Risk Drivers for Economically Motivated FoodAdulteration in China’s Farming Supply Chains
Yasheng Huang, Retsef Levi, Stacy Springs, Shujing Wang, Yanchong ZhengMassachusetts Institute of Technology, Cambridge, MA 02139, [email protected], [email protected], [email protected],
[email protected], [email protected]
We employ Heckman’s sample selection framework to empirically investigate whether and how structural
properties of China’s farming supply chains and the strength of governance within the regions in which the
supply chains operate jointly influence the risks of economically motivated adulteration (EMA) of food. We
introduce an innovative system-level supply chain perspective to study risks of EMA, and provide the first
multi-industry empirical analysis to demonstrate the value of studying EMA risks through a supply chain
lens. Our analysis focuses on the farming supply chains across five industries (eggs, honey, pork, poultry,
fish and seafood) in China. We leverage rich datasets including farming supply chain data, product sampling
data, and data related to the strength of governance. We define the important concept of supply chain
dispersion – the degree to which farming outputs are sourced from a dispersed network – and develop a
method to quantify dispersion in farming supply chains based on field data. We also develop new methods to
objectively measure the strength of city-level governance in China based on factual (as opposed to perception)
data. Our results highlight that both supply chain dispersion and weak local governance are associated with
higher EMA risks. The insights emerged from the analysis are valuable to food manufacturers, importers,
and regulators, and could ultimately allow them to more proactively and systematically identify, prevent,
and mitigate risks of EMA in food supply chains.
Keywords : Economically motivated food adulteration (EMA) | China | supply chain dispersion |
governance | Heckman’s sample selection model
1. Introduction
Food safety is undoubtedly a critical issue that concerns every single person in the world. The
above tainted infant formula scandal represents an example of economically motivated adulteration
(EMA) of food. Within the wide spectrum of food adulteration, some adulterations are due to
negligence or incompetence and considered unintentional, e.g., bacterial contamination due to bad
hygiene practices. Conversely, some adulterations are solely driven by malicious intentions, such
as bioterrorism. In between these two extremes is EMA, where some entities knowingly engage in
intentional, illegitimate actions with the primary goal of achieving economic gains. EMA is the
focus of the current paper.
Countries around the globe are challenged by various risks in and threats to the food sys-
tem, including EMA. The increasingly globalized nature of today’s food supply chains makes this
1
2 Risk Drivers for EMA in China’s Farming Supply Chains
challenge very complex to manage. For example, in the United States, 15% of food consumed is
imported, including 94% of seafood, 50% of fresh fruits, and 20% of vegetables. In 2015, over 35
million shipments of food imports arrived to the U.S. (U.S. GAO 2016, U.S. FDA 2016). Current
research and practices related to management of food adulteration risks largely focus on testing for
known harmful microbes and compounds in the final food products and, occasionally, inspecting
the manufacturing sites and their processes. While this is necessary, as a standalone approach it
faces several challenges (Kennedy 2008, Stokstad 2011, Szajek et al. 2016). First, the number and
variety of possible adulterants in food are essentially unbounded and ever-changing, requiring the
development of more robust testing methods to accommodate such complex and dynamic nature.
Second, even the most developed countries experience a serious shortage of sampling and testing
capacity. For example, the U.S. Food and Drug Administration (FDA) samples less than 2% of all
food import shipments each year (U.S. GAO 2016). Third, many adulteration activities occur at
the upstream part of food supply chains. Thus, merely relying on product sampling and manufac-
turing site inspection could often lead to delayed (or missed) detection until after negative impacts
on public health are realized. Indeed, several regulatory agencies have employed risk-based tools to
prioritize various activities such as shipment sampling (e.g., the FDA’s PREDICT tool; U.S. FDA
2015b). However, these tools primarily rely on past sampling results of a given country, product,
or company without visibility into the end-to-end supply chains. These challenges call for a more
proactive, systematic approach to tackle one of the world’s most pressing issues.
To this end, we bring forward a supply chain perspective to complement current approaches.
We empirically investigate whether and how structural properties of food supply chains and the
strength of governance within the regions in which the supply chains operate jointly impact EMA
risks in food products. Motivated by the fact that China produces 23% of the worldwide agricultural
outputs and is the world’s fourth largest exporter of agricultural products (UN FAO 2015, WTO
2016), we focus on China’s farming supply chains in various industries. Our multidisciplinary
analysis employs Heckman’s sample selection model (Heckman 1979) with rich datasets, including
multi-industry farming supply chain data, product sampling data in China’s domestic market and
in importing countries, and data related to the strength of governance in Chinese cities.
1.1. Contributions
Methodologically, our research exemplifies a systems, cross-disciplinary approach (Hammond and
Dube 2012, Zach et al. 2012) for studying complex agri-food systems by integrating the underlying
supply chain and governance aspects to examine EMA risks. First, we define the important concept
of supply chain dispersion – the degree to which farming outputs are sourced from a dispersed
network of farms – and develop a method to quantify supply chain dispersion based on field
Risk Drivers for EMA in China’s Farming Supply Chains 3
data. Such quantification allows us to empirically show that this structural property of a farming
supply chain is a significant risk driver associated with EMA. Second, we develop new methods
to quantify the strength of governance based on factual data, instead of relying on perception like
most widely-used indices (e.g., the Worldwide Governance Indicators by the World Bank; World
Bank 2016). Furthermore, we quantify the strength of city-level governance (rather than country-
level) in China. Such refined measurement captures the high regional variations in governance in
the country, thus allowing us to examine the association of local governance with EMA risks in
supply chains operating in the corresponding region.
