Abstract

Sanitation and process control costs increased the costs of producing meat and poultry

by about 0.5 percent in the period preceding the promulgation of the Pathogen

Reduction/Hazard Analysis and Critical Control Point (PR/HACCP) rule of 1996.

However, there was no benefit in trying to avoid these costs. Large slaughter plants and

all further-processing plants with poor performance of sanitation and food safety process

controls were more likely to exit their industries than other plants. Moreover, the fraction of costs required for sanitation and process control was about the same for large

plants as for small plants, suggesting that larger plants were no better able than small

plants to absorb sanitation and process control costs. Results also suggest that

PR/HACCP raised wholesale meat and poultry prices by about 1 percent.

Keywords: Food safety, production cost, manufacturing plant survival.

Acknowledgments

We wish to particularly thank John Antle, Hisham El-Osta, Tanya Roberts, Jim

MacDonald, Phil Spinelli, Dan Engeljohn, Matthew Michael, and Chuck Williams for their

very thoughtful and helpful comments. We also want to thank the members of the food

safety component of the Diet, Safety, and Health Economics Branch for their comments. 

Note: Use of firm names in this publication does not imply endorsement by the 

U.S. Department of Agriculture.

United States

Department

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

April 2003

Agricultural

Economic

Report No. 817

Managing for Safer Food

The Economics of Sanitation and

Process Controls in Meat and Poultry

Plants

Michael Ollinger and Valerie Mueller
Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iv

Acronyms in This Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vii

Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 

Chapter 2: Pre-HACCP Food Safety Regulation . . . . . . . . . . . . . . . . . . . . .3 

Wholesome Meat Act of 1967 and Wholesome Poultry 

Products Act of 1968 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 

Voluntary Quality Control Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 

Total Quality Control Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 

Partial Quality Control Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 

Streamlined Inspection, New Line Speed Inspection, and 

New Turkey Inspection Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 

How Total and Partial Quality Control Programs Vary by Plant Size . . . . .8 

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 

Chapter 3: The Shift to a New Regulatory Paradigm . . . . . . . . . . . . . . . .11 

Large Outbreak of Foodborne Illnesses Focuses Public 

Attention on Food Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Food Safety Under the Pathogen Reduction HACCP Rule . . . . . . . . . . . .13

HACCP Programs Under PR/HACCP . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Sanitation Procedures and HACCP Under PR/HACCP . . . . . . . . . . . . . . .13

Salmonella and Generic E. coli Testing Under PR/HACCP . . . . . . . . . . .14

Performance Under the WMA of 1967 and WPPA of 1968 

and PR/HACCP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Chapter 4: Process Control Effort and Plant Costs . . . . . . . . . . . . . . . . .17

A Model of Plant Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Estimation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Variable Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

Estimation and Tests for Model Selection . . . . . . . . . . . . . . . . . . . . . . . . .22

Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Product Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Economies of Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Percentage of Deficient SPCPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Percentage of Deficient SPCPs and Plant Output . . . . . . . . . . . . . . . . . . .26

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27

Chapter 5: Sanitation and Process Control Deficiencies 

and Plant Exits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36

Economic Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37

An Empirical Model of Plant Exits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

Model Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

Variable Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

Results for Slaughter Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

Results for Processing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

ii  Managing for Safer Food / AER-817 Economic Research Service/USDA
Chapter 6: Relationship Between the Performance of SPCP 

and HACCP Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

Economic Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

Model Linking HACCP Compliance to SPCP Compliance . . . . . . . . . . .48

Variable Definitions and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51

Chapter 7: Projecting the Costs of HACCP from 

the Costs of SPCPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

Previously Estimated Costs of SPCPs and the PR/HACCP Rule . . . . . . .55

Projecting the Cost of Performing at the Mean HACCP Level . . . . . . . . .56

Cost Estimates of Complying With the PR/HACCP Rule . . . . . . . . . . . . .57

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65

Economic Research Service/USDA Managing for Safer Food / AER-817  iii
Executive Summary

The U.S. Department of Agriculture has been responsible for ensuring sanitation and

process controls in meat plants since 1906. This report estimates that the cost of performing those tasks amounted to about 0.5 percent of costs: 0.2 cents per pound for

poultry and 0.6 cents per pound for beef. However, the cost of not performing sanitation and process controls may have been higher, in that plants that failed to maintain

required sanitation and process controls were more likely than others to go out of business. Additionally, this report projects the costs of the Pathogen Reduction Hazard

Analysis (PR/HACCP) rule of 1996. This most recent effort to assure wholesome meat

and poultry products mandates the use of a HACCP food safety process control program by all meat and poultry slaughter and processing plants and established maximum thresholds for the presence of pathogens in meat products. This regulation is

estimated to raise a plant’s costs of production by about 1.1 percent: 0.4 cents per

pound for poultry and 1.2 cents per pound for beef.

The U.S. Department of Agriculture (USDA) began inspecting exported pork bellies

for trichinae and live cattle, hogs, and sheep as well as discretionary meat items for

diseases and defects in 1890. The Federal Meat Inspection Act of 1906 required

USDA to ensure that slaughter and processing plants performed an appropriate amount

of sanitation. Regulations based on the Wholesome Meat and the Wholesome Poultry

Products Acts of 1967 and 1968 raised the bar on sanitation standards by compelling

plants to adhere to 15 types of sanitation and process control standards.

Concern over the presence of harmful pathogens in meat and poultry increased among

some experts during the 1960s and 1970s when the National Academy of Sciences

published a report in 1969 on the presence of Salmonella in poultry. Subsequently, the

American Public Health Association filed and then lost a 1972 Supreme Court case

that petitioned the court to declare Salmonella an adulterant. In 1977, the consulting

firm Booz-Allen expressed concern in a report to USDA about the presence of

Salmonella and other harmful pathogens in meat and poultry.

