Doctoral Dissertation

Study on Trade Effects of Food Safety
Standards: Export Promotion, Measure
Protection and Trade Duration

March, 2022
Graduate School of Social Sciences,
Hiroshima University
Linyi Tang

Doctoral Dissertation

Study on Trade Effects of Food Safety
Standards: Export Promotion, Measure
Protection and Trade Duration

Supervisor:

Toshikazu Oosawa, Professor
Yasunori Ouchida, Professor

March, 2022
Graduate School of Social Sciences,
Hiroshima University
Department of Economics
Linyi Tang

Acknowledgments
I would like to express my most sincere gratitude to my supervisor, Professor Toshikazu
Oosawa, for giving me a valuable opportunity to participate in the Ph.D. program and giving me
full support in my academic research and life.
Meanwhile, I greatly appreciate Dr. Tong Yang, a member of my research group, whoalways responds enthusiastically to my request, patiently helps me solve problems and gives me
many great suggestions.
Moreover, I am truly grateful to Professor Takashi Senda and Professor Yasunobu
Tomoda, members of my thesis committee, for their critical comments and suggestions on therevision, making my thesis more perfect.
In addition to academic studies, I would like to appreciate the Japanese government andenterprises for their financial support, which greatly alleviated the pressure in my life andallowed me to concentrate on completing my studies, considering that my parents have retired,and my father has no pension and suffers from chronic illness.
Last but not least, there are many people I want to appreciate. They are the teachers,
relatives and friends who helped me during my Ph.D. program. Without their support, I would
not be able to finish my studies smoothly.

Abstract
This thesis studies the food safety standards that need to be complied in international trade,e.g. maximum residue limits for pesticides (MRLs), and their effects on food trade exports, tradeprotectionism and trade duration, thus enriching the understanding of international trade effects.
Chapter 2: Are China’s food safety standards driven by trade protectionism? - Empiricalanalysis of maximum residue limits
Mandatory food safety standards, including maximum residue limits, may be used to replacesubstantially reduced agricultural tariffs as new non-tariff measures in international trade. Chinais the world's largest importer of agricultural products, and there is a question whether the
maximum residue limits promulgated and implemented over the years have led to protectionism.
Scholars have proposed “protectionism indices” to quantify the trade protectionism of non-tariffmeasures. This thesis collects and collates a relatively new and complete data set of maximum
residue limits in China and the Codex Alimentarius Commission. This is used to demonstrate theevolution of China’s maximum residue limits, and measure their protectionism indices relative tointernational standards. Regression analysis shows that China’s maximum residue limits havetended not to replace tariffs as a new tool for trade protection. The motivation for the rise ofprotectionism indices appears to stem from public demand for improved food safety and thegovernment’s active response to food safety incidents. There may be some misleading
implications to using the term “protectionism indices” in relation to maximum residue limits. The
use of the term “stringency indices” may be more neutral and objective.
Chapter 3: Influence of maximum residue limits for pesticides on agricultural foodexports: analysis based on a quality heterogeneous firms trade model
Some studies suggest that maximum residue limits (MRLs) discourage imports by increasing
compliance costs for exporters, similar to other non-tariff measures. This study analyzes the impact of
MRLs on trade by constructing a quality heterogeneous firms trade model, with data of agricultural
food products exported from China to the European Union (EU) from 2008 to 2020. The results showthat the trade impact depends on the comprehensive effects of three factors: demand, variable cost,and fixed cost. Heterogeneity indexes, which combine the standard quantity and level, are used tomeasure the difference in MRL standards between China and the EU. The Poisson pseudo maximum
likelihood fixed-effect estimator is applied in the regression to investigate the impact of MRLs on theexports of agri-food products. The results indicate that more stringent MRLs, whether formulated by
the EU or China, promote China’s agri-product exports to the EU. When China (the exporter) actively
raises its MRL standards, there is an even more significant promotion effect on its exports than when
China only follows the strict MRLs of the EU (the importer). The results are consistent with the rapid
development of China’s food safety standards and significant improvement in food safety levels overthe past decade.

Chapter 4: Influence of the exporter’s food safety standards on trade duration: An empirical
study of agricultural food products from China
Previous studies have revealed the duration of most international trade combinations is
unexpectedly short. Focusing on improving the trade duration may be more critical than simply
exploring diversified markets to boost trade. Based on a set of export data from 2008 to 2019, thetrade survival analysis shows the average trade duration of China's agri-food export is 3.5824
years. To further understand the trade duration, this thesis introduces the heterogeneity indices ofMaximum Residue Limits for pesticides (MRLs) between China and Codex Alimentarius
Commission (CAC), aiming to quantitatively measure the strictness level of China's MRLs
standards, and adopts the discrete-time hazard model (Cloglog) to examine the impact of foodsafety standards on the duration of agri-food export. The regression results illustrate the upgradingof MRLs standards in exporter (China) have contributed to prolong the trade duration and stabilize
bilateral trade relations. In addition, the capacity of agri-food production is also an emphasis
factor affecting the stability of export trade.

Contents
Chapter 1 Introduction .................................................................................................................. 1
Chapter 2 Are China’s food safety standards driven by trade protectionism? - Empirical analysis
of maximum residue limits ............................................................................................ 5
2.1 Introduction ......................................................................................................................... 5
2.2 Data descriptions ................................................................................................................. 7
2.3 Stringency of MRLs ............................................................................................................ 9
2.3.1 Evolution of MRLs in China ........................................................................................ 9
2.3.2 Stringency indices of MRLs ....................................................................................... 11
2.4 Motivation of China’s changing MRLs ............................................................................ 13
2.4.1 Theoretical analysis .................................................................................................... 13
2.4.2 Regression model ....................................................................................................... 15
2.4.3 Results and discussion of regression .......................................................................... 16
2.5 Conclusion and prospect ................................................................................................... 19
Chapter 3 Influence of maximum residue limits for pesticides on agricultural food exports:
analysis based on a quality heterogeneous firms trade model ..................................... 21
3.1 Introduction ....................................................................................................................... 21
3.2 Theoretical model.............................................................................................................. 24
3.2.1 Stricter MRLs in importer .......................................................................................... 24
3.2.2 Stricter MRLs in exporter .......................................................................................... 28
3.2.3 Hypothesis .................................................................................................................. 29
3.3 Empirical model ................................................................................................................ 29
3.4 Data sources ...................................................................................................................... 31
3.4.1 Dependent variable ..................................................................................................... 31
3.4.2 Explanatory variable .................................................................................................. 31
3.4.3 Data description.......................................................................................................... 34
3.5 Methods and results of estimation ..................................................................................... 36
3.5.1 Estimation method...................................................................................................... 36
3.5.2 Estimation result analysis ........................................................................................... 36
3.5.3 Robustness check ....................................................................................................... 39
3.6 Conclusion and future research ......................................................................................... 44
Chapter 4 Influence of the exporter’s food safety standards on trade duration: An empirical
study of agricultural food products from China .......................................................... 46-I -4.1 Introduction ....................................................................................................................... 46
4.2 Literature review ............................................................................................................... 48
4.2.1 MRLs impact on agricultural trade ............................................................................ 48
4.2.2 Study on trade duration .............................................................................................. 49
4.3 Estimation on trade duration ............................................................................................. 49
4.3.1 Data preprocessing ..................................................................................................... 49
4.3.2 Multiple spells ............................................................................................................ 50
4.3.3 Data censoring ............................................................................................................ 51
4.3.4 Methodology of survival analysis .............................................................................. 51
4.4 Influencing factors of trade duration ................................................................................. 53
4.4.1 Estimation model........................................................................................................ 53
4.4.2 Estimation results ....................................................................................................... 58
4.4.3 Robustness test ........................................................................................................... 62
4.5 Conclusion......................................................................................................................... 62
Chapter 5 Conclusion .................................................................................................................. 64
References ................................................................................................................................... 66- II -

Chapter 1 Introduction
Food safety is the fundamental right of human safety and health, and ensuring food safety is
one of the essential functions of governments. In the past two decades, the media worldwide,
including China, have continuously exposed food safety incidents, which has exposed the longstanding problem of pesticide and veterinary drug abuse in food production and has aroused great
attention to food safety worldwide. It makes governments issue more stringent food safety
regulations, reform food safety regulatory agencies, establish and improve the national Maximum
Residue Limit for Pesticide (MRLs) to regulate the use of pesticides strictly.
As the international food trade increasing exponentially in the past few decades, pesticide
residues are widely regarded as one of the important factors affecting the quality and safety ofagricultural products. Maximum residue limits (MRLs) for pesticides is the maximum
concentration of legally allowable pesticide residues in or on food commodities and animal feeds(expressed in mg/kg), recommended by the Codex Alimentarius Commission (CAC). Food safetyincidents around the world have triggered extensive reports in the local media, exposing longstanding problems such as excessive pesticides in imported or domestic foods and illegal use of
unregistered pesticides. Enhanced public awareness of food safety and pressure from public
opinion prompt governments to formulate more extensive and stringent MRL standards forpesticides as mandatory food import standards. Japan introduced the "The Japanese Positive List
System for Agricultural Chemical Residues in Foods" in 2003, which was implemented in May
2006. The European Union (EU) member states have adopted unified EU MRLs since 2008. In
the same year, Canada passed new MRLs. From 2010 to 2019, Australia, Hong Kong, and South
Korea successively announced their new MRLs. Based on the consideration of domestic dietary
habits, politics, economy and other factors, the MRLs of some developed countries and regions
deviated from the CAC international standard advocated by the Agreement of Sanitary andPhytosanitary Measures (SPS) of the World Trade Organization, which led to the continuous
reduction of the coordination of MRLs among countries, and the related agricultural trade disputes
continued to rise. The international community is concerned about whether MRLs have replaced
gradually reduced tariffs promised by countries and become a new form of trade protection policy
tool? This has triggered many empirical studies on the impact of MRLs on trade.
Chapter 2:
The question of whether specific food safety standards can become non-tariff trade barriers
has been the subject of considerable research. Scholars in food policy believe that food safety
standards, unlike other non-tariff barriers, have dual effects on agricultural trade. They may
increase compliance costs, hinder trade and cut social welfare. However, they may also reduce
information asymmetry or negative externalities, promote trade and improve social welfare.

-1 -China is now the world’s leading net importer of agricultural products. Large-scale
agricultural imports threaten agricultural employment and farmers’ income and bring newchallenges to food security and trade disputes. With the loss of comparative advantage in
agricultural products and the continuous expansion of the trade deficit, the question of whether
Chinese government agencies will use trade protection tools other than import tariffs has attracted
much attention. Scholars have examined the adjustment of China’s trade policy following tariff
reduction. One study found that China actively uses other policy tools to replace tariffs and extend
the protection for domestic producers. Current research on food safety standards mainly focuseson the formulation of standards in developed countries and their impact on global trade. Few
studies have examined the changes in China’s agricultural products import trade policy. Scholars
in China also tend to focus on the impact of developed countries’ MRLs on China’s agricultural
exports. There is therefore very little research on China’s own MRLs. The MRLs database of the
US Department of Agriculture (USDA) is widely used in research, but this only includes the
MRLs of pesticides prescribed by the United States. There is therefore no way of assessing the
rapid development of MRLs in China. This thesis considers the following questions:-

What is the trend in MRLs in China?

-

What are the main factors affecting the evolution of MRLs in China?

-

Will MRLs replace decreasing import tariffs in China as a new tool in trade protection

policy?
To obtain more objective and complete data for analysis, we drew on historical documents
to establish an MRLs database for China and the CAC, and analyzed the evolution of China’s
MRLs compared with CAC standards. We found that MRLs have not replaced tariffs as a new
trade protection policy tool. There may also be some misleading implications from using so-called
protectionism indices to measure the use of MRLs.
Chapter 3:
In empirical studies of international trade, gravity model is mostly used. Most research
focuses on whether the MRLs of importing countries hinder the imports of agri-products. Apartfrom the various countries, agri-products, and time periods, the main difference lies in thequantification of core explanatory variable. Early scholars used MRLs of important pesticides inimport countries as explanatory variable, and later scholars almost used bilateral heterogeneity(similarity) indices of MRLs in import and export countries as explanatory variable, consideringthat bilateral divergence, not only the MRLs of importing countries, is the fundamental factor
affecting trade. These indices integrate the number and level of MRLs standards, thus cancomprehensively reflect the relative stringency of MRLs in import and export countries.
Existing empirical studies mainly focus on the impact of MRLs in developed countries on
trade, rather than the relativity of standards between importer and exporter. The outdated timeand limited samples do not timely and comprehensively reflect the rapid development of food-2 -safety standards in developing countries. Considering that the export of agri-food products
accounts for a large proportion in the trade share of developing countries, the food safety
standards will have a profound impact on the industry and trade of agri-food products, and thenhave a social and systematic influence on the development of rural areas, agriculture and farmers.
As a developing country, China is the world's second importer and fourth exporter of agricultural
products in 2020. From 2010 to 2021, China updated the national mandatory MRLs standard –"National Standard for Food Safety - Maximum Residue Limits of Pesticides in Food" for fivetimes. The actual MRLs items specified in the standard increased from 873 in 2008 to 10092 in
2021, nearly twice that of CAC in 2020. The items of agri-food products had increased from 88
to 377, including 119 of agri-products unique to China. The items of pesticides had added from138 to 564, basically realizing that all registered pesticides had standards to follow. The standard
also stipulates the MRLs of 87 pesticides that are not prohibited but have not been approved foruse in China. The principles, methods and data requirements of dietary risk assessment ofpesticide residues adopted in China's MRLs are gradually in line with Codex Alimentarius
Commission (CAC) and developed countries after more than a decade of development. The EU
is recognized to have the most stringent MRLs standards, and its MRLs are regarded as animportant obstacle to the export of agri-food products. Studying the impact of MRLs differences
between China and the EU on the export of agri-products will help to enrich the understanding offood safety on trade effects and have certain guiding significance.
This thesis attempts to make contributions in both theoretical and empirical analysis. In
theoretical analysis, we construct a concise QHFT model. The trade effect of MRLs is extended
from two factors of demand and cost to three factors of demand, variable cost and fixed cost. On
this basis, a reasonable gravity model that effectively handles the zero value is obtained. In
empirical analysis, the heterogeneity indices are used to measure the bilateral relative stringencyof MRLs between China and the EU. A relatively complete industry-level database of agri-foodproducts in China and the EU is used to analyze the comprehensive effects of MRLs standards on
trade. To make up for the data deficiency in previous studies, we have expanded the investigation
for time, products and pesticides. This thesis finds that the current MRLs standards of the EU are
still much more stringent than China, but the gap is narrowing, and even some MRLs in China
have exceeded those in the EU. The EU's more stringent EU MRLs did not curb food exports
from China to the EU, and when China's MRLs is more stringent, it shows a certain signal effect(quality upgrade), which plays a strong role (demand enhancing) in promoting China's foodexports to the EU.
Chapter 4:
In the research on the trade effect of MRLs standards, scholars mainly focus on the impactof the improvement of food safety standards in developed countries, and these studies involve
relatively early years. Most scholars focus more on the impact of developed countries' MRLs
-3 -standards on their imports. Their research suggests that strict MRLs forces producers to bear
additional production and compliance costs, and other costs come from the extension of delivery
time in customs inspection and even refusal of entry, thus MRLs is used by importing countries
to hinder the import of agricultural products and considered a protectionist measure. On thecontract, other empirical studies suggest that seemingly strict MRLs standards can promote trade
because they reduce the risk of information asymmetry and ease consumers' concerns, thustransmitting the signal of quality improvement in exporter and strengthening consumer demand.
Different from previous studies that focus on the impact of gross trade, this thesis focuseson the impact of MRLs standards on the duration of exports. Previous trade theory assume that
trade relations will remain unchanged and continue for a long time after relationship are
established, but this is not the case. Export duration plays an important role in the improvementof a country's export volume. If the trade duration is too short, it is difficult to maintain thesustained trade growth even if there are many trading partners.
The possible marginal contribution of this thesis is mainly in following aspects: First,
different from previous studies focusing on the direct impact of food safety standards on grosstrade, this thesis focuses on the implicit factor of the upgrading food safety standards on the trade
duration of agricultural export. Second, based on the empirical data of China, this thesis explores
the enhanced food safety standards such as MRLs on the export trade of developing countries.
Thirdly, extend the research year to 2019 as to better conform to the rapid changes of MRLs
standards in international trade since 2010. This thesis is dedicated to further improve the current
research on the trade effect of food safety standards, and provide reflections for the agriculturalindustry upgrading of developing countries and the sustainable development of global agriproducts trade.
The organizational structure of this thesis is summarized as follows：
Section 2.2 is the description of data sources. Section 2.3 analyzes the evolution
characteristics of MRLs in China based on our database, and introduces the relative stringent
index. Section 2.4 discusses the basic motivation of the changing MRLs in China by applying theregression model proposed. Section 2.5 is the conclusion and prospect.
Section 3.2 of this thesis is the theoretical model of QHFT, Section 3.3 is the empirical model,
Section 3.4 is the data source, focusing on the explanation of the heterogeneity indices, andSection 3.5 is the methods and results of estimation. Section 3.6 is the conclusion and future
research.
Section 4.2 is literature review, Section 4.3 is the estimation of trade duration, Section 4.4 isthe empirical research on the influencing factors of trade duration, and Section 4.5 is theconclusion.

-4 -Chapter 2 Are China’s food safety standards driven
by trade protectionism? - Empirical analysis of
maximum residue limits
2.1 Introduction
Food safety is a fundamental human right, part of both safety and health. Ensuring foodsafety is therefore one of the essential functions of governments. In the past two decades, themedia worldwide, including in China, have exposed many food safety incidents (Swinnen, 2018).
This, in turn, has exposed a long-standing problem of pesticide and veterinary drug abuse in foodproduction and has focused attention on food safety worldwide. Governments have issued more
stringent food safety regulations, reformed food safety regulatory agencies, and established andregulated the use of pesticides through improved national maximum residue limits (MRLs) for
these chemicals (Swinnen, 2018).
The World Trade Organization (WTO) Agreement on the Application of Sanitary andPhytosanitary Measures recommends that member states formulate MRLs following theinternational standards of the Codex Alimentarius Commission (CAC). This standard is notmandatory, thus countries develop their MRLs in line with national practice. This behavior
intensifies the inconsistency of MRLs between countries. There are concerns that the increasing
number of trade disputes about agricultural products mean that food safety standards, including
MRLs, may be used by some countries to replace substantially reduced tariffs as new non-tariff
trade measures.
The question of whether specific food safety standards can become non-tariff trade barriers
has been the subject of considerable research. Scholars in food policy believe that food safety
standards, unlike other non-tariff barriers, have dual effects on agricultural trade (Swinnen, 2018;
Yeung et al., 2017). They may increase compliance costs, hinder trade and cut social welfare.
However, they may also reduce information asymmetry or negative externalities, promote tradeand improve social welfare.
Scholars in international trade research have analyzed the trade effect of food safety
standards from multiple angles and levels. The results vary between countries and products. Somestudies found that food safety standards hindered trade (Ferro et al., 2015; Hejazi et al., 2016;
Seok et al., 2018), while others found a promotion effect (Shingal et al., 2021; Xiong & Beghin,
2012; Xiong & Beghin, 2014). One study argued that countries with relatively low food safety
standards import from countries with higher standards (Sun et al., 2014). Changes in standards inthe importing country therefore do not have a significant impact on international trade.
-5 -Other studies on trade policy issues have introduced negative externality to the political
economy model of trade policy-making and explored the political economy factors that lead to
MRLs being too high or too low (Marette & Beghin, 2010; Swinnen & Vandemoortele, 2011).
Another study used ordered logit and Probit regressions to examine whether MRLs have become
trade protection policy tools (Farnsworth, 2012). It found that MRLs showed no overall protective
tendency at the national or industrial level, but might be used to protect individual products. Li
and colleagues proposed “protectionism indices” to measure the degree of protection of MRLs
(Li & Beghin, 2014). They used these indices to construct a political economy model andinvestigate the substitution of MRLs for tariffs (Li et al., 2017). They believe that MRLs havebecome a trade protection policy tool to replace tariffs. A case study on China’s dairy import
standards found that China’s domestic import rules affected the development of global norms forfood safety (Augustin-Jean & Xie, 2016). As a policy response to a problem with milk in 2008,
China has improved the standards of dairy products to enhance the competitiveness of its domestic
industries and maintain social stability. This has also reshaped the international market at theadvantage of its own producers.
China achieved its tariff reduction target and joined the WTO in 2001, committing toongoing reductions in agricultural tariffs and non-tariff measures (NTMs). China’s move frombeing a net exporter of agricultural products to a net importer in 2004 was attributed to both fooddemand growth and low tariffs. The average import tariff rate on agricultural products has fallen
to about 15%, about one-quarter of the global average, and remains at that level. In early 2009,
China comprehensively fulfilled its import tariff reduction commitments. In the same year, itestablished a food safety law, which defines the food governance system, including MRLs.
During 2009–2019, China issued four new standards for MRLs. The number of standard items
increased from 873 in 2008 to 7107 in 2019. The pace of internationalization of China's MRLs is
also accelerating, as it moves from a position of receiving international standards to being a
critical participant and leader. It became the chair of the Codex Committee on Pesticide Residues(CCPR) in 2006, and has hosted 14 CCPR conferences until 2021 1 . Several experts from China
represent the Asia–Pacific region in the United Nations Food and Agriculture Organization,
which is responsible for pesticide management and setting standards. The principles, methods,
and data requirements of pesticide residue dietary risk assessment in China are in line with CAC
international standards. In total, 11 MRLs from China have been adopted as CAC international
standards. The number of MRLs in China exceeds the CAC international standard, and 90% of
the standards there are equal to or stricter than the CAC international standard (Jiang et al., 2018).