From a practical standpoint, our research yields two key insights that pinpoint the critical struc-
tural and environmental factors in China’s farming supply chains that are associated with EMA
risks. First, we show that products from a more dispersed supply chain are associated with higher
EMA risks. Second, weak local governance is associated with less intensive quality control in the
corresponding region. Specifically, food manufacturers located in regions with weaker governance
are sampled less frequently, thus indirectly increasing EMA risks in the associated products. In
the Results and Discussion Section, we discuss how these insights can yield actionable strategies
for food manufacturers, importers, and regulators in China and abroad to more proactively and
effectively identify, prevent, and mitigate EMA risks at a more systematic level.
2. Theoretical and Statistical Framework2.1. Quantifying Supply Chain Dispersion
Our in-depth case studies of several EMA incidents (see Appendix A) indicate that the dispersion
of a Chinese food manufacturer’s farming supply network is associated with EMA risks. Take the
tainted infant formula scandal as an example. The most heavily involved dairy company, Sanlu
(defunct after the scandal), sourced its raw milk from over 50,000 small farms. The company’s
products contained the highest amount of melamine – 5,125 times higher than the European Union
safety limits (Lu et al. 2009). To the contrary, one of the very few clean companies, Sanyuan,
sourced 80% of its raw milk from 30 corporate-owned farms and the remaining 20% from large
cooperative farms (Chen et al. 2014). Exploratory evidence from this and other EMA incidents
suggest the following hypothesis:
H1. In China, products of manufacturers sourcing from more dispersed farming supply chains
are associated with higher EMA risks.
To quantify supply chain dispersion, we employ the concept of entropy in information theory
(Shannon 1948). Entropy measures unpredictability or uncertainty. A larger entropy means the
information encapsulated in a signal is more uncertain. Formally, we define supply chain dispersion,
D, of a food manufacturer sourcing from n farms as follows.
D=−n∑j=1
pj log(pj), where pj =vj∑n
k=1 vk. (1)
4 Risk Drivers for EMA in China’s Farming Supply Chains
Here pj is the fraction of total volume sourced from farm j, and vj is the output volume of farm j.
We capture two key properties of supply chain dispersion in this definition. First, a farming supply
chain involving a larger number of farmers inherently entails more uncertainty regarding whether
each individual farmer would engage in EMA. Second, if the supply coming from each farmer is
more evenly distributed (as opposed to concentrating in a single large farmer), there would also be
higher uncertainty in the extent of potential EMA in the pooled supply. Figure 1 illustrates both
properties: both a larger number of farms (B vs. A) and more evenly-distributed supply (C vs. B)
result in higher dispersion. In our empirical analysis, we quantify the supply chain dispersion of
941 food manufacturers across five industries – eggs, honey, pork, poultry, and seafood – based on
farming supply chain data that these companies register with China’s General Administration of
Quality Supervision and Inspection (AQSIQ) between 2010 and 2014 (see §4).
Dispersion Examples
M
Farm 1
𝑝1= 1
𝐷 = 0
M
Farm 2
𝐷 = 0.3
Farm 1
𝑝1= 0.5 𝑝2= 0.5
M
Farm 2
𝐷 = 0.14
Farm 1
𝑝1= 0.9 𝑝2= 0.1
A B C
Figure 1 Illustrative Examples of Supply Chain Dispersion. This figure shows three examples where a
manufacturer sources from one or two farms. Notations p1, p2, and D are defined in Eq. [1].
2.2. Quantifying the Strength of Governance
A massive scandal of gelatin adulteration in China (see Appendix A) suggests (weak) local gover-
nance as another potential risk driver for EMA. Gelatin is a commonly-used gelling agent in food
and pharmaceutical applications. Edible gelatin should be derived from collagen of animal skin
and bones. In 2012, Chinese authorities found that large-scale production of edible gelatin in Hebei
Province used instead much cheaper leather scraps that contain excessive toxic chromium (CCTV
2012b, Li 2012b). The focal company in this scandal was Xueyang Gelatin and Protein Plant.
Investigations revealed that the plant owner and several high-level officials in the local government
(including the Party Secretary and the People’s Congress Chairman of the county at that time)
were brothers of the same family, and financial records implicated bribery from the plant to local
officials (Li 2012a). Worse yet, shortly after the crackdown, the plant was allowed to reopen under
the same name in almost the same location, which was not formally terminated until September
2014 (CFDA 2014). Thus, another key hypothesis of ours is the following:
H2. In China, products manufactured in regions with weaker governance are associated with
higher EMA risks.
Risk Drivers for EMA in China’s Farming Supply Chains 5
The most widely-used measures of governance in social sciences are based on perception data
and at the country level (e.g., the Worldwide Governance Indicators by the World Bank and
the Corruption Perception Index by Transparency International; Transparency International 2015,
World Bank 2016). These measures pose two methodological challenges for our research. First,
China’s regulatory systems are highly localized and the variance of governance strength across
regions is high (Wang and You 2012, Quah 2013). Measuring governance at the country level cannot
capture regional variances. Second, the above measures have been criticized for various perception
and reporting biases in their data sources, thereby urging researchers to instead use factual data
for better measurement (Thompson and Shah 2005, Apaza 2009, Thomas 2010, Cobham 2013).
To address these challenges, we employ two new methods to measure the strength of city-level
governance in China based on objective data (see §4).
The first method utilizes regulatory misconduct cases reported by People’s Daily, the official
newspaper of the Chinese central government, between 2003 and 2015. The extent of regulatory
misconduct is widely acknowledged as an important signal for (weak) governance, including being
used in the aforementioned perceptional measures. We use misconduct case data starting in 2003
to include the entire span of Hu Jintao’s presidency (2003–2012) because the political agenda of
a presidency largely shapes China’s regulatory systems (Moses 2013, Bell 2015). We define a 0–5
governance ranking based on the ranks of government officials engaging in misconduct throughout
that time frame in each city (Table 1). A higher ranking is assigned to a city whose higher-rank
officials had engaged in misconduct, thus indicating weaker governance.
