Economic 

Research

Report

Number 75

July 2009

United States 

Department 

of Agriculture

Economic 

Research 

Service

The Interplay of Regulation

and Market Incentives 

in Providing Food Safety

Michael Ollinger

Danna Moore
www.ers.usda.gov

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Ollinger, Michael

The interplay of regulation and market incentives in providing food safety.

(Economic research report (United States. Dept. of Agriculture. Economic 

Research Service) ; no. 75)

1. Animal industry—Law and legislation—United States. 

2. Packing-houses—Sanitation—Economic aspects—United States.

3. Poultry plants—Sanitation—Economic aspects—United States.

I. Moore, Danna L. II. United States. Dept. of Agriculture. Economic 

Research Service.

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www.ers.usda.gov/Briefing/FoodSafety

Recommended format for citation: Ollinger, M., and D. Moore. The Interplay of 

Regulation and Market Incentives in Providing Food Safety, Economic Research

Report No. 75, U.S. Department of Agriculture, Economic Research Service, 

July 2009.
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Michael Ollinger, ollinger@ers.usda.gov

Danna Moore, moored@wsu.edu

The Interplay of Regulation 

and Marketing Incentives 

in Providing Food Safety

Economic

Research

Report

Number 75

July 2009

Abstract

This report examines the impact of process regulations mandated under the Pathogen 

Reduction/Hazard Analysis and Critical Control Point (PR/HACCP) rule by the Food 

Safety and Inspection Service of USDA on food safety process control. The current level 

of food safety found in U.S. meat and poultry food products is a result of process and 

performance regulations and management-determined actions brought about by market 

incentives. Processing regulations include sanitation and other tasks related to food 

safety; management-determined actions include capital investment and other actions 

independent of process regulations, but possibly driven by performance standards. 

Performance standards—regulations that allow manufacturers to reach an acceptable 

level of food safety in any manner they see fi t—are not a subject of this report. This 

study used the share of samples testing positive for Salmonella spp. as a measure of food 

safety process control in meat and poultry processing plants and found empirically that 

management-determined actions account for about two-thirds of the reduction in samples 

testing positive for Salmonella spp., while process regulations account for about a third 

of the reduction. The importance of process regulation varies, but accounts for 50 percent 

or more of process control in about a quarter of plants, and in some plants accounts for 

the entire process control system. 

Keywords: food safety, process regulations, Hazard Analysis and Critical Control Point 

(HACCP) rule, food safety regulations

Acknowledgments: The authors appreciate the reviews and suggestions of Elise Golan, 

Jay Variyam, and Laurian Unnevehr (Economic Research Service); Neil Hooker (Ohio 

State University); and Sang Nyugen and Arnold Reznek (Center for Economic Studies, 

U.S. Census Bureau).
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Previous Regulatory Standards and the 1996 PR/HACCP Rule 

Form the Basis for Current Food Safety Regulation . . . . . . . . . . . . . . . . . . . 4

 HACCP Programs, SSOPs, and FCs as Process Regulations. . . . . . . . . . 4

 FSIS Establishes Salmonella spp. Testing as an Indicator 

 of HACCP System Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

 Management-Determined Actions and Salmonella spp. 

 Performance Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 Other Pathogen Control Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Economic Incentives for Maintaining Food Safety . . . . . . . . . . . . . . . . . . . . 8

Private and Public Incentives Lead to Different Levels 

of Food Safety Process Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Economic Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 Human and Physical Capital Investments Limit the Spread 

 of Pathogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 Processing Technologies Control Pathogens . . . . . . . . . . . . . . . . . . . . . 12

 Organizational Arrangements Facilitate Food Safety Process Control . 12

 Plant Size and More Complex Operations Affect Food Safety 

 Process Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Empirical Measures Link Food Safety to Management-Determined 

Actions and Process Regulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Combining Several Datasets With Unique Data 

on Food Safety Technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

 Process Regulation Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

 Discretionary Labor and Human and Physical Capital. . . . . . . . . . . . . . 24

 Food Safety Processing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 26

 Organizational Arrangements, Plant Technology, 

 and Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Discussion of Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

 Maximum Shares of Salmonella Controlled 

 by Management-Determined Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

 Actual Shares of Salmonella Controlled by Process Regulation and 

 Management-Determined Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Limitations and Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Appendix A: Linking Food Safety to Management-Determined Actions 

and Process Regulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Appendix B: Empirical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Appendix C: Change in Salmonella shares 

if all plants take all management-determined actions . . . . . . . . . . . . . . . . . 46
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Summary

The current level of food safety found in U.S. meat and poultry products is a 

result of both Government regulations and management-determined actions 

motivated by market incentives. For meat and poultry processing plants, the 

U.S. Government mandates both food safety process regulations that require 

specifi c technologies or production practices and performance regulations 

that promulgate acceptable levels of food product safety. Meat and poultry 

processing plants are also infl uenced by market incentives, including legal 

liability, the value of their brand, and their desire to sell more of their food 

product. Companies often negotiate contracts, which, in exchange for higher 

prices or guaranteed purchases, specify food safety levels to be achieved or 

technologies to be used. 

What Is the Issue?

The Food Safety and Inspection Service (FSIS) recently made changes to its 

regulatory policies to better align its program with current food safety needs. 

FSIS faces pressure, however, to do more to protect the public in the area of 

food safety. The Economic Research Service (ERS) estimates society’s costs 

(which includes medical costs, lost labor, etc.) of just Escherichia coli STEC and 

Guillain-Barré syndrome at about $1.6 billion per year. The cost of foodborne 

illness from other sources, such as Salmonella species (spp.), is much higher.

Current levels of food safety process control are achieved through a combination of Government regulations (process and performance standards) and 

private management-determined actions, but little is known about the relative contributions of each to food safety process control. This report helps 

fi ll that knowledge void by examining the impact of food safety process 

regulations and management-determined actions as measured by the share of 

samples testing positive for Salmonella spp. in a testing program conducted 

by FSIS. The fi ndings provide lessons for the development of new regulatory 

approaches to food safety process control.

What Did the Study Find?

This study’s examination of the effects of mandatory process regulations 

and management-determined actions on Salmonella spp. provides new information on the role of regulations and market incentives in ensuring food 

safety process control. The study found that management-determined actions 

accounted for about two-thirds of the reduction in the number of samples 

testing positive for Salmonella spp. (Salmonella share)—a measure of food 

safety process control. By contrast, process regulations accounted for about a 

third of the reduction of samples testing positive. The importance of process 

regulation varied across plants, accounting for more than half of all food 

safety process control for about a quarter of the plants and for the entire food 

safety process control system of some plants. These results suggest that both 

process regulation and management-determined actions play vital roles in 

meat and poultry food safety process control. 

Mandatory process regulations include cleaning and sanitation tasks and 

tasks required to implement a Hazard Analysis and Critical Control Point 
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(HACCP) plan for pathogen reduction (PR). Management-determined 

actions include investments in human and physical capital, food safety technologies, and organizational arrangements, such as contractual relationships 

that enhance food safety process control. These management-determined 

actions are driven by market forces and federally mandated performance 

standards that establish limits on pathogens, but do not specify a way to 

reach those limits. 

The study also found that nearly half of all Salmonella spp. reduction due to 

management-determined actions was motivated by direct contractual relations in which a major customer of a meat or poultry plant or supplier to a 

meat or poultry plant, fearing a loss of public confi dence in the safety of its 

products, agreed to pay a price premium, make minimum purchases, or offer 

other inducements to suppliers in exchange for greater attentiveness to food 

safety process control. Management-determined actions were also motivated 

by indirect consumer pressure for food safety; consumers link contaminated 

food products to a supplier through branded products and other sources and 

can, in turn, cease purchases if products fail to meet their expectations for 

food safety.

The forces driving management-determined actions lead to the conclusion 

that USDA’s FSIS could increase incentives by providing consumers and 

buyers with more information about the meat and poultry food safety control 

of particular plants and fi rms. USDA’s FSIS records plant performance on 

Salmonella spp. tests and noncompliance with process regulations. Making 

this information public should encourage greater food safety investments by 

meat and poultry producers.

How Was the Study Conducted?

Food safety control is measured as the number of samples testing positive 

for Salmonella spp. as a percentage of all samples taken by FSIS for each 

round (sample set) in a Salmonella spp. testing program for four major 

products: broiler (young chicken) carcasses, cattle (cow/bull and steer/

heifer) carcasses, market hog carcasses, and ground beef. These pathogen 

test results are linked with other datasets from FSIS and from the Economic 

Research Service (ERS). FSIS data provide measures of plant-level performance for mandated sanitation and HACCP tasks, and another dataset offers 

plant characteristics, such as the value of sales, number of employees, etc. 

The ERS data include information on plant-level food safety processing 

technologies, such as steam vacuum units, contractual arrangements with 

buyers and sellers, and some plant characteristics. Since the ERS dataset 

covers only information from the year 2000, the analysis was limited to that 

timeframe.

The analysis used a Tobit regression to evaluate the impact of process regulations and management-determined actions on shares of samples testing 

positive for Salmonella spp. After fi nding the marginal effects, an estimate 

was made of the change in Salmonella shares due to 20-percent changes in 

the variables representing process regulations and management-determined 

actions. The share of changes attributed to process regulations and management-determined actions were then computed.
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Introduction

The interplay of regulation and market incentives in food safety control is not 

well understood. Yet, this information is vital for the effi cient design of food 

safety regulations. The characteristics of effi cient regulation differ depending 

on the level and extent of food safety provided by market incentives. If 

market incentives are strong, then regulation may be structured to strengthen 

those incentives. If market incentives are weak, however, task-oriented 

process requirements may be necessary. 

Current food safety regulations for meat and poultry include process requirements that mandate specifi c technologies or production practices and performance requirements that specify fi nal product quality. Process regulations 

are practices that experts believe lead to greater food safety in food 

processing. These practices include the cleaning and sanitation tasks that the 

Food Safety and Inspection Service (FSIS) has required for many years and 

the monitoring tasks of critical plant operations that are included in a plant’s 

Hazard Analysis and Critical Control Point (HACCP) plan. These process 

regulations do not specify a level of food safety. By contrast, food safety 

performance standards specify a level of food safety, but allow producers to 

reach that level any way they choose. The Pathogen Reduction/HACCP rule 

mandates performance standards for two pathogens: Salmonella spp. and 

generic E. coli.

Market incentives include general liability for food safety, brand-value 

concerns, and opportunities to enter into private contracts that specify 

food safety levels or technologies in exchange for market access or higher 

payments. Market incentives and performance standards drive managementdetermined actions. Management-determined actions are defi ned as any 

food safety initiatives undertaken by meat or poultry suppliers that are not 

mandated under Government process regulation. These actions include 

investments in human and physical capital, food safety processing technologies, Government performance standards, and organizational arrangements, 

such as contracts between buyers and processing companies. 

Few studies address the impact of meat and poultry food safety regulation on 

pathogen levels. In the most recent report that directly examined pathogen 

levels, FSIS (2005) measured the share of red meat and broilers that tested 

positive for Salmonella spp. (the number of samples testing positive from all 

plants tested divided by all samples taken). The study found that the share of 

market hog carcasses testing positive for Salmonella spp. declined between 

2000 and 2001 and then held steady through 2005. The share dropped in cow/

bull carcasses during 2001-05. From 2000 to 2005, the share trended down 

in ground beef, held steady in steer/heifer carcasses, and increased in broilers 

(table 1).1 Salmonella shares in ground chicken have also risen since 2000, 

although that observation is based on fewer samples. FSIS (2005) attributes 

changes in Salmonella shares over 2000-05 to the PR/HACCP rule and 

private efforts, such as capital investments, by companies. FSIS also notes 

that it is not statistically valid to compare the share of total samples that 

test positive for Salmonella spp. over time because external factors, such as 

weather, season, and location, affect Salmonella shares. 

1 Some Salmonella serotypes are not 

human pathogens. Campylobacter 

spp. is another commonly occurring 

pathogen in pork and poultry and has 

been the subject of mitigation efforts 

by FSIS.
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The FSIS fi ndings show changes in Salmonella spp. over time, but do 

not empirically sort out the contributions that process regulations and 

management-determined actions have made to reducing contamination. The 

purpose of this report is to evaluate, for meat and poultry processing plants, 

the impact of food safety process regulations and management-determined 

actions on the prevalence of Salmonella spp. in four products: cattle and 

hog carcasses, ground beef, and broilers. We hypothesize that economic 

forces drive management-determined actions and compel managers to adopt 

different levels of food safety process controls. Plant managers with strong 

market incentives take many management-determined actions, resulting in 

levels of food safety process control that exceed those mandated under the 

PR/HACCP rule. Plant managers who face minimal market incentives may 

take no management-determined actions, leading to levels of food safety 

process control that just meet the mandated level. Results are consistent with 

this hypothesis. 

Our analysis uses simple statistics and a Tobit regression to estimate the 

marginal effects of process regulations and management-determined actions, 

and then computes how Salmonella spp. reduction can be attributed to each. 

The availability of four unique datasets was crucial to the analysis: 

Table 1

The prevalence of Salmonella spp. in red meat products and broilers, 2000-2005

Year Broilers Market hog

carcasses

Cow/bull

carcasses

Steer/heifer 

carcasses

Ground

beef

Ground

chicken

 Ground

 turkey

Sample sets tested 

for Salmonella

spp.