USDA’s Food Safety and Inspection Service (FSIS) developed voluntary and mandatory quality control programs in the late 1970s and formalized them in regulations in the

early 1980s. Plants that participated in these programs identified and monitored control points and took over much of FSIS’s responsibility for ensuring the performance

of the Sanitation and Process Control Program (SPCPs) in exchange for greater regulatory flexibility and reduced inspector overtime costs. However, only about 5 percent of

all plants ever adopted a voluntary Total Quality Control program, the most comprehensive quality control program introduced by FSIS.

Public fears over the wholesomeness of meat and poultry products accelerated during

the 1980s with an outbreak of E. coli 0157:H7 poisonings in McDonalds restaurants in

1982, 49 deaths attributed to Listeria moncytogenes and 2,200 cases of Salmonella

poisoning in Chicago during the later 1980s, and 4 children’s deaths from an outbreak

of E. coli 0157:H7 at Jack-in-the-Box restaurants in Washington and other

Northwestern States in 1992 and 1993. In response, FSIS promulgated regulations

requiring safe handling of ready-to-cook and ready-to-eat meat and poultry, declared

E. coli 0157:H7 an adulterant in ground beef and began testing products for it, and

issued the Pathogen Reduction Hazard Analysis and Critical Control Point

(PR/HACCP) rule.

iv  Managing for Safer Food / AER-817 Economic Research Service/USDA
The use of a HACCP food safety process control program was the central feature of

the PR/HACCP rule. Other components included mandatory testing for E. coli and

Salmonella to verify that meat and poultry processes are under control as well as

mandatory sanitation and process control standards. A HACCP program comprises the

following elements: (1) an assessment of all hazards, (2) identification of critical

points necessary for maintaining food safety, (3) the setting of critical limits for each

critical control point (CCP), (4) development of procedures to monitor each CCP, (5)

determination of corrective actions, (6) implementation of a recordkeeping system, and

(7) establishment of verification procedures.

Meat and poultry process control programs help ensure the food safety quality of a

firm’s production, can yield a longer shelf life, and encourage repeat purchases, but

can also raise costs. 

To see how much costs may have changed, we estimated a cost function with process

control effort as one of the arguments in an approach similar to Antle’s. Results show

that performance of sanitation and process control tasks on average increased plant

costs in six of the eight industries. A 50-percent improvement in sanitation and process

control performance, i.e., reduction in SPCPs, caused plant costs to rise an average of

1.2 percent. Hog slaughter and processed poultry plants had the highest cost increases,

and processed meat had the lowest. Cattle slaughter showed a minuscule drop in costs,

while cured/cooked pork had a 1.5-percent decline in costs. Note, that Antle pointed

out in 2000 that this estimate likely understates food safety quality control costs

because plants likely perform other tasks to enhance food safety.

We also found that costs dropped as sanitation and process control performance

dropped, and plant size rose in all eight industries, but significantly so only in

processed poultry, suggesting modest diseconomies of scale in sanitation and process

controls. This means that increased performance of sanitation and process control

tasks increases costs more in larger plants than in smaller ones. However, this small

increase in costs for larger plants does not offset the sizeable returns to scale (lower

costs) arising from increased plant size alone. These findings are important in that an

increase in the number of sanitation and process control tasks would likely benefit neither small nor large plants.

Even though it is costly to perform sanitation and process control tasks, plants continued

to do them. Our findings (chapter 5) may explain why. These results suggest that large

slaughter plants and all meat processors in the 90th percentile of unperformed/poorly

performed sanitation and process control tasks (about twice the mean number of unperformed/poorly performed sanitation and process control practices) have an increased

likelihood of exiting the industry. Only small slaughter plants could reduce their likelihood of exiting an industry by poorly performing sanitation and process control tasks.

After finding empirically that performance of sanitation and process control practices

correlates with HACCP tasks, we estimated the costs of HACCP regulation. We found

that imposition of HACCP would increase industry variation in process control performance. For example, plants with about twice the mean level of poorly performed

HACCP tasks (about the 90th percentile of quality control effort) would have an average of $500,000 in lower process control costs than plants at the industry mean performance level. These savings suggest that incentives to reduce process control effort

Economic Research Service/USDA Managing for Safer Food / AER-817  v
may be stronger under HACCP and may require an increase in enforcement actions to

maintain regulatory compliance.

We estimated that HACCP plans and their implementation would raise meat and poultry prices by about 1.1 percent, i.e., 0.4 cents per pound for poultry and 1.2 cents per

pound for beef. The 1.1-percent increase in meat and poultry costs that we project may

sound small and is to the consumer, but to the producer it is quite large. Meat and

poultry plants have little direct control over meat input prices; yet, meat and poultry

inputs amount to anywhere from about 80 percent (cattle slaughter) to 50 percent

(sausages) of all costs. Thus, for meat and poultry plants, the cost of the PR/HACCP

rule ranges from between 2.2 and 5.5 percent of controllable costs, i.e., nonmeat costs.

These estimates of the costs of HACCP to the industry are more than seven times larger than the original FSIS-estimated costs of the PR/HACCP rule. Even so, the estimated costs reported here are less than one-half the drop in health care costs associated

with reductions in foodborne illnesses that accrue to the U.S. economy due to implementation of the PR/HACCP rule. This estimate is based on an assumed 20-percent

reduction in foodborne illnesses due to PR/HACCP and a Landefeld and Seskin value

of a statistical life, the most conservative health care cost savings estimate provided by

USDA’s Economic Research Service.

vi  Managing for Safer Food / AER-817 Economic Research Service/USDA
Acronyms in This Report

APHA American Public Health Association

CCP Critical control point

CDC Centers for Disease Control and Prevention 

EFD Enhanced Facilities Database

EPA Environmental Protection Agency

FMIA Federal Meat Inspection Act of 1906

FSIS* refers to the USDA food safety agency that preceded FSIS

FSIS Food Safety and Inspection Service

HACCP Hazard Analysis and Critical Control Point

LRD Longitudinal Research Database of the Center for Economic Studies at the Bureau of the Census