Table 1 Definition of Governance Ranking
Governance Rank of Officials Engaging in MisconductRanking Mayor Party Secretary Subordinate
5 Yes Yes Yes4 Yes Yes No3 Yes No Yes
(or) No Yes Yes2 Yes No No
(or) No Yes No1 No No Yes0 No No No
The second method utilizes value-added tax (VAT) data reported in the China Industrial Census,
published by the National Bureau of Statistics of China. The data record VAT credits and payments
by every industrial company with sales over 5 million RMB (about U.S.$600,000) in each city
between 2005 and 2007.1 By Chinese law, the VAT credit a company claims (as a percentage of
1 Although the Industrial Census data are available for 1996 to 2010, the VAT data are only available in these threeyears.
6 Risk Drivers for EMA in China’s Farming Supply Chains
input costs) and the VAT a company pays (as a percentage of revenue) are both regulated at a fixed
rate of 13% or 17%, depending on the industry (State Council of the People’s Republic of China
2008). Therefore, a company is suspected to have evaded VAT if it claimed a higher percentage in
VAT credit than the percentage it paid in a year. The more companies in a city are suspected to
have evaded VAT, the more likely that the local governance is weak. We thus use the fraction of
companies suspected to have evaded VAT in a city (averaged over the three-year data) as a second
measure of governance in that city. A higher fraction indicates weaker governance.
2.3. Defining a Manufacturer’s Risk Status
The key dependent variable in our analysis is whether or not a manufacturer in our farming supply
chain data has been involved in EMA incidents within the relevant time frame. We utilize market
sampling data by China’s Food and Drug Administration (CFDA) and shipment refusal data by
importing countries to construct this dependent variable. CFDA periodically conduct food product
sampling by taking products from local retailers and testing them against certain quality standards.
Importing countries routinely sample food import shipments at the border to detect and refuse
problematic products. When constructing our dependent variable, we only consider EMA related
to farming practices (see §4 and Appendix B).
We define three “status” labels for a manufacturer: high-risk if its products either failed in CFDA
sampling or was refused by an importing country at least once; low-risk if its products passed
all CFDA sampling; and unknown if its products were never sampled by the CFDA nor refused
by any importing country. Both high-risk and low-risk manufacturers are considered “sampled,”
whereas unknown manufacturers are not. Since we do not observe passing records of sampling by
importing countries, one caveat is that we potentially misclassify some low-risk manufacturers (only
if they also never failed CFDA sampling) as having an unknown status. Table A.2 in Appendix B
summarizes the number of manufacturers under each status across the five industries.
2.4. Statistical Framework
Because products may not be sampled uniformly randomly, we adopt Heckman’s sample selection
model (Heckman 1979) to account for potential sample selection biases. Specifically, the model
contains a selection regression and an outcome regression. The selection regression models the
chance that a manufacturer is sampled, and the outcome regression models the chance that a
manufacturer is high-risk. Let Si and Ri respectively denote manufacturer i’s sampling and risk
status: Si = 1 if it was sampled and 0 otherwise; Ri = 1 if it was high-risk and 0 if it was low-risk.
Our statistical model can be mathematically formulated as follows.
S∗i = γZi + εSi ,
R∗i = βXi + εRi .
Risk Drivers for EMA in China’s Farming Supply Chains 7
Here S∗i and R∗i are the unobserved latent variables and relate to manufacturer i’s status Si and
Ri as follows: Si = 1 if S∗i ≥ 0 and Si = 0 if S∗i < 0; Ri = 1 if R∗i ≥ 0 and Ri = 0 if R∗i < 0. The
vectors Zi and Xi represent the vectors of independent variables for manufacturer i. The vectors γ
and β are the vectors of coefficients associated with the independent variables. The key to capture
sample selection biases is to allow correlation between the two error terms εSi and εRi . We assume
that εSi and εRi follow a bivariate normal distribution with mean 0 and covariance matrix
Σ =
(σ2S ρσSσR
ρσSσR σ2R
), (2)
where ρ ∈ (−1,1) is the correlation coefficient. We allow arbitrary standard deviations σS and σR
(they are restricted to be equal to 1 in Heckman’s original model).
Our key independent variables in both regressions are supply chain dispersion, governance rank-
ing, and VAT evasion measures. In addition, we quantify other supply chain features (e.g., volume,
distances between farms and manufacturers) based on the farming supply chain data and include
them in the outcome regression (see §4). Finally, we also control for city population and GDP per
capita in both regressions. These variables are obtained from the latest city-level China census
data published in 2011.
3. Results and Discussion
We use a stepwise selection approach to determine the significant features in our model. In par-
ticular, we begin by including all independent variables that we conjecture would be correlated
with sampling and risk, then eliminate nonsignificant variables one-by-one (starting from the vari-
able with the highest p value), until all remaining variables are statistically significant. To further
strengthen the robustness of our results, we perform 300 iterations of stratified bootstrapping to
obtain the mean, median, and 90% confidence interval of the coefficients associated with the signif-
icant features. We stratify the data such that the proportion of high-risk, low-risk, and unknown
manufacturers, as well as the total number of manufacturers by industry in each bootstrapped
sample remain the same as in the original data. Table 2 summarizes the final set of significant
features and the corresponding bootstrapping results (see §4).