1

Sourced from official report: The 52nd annual meeting of the Codex Alimentarius Committee on

Pesticide residues was held online.
-6 -China is now the world’s leading net importer of agricultural products. Large-scale
agricultural imports threaten agricultural employment and farmers’ income and bring newchallenges to food security and trade disputes (Zhu et al., 2018). With the loss of comparativeadvantage in agricultural products and the continuous expansion of the trade deficit, the questionof whether Chinese government agencies will use trade protection tools other than import tariffs
has attracted much attention. Scholars have examined the adjustment of China’s trade policy
following tariff reduction. One study found that China actively uses other policy tools to replace
tariffs and extend the protection for domestic producers (Garred, 2018). Current research on foodsafety standards mainly focuses on the formulation of standards in developed countries and theirimpact on global trade. Few studies have examined the changes in China’s agricultural products
import trade policy. Scholars in China also tend to focus on the impact of developed countries’
MRLs on China’s agricultural exports. There is therefore very little research on China’s own
MRLs. The MRLs database of the US Department of Agriculture (USDA) is widely used inresearch, but this only includes the MRLs of pesticides prescribed by the United States (Li &Beghin, 2014). There is therefore no way of assessing the rapid development of MRLs in China.
This thesis considers the following questions:- What is the trend in MRLs in China?
- What are the main factors affecting the evolution of MRLs in China?
- Will MRLs replace decreasing import tariffs in China as a new tool in trade protection
policy?
To obtain more objective and complete data for analysis, we drew on historical documents
to establish an MRLs database for China and the CAC, and analyzed the evolution of China’s
MRLs compared with CAC standards. We found that MRLs have not replaced tariffs as a new
trade protection policy tool. There may also be some misleading implications from using so-called
protectionism indices to measure the use of MRLs.
Section 2.2 is the description of data sources. Section 2.3 analyzes the evolution
characteristics of MRLs in China based on our database, and introduces the relative stringent
index. Section 2.4 discusses the basic motivation of the changing MRLs in China by applying theregression model proposed. Section 2.5 is the conclusion and prospect.

2.2 Data descriptions
To obtain complete historical data about Chinese MRLs, we collected and reviewed the four
versions of MRLs issued by the government from 2005 to 2019. They were "The National
Standard for Food Safety—Maximum Residue Limits of Pesticides in Food (GB 2763-2012),(GB 2763—2014), (GB 2763—2016) " and " National food safety standard Maximum residue-7 -limits for 43 pesticides in food (GB 2763.1—2018)" 2 . Each new standard replaced the previous
version. The standards in the four documents were classified by product, pesticide and year togive a complete MRLs Database of China (2008–2019), with a total of 20396 MRLs.
Relevant data about CAC standards were obtained from the Codex pesticides residues infood online database 3. However, this database only provides the most recent annual list of MRLs.
To obtain complete historical data from 2008 to 2019, we used the 2008 CAC international
standards from the “Residue limits for pesticides and veterinary drugs in foods of major trading
countries and regions (in Chinese)”, and sorted them by product and pesticide (Wang, 2010). We
then used the updated versions for each year after that to provide a new list. This gave a databaseof MRLs set by the CAC (2008–2019), which includes 40555 MRLs.
To compare the differences and stringency between Chinese and CAC standards, wematched the food names and pesticide names of the two standards. The food names in the CAC
standard are in the Codex Classification of Foods and Animal Feeds 4, and those in the Chinese
standard are in Appendix A - Food Category and Testing Site (in Chinese) of The National
Standard for Food Safety-Maximum Residue Limits of Pesticides in Food (GB 2763) . We used
these to match the food names in the two standards. To match the pesticide names, we modified
some pesticide items in the Chinese MRLs based on CAC standard. For example, the pesticides
Fenvalerate and Esfenvalerate are combined in the China list and separated in the CAC list. We
modified China's pesticide items into separate ones in accordance with the MRLs of CAC. The
reverse approach is taken with some other items.
Both Chinese and CAC standards have repetitive MRL items. For the same pesticide,different MRLs may be specified for major and subordinate categories of a food product. We
selected the MRL value of sub-categories of food as representatives. For example, for thepesticide dichlorvos, the MRL in 2018 in China was 0.1 mg/kg for grains, but 0.2 mg/kg for corn.
We used the MRL for corn as representative.
For some pesticides, MRLs may only stipulate the standards for major categories of products
but not sub-categories. To supplement the missing data, we applied the MRLs for the major
categories downwards to the products in the sub-categories. Take Endrin as an example. China
stipulates Endrin MRLs of cucurbitaceous vegetables are 0.05mg/kg, and this MRLs should be2

Sourced from government standards: National food safety standard—Maximum residue limits forpesticides in food (GB 2763-2012); (GB 2763-2014); (GB 2763-2016); National food safety standard
Maximum residue limits for 43 pesticides in food (GB 2763.1-2018))
3

Sourced from online database: Codex Pesticides Residues in Food Online Database

4

Sourced from CAC standards: Codex Classification of Foods and Animal Feeds (CAC/MISC 4–

1993).
-8 -added to the zucchini and other products included in cucurbitaceous category. A similar operationis performed on the CAC data. This increased the number of standard items in both Chinese and
CAC standards (Table 2.1).

2.3 Stringency of MRLs
2.3.1 Evolution of MRLs in China
From 2008 to 2019, China issued three new sets of MRL standards, in 2013, 2014, and 2018.
We selected these three years as the observation years. The document “National food safety
standard Maximum residue limits for 43 pesticides in food (GB 2763.1—2018)” is the
supplementary version of “National food safety standard—Maximum residue limits for pesticides
in food (GB 2763—2016)”. We integrated the two versions into a complete Chinese MRL
standard for 2018 and compared with the CAC international standard for the same year. Taking
2008 as the reference year, we summarized the changes in MRLs across the three aspects of foodcategory, pesticide category, and pesticide functions.
2.3.1.1 Food category
The number of food categories in China’s MRLs has increased steadily over time. The
number of food classifications has increased from less than 60 in 2008 to 219 in 2018, narrowing
the gap to the CAC standard (307 in the same period). The food category names in China also
tend to be in line with the CAC, with some revision of food category names especially in the“National Standard for Food Safety - Maximum Residue Limits of Pesticides in Food (GB 27632012)”. For example, “unprocessed grain” was changed to “cereal grains”, “cabbage vegetables”
to “brassica vegetables”, and “small fruits” to “berries and other small fruits”.
The number of new food categories included in the standards also continues to rise. For
instance, nuts, edible fungi, seasonings, and beverages were added in (GB 2763-2012). (GB 27632014) added fruit juices, dried fruit, and medicinal plants, and dried vegetables were added by(GB 2763-2016). Food classification has also been refined to reflect Chinese culture. For example,
the addition of medicinal plants as a primary classification reflects the fact that medicinal plants
are widely used in traditional Chinese medicine. Beans are classified as cereals and vegetables in
China. In contrast, they are classified as leguminous vegetables in CAC, because beans are astaple food in China, and account for a considerable proportion of food consumption there.

-9 -2.3.1.2 Pesticide category
MRLs in China were not revised regularly, and the highest average annual increase of 64
pesticide categories was between 2014 and 2018. By contrast, the CAC standard is revised every
year, with an average of 6.4 new pesticide categories added each time. In 2013, there wasconsiderable development of China’s MRLs, and the largest increase (171 pesticides, 126%)
occurred in this period. Since then, the growth rate has begun to decline. The number of pesticides
in China’s MRLs exceeded the CAC list in 2013, and the gap has continued to widen. By 2018,
there were 187 more pesticide categories in China’s MRLs than in the CAC standards. There were
185 shared pesticide categories, 80% of the CAC pesticides, and 232 pesticide categories uniqueto China, or 56% of China’s pesticide categories. This shows that China’s MRLs first followed
the CAC standards for pesticide categories, but then supplemented this information to provide
standards suitable for national conditions. China’s standards show significant “localization”
characteristics while maintaining the “internationalization” direction (Table 2.1).
Table 2.1 Number of pesticide categories in Chinese and CAC standards

Categories

2008

2013

2014

2018

China

136

307 (34.2)

371 (64)417 (11.5)

CAC

166

194 (5.6)

201 (7)230 (7.25)

Both

83

122 (7.8)

164 (42)185 (5.25)

Unique to China

53

185 (26.4)

207 (22)232 (6.25)

Unique to CAC

83

72 (−2.2)

37 (−35)45 (2)

Note: The number in brackets is the average annual increase of pesticide categories compared withthe previous observation year.

2.3.1.3 Pesticide functions
The functional composition of pesticides has also changed over time, reflecting national
conditions. Like the CAC standard, the pesticides in China’s MRLs are divided into four maincategories by function: herbicides, acaricides, fungicides, and insecticides, which together covernearly 95% of the total pesticides. Table 2.2 shows that the increase in pesticide items in China
was mainly from three categories: herbicides, fungicides, and insecticides. These all exceed thenumbers in the CAC standard and show steady growth. There are slightly fewer acaricide itemsin the Chinese standard than in the CAC standards, and the gap is the smallest for this category.
There are more herbicide items in China than the CAC standards, mainly because herbicides areused more than other categories. Since the early 2000s, China has become one of the world's mostimportant producers and exporters of herbicides. The continuing decline in herbicide prices has- 10 -

encouraged farmers to use more chemical herbicides (Huang et al., 2017). The differences also
show that the agriculture sector in China faces constraints from rising labor costs because of themassive loss of agricultural population. Crop field management is developing to become laborsaving, and herbicides are frequently applied as an alternative to traditional manual weeding.
Table 2.2 Number of pesticides by function in Chinese and CAC standards

Categoryboth China and CAC

Unique to China

Unique to CAC

2008 2013 2014 2018 2008 2013 2014 2018 2008 2013 2014 2018

Herbicide

5

7

12

21

20

83

90

99

7

9

7

9

Acaricide

10

13

14

14

0

5

7

7

10

8

8

9

Fungicide

20

34

58

57

7

43

49

60

29

26

4

8

Insecticide

45

62

73

78

24

51

53

53

26

22

12

14

Other

3

6

7

15

2

3

8

13

11

7

6

5

2.3.2 Stringency indices of MRLs
2.3.2.1 Definition of stringency indicesWe used the protectionism indices proposed by Li et al. to measure the stringency of China's
MRLs standards relative to CAC international standards, and explore comparative trends (Li &Beghin, 2014). These indices have the properties of invariance to scale and regulation, convexity
in protectionism, monotonicity in MRL stringency, and boundedness. The index is also relatively
objective. The calculation formula is:𝑁𝑁

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶,𝑘𝑘𝑘𝑘𝑘𝑘 − 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶,𝑘𝑘𝑘𝑘𝑘𝑘
1
𝑆𝑆𝑞𝑞𝑞𝑞 = � 𝑒𝑒𝑒𝑒𝑒𝑒 ��𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐴𝐴𝐴𝐴,𝑘𝑘𝑘𝑘𝑘𝑘
𝑁𝑁
𝑘𝑘=1

(2.1)

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶,𝑘𝑘𝑘𝑘𝑘𝑘 , and 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶,𝑘𝑘𝑘𝑘𝑘𝑘 are the MRLs of pesticide k in product q in either the CAC

or Chinese standards in year t. Stringency indices are 𝑆𝑆𝑞𝑞𝑞𝑞 ∈ [0, 𝑒𝑒 ≈ 2.72]. When the index 𝑆𝑆𝑞𝑞𝑞𝑞 >1, the MRL for product q is more stringent in China than the CAC. A value closer to 2.72 isstricter. When the index 𝑆𝑆𝑞𝑞𝑞𝑞 = 1, the MRL of product q is the same level of stringency in China

and the CAC. When the index 𝑆𝑆𝑞𝑞𝑞𝑞 < 1, the MRL of product q is looser in China than the CAC
standard. A value closer to 0 is looser.

Equation (2.1) means that the MRLs databases for China and the CAC can be used to
calculate the stringency indices of each food. No direct comparison can be made for pesticides
that are only regulated by the CAC, or by China. To maintain the integrity and comparability of
the results, we therefore drew on previous methods to deal with this problem. If there were no- 11 -

data for a particular food in the Chinese MRLs standard database in year t, we selected other
MRLs in the database that were closest to that food in the year t as an alternative. Previous studies
have suggested that the appropriate MRL value should be the maximum MRL of the mainclassification group to which the food belongs. For pesticide items unique to China, this method
can therefore be used to fill the gaps in the MRL database of CAC (Hejazi et al., 2016; Seok etal., 2018; Shingal et al., 2021).
2.3.2.2 Stringency indices for food categories
The stringency indices of the four major food categories (cereal grains, oil, vegetables, andfruit) all indicated a consistent trend from 2008 to 2018 (see Table 2.3). Since 2014, the categories
of food with a stringency index of 𝑆𝑆𝑞𝑞𝑞𝑞 > 1 have been increasing year on year, and categories
with an index 𝑆𝑆𝑞𝑞𝑞𝑞 < 1 have continued to decrease, indicating that China's MRLs standards are
becoming more stringent than CAC international standards. In 2018, 52% of oils had an index of

𝑆𝑆𝑞𝑞𝑞𝑞 > 1, as did more than 80% of foods in the remaining three categories. It is worth noting thatgrains and oilseeds are land-intensive products, and China has lost its comparative advantage inthese products. In contrast, vegetables and fruit are labor-intensive products, and China still
maintains a specific comparative advantage. This demonstrates that the overall stringency ofChina’s MRLs has increased.
Table 2.3 Distribution of stringency indices in the four categories of food
Year
2008
2013
2014
2018

Cereal grains

Oil

Vegetables

Fruit

Sqt<1

Sqt >1

Sqt <1

Sqt >1

Sqt <1

Sqt =1

Sqt >1

Sqt <1

Sqt >1

14

8

13

5

34

2

14

28

4

(63.6%)(36.4%)(72.2%)(27.8%)(68.0%)(4.0%)(28.0%)(87.5%)(12.5%)

12

14

13

8

30

46

17

28

(46.2%)(53.8%)(61.9%)(38.1%)(39.5%)(60.5%)(37.8%)(62.2%)

5

23

10

12

11

68

8

44

(17.9%)(82.1%)(45.5%)(54.5%)(13.9%)(86.1%)(15.4%)(84.6%)

6

25

11

12

15

74

10

47

(19.4%)(80.6%)(47.8%)(52.2%)(16.9%)(83.1%)(17.5%)(82.5%)

000

Note: The value in brackets is the percentage of items in each category of stringency indices values
for the food classification in that year. Where no values are given for Sqt = 0, there were no items inthat category.

The stringency indices of the four main food categories all rose sharply in 2014 and thenstabilized (see Figure 2.1). However, the stringency of each category of food varied. That for oil
was slightly higher than one, but the increase was less than for the other three food categories.
This indicates that the overall stringency of oil is lower than that of the other three categories of- 12 -

food. The stringency indices for cereal, vegetables, and fruit are all slightly higher than 1.50 but
lower than 1.70, which is far lower than the upper bound of 2.72. The stringency of MRLs in
China therefore shows an overall upward trend, but its overall level is not high.
Stringency Indices

2
1.5
1
0.5
0

Cereal grains

Oil
2008

2013

Vegetables
2014

Fruits

2018

Figure 2.1 Distribution of the stringency indices for selected four products

2.4 Motivation of China’s changing MRLs
We wanted to assess whether differences in stringency meant that China is using MRLs as aform of trade protection. The alternatives are that increased per capita income means that the
Chinese public has higher requirements for food safety, or that the Chinese government isdetermined to address food safety issues more strictly. We used regression analysis to assess theseissues.

2.4.1 Theoretical analysis
The political economy model for MRLs provides an analytical framework for our empiricalanalysis (Li et al., 2017). In this model, more stringent MRLs will directly increase the costs offood producers while reducing the social cost of negative externality. The goal of government
agencies is to determine the optimal level of MRLs by maximizing social welfare. This consistsof the weighted sum of food consumer surplus, food producer profit, and negative externality. We
can use the first-order conditions for maximizing the objective function to derive the final impactof import tariffs, comparative advantages, market size, and other factors on MRLs. We also
further identified the influencing factors into three categories: trade protectionism, demand
upgrade, and crisis response. The next few paragraphs describe each of these in turn.
As the national economy continues to grow, a country’s agricultural and food industry will
gradually lose its comparative advantage. Food imports will expand, and the income and profits
of food producers will therefore decline. The reduction of import tariff leads to a further decreasein food producers’ profit compared with consumer surplus, reducing social welfare. However, thepolitical pressure of food producers for protection will lead to an increase in the weight of their- 13 -

profits in social welfare. To maximize social welfare, government agencies may use more
stringent non-tariff measures to replace import tariffs. This should alleviate the competition fromimported food and improve the profits of domestic food producers. When China joined the WTO,
it promised to reduce tariffs. This has gradually happened over time, and China now has arelatively low tariff level. As agriculture sectors continues to open up to the world market, China
has become the world's leading net importer of agricultural products, and the deficit continues toincrease. Large-scale agricultural imports threaten agricultural employment and farmers’ incomes,
bringing new challenges to food security and trade disputes (Zhu et al., 2018). China therefore has
motivation to use MRLs as a new trade protection tool instead of import tariffs, and we considered
three hypotheses.
Hypothesis 1: The gradual loss of food comparative advantage may encourage China to keep
strengthening MRLs, resulting in rising stringency indices and the tendency of MRLs to support
trade protectionism.
Hypothesis 2: The expansion of food imports may encourage China to keep strengthening
MRLs, resulting in rising stringency indices and the tendency of MRLs to support tradeprotectionism.
Hypothesis 3: The reduction of food import tariffs may encourage China to keep
strengthening MRLs, resulting in rising stringency indices and the tendency of MRLs to support
trade protectionism.
The second possible motivation is demand upgrade. When the scale of the food market
represented by per capita income in a country expands, the absolute scale of consumer surplus
and reduced negative externalities will increase. They will therefore make up a greater proportionof social welfare. Government agencies aiming to maximize social welfare will pay more
attention to consumers’ food safety and thus develop more stringent MRLs. The past four decades
have been a period of rapid income growth in China, during which the country has moved frommiddle-income to upper-middle-income status. The sustained growth of food demand (marketsize) is accompanied by increased demand from the public for food safety (reduction of negative
externalities). We therefore proposed:
Hypothesis 4: With the increase in per capita income, the domestic demand upgrade
encourages China to keep strengthening MRLs, resulting in rising stringency indices to meet thehigher demand from the domestic public for food safety.
Finally, we considered crisis response as a motivation. The expansion of commercial media
will lead to increased coverage of food safety issues and lower the threshold for public access to
information (Swinnen, 2018). This effectively increases public interest in food safety andimproves knowledge about the issue, which in turn increases public demand for better food safety(reduction of negative externalities). Media reports will create public opinion pressure that reflects
the public’s food safety demands, which will in turn drive government agencies to formulate more
- 14 -

stringent MRLs to elevate the weight of consumer surplus and negative externalities in social
welfare. Government agencies may also consider adopting more stringent or even over-stringent
standards following the principle of “political precaution”, the principle that politicians are forced
to act in response to public perceptions, even if the actual risk is very low (Yeung et al., 2017).
Food safety incidents have occurred frequently in China over the past two decades, and these havebeen extensively reported by both the official and commercial media. Chinese government
agencies comprehensively strengthened food safety control after a problem with tainted powderedmilk in 2008 (Augustin-Jean & Xie, 2016). Public demand for food safety and the pressure of
public opinion could therefore force the Chinese government to resolve the food safety crisis andstrengthen food safety governance. We therefore hypothesized:
Hypothesis 5: The occurrence of food safety incidents and related media reports may force
the Chinese government to improve the stringency of MRLs and strengthen food safety
governance, resulting in rising stringency indices.

2.4.2 Regression model
Based on the hypothesis above, the regression model is constructed as:
𝑆𝑆𝑞𝑞𝑞𝑞 = 𝑎𝑎0 + 𝑏𝑏1 ln(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 ) + ln(𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡−1 ) + 𝑏𝑏3 ln�𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑞𝑞𝑞𝑞 + 1�+𝑏𝑏4 ln�𝑅𝑅𝑅𝑅𝑅𝑅𝑞𝑞𝑞𝑞 � + 𝑏𝑏5 ln�𝐼𝐼𝐼𝐼𝐼𝐼𝑞𝑞𝑞𝑞 � + 𝜆𝜆𝑡𝑡 + 𝜆𝜆𝑞𝑞 + 𝜀𝜀𝑞𝑞𝑞𝑞(2.2)

Sqt is the stringency index of Chinese product q in year t, derived from the previous

calculations. a0 is a constant term, b1 to b5 are regression coefficients, λt is the fixed effect of year,
λq is the fixed effect of product, and εqt is the random error. PERGDPt is China’s per capita GDP
in year t, representing the level of per capita income, which is taken from the World Development
Indicators (WDI) database 5. Reportt−1 is the number of Chinese media reports on domestic foodsafety issues in year t−1. It expresses the time lag between media attention or reports on foodsafety incidents and the development of substantial public opinion pressure. Data were taken from
Liu et al. (Liu & Ma, 2016), but the website of database has not been updated since 2016 6. The
observations included in the analysis were therefore from 2008 to 2016. Tariffqt is the import tariff
rate imposed by China on product q in year t, taken from WTO tariff database 7 and World

5

Sourced from online database: World Development Indicators (WDI) database

6

Sourced from online database: Food scandal database of Chinese provinces

7

Sourced from online database: WTO tariff database- 15 -

Integrated Trade Solution (WITS) 8 . The tariff data used the simple average tariff rate of the foodname corresponding to the HS-6 code, which is mainly based on the WTO tariff database. Missing
data were supplemented by applying tariff data from the WITS, and some countries have importtariffs of zero. RCAqt is the comparative advantage index of product q in year t, calculated using
the UN Comtrade Database 9. IMVqt is China’s food imports of product q in year t, taken from the
UN Comtrade Database. The data for the above variables between 2008 and 2016 constitutes anunbalanced panel dataset. Table 2.4 demonstrates the descriptive statistics for the variables usedin the regression analysis.
Table 2.4 Descriptive statistics for regression variables

Standard

Variable

Observations

Min.

Max.