Number

2000 190 66 32 11 446 5 17

2001 170 97 34 13 426 6 16

2002 186 159 68 42 632 8 18

2003 127 109 45 40 547 4 16

2004 144 130 61 72 571 5 21

2005 171 113 38 24 391 5 13

Samples testing 

positive for 

Salmonella spp.1

Broilers Market hog 

carcasses

Cow/bull 

carcasses

Steer/heifer 

carcasses

Ground 

beef

Ground 

Chicken

Ground 

turkey

2000 9.1 6.2 2.2 0.4 3.3 13.8 25.7

2001 11.9 3.8 2.4 0.6 2.8 19.5 26.2

2002 11.5 3.2 1.7 0.3 2.6 29.1 17.9

2003 12.8 2.5 1.5 0.4 1.7 35.5 25.4

2004 13.5 3.1 0.8 0.3 1.6 25.5 19.9

2005 16.3 3.7 1.3 0.6 1.1 32.4 23.2

Baseline samples 

testing positive for 

Salmonella spp.2

Broilers Market hog 

carcasses

Cow/bull 

carcasses

Steer/heifer 

carcasses

Ground 

beef

Ground 

chicken

Ground 

turkey

Pre-1996 20.0 8.7 2.7 1.0 7.5 44.6 49.9

1 A test sample is the unit tested for Salmonella spp. A group of samples comprise a set. The number of samples per set varies by product 

category. FSIS determines the baseline number of samples permitted to test positive for Salmonella spp. and the number of samples needed for 

each set.

2 The baseline estimate is the pathogen level present in samples taken prior to promulgation of the PR/HACCP rule of 1996.

Source: U.S. Department of Agriculture, Food Safety and Inspection Service, Progress Report on Salmonella spp. Testing of Raw Meat and 

Poultry Products, 1998-2005 and Progress Report on Salmonella Testing of Raw Meat and Poultry Products, 1998-2003, www.FSIS.USDA.gov/

science/progress_report_salmonella_testing_1998-2004/index.
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• Economic Research Service (ERS, USDA) dataset of plant characteristics 

and meat and poultry food safety technology.

• FSIS dataset of Salmonella spp. test results for animal carcasses and 

ground meats.

• FSIS dataset on the performance of tasks required by regulation. 

• FSIS dataset of plant-level meat or poultry sample-testing results for 

Salmonella spp.
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Previous Regulatory Standards and 

the 1996 PR/HACCP Rule Form the Basis 

for Current Food Safety Regulation

FSIS and its antecedent agencies have regulated meat and poultry food 

safety process control systems since Congress mandated in 1906 that plants 

follow hygienic meat processing practices. Congress greatly expanded this 

authority under the Wholesome Meat Act (WMA) and the Wholesome 

Poultry Products Act (WPPA) of 1967 and 1968. Among other provisions, 

the WMA, WPPA, and the subsequent regulations established Standard 

Sanitation Operating Procedures (SSOPs) and facility control tasks (FCs) 

that required plants to perform a number of tasks related to 15 sanitation 

and process control practices.2 The SSOPs included sanitation tasks, such 

as cleaning and sanitizing assembled and disassembled equipment, while 

facility control requirements dealt with activities that could cause contamination of fi nished products, such as raw meat coming into contact with cooked 

products and the presence of rodents. FSIS also promulgated regulations 

dealing with cooking times and temperatures, preparation of fermented, 

smoked, and other processed products, and other matters that increase the 

risk of foodborne illness associated with meat and poultry. (See Ollinger and 

Mueller (2003) for further discussion.)

FSIS expanded its regulatory authority when it put forth the fi nal PR/HACCP 

rule on July 25, 1996. The Agency completely phased in the regulation by 

January 31, 2000. Among other provisions, the rule required plant managers 

to:

• Develop and implement HACCP process control programs for each 

product under the guidance of FSIS.

• Incorporate SSOPs, FCs, and HACCP plans into its HACCP program.

• Accept responsibility for meat and poultry food safety process control.

HACCP Programs, SSOPs, and FCs 

as Process Regulations

HACCP programs include a plan that outlines criteria to ensure food safety 

process control and associated mandated tasks. There are two key features of 

the HACCP plan:

 1. Plant managers specify their own HACCP plans and accompanying 

tasks. 

 2. Tasks are intended to spot deviations from normal operating conditions, but are not ends in themselves. If deviations from the standard are 

detected, plant managers must bring their food safety process control 

systems in line, using whatever means they deem necessary.

SSOPs and FCs, on the other hand, include specifi c tasks that meet regulatory process standards. If plants complete their prescribed tasks, then their 

obligation ends.

2 The term SSOP (standard sanitation 

operating procedures) did not come 

about until much later. We use it in this 

report to refer to sanitation practices 

mandated under the WMA and WPPA 

and its subsequent regulations and 

other sanitation practices required 

under PR/HACCP.
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FSIS inspectors monitor HACCP, SSOPs, and FCs to ensure compliance. 

If a task has not been performed, then an FSIS inspector fi les a noncompliance report. Chronic failure to comply can lead to penalties. Maintenance of 

the HACCP, SSOP, and FC tasks require labor but no capital investments, 

although plant managers may incur maintenance costs to bring a food safety 

process control system under control. 

SSOPs and FCs are considered process regulations because they are food 

safety tasks (technologies) required by regulators. HACCP plans are called 

management-based regulations by Coglianese and Lazar (2002), but we 

defi ne them as process regulations because they are mandated monitoring 

systems that must meet specifi c FSIS criteria, are checked by FSIS to ensure 

compliance, and do not specify a level of food safety product performance 

(i.e., they mandate no maximum allowed threshold of harmful pathogens).3

FSIS Establishes Salmonella spp. Testing 

as an Indicator of HACCP System Performance 

PR/HACCP also requires slaughter plants to test for generic E. coli and 

comply with the industry standard. Additionally, PR/HACCP mandates that 

slaughter plants and ground meat and ground poultry plants comply with a 

Salmonella standard. That standard requires that plants have no more than 

a maximum number of samples that test positive for Salmonella spp. out 

of a larger number of samples taken by FSIS inspectors in an FSIS testing 

program. Economists consider the generic E. coli and Salmonella spp. standards performance standards. These standards do not prescribe any type of 

process control technology. Rather, high levels of the test organism indicate 

a poorly performing meat or poultry food safety process control system and 

can be used as a reason to declare a product adulterated.

FSIS verifi es compliance with the generic E. coli and Salmonella spp.

performance standards with specifi c testing requirements. FSIS established 

pathogen target prevalence in broilers, steers/heifers, cows/bulls, and market 

hog carcasses for both generic E. coli and Salmonella spp. and in ground 

beef, chicken, and turkey for Salmonella spp. FSIS verifi es compliance with 

the Salmonella spp. standard using its own tests and requires plant managers 

to conduct their own generic E. coli tests. For more information about generic 

E. coli testing, see Ollinger and Mueller (2003). 

Robert Umholtz (personal communication on June 21, 2000) described 

FSIS’s procedures for the Salmonella spp. testing program as follows:4

 1. FSIS randomly selects plants from a pool of plants that are not undergoing testing.5

 2. FSIS evaluates a set of samples for Salmonella spp. over a test 

period that can last several days, weeks, or months, depending on the 

frequency of production runs.

 3. Plants that exceed the maximum allowed number of positive 

Salmonella spp. samples in the fi rst sample set (“A” set), of at least 50 

samples (the number varies by product category), must alter their meat 

3 Some features of HACCP programs 

differ from traditional process standards in that plants construct their own 

HACCP plans and choose the tasks 

required under it. However, once this 

HACCP plan is selected and the tasks 

outlined, it becomes the mandated 

technology and FSIS inspectors verify 

compliance, as they would with SSOPs 

or any other process task. Also, regulation 9 CFR 417.2( c)(3) requires that 

an establishment’s HACCP plan meet 

critical limits and regulatory requirements, including mandatory pathogen 

reduction standards.

4 As of January 2, 2009, FSIS was in 

the process of changing how it selects 

plants for testing.

5 FSIS now uses a risk-based approach 

to select plants for testing. 
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and poultry food safety process controls and then submit to a second 

round of testing (“B” set).

 4. If the plant fails again, it must make further changes to its processing 

system and undergo further testing (“C” set).

 5. Failure to pass another attempt (“D” set) can be a contributing factor 

to the suspension of inspection services and the resulting plant closure. 

The suspension remains in effect until the plant provides more effective process controls.6

Plants have rarely failed Salmonella spp. compliance testing. Umholtz (June 

2000) said that only about 100 of the approximately 2,050 slaughter and 

grinding plants tested through 1999 failed to pass the fi rst test (“A”) and only 

22 of those 100 plants failed their fi rst two tests (“A” and “B”). Of the 22 

plants, 19 passed the third test (“C”) and only one failed all three (Supreme 

Beef) and eventually was forced to exit the industry. The other two plants 

eventually resumed production.

FSIS is changing its Salmonella control program to The Salmonella Initiative 

Program (SIP), which scores plants based on previous pathogen tests and 

categorizes them as category 1 (performs at 50 percent below baseline 

level), category 2 (passes, but at more than 50 percent baseline level), and 

category 3 (performs at higher level than baseline level). SIP is described in 

greater detail at http://www.fsis.usda.gov/Science/Salmonella_Verifi cation_

Testing_Program/index.asp and http://www.fsis.usda.gov/pdf/scheduling_

criteria_Salmonella_sets.pdf. SIP is intended to encourage higher food safety 

performance by publishing all plants ranked as category 2 and 3. Plants 

identifi ed as category 2 and 3 performed the most poorly on Salmonella

testing. Plants in category 2 are identifi ed at http://www.fsis.usda.gov/PDF/

Category_2_Broilers.pdf and those in category 3 are at http://www.fsis.usda.

gov/PDF/Category_3_Broilers.pdf.

Management-Determined Actions 

and Salmonella spp. Performance Standards

Performance standards differ from process standards in that plant managers 

can take whatever actions they deem necessary to meet a performance standard. For example, one plant may use heat treating equipment, another may 

adopt a novel approach to personnel management, and another may install 

new ventilation systems as ways to get a system in compliance with a regulatory standard. While this type of regulation does provide fl exibility, it also 

carries risks. Plant managers must determine the technologies required for 

compliance, suggesting that some level of experimentation and risk-taking is 

necessary.

Plant managers can take management-determined actions to correct a recent 

performance test failure (corrective actions) or to avoid future scrutiny 

(preventive actions). Incentives for taking corrective actions are strong since 

plant managers could face penalties for failure to regain process control. 

Incentives for taking preventive actions are weaker since there is no immediate pressure. Penalties for failure to meet performance standards include 

extensive management time, regulatory sanctions (shutting down operations), 

6 All meat and poultry processing 

plants that ship meat and poultry 

products in interstate commerce must 

be inspected by FSIS. Suspension of 

inspection service means that plants 

cannot ship products across State lines.
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fi nes, line stoppages, retesting, and lost sales due to adverse buyer reactions 

(reputation costs). 

PR/HACCP performance standards have had weak penalties associated with 

them. Until recently, FSIS did not publicize Salmonella spp. test results, 

so reputation costs were lower than if publicized.7 Moreover, a ruling in a 

case involving Supreme Beef Company limited FSIS authority to impose 

penalties on plants failing to meet Salmonella spp. performance standards. 

Nevertheless, there are line stoppages, management attention, and other 

relevant costs.

Other Pathogen Control Programs

Aside from the PR/HACCP rule, FSIS prohibits detectable levels of E. coli 

O157:H7 and Listeria monocytogenes, two human pathogens, and excessive levels of Salmonella spp. in meat and poultry products.8 FSIS monitors 

compliance with E. coli O157:H7, Listeria monocytogenes, and Salmonella 

spp. limits and will recall products if necessary.9 Common reasons for recalls 

include linking products to illnesses by the Centers for Disease Control and 

Prevention (CDC) and State testing programs or by the companies themselves. Publicity about foodborne illness outbreaks and food supply safety 

has led to a network of monitoring programs by FSIS, CDC, State health 

agencies, and other organizations to ensure that the discovery of contaminated meat or poultry is followed by a public announcement and a recall of 

meat or poultry products. 

7 FSIS strengthened enforcement of 

the standard by posting Salmonella 

categories for individual plants, starting with broilers in 2008.

8 According to an anonymous FSIS 

offi cial, regulations require 6.5 and 

7-log10 reductions in Salmonella in 

certain ready-to-eat meat and poultry 

products, respectively, but any amount 

renders them adulterated.

9 FSIS has a risk-based verifi cation 

sampling program for Listeria monocytogenes that targets inspections at 

establishments producing ready-toeat meat and poultry products that 

support the growth of Listeria monocytogenes. The response to Listeria 

monocytogenes or Salmonella spp. 

depends on whether or not the product 

is ready-to-eat.
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Economic Incentives 

for Maintaining Food Safety

The costs to fi rms and society of imprecise meat and poultry food safety 

monitoring fi rst became apparent in the 1980s and 1990s when E. coli

O157:H7 in hamburgers at McDonald’s, Jack-in-the-Box, and other restaurants caused outbreaks of foodborne illnesses that resulted in several 

deaths, production plant closures, and near bankruptcies at other processors. 

The television show 60 Minutes highlighted the risks of Salmonella spp.

contamination in chicken, while the CDC and FSIS expanded and improved 

product testing (Ollinger and Mueller, 2003). Fearing lost sales, managers 

of chicken slaughter plants developed and installed counter-current scalders, 

bird washes, chlorine rinses, and other pathogen-reducing technologies 

(Waldroup et al., 1992). Other manufacturers sought new meat and poultry 

food safety control technologies, such as those described in Ollinger, Moore, 

and Chandran (2004).

The concern for meat and poultry food safety, fear of reputation loss for 

selling unsafe food, and the search for greater profi tability led to changes 

in the marketplace. Large restaurants, grocery chains, and other buyers that 

purchase huge amounts of meat and poultry demanded stricter meat and 

poultry food safety standards from their suppliers and, in return, granted 

lucrative single-source contracts (Ollinger and Mueller, 2003; Golan et al., 

2004). Moreover, a shift to the production of branded products provided 

consumers with a clear association between producers and their products, 

giving producers a stronger incentive to ensure meat and poultry food safety 

(Ollinger and Mueller, 2003).