We highlight three observations. First, the feature of supply chain dispersion is significantly pos-
itive in the outcome regression. That is, Chinese food manufacturers sourcing from more dispersed
farming supply chains are associated with a higher chance to be high-risk, supporting H1. Second,
both regulatory features of governance ranking and VAT evasion are significantly negative in the
selection regression. This result shows that manufacturers located in Chinese cities with weaker
governance are sampled less frequently, thus potentially leading to high-risk manufacturers in these
cities escaping detection. Hence, we find indirect support of H2. Third, the correlation between
8 Risk Drivers for EMA in China’s Farming Supply Chains
the selection and outcome regression errors are significantly positive yet with a small magnitude.
This result implies that although manufacturers who are more likely to be high-risk have a higher
chance to be sampled, current sampling overall does not target high-risk manufacturers effectively.
Table 2 Significant Features in Our Statistical Model (with 300 Iterations of Stratified Bootstrapping)
Feature Mean Median 90% Confidence Interval Regression
Supply chain dispersion 0.20 0.20 [0.10, 0.30] OutcomeGovernance ranking -0.06 -0.03 [-0.21, -0.01] SelectionVAT evasion -0.19 -0.13 [-0.45, -0.06] SelectionPopulation 0.08 0.07 [0.04, 0.13] SelectionCorrelation (ρ) 0.10 0.11 [0.02, 0.23]
Notes. The features governance ranking, VAT evasion, and population are all at the city level, where we use the
city corresponding to the manufacturer’s location. The last column indicates the regression in which the associated
feature is statistically significant.
We perform additional robustness analyses to strengthen our results. First, regarding supply
chain features, we remove dispersion and only include a manufacturer’s total output volume and
total number of supplying farms as independent variables. Neither of these two latter variables is
statistically significant. This analysis shows that it is indeed supply chain dispersion, not volume
or size per se, that is associated with EMA risks. In addition, capturing the degree of supply con-
centration (beyond the number of farms; see Eq. [1]) is essential to properly quantify dispersion.
Second, we define an alternative governance ranking in which a city is ranked 2 if both its mayors
and party secretaries had engaged in misconduct, ranked 1 if either its mayors or its party secre-
taries (but not both) had engaged in misconduct, and ranked 0 if neither its mayors nor its party
secretaries had engaged in misconduct. We reestimate our model and obtain similar results (see
Appendix B, Table A.4).
Why is supply chain dispersion associated with EMA risks? We conjecture a number of rea-
sons. First, it is more difficult to impose tight quality control or to transfer best practices in a
dispersed farming network. Second, dispersion hinders traceability, creating opportunities to hide
bad practices. Third, there often exists a substantial power asymmetry between the small farm-
ers in a dispersed farming supply chain and the much larger food manufacturer downstream (Lee
et al. 2012). In recent decades, the Chinese government has been promoting the creation of Dragon
Head Enterprises (DHE) to industrialize China’s agricultural sector. Agricultural DHEs are large-
scale companies dominating the processing and distribution of food products in China. As of 2011,
these DHEs account for 70% of pork and poultry processing and 80% of aquaculture processing in
China (Schneider and Sharma 2014). However, such consolidation in the processing and distribu-
tion stages of the food supply chain puts small farmers in a highly unfavorable position. DHEs are
Risk Drivers for EMA in China’s Farming Supply Chains 9
often the price-setting monopolies in the relationship. Furthermore, as small farmers increasingly
depend on DHEs’ contracts, they lose bargaining power and often bear most of the market risks
and price pressures (Lingohr-Wolf 2007, Hu and Hendrikse 2009, Lingohr-Wolf 2011). When facing
these pressures that threaten their only source of income, small farmers have a strong incentive to
do whatever they can to sustain the livelihood of their families.
Our results bring timely and valuable insights for food manufacturers, importers, and regulators
as experts call for better supply chain control of today’s complex food systems (Fischetti 2015). For
Chinese food manufacturers, it is beneficial to reduce farming supply chain dispersion to allow for
better traceability and training or implementation of best practices. In addition, carefully ensuring
fair risk sharing with upstream farmers through proper contracts is crucial. For Chinese regulators,
governance on DHEs should be strengthened to enhance farmers’ positions in their contractual
relationships with DHEs, e.g., by establishing guaranteed distribution channels and protective
prices for farmers. Regarding quality control, we advocate tighter inspection in cities with weaker
governance, e.g., by increasing the extent of market sampling activities, to more efficiently screen
out high-risk food manufacturers. Furthermore, based on our conversations with Chinese officials
in CFDA and China National Center for Food Safety Risk Assessment, current quality inspections
are mostly done at the product or manufacturer level with little attention to the expansive farming
network. Our results stress that regulating the upstream supply chain, including small household
farms often in remote rural regions, is critical for food quality assurance.
For food importers, we advocate that they work with Chinese suppliers who source from a less
dispersed farming network or share risks with upstream farmers more fairly, and those who operate
in regions with strong governance. As the Food Safety Modernization Act (FSMA) was signed into
U.S. law in 2011, food importers are held accountable for product safety, including being able to
verify the corresponding supply chains. Our results highlight concrete aspects of the supply chains
that food importers should pay attention to, thus generating actionable strategies to help them
better satisfy the stringent requirements of FSMA.
For food regulators outside of China (e.g., the U.S. FDA), our results emphasize the value to
combine the supply chain perspective with product sampling and site inspections to more proac-
tively and systematically identify, prevent, and mitigate EMA risks. For example, under FSMA, the
FDA has issued the Foreign Supplier Verification Programs (FSVP) for food importers (U.S. FDA
2015a). The current rules imposed in the FSVP mostly focus on sampling and testing for potential
food hazards in foreign suppliers’ materials and facilities. Our results demonstrate that it is equally
important to assess suppliers’ risks at the structural and environmental level pertaining to supply
chain dispersion and local governance. Similar considerations should also be applied when the FDA
performs foreign supplier site inspections. As another example, our results can help to improve
10 Risk Drivers for EMA in China’s Farming Supply Chains
the FDA’s PREDICT tool (U.S. FDA 2015b, U.S. GAO 2016). Currently, the PREDICT tool uses
product-specific or country-specific risk rules and a firm’s past track records to calculate a risk
score for each import shipment, in order to prioritize sampling of high-risk shipments. Our analysis
highlights the value of collecting and integrating information on supply chain dispersion and local
governance into the PREDICT tool to better assess a Chinese food manufacturer’s risk level. Such
additional information is particularly valuable for evaluating the risks of new manufacturers, whose
track records do not exist yet.