MedianMeanln(Tariffqt+1)

1672

0

4.19

2.64

2.51

0.72

ln(RCAqt)

1723

−16.5

3.07

0.1

−0.8

2.98

ln(IMVqt)

1532

−6.21

17.51

6.68

6.85

4.38

ln(Reportt-1)

1596

3.81

6.58

5.2

5.44

0.91

ln(PERGDPt)

1824

8.15

8.99

8.69

8.63

0.29

Deviation

2.4.3 Results and discussion of regression
We used the panel data with fixed effects in Stata15 to estimate, and select robust standard
errors. We considered that there might be multicollinearity among import tariffs, food imports,
and comparative advantages. We therefore first performed a basic regression excluding the two
explanatory variables of revealed comparative advantage index and food imports, and then acomplete regression that included these two explanatory variables. The results showed nosignificant difference between the two, indicating no apparent multicollinearity between the
explanatory variables. The regression results were therefore considered robust, and we used thecomplete regression for further analysis (Table 2.5). Columns (1) to (4) are the individual
regression results of the selected products in the four food categories. Because of the highhomogeneity of products within the same food category, product fixed effect was not adopted to
avoid inconsistent estimates and include more information. Column (5) is the regression resultsof the mixed products in the four categories, and the products fixed effect of the is added in the8

Sourced from online database: World Integrated Trade Solution (WITS) database

9

Sourced from online database: UN Comtrade Database- 16 -

regression.
Table 2.5 Regression results
Explanatory

(1)

(2)

(3)

(4)

(5)

variable

CerealOil

Vegetables

Fruit

Mixed

0.50780.1971−0.2104**−0.0416−0.1125(0.634)(0.575)(0.0813)(0.102)(0.0789)0.0609−0.0150−0.0969**0.00180.0127(0.0392)(0.0842)(0.0474)(0.0275)(0.0272)−0.1707**0.01840.0001−0.0586−0.0227(0.0804)(0.0359)(0.0175)(0.0429)(0.0176)

0.3381**0.1587*0.2345***0.2431***0.2182***(0.154)(0.0831)(0.0444)(0.0736)(0.0332)

12.9900**

7.0371**

7.9214***10.7579***

8.6702***(5.894)(3.247)(1.267)(2.843)(1.131)−206.0034**−112.0096**−124.7086***−169.5371***−136.6386***(95.71)(52.09)(20.31)(45.66)(18.14)

Grainsln(Tariffqt+1)
ln(RCAqt)ln(IMVqt)
ln(Reportt−1)ln(PERGDPt)_cons
Products Fixed

yes

EffectTime Fixed Effect

Yes

yes

yes

yes

yes

Observations

138

124

398

308

968

R2

0.448

0.258

0.598

0.633

0.513

Notes: Robust standard error is in parentheses. *, **, *** show significance of 10%, 5%, and 1%.

The regression coefficient of the revealed comparative advantage index (RCAqt) and foodimports (IMVqt) were similar, but the coefficients of the RCAqt were not uniform. Except forvegetables, they were also not statistically significant. The coefficient of IMVqt is also not uniform,
except for grain, which is also not statistically significant. This means that the improvement in
the stringency of China’s MRLs is not directly related to the loss of food comparative advantages
and the expansion of imports. It also suggests that the fundamental purpose of setting stringent
MRLs in China is not to protect the domestic food industry.
Except for cereal grains and oil, the coefficient of tariffs (Tariffqt) is negative, and apart fromvegetables, not statistically significant. This means that whether we look at the four categories offood as a whole or individually, China’s MRLs generally do not show a tendency towards
protectionism. This suggests that MRLs have not replaced tariffs as a new trade protection policy
tool. As a labor-intensive product, vegetables are the second-largest agricultural product exported
by China and have obvious comparative advantages. Chinese government agencies therefore haveno motivation to use MRLs as a trade protection tool. However, with the general reduction of- 17 -

import tariffs in WTO member countries and intensified competition in the international market
for high-value products such as vegetables, China is actively participating in the formulation ofinternational standards to improve product quality and enhance the international competitivenessof vegetables by strengthening its own MRLs (Augustin-Jean & Xie, 2016).
The coefficient of per capita GDP (PERGDPt) was also positive, in line with expectations.
Except for cereal grains, the PERGDPt coefficients were all statistically significant. This showsthat as China’s per capita income level increases, and especially as China transitions from being
a middle-income country to an upper-middle-income country, the number of food imports haveincreased. As a result, public awareness of food safety and health has also significantly increased,
and they prefer high-quality and safe food, requiring lower MRLs.
The coefficient of the number of media reports on food safety issues (Reportt-1) was positiveand statistically significant. It can therefore reasonably be inferred that the frequent occurrenceof food safety incidents and ongoing media reports on food safety issues have aroused public
interest and caused the government’s focus on food safety. The government is therefore likely tobe responding to public demands for improved food safety by issuing ever-more stringent MRLs
and strengthening food safety governance (Swinnen, 2018). In China, in response to people's
concerns about food safety, the government has specially established the only official food safety
media to publicize food safety policies and administrative law enforcement, popularize scientificawareness of food safety, and timely disclose or clarify food safety incidents and their response
measures 10.
The regression results do not support Hypotheses 1 to 3 about the motivation ofprotectionism. Instead, they support Hypothesis 4 about demand upgrade and Hypothesis 5 aboutcrisis response. This means that the primary goal of China’s MRLs is to deal with food safety
problems, curb food safety incidents and ensure public safety, rather than reducing food imports
or protecting the domestic food industry, even though this is gradually losing its comparativeadvantage. We suggest that the fundamental reason for this is that the policy objectives of theChinese government focus more on consumers’ demand for food safety than food producers’
income. This conclusion is consistent with previous studies that used the standard policy
preference function to measure the weight of import policy preference of primary agricultural
products (Mao et al., 2019). That study found that the priority of import trade policy is consumers,
producers, traders, and government, in that order. Import policies focus on the interests ofconsumers rather than other economic agents, whose welfare may even be reduced. In other words,
Chinese government agencies give much more weight to consumer surplus and negative
externalities than to the income of food producers when setting MRLs. The stringency of MRLs

10

Sourced from official website: China Food Safety Net, https://www.cfsn.cn/- 18 -

is not directly related to the sharp decline in import tariffs and comparative advantages or thecontinuing increase in food imports. However, it appears to be related to the level of per capita
income and media coverage of food safety incidents.

2.5 Conclusion and prospect
By collecting and processing the data of previous studies and official updates on China's
MRLs standards, we obtained a relatively new and complete database of Chinese MRLs, and used
it to analyze the evolution of these standards. The MRLs developed rapidly in China from 2008
to 2019, and the food and pesticide items covered maintained a sustained and rapid expansion.
The variety of foods included has increased from less than 60 to 219, and the gap with CAC
international standards has narrowed. The types of pesticides increased to 417, exceeding the
CAC international standard of 187. The actual number of items has increased from 859 to 8388,
an increase of 933%, which is the same as in the CAC international standard. The revision ofChina’s MRLs has drawn on the CAC international standard, reflecting a growing trend towards
internationalization. The adjustment of food and pesticide items unique to China reflects thedistinct characteristics of Chinese localization, such as agricultural production, food security
requirements, and the change of food consumption structure.
We measured the stringency of the Chinese MRLs compared with the CAC standards using
Stringency Indices. The stringency indexes of about 80% of China’s MRLs were greater than one,indicating higher stringency than the CAC standards. However, the high stringency indices do notprove that China is using MRLs as non-tariff measures or for trade protectionism. The regression
results indicate that the factors driving the rise in stringency indices are actually public demandfor higher food safety and the government’s positive response to food safety incidents. We
therefore conclude that China’s MRLs have not become a new trade protection policy tool, andare not being used as an alternative to a tariff. In the long run, the growth in China’s demand forfood and the constraints of agricultural resources determine that the trends in food import and
MRL development is irreversible.
We cannot ignore the role of media reports in reflecting public food safety demands,
increasing public food safety awareness, and promoting the revision of food safety standards.
Government agencies should take the initiative to use media channels to guide public participationin the supervision and formulation of food safety standards, so that relevant government
departments improve their governance. In the era of rapid information dissemination via theinternet, timely and transparent disclosure of food safety incidents, and scientific popularizationof food safety knowledge is helpful in avoiding irrational public focus on food safety, which
drives politicians to take excessive action as a result of “political precaution”, resulting in
unreasonably high food safety standards.
- 19 -

Our study shows that the so-called “protectionism indices” can only measure the relative
stringency of MRLs and the CAC standards (Li & Beghin, 2014). We have shown that differences
may have other causes. Using these indices as a proxy for protectionism may therefore be
misleading. The use of the term “stringency indices” may be more neutral and objective, as well
as more accurate. We believe that the impact of MRLs on international food trade requires
comprehensive research into its effects on food imports and exports, price, quality, and even social
welfare, which will be the focus of our future research.

- 20 -

Chapter 3 Influence of maximum residue limits forpesticides on agricultural food exports: analysis
based on a quality heterogeneous firms trade model
3.1 Introduction
With the exponential increase in international food trade of the past few decades, pesticide
residues have become widely regarded as an important factor affecting the quality and safety ofagricultural products. Maximum residue limits (MRLs) for pesticides are defined as the maximum
concentration of legally allowable pesticide residues in or on food commodities and animal feeds(expressed in mg/kg) recommended by the Codex Alimentarius Commission (CAC). Food safetyincidents around the world have triggered extensive media reports, exposing long-standing
problems, such as excessive pesticides in imported or domestic foods and illegal use of
unregistered pesticides. Enhanced public awareness of food safety and public pressure has
prompted governments to formulate more extensive and stringent MRL standards for pesticides
as mandatory food import standards (Swinnen, 2018). Japan announced the “Japanese Positive
List System for Agricultural Chemical Residues in Foods” in 2003 and implemented the system
in May 2006. European Union (EU) member states have adopted unified EU MRLs since 2008.
In the same year, Canada passed new MRLs. Australia, Hong Kong, and South Korea
successively announced new MRLs from 2010 to 2019. Based on the consideration of domestic
dietary habits, politics, economy, and other factors, the MRLs of some developed countries andregions deviate from the CAC international standard of the Agreement of Sanitary andPhytosanitary Measures of the World Trade Organization, which has led to a continuous reductionof coordination of MRLs among countries, and related agricultural trade disputes have continued
to rise. The international community is concerned about whether MRLs have in effect replaced
gradually reduced tariffs promised by countries and become a new form of trade protection policy
tool. This issue has triggered a large body of empirical research on the impact of MRLs on trade.
The gravity model is mostly used in empirical studies on international trade. Most research
focuses on whether the MRLs of importing countries hinder imports of agri-products. The maindifference in the research results lies in the quantification of the core explanatory variable but
others include differences in the various countries, agri-products, and time periods. Early scholars
used the MRLs of important pesticides in importing countries as the explanatory variable, andlater scholars mostly used bilateral heterogeneity (similarity) indexes of MRLs in importing andexporting countries as the explanatory variable, considering that bilateral divergence, and not the
MRLs of importing countries only, is the fundamental factor affecting trade. These indexes- 21 -

integrate the number and level of MRL standards and thus, can comprehensively reflect therelative stringency of MRLs in importing and exporting countries.
Although some scholars have found that more stringent MRLs in importing countries inhibit
imports (C. L. Chen et al., 2008; Drogué & DeMaria, 2012; Ferro et al., 2015; Fiankor et al., 2021;
Hejazi et al., 2016; Winchester et al., 2012), whereas others have found that they do not hinder
or even promote trade (Ishaq et al., 2016; Shingal et al., 2021; Xiong & Beghin, 2012; Xiong &Beghin, 2014). In addition, some scholars have found that more stringent MRL standards inexporting countries promote exports (Seok et al., 2018; Shingal et al., 2021), which is attributed
to the signaling effect of conveying product quality. Regarding the divergence in empirical studies,scholars have identified dual effects; that is, food safety standards, including MRLs (Shingal etal., 2021; Swinnen, 2018; Xiong & Beghin, 2014), not only increase compliance costs that impede
trade, but also promote trade by reducing information asymmetry or negative externalities. The
impact of food safety standards on trade depends on the comprehensive effect of these two factors
and varies by country or product. Dual effects have been introduced into the empirical trade modelto analyze their impact on trade, but there are fewer theoretical analyses amid the rich empiricalliterature.
Chen et al. analyzed the comprehensive influence of dual effects on export probability,
export market quantity, and export product category margin in a perfect competition model (M.
X. Chen et al., 2008). Xiong and Beghin used the monopoly competition model to analyze andverify the existence of demand-enhancing and trade-cost effects (dual effect) but did not consider
their comprehensive influence (Xiong & Beghin, 2014). These two studies did not properly
account for the large number of zero-trade values in trade data at the industrial and product levels,
which can be addressed by introducing firm heterogeneity and analyzing the intra-industry
adjustment caused by food safety standards. Medin used the heterogeneous firms trade (HFT)
model to analyze the impact of food safety standards on the number of export firms, averageexports of firms, and total industrial exports (Medin, 2019). However, a limitation of their study
is the function setting of the variable cost and fixed cost, which means that the dual effect can bedivided only in two independent ways: demand and variable cost, and demand and fixed cost. The
comprehensive influence of the three factors cannot be considered.
Existing empirical studies mainly focus on the impact of MRLs in developed countries on
trade rather than on the relativity of standards between importers and exporters. Outdated timeand limited samples do not reflect the rapid development of food safety standards in developing
countries in a timely manner. Considering that exports of agri-food products account for a large
proportion of the trade share of developing countries, food safety standards will have a profound
impact on the industry and trade of agri-food products, which would have a social and systematic
influence on the development of rural areas, agriculture, and farmers. As a developing country,
China was the world’s second largest importer and fourth largest exporter of agricultural products- 22 -

in 2020. From 2010 to 2021, China updated the national mandatory MRLs standard, the “National
Standard for Food Safety—Maximum Residue Limits of Pesticides in Food,” five times. The
MRLs items specified in the standard increased from 873 in 2008 to 10092 in 2021, nearly doublethat of the CAC in 2020. The number of agri-food products increased from 88 to 377, including
119 agri-food products unique to China. The items of pesticides increased from 138 to 564, andstandards for all registered pesticides are expected to follow. The standard also stipulates MRLs
for 87 pesticides that are not prohibited but have not been approved for use in China 11. After morethan a decade of development, the principles, methods, and data requirements of dietary risk
assessment of pesticide residues adopted in China’s MRLs have gradually aligned with those of
the CAC and developed countries. The EU is recognized as having the most stringent MRL
standards; its MRLs are regarded as an important obstacle to exports of agri-food products.
Studying the impact of MRL differences between China and the EU on exports of agri-products
would help enrich understanding of food safety on trade effects and provide rich implications.
The quality heterogeneous firms trade (QHFT) model is based on the HFT model proposedby Melitz (Melitz, 2003). Some scholars have considered that the difference between enterprises
lies not only in productivity, but also in quality; therefore, the influence of quality on the export
decisions of enterprises should be considered (Baldwin & Harrigan, 2011). This study contributes
to both theoretical and empirical analyses on the topic of MRLs for pesticides. For the theoreticalanalysis, we construct a concise QHFT model. We extend the trade effect of MRLs from two
factors (demand and cost) to three factors (demand, variable cost, and fixed cost). Based on thismodel, a reasonable gravity model that can effectively treat zero-trade values is obtained. For theempirical analysis, heterogeneity indexes are used to measure and compare the stringency of
MRLs between China and the EU. A relatively complete industry-level database of agri-foodproducts in China and the EU is used to analyze the comprehensive effects of MRL standards on
trade. To compensate for the data deficiency in previous studies, we expand the analysis of time,
products, and pesticides. This study finds that the current MRL standards of the EU are still much
more stringent than those of China, but the gap is narrowing, and some MRLs in China evenexceed those in the EU. The EU’s more stringent MRLs have not curbed food exports from China
to the EU, and for cases in which China’s MRLs are more stringent, there is a signal effect (quality
upgrade), which plays a strong demand-enhancing role in promoting China’s food exports to theEU.
Section 3.2 of this thesis is the theoretical model of QHFT, Section 3.3 is the empirical model,
Section 3.4 is the data source, focusing on the explanation of the heterogeneity indices, and11

Sourced from official report: Analysis of the National Standard for Food Safety - Maximum

Residue Limits of Pesticides in Food (2021 Edition)- 23 -

Section 3.5 is the methods and results of estimation. Section 3.6 is the conclusion and future
research.

3.2 Theoretical model
The Chaney model is a simplified Melitz model that is widely used in trade-margin analysis(Chaney, 2008). It assumes that the number of exporting companies is given exogenously andomits the free-entry condition in the Melitz model, thereby focusing on the analysis of the export
behavior of a company and its zero-profit condition (ZPC). This study sets up a concise QHFT
model by introducing the dual effects of MRLs on trade into the Chaney model. The differencein MRLs between importers and exporters affects consumers’ preferences, variable cost, and fixed
cost. We find that the overall impact of MRLs on agri-food trade depends on the three factors
mentioned above. The model can be further divided into two cases, in which the MRLs of theimporter or exporter are more stringent. Stricter MRLs in the importing country are more
complicated, and the following derivation is mainly based on this case, which can be easily
extended to the more stringent case of MRLs in the exporting country.

3.2.1 Stricter MRLs in importer
3.2.1.1 Import demand
We assume that there are 𝑁𝑁 importers. The consumers in each importer 𝑗𝑗 have a constantelasticity of substitution (CES) utility function,Ω

𝑈𝑈 = ��

0

𝜖𝜖
𝜖𝜖−1
𝜖𝜖−1
𝜃𝜃(𝑣𝑣)𝛽𝛽 𝑞𝑞(𝑣𝑣) 𝜖𝜖 𝑑𝑑𝑑𝑑�

(3.1)

where Ω represents the available varieties of agri-food products in importer 𝑗𝑗; 𝑞𝑞 and 𝜃𝜃 denotethe quantity and quality of agri-food product 𝑣𝑣 consumed, respectively; 𝜖𝜖 is the substitution
elasticity between agri-food products with horizontal differentiation; and 𝜖𝜖 > 1 . Verticaldifferentiation also exists between the same agri-food products, which have a different quality 𝜃𝜃.

Assuming that the importer has formulated the mandatory MRL standard 𝜃𝜃𝑗𝑗 , all product 𝑣𝑣 in theimporter market must meet this standard. Hence, the MRL standard 𝜃𝜃𝑗𝑗 determines the quality 𝜃𝜃of agri-products in the importer market. Consumers can perceive this quality difference and reactaccordingly, and firms are not motivated to exceed the standard because of additional costs.
Specifically, when the MRL standards of the importing country are more stringent, the exporting

firms must comply with the more stringent MRLs to produce, which convinces consumers in the- 24 -

importing country that the quality of imported agri-products has improved compared with that
produced according to the MRLs of the exporting country (Fiankor et al., 2021). More stringent
MRLs enhance consumers’ preferences for the quality of imported agri-products and increase
their consumption demand, which is the demand-enhancing effect of MRLs. β > 0 indicatesconsumers’ perception and acceptance of the quality represented by MRLs by the importer.

We assume that the total expenditure on agri-food products of consumers in importer 𝑗𝑗 isΩgiven by 𝐸𝐸𝑗𝑗 = ∫0 𝑝𝑝(𝑣𝑣)𝑞𝑞(𝑣𝑣) 𝑑𝑑𝑑𝑑 . Consumers maximize the utility function subject to total

expenditure. Then, the equilibrium demand for the agri-food product 𝑣𝑣 produced by country 𝑖𝑖 isgiven by𝛽𝛽𝛽𝛽−𝜖𝜖 𝜖𝜖−1
𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗
𝑞𝑞𝑖𝑖𝑖𝑖 = 𝜃𝜃𝑗𝑗 𝑝𝑝𝑖𝑖𝑖𝑖(3.2)

Where 𝑝𝑝𝑖𝑖𝑖𝑖 is the price of agri-food product 𝑣𝑣 from exporter i to importer j. 𝑃𝑃𝑗𝑗 =1

Ω[∫0 𝑝𝑝(𝑣𝑣)1−𝜀𝜀 𝑑𝑑𝑑𝑑]1−𝜖𝜖 is defined as the price index of all agri-food products 𝑣𝑣 in importer 𝑗𝑗.
3.2.1.2 Export supply

Assume that an exogenous variable 𝑛𝑛𝑖𝑖 denotes the number of producers of agri-products 𝑣𝑣

in exporting country 𝑖𝑖. Production technology increases returns to scale. Labor is the only factor

of production and it moves freely among the differentiated sectors. The wage rate can be set equal
to one. The cost function for an enterprise to produce and export agri-food products v is𝐶𝐶𝑖𝑖 (𝑣𝑣) =𝜏𝜏𝑖𝑖𝑖𝑖 𝜃𝜃𝑗𝑗𝛼𝛼
𝜑𝜑

(3.3)

𝑞𝑞𝑖𝑖𝑖𝑖 + 𝑓𝑓𝑖𝑖𝑖𝑖

where 𝜑𝜑 is productivity, which obeys the Pareto distribution on [1, +∞) with the parameter 𝛾𝛾,

and 𝛾𝛾 is positively correlated with the mean and variance of enterprise productivity. We assume𝛾𝛾that the lower bound of productivity is 𝜑𝜑𝑚𝑚𝑚𝑚𝑚𝑚 , and the density function is g(𝜑𝜑) = 𝛾𝛾𝜑𝜑𝑚𝑚𝑚𝑚𝑛𝑛 𝜑𝜑−𝛾𝛾−1 .

If the lower bound of productivity is normalized to unity, the density function is simplified tog(𝜑𝜑) = 𝛾𝛾𝜑𝜑−𝛾𝛾−1 .

𝜏𝜏𝑖𝑖𝑖𝑖 𝜃𝜃𝑗𝑗𝛼𝛼
𝜑𝜑

𝜃𝜃

𝜂𝜂

𝑞𝑞𝑖𝑖𝑖𝑖 is the variable cost, 𝑓𝑓𝑖𝑖𝑖𝑖 = 𝜂𝜂𝑗𝑗 𝐷𝐷𝑖𝑖𝑖𝑖 is the fixed cost, where 𝜏𝜏𝑖𝑖𝑗𝑗 is the advalorem (variable) trade cost from exporter 𝑖𝑖 to importer 𝑗𝑗, that is, the “iceberg cost,” including

tariffs. 𝐷𝐷𝑖𝑖𝑖𝑖 is the trade cost resulting from the differences in distance, language, religion, and soon between importers and exporters. 𝜃𝜃𝑗𝑗𝛼𝛼 is the additional input of production factors generated by
𝜂𝜂

𝜃𝜃𝑗𝑗

satisfying the quality standard of country j, and 𝜂𝜂 represents the incremental fixed costs incurredby exporters to operate in country j of stricter MRL standards, such as new technologies applied- 25 -

or labor training, assuming that both are specific to the quality level 𝜃𝜃 embodied in the standard.

