to identify product identification, using labels as medium to convey to consumers) can alleviate information asymmetry in the food industry (Unnevehr et al., 2010; Xie et al., 2016) and urge producers to adopt better self-discipline and responsibility to provide quality assurance and help reduce food safety risks (Hobbs, 2004; Janssen and Hamm, 2012). To improve food safety levels—increasingly demanded by Chinese consumers—the Chinese government has gradually established a diversified organic certification system using both domestic and foreign certifications, under which organic food with a certification2 from different countries or regions, (e.g. China, the U.S., and the EU) is sold in mainland China (Yin et al., 2019). To limit food safety risks, China is also establishing a traceable food system, primarily for meat, vegetables and infant food (Wu et al., 2017; Yin et al., 2017a, 2017b). In this process, compared to suppliers of conventional non-organic food, those of both organic and traceable food incur additional costs. For instance,

⁎ Corresponding author at: Research Center for Food Safety and Green Agricultural Development, Qufu Normal University, 80 Yantai Road, Rizhao, Shandong Province, China. E-mail addresses: [email protected] (S. Yin), [email protected] (F. Han), [email protected] (M. Chen), [email protected] (K. Li), [email protected] (Q. Li). 1 In November 2016, the Beijing Food and Drug Administration of China issued a note saying that some fresh fish sold by Walmart, Yonghui, Tesco and other supermarkets were contaminated with malachite green, added during the products' transportation and sales. 2 In the Chinese food market, only organic food certified by a specific certification agency is allowed to be sold as organic food. Therefore, in this paper, organic food refers to food certified as organic food.

https://doi.org/10.1016/j.aquaculture.2020.735047 Received 30 May 2019; Received in revised form 11 October 2019; Accepted 30 January 2020 Available online 04 February 2020 0044-8486/ © 2020 Elsevier B.V. All rights reserved.

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and quality control in the food chain, they had a higher WTP for tuna containing traceable information. Boncinelli et al. (2018) looked at seafood salad (marinated anchovies) and found that Italian consumers were willing to pay a 4.75% premium for seafood containing traceable information about the fishing area. Because both organic labels and traceable information provide useful clues about producers and their products, consumers can assess both food safety and quality in their choices of products. However, to the best of our knowledge, few studies exist regarding consumer WTP for these issues in developing countries such as China. Our research largely makes two contributions. We first estimated consumer WTP for organic and traceable information labels and examined the cross-effect between the two attributes. Second, considering Chinese consumers' overall low awareness about these issues, we studied how consumers' organic or traceable information influenced their preferences.

the prohibition of chemical synthesis and transgenic technology in the production of organic food tends to reduce output and increase labour and other costs (Altenbuchner et al., 2018; Ram and Verma, 2017; Hempel and Hamm, 2016; Meena et al., 2010; Wilson et al., 2008). Traceable food producers need to collect, record and label traceable information; establish traceable information databases and information transmission systems as well as finance production and testing equipment (Chung et al., 2018; Schroeder and Tonsor, 2012; Resende-Filho and Buhr, 2008). As the direct beneficiaries of food quality improvements, consumers will shoulder most of these additional production costs (Yin, 2013). Therefore, information about consumer willingness to pay (WTP) for organic or traceable food has become essential to the development of organic certification and traceable information systems, and this has triggered extensive academic interest (Zhang et al., 2018a; Alfian et al., 2017). However, much less attention has been given to understand how these attributes may interact with each other. Taken white shrimp as an example, the paper studies consumer WTP for organic and traceable information labels and the cross-effect between the two attributes, further investigating how the delivery of organic and traceable knowledge to consumers (hereinafter, ‘knowledge introduction’) affects their WTP. Shrimp is the world's largest aquatic product in sales quantity, accounting for about 15% of the global sales.3 The sale of shrimp in China has grown rapidly in recent years (Li et al., 2016; ITE Food and Drinks, 2017). White shrimp is the most important species of shrimp both in China and other regions(Chang, 2016). Therefore, white shrimp were selected in this study.

3. Survey design and data 3.1. Choice experiment design 3.1.1. Selection of choice attribute levels This research uses a choice experiment to elicit consumer WTP for organic labels and traceable information. Choice experiment is a tool that economists commonly use to estimate WTP and it is particularly useful when the consideration of multiple attributes (Hanley et al., 2001; Czajkowski et al., 2014). It is a non-market valuation method using stated preferences opposed to preferences observed from markets. This distinction is important as some researches focus on goods or services that do not currently exist in markets (e.g. new products), and that are not usually traded in these markets or have no markets. In combination with the construction of China's traceable system, the development of organic certification and the actual aquatic product market situation, this study selects three attributes—organic label, traceable information and price—and sets corresponding attribute levels (see Table 1). In addition to the main effect of organic labels and traceable information on consumer preferences, we also consider the interaction effects between the two attributes, for the following reasons. First, these issues are important in the context of Chinese consumers' increasing concerns about food safety (Wang et al., 2018; Ortega et al., 2015). Empirical studies have shown that consumers' preference in a product's attributes may also help improve their preference in the overall product and in other related attributes (Muringai et al., 2017). Second, according to the classic choice experiment scenario described by Dawes and Corrigan (1974), the main effect of explanatory variables in decision-making model usually explains 70–90% of the estimated results' variance; the second-order interaction term explains 5–15% of the variance and a higher-order interaction term explains the remaining variance. Therefore, all or part of the second-order interaction term's estimation can reduce the error of the main effect estimation (Louviere et al., 2010). Organic labels in this study have three levels: the European Union (EU) organic label (EUORG), China's organic label (CNORG), or no organic label (NOORG). Yin et al., 2017a, 2017b studied consumer preference for organic tomato and found that the EU organic label is the most familiar one for consumers in mainland China. Therefore, this study chooses the EU organic label to represent overseas organic labels. Considering that the market share of organic aquatic products in China is still low and that most aquatic products do not have organic labels, this study takes ‘no organic labels’ as the status quo. The construction of traceable systems in China is still in a pilot stage, and disputes remain about what information should be included (Zhang et al., 2018b; Ministry of Agriculture and Rural Affairs of China, 2018; Wu et al., 2016). Wu et al. (2016) proposed that traceable information should cover the key areas that may jeopardize food safety. According to our field research about China's white shrimp industry, most safety risks exist in the following three stages: shrimp seed