MIA Meat Inspection Act of 1890

NAS National Academy of Sciences

NELS New Line Speed Inspection System

NTIS New Turkey Inspection System

PBIS Performance Based Inspection System

PPIA Poultry Products Inspection Act

PQC Partial Quality Control programs

PR/HACCP Pathogen Reduction Hazard Analysis and Critical Control Point Rule of 1996

RTI Research Triangle Institute

SIC Standard Industrial Classification

SIS Streamlined Inspection System

SOP Standard Operating Procedure

SPCP Sanitation and process controls practices 

SSOP Sanitation standard operating procedures

TQC Total Quality Control programs

USDA U.S. Department of Agriculture

WMA Wholesome Meat Act of 1967

WPPA Wholesome Poultry Products Act of 1968

Economic Research Service/USDA Managing for Safer Food / AER-817  vii
Food safety regulation of meat and poultry plants has

been controversial. Plant operators have long argued

that food safety regulation raises their production costs

and imposes proportionately higher costs on small

plants than on large ones. Some consumers and public

health advocates, on the other hand, assert that an

absence of food safety regulation encourages plants to

sell products that may be harmful to humans. Despite

this controversy, there have been few studies of the

economic effects of food safety regulation on meat and

poultry slaughter and processing plants. This report

aims to address that deficiency.

The Federal Meat Inspection Act (FMIA) of 1906

mandated that the U.S. Department of Agriculture

(USDA) inspect cattle, hogs, and sheep for animal diseases, verify that carcasses are fit for human consumption, and ensure the cleanliness of slaughter and processing plants. More recent regulations stemming from

the enactment of the Wholesome Meat Act (WMA) of

1967 and the Wholesome Poultry Products Act

(WPPA) of 1968 charged the Food Safety and

Inspection Service (FSIS) of USDA with the responsibility of monitoring plant performance of a detailed set

of sanitation and process controls practices (SPCPs).1

Between 1967 and 1996, FSIS took a series of steps

toward devoting more of its resources to the control of

pathogens in meat and poultry. Then, in 1996, it promulgated the Pathogen Reduction/Hazard Analysis and

Critical Control Point (PR/HACCP) rule. This regulation mandated the use of a HACCP food safety,

process control program by all meat and poultry

slaughter and processing plants and established a set of

pathogen performance standards to which raw meat

and poultry products were required to adhere.

The brief regulatory history illustrates a progressive

but discontinuous march toward regulatory oversight

designed to reduce cases of foodborne illnesses. In this

report, we focused on the costs of food safety regulation. Policymakers, meat and poultry plants, consumers, and others want to know how much food

safety regulation costs. Moreover, they want to know

how costs change and who pays those costs as food

safety regulatory requirements change. For example,

does food safety regulation favor large or small plants?

Additionally, if food safety process control tasks are

costly, are plants with larger food safety process control costs penalized? Further, does food safety process

control performance change under alternative food

safety regulatory regimes?

The main purpose of this report is to examine the cost

implications of food safety regulation under the regulatory authority vested in FSIS stemming from the

FMIA, WMA, WPPA, and PR/HACCP rule. We started by establishing the historical context within which

food safety regulation exists. Then, we examined the

production costs of SPCPs and the effect of SPCP performance on plant survival. Finally, after establishing

that food safety process control performance under the

regulations associated with the WMA and WPPA is

correlated with food safety process control performance under PR/HACCP, we projected the costs of

PR/HACCP from estimated costs of SPCPs.

The report differs from other analyses in several ways.

To our knowledge, there have been no economic studies of food safety regulation that have been cast in a

historical context nor any studies of the effect of food

safety regulation or control measures on plant survival.

Additionally, we are aware of no reports showing the

relationship between performance of SPCPs under

WMA and WPPA and performance of HACCP tasks

under PR/HACCP. There have also not been any cost

studies of SPCPs, but there have been such studies of

PR/HACCP. The first of these studies (Knutsen et al.,

1995, and FSIS, 1996) used accounting methods and

projected labor requirements to provide preliminary

cost estimates of PR/HACCP. Recently, Antle (2000)

estimated costs based on a cost function analysis and

Economic Research Service/USDA Managing for Safer Food / AER-817  1

Chapter 1

Introduction

1 The acronym SPCP refers to the type of cleanliness standards in

place prior to 1996 and includes both sanitation and process control tasks. A process control task may be to keep raw and finished

products in separate areas.
Boland et al. (2001) published the costs of PR/HACCP

for 50 meat plants in the Great Plains.

Like Antle (2000), we took a cost function analysis

approach. Our work differs from Antle (2000) in that

we used a direct measure of food safety, process control effort that likely understates food safety, quality

control costs because food safety quality depends on

factors other than process controls. Antle’s (2000)

measure of food quality, on the other hand, likely overstates food safety, quality control costs because food

quality includes nonhealth-related factors. Combined,

the two studies provide a window within which the

costs of PR/HACCP likely fall.

We cast our analysis in a historical context in order to

illustrate the progression of events that led up to enactment of the PR/HACCP rule of 1996. We argued that

PR/HACCP marked an acceleration of a long-term

trend toward devoting more effort to protecting the

public from unobservable foodborne pathogens.

Viewed in this way, implications about the effects of

food safety regulation prior to promulgation of

PR/HACCP differ from those under PR/HACCP only

in scale.

The analysis is based on the Census Bureau’s 1992

Longitudinal Research Database (LRD) and Food

Safety and Inspection Service’s 1992, 1997, and 2001

Enhanced Facilities Database (EFD). It also uses a

database containing SPCP and HACCP performance

data obtained in private correspondences with FSIS

personnel.2 We relied on 1992 data for much of the

analysis because this was the only year for which both

SPCP and Census data were available.