Specifically, when the importing country’s MRLs are more stringent, the exporter must fullycomply with the importer’s stricter MRLs. This incurs additional compliance costs 𝜃𝜃𝑗𝑗𝛼𝛼 and

𝜂𝜂

𝜃𝜃𝑗𝑗
𝜂𝜂

over production complying with the MRLs of the exporting country; these are the variable andfixed trade-cost effects of MRLs, respectively. 𝛼𝛼 > 0 and 𝜂𝜂 > 0 indicate the impact of the
MRLs of the importing country on variable cost and fixed cost, respectively.

𝜋𝜋𝑖𝑖 = �𝑝𝑝𝑖𝑖𝑖𝑖 −𝜏𝜏𝑖𝑖𝑖𝑖 𝜃𝜃𝑗𝑗𝛼𝛼
𝜑𝜑

� 𝑞𝑞𝑖𝑖𝑖𝑖 − 𝑓𝑓𝑖𝑖𝑖𝑖(3.4)

The firm chooses a price to maximize its profit. The first-order condition is𝜏𝜏𝑖𝑖𝑖𝑖 𝜃𝜃𝑗𝑗𝛼𝛼 𝜕𝜕𝑞𝑞𝑖𝑖𝑖𝑖
𝜖𝜖 𝜏𝜏𝑖𝑖𝑖𝑖 𝜃𝜃𝑗𝑗𝛼𝛼
𝜕𝜕𝜕𝜕= 𝑞𝑞𝑖𝑖𝑖𝑖 −= 0 ⇒ 𝑝𝑝𝑖𝑖𝑖𝑖 =𝜑𝜑 𝜕𝜕𝑝𝑝𝑖𝑖𝑖𝑖
𝜖𝜖 − 1 𝜑𝜑𝜕𝜕𝑝𝑝𝑖𝑖𝑖𝑖(3.5)

It can be seen from (3.5) that the price of agri-food products v is set equal to the mark-up
over the marginal cost. pij depends on the firm’s productivity and the impact of the importing
country’s MRLs on the variable costs. According to (3.2) and (3.5), the exports of each firm canbe given by:𝛽𝛽𝛽𝛽𝜖𝜖 1−𝜖𝜖 𝜏𝜏𝑖𝑖𝑖𝑖 1−𝜖𝜖 𝛽𝛽𝛽𝛽−𝛼𝛼(𝜖𝜖−1) 𝜖𝜖−1
) ( ) 𝜃𝜃𝑗𝑗
𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗
𝜑𝜑𝜖𝜖−11−𝜖𝜖 𝜖𝜖−1𝑝𝑝𝑖𝑖𝑖𝑖 𝑞𝑞𝑖𝑖𝑖𝑖 = 𝜃𝜃𝑗𝑗 𝑝𝑝𝑖𝑖𝑖𝑖
𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗 = ((3.6)

It can be seen from (3.6) that the impact of MRLs on the firm’s exports depends on whether
𝛽𝛽𝛽𝛽 − 𝛼𝛼(𝜖𝜖 − 1) > 0 or 𝛽𝛽𝛽𝛽 − 𝛼𝛼(𝜖𝜖 − 1) < 0 , In other words, whether the demand-enhancing

effect or the variable trade-cost effect dominates. Assuming that the elasticity of substitution 𝜖𝜖 isconstant, if 𝛼𝛼 is large enough, the variable trade-cost effect dominates, and the firm's exports
decrease with stricter MRLs. On the contrary, if 𝛽𝛽 is sufficiently large, the demand-enhancing

effect dominates and the firm’s exports increase. Moreover, firms productivity 𝜑𝜑 is positively
related to the firm’s exports.

3.2.1.3 Total exports of agri-food products
By setting the firms’ ZPC in (3.4) and using (3.2), (3.3), and (3.5), we can obtain the cut-off
productivity for exporting (cpe) that the firms of country i need to achieve when exporting tocountry j:- 26 -

[𝜂𝜂+α(ϵ−1)]−βϵ
𝜖𝜖−1

1
𝜖𝜖
𝜂𝜂
𝜖𝜖
)1−𝜖𝜖 ( )𝜖𝜖−1 𝜃𝜃𝑗𝑗
𝜖𝜖−1
𝜖𝜖−1

𝜑𝜑 = (

11

𝜖𝜖−1 −1 1−𝜖𝜖𝜏𝜏𝑖𝑖𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗(3.7)

From (3.7), we know that cpe increases with ad valorem (variable) trade costs, such as importtariffs, and fixed trade costs, such as languages, and decreases with the income of importer 𝑗𝑗. The

impact of MRLs on cpe is uncertain, depending on whether [𝜂𝜂 + 𝛼𝛼(𝜖𝜖 − 1)] − 𝛽𝛽𝛽𝛽 > 0 or [𝜂𝜂 +𝛼𝛼(𝜖𝜖 − 1)] − 𝛽𝛽𝛽𝛽 < 0 , that is, whether the demand-enhancing or trade-cost effects dominate.

Assuming that the substitution elasticity 𝜖𝜖 is constant, if 𝛼𝛼 and 𝜂𝜂 are sufficiently large relativeto 𝛽𝛽, the trade-cost effect dominates, and cpe increases with the MRLs. This means that thethreshold of exports increases with the MRLs, some export firms exit, and the number of export

firms decreases. Conversely, if 𝛽𝛽 is sufficiently large, the demand-enhancing effect dominatesand cpe decreases with the MRLs. The threshold for exports decreases with the MRLs, some nonexporting firms enter, and the number of export firms increases. The MRLs lead to adjustments
within the firms by affecting the entry or exit choices of the firms, which is the fundamental
difference between the QHFT and the monopoly competition model.
Integrating (3.6), that is, adding up the exports of all export firms, the total exports of agrifood products 𝑣𝑣 exported by exporter 𝑖𝑖 to importer 𝑗𝑗 can be obtained as∞

𝐸𝐸𝐸𝐸𝐸𝐸𝑣𝑣𝑣𝑣𝑣𝑣 = � 𝑛𝑛𝑗𝑗 𝑝𝑝𝑖𝑖𝑖𝑖 𝑞𝑞𝑖𝑖𝑖𝑖 𝑔𝑔(𝜑𝜑)𝑑𝑑𝑑𝑑 =𝜑𝜑𝛽𝛽𝛽𝛽−[𝛼𝛼(𝜖𝜖−1)+𝜂𝜂(𝛾𝛾−𝜖𝜖+1)]𝜖𝜖−1𝛾𝛾𝜂𝜂 𝜖𝜖−𝛾𝛾−1 𝜖𝜖 𝜖𝜖−𝜖𝜖𝜖𝜖−1( ) 1−𝜖𝜖 ( ) 𝜖𝜖−1 𝑛𝑛𝑗𝑗 𝜃𝜃𝑗𝑗
𝛾𝛾−𝜖𝜖+1 𝜖𝜖−1
𝜖𝜖−1

−𝛾𝛾

𝛾𝛾−𝜖𝜖−1−𝛾𝛾𝛾𝛾

𝜏𝜏𝑖𝑖𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖 𝜖𝜖−1 𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗𝜖𝜖−1(3.8)

According to (3.8), the elasticity of the total exports of agri-food products 𝑣𝑣 with respect tothe MRLs is given by

𝜕𝜕𝐸𝐸𝐸𝐸𝐸𝐸𝑣𝑣𝑣𝑣𝑣𝑣
𝜕𝜕𝜃𝜃𝑗𝑗𝜃𝜃𝑗𝑗

𝐸𝐸𝐸𝐸𝐸𝐸𝑣𝑣𝑣𝑣𝑣𝑣= 𝛽𝛽𝛽𝛽 − [𝛼𝛼(𝜖𝜖 − 1) + 𝜂𝜂(𝛾𝛾 − 𝜖𝜖 + 1)](3.9)

For formulas (3.7) and (3.8) to be valid, it is generally assumed 𝜖𝜖 > 1 and 𝛾𝛾 − 𝜖𝜖 + 1 > 0.

Comparing (3.6) and (3.8), and reminding (3.7), the meaning of (3.9) is clear. The importer’s
MRLs affect the exports of each firm, as well as the behavior of entering or exiting the export,
which will result in an increase or decrease in the export firms, eventually affecting the totalexports of agri-products v.
Specifically, due to the presence of fixed costs, exporting firms with a productivity lower
than cpe will exit; thus, the number of export firms decreases. If 𝛽𝛽𝛽𝛽 − 𝛼𝛼(𝜖𝜖 − 1) < 0, both thenumber of exporting firms and the exports of remaining firms reduce, resulting in total exports- 27 -

drop of the entire industry. If 𝛽𝛽𝛽𝛽 − 𝛼𝛼(𝜖𝜖 − 1) > 0, the exports of each existing export firm

increase，but when 𝛽𝛽𝛽𝛽 − 𝛼𝛼(𝜖𝜖 − 1) < 𝜂𝜂(𝛾𝛾 − 𝜖𝜖 + 1), which means the demand-enhancing effectis larger than the variable trade-cost effect but smaller than the fixed trade-cost effect.
Accordingly, the decreased exports generated by exiting firms are greater than the increased
exports of the remaining high-productivity firms, comprehensively showing a decline in the totalexports of the industry. Conversely, when 𝛽𝛽𝛽𝛽 − 𝛼𝛼(𝜖𝜖 − 1) > 𝜂𝜂(𝛾𝛾 − 𝜖𝜖 + 1), that is, the demand-

enhancing effect is larger than the sum of the variable trade-cost effect and the fixed trade-costeffect. The decreased exports resulting from exiting firms are smaller than the increase in theremaining firms, which leads to an increase in total exports.

3.2.2 Stricter MRLs in exporter
When exporters’ MRLs are more stringent, exporting firms comply with the public quality
standards 𝜃𝜃𝑖𝑖 given by native country 𝑖𝑖 for production. Consumers believe that the quality ofagri-products produced according to MRL standards in the exporting country is higher than thatin importing countries, thus generating higher consumption demand. Similar to the first-ordercondition of maximizing consumer utility, the demand of consumers in importing country 𝑗𝑗 foragri-food products 𝑣𝑣 is given as follows:𝛽𝛽𝛽𝛽−𝜖𝜖 𝜖𝜖−1
𝑞𝑞𝑖𝑖𝑖𝑖 = 𝜃𝜃𝑖𝑖 𝑝𝑝𝑖𝑖𝑖𝑖
𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗(3.10)

However, when the exporter’s MRLs are more stringent, there is no additional compliancecost for export firms of country 𝑖𝑖 in trading with country 𝑗𝑗, namely, no trade-cost effect. The costfunction of export firms is simplified as𝐶𝐶𝑖𝑖 (𝑣𝑣) =𝜏𝜏𝑖𝑖𝑖𝑖𝑞𝑞 + 𝐷𝐷𝑖𝑖𝑖𝑖
𝜑𝜑 𝑖𝑖𝑖𝑖(3.11)𝜖𝜖 𝜏𝜏𝑖𝑖𝑖𝑖
𝜖𝜖 − 1 𝜑𝜑(3.12)

According to the first-order condition of profit maximization, the following equations are
obtained:
𝑝𝑝𝑖𝑖𝑖𝑖 =𝜖𝜖 1−𝜖𝜖 𝜏𝜏𝑖𝑖𝑖𝑖 1−𝜖𝜖 𝛽𝛽𝛽𝛽 𝜖𝜖−1
) ( ) 𝜃𝜃𝑖𝑖 𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗
𝜑𝜑𝜖𝜖−1𝑝𝑝𝑖𝑖𝑖𝑖 𝑞𝑞𝑖𝑖𝑖𝑖 = ((3.13)

Let the profit of firm be 0 and the cut-off productivity that all firms in country i must achieve
when exports to country j are given by- 28 -

−βϵ

1

1

1
𝜖𝜖
𝜂𝜂
𝜖𝜖
𝜖𝜖−1 −1 1−𝜖𝜖
)1−𝜖𝜖 ( )𝜖𝜖−1 𝜃𝜃𝑖𝑖𝜖𝜖−1 𝜏𝜏𝑖𝑖𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗
𝜖𝜖−1
𝜖𝜖−1

𝜑𝜑=(

(3.14)The total exports of agri-food product v from country i to country j were obtained byintegration.

𝐸𝐸𝐸𝐸𝐸𝐸𝑣𝑣𝑣𝑣𝑣𝑣 =𝛽𝛽𝛽𝛽

𝛾𝛾−𝜖𝜖−1𝛾𝛾𝛾𝛾
𝜂𝜂 𝜖𝜖−𝛾𝛾−1 𝜖𝜖 𝜖𝜖−𝜖𝜖𝜖𝜖−1−𝛾𝛾 −𝛾𝛾( ) 1−𝜖𝜖 ( ) 𝜖𝜖−1 𝑛𝑛𝑗𝑗 𝜃𝜃𝑖𝑖𝜖𝜖−1 𝜏𝜏𝑖𝑖𝑖𝑖 𝐷𝐷𝑖𝑖𝑖𝑖 𝜖𝜖−1 𝑃𝑃𝑗𝑗 𝐸𝐸𝑗𝑗𝜖𝜖−1
𝛾𝛾−𝜖𝜖+1 𝜖𝜖−1
𝜖𝜖−1(3.15)

By comparing the above equations, the difference between the two cases with more stringent
MRLs for the importer or exporter lies in the presence of the trade-cost effect. Exporting firms do
not incur additional compliance costs by complying with stricter domestic MRLs, and stricter
MRLs of exporting countries signal improved product quality to consumers in importing countries.

3.2.3 Hypothesis
The MRLs of importers and exporters directly affect the total exports of agri-products byquality θ, and indirectly affect exports by the demand-enhancing effect (β), variable trade-costeffect (α), and fixed trade-cost effect (η). The changes in β, α, and η can be attributed to the
public’s awareness of safety standards and government investment, such as improving quarantine
capacity.
Hence, we propose the following testable hypothesis.
If the MRLs between importing and exporting countries have a demand-enhancing effect, avariable trade-cost effect, and a fixed trade-cost effect, then:(1) when MRLs are more stringent in an importing country, their impact on total exports ofagri-food products is uncertain, depending on the combined effects of the above three (totalexports increase only when the demand-enhancing effect is greater than the sum of the variable
and fixed trade-cost effects); and(2) when MRLs are more stringent in an exporting country, there is only a demandenhancing effect, which promotes exports of agri-food products.

3.3 Empirical model
Take the logarithm of equation (3.8) and (3.15) to derive the gravity equations：𝑙𝑙𝑙𝑙𝐸𝐸𝐸𝐸𝐸𝐸𝑣𝑣𝑣𝑣𝑣𝑣 = 𝜙𝜙0 + 𝜙𝜙1 + 𝜙𝜙2 +𝛽𝛽𝛽𝛽−[𝛼𝛼(𝜖𝜖−1)+𝜂𝜂(𝛾𝛾−𝜖𝜖+1)]
𝛾𝛾−𝜖𝜖−1𝛾𝛾ln𝜃𝜃𝑗𝑗 − γln𝜏𝜏𝑖𝑖𝑖𝑖 −𝑙𝑙𝑙𝑙𝐷𝐷𝑖𝑖𝑖𝑖 +𝑙𝑙𝑙𝑙𝐸𝐸𝑗𝑗
𝜖𝜖−1
𝜖𝜖−1
𝜖𝜖−1

(3.16)- 29 -

𝑙𝑙𝑙𝑙𝐸𝐸𝐸𝐸𝐸𝐸𝑣𝑣𝑣𝑣𝑣𝑣 = 𝜙𝜙0 + 𝜙𝜙1 + 𝜙𝜙2 +where 𝜙𝜙0 = ln

𝛽𝛽𝛽𝛽𝛾𝛾−𝜖𝜖−1𝛾𝛾ln𝜃𝜃𝑖𝑖 − γln𝜏𝜏𝑖𝑖𝑖𝑖 −𝑙𝑙𝑙𝑙𝐷𝐷𝑖𝑖𝑗𝑗 +𝑙𝑙𝑙𝑙𝐸𝐸𝑗𝑗
𝜖𝜖−1
𝜖𝜖−1
𝜖𝜖−1

(3.17)𝛾𝛾
𝜖𝜖−𝛾𝛾−1𝜂𝜂𝜖𝜖−𝜖𝜖𝜖𝜖−1
𝜖𝜖
+
ln
+
ln
is a constant term. 𝜙𝜙1 = 𝛾𝛾𝛾𝛾𝛾𝛾𝑃𝑃𝑗𝑗 is the price
𝛾𝛾−𝜖𝜖+1
1−𝜖𝜖
𝜖𝜖−1
𝜖𝜖−1
𝜖𝜖−1

index of agri-food products, which is controlled by the fixed effect for the importer in our study.

𝜙𝜙2 = 𝑙𝑙𝑙𝑙𝑛𝑛𝑗𝑗 is the number of firms in the exporter country, which is replaced by the gross agri𝛾𝛾

𝑙𝑙𝑙𝑙𝐺𝐺𝐺𝐺𝐺𝐺𝑗𝑗𝑗𝑗 is the total expenditure

the ad valorem (variable) trade cost, including import tariffs.

𝛾𝛾−𝜖𝜖−1𝛾𝛾−𝜖𝜖−1
𝑙𝑙𝑙𝑙𝐷𝐷𝑖𝑖𝑖𝑖 =𝛿𝛿𝛿𝛿𝛿𝛿𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖
𝜖𝜖−1
𝜖𝜖−1

food output of China based on data availability 12.

𝛾𝛾

𝜖𝜖−1

𝑙𝑙𝑙𝑙𝐸𝐸𝑗𝑗 =𝜖𝜖−1

of agri-food products, depending on the GDP of the importer. γln𝜏𝜏𝑖𝑖𝑖𝑖 = 𝛾𝛾ln(1 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 ) isdenotes the fixed trade cost, including the transportation cost represented by the distance between
the importer and exporter 13. As more than 70% of EU agri-product trade is intra-EU, this study
also considers the impact of the supply capacity of agri-products among EU member states on
external imports. As panel data are used for the estimation, the gravity model is given by𝑙𝑙𝑙𝑙𝐸𝐸𝐸𝐸𝐸𝐸𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 = 𝛽𝛽0 + 𝛽𝛽1 𝜃𝜃𝑖𝑖𝑖𝑖 + 𝛽𝛽2 𝑙𝑙𝑙𝑙𝐺𝐺𝐺𝐺𝐺𝐺𝑗𝑗𝑗𝑗 + 𝛽𝛽3 𝑙𝑙𝑙𝑙𝑃𝑃𝑃𝑃𝑃𝑃𝑣𝑣𝑣𝑣𝑣𝑣 +𝛽𝛽4 𝑙𝑙𝑙𝑙𝑃𝑃𝑃𝑃𝑃𝑃𝑣𝑣𝑣𝑣𝑣𝑣+𝛽𝛽5 𝑙𝑙𝑙𝑙𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 + 𝛽𝛽6 ln(1 + 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 ) + κ𝑣𝑣𝑣𝑣 + 𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖(3.18)

where EXPvijt represents exports of China’s agri-product v to EU member states in year t. To avoid
the bias caused by the single use of importers’ MRLs in previous studies, this study adopts therelative stringency 𝜃𝜃𝑖𝑖𝑖𝑖 of MRLs in importer and exporter countries, which can be measured by

the heterogeneity indexes. GDPjt is the GDP of EU member states. PROvit is the total productionof China’s agri-products v in year t, PROvjt is the total production of EU agri-products in year t 14.
DISTij is the geographical distance between China and EU members, and is multiplied by the West

Texas Intermediate oil price in the regression to reflect the yearly impact of geographical12

It is generally assumed that the number of exporting firms is directly proportional to the income

or labor of the exporter, but in the case of incomplete data of the gross value of agricultural output andlabor, the gross yield of agricultural production related to the above two is used instead.
13

Since the exporter is China and the importer is the EU members, this study does not include thefixed trade cost terms, such as common boundary, common language, and common religion, in thegeneral gravity model.
14

The varieties of agri-products included in this study are not produced in every EU member state,

and thus, some production data are missing. The total production of EU member states is used torepresent their supply capacity, which reflects the reality of intra-EU trade and retains as manyobservations as possible.
- 30 -

distance 15. Tariffvijt is the average tariff rate of customs duties charged by the EU on China’s agriproducts v in year t. β0 is a constant. β1–β6 are parameters to be estimated. κvt is the fixed effect of
product-year. μjt is the fixed effect of EU member states-year 16. εijt is the error term and is normally
distributed.

3.4 Data sources
3.4.1 Dependent variable
The dependent variable is China’s food exports to EU members, derived from the EU
Eurostat database 17. This study selects 67 representative agri-food products, which are identified
by the HS6 Codes (Harmonized System). These products belong to the eight plant-based
categories in the HS2 code: vegetables (HS07), fruits (HS08), tea and spices (HS09), cereals(HS10), flour (HS11), oil seeds (HS12), vegetable oil (HS15), and fruit juices (HS20). The
exporting country i is China, and the importing country j is the 28 EU member states. The study
period is from 2008 to 2020. Thus, the sample consists of panel data of 13 (year) × 28 (country)
× 67 (product) 18.

3.4.2 Explanatory variable
To investigate the influence of the relative stringency 𝜃𝜃𝑖𝑖𝑖𝑖 of MRLs between China and the

EU on China’s food exports, this study draws on the heterogeneity indexes proposed by Shingal

et al. (Shingal et al., 2021). They averaged the sum of the stringency indexes of each pesticide by
the total number of pesticides and made the heterogeneity indexes a stable characteristic invariant
to the regulation intensity. Using a simple average method can avoid simply assigning higher
values to certain products, which are usually specified by different types of pesticides.