2. Literature review Numerous empirical studies have shown that, generally, consumers are willing to pay higher prices for organic food (Renee et al., 2010; Schaeufele and Hamm, 2017). A study of organic tomatoes in Albania (Skreli et al., 2017), an organic mint study in Morocco (Faysse et al., 2017), an organic yogurt survey in Germany (Emberger-Klein et al., 2016) and a study on organic eggs in Spain (Mesias et al., 2011), all found that consumers had a higher WTP for organic food. Choi (2018) showed that even in developing countries such as Malawi, consumers' preference for organic chicken increased with its per-capita income. Zhang et al.'s (2018a) survey in Beijing showed that 65.8% of consumers were willing to pay a premium price for organic vegetables. Using a choice experiment, Olesen et al. (2010) investigated Norwegian consumers' WTP for organic salmon and found that compared with conventional salmon products they were willing to pay about a 15% premium (2 euros/kg.) for organic salmon. In addition, using a choice experiment, Risius et al. (2019) studied German consumers' preference for smoked trout fillets with certain safety information attributes (e.g. organic or traceable labels, country of origin) and found that 58% of consumers took organic labels and other safety attributes into consideration when purchasing fish. Many scholars have focused on consumer preferences for traceable foods. Matzembacher et al. (2018) showed that traceable information about food products, especially regarding pest control and intermediate product inputs, significantly improved Brazilian consumers' WTP. Menozzi et al. (2015) studied French and Italian consumers' intentions to purchase traceable food (specifically, chicken and honey) and found that consumers' consumption habits, trust, experience, economic conditions, age, and other factors significantly affected their WTP for traceable food. Yin et al., 2017a, 2017b survey about milk and Wu et al.'s, 2016 survey about pork found that Chinese consumers were more willing to pay higher prices for traceable information during the farming stage. Metref and Calvo-Dopico (2016) found that after providing consumers with information about the benefits of traceability 3

Data Source:

https://unstats.un.org/unsd/trade/data/tables.asp#annual

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Table 1 Attributes and levels used in the choice experiment design. Attribute

Level

Describe

Organic label (ORG)

China's organic label (CNORG) EU organic label (EUORG) No certification (NOORG) Shrimp seed rearing traceable information (SEETRACE)

Certified organic white shrimp carry an organic label, from either China or the EU.

Traceable information (TRACE)

Price

Adult shrimp farming traceable information. (ADUTRACE) Adult shrimp sales traceable information. (SALTRACE) No traceable information (NOTRACE) 4.34 USD/500 g 5.50 USD/500 g 7.67 USD/500 g 9.84 USD/500 g

To include corresponding traceable information in the traceable code, shrimp seed rearing's traceable information includes data about the seedling company, the batch and information about shrimp inspections. Adult shrimp farming traceable information includes information about the feed batch and inspections. Adult shrimp sales traceable information includes information about inspections, quarantines, transportation, stockholders, sellers and others In the actual survey, respondents were asked if they would pay the baseline price for 500 g of white shrimp.

Moser et al. (2014), we read a cheap talk script to consumers before presenting the choice set (see Appendix I). Before the experiment, respondents were told that no significant differences could be detected in the appearance, smell or other aspects of the white shrimp products presented, apart from the organic labels, traceable information and price attributes included in the choice set. To test the representativeness of the sample, after the choice experiment, a simple structured questionnaire was used to collect data, such as the individual characteristics of respondents. Each respondent took 30–35 min to complete the choice experiment and questionnaire, and each respondent, as compensation, was given 19 yuan (2.75 USD) after completion, to minimise the experimenter demand effect in the choice experiment (Ekstrom, 2012).6

rearing, adult shrimp farming and adult shrimp sales (FAO, 2018). Therefore, referring to Yin et al. (2017a, 2017b), this paper divides traceable information (TRACE) attributes into the following four levels: shrimp seed rearing traceable information (SEETRACE), adult shrimp farming traceable information (ADUTRACE), adult shrimp sales traceable information (SALTRACE) and no traceable information (NOTRACE) (see Table 1 for the specific attribute descriptions at different levels). Based on the fact that traceable shrimp are still relatively rare in the Chinese market, this study takes NOTRACE as the status quo. A total of four levels are set for the price attribute to avoid, as much as possible, the Quantitative Effect of levels.4 A baseline price of 5.50 USD/500 g is observed according to the per-month average price of conventional shrimp during the experimental period. Following Quan et al. (2018) and Liu et al. (2017), we use four price levels: 4.34 USD/ 500 g (20% below the baseline), 5.50 USD/500 g (the baseline), 7.67 USD/500 g (40% above the baseline), and 9.84 USD/500 g (80% above the baseline).5

3.1.3. Introduction of organic and traceable knowledge Organic aquacultural food is not widely sold in China and as a result, consumers' awareness about organic aquacultural food is not very high (Tariq et al., 2019). Construction of traceable systems also is in a pilot stage (Hou et al., 2019). Since respondents' knowledge of organic or traceable food may affect their WTP (Ponce et al., 2019), we conducted two versions of the choice experiment to observe the impact of consumers' knowledge on their preferences for organic labels or traceable information. One version of the choice experiment provided interviewees with knowledge about organic labels and traceable information by asking them to read the knowledge card (see Appendix II for details), while the other version did not offer this knowledge. The knowledge card was expressed in an understandable way, to simulate the real situation that consumers get some plain facts about traceable information. In this way, the CE can mimic a real-word market purchase situation. We refer to the respondents who did not receive the knowledge card as the ‘control group’ and the respondents who received it as the ‘treatment group’. Participants were randomly selected to participate in these two groups. Some respondents assigned to the control group may already have acquired varying degrees of knowledge about organic food or traceable information. Therefore, WTP differences between the treatment group and the control group estimated below, constitute the lower bound of information effects (Xie et al., 2016).