This report is the first in a series of planned reports on

the costs and technologies associated with food safety

process control. This report provides some long-term

economic implications of food safety regulation based

on the performance of SPCPs under WMA of 1967

and WPPA of 1968. These implications are still valid

under the PR/HACCP rule of 1996 because the two

regulatory regimes are linked. A planned report on the

costs of PR/HACCP based on plant survey results will

discuss the short-term plant costs of adhering to the

requirements of PR/HACCP. Other reports will investigate the adoption and performance of various types of

food safety technologies.

The LRD has detailed establishment records for all

manufacturing establishments for 1963 and 1967-97.

We used the 1992 data for the cost analysis of SPCPs

because 1997 LRD data were not available at the time

of the analysis and percent-deficient SPCPs were

available only for 1992. Data records include physical

quantities of meat production, number of employees,

electricity use and dollar values of worker’s wages,

plant shipments, material costs, fuel use, plant assets,

and many other items. The LRD also notes ownership

and location of establishments.

Researchers can access LRD records for research purposes only at a Census facility. Additionally, stringent

disclosure requirements dictate that researchers can publish only aggregated information. We follow those same

disclosure rules for FSIS data. Any references to specific company or plant names are based on publicly available records, and not on any Census or FSIS source.

The EFD details animal counts by animal species,

types of production processes, plant names, and, until

1997, meat and poultry production volume. Since FSIS

identifies plants by the same plant number for each of

its databases, we matched these EFD data with the

SPCP data and HACCP performance data.

The report proceeds as follows. In the second and third

chapters, we reviewed some key food safety regulatory

policies and key events. In the fourth and fifth chapters,

we used the performance of SPCPs as a measure of

process control effort to investigate the costs of food

safety process controls and whether it is profitable to

reduce performance of them. Finally, in chapters 6 and

7, we compared plant regulatory performance of SPCPs

with performance of tasks under the PR/HACCP rule

and estimated the costs of PR/HACCP, based on our

estimated costs of SPCPs.

2  Managing for Safer Food / AER-817 Economic Research Service/USDA

2 SPCP performance data are based on inspection tasks as specified in the Inspection System Guide of FSIS. The inspection tasks

are pre-operational and operational sanitation and process control

tasks as detailed in the Performance Based Inspection System.
USDA, through its food safety agency, FSIS, has

played a significant role in regulating meat and poultry

quality and safety since 1890. During the 1880s,

European countries began restricting American pork

and livestock imports out of fear of trichinosis in pork

and animal diseases in livestock. American meatpackers, fearing that they would lose export sales, petitioned the U.S. Congress for a way to guarantee the

safety of American exports. After several years of

effort, meatpacker lobbying efforts resulted in the

Meat Inspection Act (MIA) of 1890. The MIA granted

USDA the authority to inspect livestock, salted pork,

and bacon intended for export and to quarantine animal imports to ensure that diseased animals were not

imported. However, this legislation had many loopholes, so Congress strengthened it with more stringent

legislation in 1891 that required the inspection of cattle for export and cattle, hogs, and sheep in interstate

commerce for animal diseases, such as hog cholera

and tuberculosis. The law also required all fresh beef

for export to have a certificate verifying that the meat

had come from inspected cattle and the inspection of

pork products for trichinosis and some other animal

diseases. To comply with this legislation, FSIS*

inspectors made direct visual inspections of the animals and meat products and used microscopes to

check for unwanted bacteria.1 The MIA was further

amended in 1895 to prevent the diversion into commerce of condemned carcasses and parts.

During the 1890s and early 1900s, inspectors examined an ever-increasing number of animals and volume

of pork but did not monitor the cleanliness of packing

plants. By some accounts, sanitation in plants became

very inadequate, setting the stage for Upton Sinclair’s

novel, The Jungle. This book raised anxiety in the

United States, but, according to Wiser et al. (1986), it

was the international reaction that caused the most

concern among meatpackers. Fearing lost export sales,

meatpackers lobbied for legislation that would assuage

European food safety concerns. In response, Congress

enacted the Federal Meat Inspection Act (FMIA) of

1906. This legislation did reduce concerns in export

markets but was mainly directed at the domestic market, covering all meat plants that shipped products

across State lines and to export markets. It greatly

increased expenditures for Federal meat inspection

activities and mandated that FSIS* inspect live cattle,

hogs, sheep, and goats just prior to slaughter and their

carcasses after slaughter. Legislation also required

FSIS* to inspect meat further-processing lines to

ensure proper sanitation and required producers to

affix a label indicating that the meat was FSIS*

inspected on all domestically shipped products.2 If an

inspector condemned a meat product, then that meat

required a stamp of condemnation and had to be disposed of in the presence of an inspector. Since legislation mandated that meat products sold across State

lines must pass inspection, the FMIA served as a vehicle for ensuring product wholesomeness and the

Federal Government became a guarantor of meat quality and safety. Inspection also took place in plants selling products within State boundaries, but State agencies monitored these plants.

Poultry products were not covered under FMIA,

probably because chickens were often raised in backyards and home butchered. With the advent of more

commercial poultry slaughter and processing plants,

the Federal Government instituted a fee-for-service

inspection system. Processors participated in this program in order to assure the public that their products

were wholesome.

Rising demand and the possibility of the use and sale

of meat from sickly animals by some unscrupulous

producers prompted Congress to mandate compulsory

Economic Research Service/USDA Managing for Safer Food / AER-817  3

Chapter 2

Pre-HACCP Food Safety Regulation

1 The USDA agency responsible for the inspection of meat and

poultry products became an independent agency of USDA known

as the Food Safety and Inspection Service in 1981. This agency

had previously been called the Food Safety and Quality Service

and, under various names, has been part of USDA operations since

the FMIA of 1906. We use FSIS* to refer to the USDA food safety

agencies that preceded FSIS.

2 The 1891 act required labels to be affixed to products for export. 
inspection in the Poultry Products Inspection Act

(PPIA) of 1957. As with meat products, Federal

inspectors used clinical symptoms and conditions as an

indicator of disease conditions and visual and olfactory

inspection as an indicator of animal and poultry meat

microbiological safety.