Economists have examined the effects of reputation loss due to producing 

unsafe food. Thomsen and McKenzie (2001) found that fi rms that voluntarily recalled contaminated meat and poultry products suffered a decline in 

longrun profi tability (i.e., signifi cant declines in stock prices). Additionally, 

Ollinger and Mueller (2003) report anecdotal evidence indicating that plants 

suffering recalls incurred higher liability and process control costs. A number 

of studies (Piggott and Marsh, 2004; Marsh, Schroeder, and Mintert, 2004) 

determined that adverse meat and poultry food safety events led to temporary 

declines in meat and poultry consumption. Thomsen, Shiptsova, and Hamm 

(2006) established that sales of branded frankfurter products declined more 

than 20 percent after a product recall. Hudson Meats suffered a massive 

recall in 1998 and eventually exited the industry.

Adverse media publicity, fear of reputation loss, and demand from customers 

for safer products put pressure on meat and poultry fi rms to take their own 

actions. At the same time, FSIS mandated new performance standards 

(Salmonella spp. and generic E. coli performance criteria) that require no 

specifi c technology, giving plant managers the option to take any action 

necessary to meet the standards. Finally, FSIS promulgated more stringent 

meat and poultry food safety process regulations (SSOPs and HACCP tasks). 
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Private and Public Incentives Lead 

to Different Levels of Food Safety 

Process Control

A fundamental belief among economists is that fi rms want to maximize their 

profi ts. In terms of food safety, this means that plant managers undertake 

investments only up to the point at which it is no longer profi table for them 

to do so. Plants facing market pressures for food safety from several sources 

would likely make substantial investments in food safety process control, 

whereas a plant facing minimal pressure may make no such investments 

(fi g. 1). 

The buildings in fi gure 1 are hypothetical points representing plants with 

different levels of food safety process control. Plant A chooses the minimal 

level of food safety process control allowed by the PR/HACCP rule. Two 

other plants—plants B and C—choose levels that are slightly more stringent 

than those dictated under the PR/HACCP rule, while plants D and E select 

levels of food safety process control much greater than that demanded under 

PR/HACCP.

The levels of food safety process control shown in fi gure 1 vary because 

plants face different market demands. Plant A takes no management-determined actions and has a relatively low level of food safety process control. 

Plants like this may be willing to invest only to the level of food safety 

process control required under PR/HACCP because they sell generic products that are commingled with other products. These plants face very little 

pressure to provide food safety because it is diffi cult for buyers to link these 

plants to their products, eliminating potential liability for the sale of contamiFigure 1

Hypothetical mean level of food safety under the 1996 PR/HACCP rule

Source: Economic Research Service calculations.

Level of food safety

Level of food safety process control

Minimum PR/HACCP level of food safety

Plant A

Plant B

Plant C

Plant D

Plant E
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nated products. Other plants face various levels of demand for food safety 

process control from their markets. Plants B and C may sell branded products 

and may be identifi ed in the event of a foodborne illness outbreak. Plants D 

and E may face even more pressures. For example, they may sell brand-name 

products, operate as single sellers in some markets or to some retail outlets, 

and face buyer requirements from a major fast food restaurant and, thus, be 

closely linked to its products and be held liable for any contamination.
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Economic Framework

Plants achieve a level of food safety process control through their own 

management-determined actions and compliance with mandated process 

regulations. Management-determined actions are manifested in inputs of 

labor, human and physical capital, and technology and can be either direct or 

indirect.10

Direct actions include: 

 1. Meat and poultry food safety processing technologies, such as carcass 

pasteurizers and chlorinated water baths.

 2. Organizational arrangements, including contractual agreements with 

buyers and sellers.

Indirect actions include:

 1. A plant’s own production technology (e.g., plant size).

 2. Other factors beyond the control of plant managers that affect food 

safety, such as seasonality or the cleanliness and condition of the 

animal at the time of slaughter.

We examine the impact of management-determined actions and process regulations on food safety with a reduced-form production function. This model 

has no specifi ed functional form, but does provide a framework in which 

inputs of labor, capital, and food safety technologies are combined to produce 

food safety. Refer to appendix A for a complete discussion of this model.

Human and Physical Capital Investments 

Limit the Spread of Pathogens

Human capital includes general actions, such as hiring more workers devoted 

to meat and poultry food safety process control, targeting investments to 

worker training, or empowering workers to take greater responsibility for 

meat and poultry food safety process controls. Hatfi eld Meats provides an 

example of a targeted investment. This company set up a program in which 

production workers further up the processing line alert downstream workers 

when process control failures arise so downstream workers can make corrective adjustments (Bolton et al., 1999).

Physical capital investments are expenditures on the plant and specialized 

processing equipment designed to control pathogens. Plant investments, such 

as modifying ventilation systems or improving drainage, are ways to reduce 

the threat of food contamination by eliminating or controlling sources of 

pathogens. For example, Sara Lee invested millions of dollars in its ventilation systems and other plant facilities after it discovered that those systems 

were sources of pathogen contamination (Kenneth B. Moll & Associates, 

January 1999).

Plant managers can also change plant layouts to alter product fl ows and eliminate critical points that may be prone to cross-contamination. For example, 

10 Human and physical capital investments are generally managementdetermined actions because Government regulations do not mandate these 

investments. Investment is required for 

validating HACCP plans, but that type 

of investment is not included here.
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some plant managers choose to redesign their plant layout to prevent raw 

meat from coming in contact with fi nished products.

Processing Technologies Control Pathogens

Plant managers have developed innovative sanitation and operating procedures, product and environmental pathogen testing approaches, and equipment and processing techniques that kill, control, or detect pathogens. 

Advanced hide-removal equipment, for example, limits contamination by 

peeling away the hide so it does not touch the exposed meat. Additionally, 

steam vacuum units, which heat carcass exteriors, and other heat-treating 

equipment and chemical control devices, such as chlorine baths, kill pathogens outright. Also, more intensive cleaning can prevent pathogens from 

ever establishing themselves. See Ollinger, Moore, and Chandran (2004) and 

Roberts (2005) for examples of other technologies.

Organizational Arrangements Facilitate 

Food Safety Process Control

Organizational arrangements—vertical and horizontal management relationships within the fi rm and contractual relations with buyers and suppliers 

outside the fi rm—are other ways to control pathogens. Williamson (1985) 

argues that spot market purchases—transactions with minimal contact 

between a buyer and producer—are the simplest type of contract. These 

purchases, however, require that quality be obvious to the buyer because 

suppliers lose the incentive to provide adequate product quality if they 

cannot be held accountable for hidden fl aws. 

Williamson (1985) suggests that fi rms enter into contractual arrangements 

when product quality cannot be easily evaluated and the potential costs 

of purchasing low-quality products are high. For meat and poultry plants, 

failure to detect contamination in meat and poultry, in addition to being 

very costly, can become a source of unwanted negative publicity. One major 

outbreak at a Jack-in-the-Box restaurant in the early 1990s cost three people 

their lives, prompting Jack-in-the-Box and other large meat and poultry 

buyers in the United States to demand more sophisticated meat and poultry 

food safety process control systems from suppliers (Ollinger and Mueller, 

2003; Golan et al., 2004). Today, most large restaurant chains, grocery 

stores, and other large buyers enter into highly detailed contracts with 

suppliers in which buyers make large-volume purchases and suppliers use 

the food safety practices and technologies demanded by the buyer. Buyers 

benefi t from reduced business risks, and sellers benefi t from higher prices or 

guaranteed markets (Ollinger and Mueller, 2003; Golan et al., 2004). Henson 

and Northern (1998) and Balsevich et al. (2003) identify similar cases in the 

United Kingdom and Latin America. Starbird (2005) notes that contracts 

reinforce product food safety, since sellers must adhere to quality standards.

Other marketing methods may also encourage investments in meat and 

poultry food safety process controls. Klein and Leffl er (1981) argue that 

producers of branded products must support their brands by investing in 

quality control. To our knowledge, however, there is no empirical evidence 
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either supporting or refuting their argument in the case of meat and poultry 

food safety.

Some evidence shows that export markets impose food safety process control 

requirements. For example, Japanese and South Korean buyers imposed 

controls on U.S. suppliers due to concerns over bovine spongiform encephalopathy (BSE) or “mad cow” disease. Additionally, economists (Henson, 

Brouder, and Mitullah, 2000; Jaffee and Masakure, 2005) report that Kenyan 

fi sh and vegetable exporters had to comply with European Union standards 

before they gained access to the European market. 

Sometimes contractual arrangements become burdensome, but production 

processes are compatible enough within a plant that managers choose to 

vertically integrate. Williamson (1985) and Reimer (2006) argue that vertical 

integration offers greater control over product quality because one management controls the entire process, thereby eliminating the need for contractual 

negotiations. 

Ground meat and poultry producers may be more susceptible to selling 

contaminated meat because they rely on the food safety quality of inputs 

supplied to them by outside fi rms.11 In a vertically integrated operation, the 

manager of a ground meat plant knows the conditions under which their meat 

or poultry inputs were produced and can control that quality through lines of 

authority within the plant. Vertical integration should be more likely between 

slaughter plants and ground meat facilities or fi nal processors than between 

slaughter plants and growers or retailers, because ground meat production 

is a production process and easily integrated into a slaughter plant, whereas 

animal husbandry and retailing are not.

Plant Size and More Complex Operations 

Affect Food Safety Process Control

Plant size and other elements of basic plant technology indirectly affect 

meat and poultry food safety process control. Williamson (1985) argues that 

plant size drives economies of scale in production, but yields diseconomies 

of scale in bureaucracy. Economies of scale in production means that larger 

plants can spread the cost of a microbiological laboratory and other food 

safety technologies over a much larger volume of output, resulting in a lower 

fi xed cost per unit relative to smaller plants. Large plants, however, may 

also have higher bureaucratic costs due to added controls that ensure smooth 

information fl ow over longer and more complex lines of control and the need 

to rapidly process information about a broader mix of inputs and outputs. 

Increased bureaucracy might mean slower responses to food safety process 

control failures and greater reporting and management costs. 

Different animal species may also inherently harbor certain pathogens. 

For example, E. coli O157: H7 occurs in cattle but not in poultry, whereas 

Salmonella spp. is more prevalent in poultry and hogs. These pathogens may 

require different process control methods and record-keeping techniques, 

raising the cost of food safety process controls. Food safety experts also indicate that pathogen prevalence may vary within species (e.g., cows and bulls 

versus steers and heifers) and by season and region of the country.

11 As pointed out by an anonymous 

reviewer, it has not been proven that 

ground meat producers with multiple 

suppliers are more likely to sell contaminated meat. It depends on the food 

safety process controls of the supplier 

(i.e., a plant with one supplier with 

weak controls may be more susceptible to contamination than a producer 

with several suppliers using strong 

controls). Nevertheless, if all suppliers 

are equal, the probability of producing 

contaminated products does rise with 

the number of suppliers.
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Empirical Measures Link Food Safety 

to Management-Determined Actions 

and Process Regulations

We have sketched out a food safety production function in which inputs of 

labor, capital, and food safety technologies are combined to produce food 

safety (i.e., control pathogens). The food safety variable should be the actual 

level of pathogens in the meat or poultry produced by the plant. This level 

may or may not exceed the tolerance level (i.e., the level of pathogens that 

is safe for human consumption) and is strictly regulated by FSIS. The inputs 

(independent variables) are manifested in management-determined actions 

and process regulations.

Data on Salmonella spp. shares from FSIS’s testing program served as our 

measure of food safety. In this program, FSIS permits a maximum number 

of samples to test positive for Salmonella spp. out of a certain number of 

samples taken. For example, for a plant to pass inspection, FSIS might 

require that no more than 2 samples out of 50 samples taken from hog 

carcasses test positive for Salmonella spp. We defi ne our measure of food 

safety—Salmonella share—as the number of meat or poultry samples taken 

by FSIS that test positive for Salmonella spp. divided by the total number of 

Table 2

Plants with Salmonella shares, by product category, 20001

Product categories

Salmonella shares Cattle

carcasses Ground beef

Hog

carcasses Broilers Mean

Share of plants

0 53.6 43.2 46.1 12.2 38.8

0-2 20.2 20.2 8.4 13.3 15.5

3-5 13.1 13.3 13.5 16.0 14.0

6-10 10.7 16.3 15.2 23.4 16.4

11-20 1.2 5.6 9.0 20.2 9.0

21-30 1.2 0.9 5.0 9.0 4.0

Greater than 30 0 0.5 2.8 5.9 2.3

Total 100 100 100 100 100

Test sets

Cattle

carcasses Ground beef

Hog

carcasses Broilers Mean

Number of plants

0 45 354 82 23 126.0

0-2 17 166 15 25 56.0

3-5 11 109 24 30 43.5

6-10 9 134 27 44 53.5

11-20 1 46 16 38 25.2

21-30 1 7 9 17 8.5

Greater than 30 0 4 5 11 5.0

Total2 84 8203 178 188 -

1 FSIS tests 50 or more samples in sequence for Salmonella spp. from each plant that produces cattle or hog carcasses, ground meat or poultry, 

or broilers. Salmonella shares equal the number of samples testing positive for Salmonella spp. divided by the number of samples in the test set. 

A test set is the total number of samples FSIS takes each time a plant undergoes testing. If a plant has enough positives to fail a test or enough 

negatives to pass a test, FSIS stops sampling.

2 Total number of plants undergoing testing for Salmonella spp. in 2000.

3 Includes many nonmanufacturing plants, such as retailers and wholesalers. 

Source: Economic Research Service estimates based on FSIS Salmonella spp. testing data.
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samples.12 The hypothetical range of the Salmonella share includes zero, one, 

and all values in between. In practice, many plants had some positive samples 

but no plant had only positive ones, making the distribution bounded from 

below but not from above (table 2).