3.1.2. Choice experiment task design Respondents were asked to choose between two different types of white shrimp and were also given an ‘opt-out’ option (Fig. 1 provides examples of these product profiles). Based on the attributes and corresponding levels in Table 1, the full factorial experiment design requires C482 or 1128 choice sets. Because respondents can become fatigued after identifying the 15 to 20 profiles, it would be impossible for them to evaluate such a large number of choices (Kim and Park, 2017; Yin et al., 2017a, 2017b; Wu et al., 2015). Therefore, to construct the experiment, we used a partial-factor design method and considered the principal interactions between an organic label and traceable information attributes. Using the OPTEX programme in SAS, we obtained 16 sets of non-selective scenarios and D optimal designs that estimated the main effect and the first-order interaction effect (Yin et al., 2017a, 2017b; Loureiro and Umberger, 2007; Lusk and Schroeder, 2004). The choice set was shown to the interviewees through colour images (a sample is shown in Fig. 1). In each choice set, respondents were asked to select one of three options. To minimise the learning effect of consumers, choice sets were presented in a random order, according to the method recommended by Savage and Waldman (2008). To further reduce experimental bias, using the methods of Silva et al. (2011) and 4 With a number of levels, people tend to not be able to distinguish the differences of prices all the time making decisions, which could lead to bias of results (Van Loo et al., 2011). 5 In the actual survey, we marked the price in RMB. However, to help international readers, we changed all RMB units to USD, at an exchange rate of 1 USD = 6.9116 RMD (checked on December 10, 2018). Data from: https:// www.refinitive.com.

6 2017 per-capita pre-tax disposable income of urban residents in Shandong province was RMB 36,789 (5322.79 USD), and their hourly wage was about RMB 19(2.75 USD), assuming an eight-hour work day for five days per week. Since each individual experiment took 30–35 min, a payment of 10 RMB (about USD 1.45) compensated participants for their time and avoided a portfolio effect.

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Organic Label

Traceability Information

Price

I would choose neither of them.

Adult Shrimp Sales Information

No Traceability Information

7.67USD/500g

9.84USD/500g

A

B

C

Fig. 1. Sample of the selection task.

to interviewees the steps involved in answering the questionnaire. In January 2018, we selected 50 respondents in Rizhao city, Shandong province for preliminary testing, of which 42 completed all questions. We then adjusted the questionnaire according to these city preliminary research results. The final questionnaire was used in the above nine cities in February and March of 2018. A total of 1215 consumers (about 135 in each) were randomly intercepted and invited to participate in the survey on an open place outside the supermarkets and 996 valid questionnaires were recovered, an effective recovery rate of 81.98%. Demographic characteristics of the information treatment and control groups are reported in Table 2. The chi-square tests cannot reject the null hypothesis that these two groups share the same demographic characteristics. Referring to the 2017 statistical yearbook of Shandong province in 2016 and Chinese Family Tracking Survey data, while the proportion of families with children under age 18 are in line with the general population, the percentage of high-income groups and female in the sample are higher than the province's average. A high proportion of female in the sample is consistent with the fact that most household food buyers in China are women (Yin et al., 2019). The reason for the high proportion of middle- and high-income groups in the sample is that, in China, high-income groups purchase food in large supermarkets more often, while lower-income consumers buy food in farmers' markets more often (Geng, 2014; Michelson et al., 2018).

3.2. Data collection and descriptive statistics We chose Shandong province, China as the site for the survey. Located on the East coast of China, Shandong province is an important producer and consumer of aquatic products, including white shrimp. At the same time, a large gap exists between the levels of economic development in different regions of Shandong province. In general, the Eastern part is the most developed and the Western part is relatively backward, reflecting a similar pattern of economic development in China (Yin et al., 2017a, 2017b). For our survey, we selected Rizhao, Qingdao and Weifang cities in Eastern Shandong; Tai'an, Jinan and Zaozhuang cities in Central Shandong and Heze, Liaocheng and Dezhou cities in Western Shandong.7 During October and November 2017, focus group discussions, each lasting about two hours, were conducted in each of these nine cities. The discussions aimed to obtain basic consumer information and to understand participants' preferences for key shrimp attributes. Each discussion group involved 8–10 participants, and all respondents were over 18 years of age, were the primary grocery shoppers in their respective households and had purchased seafood within the last month. Empirical research and our focus group discussions indicated that farmers' markets and supermarkets selling aquatic products were the most important places for local consumers to buy white shrimp. However, since organic and traceable food is mainly sold in city supermarkets, we recruited participants from these consumers and largely ignored general farmers' markets or rural markets (Zhang et al., 2017). Two supermarkets were selected in each of the above nine cities (including one local supermarket and one national chain supermarket). The respondents were intercepted by experiment assistants in the above supermarkets. To enhance the randomness of the sample selection, it was determined that the third consumer coming into view should be selected as the respondent (Wu et al., 2014). Once agreed, they were escorted to an office (or a corner) of the market where the experiments were conducted. Trained investigators conducted each direct, face-toface interview. Before each interview, the investigator briefly explained

4. Econometric models 4.1. The generalised mixed logit model Consumers' decision-making process regarding white shrimp can be modelled using a random utility framework (Morey et al., 1993). In this study, the decision-maker's white shrimp purchaser, n, faced T choice scenarios and J options for each choice scenario. The utility level of purchaser n choosing white shrimp type j in scenario t can be expressed as

Unjt = Vnjt + εnjt , n = 1, …, N , j = 1, …, J , t = 1, …, T .

(1)

7

These cities are located in the east, center and west part of Shandong province, with different levels of economic development and city sizes. Due to the limitation of paper length, we did not introduce the basic situation of these cities in details in the paper.