Guaranteeing truthful labeling regarding product formulation became markedly more complex after World

War II. FSIS* had long been concerned about the use

of water, vegetable protein, and other nonmeat fillers

that could be used by processors as cheap replacements for meat without being readily detected by consumers. However, concern about the use of these fillers

increased after the war because frozen pizzas, prepackaged hamburgers, ham products, and other processed

meat products made their way into grocery store meatcases. Later, poultry followed this path. 

FSIS*’s meat and poultry food safety and labeling

assurance inspection program became even more

complicated with further changes in meat and poultry

processing. Silent Spring by Rachel Carson and other

books during the 1960s heightened consumer awareness of the presence of pesticides and other residues

in food. In 1963, a congressionally mandated FSIS*

study showed that: (1) few States had strong inspection programs, (2) there was a lack of uniformity

among States concerning inspection requirements, and

(3) sanitary conditions varied widely among Stateinspected plants. In response, Congress amended the

FMIA and PPIA with the WMA of 1967 and the

WPPA of 1968.

Wholesome Meat Act of 1967 and

Wholesome Poultry Products Act of 1968

The WMA and WPPA amendments greatly expanded

FSIS*’s statutory authority over the number of establishments inspected, enhanced enforcement powers,

and increased the detail with which FSIS* carried out

inspections. These acts increased the number of establishments inspected in three ways. First, they increased

the range of establishments over which FSIS* had

authority by mandating that FSIS* has authority over

renderers, food brokers, animal food manufacturers,

freezer storage concerns, transporters, retail outlets,

and wholesalers in addition to slaughter plants and

processing plants. Today, FSIS* inspects more of the

processing and slaughter operations of these types of

establishments than plants defined by the Census

Bureau to be either an animal slaughter or meat processing plant.

The acts also granted FSIS* indirect oversight of Stateinspected plants and direct monitoring of foreign

exporters by requiring that State inspection systems and

the systems of foreign country facilities of meat and

poultry exporters to this country be at least equal to the

Federal system. The “at least equal to” clause meant

that FSIS* had to ensure that State-inspected systems

and plants exporting products to the United States met

FSIS* standards. This “at least equal to” clause had a

huge effect on FSIS*’s inspection load. Shortly after

enactment of the WMA and WPPA, 24 States discontinued State poultry inspections and 17 States decided

not to pursue State meat inspection, so FSIS* had to

take over their former responsibilities. Due to changes

stemming from the WMA and WPPA, the number of

State-inspected plants dropped by about 30 percent to

5,219, forcing plants without State inspection to seek

FSIS* regulation or go bankrupt. Combined, the number of plants switching from State to Federal inspection

and normal plant entries raised the number of federally

inspected plants by about 50 percent to 7,093 over the

1972-76 period (Booz-Allen, 1977).

The WMA and WPPA also provided stronger enforcement tools. Regulations based on these mandates permitted product detentions, the withdrawal of inspection

services from offending plants, and injunctions and

investigations of allegations of food safety violations.

However, Booz-Allen (1977) asserted that enforcement

powers remained weak, in that violations depended on

the presence of a meat or poultry inspector.

Booz-Allen maintained that many very small processing plants had inspectors present onsite only part of

the time for a periodic plant inspection. Thus, although

these plants were supposed to comply with the law all

of the time, they could operate under potentially

unsanitary conditions when the inspector was not present, enabling them to potentially ship products that

did not meet FSIS* standards. Additionally, the penalties themselves were much weaker than they appeared.

Plant inspectors commonly detained products that

failed to meet FSIS* standards and withheld inspection

services until sanitary conditions were met. However,

since quality control managers in companies usually

issued similar directives, these practices were really

only usual control measures and, thus, just a normal

cost of doing business.

4  Managing for Safer Food / AER-817 Economic Research Service/USDA
Other enforcement measures had more coercive power

but may have been less effective. The WMA and

WPPA permitted the FSIS* to shut down a plant by

canceling the meat or poultry grant (a license to sell

meat or poultry) if the plant tried to bribe an inspector,

failed to destroy condemned meat, or had unsanitary

conditions that led to adulterated products. However,

this enforcement tool may have been much weaker

than it appeared. Booz-Allen (1977) indicated that

FSIS* preferred to negotiate weaker agreements rather

than seek permanent denial of inspection services

because experience from court proceedings shortly

after enactment of WMA and WPPA showed that judicial hearings were too protracted to be an effective

deterrent. For example, FSIS* threatened 20 firms with

closure after they tried to bribe FSIS* inspectors in the

early 1970s. However, none of these firms ever did

close. Rather, out-of-court settlements permitted them

to terminate some of their employees but remain in

operation. Booz-Allen (1977) also suggested that the

threat of criminal penalties also proved to be ineffective, as FSIS* successfully prosecuted only 26 out of

90 cases in 1977.

Regulations based on the WMA and WPPA also

described the terms under which meat or poultry products could be classified as adulterated, permitted the

FSIS* to establish tolerance levels for adulterants, and

addressed mislabeling issues. The acts defined adulterated products as those products processed under

unsanitary conditions, lacking a valuable ingredient, or

containing harmful substances, chemical pesticides, or

diseased meat or poultry. Under this broad definition,

FSIS* defined pesticides as adulterants in the 1960s

and E. coli 0157: H7 as an adulterant in raw ground

beef in the 1990s. For ready-to-eat products, such as

luncheon meats, the detection of this pathogen or any

other pathogen of public health concern, such as

Listeria monocytogenes or Salmonella, could be the

basis for declaring the product adulterated. 

Having meat or poultry identified as adulterated is

costly for meat and poultry plants because adulterated

products must be condemned, appealed, or reconditioned to correct for the adulterating condition, for

example, to kill E. coli 0157: H7. A condemned product requires disposal, while an appealed product can

be retained and reconditioned for an alternative use,

such as cooked products. Regardless of final use, precise records of all adulterated products must be maintained and the final dispositions of adulterated products generally yield much lower revenue than products

that pass inspection.