Our research centers on one of the largest food exporting countries in the world – China. In
other top agricultural countries such as Brazil, India, and Mexico, dispersed farming models and
highly localized regulatory systems also prevail (Khan and Parashari 2014, Graeub et al. 2016,
Charron 2010, Ferraz et al. 2012). In addition, similar EMA incidents are observed (Doyle et al.
2013, Handford et al. 2016). Future research can extend our methodology to examine the impacts
of supply chain structure and regional governance strength on food adulteration risks in products
originating from these countries.
4. Materials and Methods4.1. Data
We utilize (i) farming supply chain data, (ii) regulatory misconduct case data, and (iii) China
industrial and city census data to construct the independent variables in our model. We use (iv)
CFDA domestic market sampling data and (v) shipment refusal data by importing countries to
construct the dependent variables. This section and Appendix B provide more details on these
data sources.
4.1.1. Farming supply chain data We collect this data from public websites of China’s
AQSIQ, a government agency responsible for entry-exit commodity inspection, certification, accred-
itation, and import-export food safety. Effective since March 2012, companies involved in the
planting, breeding, and processing of raw materials of exported food are required to file a record
with AQSIQ (State Administration of Quality Supervision and Quarantine 2011). Complete infor-
mation filed in each record includes: company name and address, a list of farms supplying to the
company, the farms’ names, addresses, and annual output volume. For a considerable number of
manufacturers, however, farm-level information is incomplete or missing. Therefore, we perform
appropriate data imputation to obtain the largest sample size we can (see Appendix B). The final
usable data includes 941 food manufacturers in total, with 122, 110, 89, 99, and 521 manufacturers
in the eggs, honey, pork, poultry, and seafood (including freshwater fish) industry respectively.
We utilize the farming supply chain data to construct several supply chain features for our
analysis, including dispersion, total number of supplying farms, total annual output volume, average
Risk Drivers for EMA in China’s Farming Supply Chains 11
and standard deviation of the distances between a manufacturer and its supplying farms, as well as
average and standard deviation of the distances among a manufacturer’s supplying farms. Summary
statistics of all supply chain features are available in Appendix B, Table A.1.
4.1.2. Regulatory misconduct case data This data is collected from the archive of People’s
Daily based on legal terms of regulatory misconduct in China and contains 467 unique cases. Each
case article typically reports the name and position of the primary government official involved in
misconduct. When such information is missing, we complement with open search on the Internet.
We note that the handful of recent studies using misconduct cases to measure governance mainly
use the number of misconduct cases reported in a region as the measure (Cole et al. 2009). We
posit that this measure more closely captures efforts to punish misconduct as opposed to the extent
of misconduct itself in a political system where misconduct is pervasive and systemic, such as in
China (Wedeman 2012). Focusing on more detailed case information can alleviate this problem.
We specifically use information about the rank of the officials involved in misconduct because it is
available for every case in our data and allows us to develop our measure using all data. Future
research can examine other case information such as punishment decisions and monetary value
involved, although such information is much harder to collect (e.g., they are not available in over
half of our cases).
4.1.3. CFDA domestic sampling data and import shipment refusal data Beginning
in early 2016, CFDA publishes results of food product sampling it conducted nationwide in China’s
domestic market. We scraped the entire dataset as of August 2016, which contains 117,218 pass-
ing records (when the sampled product met all standards) and 3,253 failing records (when the
sampled product did not meet at least one standard) in 2014 and 2015. Most records report the
sampled product’s manufacturer (name and address), product name, quality items being tested,
and detected problem(s) in the case of failing records. We search within these records for every
manufacturer in our farming supply chain data to identify whether a manufacturer’s product has
been sampled by the CFDA and if so, whether the sampling yielded a passing or failing record.
In addition to domestic sampling, we also investigate whether a manufacturer’s product has been
refused by an importing country. For this purpose, we perform both open search on the Internet and
targeted search of shipment refusal records published by leading importing countries and regions,
including Australia, Canada, European Union, Japan, South Korea, and the U.S. We use import
shipment refusal data between 2010 and 2016.
12 Risk Drivers for EMA in China’s Farming Supply Chains
4.2. Model and Estimation
In every step of the stepwise model selection procedure, we use maximum likelihood estimation to
estimate the coefficients of the independent variables. Formally, we can characterize the following
three probabilities given our data and the probit nature of our model (Greene 2011, pp. 686–688):
P(Si = 0) = P(S∗i < 0) = Φ
(−γZiσS
),
P(Ri = 1, Si = 1) = P(R∗i ≥ 0, S∗i ≥ 0)
=
∫ ∞−βXi
∫ ∞−γZi
φ(u, v)dudv,
P(Ri = 0, Si = 1) = 1−P(Si = 0)−P(Ri = 1, Si = 1).
These three probabilities correspond to the probability of manufacturer i being unknown, high-risk,
and low-risk, respectively. The function Φ(·) is the cumulative distribution function of the univariate
standard normal distribution, and φ(·, ·) is the probability density function of the bivariate normal
distribution with mean 0 and covariance matrix Σ defined in Eq. [2]. Hence, our model estimation
is equivalent to the following maximization problem:
maxγ,β,ρ,σS ,σR
LL ≡∑
i∈{i:Si=0}
logP(Si = 0)
+∑
i∈{i:Ri=1}
logP(Ri = 1, Si = 1)
+∑
i∈{i:Ri=0}
logP(Ri = 0, Si = 1).