Vnjt is the deterministic part of the utility level and εnjt is the stochastic part. Decision-makers tend to choose alternatives that provide the highest utility (decision-makers choose option j if and only if ∀i ≠ j, 4

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Table 2 Socio-demographic characteristics of the sample. Variable

Elements

Treatment group

Control group

Full sample

Shandong statistical indexa

Gender

0 = women 1 = male χ2(1) = 0.591, p = 0.442 Junior high school and below High school or technical secondary school College degree or above χ2(2) = 3.348, p = 0.188 Below US $7235 US $7235–US $14,468 US $14,569–US $28,936 US $28,936–$72,342 US $72,343 or more χ2(4) = 6.401, p = 0.171 1–2 people 3–4 people Five or more people χ2(2) = 0.588, p = 0.745 No children One Two Three or more χ2(3) = 2.671, p = 0.445

58.43% 41.57%

56.04% 43.96%

57.23% 42.77%

49.23% 50.77%

13.25% 41.57% 45.18%

9.64% 42.17% 48.19%

11.44% 39.46% 46.70%

71.55% 18.63% 9.82%

7.23% 24.10% 39.16% 24.70% 4.81% 39.16% 45.18% 15.66%

9.64% 21.69% 42.77% 19.88% 6.02% 40.96% 42.77% 16.27%

8.43% 22.89% 40.96% 22.29% 5.43% 40.06% 43.98% 15.96%

38.15% 40.16% 17.47% 4.22%

33.73% 40.96% 18.67% 6.63%

35.94% 40.56% 18.07% 5.43%

43.04% 33.98% 16.83% 4.98% 1.17% 32.13% 43.58% 24.29% Average: 3.7 47.56% 33.51% 15.97% 3.96%

Education

Annual household income

Family size

Number of children in the family

a Data about gender and education were obtained from the 2018 statistical yearbook of Shandong province (Shandong Bureau of Statistics, 2018). However, because annual household income, family size and number of children in a family were not available from this yearbook, this data was obtained from the China Family Panel Studies (CFPD) (Institute of Social Science Survey in Peking University, 2016).

(Xie et al., 2016). In this research, we allow γ = 0 satisfying the scale mixed logit model βn = σn[β + Γωn], which was named the G-MNL-II model by Fiebig et al. (2010). In these models, random parameters are assumed to be uncorrelated with each other, i.e. Γ is a diagonal matrix. In addition, Σ = ΓΓ' represents the covariance matrix of random parameters, where the diagonal line of Σ represents the variances of random parameters. Because Γ is a diagonal matrix, the non-diagonal elements of Σ are all zero, indicating that no correlation exists between random parameters. Considering that each respondent has multiple selection tasks in the survey data, if respondent n chooses option j under scenario t, let ynjt = 1; otherwise, ynjt = 0. Then, the simulated selection probability of a series of choices {ynjt}t=1T adopted by respondent n becomes

i ∈ Integers, Unjt > Unit). Referring to the method proposed by McFadden (1974), the probability that decision-maker n selects option j in the selection scenario t can be expressed as

Prob (ynt = j | β ) = Prob (Vnjt + εnjt > Vnit + εnit ,∀ i ≠ j ) = Prob (εnit < εnjt + Vnjt − Vnit ,∀ i ≠ j )

(2)

In this paper, we assume that the deterministic part, Vnjt, is a nonorganic, linear parameter utility function:

Vnjt = βp ′pnjt + β1 ′Onjt + β2 ′Trnjt + β3 ′Onjt × Trnjt ,

(3)

where p refers to the price of white shrimp products i paid by decisionmakers n in choice scenario t. Dummy vector O represents whether the shrimp is organic or non-organic (non-organic is the basic level). Dummy vector Tr represents whether the shrimp is traceable (nontraceable shrimp is the basic level). β refers to coefficients of above attributes or two-order effects of two attributes. The generalised mixed logit model (G-MIXL) relaxes the assumptions of a traditional, conditional logit model of independence of choice (IIA) and captures consumers' heterogeneous preferences. A G-MIXL model is a generalised model that integrates mixed logit models, scaled, heterogeneous, polynomial logit models and conditional logit models (Fiebig et al., 2010). Because of scale heterogeneity, which means different utilities are brought to consumers with different traits by the same attribute, a G-MIXL model significantly improves the model's fitness (Lew and Wallmo, 2017; Sheremet et al., 2017). The utility function of the nth interviewee selects option j in purchase (or selection) scenario t, which can be restated in a more concise form:

n = 1 P D

d=1

exp[σ d (β + Γωd Xnit]

⎞ J ⎟ d d ∑ exp[σ (β + Γω ) Xnit ] ⎠ ⎝ j=1 ⎛

ynjt

(5)

where σ = exp (−τ /2 + τv ). d

2

d

4.2. Estimation of the WTP space The traditional method to estimate consumer WTP for specific attributes is to express it as the inverse of the ratio of an attribute coefficient and a price coefficient.8 Referring to Train and Weeks's (2005) method, we divide the utility function in Formula (4) into scale parameters, sn, obtaining a new error term with different variances without changing the simulation of consumer behaviour. Then, the utility function becomes

Unjt = −

Unjt = βn ′Xnjt + εnjt ; βn = σn β + [γ + σn (1 − γ )] Γωn 0 ≤ γ ≤ 1, ωn ~N (0, 1); σn = exp(‐τ 2/2 + τvn), vn ~N [0, 1]

D

∑ ΠΠ t j ⎜

βp sn

pnjt +

β′ x njt + vnjt . n = 1, …, N , j = 1, …, J , t = 1, …, T sn

(6)

Furthermore, we can use Formula (7) to obtain the marginal WTP vector of all attributes facing individual n and directly estimate his/her WTP for any attribute Wn. By adding marginal WTPs of all attributes and their interaction effects to the original WTP of the conventional product, we get the WTP of the product (Yip et al., 2017).

(4)

where Xnjt is a vector that contains all attributes and interaction terms between attributes that interviewee n selects option j from scenario t. The mean of σn, which represents random scale factors, is 1, and its variance is exp(τ2 − 1). In the equation, τ and γ are scalars, while Γ is a diagonal matrix containing a non-zero element. Specific values of τ and γ can be used to represent specific models

8 The method is correct when the attribute hierarchy is encoded through dummy variables. Other attribute-level encoding methods may result in similar but slightly different WTP calculations.