Table 3 shows how Salmonella share (top panel), plant size (middle panel), 

and the total number of plants (bottom panel) vary with managementdetermined actions in cattle and hog carcass, ground beef, and broiler 

plants. Salmonella shares drop sharply as the number of managementdetermined actions rises from zero to one in three of the four industries.13

This lower Salmonella share is followed by a modest, if any, decline in 

Salmonella shares after one management-determined action. The data also 

show that larger plants tend to have more management-determined actions 

than smaller ones. Combined, these trends suggest a possible relationship 

between plant size and Salmonella shares. It is hard to say if any relationship 

between specifi c process regulations, management-determined actions, and 

Salmonella shares exists.

Appendices A and B detail an empirical model of the reduced-form production function discussed in the previous section and describe the empirical 

methodology. The variables included in the model follow from discussions 

of process regulations and management-determined actions defi ned in table 4 

and are given in equation B.1.

Table 3

Salmonella shares decline as the number of management-determined actions and plant size rise, 20001

Number of management-determined actions

Industry 0 1 2 3 4 5 Industry mean

Mean percent of samples testing positive for Salmonella spp.

Cattle carcass 22.0 0.0 1.0 1.0 0.0 n.a. 1.3

Ground beef 2.0 5.7 3.3 3.3 n.a. n.a. 4.1

Hog carcass 43.9 8.0 6.6 3.7 6.0 0.0 7.0

Broilers 23.9 9.2 12.7 12.1 8.3 n.a. 11.8

Mean Salmonella share 14.6 6.4 5.3 4.9 5.7 0.0 5.8

Mean plant size

Cattle carcass (thousands of head) 14 64 106 155 329 n.a. 139

Ground beef (millions of pounds) 12.1 13.2 98.3 144.4 n.a. n.a. 71.9

Hog carcass (thousands of head) 201 867 682 850 1,456 123 796

Broilers (millions of birds) 18.4 29.9 37.9 38.8 47.1 n.a. 38.1

Number of management-determined actions 

01234 5

Total plants,

all actions

Number of plants

Cattle carcass 2 4 34 35 6 0 81

Ground beef 8 46 38 30 0 0 122

Hog carcass 2 22 31 22 4 1 82

Broilers 3 7 18 22 22 0 72

Total plants2 15 79 121 109 32 1 357

n.a.= Not applicable.

1 Management-determined actions include increases in human or physical capital, use of food safety process control technologies, vertical 

integration, contracting for enhanced food-safety with suppliers, and contracting for enhanced food safety with buyers. Only fi ve statistically 

signifi cant management-determined actions affect Salmonella spp. shares in each industry. Further discussion given in the text.

2 Total number of plants in the ERS survey that underwent testing for Salmonella spp. in 2000.

Source: Economic Research Service data.

12 As pointed out by a reviewer, there 

are many measures of food safety and 

Salmonella shares is only one of them.

13 As pointed out by an anonymous 

reviewer, all plants have management 

actions that are chosen to meet specifi c 

needs. For example, large plants may 

focus on different actions than small 

plants. Here, we can only examine 

management actions considered in the 

ERS survey, but we believe they cover 

a variety of actions able to accommodate plants of many different sizes.
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Process Regulations, Management-Determined 

Actions, and Other Variables That Explain 

Salmonella Shares

Data for the process regulation variables (no SSOP noncompliances, no FC 

noncompliances, and HACCP noncompliance share) come from the FSIS 

inspection reports completed by FSIS inspectors. If plants do not comply 

with mandated SSOPs, FCs, or HACCP tasks, the inspector may issue a 

noncompliance report. FSIS maintains several categories of record-keeping. 

The precise categories used in the defi nition were provided by Ron Eckel and 

other regulatory experts at the FSIS Omaha, Nebraska Technical Center.14 

The variables representing SSOPs and FCs are binary variables because 

many, but not most, plants reported no SSOPs or FCs out of compliance with 

FSIS regulations. Since the vast majority of plants had at least one noncompliance report for HACCP, the HACCP variable was defi ned as the number 

of HACCP tasks out of compliance with FSIS regulations divided by the 

total number of HACCP tasks performed.

Most of the management-determined action variables come from a meat and 

poultry food safety technology survey conducted by ERS (http://www.ers.

usda.gov/Data/haccpsurvey/). Only variables representing vertical integration, size, and the presence of multiple processes come from elsewhere—the 

Enhanced Facilities Database (EFD) of FSIS.

The food safety (FS) labor share variable (FS labor share) indicates the share 

of food safety workers devoted to process control. Studies showing improved 

food safety process control at plants like Hatfi eld Meats led us to include 

a human capital variable—employee actions—that captures employee 

involvement in maintaining meat and poultry food safety process control 

(see p. 6, “Management-Determined Actions and Salmonella spp. 

Performance Standards”). The physical capital variable (high capital expenditures) and the variable measuring the extent to which plants may have 

remodeled to enhance meat and poultry food safety process control (change 

plant layout) are based on questions in the ERS survey. They asked whether 

plants made investments beyond those which plant managers believed were 

necessary for compliance with the PR/HACCP rule and the extent to which 

the plant changed its layout to enhance product fl ows. 

Food safety process technology represents meat and poultry food safety 

process control technologies for different industries. Defi nitions vary for 

each industry (table 4) and are based on responses to the ERS survey. 

Technology indices for cattle slaughter and ground beef give a comprehensive measure of technologies, such as food safety equipment, and come from 

Ollinger, Moore, and Chandran (2004). The indices are based on the idea 

that some technologies in some industries work best as a group rather than 

as individual units, representing a “multiple hurdle system.” Indices were 

created for sanitation, operating practices, equipment or sprays that control 

or kill pathogens, and, for cattle carcass plants, hide-removal practices. 

Each index is based on the use of several similar, yet distinct, technologies. 

For example, the sanitation index includes how often hands are washed and 

knives sanitized. 

14 The SSOPs come from FSIS process 

codes 01B02 and 01C02, and the FCs 

come from FSIS process codes 06D02, 

06D03, 06E01, 06F01, 06F02, and 

06G01. HACCP tasks for slaughter 

operations include tasks identifi ed in 

FSIS process codes 03B01, 03B02, 

03C01, 03C02, 03J01, and 03J02. 
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Table 4

Explanation of variables

Label (Appendix A equation 3) Basis1 Label type Defi nition

Independent variable: 

 Salmonella share

S Continuous Number of samples testing positive for Salmonella spp. divided 

by number of samples tested.

Process regulation:

 No SSOP noncompliances R Binary One for plants with no SSOPs out of compliance with FSIS 

requirements; zero otherwise.

 No FC noncompliances R Binary One for plants with no facility control tasks out of compliance 

with FSIS requirements; zero otherwise.

 HACCP noncompliance 

 shares

R Continuous Number of HACCP tasks not in compliance with FSIS 

requirements as a share of all HACCP tasks.

 Discretionary labor—

 Labor share comprised 

 of FS workers

L Continuous Number of food safety (FS) process control workers hired after 

1996 as a share of all workers hired after 1996. They may or 

may not be trained to monitor HACCP performance.

 Human capital—

 Employee actions K Binary One if production workers correct own unsanitary conditions, 

tell others about their unsanitary conditions, or report 

conditions to supervisors; zero otherwise.

 Physical capital—

 High capital expenditures K Binary One if FS fi xed investment exceeds that required under PR/

HACCP; zero otherwise.

 Change plant layout K Binary One if plant modifi ed processing facilities for pathogen control 

since 1996; zero otherwise.

 Process technology—

 Food safety processing 

 technology

τ Continuous or 

binary

Varies; index values for hide-removal for cattle carcass and 

food safety processing equipment for ground beef. One for hog 

plants using steam vacuum units and one for broiler plants with 

seven food safety processing technologies; zero otherwise. Organization—

 Vertical integration O Binary Varies; one for cattle plants with ground beef operations, 

ground beef plant with slaughter operations, and hog or broiler 

with further processing operations; zero otherwise.

 Supplier contract O Binary One if supplier must meet plant FS standards; zero otherwise.

 Buyer contract O Continuous Probability of a plant’s having a buyer contract in which a 

plant’s customers impose stricter FS requirements on the 

plant than those imposed on it by FSIS or the plant produces 

branded products. Plant technology:

 Size P Continuous Number of animals slaughtered (cattle/hog carcasses, broilers) 

or pounds of meat produced (ground beef). 

 Multi-process P Binary One if cattle, hog, broiler plant slaughters more than one 

animal species or ground beef plant produces further 

processed products; zero otherwise.

Control variables:

 Share samples, fi rst quarter X Continuous Share of Salmonella spp. samples taken from January to 

March.

 Share samples, third quarter X Continuous Share of Salmonella spp. samples taken from July to August. 

 Previous FSIS testing X Binary One if previously subject to Salmonella spp. tests; zero 

otherwise.

 Year testing conducted X One for test results from 2001; zero otherwise.

 Western plant X Binary One if plant located in Colorado, Idaho, Oklahoma, Texas, Utah, 

or Wyoming; zero otherwise.

 Western Corn Belt plant X Binary One if plant in Iowa, Kansas, Minnesota, Missouri, Nebraska, 

North or South Dakota; zero otherwise.

 Cow/bull plant X Binary One if cattle plant processes cows and bulls; zero otherwise. 

1 These variables are included in Appendix A equation 2, which serves as the justifi cation for the associated empirical variable. S is a food safety 

variable, R is a vector of food safety process regulations, L is a vector of discretionary labor variables, K is a vector of physical and human capital 

variables, τ is vector of innovative technologies devoted to food safety, O is a vector of organizational relationship variables, P is a vector of plant 

technology variables, and X is a vector of control variables.
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The seven types of food safety control equipment for poultry slaughter establishments comprise a multiple-hurdle system in which continuous applications of food safety process control equipment results in lower pathogen 

levels. Systems with fewer than all seven types of equipment may not work 

as well because pathogens may have time to recover before the poultry 

encounters the next type of pathogen control equipment.15 The equipment 

controls pathogens by either limiting human contact or applying chemicals. 

The units include:

• A counter-fl ow scalding tank. 

• An automatic transfer to the eviscerator conveyor.

• Viscera separation machinery. 

• Equipment that pushes the crop through the front of the bird.

• Chlorinated water in the chiller. 

• Inside-outside antimicrobial sprays.

• On-line reprocessing for contamination control.

There is no reason to believe that any particular type of equipment is superior 

to another; thus, we assume equivalency across all types of equipment.

We also evaluated the effects of many food safety technology variables, but 

only two—steam vacuum units on hog carcasses and the use of seven types 

of food safety control equipment in young chickens—had measurable effects 

on pathogens. Steam vacuum units for hog carcasses heat a carcass’s surface 

high enough to kill pathogens and then remove any condensation with a 

vacuum system.

Recall that vertical integration and supplier and buyer contracts are management technologies in which plant managers can control food safety quality 

more closely. Vertical integration refers to either backward integration into 

slaughtering by ground beef plants or forward integration for cattle and hog 

carcass and broiler plants into processing (e.g., ground beef for cattle carcass 

plants). Supplier contracts relate to agreements up the supply chain. Animal 

suppliers, for example, might commit to using fencing to prevent rodents 

from entering animal pens or take other food-safety-related measures. If 

the animal supplier does not uphold its food safety promises, it may lose 

a customer and future revenues if managers of the buying plant choose to 

purchase animals elsewhere. Buyer contracts bind plants to buying fi rms 

(buyers), such as fast food restaurants, major grocery stores, and other meat 

and poultry retailers and wholesalers, that need to protect their reputations 

for food safety. Under these contracts, a buyer might require plant managers 

to take specifi c preventive and corrective actions, such as product testing 

or more stringent cleaning, that exceed the actions required by FSIS. Data 

on the use of these contracts came from the ERS survey. The question upon 

which buyer contracts are based asks specifi cally whether the terms of the 

contract required the plant to perform tasks that were more demanding than 

those imposed by FSIS.

15 We determined that plants needed all 

seven types of equipment through trial 

and error. Fewer than seven types of 

equipment did not yield any signifi cant 

change in the share of samples testing 

positive for Salmonella whereas seven 

types of equipment did lower the share 

of positive Salmonella samples.
19

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Buyer contracts affect a plant’s operations by making managers more vigilant about controlling pathogens. For example, instead of asking employees 

just to wash their hands, a plant may also require the employee to sanitize 

his/her knives more frequently. Plant managers feel pressure to act because 

they fear that a buyer will shift purchases to competitors. We call this 

increase in management diligence the management effect.

There is also a technology effect in which a buyer may require a plant to 

make capital investments, buy food safety process control equipment, or 

make some other change intended to enhance food safety process control. 

Since some economists may prefer to separate the management effect from 

the technology effect, we created an instrumental variable to represent the 

management effect. It is defi ned as the probability of a plant’s having a buyer 

contract.16

The fi nal two groups of variables, plant technology and control variables, 

are also defi ned in table 4. Plant technology variables are determined by the 

volume of output (size) and by a plant’s product line (multiprocess). Control 

variables account for unique factors, due mainly to either the season of the 

tests or the geographic location of the plant. We also control for: 

• Plant testing experience: plant managers may improve their food safety 

process controls after their fi rst round of testing.

• Quarter of the year and geographic location: FSIS (2005) indicates that 

Salmonella spp. levels can vary with the time of the year, and Onal, 

Unnevehr, and Bekric (2000) found that Salmonella spp. levels vary 

geographically. 

• Control variables: most are used for only one or two industries and only 

after fi nding a statistically signifi cant impact in the reported regression 

analyses.

16 An anonymous reviewer pointed out 

that the technology effect might be 

associated with other managementdetermined actions included in the 

model and that this technology effect 

would reduce the impact of capital investments and processing technologies 

on the dependent variable. This is not a 

drawback. Rather, it is precisely what 

should happen since there would be 

no such technology without a contract. 