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16 Risk Drivers for EMA in China’s Farming Supply Chains
Appendix A: EMA Case Studies
A.1. The distributed farming supply chain model in China
Based on several of our in-depth case studies, one farming supply chain model that turns out to be prevalent
in China is the distributed farming model (Pi et al. 2014, Schneider and Sharma 2014, Sharma and Rou 2014).
Under this model, farming outputs are sourced from a large number of small household farms, who typically
raise a handful to a dozen of animals in their backyards. These farming outputs are sold to and integrated by
middlemen and eventually sold to downstream food manufacturers who use the farming outputs to produce
the final products.
One case that illustrates the association between the distributed farming supply chain model and EMA
risks is the tainted infant formula scandal mentioned in the paper. Prior to 2008, China’s dairy industry
depended heavily on small farmers. An average household only raised 3 cows in the mid 1990s, and more than
80% of raw milk came from such backyard farmers by the mid 2000s (Zhou et al. 2002, Lu et al. 2009). These
small farmers typically sold their raw milk to milking stations or traders, who then pooled the raw milk from
different farmers and sold to large dairy companies. For example, Sanlu has an extremely distributed supply
chain by sourcing all of its raw milk from 52,000 small farmers. Similarly, Mengniu sourced 90% of its raw
milk from 91,600 small farmers. While sourcing from small farms was a common practice, companies such
as Sanyuan and Bright Dairy adopted a more vertically integrated model. In particular, Sanyuan sourced
80% of its raw milk from 30 large company-owned farms and the remaining 20% from cooperative farms.
Similarly, Bright Dairy sourced 95% of its raw milk from large company-owned or cooperative farms and
the remaining 5% from small backyard farms (Zhao et al. 2014). Both Sanlu’s and Mengniu’s products were
found to have been adulterated by melamine, whereas Sanyuan and Bright Dairy were among the very few
companies that passed all inspections and stayed intact (Chen et al. 2014).
Another example relates to poultry farming. The distributed farming model is also prevalent in China’s
poultry industry, with an average household farmer raising only a dozen or so chickens. Outbreaks of avian
flu prior to 2008 had led to increased misuse of antibiotics, antivirals, and Chinese traditional medicine by
poultry farmers. We find that such extensive use of animal drugs in poultry farming were sporadic and
without proper supervision by authorities. Online forums existed to recommend “recipes” for preventing and
treating avian flu, with the corresponding drugs being available for sale freely. We have mapped 95 different
poultry medicines being used in China, examples including amikacin, toad venom, streptozotocin, aristolochic
acid, and ribavirin. In 2012, CCTV released a report that chickens supplied to KFC restaurants in China
contained excessive levels of antibiotics, antivirals, steroids, and heavy metals used to promote growth and
prevent diseases. These chickens were sourced from over 1,000 small farms through three companies (CCTV
2012a, Xinhuanet 2012a).
A.2. EMA of gelatin capsules
In 2012, CCTV reported that tainted gelatin was being provided to capsule makers in Zhejiang Province,
focusing on the firm Xueyang Gelatin and Protein Plant in Qiansong Village, Fucheng County, Hebei
Province. The company purchased leather scraps at 200–400 RMB per ton (compared to 4,000–5,000 RMB
per ton for clean rawhide) from a large number of small enterprises as raw materials to produce gelatin
Risk Drivers for EMA in China’s Farming Supply Chains 17
(CCTV 2012b, Li 2012a). These leather scraps contain excessive amount of toxic chromium and were only
allowed to be used for producing industrial gelatin, not edible gelatin. The investigation into Xueyang Gelatin
and Protein Plant led to a massive crackdown on gelatin producing companies adopting the same practice
across China (Zhao 2014). However, Xueyang Gelatin and Protein Plant managed to reopen shortly after the
crackdown and stayed operating until September 2014. Its close connection with local government officials
was believed to be a key factor that allowed its continued operation (Li 2012a).
The company was a family-owned business registered to Haixin Song, with his son Xunjie Song being
the plant manager. During the crackdown, Xunjie Song burned down the factory to conceal records of
wrongdoings (Xinhuanet 2012b). Nevertheless, partial records describing cash payments from the company
to Jiangxin Song, Hexin Song, and Zhenjie Song, all of whom were brothers of Haixin Song, were recovered.
At that time, Zhenjie Song was the Party Secretary of Fucheng County; Jiangxin Song was the People’s
Congress Chairman of Fucheng County; and Hexin Song was the company’s sales manager. These records
implicated that bribes were paid to the Song brothers who served as county officials, to ignore the fact that
the company was making and selling tainted gelatin.
Appendix B: Data and Model
B.1. Farming supply chain data
At the time of our research, AQSIQ published farming supply chain data for
the eggs, honey, pork, poultry, seafood (including freshwater fish), and vegetable
industries on two public websites: http://jckspaqj.aqsiq.gov.cn/xz/backzzyzjdmd/ and
http://en.ciqcid.com/Registered/Registered2/Food1/index4.htm. We cannot use the data for the vegetable
industry because the farm data and the manufacturer data are disconnected, and there is no way to link
the two data to map the farming supply chain of each vegetable manufacturer. For the remaining five
industries, the farm-level data for some manufacturers are incomplete or missing altogether. As a result,
we perform the following data cleaning and imputation. First, we discard all manufacturers without any
farm-level data. This step removes 9 and 117 manufacturers in the pork and seafood industries, respectively.