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Table 3 Consumer preference estimation results of the Generalised Mixed Logit Model. Control group Main effect

⁎

EUORG CNORG SEETRACE ADUTRACE SALTRACE PRICE CNORG × SEETRACE CNORG × ADUTRACE CNORG × SALTRACE EUORG × SEETRACE EUORG × ADUTRACE EUORG × SALTRACE

Interactive effect

Alternative selection constant (ASC) Estimated standard deviation

s.e. s.e. s.e. s.e. s.e. s.e. s.e. s.e. s.e. s.e. s.e. s.e. s.e.

7.008 4.497 2.435⁎⁎⁎ 3.297⁎⁎⁎ 1.634⁎⁎⁎ 1.019⁎⁎ 0.158⁎ 0.437⁎⁎⁎ 0.212⁎ 0.042⁎⁎⁎ 0.183⁎⁎⁎ 0.270⁎⁎⁎ 22.829⁎⁎⁎ 0.348⁎⁎⁎ 4.818⁎⁎ 0.291⁎⁎⁎ 1.250⁎⁎⁎ 0.858⁎⁎ 0.380⁎ 1.572⁎⁎⁎ 0.792 0.242⁎ 1.064⁎ 0.042⁎⁎⁎ 0.692⁎⁎ 1.064⁎⁎⁎ 0.409⁎⁎ 5976 8636.976

PRICE ASC CNORG EUORG SEETRACE ADUTRACE SALTRACE CNORG × SEETRACE CNORG × ADUTRACE CNORG × SALTRACE EUORG × SEETRACE EUORG × ADUTRACE EUORG × SALTRACE

Tau (τ) Observed quantity Logarithmic likelihood ratio (L)

Note:

⁎⁎⁎ ⁎⁎

,

and

⁎

Treatment group 7.548⁎⁎⁎ 4.820⁎⁎⁎ 2.628⁎⁎⁎ 3.429⁎⁎⁎ 1.739⁎⁎⁎ 0.938⁎⁎⁎ 0.125 0.224⁎ 0.096 −0.417⁎ −0.315 −0.350⁎ 26.760⁎⁎⁎ 0.201⁎⁎⁎ 4.493⁎⁎⁎ 0.302 1.067⁎⁎⁎ 0.713⁎⁎ 0.317 1.440⁎⁎⁎ 0.597⁎⁎⁎ 0.134⁎ 0.869⁎⁎ 0.026⁎⁎⁎ 0.653⁎⁎⁎ 0.300⁎⁎⁎ 0.150 5976 3594.717

represent significance levels of 1%, 5% and 10%, respectively. Halton sampling = 1000.

Wn = −β′/ βp

two attributes generally do not exist. Coefficients of interaction terms between EUORG and SEETRACE, EUORG and ADUTRACE and EUORG and SALTRACE even turn negative, indicating that the complementary relationship between these two attributes has changed into a substitute relationship, in which one possible reason could be organic and traceable knowledge improves consumers' recognition of organic labels and traceable information; and some consumers believe that only one of these information sources is enough to guarantee the safety of shrimp. Furthermore, supporting the conclusion of Yin (2013), Chinese consumers may buy organic food for safety reasons rather than for environmental ones, because both organic label and traceable information can help reduce the input of pesticides (fish medicine) and other chemicals in the production of agricultural products (aquatic products), and especially help relieve the illegal usage of highly toxic pesticides issue, which is particular concerns of Chinese consumers, so as to improve the overall food safety level. Some participants expressed concerns about the competence of the EU organic certification process in China, changing the original preference between EUORG and traceable information. The possible reason is that, on the basis of the results from the focus group discussions, some participants do not trust the Chinese cooperated entities with some global organic certification agents, rather than distrust the EU organic system; in China, foreign certification bodies are not allowed to provide organic certification services directly, instead, they can enter the Chinese market only by cooperating with Chinese certification bodies. Table 3 also shows that the standard deviations of most parameters are significant, indicating the heterogeneity of consumer preferences. In other words, for the same attribute, consumers have different preferences, attitudes, and WTP. Standard deviations in most treatment groups were smaller than those in control groups. This suggests that providing more information can narrow the heterogeneity of consumer preferences.

(7)

5. Empirical results and discussion 5.1. Estimation results of the generalised mixed logit model The survey data were processed with Nlogit5.0 software to obtain the estimation results of the generalised mixed logit model (shown in Table 3). Conventional white shrimp with no organic label and no traceable information were used as benchmarks in the model. The coefficients of the two organic label attributes in the treatment group are significant at 1%; in contrast, in the control group, the coefficient of the EU organic label is significant at the 10% level, and the coefficient of the Chinese organic label is not significant (Table 3). This implies that consumers in the treatment group differentiate more between organic and conventional products, especially between Chinese organic and conventional products than those in the control group. China's organic food market is still in its infancy, and consumers' awareness of organic food is generally low (Wu et al., 2014). Therefore, it may be easier to promote consumer preferences by introducing organic knowledge to respondents. TRACE coefficients are significantly positive for consumers in both groups. Compared with white shrimp with no traceable information, the two groups generally prefer white shrimp with traceable information. This confirms the studies of Wu et al. (2011) and Ortega et al. (2016) who concluded that ‘compared to the conventional food, consumers prefer traceable food’. In the control group, coefficients of the interaction terms between organic label (ORG) and traceable information (TRACE) are significantly positive, indicating that a complementary relationship exists between these two factors and suggesting that combining organic labels and traceable information can improve consumer preferences. In the treatment group, coefficients of the interaction terms of the organic label (ORG) and the traceable information (TRACE) are generally not significant, showing that complementary relationships between these 6

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5.2.1. Estimation of consumer WTP for organic labels Panel 1 in Table 4 compares consumer WTP estimates for organic shrimp at the same traceable level. Results show that the average consumer WTP for organic shrimp is significantly higher than that for non-organic shrimp under the same traceability condition. Similar to Wu et al.'s (2014) conclusions, which stated that Chinese consumers prefer organic labels from developed countries, such as Europe and the U.S.; average consumer WTP for EUORG was higher than that for CNORG. Despite consumer's high WTP backed up by this study and many other studies (Olesen et al., 2010; Risius et al., 2019), currently organic seafood still only has a niche market in the world as well as in developed countries. The possible reasons are as follows. Firstly, the choice experiment adopted in this paper is a hypothetical method, and consumers may exaggerate their real preferences (Lusk and Schroeder, 2004). Secondly, consumers' willingness to pay does not represent their real purchasing behaviour. The latter is also influenced by many factors, such as consumers' income level and purchasing habits (Liu et al., 2013; Breidert et al., 2006; Yiridoe et al., 2005). Thirdly, although consumers are generally willing to pay higher prices for organic seafood, it may still be difficult for many producers to make up for the increased costs caused by their switch to organic production, resulting in few supplies of organic food.