Labeling requirements were instituted to ensure product consistency, meaning that labels had to correctly

specify the contents of a given product and similar

products had to meet uniform standards. For example,

an item labeled “chili con carne” had to meet maximum fat and minimum meat content standards in order

to pass inspection. Substitution of other products for

ingredients on labels or in compensation of the

amounts mandated by code was considered misbranding and a violation of the law, possibly resulting in

punitive measures.

Regulations based on the WMA and WPPA also outlined 15 sanitary processes that formed the basis for a

new direction in FSIS* meat and poultry inspection.

Requirements differed from plant to plant but generally included good management practices and such commonly accepted food safety practices as the prevention

of raw products from coming into contact with cooked

products and processed products from coming into

contact with walls, floors, ceilings, rails, etc. Other

stipulations included requiring operations, procedures,

clothing, and utensils to be clean and sanitary and

handwashing by employees before touching an

exposed meat product or after handling a dirty 

shipping container.

As a way to ensure hygienic facilities, the regulations

also mandated that FSIS* approve blueprints in

advance of construction and examine the facilities and

equipment outlined in those drawings before granting

inspection. To have an FSIS*-approved facility, plants

had to have an ample water supply, efficient drainage,

and other basic infrastructure. FSIS*-approved equipment had to have contact surfaces constructed of stainless steel or some other rust-resistant material and that

could be cleaned of all microbial contamination.

By 1992, the sanitation and process controls practices

(SPCP) requirements had evolved into a computergenerated scheduling process called the Performance

Based Inspection System in which FSIS process-control inspectors examined plants for five types of sanitation and five types of process control activities. The

sanitation activities included pre-operation sanitation

of facilities, assembled and disassembled equipment,

product-handling equipment, sanitation of operations,

and proper handling of contaminated and adulterated

products. Process control activities included water 

Economic Research Service/USDA Managing for Safer Food / AER-817  5
supply/sewage disposal, facilities sanitation/personal

hygiene, pest and rodent control, receipt and control of

incoming material, and product handling and preparation. There were also specific requirements for particular products and product integrity concerns. Cooking

time and temperature controls for roast beef and other

cooked products and requirements for fermented,

smoked, and other processed products became particularly important as the production of processed products

became a larger share of the products inspected by

FSIS*.3

Inspection enforcement took the following form.

Process control inspectors periodically made rounds in

the plant to verify compliance and examined all available records. Inspectors designated poorly performed

operations as deficient tasks and held or condemned

products for serious violations. If the deficiencies were

particularly egregious or if the number of deficiencies

relative to total operations was excessive, then the

inspector asked the plant manager to make corrections.

If operations remained deficient, inspectors had the

authority to retain and condemn the product and temporarily prevent the use of rooms and equipment that

could contaminate the product. No other actions could

immediately be pursued. In the longer term, FSIS* had

the authority to withdraw inspectors for serious, persistent violations but, due to the protracted nature of

court hearings, was unable to use this tool successfully

(Booz-Allen, 1977).

Voluntary Quality Control Programs

The WMA and WPPA greatly expanded FSIS*’s

responsibilities in terms of the number of inspected

facilities, types of inspection tasks, and administrative oversight, but according to Booz-Allen (1977),

provided little additional funding for inspectors.

Moreover, FSIS* was becoming increasingly aware

that it needed to target more of its resources toward

food safety rather than nonfood safety activities in

processing, such as product formulations. Thus, since

regulatory provisions yielded a framework for giving

incentives to plants to administer their own process

control programs, FSIS initiated several voluntary

programs. FSIS envisioned that, under these programs, inspectors would verify the accuracy of

records rather than directly monitor net weights,

product formulations, etc.

Starting in the late 1970s, FSIS* instituted five voluntary programs—Total Quality Control (TQC),

Partial Quality Control (PQC), Streamlined

Inspection System (SIS), New Line Speed Inspection

System (NELS), and New Turkey Inspection System

(NTIS)—that shifted some of the inspection workload to plants in exchange for either a relaxation of

inspection frequency or increased line speeds. FSIS*

could also require a plant to adopt a PQC program if

a sanitation, process control, or product quality deficiency persisted.

Total Quality Control Programs 

FSIS* instituted the TQC program in 1980. Under this

program, the responsibility for documenting process

control matters for food safety and nonhealth meat and

poultry standards set by FSIS* fell to the plant. FSIS*

continued to inspect products for compliance with the

statutes, but focused more on the written documentation, with occasional, but scheduled, hands-on verification of compliance.

To qualify, plants had to design and implement a quality control program that encompassed all aspects of the

plant’s production processes from ante-mortem to

post-mortem inspection. TQC plans typically dealt

with the treatment of incoming raw materials, processing procedures, important food safety targets for processing operations, and action limits for plant quality

control personnel. The FSIS regulations also required

plants to specify sanitizing rinses and other inputs or

devices used to control product wholesomeness or

product quality.

Both FSIS and processing plants could gain from

having a TQC program. FSIS benefited by easing the

burden on inspection resources, while plants benefited from greater flexibility in establishing a quality

control program most suitable to the plant’s circumstances. For example, TQC processing plants could

more quickly introduce new products due to expedited approval procedures, ship products without direct

inspector presence, and did not have to pay for

inspector overtime if the FSIS inspector’s time

exceeded the normal 8-hour workday.4 TQC plants

could also use a special logo that advertised their

quality control program.

6  Managing for Safer Food / AER-817 Economic Research Service/USDA

3 The Inspection System Guide details each of the 10 sanitation

and process control activities.