Nonetheless, we estimated the management effect of buyer contracts separately with an instrumental variable 

approach in which we regressed buyer 

contracts on the independent variables 

in the equation from Appendix B, a 

market size variable (whether the plant 

serves national or local markets), and 

a number of technology variables 

not related to Salmonella shares. The 

resulting estimator captures greater 

management vigilance only and is 

free of any association with specifi c 

food safety processing technologies 

or capital or labor investments. This 

buyer contract variable is defi ned as 

the probability of a plant’s having a 

buyer contract.
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Combining Several Datasets 

With Unique Data on Food Safety Technologies

FSIS data include the total number of samples tested by FSIS in 2000 and the 

number of test samples found to be positive for Salmonella spp. For cattle 

carcass plants, we also used 2001 data because we have data for only 40 

plants in 2000. FSIS regulatory compliance data, also called noncompliance 

reports, covered the same years as the Salmonella spp. data and included the 

number of SSOP, HACCP, and facility control tasks out of compliance with 

FSIS standards, the number of tasks performed, and other process control 

data. The FSIS Enhanced Facilities Database (EFD) for 2000 gives detailed 

information on the numbers and types of animals slaughtered, SIC codes 

(Standard Industrial Classifi cation), pounds of meat or poultry produced, 

whether a plant produced meat or poultry, and categorical data on process 

types for each plant inspected by FSIS.

The EFD and regulatory compliance dataset include data for the more than 

6,000 plants inspected by FSIS. Far fewer observations of plants, however, 

were included in the analysis because many plants in the EFD do not produce 

carcasses or ground meat, were not tested for Salmonella spp. in 2000, or had 

missing data. 

We also deleted observations of plants testing in the B, C, or higher sets. 

Recall that FSIS randomly selects plants for Salmonella spp. testing in an 

initial set of tests (“A” set) and, if the plant fails the “A” set, it is subjected 

to followup sets. Since only “A” set plants are randomly selected, we use 

only “A” set plants in the analysis and drop other nonrandomly selected 

sets.17 Thus, plants eligible for our analysis had to have undergone 

Salmonella spp. testing in the year 2000 (2000 or 2001 for cattle carcasses), 

had been subject to testing in the “A” set, and had been a producer of cattle 

or hog carcasses, broilers, or ground beef. Note, if a plant was in the middle 

of a test set at either the beginning or end of the year, the Salmonella spp.

samples from the previous or subsequent year were included to make a 

complete set; thus, the dependent variable S for any plant is always based 

on a complete set of samples. After deletions for incomplete data, 162 cattle 

carcass, 646 ground beef, 175 hog carcass, and 162 broiler plants were 

eligible for the analysis.

The Economic Research Service (ERS) has a unique dataset containing information on plant characteristics, market relationships with buyers and sellers, 

and meat and poultry food safety technologies. The data were obtained in 

a survey containing approximately 40 questions on meat and poultry food 

safety technology, 15 questions on the costs of PR/HACCP regulation, 

various plant characteristics, and the types of markets plants serve. The 40 

meat and poultry food safety responses were used to create fi ve meat and 

poultry food safety technology indices: food safety equipment, food safety 

tests, hide-removal, sanitation, and food safety operating practices. Index 

values are higher for large and small plants with more intensive meat and 

poultry food safety activities. Refer to Ollinger, Moore, and Chandran (2004) 

for a complete description of the indices and the ERS survey. 

17 If we were to include B, C, and D 

tests, then we would bias our sample 

toward plants that are less able to pass 

the Salmonella test because the sample 

would include more poor performers 

than would randomly occur. 
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The ERS survey covered only establishments in the EFD that ERS defi ned 

as manufacturers—about a third of the establishments inspected by FSIS.18

Excluded establishments included retailers, wholesalers, and other nonmanufacturers. About 60 percent of the population of plants selected by ERS 

responded to the survey. Data used in the analysis included data from 131 

ground beef plants, 73 hog carcass plants, and 72 broiler plants that underwent Salmonella spp. testing in 2000 and 73 cattle carcass plants that had 

Salmonella spp. testing in 2000 or 2001. These data accounted for 44 percent 

of the cattle and hog carcass and broiler plants and about 20 percent of the 

ground beef plants. The small number of ground beef plants (131 of the 641 

ground beef plants eligible for analysis) is due to the wide diversity of establishments that grind meat. For example, many grocery stores and wholesalers 

grind meat as a side business. 

The ERS survey was not nationally representative, meaning that results 

cannot be generalized. Two factors, however, suggest that the bias due to the 

use of a nonrepresentative sample is small.19 First, the share of total output 

by respondents closely tracks the number of plants that participated in the 

survey, and a regression analysis by the authors suggests that no correlation 

exists between plant size and survey response. Second, the data were treated 

with a post-stratifi cation adjustment (Gelman and Carlin, 2000) in which the 

regression is adjusted with a response weight equal to the reciprocal of the 

share of plants responding to the survey within each of eight size strata for 

each industry.

18 The EFD identifi es the primary 

Standard Industrial Classifi cation (SIC) 

of all establishments. An establishment 

was assumed to be a manufacturer if 

it had a 2011, 2103, or 2015 SIC or 

slaughtered animals.

19 An anonymous reviewer asserts that 

a large degree of heterogeneity in the 

operations of establishments would 

increase the bias.
22

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Descriptive Statistics

Table 5 provides the means of the variables. Notice that the share of meat 

and poultry samples testing positive for Salmonella spp. ranged from about 

1.4 percent for cattle carcasses to nearly 12 percent for broilers. The table 

also shows that nearly all broiler plants were vertically integrated, more than 

half of all plants in each industry had buyer contracts, and workers in cattle 

slaughter plants had much greater control over food safety process control 

(employee actions) than employees in other industries.
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Table 5

Means of all variables used in models

Product category

Variable

Variable 

type

Cattle

carcass

Ground

Beef

Hog

carcass

Broiler

Dependent variable:

 Salmonella share Continuous 0.014 0.041 0.070 0.118

Process regulation:

 No SSOP noncompliances Binary 0.086 0.115 0.073 0.026

 No FC noncompliances Binary 0.160 0.197 0.134 0.038

 HACCP noncompliance share Continuous 0.020 0.007 0.011 0.057

Management-determined actions:

 Discretionary labor—

 Labor share comprised of FS workers Continuous 0.021 0.020 0.028 0.056

 Human capital—

 Employee actions Binary 0.827 0.631 0.573 0.513

 Physical capital—

 High capital expenditures Binary 0.407 0.344 0.280 0.436

 Change plant layout Binary n.a. n.a. n.a. 0.795

 Process technology—

 Food safety processing technology:

 hide-removal index

Continuous 0.439 n.a. n.a. n.a.

 Food safety processing technology:

 food safety equipment index

Continuous n.a. 0.520 n.a. n.a.

 Food safety processing technology:

 uses steam vacuum unit

Binary n.a. n.a. 0.122 n.a.

 Food safety processing technology:

 seven equipment types

Binary n.a. n.a. n.a. 0.038

 Organization—

 Vertical integration: makes ground beef Binary 0.395 n.a. n.a. n.a.

 Vertical integration: slaughters animals Binary n.a. 0.369 n.a. n.a.

 Vertical integration: further processes meat Binary n.a. - 0.512 0.987

 Supplier contract Binary n.a. 0.279 0.220 0.590

 Buyer contract Continuous 0.852 0.746 0.659 0.577

 Plant technology—

 Size: animals slaughtered1 Continuous 0.139 n.a. 0.796 0.038

 Size: pounds of ground meat2 Continuous n.a. 0.072 n.a. n.a.

 Multiprocess: slaughters multiple species Binary 0.370 n.a. 0.659 0.218

 Multiprocess: produces multiple 

 processed products

Binary n.a. 0.606 n.a. n.a.

 Control variables—

 Share samples, fi rst quarter Continuous n.a. 0.238 0.445 0.559

 Share samples, third quarter Continuous 0.259 n.a. n.a. n.a.

 Had Salmonella spp. testing Binary n.a. 0.426 n.a. 0.846

 Year of Salmonella spp. testing Binary 0.494 n.a. n.a. n.a.

 Western plant location Binary 0.247 n.a. n.a. 0.090

 Western Corn Belt plant location Binary n.a. n.a. n.a. 0.103

 Cow/bull plant Binary 0.617 n.a. n.a. n.a.

n.a. = Not applicable.

1 Cattle and hog carcasses are in millions and broilers are in billions.

2 Ground beef is in billion pounds.

Source: Economic Research Service technology data and various FSIS data sources.
24

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Results

Process Regulation Variables

Results (table 6) indicate that cattle and hog carcass plants with no SSOP 

noncompliance reports were associated with a statistically signifi cant, lower 

percentage of samples testing positive for Salmonella spp. Plants with no FC 

noncompliance reports were associated with a marginally signifi cant, lower 

percentage of samples testing positive for Salmonella spp. in hog carcass 

plants, but a signifi cantly higher percentage of samples testing positive for 

Salmonella spp. in broiler plants. Table 6 also shows that the share of tasks 

not in compliance with HACCP plans had a statistically signifi cant and positive effect on the number of samples testing positive for Salmonella spp. in 

cattle carcass, ground beef, and broiler plants.

Table 7 shows the impact of regulations and actions on Salmonella shares 

with a 20-percent increase in the stringency of process regulations and 

management-determined actions (see box, p. 25, “Analytical Approach to 

Econometric Analysis”). Results show that SSOPs have their largest hypothetical effect in hog carcasses. A 20-percent increase in the number of plants 

with no SSOP noncompliance reports was associated with a 17-percent 

reduction in Salmonella share.20 HACCP noncompliance share had its 

greatest impact in cattle carcasses. A 20-percent increase in the HACCP 

noncompliance share was associated with an 18.5-percent increase in the 

Salmonella share.21

The regulatory impact varies by industry. SSOP noncompliance reports had 

no statistically signifi cant association with the Salmonella share in ground 

beef and broilers, and the HACCP noncompliance share had no statistically 

signifi cant association with the Salmonella share in hog carcasses (table 6). 

Only in cattle carcasses were plants with no SSOP noncompliance reports 

and HACCP noncompliance shares of the correct sign and signifi cant. Plants 

with no FC noncompliance reports had mixed results, suggesting little 

impact—one empirical result was signifi cant and of the correct (negative) 

sign and one was signifi cant and of the incorrect (positive) sign.

Discretionary Labor and Human and Physical Capital

Results show that a higher labor share was associated with a statistically 

signifi cant increase in Salmonella spp. in cattle carcass and ground beef 

plants, but had no impact in the other industries (table 6). This is contrary to 

what was expected, perhaps because cattle carcass and ground beef plants 

added meat and poultry food safety workers in an effort to reduce Salmonella 

spp. when they already had high Salmonella shares. 

Human and physical capital gave expected results. Cattle and hog carcass 

plants that permitted employee actions had a lower share of samples testing 

positive for Salmonella spp. (table 6). Also, greater investment in plant, property, and equipment than required for compliance with the PR/HACCP rule 

in cattle carcass and ground beef plants and changes in plant layout in broiler 

plants were associated with statistically signifi cant and negative impacts on 

the share of samples testing positive for Salmonella spp.

20 We computed the marginal effects 

of binary and continuous variables in 

the same way to weigh all independent 

variables equally. For binary variables, the right-hand side of table 7 is 

multiplied by 0.20 and the mean of the 

independent variable is the mean of the 

binary variable.

21 We assume linearity, such that a 

20-percent reduction in the noncompliance share has an equal and opposite 

effect on the Salmonella share as for a 

20-percent increase in noncompliance. 
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Regression analysis was used to evaluate the impact of process regulations 

and management-determined actions on Salmonella shares in the meat and 

poultry industry. Each of the four industries—cattle and hog carcass, ground 

beef, and broilers—were examined separately because they have different 

processing technologies and Salmonella spp. standards. The hog carcass, 

ground beef, and broiler plant analyses were based on 2000 data because 

ERS data were available only for that year. The analyses also relied on 

FSIS’s Enhanced Facilities Database (EFD) data for 2000. Since there were 

not enough cattle carcass observations for a 1-year analysis, Salmonella spp. 

and process regulation data for 2000 and 2001 were used.

Tobit regressions and STATA econometric software were utilized to 

conduct the analysis. The Tobit regressions are discussed in more detail in 

appendix B. STATA allowed us to use the sample weights that were calculated as proposed by Gelman and Carlin and gives both parameter values and 

marginal effects. Marginal effects are reported because they provide a way 

to estimate the impact of the independent variables on the dependent variable in tobit regressions. For example, they allow us to report how much a 

10-percent increase in an independent variable, such as a buyer contract, has 

on the Salmonella share.1

We used the following procedure: 

 1. We used a likelihood test to show that a model containing all of the 

process regulation and management-determined actions (independent) 

variables had a statistically signifi cant impact on Salmonella shares. 

The results are available from the authors. 

 2. Parameter estimates and t-statistics from the econometric estimates 

model were then used to evaluate the impact of the independent variables on Salmonella shares (dependent variable). The coeffi cient on 

each variable is called the marginal effect and is used to determine the 

change in the dependent variable. The t-statistic indicates the degree of 

certainty associated with the estimated parameter.

 3. We estimated the change in the share of samples testing positive for 

Salmonella spp. We multiplied the estimated coeffi cients (table 6) 

times 0.20 (20 percent) times the mean of the independent variable 

(e.g., HACCP noncompliance share-table 5) and divided both sides of 

the equation by the mean Salmonella share (table 5).2 

Table 7 shows the hypothetical effect of 20 percent increases in independent 

variables on Salmonella shares. All variables with parameter signs consistent with their expected sign were included in the total impact, regardless of 

whether the parameter estimate was statistically signifi cant. For example, 

an estimate of the impact of SSOPs on Salmonella shares in broilers was 

included even though the parameter estimate was not statistically signifi cant. 