Table 4 Consumer WTP for white shrimp (Generalised Mixed Logit Model) (USD/ 500 g)a, b, c. Control group

Panel 1. Organic (ORG) vs. Non-Organic (NOORG) CNORG × NOTRACE vs. NOORG 4.42, × NOTRACE [3.26, 5.58] EUORG × NOTRACE vs. NOORG 6.88 × NOTRACE [5.13.8.63] CNORG × SALTRACE vs. NOORG 4.62 × SALTRACE [3.01, 6.24] EUORG × SALTRACE vs. NOORG 7.15 × SALTRACE [5.35, 8.94] CNORG × ADUTRACE vs. 4.84 NOORG × ADUTRACE [3.90, 5.79] EUORG × ADUTRACE vs. NOORG 7.06 × ADUTRACE [6.07, 8.05] CNORG × SEETRACE vs. NOORG 4.57 × SEETRACE [3.92, 5.21] EUORG × SEETRACE vs. NOORG 6.92 × SEETRACE [5.88, 7.96]

Treatment group

ΔWIP

5.14 [3.78, 6.50] 8.05 [6.41, 9.69] 5.24 [3.73, 6.76] 7.68 [5.92, 9.44] 5.38 [4.29, 6.46] 7.72 [6.79, 8.65] 5.27 [4.65, 5.89] 7.61 [6.60.8.62]

0.72 [0.52, 0.92] 1.17 [1.02, 1.32] 0.62 [0.52.0.72] 0.53 [0.50, 0.56] 0.54 [0.46, 0.62] 0.65 [0.59, 0.72] 0.70 [0.67, 0.73] 0.68 [0.65, 0.71]

Panel 2. Traceable (TRACE) vs. Untraceable (NOTRACE) SEETRACE × NOORG vs. NOORG 2.41 2.8 × NOTRACE [1.36, 3.46] [1.88, ADUTRACE × NOORG vs. 3.24 3.66 NOORG × NOTRACE [2.20, 4.28] [2.43, SALTRACE × NOORG vs. NOORG 1.6 1.85 × NOTRACE [0.93, 2.27] [0.97, CNORG × SEETRACE vs. 2.56 2.94 CNORG×NOTRACE [2.33, 2.80] [2.09, CNORG × ADUTRACE vs. CNORG 3.67 3.9 × NOTRACE [2.80, 4.53] [3.05, CNORG × SALTRACE vs. CNORG 1.81 1.96 × NOTRACE [1.17, 2.45] [1.11, EUORG × SEETRACE vs. 2.45 2.36 EUORG×NOTRACE [1.40, 3.50] [1.44, EUORG × ADUTRACE vs. 3.42 3.32 EUORG×NOTRACE [2.24, 4.60] [2.23, EUORG × SALTRACE vs. 1.87 1.48 EUORG×NOTRACE [1.46, 2.28] [1.13, Panel 3. Organic (ORG) vs. Traceable (TRACE) CNORG × NOTRACE vs. NOORG 2.01 × SEETRACE [1.07, 2.94] CNORG × NOTRACE vs. NOORG 1.18 × ADUTRACE [0.52, 1.83] CNORG × NOTRACE vs. NOORG 2.81 × SALTRACE [1.60, 4.03] EUORG × NOTRACE vs. NOORG 3.04 × SEETRACE [2.02, 4.06] EUORG × NOTRACE vs. NOORG 3.64 × ADUTRACE [2.60, 4.68] EUORG × NOTRACE vs. NOORG 5.28 × SALTRACE [3.91, 6.65]

2.34 [1.27, 1.48 [0.61, 3.29 [1.85, 3.28 [2.10, 4.39 [3.16, 6.2 [4.75,

3.72] 4.89] 2.73] 3.79] 4.75] 2.81] 3.28] 4.41] 1.83]

3.72] 2.32] 5.14] 4.46] 5.62] 7.65]

0.4 [0.21, 0.43 [0.22, 0.25 [0.22, 0.37 [0.29, 0.23 [0.02, 0.15 [0.10, 0.09 [0.11, 0.1 [0.15, 0.39 [0.50,

0.59]

5.2.2. Estimation of consumer WTP for traceable information Panel 2 in Table 4 compares consumer WTP for shrimp with and without traceable information. In accordance with earlier research about consumer WTP for traceable products (Wu et al., 2011; Louviere et al., 2010), traceable information generally increases average consumer WTP. However, after the addition of traceable information about EU organic shrimp, consumer WTP in the treatment group slightly decreased, which is related to the weak substitution relationship between EU organic labels and traceable information in the treatment group, described in Table 3 above. No matter whether or how shrimp are organically labelled, consumers have the highest average WTP for ADUTRACE, followed by SEETRACE and then SALTRACE. This suggests that consumers may be concerned about food safety risks in shrimp farming.