4 U.S. law requires USDA inspectors to be present for some operations, such as slaughtering animals in order to make an antemortem or post-mortem inspection of every animal/carcass.
Plants had to meet several requirements before FSIS

would recognize their plant-administered TQC programs. First, the plants had to demonstrate their ability

to monitor the quality of their production by demonstrating the independence of quality control from production. So, plants had to have at least one full-time

person whose primary responsibility was quality control, who reported to a manager whose responsibilities

were not predominantly production-related, and who

had authority to halt production or stop product shipments if production did not meet TQC standards. A

plant without at least one full-time quality control person had to outline the responsibilities of the person in

charge of the quality control program. 

Plants also had to detail the manner in which their

TQC programs would function and had to show how

assorted components of the quality program could

maintain compliance with health and nonhealth standards established by FSIS. The components of a TQC

plan usually included raw material controls and

process control points at assorted points in the production process. For process control points, plants specified tolerances and the nature of corrective actions in

the event that tolerances were breached.

The FSIS inspector’s role changed in TQC plants to

one in which he or she used plant production records

and in-plant observations to verify plant compliance

with regulatory requirements and consulted with plant

management.5 As a result, each TQC plan had a

description of the plant’s quality control actions, the

nature and frequency of tests, the types of charts and

other records, and the length of time for which TQC

records would be maintained. A plant also had to agree

to maintain all of the analyses and information generated by its quality control system such that the records

could easily be monitored for compliance. FSIS, in

turn, emphasized accurate recordkeeping and expected

appropriate action when a product exceeded or reached

a tolerance limit. 

Partial Quality Control Programs 

Partial Quality Control programs emerged during the

1970s as a way for establishments to better control

plant sanitation and processes or parts of processes and

accurately adhere to net weight, labeling, and other

economic and public health safety requirements of

individual products.6 Many PQC programs, particularly the economic programs, were voluntary, but FSIS

mandated many public health programs, such as those

for the production of cooked roast beef. FSIS inspectors monitor all PQC programs.

Plants with PQC programs had to fully document how

they dealt with a particularly troublesome node of production that periodically got out of control and threatened the wholesomeness, quality, or economic value of

the product. For example, if an inspector noticed condensation coming from the ceiling adjacent to an

exposed product, then the plant could have been

required to continuously address the problem or chosen voluntarily to develop a PQC program to demonstrate process control. With a PQC program, a plant

could determine the expected variability of the problem and then address it on a statistical basis.

Plants voluntarily choosing PQC programs faced new

regulatory requirements but also realized some benefits. FSIS required that plants have a written PQC program that outlined the nature and frequency of tests

and detailed raw material controls, critical checks, and

control limits. In exchange, plants benefited from the

expertise of the specially trained, more highly skilled

FSIS inspectors specializing in quality control.

All PQC programs for slaughter plants were voluntary.

As with processors, PQC programs could be used to

address economic and public health quality. Programs

included finished product, preoperational sanitation,

and carcass presentation standards. Three special quality control programs for poultry—the SIS, NELS, and

NTIS—required PQC programs for poultry eviscerating lines.

Streamlined Inspection, New Line 

Speed Inspection, and New Turkey

Inspection Systems 

The SIS shifted routine tasks that affected nonfood

safety from inspectors to plant employees. FSIS felt

that plants producing branded products had a strong

incentive to ensure their products would have no visible, unpalatable defects. So, plant employees, working

Economic Research Service/USDA Managing for Safer Food / AER-817  7

5 Inspectors selected for TQC programs received specialized training, particularly in statistical process control, and received a premium salary for their work.

6 Economic qualities—including product weight, fat, and other

measurable attributes that consumers can observe or otherwise

believe to be present in similar products—often lead to different

prices. 
under FSIS supervision, detected and then trimmed

meat defects that affect product economic quality but

did not affect or harm public health safety.

FSIS established preventive systems of nonfood safety

process control for broilers and cornish hens with the

NELS and for turkeys with the NTIS. Under these

slaughter process control systems, the establishment

had to: (1) demonstrate compliance with regulatory

requirements by identifying process control points that

were important to regulatory compliance, (2) set realistic standards for these points and observe them frequently enough to ensure compliance and identify the

action that would be taken if a standard was not met,

(3) maintain records of observations that FSIS could

monitor to verify compliance, (4) have quality control

personnel that reported to supervisors who were independent of production and had the authority to halt

production or shipment of products if necessary, and

(5) make owners, operators, or designees available for

consultation with FSIS.

Plants using SIS, NELS, and NTIS systems benefited

by being permitted to increase line speeds beyond the

FSIS-mandated speed of 70 birds per minute, while

FSIS benefited by shifting bird dressing requirements

to plant personnel from FSIS staff. By 1995, about 22

percent of all poultry slaughter plants used the SIS,

NELS, and NTIS. This percentage included 45 broiler

and cornish hen slaughter plants and 27 turkey slaughter plants. There is no detailed information on the size

of these plants.

How Total and Partial Quality Control

Programs Vary by Plant Size

Table 2.1 shows the number of Total and Partial

Quality Control programs for processing and hoofed

animals and poultry slaughter. The adoption of Total

Quality Control programs may be a better indicator of

plant commitment to public health quality control

because TQC programs were entirely voluntary while

Partial Quality Control programs could be either voluntary or mandated. Moreover, since TQC programs

dealt with the entire plant, use of these programs illustrates the willingness of plants to adopt programs that

control overall plant operations. Also, since plants

used PQC programs to address specific problem areas

in the plant, large plants, due to their more complex

operations, likely had more such programs per plant.

Finally, note that if a PQC was required for a particular process and the plant was a TQC plant, then the

plant incorporated the PQC program into its specifications for its TQC program. 

An inherent feature of all voluntary programs is that

plants adopt a program only if the benefits exceed the

costs of adoption. For plants with TQC and other quality programs, these benefits included a reduction in

overtime costs, more rapid introduction of new products, scheduling flexibility, and, in the case of poultry,

faster line speeds. If these cost reductions exceeded the

costs of the program, then a plant would not adopt the

program. The low overall adoption rates for TQC programs (table 2.1) suggest that the costs of program

adoption exceeded the benefits for most plants. 