An estimate of the HACCP noncompliance share for hog carcasses, on the 

other hand, was not included because the parameter is negative and a positive sign was expected.3

Analytical Approach to Econometric Analysis

1 In ordinary least squares regressions, 

the impact of independent variables 

on the dependent variable can be computed directly from parameter values 

because the statistical distribution of 

the dependent variable is a full distribution. However, parameter values for 

tobit regressions cannot be used in the 

same way because they are estimated 

for an independent variable with a 

truncated distribution (Salmonella test 

results exist only in the positive portion of the distribution of the dependent variable, S). Econometricians, 

such as Greene (1993), have derived 

measures that do defi ne direct impact. 

These marginal effects are presented 

in table 6. STATA, our econometric 

statistical package, constructs these 

marginal effects by evaluating continuous variables at their means and 

estimating binary variables as discrete 

changes from zero to one.

2 The percent change could have been 

any value as long as the change was 

identical for each variable. The goal 

was to estimate the impact of process 

regulations relative to managementdetermined actions.

3 An anonymous reviewer points out 

that this could bias the results if statistically insignifi cant values are used for 

estimation purposes. We use insignifi cant but theoretically consistent 

terms because we are talking about an 

average relative contribution and not 

an absolute relative contribution. 
26

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A 20-percent increase in employee actions led to a 122-percent decline 

in Salmonella spp. in cattle carcasses. Broilers and hog carcasses showed 

declines of 16.0 and 1.5 percent, respectively (table 7). By contrast, a 

20-percent increase in the number of plants that made high physical investments or changed their plant layouts led to reductions in Salmonella spp.

of 54.0, 7.9, and 7.5 percent in cattle carcasses, ground beef, and broilers, 

respectively.22 There was no impact in hog carcasses.

Food Safety Processing Technologies

Numerous stand-alone meat and poultry food safety technologies and meat 

and poultry food safety technology indices were examined, but only a few 

were statistically signifi cant. All of the food safety processing technology 

variables we examined were recommended by at least one expert and were 

reported in the ERS technology survey. Test reports for some equipment 

also showed reductions in pathogens. For example, steam pasteurizers for 

cattle carcasses have been associated with statistically signifi cant declines 

in pathogens. Several technology indices were also considered (see earlier 

discussion).

Cattle carcass plants with higher index values for hide removal were associated with a statistically signifi cant lower share of samples testing positive for

Salmonella spp., as were broiler plants that used the seven types of poultry 

processing equipment designed for better pathogen control. Hog carcass 

plants that use steam vacuum units and ground beef plants with a high food 

safety equipment index had statistically insignifi cant declines in Salmonella 

spp.

None of the stand-alone equipment tested, including steam pasteurizers, 

steam vacuums, sanitizing washes, and other equipment identifi ed in 

Ollinger, Moore, and Chandran (2004), signifi cantly reduced the number of 

positive test samples. Groups of technologies working in concert in broiler 

and cattle carcass plants did provide superior process control and support for 

meat and poultry food safety experts who promote the use of multiple technologies to control pathogens.

As shown in table 7, a 20-percent increase in the hide-removal index is 

associated with about a 45-percent decline in samples testing positive for 

Salmonella spp. in cattle carcasses. Additionally, a 20-percent change in the 

equipment index in ground beef, number of plants using steam vacuum units 

in hog carcasses, and number of plants using seven food safety technologies 

in broilers was associated with reductions in the share of samples testing 

positive for Salmonella spp. that varied from 7.6 percent in ground beef to 

0.9 percent in broilers.

Organizational Arrangements, Plant Technology, 

and Control Variables

Results for organizational arrangement variables show that cattle carcass 

plants that vertically integrated into ground beef production, hog carcass 

plants that integrated into processing, and ground beef plants that backward 

integrated into slaughter had a statistically signifi cant lower share of samples 

22 Small changes in the samples testing 

positive for Salmonella spp. may only 

be applicable in the cattle slaughter 

regression because large changes may 

be out of the statistically valid region. 

However, because our main interest 

is to evaluate the contributions of the 

independent variables relative to each 

other (i.e., process regulations relative 

to employee actions), it does not matter whether we change the independent 

variables by 2 percent, 20 percent, or 

200 percent as long as we change all 

independent variables by the same 

amount. We used 20 percent because 

it is an easy number to work with and 

gives reasonable results for the other 

three products. 
27

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Table 6

The marginal effects of process regulations and management-determined actions on Salmonella shares, 

by type of meat or poultry plant, 20001

Variable Cattle carcass Ground beef Hog carcass Broilers

Process regulation:

 No SSOP noncompliances -0.088***

(0.018)

0.018

(0.025)

-0.629***

(0.107)

-0.096

(0.217)

 No FC noncompliances -0.002

(0.010)

-0.020

(0.029)

-0.099*

(0.060)

0.119***

(0.030)

 HACCP noncompliance share 0.649***

(0.193)

2.114**

(1.078)

-0.002

(0.567)

0.354*

(0.220)

Management-determined actions:

 Discretionary labor—

 Labor share comprised 

 of food safety workers

0.866***

(0.164)

1.073***

(0.362)

0.109

(0.080)

-0.647

(1.36)

 Human capital—

 Employee actions -0.111***

(0.014)

0.014

(0.016)

-0.098***

(0.037)

-0.017

(0.017) Physical capital—

 High capital expenditures -0.093***

(0.015)

-0.047***

(0.017)

 0.118**

(0.048)

-0.022

(0.017)

 Change plant layout n.a. n.a. n.a. -0.044*

(0.029) Process technology—

 Food safety processing technology2 -0.071***

(0.024)

-0.030

(0.021)

-0.069

(0.056)

-0.143***

(0.040)

 Organization—

 Vertical integration3 -0.128***

(0.024)

 -0.042***

(0.016)

-0.083*

(0.045)

 0.039

(0.029)

 Supplier contract n.a. -0.027*

(0.018)

-0.023

(0.042)

-0.025+

(0.017)

 Buyer contract -0.077***

(0.012)

-0.030

(0.025)

-0.182**

(0.077)

-0.047*

(0.028) Plant technology:

 Size -0.711***

(0.287)

0.045

(0.04)

 -0.024**

(0.011)

-0.777*

(0.440)

 Multi-process4 0.010

(0.011)

0.012

(0.014)

 -0.144**

(0.048)

 0.040

(0.027) Control variables:

 Share samples, fi rst quarter n.a. 0.098***

(0.038)

-0.104

(0.096)

-0.144***

(0.039)

 Share samples, third quarter 0.189***

(0.034)

n.a. n.a. n.a.

 Had Salmonella spp. testing -0.002

(0.011)

-0.040

(0.034)

n.a. 0.008

(0.019)

 Year of Salmonella spp. testing -0.011

(0.001)

n.a. n.a. n.a.

 Western plant location 0.176***

(0.027)

n.a. n.a. -0.134***

(0.042)

 Western Corn Belt plant location n.a. n.a. n.a. -0.134*

(0.076)

 Cow/bull plant 0.172***

(0.036)

n.a. n.a. n.a.

Observations5 73 124 73 72

X2 436*** 32** 73** 252***

+, *, **, *** statistically signifi cant at the 20, 10, 5, and 1 percent level, respectively.

n.a. = Not applicable. Dependent variable: S (share of samples testing positive for Salmonella spp. in FSIS testing program).

1 For marginal effects, continuous variables are evaluated at the means and binary variables are computed as discrete changes from zero to one. 

Standard errors are in parentheses.

2 Food safety processing technology is a hide-removal index for cattle slaughter, equipment index for ground beef, and one for hog carcass plants 

that use steam vacuum units and broiler plants that use seven types of modern chicken slaughter equipment; zero otherwise.

3 Vertical integration equals one for cattle carcass plants that grind meat, ground meat plants that slaughter animals, and hog carcass and chicken 

broiler plants that further process meat; zero otherwise.

4 One if a cattle, hog, broiler plant slaughters more than one animal species or a ground beef plant produces further processed products; zero otherwise.

5 The total number, 342 plants, is less than the 356 plants reported in table 3 because some plants had incomplete data and were dropped.

Source: Economic Research Service estimates.
28

The Interplay of Regulation and Market Incentives in Providing Food Safety / ERR-75

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testing positive for Salmonella spp. than did other plants. Table 7 shows that 

a 20-percent increase in the number of vertically integrated plants producing 

cattle carcasses was associated with a 72.2-percent reduction in the share of 

samples testing positive for Salmonella spp. Ground beef and hog carcass 

plants experienced Salmonella reductions of 7.6 and 12.1 percent with a 

20-percent increase in the number of vertically integrated plants.

Results also show that ground beef and broiler plants with food-safetyrelated supplier contracts had signifi cantly fewer samples testing positive for

Salmonella spp. Hog carcass plants had a negative but insignifi cant relationship. A 20-percent increase in the number of plants with supply contracts 

Table 7

Reductions in Salmonella share if independent variables are changed by 20 percent, by product category 1

Source of impact Variables Cattle

Ground 

beef

Hog 

carcass Broiler

Mean2

(no cattle)

Salmonella share

Process regulation No SSOP noncompliances 

+ No FC noncompliances

HACCP noncompliance 

share

11.3

18.5

1.9

7.2

17.0

No effect

0.4

3.4

6.5

3.5

Total process regulation 29.8 9.1 17.0 3.8 10.0

Management-determined 

actions:

 Capital—

 Human capital

 Physical capital 

 

Employee actions

High capital expenditures +

 change plant layout

132.2

54.0

No effect

7.9

16.0

No effect

1.5

7.5

 5.9

 5.1

 Sum of capital 186.2 7.9 16.0 9.0 11.0 

Process technology: Food safety processing 

technology3

44.5 7.6 2.4 0.9 3.6

 Sum of process technology 44.5 7.6 2.4 0.9 3.6

Organization:

 

 

Vertical integration4

Supplier contract 

Buyer contract

72.2

No effect

93.6

7.6

3.7

10.9

12.1

1.4

34.3

No effect

2.5

4.6

6.6

2.5

16.6

 Sum of organization 165.8 22.2 47.8 7.1 25.7

Sum of all managementdetermined actions396.5 37.7 66.2 17.0 40.3

Maximum effect—all factors 426.8 46.8 83.2 20.8 50.3

1 Estimates are based on a 20-percent change in all independent variables. Using a HACCP noncompliance share as an example, percentage 

change in S = β*0.20* HACCP noncompliance share / Salmonella share, where HACCP noncompliance share and Salmonella share are sample 

mean values (table 5).

2 Mean values based on ground beef, hogs, and broilers. Cattle slaughter excluded because it gives too much infl uence over the mean values.

3 Food safety processing technology is a hide-removal index for cattle slaughter, equipment index for ground beef, and one for hog carcass plants 

that use steam vacuum units and broiler plants that use seven types of modern chicken slaughter equipment; zero otherwise.

4 Vertical integration equals one for cattle carcass plants that grind meat, ground meat plants that slaughter animals, and hog carcass and chicken 

broiler plants that further process meat; zero otherwise.

Source: Economic Research Service estimates.
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was associated with a 3.7-, 1.4-, and 2.5-percent reduction in samples testing 

positive for Salmonella spp. in ground beef, hog carcasses, and broilers, 

respectively (table 7). 

Table 6 shows that the probability of having a buyer contract is associated 

with signifi cantly fewer samples’ testing positive for Salmonella spp. The 

impact of a 20-percent increase in the probability of having a contract varied 

from 4.6 percent fewer samples testing positive for Salmonella spp. in broiler 

plants to 93.6 percent in cattle carcass plants (table 7).23

Results (table 6) also show that cattle and hog carcass and broiler plants, but 

not ground beef plants, realized reductions in the share of samples testing 

positive for Salmonella spp. as plant size rose. A 20-percent increase in plant 

size was associated with a 141-percent reduction in Salmonella spp. for cattle 

carcasses and about a 5.4- and 5.0-percent reduction for hogs and broilers.

One plausible explanation for the advantage to larger plants is that they have 

a much lower cost of purchasing and maintaining quality control laboratories 

and other fi xed-cost food safety technologies because the cost per unit of 

output drops as plant size rises. Ground beef plants, on the other hand, have 

few equipment-based food safety technologies available for Salmonella spp.

control. Rather, they rely on methods that incur costs that vary with output. 

For example, they must ensure that incoming meat is not contaminated with 

pathogens and then rigorously clean and sanitize to avoid introducing or 

spreading pathogens.

Horizontal growth into multiproduct or multispecies plants did not affect 

shares of Salmonella spp. One of the coeffi cients for these multiprocess 

plants was negative, and three were positive. The control variables show 

that cows and bulls have a higher share of test samples testing positive for 

Salmonella spp. than do other cattle and that geography and seasonality of 

FSIS tests affect the share of samples testing positive for Salmonella spp.

Many other characteristics and technologies discussed in Ollinger, Moore, 

and Chandran (2004) were examined, but were not statistically signifi cant 

and were dropped. For example, broiler and hog carcass plants often remove 

animals from feed prior to slaughter to reduce fecal matter, but our results 

show no discernible effect for this process. Finally, numerous variations of 

the SSOP regulatory variable were tested, but were dropped in favor of the 

one used in the model because results were similar and experts at FSIS had 

more confi dence in the defi nition used. A continuous SSOP variable defi ned 

as tasks not in compliance with SSOPs as a share of all SSOPs and FCs not in 

compliance as a share of all FCs were also tested. Binary variables were used. 

We did test continuous variables, but results were insignifi cant in all cases, 

perhaps because of nonlinearity due to many plants’ reporting no noncompliance reports.

23 Recall that we are using the probability of having a buyer contract as 

an instrumental variable for buyer 

contracts. Parameter estimates for this 

variable are nearly identical to the 

binary variable for buyer contracts.
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Discussion of Results

Maximum Shares of Salmonella Controlled 

by Management-Determined Actions

The “maximum effect—all factors” is shown at the bottom of table 7. 