15

Sourced from online database: West Texas Intermediate (WTI) crude oil prices

16

By introducing product-year fixed effect and importer-year fixed effect to control factors of

product-time and importer-time respectively, the endogeneity caused by omitted variables can beaddressed.
17

Sourced from online database: EU Eurostat database18

For ensuring the continuity and reducing the outliers, the observations used meet the following

conditions simultaneously: (1) the gross food output data of 28 EU member states for more than 10
consecutive years from 2008 to 2020 can be obtained; (2) the gross food output data of China for morethan 10 consecutive years from 2008 to 2020 can be obtained; and (3) the data of food import tariffs
levied by the EU on China for more than 10 consecutive years from 2008 to 2020 can be obtained.
- 31 -

3.4.2.1 Heterogeneity indicesFirst define two indices for relative stringency of MRLs:
𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 −𝑀𝑀𝑀𝑀𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸

if 𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 > 𝑀𝑀𝑀𝑀𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸

𝑎𝑎𝑎𝑎𝑎𝑎(𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 −𝑀𝑀𝑀𝑀𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 )𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 +𝑀𝑀𝑀𝑀𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸

if 𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 ≤ 𝑀𝑀𝑀𝑀𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸

𝑓𝑓𝑣𝑣𝑣𝑣𝑣𝑣 = �𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 +𝑀𝑀𝑀𝑀𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸
0
𝑚𝑚𝑣𝑣𝑣𝑣𝑣𝑣 = �0𝑜𝑜𝑡𝑡ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒(3.19)(3.20)

where MRLCHNvkt is the MRL of product v and pesticide k in China in year t. MRLEUvkt is the MRLof product v and pesticide k in the EU in year t. fvkt (mvkt) indicates that for agri-food products v,
the MRL standard of the EU (China) for pesticide k is more stringent than that of China (EU).
Then fvkt and mvkt are respectively simple average to obtain the corresponding heterogeneity
indices of product v:
𝐹𝐹𝑣𝑣𝑣𝑣 = ∑𝐾𝐾
𝑓𝑓
𝐾𝐾 𝑘𝑘=1 𝑣𝑣𝑣𝑣𝑣𝑣

1(3.21)

𝑀𝑀𝑣𝑣𝑣𝑣 = ∑𝐾𝐾
𝑘𝑘=1 𝑚𝑚𝑣𝑣𝑣𝑣𝑣𝑣

1
𝐾𝐾(3.22)

where K is the total type of pesticide specified for product v in the MRLs, that is, the number of
MRL items of product v. The range of Fvkt (Mvkt) is [0, 1]. The closer the value is to 1, the more
stringent the EU (China) MRL regulation for agri-food products v.
3.4.2.2 Description of MRLs data
For China’s MRL data, this study collects 10 versions of “National food safety standard—
Maximum residue limits for pesticides in food” implemented in China from 2005 to 2020 19. Eachnew version clearly specifies updates to the old standard. The prescribed items in the above 10
documents are sorted according to products-pesticides-years. Then, a database of China’s MRL
standards from 2008 to 2020 is established. The database of EU MRL standards comes from the
EU Pesticides Database 20.

19

Sourced from online database: National food safety standard-Maximum residue limits forpesticides in food20

Sourced from online database: EU Eurostat database for pesticide- 32 -

Due to the differences in item names and food classifications between MRLs of China andthe EU, this study unifies the diverse names of products and pesticides, and reclassifies the foods
according to the abovementioned eight categories of the HS2 code. As shown in Table 3.1, thecategories of pesticides jointly regulated by China and the EU are increasing. Although the total
number of pesticides regulated by the EU is greater than that of China, the number of pesticides
exclusively regulated by the EU is gradually decreasing, whereas that of China is increasing
rapidly.
Table 3.1 The number of prescribed pesticides of MRLs in China and EU

Year

China

EU

China and EU

China only

EU only

2008

138

442

104

34

338

2011

161

453

122

39

331

2012

219

458

160

59

298

2013

323

465

219

104

246

2015

387

480

263

124

217

2017

433

504

286

147

218

2019

434

518

288

146

230

2020

483

521

315

168

206

Note: All the years in the table are the implementation years of the new standard except 2008.

The MRL standards of China and the EU have specified some pesticide items separately,
resulting in missing MRL data for these pesticides. The EU clearly stipulates that the default valueof MRLs for pesticides not specified in its MRLs is 0.01 mg/kg, while China does not specify thedefault value of MRLs. This study draws on relevant studies and adopts the maximum score in
China’s MRLs of that year to supplement the default, which represents low stringency forpesticide residues (Hejazi et al., 2016; Seok et al., 2018; Shingal et al., 2021).
3.4.2.3 Variation of heterogeneity indicesWe use (3.19), (3.20), (3.21), and (3.22) to calculate the heterogeneity indexes of each agrifood product in our data set. Then, we simply average the heterogeneity indexes contained in a
category of agri-food products to obtain the heterogeneity indexes of the category. The variationin the heterogeneity indexes of the eight categories over time is shown in Figure 1. The left (right)
axis represents the value of Fvt (Mvt).

- 33 -

Figure 3.1 Variation of 𝐹𝐹𝑣𝑣𝑣𝑣 and 𝑀𝑀𝑣𝑣𝑣𝑣 values with years

Fvt exceeds 0.91, and Mvt is less than 0. 008.The value of Fvt is much higher than that of Mvt,
indicating that the MRLs of the EU are much more stringent than those of China in general;
however, the gap is gradually narrowing. There are no significant changes in vegetable oils (HS15)
and fruit juices (HS20). By contrast, the Fvt of vegetables (HS07), fruits (HS08), cereals (HS10),
and oil seeds (HS12) all show a notable decrease, while the decline of tea and spices (HS09) andflour (HS11) is relatively gentle, maintained above 0.97. The change in Mvt is basically opposite
to that in Fvt, showing an obvious upward trend, but the increase is much smaller. This result
demonstrates that the stringency of some MRL items in China has gradually exceeded that of the
EU and has maintained an increasing trend.

3.4.3 Data description
For the 67 agri-food products selected in this study, there is a data shortage (N/A) of agriproduct output and import tariffs with regard to the EU. The data used in the estimation consistof four sets of panel data. The first set comprises unbalanced panel data, including all control
variables. It comprises 43 agri-products from 2008 to 2020 and 13 agri-products from 2008 to
2017. The observations are 19292 (13 years × 28 countries × 43 products + 10 years × 28 countries
× 13 products), with an observed value of zero of 16359 exports from China to EU members,
giving a zero frequency of 84.8%. The second set also comprises unbalanced panel data, but thecontrolled variables do not include the import tariff rate of the EU. This data set comprises 52
agri-products from 2008 to 2020 and 15 agri-products from 2008 to 2017. The sample
observations are 23128 (13 years × 28 countries × 52 products + 10 years × 28 countries × 15
- 34 -

products), with an observed value of zero of 19584 exports from China to EU members. The
frequency of zeros is 84.68%. The third data set consists of balanced panel data. The controlled
variables do not contain the output of EU agricultural food products. The sample observations are
20384 (13 years × 28 countries × 56 products), with an observed value of zero of 17148 exports
from China to EU members, giving a zero frequency of 84.12%. The fourth data set comprises
balanced panel data from 2008 to 2017, including all control variables. The sample observations
are 15680 (10 years × 28 countries × 56 products), with an observed value of zero of 13185
exports from China to EU member states, giving a zero frequency of 84.09%.
Table 3.2 Statistical characteristics of variables

Unit

Obs.

Mean

St.Dev

Min.

Max.

Euro

24,3881.64783.9889017.7165

Fvt-all

24,38896.4183

3.098083.572299.9334

Mvt-all

24,3882.93532.9650018.0746

Fvt-both

24,38894.04885.649368.565499.9678

Mvt-both

24,3885.5462

6.5487039.5489

lnEXPvit

lnGDPjt

Euro24,38825.8883

1.552422.5488

28.8761

lnPROitTonne

24,38815.00222.75127.804719.3958

lnPROjt

Tonne

23,12813.97072.82224.043118.8983

lnDISTijkm.

24,38812.81210.611511.0277

13.7866ln(1+Tariffvit)%

20,384

1.3764

1.056703.2581The statistical characteristics of all the variables are shown in Table 3.2. Fvt-all and Mvt-all are
the heterogeneity indexes calculated from the MRLs of all relevant pesticides in China and theEU, respectively. Fvt-both and Mvt-both are the heterogeneity indexes calculated for pesticide MRLs
jointly prescribed by China and the EU. To avoid bias in the Poisson pseudo maximum likelihood(PPML) estimation results due to the large numerical difference between variables, this study
expands the heterogeneity indexes F by 100 times and M by 1000 times. Some tariff data are notrecorded and, therefore, there are fewer observations, but the missing data have little significant
impact on the regression results.
The GDP of EU member states derived from the EU Eurostat database 21 . The foodproduction data of China and EU are from the Food and Agriculture Organization (FAO) of the21

Sourced from online database: EU Eurostat database- 35 -

United Nations 22. Bilateral distance data comes from CEPII 23. EU import tariffs are derived fromthe World integrated Trade Solutions 24.

3.5 Methods and results of estimation
3.5.1 Estimation method
Zeros are heavily present in export data on agri-food products of China to the EU. The
frequency of zeros exceeds 80%, which leads to biased estimates and therefore, needs to beeffectively treated. Silva and Tenreyro pointed out that the PPML estimator can effectively solve
heteroscedasticity and zeros, and is relatively robust to various forms of heteroscedasticity andmeasurement errors (Silva & Tenreyro, 2006). Assuming that the conditional variance is
proportional to the conditional mean (not necessarily equal), the PPML estimator is optimal; the
PPML estimation results are consistent even when the two are not proportional. Silva andTenreyro further pointed out that PPML is still a well-behaved estimator, even if the frequencyof zeros is large (Santos Silva & Tenreyro, 2011). Thus, PPML is an appropriate estimator of thegravity equation. The estimation in this study adopts PPML with fixed effects and cluster-robust
standard errors for importing countries (28 EU member states), which can properly treat
overdispersion.

3.5.2 Estimation result analysis
The baseline estimate uses the first set of panel data, including all controlled variables. The
estimation results are shown in column (1) of Table 3.3. Because the exports of agri-food products(dependent variable) take a logarithmic value, while the heterogeneity indexes (explanatory
variable) do not, the regression coefficient of the heterogeneity indexes is semi-elastic. The
regression coefficients of the other logarithmic control variables are elastic.
The coefficient of Fvt-all is positive and statistically significant. This indicates that although
there is still a certain gap between China’s MRL standards and those of the EU in terms ofstringency, with the upgrading of China’s MRs standards, the demand-enhancing effect is higher
than the variable and fixed trade-cost effect. Therefore, when EU MRLs are more stringent, they
do not restrain China’s agri-food product exports to the EU; on the contrary, they play a role in22

Sourced from online database: FAOSTAT database

23

Sourced from online database: CEPII database24

Sourced from online database: World Integrated Trade Solution (WITS) database- 36 -

promoting trade. When the stringency indexes of the EU MRLs exceed those of China by 1%,
exports of agri-products from China to the EU increase by 37.3%. In the case in which the MRL
gap is narrowing year by year, the influence of EU MRL standards on China’s agri-food productexports continues to weaken.
Table 3.3 Results of PPML estimation - unbalanced panel

Fvt-all
Mvt-all(1)(2)

0.3730***0.3896***(7.5865)(11.7035)

0.1612***0.1571***(4.6324)(6.7490)

Fvt-bothMvt-bothlnGDPjt
lnPROitlnPROjt
lnDISTijln(1+Tariffvit)
Constants

(3)

(4)

0.1839***0.1993***(7.3495)(11.9979)

0.0703***0.0703***(4.4171)(6.4334)

0.3130***0.3434***0.3131***0.3434***(5.8501)(6.4406)(5.8727)(6.4107)0.2257***0.2392***0.2327***0.2436***(14.9480)(15.1427)(14.9936)(16.1930)-0.1286***-0.1687***-0.1343***-0.1692***(-8.3734)(-8.1642)(-8.0400)(-8.0439)-0.4584-0.8509***-0.4731-0.8594***(-1.4550)(-3.8512)(-1.6070)(-4.1946)-0.8694***-0.8819***(-16.1476)(-16.4023)-39.6979***-37.5995***-20.6895***-18.5832***(-5.9066)(-8.7411)(-4.7552)(-6.8171)

Product-Year FE

Yes

Yes

Yes

Yes

Importer-Year FE

Yes

Yes

Yes

Yes

Observations

15402

18611

15402

18611

R2

0.49000.42160.48410.4167

Note: P < 0.05, ** P < 0.01, *** P < 0.001, Within the brackets are the cluster-robust standard errorof the importers. Due to the applying of fixed effect, some observations were dropped out in theestimation process of PPML.

The coefficient of Mvt-all is also positive and statistically significant. More stringent Chinese
MRL standards have enhanced EU consumers’ cognition and preference for the quality of Chinese- 37 -

agri-food and play a role in signaling product quality, which reflects a significant demandenhancing effect and effectively promotes exports of Chinese agri-food products. For every 1%
increase of the stringency of China’s MRL standards compared to those of the EU, there is anincrease of 16.12% in China’s exports of agri-products to the EU. Thus, increasingly stringent
MRLs in China further promote exports of agri-food products from China to the EU. By
comparing the coefficients of Mvt-all and Fvt-all, export promotion of agri-food produced byfollowing China’s stricter MRL standards is more effective than that of following the EU. This
also means that actively raising China’s MRL standards would generate an even more significant
promotion effect on its exports than following only the strict MRLs of the EU.
The regression results are in accordance with the expectations of hypotheses (1) and (2)based on the QHFT model. Stricter MRL standards, whether formulated by the EU or China,
promote China’s exports to the EU. The difference between our results and those of previous
studies lies in the coefficient of Fvt-all, which indicates that more stringent MRLs in the EU do nothinder exports of agri-products from China. This result is similar to that reported by Shingal et al.
(Shingal et al., 2021). When the MRLs of importing countries (EU) are more stringent, using only
the MRLs of importers as the explanatory variable leads to the traditional conclusion that MRLs
act as trade barriers to exporting countries (in our case, China). However, as analyzed in this study,
if the demand-enhancing effect exceeds the trade-cost effect of MRLs, it can be concluded that
MRLs promote trade. The application of heterogeneity indexes compensates for the deficiency ofusing only the MRLs of the importer, and the PPML with fixed effects can effectively solve theendogeneity problem.
The coefficients of other controlled variables are consistent with expectations andstatistically significant, indicating that the EU’s demand for high-quality agri-products is the “pull”
factor of imports, and the production capacity of Chinese agri-products is the “push” factor for
China’s exports to the EU. Geographical distance and tariffs still have no negligible trade costs
for China’s agri-product trade.
In addition, the public investment and services of the exporting country can help enterprises
reduce the compliance cost of MRLs, thereby reducing the trade-cost effect and syntheticallypromoting product exports. For example, Chinese government agencies have increased
investment in export quarantine facilities and capabilities, strengthened the collection and sortingof EU MRLs and quarantine early warning, established national and provincial demonstration
zones for the quality and safety of exported agri-food products, and helped enterprises learn andadapt to the MRLs of developed countries, such as those of the EU 25 . This is in line with

25

Sourced from government document: Opinions on improving the quality and safety risk early

warning and rapid response supervision system- 38 -

continuous improvements in food safety in China in the past decade. As Yuan and Chen pointed
out, the notification of food imported from China by the Rapid Alert System for Food and Feedof the EU has decreased year by year in terms of quantity and proportion of the total notification(Yuan & Chen, 2019). Among these notifications to China, pesticide residue notifications, whichranked second in the food safety crisis, demonstrated a significant decline annually. The impactof MRLs on China’s agri-product trade has presented new characteristics with the rapid
development of MRLs.

3.5.3 Robustness check3.5.3.1 Dealing with MRL default values
To investigate whether the treatment of MRL default values would affect the regression
results, we select the pesticide items jointly stipulated by China and the EU to calculate anotherset of heterogeneity indexes. Fvt-both and Mvt-both are heterogeneity indexes that include only thepesticide items jointly specified in the MRLs of China and the EU. Fvt-all and Mvt-all areheterogeneity indexes that include all regulated pesticides in China and the EU. In Tables 3, 4, 5,
and 6, the regression coefficients of Fvt-all and Mvt-all are larger than those of Fvt-both and Mvt-both,while the coefficients of the other variables change little, which shows that the treatment of MRL
default values does not affect the basic conclusions of this study.
The gap in the regression coefficients between Fvt-all and Mvt-all is greater than that between
Fvt-both and Mvt-both, which is due to the introduction of the default values of the MRLs. For pesticide
items regulated only by the EU, China does not specify the corresponding default value of the
MRLs. This study uses the maximum of China’s MRLs in that year as the default value. Although
the stringency of the MRL levels is higher, it is still lower than that of the EU and has a moderate
impact on the heterogeneity indexes. This is because the default value of the MRLs regulated by
the EU is 0.01 mg/kg, which is much more stringent than that of China. For pesticide items
regulated only by China, the corresponding heterogeneity indexes also show that the EU is more
stringent. When all pesticides are included in Fvt-all and Mvt-all, the pesticide item K in agri-productv increases, Fvt-all increases, and Mvt-all drops, with the gap between Fvt-all and Mvt-all being obviously
wider than that between Fvt-both and Mvt-both, leading to a larger gap in the regression coefficients.
3.5.3.2 Test of multicollinearity
Li et al. considered that MRLs are replacing import tariffs as a new trade protection measure(Li et al., 2017). We examined the multicollinearity between MRLs and import tariffs and theirimpact on the estimation results. The Pearson correlation test demonstrates a significant negative- 39 -

correlation between import tariffs and Fvt. We use the second set of panel data excluding the EU’s
import tariff rate for the estimation, as shown in columns (2) and (4) in Table 3.3. Compared withcolumns (1), (2), (3), and (4) in Table 3.3, the regression coefficient of the heterogeneity indexeschanges slightly, but the sign and significance does not change, indicating no serious
multicollinearity problem. Some scholars have pointed out that MRLs tend to protect the safety
and health of domestic food consumers (Yeung et al., 2017). Even if they show some tradedistortion, they cannot be classified as a non-tariff measure deliberately designed to replace
import tariffs.
3.5.3.3 Sample observations
To investigate whether the unbalanced panel data used in the basic estimation affect the
regression results, the third and fourth sets of balanced panels are used for the estimation. As
mentioned earlier, the two balanced panels differ in the studied years and the controlled variableof EU agri-product output. The results are shown in Table 3.4. The coefficients and statisticalsignificance of Fvt-all, Mvt-all, Fvt-both, and Mvt-both demonstrate little difference to the results in Table
3.3, except for a slight change in geographical distance (lnDISTij).
3.5.3.4 Check for overdispersion
There are many methods for dealing with overdispersion and inflated zero-trade values.
Considering heteroscedasticity and the panel data used, this study applies negative binomial
regression for panel data to test whether the inflated zero-trade values and overdispersion affectthe estimation results. First, NBREG in STATA 15 is used for the regression (Table 3.5), where
the coefficient of overdispersion (lnα) shows the existence of overdispersion. Then, XTNBREG
with random effects and fixed effects are used for the regression. Both the likelihood ratio test
and Hausman test indicate that random effects should be adopted. The estimation results for
XTNBREG with random effects are shown in Table 3.6.
In Table 3.6, the absolute regression coefficient of geographical distance decreases
obviously, while the absolute value of the regression coefficient of heterogeneity indexes andother controlled variables increases obviously, but the expected sign and significance do notchange. Thus, the problem of overdispersion does not affect the basic conclusion of this study.

- 40 -

Table 3.4 Results of PPML estimation - balanced panel

2008-2020(1)
Fvt-all
Mvt-all

2008-2017
(2)

(3)

(4)

0.3726

0.3379(8.8249)(8.1539)0.1176***0.1829******(4.1675)(6.5210)0.1733

Fvt-bothMvt-both******0.1583***(8.1343)(7.6078)

0.0342**0.0850***(2.6880)
lnGDPjt
lnPROit

0.3266(6.1571)(6.2244)(5.7464)(5.7853)

0.1871***0.1935***0.2186***0.2213***(17.8760)(17.2541)(15.4015)(15.6604)-0.1312***-0.1423***(-9.1400)(-9.1857)***

lnPROjt
lnDISTijln(1+Tariffvit)(6.5411)0.3240***0.3171***0.3188***-0.8410**-0.9365***-0.5964-0.6644(-3.2453)(-3.8836)(-1.4173)(-1.6308)-0.8527-0.8489-0.8469***-0.8528***

***

***

(-14.9737)(-15.8284)(-16.0151)(-16.3236)-36.1183***-15.2468***-34.5547***-15.7320**(-6.5908)(-4.4273)(-5.0079)(-3.1314)

Product-Year FE

Yes

Yes

Yes

Yes

Importer-Year FE

Yes

Yes

Yes

Yes

Observations

16427

16427

12663

12663

R2

0.48480.4796

0.48180.4767

Constants

Note: P < 0.05, ** P < 0.01, *** P < 0.001, Within the brackets are the cluster-robust standard errorof the importers. Due to the applying of fixed effect, some observations were dropped out in theestimation process of PPML.

- 41 -

Table 3.5 NBREG estimation results

Fvt-all
Mvt-all(1)(2)

0.4730***0.5731***(5.6895)(8.2620)0.1396*0.2337***(2.0907)(5.2044)

Fvt-bothMvt-bothlnGDPjt
lnPROitlnPROjt
lnDISTijln(1+Tariffvit)
Constants

(3)

(4)

0.2505***0.3062***(5.7383)(8.9340)0.0771*0.1170***(2.4891)(5.3649)

0.5233***0.4076***

0.5247***0.4080***(4.8066)(5.8707)(4.7006)(5.8251)

0.2985***0.3008***0.3078***0.3184***(10.2722)(13.7125)(9.9983)(13.4004)-0.1436***-0.1319***-0.1494***-0.1392***(-7.6286)(-7.7364)(-7.6332)(-8.1524)-1.1555*-1.8908***-1.2857**-2.1745***(-2.5346)(-4.4807)(-2.8449)(-4.7047)-1.1845***-1.2069***(-10.2796)(-10.3541)-46.5644***-45.5906***-22.8450***-15.5590**(-4.3326)(-5.3408)(-3.3419)(-2.7409)

1.8366***

1.9846***

1.8433***1.9902***(17.6481)(15.9288)(17.6638)(15.8824)

Product-Year FE

Yes

Yes

Yes

Yes

Importer-Year FE

Yes

Yes

Yes

Yes

Observations

19292

23128

19292

23128

lnalphaNote: P < 0.05, ** P < 0.01, *** P < 0.001, Within the brackets are the cluster-robust standard errorof the importers.