0.63] 0.28] 0.45] 0.48] 0.19] 0.07] 0.05] 0.28]

0.33 [0.26.0.38] 0.3 [0.28, 0.35] 0.47 [0.34, 0.59] 0.24 [0.21, 0.27] 0.75 [0.65, 0.85] 0.92 [0.03, 1.87]

5.2.3. Comparison of consumer WTP for organic labels and traceable information Panel 3 in Table 4 compares consumer WTP for organic labels and traceable information. The first three rows of data compare WTP for Chinese organic labels and three types of traceable information, and the last three rows of data compare WTP for European organic labels and three types of traceable information. The model's estimation shows that the average consumer WTP for Chinese organic labels and EU organic labels is higher than that for traceable information. Results in the first two rows of Panel 1 and the first three rows of Panel 2 in Table 4 also show that compared with conventional shrimp (shrimp that is neither organic nor traced), average consumer WTP for organic shrimp is higher than that for shrimp containing any single type of traceable information. However, if shrimp simultaneously contain complete and traceable information about shrimp seed rearing, adult shrimp farming, and adult shrimp sales, consumer WTP may be much higher than WTP for organic labels in China or the EU.9

a

The number in brackets is the confidence interval for 95% WTP confidence. The mean and confidence intervals of WTP were determined by the Krinsky-Robb bootstrap method, using 1000 bootstrap WTP estimates. c ΔWTP values were calculated using 1000 estimated WTP bootstrap parameters in the treatment group versus the control group. b

5.2. Consumer WTP for white shrimp and the influence of knowledge introduction

5.2.4. The influence of knowledge on consumer WTP The effect of knowledge on consumer preferences can be obtained by comparing the WTP estimations of the control group and the treatment group (see the third column of Table 4). The introduction of

To simulate the WTP estimates of 1000 individuals for white shrimp with different attributes, estimated coefficient mean and standard deviations in generalised mixed logit models are used in the bootstrap method put forward by Krinsky and Robb (1986). The confidence intervals for the WTP mean and for 95% confidence are given in the first two columns of Table 4. Differences in WTP values between the treatment and control groups (WTPtreatment − WTPcontrol) are given in the last column.

9

In this research, we provided traceable information about shrimp seedling cultivation, adult shrimp cultivation, and adult shrimp sales separately in the experimental design. Therefore, we could not estimate consumer WTP for shrimp with the combined traceable information of all three stages. This is a topic for further study. 7

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certification, is calculated by the following formula:

additional information as knowledge significantly improves consumer WTP mean values for various products. This suggests that the provision of organic and traceable knowledge generally increases consumer preferences for organic labels or traceable information. But if traceable information is added for EU organic shrimp, WTP for the treatment group is even lower than for the control group, suggesting that knowledge reduces average consumer WTP. The reason is that, in the control group, the interaction terms' coefficients between organic labels and the three types of traceable information (SEETRACE, ADUTRACE, and SALTRACE) are significantly positive, while they were negative in the treatment group (Table 3). Table 4 also shows how knowledge influences WTP for organic labels more than for traceable information. The first two lines of Panel 1 show that, compared with the control group, the average consumer WTP for Chinese and EU organic labels in the treatment group increased by 0.72 USD/500 g and 1.17USD/500 g, respectively. The first three lines of data in Panel 2 show that, compared with the control group, the average WTP for traceable information related to shrimp seedling cultivation, adult shrimp cultivation and adult shrimp sales in the treatment group increased by 0.40 USD/500 g, 0.43 USD/500 g and 0.25 USD/500 g, respectively. These differences are lower than the WTP differences for the two organic labels. The data in the last column of Panel 3 indicate that the differences between consumer WTP for organic labels and traceable information are positive. Because of the improved knowledge as a result of introducing additional information, consumer WTP increased more for organic labels than for traceable information. This may be because Chinese consumers have a relatively poor understanding of organic labels, or because the concept of traceable food is more easily understood in Chinese expressions. Although the traceability system for organic food in China are nascent and consumer perceptions are low in general, consumers may understand traceable concepts relatively easier from Chinese expressions. Moreover, while more consumers derive an intuitive understanding of organic food, Chinese consumers often mistake organic food for green food and hazard-free food (Yin et al., 2017a, 2017b).

13

post

9

pre

2 ln (∑k = 1 eVik ) − 2 ln (∑k = 1 eVik ) −βp

(8)

To simplify the calculation, the dimension of βp is unified to −1. Each symbol has the same definition as in Section 4. Combined with Table 3 data, Eq. (8) shows that introducing EU organic certification in scenario 2 increased consumer surplus of each selection (500 g of shrimp) on average by 84.06%. After introducing knowledge to consumers, the average consumer surplus increased by 120.16%, which are astonishing increases and witnessed the enhance of consumer welfare primarily. 6. Conclusions and implications Using white shrimp, this study examined Chinese consumer WTP for two attributes—organic labels and traceable information—and investigated the influence of providing consumers with additional knowledge about organic products and their processing. We concluded that consumers are generally willing to pay a premium for organic versus conventional white shrimp. Comparing two organic labels, consumers will pay more for EU organic labels than for the Chinese label. This indicates that the Chinese government should continue to establish a diverse organic certification system, and on the other hand, Chinese domestic organic certification should be managed strictly and must focus on promoting international cooperation to enhance credibility of current certification procedures, which will help promote the development of China's organic food market. Second, compared with conventional white shrimp, consumers are generally willing to pay a premium for traceable information labels as well. Among three types of labels, consumers have the highest WTP for farmed adult shrimp traceable information, followed by shrimp seed rearing traceable information and adult shrimp sales traceable information. The construction of traceable information systems or food safety supervision systems should incorporate farming as the key stage and focus on strengthening the supervision of the farming link, while commit to make the entire process traceable. Third, introducing additional information as knowledge, which can generally increase consumer WTP for organic labels and traceable information, weakens the complementary relationship between the two attributes of organic labels and traceable information, and may even turn the relationship into a substitution. In the Chinese food market, though, improving consumers' organic certification and traceable knowledge will not necessarily improve consumer WTP. Food suppliers should take this into consideration when making decisions about organic certification or traceable systems. Fourth, we showed that EU organic certification in China will increase consumer WTP by an average of 84.06%, and the introduction of additional knowledge to consumers will further increase the consumer WTP by 120.16%. This further illustrates the practical value of introducing international certification (such as EU organic certification) into the Chinese market and the need for the Chinese organic food market to improve consumer knowledge through advertising and other means. We cannot, however, reach a conclusion about the net impact of knowledge, because it would require a complete cost-benefit analysis that incorporates social costs. Such an analysis remains to be a useful future exploration. It has to be pointed out that this study has some limitations. First, the major limitation was that this research was conducted only in one province of China, and with a small sample-sized leading us to strongly caution the generalizability of the findings. In future studies, we will try to collect more and more representative samples nationwide in China. Second, since organic and traceable food is mainly sold in city supermarkets, we recruited participants from these consumers and largely ignored general farmers' markets or rural markets. The sampling is biased towards the better educated and higher-income population. Some empirical studies show that (Liu et al., 2013; Yiridoe et al., 2005),