As indicated on p. 25 in the box “Analytical Approach to Econometric 

Analysis,” this value is the impact on Salmonella spp. due to a 20-percent 

increase in process regulations and all management-determined actions 

described in table 7. That is, the maximum effect is the most that process 

regulations and management-determined actions can affect Salmonella spp.

levels if all factors were changed by 20 percent. For example, a 20-percent 

increase in the fi ve relevant management-determined actions for broilers—

human and physical capital, process technology, and supplier and buyer 

contracts—would lead to a 17-percent reduction in the number of samples 

testing positive for Salmonella spp. For both process regulations and 

management-determined actions, the maximum effect for broilers is 20.8 

percent. Notice that vertical integration is assumed to have no effect since the 

sign on its estimated coeffi cient does not match expectations.

The maximum effect for poultry is the smallest value; ground beef is nearly 

twice as high and hog slaughter four times higher than poultry. Cattle 

slaughter is dramatically higher than any of the other three industries, even 

though the coeffi cients for process regulation and management-determined 

action variables (table 6) do not vary greatly from those of poultry. One 

possible explanation for this level of variance is that a 20-percent change 

in management-determined actions in cattle slaughter is out of the sample 

range. These changes may also have a greater proportional impact in 

cattle carcasses and a smaller impact in broilers because the mean share of 

samples testing positive for Salmonella spp. is higher in broilers, making any 

percentage change in the share of samples testing positive for Salmonella 

spp. smaller with relatively equal absolute reductions.

The relative effect of process regulation and management-determined actions 

gives the percentage contribution of each type of process regulation and 

industry action. The method of calculation is given in the box “Computing 

the Maximum Effect: All Factors and the Relative Shares” p. 31 and the 

percentages in the Appendix C table. For ease of presentation, the relative 

contribution shares are included in fi gures 2-6.

Consider the shares of process regulations and management-determined 

actions. As shown in fi gures 2-6, process regulation varies from a relative contribution of about 7 percent in cattle carcasses to 20 percent in hog 

carcasses. By contrast, the relative effect of human and physical capital 

varied from 19 percent in hog carcasses to about 44 percent in broilers and 

cattle slaughter, food safety technology ranged from 2.9 percent in hog 

carcasses to 16.2 percent in ground beef, and organization’s contribution 

(vertical integration and contract with suppliers and buyers) varies from 

about 34 percent in broilers to nearly 58 percent in hog carcasses.

The fi gures show that food safety technologies are less effective than 

other management-determined actions, perhaps suggesting endogenous 
31

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use. Haavelar, Nauta, and Jansen (2004) argue that plants using fewer new 

technologies may need them the least and plants using more may have the 

greatest diffi culty controlling pathogens. This minimalist approach to using 

food safety technology is consistent with its economic value. Plants that add 

equipment to improve product tastiness, texture, or some other observable 

food quality do so to increase sales or raise prices. Food safety, however, 

offers fewer direct economic benefi ts because consumers may assume all 

purchased meat and poultry is safe.24

Actual Shares of Salmonella Controlled by Process 

Regulations and Management-Determined Actions

The maximum contribution to process control by process regulations and 

management-determined actions depends on whether plant managers take all 

such actions. Table 3 shows that they do not—the actual number of management-determined actions adopted is much lower. For example, 15 plants had 

no management-determined actions, 79 plants had only 1, and most plants 

(about 60 percent) had 2 or fewer management-determined actions (table 3). 

Fewer management-determined actions mean a relatively weaker infl uence 

by management-determined actions and a stronger infl uence by process regulations than that shown in fi gures 2-6. 

Table 8 shows the changes in Salmonella shares by the number of management-determined actions for all four industries. If a ground beef plant, for 

example, took only two management-determined actions, then the reducThe maximum effect—all factors is the sum of all of the changes to the 

Salmonella share due to a 20-percent increase in each process regulation 

and management-determined action variable. Only variables with an estimated sign on the coeffi cient that matched expectations were included in 

the sum. Ground beef, for example, includes FCs, which has a coeffi cient 

that matches expectations, but does not include SSOPs, which has a coeffi cient that does not match expectations. The sum for ground beef also 

excludes human capital because that positive coeffi cient differs from the 

expected negative value since human capital should reduce not increase 

the Salmonella spp. share.

The relative contribution to Salmonella spp. reduction (i.e., shares of the 

maximum effect—all factors as demonstrated in fi gs. 2-5), was obtained 

by dividing all cells of table 7 by the corresponding value in the maximum 

effect—all factors cell. This calculation provides a percentage called the 

percent (or share) of the maximum effect. For example, each cell of the 

table pertaining to broilers was divided by 20.8. The resulting percentages, 

shown in table 1 of Appendix C, indicate that process regulation accounts 

for about 18.3 percent of the maximum effect—all factors and that 

management-determined actions account for the remaining 81.7 percent. 

Computing the Maximum Effect: 

All Factors and the Relative Share

24 Companies that tend to invest 

less in food safety do make exceptions. They likely would make 

investments if consumers have 

reason to believe that food is not 

safe. For example, Sara Lee and a 

Conagra plant in Greeley, CO, improved their meat and poultry food 

safety technologies and controls 

only after they suffered recalls.
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Figure 2

Cattle slaughter: Share of change due to process regulations 

and management-determined actions if all variables changed 

by the same amount

Source: Economic Research Service estimates.

Physical capital

Buyer contracts

Vertical 

integration

Human

capital

Process regulation

Process technology

22%

7%

31%

10% 13%

17%

Supplier

contracts

0%

Figure 3

Ground beef: Share of change due to process regulations 

and management-determined actions if all variables changed 

by the same amount

Source: Economic Research Service estimates.

Physical capital

Buyer contracts

Vertical 

integration

Human

capital

0%

Process regulation

Process technology

23% 20%

17%

16%

16%

Supplier 8%

contracts
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Figure 4

Hog slaughter: Share of change due to process regulations 

and management-determined actions if all variables changed 

by the same amount

Source: Economic Research Service estimates.

Human capital

Buyer contracts

Vertical integration

Physical capital 0%

Process regulation

Process technology

41%

20%

19%

15% 3%

Supplier contracts

2%

Figure 5

Broiler: Share of change due to process regulations and managementdetermined actions if all variables changed by the same amount

Source: Economic Research Service estimates.

Physical

capital

Buyer contracts

Vertical integration

0%

Human capital

Process regulation

Process

technology

4%

18%

37%

12%

22%

Supplier

contracts

7%
34

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tion in Salmonella shares due to management-determined actions for that 

plant would be about 16.9 percent. Since process regulations account for 

a 9.1-percent reduction in Salmonella shares (table 7), the contribution to 

Salmonella spp. reduction by management-determined actions and process 

regulations equals 26 percent (9.1 percent + 16.9 percent). Managementdetermined actions (16.9 percent) account for about two-thirds of Salmonella

reduction; process regulation accounts for the remaining third of the total 

reduction.

Figure 7 shows the relative shares of process regulation and the comparison 

of management-determined actions with Salmonella reduction.25 Plant 

managers taking no management-determined actions rely only on process 

regulation for food safety process control, making the process regulation 

share of Salmonella spp. reduction equal to 100 percent The mean share of 

Salmonella spp. reduction due to management-determined actions across all 

product categories moves up sharply from 0 to about 52 percent, and process 

regulation’s share drops from 100 to 48 percent for the fi rst managementdetermined action. The mean share of Salmonella spp. reduction due to 

Figure 6

Mean values (excluding cattle): Share of change due to process 

regulations and management-determined actions if all variables 

changed by the same amount

Notes:

1. Cattle slaughter excluded from chart of mean values because cattle slaughter gives too 

 much influence over the mean values.

2. Values are the normalized values from table 7. They are normalized by dividing all 

 independent variables by the maximum effect due to all factors. See Appendix C table 

 for detailed presentation.

4. Processing technology is a hide-removal index for cattle slaughter and equipment index 

 for ground beef. It also equals one for hog carcass plants that use steam vacuum units 

 and broiler plants that use seven types of modern chicken slaughter equipment; zero 

 otherwise.

5. Vertical integration equals one for cattle carcass plants that grind meat, ground meat plants 

 that slaughter animals, and hog carcass and chicken broiler plants that further process 

 meat; zero otherwise.

Source: Economic Research Service estimates.

Physical capital

Buyer contracts

Vertical integration

Human

capital

Process regulation

Process technology

7%

19%

18%

8%

29%

Supplier contracts

9%

10%

25 Values are computed as follows. 

First, we found the actual value of 

management-determined actions. 

This actual value equals the sum of 

all values arising from managementdetermined actions actually taken by 

a plant. For example, suppose ground 

beef plant A is vertically integrated, 

has a buyer contract, and has high 

physical capital investment, then its 

actual management-determined actions value equals 7.9 percent plus 7.7 

percent plus 10.9 percent (table 7) or 

26.5 percent. Since process regulations equal 9.1 percent of Salmonella

reduction (table 7), the actual process 

regulation effect must be adjusted. We 

do this by dividing process regulations 

(9.1 percent) by the sum of process 

regulations (9.1 percent) and actual 

management-determined actions (26.5 

percent), which equals 34.6 percent. 

Thus, the actual process regulation 

effect equals 25.6 percent for this 

example. 
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management-determined actions rises more slowly as the number of management-determined actions increases. Overall, the mean share of Salmonella 

spp. reduction due to process regulation is about 34 percent and the mean 

share of Salmonella spp. reduction due to management-determined actions is 

about 66 percent. 

Table 8 also shows that the impact of management-determined actions on 

food safety process control diminishes sharply after three actions, suggesting 

that food safety process control would increase substantially if plants 

currently taking two or fewer management-determined actions would take 

three or more such actions.

Table 8

The actual effect of management-determined actions: Change in Salmonella share based on the number 

of management-determined actions taken 1

 

Number of management-determined actions

Industry 1 2 3 4 5 Mean Maximum managementdetermined actions effect

Percent change

Cattle carcass 132.2 200.1 284.0 352.0 n.a. 239.6 396.5

Ground beef 9.8 16.9 23.4 n.a. n.a. 14.7 37.7

Hog carcass 22.6 36.8 55.0 61.6 66.2 38.6 66.2

Broilers 4.3 8.5 12.5 15.4 n.a. 10.3 17.0

Mean managementdetermined actions 

(excludes cattle)2

12.8 21.3 28.1 26.3 66.2 20.4 40.3

n.a. = Not applicable. 

1 The actual management-determined actions effect only includes contributions from management-determined actions taken by a plant. Some 

plants in the same industry may take three actions while others take only one, suggesting that the actual impact of management-determined 

actions varies from plant to plant and is less than the maximum amount. For example, the share of hog carcasses testing positive for Salmonella

spp. changed 36.8 percent when taking two management-determined actions but changed 61.6 percent when taking four actions. If no actions 

are taken, then the contribution due to management-determined actions is zero percent and, for process regulation, it is 100 percent.

2 We do not include cattle slaughter in the mean value because it is an outlier in discussions of absolute effects. We include it otherwise because 

relative effects are meaningful and cattle slaughter is a very important industry.

Source: Economic Research Service estimates.
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Figure 7

Mean share of Salmonella reduction due to process regulations 

in four meat and poultry industries, by number of managementdetermined actions

Notes: 

1. A maximum of five management-determined actions have statistically significant effects 

 (table 6), but most plants do not take all actions, giving process regulation a bigger role 

 in food safety process control than suggested in figures 2-6. Table 3 shows that 15 plants 

 took no actions, 79 took only one action, and 120, 109, 32, and 1 plant took 2, 3, 4, or 5 

 actions, respectively.

2. Shares given above show the actual share of Salmonella spp. reduction due to process 

 regulation. It equals one minus the actual share of Salmonella spp. reduction due to 

 management-determined actions. This actual share equals the average for all plants with 

 a given number of management-determined actions (e.g., two actions). The management-

 determined actions could be any of the six management-determined actions and differ from 

 plant to plant: For example, one plant may take one management-determined action, such 

 as a buyer contract, while another may also take one action, such as vertical integration. 

 Each plant would have one action, but each plant would also have different shares of 

 management-determined actions because different management-determined actions yield 

 different amounts of Salmonella reduction.

3. The actual share of Salmonella spp. reduction due to management-determined actions = 

 (actual management-determined effects)/(actual management-determined effects + 

 regulatory effects), where actual management-determined effects for a given number of 

 private actions comes from table 8 and regulatory effects comes from the top panel of table 

 7. For example, the actual management-determined effect for a cattle slaughter plant with 

 one action is 132.2 percent (table 8, second column for cattle slaughter). Regulatory effects 

 is 29.8 percent (table 7, third column of the table and top panel), does not change with the 

 number of management-determined actions. Thus, the actual share of Salmonella spp. 

 reduction due to management-determined actions equals 81.6 percent and the share of 

 Salmonella spp. reduction due to process regulation is 18.4 percent.

Source: Economic Research Service estimates.

No

private

actions

One

private

action

Two

private

actions

Three

private

actions

Four

private

actions

Cattle carcass

Ground beef

Hog carcass

Broiler

Mean (excluding cattle)

19%

29%

Percent

0 20 40 60 80 100
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Conclusion

This paper empirically examines how market incentives affect the ability 

of meat and poultry plants to control the prevalence of Salmonella spp. 

occurring in meat and poultry products. Using management-determined 

actions as a measure of those incentives, the report examines the impact of 

process regulations and management-determined actions on the share of 

samples testing positive for Salmonella spp. in an FSIS testing program. 

It is estimated that management-determined actions actually taken (not all 

managers take all actions) account for about two-thirds and process regulations about one-third of reductions in Salmonella spp. in meat and poultry. 