- 42 -

Table 3.6 XTNBREG-RE estimation results

Fvt-all
Mvt-all(1)(2)

0.4442***0.4333***(9.6908)(11.1146)

0.2968***0.2267***(7.7109)(6.9709)

Fvt-bothMvt-bothlnGDPjt
lnPROitlnPROjt
lnDISTijln(1+Tariffvit)
Constants

(3)

(4)

0.1473***0.1993***(5.4731)(8.9817)0.0627**0.0745***(3.1300)(4.7185)0.4363***0.4077***0.4175***0.3981***(11.7636)(11.1494)(10.9320)(10.6829)

0.2559***0.2220***0.2484***0.2148***(13.0587)(11.3290)(12.2977)(10.9160)-0.2679***-0.3230***-0.2550***-0.3139***(-15.5163)(-18.2159)(-13.5811)(-16.6255)-0.1622-0.5548+-0.4712-0.6894*(-0.5097)(-1.9408)(-1.4525)(-2.3904)-1.1662***-1.1554***(-21.6815)(-21.2635)-54.1792***-47.0990***-20.1606***-21.7856***(-8.5336)(-8.5246)(-3.9828)(-4.9844)-0.4289***-0.4940***-0.4501***-0.5017***(-7.8178)(-10.4495)(-8.2899)(-10.6458)-0.3380**-0.4420***-0.4008***-0.4607***(-3.1333)(-4.8849)(-3.7766)(-5.1161)

Product-Year FE

Yes

Yes

Yes

Yes

Importer-Year FE

Yes

Yes

Yes

Yes

Observations

19292

23128

19292

23128

ln_r
ln_s

Note: P < 0.05, ** P < 0.01, *** P < 0.001, Within the brackets are the cluster-robust standard errorof the importers.

- 43 -

3.6 Conclusion and future research
Growing public concern about food safety has caused governments to actively formulate
their own food safety standards, and the impact of these different but mandatory safety standards
on trade has been widely discussed. Previous empirical studies have mainly focused on the impactof MRLs in developed countries on trade rather than on the relativity of standards between
importers and exporters. Outdated time and limited samples do not reflect the rapid developmentof food safety standards in developing countries in a timely manner. This study attempts to reveal
the comprehensive impact of MRLs on agricultural food industry trade based on theoretical andempirical analyses.
This study constructs a QHFT model to explain the influence of MRLs on exports andderives an appropriate empirical model. The basic logic is that, on one hand, when the MRLs in
an importing country are more stringent than those in an exporting country, firms in the exporting
country must comply with the stricter MRLs of the importing country. Goods produced according
to the MRLs of importing countries can enhance product quality compared with the relatively
loose MRLs of exporting countries. Consumers in importing countries with quality preference
perceive the improvement in product quality, which increases consumer demand. However, therelative improvement in quality requires input, resulting in additional costs for firms (products).
On the other hand, when the MRLs in an exporting country are more stringent than those in an
importing country, the products produced in the exporting country make a high-quality
impression on consumers, and the firms do not input additional trade costs in this case. The
comprehensive impact of MRLs on trade should be determined by three effects: demandenhancing, variable trade-cost, and fixed trade-cost effects.
We employ a relatively new and complete MRL database for China and the EU from 2008
to 2020 for the empirical analysis. The heterogeneity indexes integrate the quantity and level ofstandards to measure the relative stringency of MRLs between China and the EU. The PPML
fixed-effect model is used to treat heteroscedasticity, inflated zero-trade values, and endogeneity
in the trade data. Finally, the robustness of some key influencing factors is tested. We find that
more stringent EU MRLs do not curb food exports from China to the EU. A more plausible
explanation is that the stricter MRLs signal to consumers that the quality of products has improved,
thereby generating a demand-enhancing effect that can boost the consumption demand ofimporting countries. The demand-enhancing effect plays a leading role in promoting agri-foodexports from China to the EU. Meanwhile, the EU MRLs are still more stringent than those of
China in general, but the gap continues to narrow, and the stringency of certain MRLs forpesticides in China exceeds that of the EU. The rapid upgrading of China’s food safety standards
can dynamically adjust the domestic agricultural industry to a high quality and efficiency. China’s
efforts to adopt more stringent domestic MRL standards has boosted exports over and above- 44 -

passively complying with EU MRLs.
The results illustrate that MRL standards are not intentionally designed as non-tariffmeasures, and their impact on trade depends on the combined effect of demand and cost. MRLs
as a food safety standard and their impact on trade also show phased characteristics with
development. For example, as China’s economy has grown and production capacity for agriproducts increased, public attention has shifted from food security to a healthy diet. The impact
of stricter MRLs in developed countries and regions on China’s exports has shifted from being abarrier to trade between high-income countries and a low-income country to one of mutual
promotion between high-income countries. Owing to the significant differences in productivity
and quality of enterprises, an in-depth understanding of the impact of MRLs on quality
improvement, imports and exports, and other micro behaviors of agri-food enterprises would help
to comprehensively and accurately evaluate the impact of MRL standards on trade and welfare.
The QHFT model proposed in this study provides an analytical basis. Customs and enterprise data
could be used to conduct empirical research of various trade margins, which is a direction forfurther research.

- 45 -

Chapter 4 Influence of the exporter’s food safety
standards on trade duration: An empirical study ofagricultural food products from China
4.1 Introduction
Despite long-standing debate, mainstream discourse continues to suggest that free trade in
agricultural and food products can strengthen global food security and sustainable development;
typically, the rationale is that such free trade will enable people living in poverty to live healthierlives and thus have access to higher return on non-agricultural activities. In developing countries,
where agriculture accounts for a relatively large share of the economy, the potential benefits ofagricultural trade liberalization will be greater. In 2020, during the COVID-19 pandemic,
agricultural growth in developing countries was dynamic and significantly helped to stabilize theworld’s food trade (notably, during this same period, exports declined in developed countries) 26.
The SPS 27 Agreement guarantees trading countries sovereignty in adopting restrictions onimported animal and plant trade products to protect national health and sustainable development.
In addition, to promote international trade, it coordinates the quarantine of animals and plants ininternational trade and offers guidelines for resolving disputes between nations. To ensure astrong balance between scientific rationality and fair trade, the SPS Agreement encourages
nations to adopt the principles, guidelines, and recommended technical standards developed by
the CAC28, including MRLs 29 for pesticides. However, the CAC standards are not mandatory. In
practice, participating countries have formulated different mandatory technical standards,
including MRL standards, based on their complex national conditions. Owing to the heterogeneity26

Sourced from FAO report: FAO's Food Outlook: Developing countries buoy global food trade

27 The Agreement on the Application of Sanitary and Phytosanitary Measures (SPS) is a multilateral

trade agreement under the jurisdiction of the World Trade Organization (WTO).
28 The Codex Alimentarius Commission (CAC) is an intergovernmental organization establishedby the Food and Agriculture Organization of the United Nations (FAO) and the World HealthOrganization (WHO) in 1963 to coordinate intergovernmental food standards and establish aninternational food standards system.
29 The maximum residue limit for pesticides (MRL) is the maximum concentration of a pesticide

residue (expressed as mg/kg) the CAC recommends nations should legally permit in or on foodcommodities and animal feeds. MRLs are based on GAP data. Foods derived from commodities thatcomply with the respective MRLs are viewed as toxicologically acceptable.
- 46 -

of standards and the popular trend of annually reducing international trade tariffs, sanitary andquarantine measures based on these standards are often considered non-tariff measures withstrong concealment.
Existing research on how MRL standards may affect trade mainly focuses on the impact ofimproved food safety standards in developed countries; notably, these studies are relatively old
and mostly focus on how developed countries’ MRL standards impact their imports (Curzi et al.,2018; Fernandes et al., 2019; Kareem et al., 2018; Xiong & Beghin, 2012). Generally, thisresearch suggests that strict MRLs force producers to bear additional production and compliancecosts (C. L. Chen et al., 2008; Otsuki, 2001), and that other costs emerge if customs extends
delivery times or refuses entry (Xiong & Beghin, 2014). Thus, MRLs are used by importing
countries to hinder the import of agri-products and are considered a protectionist measure (Ferroet al., 2015). In contrast, other empirical studies suggest that seemingly strict MRL standards canpromote trade because they reduce the risk of information asymmetry and ease consumer concerns,
thus signaling quality improvement to exporters and strengthening consumer demand (Ishaq etal., 2016; Li et al., 2017; Seok et al., 2018; Shingal et al., 2021).
Unlike previous studies, which focused on the impact of gross trade, this study focused on
the impact of MRL standards on the duration of exports. Previous trade theory assumes that traderelations remain unchanged and continue for a long time after the relationship is established, but
this is not the case (Brenton et al., 2010). Export duration plays an important role in improving acountry's export volume. If the trade duration is too short, it is difficult to maintain sustained trade
growth even if there are many trading partners (Besedes & Blyde, 2010).
Some domestic Chinese scholars have discussed various factors affecting trade duration;
however, they primarily explore MRL standards in importing countries and fail to reflect the rapid
development of China's MRL standards in recent years. The latest version of China’s National
Food Safety Standard containing the nation’s MRL standards—“Maximum Residue Limits for
Pesticides in Food” (GB2763–2021)—was formally implemented on September 3, 2021. This
document presents 10092 MRL standards, involving 564 pesticides and 376 foods. Compared to
the 2019 version, these standards cover 81 new varieties of pesticides (an increase of 16.7%) and
2985 new MRLs (an increase of 42%)—ultimately, they cover nearly twice the number ofpesticide varieties and MRLs as the CAC standards 30. In 2020, China was the world's second
largest importer and fifth largest exporter of agri-products; additionally, it had the world’s second
highest gross trade value. However, from 2000 to 2020, China's agri-food imports grew fasterthan its exports. When China joined the WTO in 2004, it quickly shifted from having an

30

Sourced from official report: China's pesticide residue limit standard exceeded 10000 items, fully

covering the approved pesticide varieties and main plant-derived agricultural products in China- 47 -

agricultural trade surplus to an agricultural trade deficit (Han et al., 2021). Therefore, China is a
useful sample for an export duration study because it may serve as a good reference for developing
countries transitioning from agricultural to non-agricultural economies.
This study contributes to existing literature in the following ways. First, unlike previous
studies focusing on the direct impact of food safety standards on gross trade, this study focusedon how the implicit factor of upgraded food safety standards impacts the trade duration ofagricultural exports. Second, based on empirical data from China, this study explored how
enhanced food safety standards, such as MRLs, impact exports in a developing country. Third,
this study’s sample period extended to 2019, which better reflects the rapid changes in MRL
standards in international trade since 2010. This paper is dedicated to further improving current
research on how food safety standards impact trade and to offering useful insights on the
agricultural upgrading of developing countries and the sustainable development of the global agriproduct trade.
The remainder of the paper unfolds as follows. Section 4.2 is literature review, Section 4.3
is the estimation of trade duration, Section 4.4 is the empirical research on the influencing factorsof trade duration, and Section 4.5 is the conclusion.

4.2 Literature review
This thesis focuses on the influence of MRLs standards on the duration of agricultural
exports. The relevant literature mainly includes the impact of MRLs on agricultural trade andtrade duration.

4.2.1 MRLs impact on agricultural trade
Otsuki et al. studied the impact of the EU’s aflatoxin standard on Africa's exports using thegravity model (Otsuki, 2001). They found that Africa's exports decreased by 11% for every 10%
increase in the EU’s standard. Meanwhile, Xiong and Beghin used the same method to study the
impact of the EU’s aflatoxin standard on African exports and found that the standard had no
significant impact on the exports of African countries (Xiong & Beghin, 2012). Recently, scholars
have studied trade effects by comparing the heterogeneity of standards in different countries andorganizations. Xiong and Beghin found that strict MRL standards in high-income countries
increased demand for imports but hindered foreign exports (Xiong & Beghin, 2014). Kareem etal. also pointed out when the EU’s MRL standards became more comprehensive than the CAC
standards (Kareem et al., 2018), Africa's tomato exports to the EU gradually decreased.
Additionally, Curzi et al. measured the standard stringency of the EU and its major trading
partners using the CAC standard as a baseline (Curzi et al., 2018). They found that the EU's
- 48 -

stringent MRLs hindered the agri-product exports of developing countries but promoted EU
member state exports. Last, using firm-level data, Fernandes et al. found that when importers havestringent MRLs, exporters are less likely to enter their markets and existing enterprises are more
likely to exit their markets; notably, this effect was stronger for relatively small enterprises(Fernandes et al., 2019).

4.2.2 Study on trade duration
Besede and Prusa introduced a survival analysis method from medical research to tradestudies and found that the median trade duration of most imports in the US was only 2–4 years(Besedes & Prusa, 2006). Meanwhile, Nitsch and Hess and Persson compared the trade situationsof Germany and the EU and arrived at similar conclusions (W. Hess & M. Persson, 2011; Nitsch,2009). In a study on agri-products, Peterson et al. examined the phytosanitary measures and theimport duration of fruits and vegetables in the US and found that phytosanitary measures had no
significant impact on import duration (Peterson et al., 2017). Additionally, Straume studied theexport of Norwegian salmon and found that enterprise- and market-level factors affect the stabilityof export duration (Straume, 2017). Zhang and Tveteras studied the influence of the generalized
scheme of preferences (GSP) of the EU on the export of aquatic products from developing
countries (Zhang & Tveterås, 2019). They found that this measure prolongs the export durationof aquatic products, but hinders the export of primary aquatic products.
In summary, most studies on food safety standards and agri-food trade have been based on
the standards of northern countries (importers). Regarding the effect of MRL standards on trade,scholars mainly discussed the impact of MRLs on trade volumes, and some scholars extended themarginal impact to product quality. However, research on trade duration is limited. In recent years,
the economic development of southern countries and the continuous reform of food safety
standards have required timely attention. This study examined the drastic changes in China’s
MRLs and explored how its agri-food exports affect trade duration. Our findings offer insightsthat may be conducive to further studies into the effect of MRL standards on trade and serve as
empirical references for southern countries seeking to catch-up.

4.3 Estimation on trade duration
4.3.1 Data preprocessing
Since 1989, most products in international trade flows have been classified as harmonizedsystems (HS). Based on the research subject and data availability, 113 product items specified
by the HS 6-digit level were selected. These items belong to ten categories of agri-food products- 49 -

identified by the HS 2-digit code, namely: HS02, HS04, HS07, HS08, HS09, HS10, HS11,
HS12, HS18, and HS20. In this study, the single exporter i is China, and importer j includes 155
importing countries participating in bilateral trade. The sample period was 2008–2019. These
data constituted a panel dataset of 12 (years) × 155 (countries) × 113 (products).
“Trade duration” refers to the period or “spell” beginning when an exporter in a bilateral
trade relationship enters the market and ending when it withdraws for any reason. Existing
economic studies on trade duration mainly measure duration based on yearly product trade due
to data availability and the ease of this approach. Defects in data censoring and multiple spells
in statistical methodologies must be handled properly.

4.3.2 Multiple spells
In this study, a "spell" comprises the continuous years with non-zero trading between
importers and exporters in the dataset. Multiple spells may occur in the sample; namely, traderelations restart after being interrupted during the observed period. To control for thisphenomenon, Besedes and Prusa (2006a) advice regarding multiple spells as independent
segments for analysis or situating the first spell of multiple spells as the observation (Besedes &Prusa, 2006; Molina & Fugazza, 2009). Neither approach qualitatively impacts the estimatedresults of trade duration. Considering the additional advantage of maximizing the sample size,
this study treated multiple spells as independent fragments for analysis.
As shown in Table 4.1, the 4111 trade relations have only one spell, accounting for 37.8%of the total. Of the total trade relations, 51.8% of trade relations can only last for one year, 14.2%
for five years or more, and 2.2% for 10 years or more. In terms of trade duration, the sample ofonly one spell recorded a maximum of 12 years, compared to 10 years for multiple spells. In
addition, only 6.6% of the multiple spells had a duration of more than 5 years, compared with
16.2% in only one spell. China's agri-food exports are mainly short-term, and only one spell had
a better trade duration performance than multiple spells. This can be explained by theheterogeneity or low substitution elasticity of product types in only one spell or the mutual trust
and trade preference of bilateral traders, which results in less trade interruption.

- 50 -

Table 4.1 The number of spells and trade relationships
Duration(years)Only One Spell

Multiple Spells

Total

Number

Trade

Number

Trade

NumberTradeof Spells

Relationshipsof Spells

Relationshipsof Spells

Relationships

1

2394

2394

(22.0%)

5458

3247

(29.8%)

7852

5641

(51.8%)

2

518

518

(4.8%)

1810

1546(14.2%)

2328

2064

(19.0%)

3

266

266

(2.4%)

901

827(7.6%)

1167

1093(10.0%)

4

149

149

(1.4%)

404

392(3.6%)

553

541(5.0%)

5

123

123

(1.1%)

317

314(2.9%)

440

437(4.0%)

6

96

96(0.9%)

206

206(1.9%)

302

302(2.8%)

780

80(0.7%)

121

121(1.1%)

201

201(1.8%)

8

167

167

(1.5%)

66

66(0.6%)

233

233(2.1%)

994

94(0.9%)

37

37(0.3%)

131

131(1.2%)

1085

85(0.8%)

18

18(0.2%)

103

103(0.9%)

1185

85(0.8%)

85

85(0.8%)

1254

54(0.5%)

54

54(0.5%)

Total

4111

4111

(37.8%)

9338

6774

(62.2%)

13449

10885

(100.0%)Note: The number in parentheses is the percentage of the trade relations in the total trade relations.

4.3.3 Data censoring
The sample data in this study span from 2008 to 2019; hence, the export status of the
product level before 2008 and after 2019 is unknown. When bilateral trade is observed in 2008,
the starting time of this trade is not included in the sample, giving rise to the problem of left
censoring. In contrast, for the ongoing bilateral trade in 2019, the time of trade termination
cannot be observed in the sample, leading to a right-censoring problem. To obtain unbiased
parameters of estimation for the left-censoring problem, the left-censored observations were
excluded from the data (Besedes & Prusa, 2006; Besedeš & Prusa, 2006). Notably, our sample
only contained data on agri-food products that exporter i (China) did not trade in 2007 and only
began to export to importer country j in 2008 and later. Thus, the longest duration of agriproduct exports in our study was 12 years. Meanwhile, discrete-time hazard models caneffectively solve the correct censoring problem (W. Hess & M. Persson, 2011).

4.3.4 Methodology of survival analysis
The survival (hazard) function is used to describe the characteristics of the survival(hazard) rate in the trade survival analysis. As mentioned above, survival analysis caneffectively handle right censoring. We introduced a survival function to estimate the distribution- 51 -

of the trade duration for China's agri-food exports.
Let i denote the continuous trade duration (spell) of various trade relationship combinations(China’s product=v; importer=j): i＝1，2，3,…. Let T be a discrete random variable that denotes
the length of spell i, reflecting the duration of the trade combination for China's agri-product, v,
exported to importer j. T is taken as t = 1, 2, 3,..., (years) in this study. Therefore, thecorresponding survival function is 𝑆𝑆𝑖𝑖 (𝑡𝑡) = 𝑃𝑃𝑃𝑃(𝑇𝑇𝑖𝑖 > 𝑡𝑡), implying that the conditional probabilityof Ti exceeds year t given that the trade has survived to year t. Thus, the Kaplan-Meier estimatorof the survivor function is given as:𝑡𝑡

𝑆𝑆�𝚤𝚤 (𝑡𝑡) = �
𝑘𝑘=1

𝑛𝑛𝑘𝑘 − 𝑑𝑑𝑘𝑘
𝑛𝑛𝑘𝑘

(4.1)

where 𝑛𝑛𝑘𝑘 refers to the number of spells that survived (but were at risk of trade interruption) in
year k. Similarly, 𝑑𝑑𝑘𝑘 denotes the number of spell-observed trade termination events in year k.

The hazard function expresses the conditional probability of export ending in year t + Δt

under the condition that the export of agri-products of China survived in year t.

ℎ𝑖𝑖 (𝑡𝑡) = Pr(𝑇𝑇𝑖𝑖 = 𝑡𝑡|𝑇𝑇𝑖𝑖 ≥ 𝑡𝑡) =p(𝑡𝑡)𝑆𝑆𝑖𝑖 (𝑡𝑡 − 1)

The nonparametric estimation of the hazard function is as follow:ℎ�𝚤𝚤 (𝑡𝑡) =

𝑑𝑑𝑘𝑘
𝑛𝑛𝑘𝑘

(4.2)

(4.3)

The survival and hazard functions of the trade duration of the agri-food product exports in
China were estimated based on the Kaplan-Meier estimator, as shown in Figure 4.1.

Figure 4.1 Estimation of survival rate
- 52 -

The duration of China's agri-food export is generally short, with an average of 3.58 years.
As Shown in Figure 4.1, the survival rate of Chinese export firms dropped sharply in the earlystage, and then decreased slowly and flattened. A nonparametric analysis of the survival rateand survival time demonstrated a significant negative duration dependence; this is consistent
with the conclusions of most studies (Besedes & Prusa, 2006; W. Hess & M. Persson, 2011;
Yang et al., 2021).
The negative duration dependence may be due to heterogeneous data clusters (destination
markets, products, or enterprises) (Esteve-PÉRez et al., 2013), the sunk costs of market entry(Brenton et al., 2010), or the learning effect (experience accumulation) (Esteve‐Pérez, 2021).
Thus, the sustainable development of trade relations may be a determinant rather than a result ofextensive margins in trade diversification.