Second, there exist a subset of honey and seafood manufacturers for which we can approximate their
dispersion measures based on partial farm-level data. Specifically, for honey manufacturers with complete
farm-level data, we identify a strong linear relationship between their dispersion measure and the logarithm
of the number of supplying farms, as shown in Figure A.1. A simple linear regression yields the relationship
Di = −0.0075 + 0.41 log(Ni) with both coefficients being statistically significant (p < 0.01) and R2 = 0.99,
where Di and Ni are the dispersion and the number of supplying farms for manufacturer i respectively.
Given this strong linear relationship, we approximate the dispersion of 31 honey manufacturers for which
we only have data on the number of supplying farms (but not the individual output volume of each farm).
For seafood manufacturers, the data contains two types of information that can be associated with the
output volume of each farm: the output volume itself and the area of wetland. It is reasonable to assume that
farms with larger areas of wetland can produce higher volume. There are 426 seafood manufacturers with
both types of information. For these manufacturers, we compute an alternative dispersion measure DA by
18 Risk Drivers for EMA in China’s Farming Supply Chains
0.0
0.5
1.0
0 1 2 3
Log (number of supplying farms)D
ispe
rsio
n
Figure A.1 Strong linear relationship between dispersion and logarithm of the number of supplying farms for
honey manufacturers with complete farm-level data
replacing vj , the output volume of farm j, in Eq. [1] with aj , the area of wetland at farm j. We compare the
original dispersion measure D (computed based on output volume) to this alternative dispersion measure DA
(computed based on area of wetland) and observe a strong linear relationship between the two (see Figure
A.2). A simple linear regression yields Di = 0.0049 + 1.018DAi with both coefficients statistically significant
(p < 0.01) and R2 = 0.95. Hence, we use this strong linear relationship to approximate the dispersion of 95
seafood manufacturers for which we only have data on the area of wetland at each farm (but not the output
volume).
0.00
0.25
0.50
0.75
0.00 0.25 0.50 0.75 1.00
Dispersion computed based on area of wetland
Dis
pers
ion
com
pute
d ba
sed
on o
utpu
t vol
ume
Figure A.2 Strong linear relationship between dispersion based on output volume and dispersion based on area
of wetland for seafood manufacturers with both types of farm-level data
We also note some time discrepancies in the farming supply chain data. Specifically, the data for the
eggs, pork, and poultry industries and part of the data for the seafood industry are from 2010, while the
rest of the data are from 2014. Such time discrepancies are solely driven by what data are available from
AQSIQ. One may question the use of the 2010 data because registration of supply chain data with AQSIQ
was required only since March 2012. We confirm that using data from 2010 does not impact our conclusions.
First, 405 seafood manufacturers appear in both the 2010 and 2014 data. We compare these manufacturers’
data between the two years and do not observe any changes in the registered information. This consistency
suggests we can reasonably assume that the farming supply chains of food manufacturers in our data do not
Risk Drivers for EMA in China’s Farming Supply Chains 19
Table A.1 Mean [Median] (Standard Deviation) of Farming Supply Chain Features
Features Eggs Honey Pork Poultry Seafood
No. of Mfg’s 122 110 89 99 521Dispersion 0.17 [0] 0.53 [0.56] 0.65 [0.57] 0.87 [0.87] 0.18 [0]
(0.26) (0.37) (0.48) (0.51) (0.28)No. of farms 2.23 [1] 5.45 [4] 14.05 [4.5] 20.1 [8.5] 2.65 [1]
(3.59) (5.59) (34.75) (50.62) (4.10)Volume 5876.98 [3400.00] 36038.1 [23247] 46821.71 [22.52] 945.04 [473] 4912.25 [2500]
(7267.97) (31823.26) (98895.04) (1418.93) (6232.56)Avg Mfg-farm distance 68.06 [10.32] 341.48 [223.63] 81.87 [29.97] 36.16 [24.58] 58.55 [23.06]
(254.28) (363.19) (178.49) (41.60) (124.56)StDev of Mfg-farm distance 28.44 [0] 198.24 [42.01] 45.46 [12.71] 23.23 [5.66] 10.20 [0]
(156.58) (305.15) (92.07) (43.98) (33.79)Avg farm-farm distance 43.11 [0] 317.05 [74.53] 72.07 [24.19] 30.01 [4.83] 18.84 [0]
(225.93) (457.76) (145.77) (45.67) (52.85)StDev of farm-farm distance 6.91 [0] 204.11 [45.49] 57.28 [13.32] 32.57 [15.21] 9.86 [0]
(19.21) (305.38) (119.55) (58.08) (32.18)
Notes. Notations “Mfg”, “Avg”, and “StDev” stand for “manufacturer”, “average”, and “standard
deviation” respectively.
change from 2010 to 2014. Second, as a robustness analysis, we verify that our results remain the same if we
only examine food manufacturers with farming supply chain data in 2014 (see Tables A.5 and A.6). Hence,
we report results based on all available data in the paper.
Table A.1 presents the summary statistics of the supply chain features for all five industries in our anal-
ysis. As shown in Table A.1, the empirical distributions of the supply chain features vary across different
industries. In addition, output volumes are measured in industry-specific units and hence not comparable
across industries. Thus, we normalize each feature by subtracting the feature value of manufacturer i by the
corresponding industry average and then dividing the difference by the industry standard deviation. This
normalization ensures that the cross-industry variations do not bias our results.