5.3. The value of knowledge introduction China is developing a diversified organic certification policy, incorporating both Chinese organic labelling policies (which will play a primary role) and foreign labels, such as EU organic labels (Yin et al., 2019). Because Chinese consumers know less about EU organic certification than about Chinese certification (Yin et al., 2019), we proposed a simple, two-scenario model to assess whether or not information can improve social welfare. In the first scenario, we assumed no EU organic certification in the Chinese shrimp market; in the second scenario, EU organic certification was introduced. In scenario 1, the consumer had the following nine options: 1) China organic shrimp, 2) reared shrimp seed with traceable information, 3) adult shrimp with traceable information, 4) adult shrimp with traceable sales information, 5) reared shrimp seed with traceable information and China's organic certification, 6) adult shrimp with traceable information and China's organic certification, 7) adult shrimp with traceable sales information and China's organic certification, 8) conventional shrimp and 9) a no-choice option. After introducing EU organic certification in scenario 2, the number of options increased to 12, i.e. the aforementioned nine options with additional four: 10) EU organic shrimp, 11) farmed adult shrimp with traceable information and the EU's organic certification and 12) adult shrimp with traceable sales information and the EU's organic certification, 13) shrimp with shrimp seed traceable information and the EU's organic certification. We first compared changes in consumer surpluses (average total WTPs for all products) in the two scenarios and then compared changes in consumer welfare after providing knowledge about EU organic certification. Using Brooks and Lusk's (2010) method for reference, consumer surplus changes in scenario 2, after introducing the EU's organic 8

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This paper was supported by the study of evaluation of social welfare of quality and safety certification policy for agricultural products in the New era in China—a project of the Social Science Foundation of China (Approval No. 18BJY153); the study of consumer preference for certified food: modeling, empirical testing and policy application—a project of Nature Science Foundation of Shandong Province (Approval No. ZR2017MG018); and the study of the policy system supporting green agricultural development under the background of rural revitalization and food security stratagems—a project of Science and technology suppory plan for outstanding youth innovation team of colleges in Shandong Province (Approval No. 2019RWG009).

supply chain and clarify the responsibilities of the relevant parties but also helps suppliers recall risky food quickly, which reduces social costs. Using Radio Frequency Identification, bar codes, CPU cards and other technologies, a complete traceable system (as the information carrier) should include the entire supply chain of shrimp farming, processing and distribution. In theory, the wider the traceable system, the deeper its depth, the greater its accuracy, the more comprehensive the record provided by traceable and safety information and the easier it is for consumers and stakeholders to identify and prevent food safety risks. However, the cost of tracing food production will increase, and it will become more technical and therefore more difficult. Furthermore, supply chain processes differ for different food categories in China, and most existing tracing systems cover only part of the supply chain. Note: * Information about the content of organic food was obtained from the Chinese Administrative Measures for Organic Product Certification in 2005 (Order No. 67 of China State Administration of Quality Supervision, Inspection and Quarantine) and the related National Standard of Organic Products (GB/T 19630.1–19,630.4-2005). Information about the content of traceable food was obtained from the Notice on Issues Related to the Pilot Construction of Traceable Systems for Meat and Vegetable Circulation in 2011 (Order No. 12 of the General Office of the Ministry of Finance) and the Guidelines on Promoting the Construction of Information Traceable Systems for Important Products (Order No. 8 of the Department of Quality and Safety Supervision of Agricultural Products) Five scholars were invited to comment and edit the content to ensure better comprehension by the general public.

Appendix I Cheap talk script⁎

References

In studies similar to this one, a group of participants just like you were asked, in a survey-type questionnaire, to declare the maximum amount they would pay for a product. The researchers observed a difference between values given by those who participated in the survey (like you) and those who actually purchased the product. This difference, observed in many published studies, has been called the hypothetical bias. How can we explain this difference? One possibility is that survey participants do not really consider their available money and the fact that the amount spent on the product afterwards would no longer be available. For these reasons, we ask that you try to answer the following questions by imagining that you actually have to pay the amount that you will indicate for the products described. Note: Content of the script refers the former edition of article: Bergeron,S., Doyon,M., Muller,L. Strategic response: A key to understand how cheap talk works 2019, 3, Can. J. Agr. Econ., 67(1): 75–83. doi:https://doi.org/10.1111/cjag.12182.

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consumers with high income or higher education generally have a higher willingness to pay for organic and traceable food. Therefore, our study overestimated consumers' willingness to pay for organic and traceable food. With organic and traceable food becoming increasingly popular in the Chinese market, future research should focus on general farmers' markets or rural markets. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements

Appendix II The information card for organic and traceable knowledge* Organic food: the term ‘organic’ can only be used to describe food sold in China that meets all of the following requirements. First, organic food needs to follow established production, processing, packaging and transportation standards. It cannot be genetically modified, and its products must be free of synthetic pesticides, fertilisers, growth regulators and feed additives. Organic food has to be certified by 23 government-approved entities, and its appearance or packaging should contain an organic label. China's regulators only recently recognised foreign certification bodies. Therefore, in addition to organic food carrying China's organic label, some domestically produced food may carry a label issued by foreign entities, such as the EU's certification organisation. Traceable information: The Chinese government began a traceable system for food in 2000. This system monitors the food production process and its flows, which not only helps identify risks in the food 9

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