4.4 Influencing factors of trade duration
4.4.1 Estimation model
4.4.1.1 Dependent variable
In the general trade duration model, food safety standards affect trade duration through thehazard/survival rate. Food safety standards partly increase trade costs, resulting in the withdrawal
of existing exporters, that is, the interruption of trade relations. Meanwhile, food safety standards
can discourage exporters from entering the market (non-exporters), thus strengthening thepersistence of existing exporters and prolonging trade duration. Some scholars researching trade
have adopted Cox proportional hazards models (Besedeš & Prusa, 2006; Molina & Fugazza, 2009;
Nitsch, 2009), while others (Esteve-PÉRez et al., 2013; W. Hess & M. Persson, 2011; Peterson etal., 2017; Wang et al., 2019) have adopted discrete-time hazard models. Notably, the Cox modelhas the following defects: ① the model is applicable to continuous-time specifications, whileobserved time on trade duration studied is yearly discrete—"tied" duration times cause asymptotic
bias in an estimation; ② it ignores unobserved heterogeneity and causes parameter andestimation biases in the survivor function; and ③ its assumptions of proportional hazards arerather restrictive—incorrectly imposing proportionality causes estimation bias in covariates.
Discrete-time hazard models can effectively address the above three defects and can be used toestimate the base hazard in a nonparametric mode similar to the Cox model (Wolfgang Hess &Maria Persson, 2011). Accordingly, this study used a discrete-time hazard model to examine theinfluence of food safety standards on the export hazard rate of agri-products.
Discrete-time hazard models are used to study export duration because they can effectively
estimate the "failure" probability of "spell" i in a specific trade relationship combination in- 53 -

discrete time, namely the discrete-time hazard rate, ℎ𝑖𝑖 (𝑡𝑡). The functional form of the hazard rate

must be set before estimating parameters in the survival analysis model. The hazard rate
distribution function in discrete-time hazard models typically follows normal, logistic, andextreme-value minimum distributions, corresponding to the probit, logit, and cloglog(complementary log-log) models, respectively (Sueyoshi, 1995). This study assumed that ℎ𝑖𝑖 (𝑡𝑡)yields the extreme-value minimum distribution. The cloglog estimator was applied in thebenchmark regression. Subsequently, the logit and probit models were selected for robustness
tests. The corresponding discrete cloglog model was as follows:
Cloglog[ℎ𝑖𝑖 (𝑡𝑡, 𝑋𝑋)] = log(− log[1 − ℎ𝑖𝑖 (𝑡𝑡, 𝑋𝑋)]) = 𝑋𝑋 ′ 𝛽𝛽 + 𝛾𝛾𝑡𝑡 + 𝑢𝑢

(4.4)

where ℎ𝑖𝑖 (𝑡𝑡, 𝑋𝑋) denotes the discrete-time hazard rate. The covariate 𝑋𝑋′ , which includes the

explanatory variables, is the vector that embodies the characteristics of the impact factors. 𝛽𝛽 is

the regression coefficient vector to be estimated. 𝛾𝛾𝑡𝑡 is the baseline hazard, which integrates thetime-varying risk faced by the samples; 𝑢𝑢 is the error term normally distributed, which is used tocontrol the individual unobservable heterogeneity affecting trade duration (individual random
effect).

To examine the influence of food safety standards on the export duration of China's agrifood product exports, we referred to previous studies and carefully constructed the following basic
empirical model:

𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 = 𝛽𝛽0 + 𝛽𝛽1 ∆𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 + 𝛽𝛽2 ln 𝑔𝑔𝑔𝑔𝑔𝑔𝑗𝑗𝑗𝑗 + 𝛽𝛽3 ln 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑗𝑗𝑗𝑗 + 𝛽𝛽4 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐+𝛽𝛽5 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐+𝛽𝛽6 ln 𝑝𝑝𝑝𝑝𝑝𝑝𝑣𝑣𝑣𝑣𝑣𝑣 + 𝛽𝛽7 ln 𝑝𝑝𝑝𝑝𝑝𝑝𝑣𝑣𝑣𝑣𝑣𝑣 + 𝛽𝛽8 ln 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖+𝛽𝛽9 ln(1 + 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡)𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 + 𝛼𝛼𝑡𝑡 + 𝛼𝛼𝑣𝑣 + 𝜇𝜇0(4.5)

where 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 is a binary dependent variable that represents the outcome of each traderelationship. 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 =1 represents a "fail" and the interruption of trade duration; the "fail" does
not occur when 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 =0. ∆MRLsvijt is the gap in MRL stringency between importers andexporters, GDPjt is the GDP of importer j in year t, and counnumijt is the total number of importers
for a product in year t, indicating market diversification. contig is the adjacency between theimporter and exporter; comlang indicates whether the importer and exporter have a common
language; PROvit is the exporter’s total production of product v in year t; PROvjt is the importer’s- 54 -

total production of product v in year t 31; DISTij is the geographical distance between the exporterand importer; Tariffvijt is the average tariff rate levied by the importer on export product v in yeart; β0 is a constant term; and β1–β9 are the parameters to be estimated. 𝛼𝛼𝑡𝑡 is the fixed effect of theyear, 𝛼𝛼𝑣𝑣 is the fixed effect of the product. The year fixed effect and product fixed effect enable

the proper handling of the endogeneity caused by omitted variables. εijt is the normally distributed
error.

4.4.1.2 Core explanatory variable
The framework of the WTO’s SPS Agreement situates CAC food safety standards, including
MRLs, as the only international standards jointly recognized by the FAO and the WTO. This
study introduces heterogeneity indices between China and the CAC, which concisely andreasonably reflect the relative strictness of China's MRL standards compared to the CAC
standards. Furthermore, it can be used to measure the influence of the relative strictness of China's
MRL standards on the hazard rate of agri-food exports.
Li and Beghin argue that simply averaging the gap between the MRLs of importers andexporters means that the MRL stringency on trade is homogeneous (Li & Beghin, 2014). In
contrast, the more stringent the MRLs, the greater the impact on trade. They reason that index
weighting should be used to give more weight to stricter MRL standards in the process of index
calculation and their advocated index is widely used in empirical research on MRL standards.
Based on their views, new indices suitable for our study are presented below.
Firstly, we defined the indices measuring the gap of MRLs stringency as follows:

𝑖𝑖𝑣𝑣𝑣𝑣𝑣𝑣 = exp �

𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 − 𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶
�
𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶(4.6)

where MRLCACvkt (MRLCHNvkt) is the MRL of pesticide k in agri-products v specified by CAC(China) in year t. K denotes the number of pesticide items specified for agri-product v in MRLs,
and the number of MRL items of agri-product v. The Ivt range is [0, 2.72]—if the value is closer
to 2.72, then China's MRL regulation of agri-product v is more stringent; if the value is closer to0, then the CAC’s MRL regulation of agri-product v is more stringent; and if the value is closer
to 1, then the CAC and China have the same level of MRL regulations for agri-product v.

31

The outputs of agri-products v involved is not produced by each importing country and results in the

missing data. This paper uses the gross outputs of agri-products exported by the importing country to theworld in that year to represent its supply capacity and expand the observation.
- 55 -

In addition, this paper also introduces the heterogeneity indices proposed by Shingal et al.
for a robustness test (Shingal et al., 2021). They simply averaged the relative stringency index of
product items v by using the total pesticide items, K; thus, their heterogeneity indices have stablecharacteristics that do not vary with the intensity of the regulations. The simple averaging method
prevents a certain product (v) from being given too much weight when there are many specified
pesticide types.
Similarly, we defined the relative stringency indices as follows:

𝑖𝑖𝑣𝑣𝑣𝑣𝑣𝑣 =𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 − 𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶
𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 + 𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶(4.7)

Then, we simply averaged 𝑖𝑖𝑣𝑣𝑣𝑣𝑣𝑣 by using the total pesticide items, K. The heterogeneity

indices were given as follows:

𝐾𝐾

1
𝐼𝐼𝑣𝑣𝑣𝑣 = � 𝑖𝑖𝑣𝑣𝑣𝑣𝑣𝑣
𝐾𝐾

(4.8)

𝑘𝑘=1

where the range of 𝐼𝐼𝑣𝑣𝑣𝑣 is [-1, 1]— if the value is closer to -1, then the CAC’s MRL regulation for

agri-product v is more stringent; if the value is closer to 1, then China's MRL regulation for agriproduct v is more stringent; and if the value is closer to 0, then the CAC and China have the samelevel of MRL regulations for agri-product v.
Due to the differences in the classifications and names of MRL items between China and
CAC, this study unified the names of agri-products and pesticides and reclassified the foods ofboth entities according to customs-based HS-2 digit codes (see Table 4.2). Taken together, theMRLs of China and the CAC increased on an annual basis; this reflects an overall increase inpesticide residue control. However, the number of pesticides specified by China alone isincreasing rapidly, whereas the number of pesticides specified by the CAC alone is gradually
decreasing; this signifies that China is developing its pesticide regulations more rapidly than the
CAC.
Table 4.2 The number of pesticides specified in the MRLs of China and CAC

Year

China

CAC

Both

China only

CAC only

2008

137

160

86

51

74

2013

322

189

127

195

62

2015

386

206

172

214

34

2017

432

218

188

244

30

2019

434

230

192

242

38

- 56 -

Notably, the pesticides collected under “both” in Table 4.2 comprise pesticide items
specified by China but not by the CAC and vice versa, which led to missing data. Since neither
China nor the CAC defined a default value for the MRLs, this study used the maximum values intheir MRL databases as a supplement for their default values, which correspond to the minimum
stringency of the MRLs in a given year.
4.4.1.3 Variable implication and data source
The meanings of the variables involved in the empirical model and the source of the sample
data are summarized in Table 4.3. The Table 4.4 presents the statistical characteristics of thevariables.

Table 4.3 Variable implication and data source
Variable

Definition

Implication

failvijt"failure" event

Trade disruption

PROvitcounnumijt
Tariffvit

MRLCHNvkt

MRLCACvkt

GDPjt

Output of exporting
country (China)
trade relations
combinations

Supply capacity
Product competitivenessvariable costs

MRLs standard ofCore explanatory

MRLs standard of
CAC
GDP of importing
country

World Integrated Trade
Solution (WITS) 32

and market diversification

tariffChina

Data source

variableCore explanatory
variable
Consumption capacity

National food safety
standard—Maximum residue
limits for pesticides in food 33
CAC pesticide residue
database 34
World Development
Indicators (WDI) 35

32

Sourced from online database: World Integrated Trade Solution (WITS) database

33

Sourced from online database: National food safety standard-Maximum residue limits forpesticides in food34

Sourced from online database: CAC pesticide residue database

35

Sourced from online database: World Development Indicators (WDI) database- 57 -

contig

contiguity

Trade convenience

comlangcommon language

Trade convenience

DISTijGeographicaldistance

CEPII 36

fixed cost

4.4.2 Estimation results
The estimation results of the cloglog, probit, and logit models are shown in Tables 5 and 6,
respectively. The core explanatory variables in columns (1), (3), and (5) are the heterogeneity
indices Ivt-all, which includes all pesticides specified by China and the CAC. The explanatory
variables in columns (2), (4), and (6) are the heterogeneity indices Ivt-both, which only include
pesticides jointly specified by China and the CAC. Notably, while the dependent variable failvijt
is a binary variable, the heterogeneity indices of the core explanatory variables do not takelogarithms; thus, the coefficient of the heterogeneity indices is semi-elastic and the coefficientsof the other control variables are elastic. Moreover, all heterogeneity indices are negative,
indicating that China’s MRL regulation on agri-product v is higher than that of CAC. Therefore,
in the regression process, this study took it as a positive number, situating a larger difference
between the indices as indicating that China is stricter than the CAC.
In columns (1)– (6) of Table 4.5, the heterogeneity indices of the core explanatory variables
and coefficients of the control variables show unified characteristics.
The Ivt coefficients were all negative and statistically significant; this illustrates that the
improvement of China's MRL standards helps stabilize the bilateral trade relations of agri-foodproducts. Based on the regression results of the cloglog model, the stringency of China's MRLs
is one percent higher than that of the CAC; that is, every one percent increase in Ivt will prolong
the duration of China's agri-food product export by 0.32%–0.68%. The regression coefficient of
the heterogeneity index Ivt-all is significantly higher than that of Ivt-both, indicating that the
pretreatment of pesticide types and the default value of MRLs in the sample data is meaningful.
The high significance of both regression coefficients proves that data pre-processing did not affectour basic conclusions.
In terms of other controlled variables, lnGDPjt, as a typical representative of the importer’s
consumption demand, has significant negative coefficients; this clearly indicates that higher
import demand reduces the hazard of trade disruption.
Meanwhile, the regression coefficient of lncounnum is negative and highly significant.

36

Sourced from online database: CEPII database- 58 -

Notably, the exporter variable in year t was controlled to distinguish between heterogeneous
products. Product types widely demanded by the markets were assumed to face less hazard
because they are more competitive.
Table 4.4 Descriptive statistics of variables

Unit

Obs.

Mean

St.DevMin.

Max.

failvijt

13,001

0.34830.47640.00001.0000Ivt-all_Li

13,001

12.2456

2.05126.752116.6506

1.0510Ivt-both_Li

13,001

6.5644

1.41342.43069.6122

1.0451

Ivt-all_Shingal

13,0018.0206

4.2214

0.109016.6563

1.0830Ivt-both_Shingal

13,001

3.76102.77710.01499.61641.086013,00125.6686

1.844519.502730.6569

1.4040lncounnum

13,0013.4316

0.52260.00004.31751.1330contig13,0010.10950.31230.00001.00001.5064

comlang

13,0010.02680.16140.00001.00001.1046lnGDPjt

USD

VIF

lnPROitTonne

13,001

9.3341

3.2682-6.2146

14.4488

1.1550lnPROjt

Tonne

13,001

4.52644.2485-9.210317.3592

1.2150lnDISTijkm.

13,0018.91040.5041

6.86249.86771.5895ln(1+Tariffvit)%

13,001

1.69591.35230.00006.61741.1167

Note: Ivt-all is the heterogeneity indices calculated by all pesticides including China and CAC, and Ivt-

both denote the heterogeneity indices calculated only for pesticides prescribed jointly by China and

CAC. For avoiding bias in the estimation due to the large numerical difference between variables, thisthesis expands the heterogeneity indices I for ten times.

The variable contigs, comlang, and lnDISTij are considered important factors affecting costsin the general trade gravity model. Trade combinations with cultural proximity, a common
language, and close distance share lower costs. In contrast, trade relationships with lower trade
costs are better able to withstand the negative impact of the market and reduce the probability oftrade interruption. Accordingly, the regression results show that contig and comlang havenegative coefficients and lnDISTij has positive coefficients.

- 59 -

Table 4.5 Estimation results by applying heterogeneity indices (Li Y)

Cloglog(1)
Ivt-all_Li

Probit(2)-0.6750(3)(4)-0.5527***(-4.6610)

lnGDPjt
lncounnum
contigcomlang
lnPROit-0.1184-0.1184***(6)-0.8968(-5.1161)***(-5.0107)-0.2665***(-4.9408)
***

(5)

***

-0.3263***

Ivt-both_Li

Logit-0.4321***(-5.4171)-0.0948***-0.0949***(-5.3002)-0.1551***-0.1553***(-12.2009)(-12.2076)(-12.5266)(-12.5354)(-12.4203)(-12.4278)-0.3834***-0.3827***-0.2998***-0.2993***-0.4946***-0.4939***(-9.7941)(-9.7773)(-9.5591)(-9.5426)(-9.6063)(-9.5910)-0.2820-0.2807-0.1975-0.1968-0.3392***-0.3379***

***

***

***

***

(-4.2773)(-4.2564)(-4.2141)(-4.1993)(-4.2882)(-4.2723)-0.4738***-0.4738***-0.3634***-0.3638***-0.6063***-0.6067***(-3.9058)(-3.9060)(-4.4034)(-4.4071)(-4.2781)(-4.2809)-0.0280-0.0280-0.0235-0.0235-0.0378***-0.0379***

***

***

***

***

(-5.5516)(-5.5576)(-5.8710)(-5.8789)(-5.7695)(-5.7761)

0.0111**0.0110**0.0098**0.0097**0.0159**0.0157**(2.7654)(2.7366)(3.1801)(3.1491)(3.1250)(3.0943)

0.15770.15850.11610.11650.1931***0.1938***(3.9471)(3.9667)(3.9664)(3.9801)(3.9504)(3.9639)0.1002***0.1003***0.0787***0.0788***0.1284***0.1286***(8.0990)(8.1102)(8.4685)(8.4834)(8.3375)(8.3501)

2.89232.30762.87532.3982

4.6799***3.9040***(5.2468)(4.3175)(6.5561)(5.6339)(6.3715)(5.4750)

Year fixed

Yes

Yes

Yes

Yes

Yes

Yes

Product fixed

Yes

Yes

Yes

Yes

Yes

Yes

Observation

13001

13001

13001

13001

13001

13001

lnPROjt
lnDISTijln(1+Tariffvit)
_cons

***

***

***

***

***

***

***

***

Note: p < 0.05, p ** < 0.01, p *** < 0.001, the brackets are robust standard error.

- 60 -

Table 4.6 Estimation results by applying heterogeneity indices (Shingal A)

Cloglog(1)
Ivt-all_Shingal

Probit(2)-0.9280(3)(4)-0.7625***(-4.5052)

lnGDPjt-0.1184-0.1183***(6)-1.2367(-4.9400)***(-4.8414)-0.4524***(-5.5620)
***

(5)

***

-0.5623***

Ivt-both_Shingal

Logit-0.7368***(-6.0440)-0.0948***-0.0948***(-5.9185)-0.1552***-0.1552***(-12.2042) (-12.1977) (-12.5252) (-12.5280) (-12.4200) (-12.4200)
lncounnum
contigcomlang
lnPROit-0.3891***-0.3821***-0.3049***-0.2986***-0.5028***-0.4927***(-9.9576)(-9.7621)(-9.7347)(-9.5201)(-9.7779)(-9.5667)-0.2820-0.2783-0.1976-0.1950-0.3392***-0.3349***

***

***

***

***

(-4.2764)(-4.2204)(-4.2172)(-4.1587)(-4.2888)(-4.2328)-0.4724***-0.4721***-0.3626***-0.3617***-0.6046***-0.6037***(-3.8943)(-3.8911)(-4.3929)(-4.3832)(-4.2664)(-4.2587)-0.0273-0.0276-0.0228-0.0232-0.0369***-0.0375***

***

***

***

***

(-5.4147)(-5.4896)(-5.7118)(-5.8107)(-5.6150)(-5.7092)

0.0113**0.0107**0.0099**0.0094**0.0161**0.0153**(2.8141)(2.6547)(3.2261)(3.0626)(3.1713)(3.0065)

0.15760.16030.11600.11780.1930***0.1961***(3.9444)(4.0115)(3.9630)(4.0234)(3.9486)(4.0086)0.1005***0.1014***0.0789***0.0798***0.1288***0.1301***(8.1195)(8.1943)(8.4898)(8.5822)(8.3590)(8.4422)2.66012.32742.68602.41424.3723***

3.9277***(4.9106)(4.3490)(6.2247)(5.6656)(6.0487)(5.5039)

year fixed

Yes

Yes

Yes

Yes

Yes

Yes

Product fixed

Yes

Yes

Yes

Yes

Yes

Yes

Observation

13001

13001

13001

13001

13001

13001

lnPROjt
lnDISTijln(1+Tariffvit)
_cons

***

***

***

***

***

***

***

***

Note: p* < 0.05, p ** < 0.01, p *** < 0.001, the brackets are robust standard error.

The terms ln(1+Tariffvit) were highly significant in all the regressions. The sign of thecoefficient illustrates that duties intensify trade interruptions and shorten trade durations, which
is logically consistent with our expectations.
lnPROit had a significant negative regression coefficient, indicating that the exporters’
excellent supply capacity is an eventful aspect of reducing trade hazards, and numerous low-priceand high-quality products will be sought by the market, thereby enhancing the stability of traderelations. However, the significance was weaker than that of lnPROit, perhaps due to the impacts- 61 -

of product diversification and complementarity. Considering that lnPROit is the controlled
variable in this study, this paper will not discuss this in more detail.

4.4.3 Robustness test
By applying heterogeneity indices of (Li Y) and (Shingal A) and using the log, probit, andlogic models, six main groups of regression results were obtained. Columns (1) and (2) in Table
4.5 list the basic regressions; the other regression results were used as robustness tests. As can beseen from Table 4.5 and 4.6, the regression coefficients are all statistically significant. The
coefficients of the heterogeneity indices Ivt-all and Ivt-both were similar. The sign and statisticalsignificance of the variables did not change. Thus, the regression results are robust.

4.5 Conclusion
This study sought to examine the effect of food safety standards on trade duration. We used
a set of Chinese data from 2008 to 2019 on agri-food product exports to 115 countries at the HS6 level, and performed empirical description and analysis with a trade survival analysis. Using
this data, we established the distribution of trade relations in China's agri-food product exports.
Discrete-time hazard models (cloglog) were adopted to analyze the impact of food safety
standards represented by MRLs on the export hazard rate of agri-food products in China.
We found that 51.8% of China's agri-food export trade relations can only last for one year,
14.2% can last for five years or more, and 2.2% can last for 10 years or more; the average trade
duration was 3.85 years. As the world's fifth-largest exporter of agri-products, China’s short trade
duration was unexpected. Notably, the survival rate also presents negative duration dependence.
In terms of parameter analysis, we constructed a trade gravity equation based on the log–log
estimator in the survival analysis. We introduced a heterogeneity index for the core explanatory
variable, which meaningfully quantified the stringency of food safety standards. The regression
results confirm that upgrading China's MRLs can lower the hazard of trade interruption, thus
stabilizing exports and extending trade duration. Each 1% increase in the relative stringency ofChina's MRL standards increases the duration of China's agri-food product export trade by
0.32%–0.68%. The remaining control variables are statistically significant, indicating that thevariable settings of our model were appropriate. Regarding limiting the reduction of duties ininternational trade and other factors that cannot be controlled, we discovered that the agri-foodproduction capacity of exporters is a crucial factor in stabilizing trade relations.
For many developing countries, the rapid loss of agri-food exports will create obstacles toexport growth; thus, it is critical to study trade duration and influencing factors in such contexts.
- 62 -

The factors analyzed in this study may not be easily changed through policies. However, our study
offers some insights on the Chinese context that may be worth applying to other developing
countries. First, developing southern countries may improve their discursive power and thecompetitiveness of their products in the international market by accelerating their construction of
MRL standards 37 ; to date, northern countries still generally have the most stringent MRL
standards. Second, increasing investment in their inspection institutions—specifically in researchand detection 38—may help nations control export quality, enhance their international images, andattract high premiums for their products. Customs will refuse agri-products with excessive
pesticide residue, which can cause huge economic losses and resource waste. Meanwhile, using
online marketplaces can strengthen the circulation of agri-products, reduce food waste, andincrease agri-product yields 39. In countries largely dependent upon agriculture, food security andnational development depend on the tradable output of high-quality agri-food products.
Subsequent studies may extend the work of this study to the firm-country or firm-product levels.
To date, research on MRLs remains limited. The profound changes in food safety standards inrecent years, such as MRLs and economic dynamics, can be clarified through studies that apply
more recent and complete data.