We also found that the importance of process regulation varies across plants, 

accounting for 50 percent or more of all food safety process control for about 

a quarter of the plants and for the entire food safety process control system of 

some plants.

The results demonstrate that both process regulation and managementdetermined actions play vital roles in meat and poultry food safety process 

control, and that meat and poultry food safety process regulation is a fl oor 

that some plants use as their only means of food safety process control, while 

the majority of plants use it as a basis for building a more sophisticated food 

safety process control system. 

This report also illustrates a variety of ways to enhance food safety process 

control. Given this variety, the most successful strategy for controlling pathogens may be to devote effort across a range of practices and technologies. 

Focus on a single type of control may yield some gains, but leave many other 

avenues unexploited.

Results suggest that management-determined actions make a substantially 

greater contribution to meat and poultry food safety process control than 

process regulation for most plants. For example, high capital expenditures 

and improved organizational arrangements each had twice the impact as 

process regulation did. Since health benefi ts data were not available, it was 

not possible to evaluate the costs of these management-determined actions 

relative to the public health benefi ts. 

Specifi c food safety processing technologies provide few advantages in terms 

of Salmonella spp. reduction. Only the use of steam vacuum units in hog 

carcass plants generated lower Salmonella shares. Combinations of specialized equipment and practices did affect Salmonella shares in cattle carcass 

plants and broilers. Salmonella spp. reduction provided by those technologies, however, was still less than Salmonella spp. reduction achieved through 

increases in employee actions, high capital expenditures, or organizational 

arrangements.

Questions remain about whether management-determined actions are motivated by market forces or compliance with performance standards. The 

case that market forces have a strong infl uence on management-determined 

actions is quite clear, however. Market forces are driven by fears of losing a 

brand image, a reputation for quality products, or a major customer because 

of inadequate food safety process control. The effects of losing a reputa-
38

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tion for food safety due to product recalls are well documented (Thomsen 

and McKenzie, 2001). Additionally, the response of the broiler industry 

to adverse media coverage indicates the effectiveness of market forces 

(Waldroup, 1992). Finally, anecdotal evidence suggests that many plants 

suffering large meat or poultry product recalls also increased their investments (Kenneth B. Moll & Associates, 1999) or, as in the cases of Hudson 

Meats and the Topps Meat Company, were forced to exit their industries.

There is little evidence showing the extent to which performance standards 

affect management-determined actions. For example, no documented reports 

have indicated that plants bought food safety processing equipment or took 

any other management-determined action to comply with the Salmonella 

spp. or generic E. coli performance standards. Plant managers probably 

were not thinking about performance standards when they entered buyer 

contracts because these arrangements are strictly between buyers and sellers. 

Performance standards leading to high capital expenditures (as used in this 

report) are also not likely because the ERS survey question upon which it is 

based asked the respondent whether plant managers had made capital investment beyond those required to comply with the PR/HACCP rule.

Government policies affect management-determined actions in more ways 

than through performance standards. Food safety information provided by 

Government offi cials plays an important role in shaping consumer and buyer 

demands. FSIS enforces zero tolerance rules for two human pathogens—

Listeria monocytogenes and E. coli O157: H7—and a less strict standard for 

Salmonella spp. by testing products in the marketplace. State agencies test 

some products in the marketplace, and public health experts isolate victims 

of foodborne illnesses and trace the pathogens to the source. If State agencies or other sources fi nd that products in the marketplace are contaminated, 

then FSIS has the power, credibility, and obligation to warn consumers about 

the potentially harmful food product and recall products. Warnings about 

product safety take place via public health announcements that both warn 

consumers about potentially harmful products and adversely affect a fi rm’s 

reputation for food safety. Economists have shown that a loss of reputation 

negatively affects profi tability and encourages managers to take actions to 

enhance food safety process control. 

FSIS could further encourage market forces and increase managementdetermined actions by increasing product testing in the marketplace and 

providing consumers and buyers with more information about the meat and 

poultry food safety quality of particular plants and fi rms. FSIS has recently 

begun publishing accounts of plant performance on Salmonella spp. tests and 

noncompliance reports with SSOPs and HACCP plans. These are positive 

activities that give consumers, consumer advocate groups, and others information about plant food safety process controls and should encourage greater 

food safety investments by meat and poultry producers.
39

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Limitations and Further Research

Four limitations to this study may drive future research:

• The available Salmonella spp. test results did not give a complete picture 

of meat and poultry food safety for the entire industry because not all 

plants were sampled.

• Salmonella spp. is only one of many harmful pathogens, and current data 

may give an incomplete picture of meat and poultry food safety. 

• The analysis could not precisely determine whether management-determined actions were driven by performance standards or market effects, 

making it diffi cult to assess the contributions of performance standards to 

Salmonella spp. control. 

• The ERS survey and the fi nal dataset were not nationally representative, 

so results cannot theoretically be generalized.26

The initial data were collected in 2001, but further study with more current 

data may show how improvements have progressed.

26 As discussed earlier, the bias 

may have been minimal since the 

share of total output of respondents 

closely tracks the share of plants 

that participated in the survey. 

Plant size and survey response 

were not correlated, and the data 

were treated with a post-stratifi -

cation adjustment (Gelman and 

Carlin, 2002). 
40

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Enhanced Facilities Database, 2001, U.S. Department of Agriculture, 

Food Safety and Inspection Service, 2001.
42

The Interplay of Regulation and Market Incentives in Providing Food Safety / ERR-75

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U.S. Department of Agriculture, Food Safety and Inspection Service. PBIS 

Database, 1998-2003, U.S. Department of Agriculture, Food Safety and 

Inspection Service, 2004.

U.S. Department of Agriculture, Food Safety and Inspection Service. 

Salmonella Test Results, 1998-2003, U.S. Department of Agriculture, 

Food Safety and Inspection Service, 2004.

U.S. Department of Agriculture, Food Safety and Inspection Service. 

Progress Report on Salmonella Testing of Raw Meat and Poultry 

Products, 1998-2004, U.S. Department of Agriculture, Food 

Safety and Inspection Service, 2005, www.fsis.usda.gov/science/

Progress_Report_Salmonella_Testing_1998-2004/.

U.S. Department of Agriculture, Food Safety and Inspection Service. 

Salmonella Database, 1998-2003, U.S. Department of Agriculture, Food 

Safety and Inspection Service, ongoing.

Waldroup, Amy L., Bruce M. Rathberger, Richard Forsythe, and Les Smoot. 

“Effects of Six Modifi cations on the Incidence and Levels of Spoilage and 

Pathogenic Organisms on Commercially Processed Postchill Broilers,” 

Journal of Applied Poultry Research (1992) pp. 226-234.

Williamson, O. The Economic Institutions of Capitalism, New York: The 

Free Press, 1985.
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Appendix A:

Linking Food Safety to ManagementDetermined Actions and Process 

Regulations

Meat and poultry food safety is just one product attribute among many, 

including marbling, taste, fat content, etc. Some of these product attributes 

may be mutually dependent and must be examined jointly. For example, 

marbling may make meat tender, but may also increase fat content, 

suggesting that tenderness and fat must be examined jointly. Attributes such 

as harmful pathogens, however, are separable because pathogens thrive in all 

types of meat products. This separability of harmful pathogens means that 

food safety can be examined separately from other attributes.

Equation 1 is a production function in which meat and poultry plants produce 

food safety (S) with inputs that include the discretionary labor needed to 

meet demands for meat and poultry food safety (L), physical and human 

capital (K), technology (t) and a vector of control variables (X) that accounts 

for differences in seasonality, location, and other factors that can affect food 

safety.

Equation 1: S = f (L, K, t, X)

Technology includes any method, equipment, or other means used to produce 

food safety. For our case, these are food safety process regulations (R), innovative food processing technologies devoted to meat and poultry food safety 

(τ), organizational innovations (O), and plant technology (P). Process regulations specify cleaning practices and monitoring activities, organizational 

innovations enhance management’s ability to control food safety, and plant 

technology refers to plant size and scope. Control variables include seasonality, location, etc. All of these variables are discussed in detail in the text 

and expressed in equation 2.

Equation 2: S = f (R, L, K, τ, O, P, X) 

Equation 2 was used to develop an empirical model (Appendix B) and 

includes two types of variables: process regulations and management-determined actions. Process regulations are sanitation and process control tasks; 

management-determined actions are investments in physical and human 

capital, food safety technology, and organizational arrangements.
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Appendix B: 

Empirical Modeling

An empirical model is needed to examine the impact of the factors affecting 

food safety, as described in equation 2 of Appendix A. The variable S is 

defi ned as the share of samples testing positive for Salmonella spp. It is 

bounded below by zero (plants with no positive test samples) and bounded 

above by one (plants with only positive test samples). In practice, many 

plants had some positive samples, but no plant had only positive ones, 

making the distribution bounded from below but not from above (table 2). 

Since most plants have none or very few samples testing positive for

Salmonella spp., it is assumed that S has a normal distribution centered 

around and truncated at zero, meaning that, if it were possible, some values 

would be less than zero.1 Managers at plants with samples that fall in this 

truncated region are concerned about ensuring meat and poultry food safety.

It may seem that S should be modeled as a proportion. Two problems emerge, 

however, by doing so. First, Greene points out that the estimation breaks 

down if any of the proportions are zero or one. Table 2 shows that there are 

many observations with zero proportions. Second, a wide variety of proportions are possible since FSIS ceases testing if a plant exceeds its tolerance 

level for samples testing positive for Salmonella spp. For example, if a tolerance is 5 samples out of 50, then it will stop testing after 5 samples if the fi rst 

5 samples test positive for Salmonella spp. Similarly, FSIS stops testing once 

a plant reaches a point at which it is impossible for it to fail the test set. For 

example, FSIS stops testing after 45 samples test negative for Salmonella spp.

if it was allowed 5 positive samples and the test set consists of 50 samples. 

Similarly, it would stop testing after 46 samples if only one sample tests 

positive.

A more appropriate regression approach is a Tobit model since it allows for 

a continuous, censored dependent variable that is truncated at zero. Tobin 

(1958) was the fi rst to consider regressions with censored dependent variables, like S, making his econometric method particularly well-suited for our 

analysis. Using a tobit regression, as given by Greene (1993), we specify the 

following model: 

1 Table 2 shows that about 40 percent 

of all observations have zero values 

and only about 2 percent have values 

that exceed 30 percent. This type of 

distribution forms half of a typical bellshaped distribution as assumed in the 

empirical model.
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The independent variables are components of the vectors given in equation 

2 in Appendix A. Defi nitions are given in table 4. We include only the label 

in the table since the label and the variable name are nearly identical. The 

middle column of table 4 shows the vector from equation 2 to which the 

label corresponds. For example, the regulatory vector, R, in equation 2 is 

comprised of three regulatory variables: 

• No_SSOP_noncompliances. 

• No_FC_noncompliances. 

• HACCP_noncompliance_share.

 Si

 = β1No_SSOP_noncompliancesi + β2No_FC_noncompliancesi +

β3HACCP_noncompliance_sharei

 + β4labor_share_comprised_of_FS_workersi +

β5Employee_actionsi + β6High_capital_expendituresi

 + β7Change_plant_layouti +

β8FS_processing_technologyi

 + β9Vertical_integrationi + β10Supplier_contracti +

β11Buyer_contracti

 + β12Sizei + β13Multi_processi + β14Share_samples_quarter1i +

β15Share_samples_quarter3i

 + β16Prior_Salmonella_testingi + β17Year_of_testingi +

β18Western_locationi

 + β19Western_corn_locationi + β20Cow_bull_plant + εi
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Appendix C

Change in Salmonella shares if all plants take all management-determined actions1

Source of impact Variables Cattle

Ground 

beef

Hog 

carcass Broilers

Mean2

(no cattle)

Salmonella share

Process regulation No SSOP 

noncompliances + no FC 

noncompliances

HACCP noncompliance 

share

 2.7

4.3

4.1 

15.4

20.4

0.0

1.9

16.4

8.8

10.6

Total process regulation 7.0 19.5 20.4 18.3 19.4

Management-determined 

actions

 Capital

 Human capital

 Physical capital 

 

Employee actions

High capital expenditures 

+

 change plant layout

31.0

 12.7

0.0

 16.9

19.2

 0.0

7.2

 36.1

8.8

 17.7

 Sum of capital 43.7 16.9 19.2 43.3 26.5

 Process technology Food safety processing 

technology3

10.4 16.2 2.9 4.3 7.8

Sum of process technology 10.4 16.2 2.9 4.3 7.8

 Organization

 Vertical integration 

 Contracts with suppliers

 Contracts with buyers 

Vertical integration4

Supplier contract 

Buyer contract

16.9

0.0 

22.0

16.2

7.9

23.3

14.5

1.7

41.3

0.0 

12.0

22.1

10.2

7.2

28.9

 Sum of organization 38.9 47.4 57.5 34.1 46.3

Share of all managementdetermined actions93.0 80.5 79.6 81.7 80.6

Maximum effect—all factors 100 100 100 100 100

1 Values come from table 7 and are put in terms of percentages. It is assumed that each plant takes all management-determined actions. Actual 

percent shares of total Salmonella reductions are shown in fi gure 7 and are based on the management-determined actions actually taken (i.e., 

they adjust Salmonella performance). 2 Mean values are based on ground beef, hogs, and chicken. Cattle slaughter is excluded from mean calculations because it gives too much infl uence over the mean values.

3 Food safety processing technology is a hide removal index valued between zero and one for cattle slaughter, an equipment index valued between zero and one for ground beef and dummy variables equal to one if a hog carcass plants uses steam vacuum units, or a broiler plants uses 

seven types of modern chicken slaughter equipment and zero otherwise.

4 Vertical integration means cattle carcass plants that grind meat, ground meat plants that slaughter animals, and hog carcass and chicken broiler 

plants that further process meat.