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, yshuang@mit.edu, retsef@mit.edu, ssprings@mit.edu,

shujing@mit.edu, yanchong@mit.edu

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

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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

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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.