37

Sourced from government document: Reply - suggestions on strengthening the supervision ofpesticide use
38

Sourced from government administration: Carry out the verification of the technical ability ofnational agricultural product quality and safety inspection and testing
39

Sourced from official report: Suggestions on the construction of agricultural products

circulation system- 63 -

Chapter 5 Conclusion
In the study of measure protection, we measured the stringency of the Chinese MRLs
compared with the CAC standards using Stringency Indices. The stringency indexes of about 80%
of China’s MRLs were greater than one, indicating higher stringency than the CAC standards.
However, the high stringency indices do not prove that China is using MRLs as non-tariffmeasures or for trade protectionism. The regression results indicate that the factors driving therise in stringency indices are public demand for higher food safety and the government’s positive
response to food safety incidents. We therefore conclude that China’s MRLs have not become anew trade protection policy tool, and are not being used as an alternative to a tariff. In the longrun, the growth in China’s demand for food and the constraints of agricultural resources determinethat the trends in food import and MRL development is irreversible.
We cannot ignore the role of media reports in reflecting public food safety demands,
increasing public food safety awareness, and promoting the revision of food safety standards.
Government agencies should take the initiative to use media channels to guide public participationin the supervision and formulation of food safety standards, so that relevant government
departments improve their governance. In the era of rapid information dissemination via theinternet, timely and transparent disclosure of food safety incidents, and scientific popularizationof food safety knowledge is helpful in avoiding irrational public focus on food safety, which
drives politicians to take excessive action as a result of “political precaution”, resulting in
unreasonably high food safety standards.
In terms of export promotion effect, this thesis constructs a Quality Heterogeneous Firm
Trade (QHFT) model to explain the influence of MRLs on the exports and derives an appropriate
empirical model. Its basic logic is: when the MRLs in importing country are more stringent, firms
in exporting countries must comply with stricter MRLs. Compared with relatively loose MRLsof exporting countries, produced according to MRLs of importing countries can enhance productquality. Consumers in importing countries with quality preference perceive the improvement of
product quality and increase consumer demand. But the relative improvement of quality requires
input, resulting in additional costs for firms (products). When the MRLs in exporting country are
more stringent, the products produced in the exporting country make a high-quality impression
on the consumers, and the firms do not input additional trade costs in this case. The
comprehensive impact of MRLs on trade should be determined by the demand-enhancing effect,
variable trade-cost effect and fixed trade-cost effect.
We establish a relatively new and complete MRLs database of China and the EU from 2008
to 2020 for empirical analysis. The heterogeneity indices integrating quantity and level ofstandards to measure the relative stringency of MRLs between China and EU. PPML Fixed Effect(FE) model is used to handle the heteroscedasticity, inflated zeros and endogeneity in trade data.
- 64 -

Finally, the robustness of some key influencing factors is tested. We found more stringent EU
MRLs did not curb food exports from China to the EU. A more plausible explanation is that thestricter MRLs signal to consumers that the quality of products has been improved, and thus
generate the demand-enhancing effect that can boost consumption demand of importing countries.
The demand-enhancing effect plays a leading role and promotes the agri-food exports from China
to the EU. Meanwhile, the EU MRLs is still more stringent than China in general, but the gap
continues to narrow and the stringency of certain pesticides MRLs in China has exceeded that of
EU. The rapid upgrading of China's food safety standards can dynamically adjust the domestic
agricultural industry to high quality and efficiency. China's (exporter) initiative to adopt more
stringent domestic MRLs standards has led even more boost effect on exports than passively
complying the EU (importer) MRLs.
Regarding trade duration analysis, the aim of this thesis is to carry forward the study of foodsafety standards on trade duration. We used a set of the China export data of agri-food products
to 115 countries at the HS-6 level in 2008 to 2019, and performed empirical description andanalysis by the trade survival analysis. With these data, we demonstrate the distribution of traderelations on China's agri-food products exports. The discrete-time hazard models (cloglog) are
adopted to analyze the impact of food safety standards represented by MRLs on the export hazard
rate of agri-food products in China.
For China's agri-food export, 51.8% of the trade relations can only last for one year, only
14.2% of the trade relations can last for 5 years or more, and only 2.2% of which can last for 10
years or more, with the average trade duration of 3.85 years. As the world's fifth largest exporterof agricultural products, the short trade duration of China seems unexpected, and the survival rate
also presents a negative duration dependence.
In terms of parameter analysis, we constructed the trade gravity equation based on thecloglog estimator in survival analysis. As for the core explanatory variable, we introduce theheterogeneity index, which provides a fabulous method to quantify the stringency of food safety
standards. The regression results confirm the upgrading of China's MRLs can lower the hazard of
trade interruption, thus helping to stabilize the export and extend trade duration. Each 1% increasein the relative stringency of China's MRLs standards will increase the duration of China's
agricultural food products export trade by 0.32% to 0.68%. The remaining control variables were
statistically significant, indicating that the variable setting of our model is appropriate. For thelimit reduction of duty in international trade and other factors that cannot be controlled, we
discover the agri-food production capacity of exporter is also a crucial factor in stabilizing traderelations.

- 65 -

References
Augustin-Jean, L., & Xie, L. (2016). Food Safety Standards and Market Regulations as
Elements of Competition-Case Studies from China's International Trade. Asian J. WTO& Int'l Health L & Pol'y, 11, 289.
Baldwin, R., & Harrigan, J. (2011, May). Zeros, Quality, and Space: Trade Theory and Trade
Evidence. American Economic Journal-Microeconomics, 3(2), 60-88.
https://doi.org/10.1257/mic.3.2.60
Besedes, T., & Blyde, J. (2010). What drives export survival? An analysis of export duration in
Latin America.
Besedes, T., & Prusa, T. J. (2006, Feb). Ins, outs, and the duration of trade. Canadian Journal of
Economics-Revue Canadienne D Economique, 39(1), 266-295. https://doi.org/DOI
10.1111/j.0008-4085.2006.00347.x
Besedeš, T., & Prusa, T. J. (2006). Product differentiation and duration of US import trade.
Journal of International Economics, 70(2), 339-358.
https://doi.org/10.1016/j.jinteco.2005.12.005
Brenton, P., Saborowski, C., & von Uexkull, E. (2010). What Explains the Low Survival Rateof Developing Country Export Flows? The World Bank Economic Review, 24(3), 474499. https://doi.org/10.1093/wber/lhq015
CEPII. CEPII database. Retrieved June 3rd fromhttp://www.cepii.fr/cepii/en/bdd_modele/bdd.asp
Chaney, T. (2008, Sep). Distorted Gravity: The Intensive and Extensive Margins ofInternational Trade. American Economic Review, 98(4), 1707-1721.
https://doi.org/10.1257/aer.98.4.1707
Chen, C. L., Yang, J., & Findlay, C. (2008, Apr). Measuring the effect of food safety standards
on China's agricultural exports. Review of World Economics, 144(1), 83-106.
https://doi.org/10.1007/s10290-008-0138-z
Chen, M. X., Wilson, J. S., & Otsuki, T. (2008). Standards and export decisions: Firm-level
evidence from developing countries. Journal of International Trade & Economic
Development, 17(4), 501-523.
https://doi.org/https://doi.org/10.1080/09638190802250027- 66 -

Codex Alimentarius Commission. CAC pesticide residue database. Retrieved October 23rd
from http://www.fao.org/fao-who-codexalimentarius/codextexts/dbs/pestres/pesticides/en/
Codex Alimentarius Commission. Codex Pesticides Residues in Food Online Database.
Retrieved November 19th from http://www.fao.org/fao-who-codexalimentarius/codextexts/dbs/pestres/en/
Codex Alimentarius Commission. (1993). Codex Classification of Foods and Animal Feeds(CAC/MISC 4–1993). https://www.fao.org/fao-who-codexalimentarius/codex-texts/allstandards/en/
Curzi, D., Luarasi, M., Raimondi, V., & Olper, A. (2018). The (lack of) international
harmonization of EU standards: import and export effects in developed versus
developing countries. Applied Economics Letters, 25(21), 1552-1556.
https://doi.org/10.1080/13504851.2018.1430327
Drogué, S., & DeMaria, F. (2012). Pesticide residues and trade, the apple of discord? Food
Policy, 37(6), 641-649. https://doi.org/10.1016/j.foodpol.2012.06.007
Esteve-PÉRez, S., Requena-Silvente, F., & PallardÓ-Lopez, V. J. (2013). The Duration of FirmDestination Export Relationships: Evidence from Spain, 1997-2006. Economic Inquiry,
51(1), 159-180. https://doi.org/10.1111/j.1465-7295.2012.00460.x
Esteve‐Pérez, S. (2021). Previous experience, experimentation and export survival: Evidence
from firm‐product‐destination level data. The World Economy, 44(9), 2638-2682.
https://doi.org/10.1111/twec.13115
European Commission. EU Eurostat database. Retrieved November 14th fromhttps://ec.europa.eu/eurostat/data/database
European Commission. EU Eurostat database for pesticide. Retrieved November 16th fromhttps://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/mrls/?event=search.pr
Farnsworth, D. (2012). Maximum Residue Limits: Protectionism or Food Safety?
https://ideas.repec.org/p/ags/aaea12/124931.html
Fernandes, A. M., Ferro, E., & Wilson, J. S. (2019, Jun). Product Standards and Firms' Export
Decisions. World Bank Economic Review, 33(2), 353-374.
https://doi.org/10.1093/wber/lhw071
Ferro, E., Otsuki, T., & Wilson, J. S. (2015, Jan). The effect of product standards on agricultural
exports. Food Policy, 50, 68-79. https://doi.org/10.1016/j.foodpol.2014.10.016- 67 -

Fiankor, D.-D. D., Curzi, D., & Olper, A. (2021). Trade, price and quality upgrading effects ofagri-food standards. European Review of Agricultural Economics, 48(4), 835-877.
https://doi.org/10.1093/erae/jbaa026
Food and Agriculture Organization. FAOSTAT database. Retrieved August 18th fromhttp://www.fao.org/faostat/en/#home
Food and Agriculture Organization. (2021). FAO's Food Outlook: Developing countries buoy
global food trade. https://www.fao.org/news/story/en/item/1329605/icode/
Food Codex Data. National food safety standard-Maximum residue limits for pesticides in food.
Food Codex Data. Retrieved June 5th from https://www.sdtdata.com/fx/fmoa/tsLibList
Garred, J. (2018, Sep). The persistence of trade policy in China after WTO accession. Journal ofInternational Economics, 114, 130-142. https://doi.org/10.1016/j.jinteco.2018.06.001
Han, X., Zhang, Y., & Hu, X. (2021). China agricultural sector development report 2020.
China Agricultural Science and Technology Press.
Hejazi, M., Grant, J., & Peterson, E. (2016). Hidden Trade Costs? Maximum Residue Limits and
US Exports to Trans-Atlantic and Trans-Pacific Trading Partners.
https://EconPapers.repec.org/RePEc:ags:aaea16:235847
Hess, W., & Persson, M. (2011). The duration of trade revisited. Empirical Economics, 43(3),1083-1107. https://doi.org/10.1007/s00181-011-0518-4
Hess, W., & Persson, M. (2011, Nov). Exploring the duration of EU imports. Review of World
Economics, 147(4), 665-692. https://doi.org/10.1007/s10290-011-0106-x
Huang, J., Wang, S., & Xiao, Z. (2017). Rising Herbicide Use and Its Driving Forces in China.
The European Journal of Development Research, 29(3), 614-627.
https://doi.org/10.1057/s41287-017-0081-8
Ishaq, M., Ping, Q., Haq, Z., Li, C. G., & Tong, C. (2016). Maximum residue limits andagrifood exports of China: choosing the best estimation technique. Agricultural
Economics-Zemedelska Ekonomika, 62(2), 78-92. https://doi.org/Doi
10.17221/17/2015-Agricecon
Jiang, N., Wang, T., & Li, G. (2018). Record of China's pesticide development in the past 40
years of reform and opening up. People's Forum. Retrieved July 8th fromhttp://www.rmlt.com.cn/2018/1112/532826.shtml- 68 -

Kareem, F. O., Martinez-Zarzoso, I., & Brummer, B. (2018, Jan). Protecting health orprotecting imports? Evidence from EU non-tariff measures. International Review of
Economics & Finance, 53, 185-202. https://doi.org/10.1016/j.iref.2017.08.012
Li, Y., & Beghin, J. C. (2014, Apr). Protectionism indices for non-tariff measures: An
application to maximum residue levels. Food Policy, 45, 57-68.
https://doi.org/10.1016/j.foodpol.2013.12.005
Li, Y., Xiong, B., & Beghin, J. C. (2017). The political economy of food standard
determination: International evidence from maximum residue limits. In Nontariffmeasures and international trade (pp. 239-267). World Scientific.
https://doi.org/https://doi.org/10.1142/9789813144415_0014
Liu, P., & Ma, L. (2016, Aug). Food scandals, media exposure, and citizens' safety concerns: A
multilevel analysis across Chinese cities. Food Policy, 63, 102-111.
https://doi.org/10.1016/j.foodpol.2016.07.005
Macrotrends.net. West Texas Intermediate (WTI) crude oil prices. Retrieved September 21 fromhttps://www.macrotrends.net/1369/crude-oil-price-history-chart
Mao, X., Du, R., & Yang, J. (2019). An Economic Analysis on the Preference of China’s
Agricultural Import Policy and its Policy Implication (in Chinese). Journal of
Agrotechnical Economics(10), 129-142. https://doi.org/10.13246/j.cnki.jae.2019.10.009
Marette, S., & Beghin, J. (2010). Are Standards Always Protectionist? Review of International
Economics, 18(1), 179-192. https://doi.org/10.1111/j.1467-9396.2009.00856.x
Medin, H. (2019, Apr). Trade barriers or trade facilitators? The heterogeneous impact of foodstandards in international trade. World Economy, 42(4), 1057-1076.
https://doi.org/10.1111/twec.12756
Melitz, M. J. (2003). The Impact of Trade on Intra-Industry Reallocations and Aggregate
Industry Productivity. Econometrica, 71(6), 1695-1725. https://doi.org/10.1111/14680262.00467
Ministry of Agriculture and Rural Affairs of the PRC. (2012). National food safety standard—
Maximum residue limits for pesticides in food (GB 2763-2012). foodmate.net. Retrieved
May 28st from http://down.foodmate.net/standard/sort/3/31784.html
Ministry of Agriculture and Rural Affairs of the PRC. (2014). National food safety standard—
Maximum residue limits for pesticides in food (GB 2763-2014). foodmate.net. Retrieved
May 15st from http://down.foodmate.net/standard/sort/3/40372.html- 69 -

Ministry of Agriculture and Rural Affairs of the PRC. (2016). National food safety standard—
Maximum residue limits for pesticides in food (GB 2763-2016). foodmate.net. Retrieved
May 18st from http://down.foodmate.net/standard/sort/3/50617.html
Ministry of Agriculture and Rural Affairs of the PRC. (2018). National food safety standard
Maximum residue limits for 43 pesticides in food (GB 2763.1-2018). foodmate.net.
Retrieved May 19st from http://down.foodmate.net/standard/sort/3/53151.html
Ministry of Agriculture and Rural Affairs of the PRC. (2021a). The 52nd annual meeting of theCodex Alimentarius Committee on Pesticide residues was held online.
http://www.moa.gov.cn/xw/zwdt/202107/t20210727_6372933.htm
Ministry of Agriculture and Rural Affairs of the PRC. (2021b). Analysis of the National
Standard for Food Safety - Maximum Residue Limits of Pesticides in Food (2021
Edition). http://www.jgs.moa.gov.cn/gzjb/202108/t20210826_6374940.htm
Ministry of Agriculture and Rural Affairs of the PRC. (2021c). Carry out the verification of thetechnical ability of national agricultural product quality and safety inspection andtesting. http://www.jgs.moa.gov.cn/jyjc/202104/t20210429_6367022.htm
Ministry of Agriculture and Rural Affairs of the PRC. (2021d). China's pesticide residue limit
standard exceeded 10000 items, fully covering the approved pesticide varieties andmain plant-derived agricultural products in China.
http://www.moa.gov.cn/xw/zwdt/202104/t20210401_6365132.htm
Ministry of Agriculture and Rural Affairs of the PRC. (2021e). Reply - suggestions on
strengthening the supervision of pesticide use.
http://www.moa.gov.cn/govpublic/ZZYGLS/202108/t20210806_6373686.htm
Ministry of Agriculture and Rural Affairs of the PRC. (2021f). Suggestions on the constructionof agricultural products circulation system.
http://nmfsj.moa.gov.cn/xwzx_25646/dfdt/202109/t20210922_6376969.htm
Molina, A. C., & Fugazza, M. (2009). The determinants of trade survival.
https://EconPapers.repec.org/RePEc:gii:giihei:heidwp05-2009
Nitsch, V. (2009, Apr). Die another day: duration in German import trade. Review of World
Economics, 145(1), 133-154. https://doi.org/10.1007/s10290-009-0008-3
Otsuki, T. (2001). What price precaution? European harmonisation of aflatoxin regulations andAfrican groundnut exports. European Review of Agriculture Economics, 28(3), 263284. https://doi.org/10.1093/erae/28.3.263- 70 -

Peterson, E. B., Grant, J. H., & Rudi‐Polloshka, J. (2017). Survival of the Fittest: Export
Duration and Failure into United States Fresh Fruit and Vegetable Markets. American
Journal of Agricultural Economics, 100(1), 23-45. https://doi.org/10.1093/ajae/aax043
Santos Silva, J. M. C., & Tenreyro, S. (2011). Further simulation evidence on the performanceof the Poisson pseudo-maximum likelihood estimator. Economics Letters, 112(2), 220222. https://doi.org/10.1016/j.econlet.2011.05.008
Seok, J. H., Saghaian, S., & Reed, M. R. (2018, Sep). The 'Signaling Effect' and the impact ofhigh maximum residue limit standards on U.S. vegetable exports. Economic Analysis
and Policy, 59, 150-159. https://doi.org/10.1016/j.eap.2018.04.004
Shingal, A., Ehrich, M., & Foletti, L. (2021, Mar). Re-estimating the effect of heterogeneous
standards on trade: Endogeneity matters. World Economy, 44(3), 756-787.
https://doi.org/10.1111/twec.13015
Silva, J. M. C. S., & Tenreyro, S. (2006, Nov). The log of gravity. Review of Economics andStatistics, 88(4), 641-658. https://doi.org/DOI 10.1162/rest.88.4.641
State Council of the PRC. (2017). Opinions on improving the quality and safety risk early
warning and rapid response supervision system.
http://www.gov.cn/zhengce/content/2017-09/27/content_5227963.htm
Straume, H. M. (2017). Here today, gone tomorrow: The duration of Norwegian salmon
exports. Aquaculture Economics & Management, 21(1), 88-104.
https://doi.org/10.1080/13657305.2017.1262477
Sueyoshi, G. T. (1995). A class of binary response models for grouped duration data. Journal ofApplied Econometrics, 10(4), 411-431. https://doi.org/10.1002/jae.3950100406
Sun, D., Huang, J., & Yang, J. (2014). Do China's food safety standards affect agricultural
trade? The case of dairy products. China Agricultural Economic Review, 6(1), 21-37.
https://doi.org/10.1108/caer-06-2012-0062
Swinnen, J. (2018). The Political Economy of Agricultural and Food Policies. Palgrave
Macmillan. https://doi.org/10.1057/978-1-137-50102-8
Swinnen, J. F. M., & Vandemoortele, T. (2011, Jun). Trade and the Political Economy of FoodStandards. Journal of Agricultural Economics, 62(2), 259-280.
https://doi.org/10.1111/j.1477-9552.2011.00294.x
United Nations. UN Comtrade Database. Retrieved March 2nd from https://comtrade.un.org/- 71 -

Wang, N. (2010). Residue limits for pesticides and veterinary drugs in foods of major trading
countries and regions (in Chinese) (2 ed., Vol. 1). Standards Press of China.
Wang, P., Tran, N., Wilson, N. L. W., Chan, C. Y., & Dao, D. (2019, Jan 1). An Analysis of
Seafood Trade Duration: The Case of ASEAN. Marine Resource Economics, 34(1), 5976. https://doi.org/10.1086/700599
Winchester, N., Rau, M. L., Goetz, C., Larue, B., Otsuki, T., Shutes, K., Wieck, C., Burnquist,
H. L., de Souza, M. J. P., & de Faria, R. N. (2012, Aug). The Impact of Regulatory
Heterogeneity on Agri-food Trade. World Economy, 35(8), 973-993.
https://doi.org/10.1111/j.1467-9701.2012.01457.x
World Bank. World Development Indicators (WDI) database. Retrieved February 1st fromhttp://datatopics.worldbank.org/world-development-indicators/
World Integrated Trade Solution. World Integrated Trade Solution (WITS) database. Retrieved
June 3rd from https://wits.worldbank.org
World Trade Organization. WTO tariff database. Retrieved June 15th fromhttp://tariffdata.wto.org
Wu, H. (2016). Food scandal database of Chinese provinces. ZZCW. Retrieved December 28
from http://www.zccw.info/index
Xiong, B., & Beghin, J. (2012). Does European aflatoxin regulation hurt groundnut exportersfrom Africa? European Review of Agricultural Economics, 39(4), 589-609.
https://doi.org/10.1093/erae/jbr062
Xiong, B., & Beghin, J. (2014, Jul). Disentangling Demand-Enhancing and Trade-Cost Effectsof Maximum Residue Regulations. Economic Inquiry, 52(3), 1190-1203.
https://doi.org/10.1111/ecin.12082
Yang, B. X., Anderson, J., & Fang, Y. K. (2021, Jul 3). Trade duration of Chinese shrimp
exports. Aquaculture Economics & Management, 25(3), 260-274.
https://doi.org/10.1080/13657305.2020.1737891
Yeung, M. T., Kerr, W. A., Coomber, B., Lantz, M., & McConnell, A. (2017). Declining
International Cooperation on Pesticide Regulation: Frittering Away Food Security.
Springer.
Yuan, Y., & Chen, H. (2019, May). Empirical Analysis of China's Food Safety Notifications by
EU Rapid Alert System for Food and Feed (RASFF) from 2013 to 2017 (in Chinese).
Forum on Science and Technology in China(05), 181-188.
- 72 -

Zhang, D., & Tveterås, R. (2019). A fish out of water? Survival of seafood products fromdeveloping countries in the EU market. Marine Policy, 103, 50-58.
https://doi.org/10.1016/j.marpol.2019.02.030
Zhu, J., Li, T., & Lin, D. (2018). China's Agricultural Trade in Economic Opening-up:
Development, Challenges and Future Policy Alternatives (in Chinese). Issues inAgricultural Economy(12), 19-32. https://doi.org/10.13246/j.cnki.iae.2018.12.003- 73 -