B.2. China industrial census data
We utilize VAT-related data from the China Industrial Census between 2005 and 2007 to construct our
second measure of governance. Each year’s census reports economic and demographic data of every medium-
to large-size industrial company in each city in China, including industry classification, employment and
training status, and financial and gross production data. The key data we use to develop the VAT evasion
measure is the annual input cost, annual revenue, VAT credit claimed, and VAT payment of each company in
each city and each year, all measured in present monetary value (in RMB). The ratio of VAT credit claimed to
annual input cost shows the percentage VAT credit, and the ratio of VAT payment to annual revenue shows
the percentage VAT payment. We use these percentages to develop our city-level VAT evasion measure. We
focus on VAT rather than other taxes because (i) VAT is regulated at fixed rates (varies only by industry),
and (ii) very few exemptions are granted for VAT. These properties of VAT simplify the identification of
suspected tax evasion behavior compared to other taxes.
20 Risk Drivers for EMA in China’s Farming Supply Chains
B.3. Determining manufacturers’ sampling and risk status
The CFDA domestic market sampling data are published on the following public website:
http://app1.sfda.gov.cn/datasearch/face3/dir.html. Due to our focus on farming supply chains, we only con-
sider situations where the items tested in CFDA’s domestic sampling or the reason for refusal by an importing
country concerned EMA in farming practices. These include misuse of antibiotics and banned drugs, farmers
faking certificates, mistreatment of dead animals, mixing with low-quality substitutes (e.g., adding sugar to
honey), and adulteration with toxic chemicals (e.g., melamine in eggs). Table A.2 summarizes the number
of manufacturers under each status across the five industries.
Table A.2 Sample Size by Sampling and Risk Status
No. of manufacturers Eggs Honey Pork Poultry Seafood
High-risk 9 28 4 10 68Low-risk 28 27 28 27 22Unknown 85 55 57 62 431Total 122 110 89 99 521
B.4. Model estimation and robustness analysis
In the most general version of our statistical model, we include the following independent variables in the
selection and outcome regressions:
• Selection regression: supply chain dispersion, manufacturer annual volume, city governance ranking,
city VAT evasion measure, city population (logged value), city GDP per capita (logged value), and a
dummy variable for the seafood industry;
• Outcome regression: supply chain dispersion (or number of supplying farms for a manufacturer), manu-
facturer annual volume, one of the distance features, city governance ranking, city VAT evasion measure,
city population (logged value), and city GDP per capita (logged value).
We include a dummy variable for the seafood industry in the selection regression because the proportion of
unknown manufacturers in this industry is substantially larger than that in the other industries (83% versus
50-70% in the other four industries), suggesting a substantially lower sampling probability for seafood man-
ufacturers. We do not consider other supply chain features except dispersion and the manufacturer’s annual
output volume in the selection regression because, based on informal conversation with CFDA officials, the
agency has not considered supply chain information when deciding which products to sample. Nevertheless,
it is reasonable to assume that larger manufacturers (proxied by annual volume) may be sampled more fre-
quently. The city-level regulatory, demographic, and economic variables all use the city corresponding to the
food manufacturer’s address. Table A.3 shows the correlation matrix for all independent variables we could
include in our model. We do not include dispersion and number of supplying farms simultaneously because
these two variables are highly correlated. For the same reason, we consider the four distance features one at
a time. The pairwise correlations for the remaining variables are all sufficiently small that multicolinearity
is not an issue.
Risk Drivers for EMA in China’s Farming Supply Chains 21
Ta
ble
A.3
Pa
irw
ise
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rrel
ati
on
of
All
Ind
epen
den
tV
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s
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Mfg
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rmdis
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-fa
rmdis
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StD
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-fa
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tance
Gov
ernan
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AT
evasi
on
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tion
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a
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dis
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-farm
0.32
***
0.2
0***
0.0
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*1
dis
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0.32
***
0.1
8***
0.0
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*1
dis
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-farm
0.44
***
0.3
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0.15
***
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***
0.75
***
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***
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***
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.020
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Rankin
g
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*-0
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0.0
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-0.0
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tion
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0075
-0.0
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-0.0
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19
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Note
s.N
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tions
“M
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“A
vg”,
and
“StD
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stand
for
“m
anufa
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“av
erage”
,and
“st
andard
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resp
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0.0
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22 Risk Drivers for EMA in China’s Farming Supply Chains
Table A.4 Robustness: Significant Features in the Model with 0–2 Governance Ranking
Feature Mean Median 90% Confidence Interval Regression
Supply chain dispersion 0.20 0.19 [0.10, 0.30] OutcomeGovernance ranking -0.13 -0.07 [-0.35, -0.01] SelectionVAT evasion -0.20 -0.11 [-0.52, -0.06] SelectionPopulation 0.07 0.07 [0.02, 0.13] SelectionCorrelation (ρ) 0.13 0.11 [0.02, 0.28]
Table A.5 Robustness: Significant Features in the Model with 2014 Farming Supply Chain Data only and 0–5
Governance Ranking
Feature Mean Median 90% Confidence Interval Regression
Supply chain dispersion 0.20 0.19 [0.05, 0.32] OutcomeGovernance ranking -0.04 -0.03 [-1.02, -0.01] SelectionVAT evasion -0.18 -0.09 [-0.66, -0.06] SelectionPopulation 0.11 0.10 [0.05, 0.23] SelectionCorrelation (ρ) 0.13 0.05 [-1, 0.40]
Table A.6 Robustness: Significant Features in the Model with 2014 Farming Supply Chain Data only and 0–2
Governance Ranking
Feature Mean Median 90% Confidence Interval Regression
Supply chain dispersion 0.19 0.19 [0.05, 0.32] OutcomeGovernance ranking -0.10 -0.03 [-0.35, -0.01] SelectionVAT evasion -0.22 -0.09 [-0.75, -0.06] SelectionPopulation 0.09 0.08 [0.04, 0.20] SelectionCorrelation (ρ) 0.17 0.04 [-1, 0.52]
Tables A.4–A.6 demonstrate the significant features and the statistics of their coefficients for various
robustness analyses. We perform 300 iterations of stratified bootstrapping in all of our robustness analyses.