Table 2.1 also shows that adoption of TQC programs

was much weaker for slaughter operations. This is not

surprising because TQC and PQC programs dealt with

post-mortem meat and poultry processing practices. As

such, slaughter plants benefited from the program only

if they had cut-up or further-processing operations.

Second, as plant size increased, there was a striking

increase in the percentage of plants, particularly further-processing plants, using TQC programs. For further-processors, participation increased from 2.4 percent of plants in the smallest plant category to 21.4

percent in the largest one. This makes a lot of sense.

One of the chief benefits granted to plants with TQC

programs was permission to introduce new products

prior to formal approval of all the necessary labels,

greatly reducing the time from product development to

product introduction. This savings of time and money

favored large plants because new product introductions

were costly for small plants.

Another benefit of TQC was more flexibility in scheduling for FSIS inspections. FSIS inspectors were

required to be present for some operations, and if these

operations occurred outside of normal operating times,

the plant was required to pay the overtime costs of the

inspector. In TQC plants, many of these tasks were

allowed to occur without an FSIS inspector’s presence,

but documentation had to be accurately maintained

prior to final inspection and release by FSIS. So,

added flexibility meant that plants could avoid paying

the inspector overtime and, perhaps, more efficiently

schedule production. This benefit probably helped

larger plants more than smaller ones because large

plants typically have more complex operations calling

for greater labor specialization. This greater specialization permits less leeway in scheduling, making inspec8  Managing for Safer Food / AER-817 Economic Research Service/USDA
tor flexibility more attractive. Additionally, inspector

overtime costs could be greatly reduced by a plant’s

becoming a TQC plant. Finally, plants with TQC programs could apply labels stating that products were

produced under a TQC program, but this benefit was

not widely used.

The costs of TQC programs appear to have been higher for smaller plants. Smaller plants are less likely to

have quality control personnel devoted only to product

quality, so a TQC program would require them to hire

such a person. Since the small plant may have lower

revenues over which to spread fixed costs, average

costs go up.

Adoption of TQC programs is important to current

policy for two reasons. First, the program was very

similar to Hazard Analysis and Critical Control Point

(HACCP) programs and other process control measures related to public health. These similar elements—i.e., the use of sanitation programs, the monitoring of critical control points with FSIS verification,

and flexibility in designing food safety process control

program—suggest that the cost differences between

the two would be small.

There are some differences, however, the terms of

enforcement being the most striking. Whereas FSIS

can shut down a plant if it does not fulfill the requirements of its HACCP program, it could not shut down a

TQC plant for failing to adhere to its TQC plan as long

as the product was not adulterated. FSIS could force a

TQC plant that was not practicing its quality program

to relinquish its TQC status, however. Another difference was that TQC encompassed more than food safety control practices while HACCP deals only with

food safety. Furthermore, FSIS clarified the rules related to enforcement for HACCP.

Summary

This chapter shows that efforts to regulate meat and

poultry for public health reasons began in the 1890s

but only recently began to emphasize these elements.

In the 1980s, FSIS introduced the TQC, PQC, SIS,

NELS, and NTIS programs that shifted some of the

inspection burden to plants in exchange for more flexibility in regulatory requirements in the case of TQC

and PQC programs and increased line speeds for the

SIS, NELS, and NTIS systems. FSIS also granted

TQC plants the right to apply a label to its products

stating that the plant used an approved quality control

program. At the same time, these programs applied the

same sanitation and other regulatory requirements to

all establishments, regardless of the type of program.

Although there were some apparent benefits from participation in an FSIS-sponsored quality control program, industry showed only a modest interest. By

1992, less than 5 percent of all plants had adopted a

TQC program, and the number of PQC programs for

public health purposes as a share of total plants was

only 36.4 percent. These were both voluntary and

mandatory, and there could be more than one per

plant. Large meat processors were the most likely

plants to adopt a TQC program, with almost 20 percent of all large plants having such a program.

Slaughter plants had much lower adoption rates. Still,

larger slaughter plants were more likely to adopt TQC

than smaller ones. This low adoption rate could

explain why FSIS made the adoption of HACCP programs mandatory. Note, the higher adoption rate of

larger plants does not mean that small plants had less

effective process control programs, only that large

plants derived more benefits from such programs.

Economic Research Service/USDA Managing for Safer Food / AER-817  9
10  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 2.1—Total and Partial Quality Control programs as shares of plants, 19921

Partial Quality Control

Size of plant Total plants Total Quality Nonfood safety Food safety

Control programs

Number Number Percent Number Percent Number Percent

Processing plants:

Pounds of meat—

Fewer than 500,000 3,516 83 2.4 438 12.5 471 13.4

500,000 - 999,999 462 16 3.5 150 32.5 186 40.2

1 million - 9.9 million 1,063 76 7.1 764 7.1 687 64.6

10 million - 99 million 475 63 13.3 964 202.0 601 127.0

100 million or more 56 12 21.4 163 291.0 85 152.0

Total 5,572 250 4.5 2,479 44.5 2,030 36.4

Hoofed animal slaughter plants:

Number of hoofed animals—

Fewer than 1,000 330 2 0.7 33 10.0 72 21.8

1,000 - 9,999 337 3 0.9 20 5.9 144 42.7

10,000 - 99,999 187 1 0.5 91 48.6 110 58.8

100,000 - 999,999 90 3 3.3 111 123.0 99 110.0

More than 1 million 46 3 6.5 155 337.0 85 185.0

Total 990 12 1.2 410 41.4 510 51.5

Poultry slaughter plants:

Number of birds—

Fewer than 10,000 72 0 0.0 14 19.4 33 45.8

10,000 - 999,999 50 0 0.0 12 24.0 10 20.0

1 million - 9.9 million 61 2 3.3 114 187.0 41 67.2

10 million - 49.9 million 130 2 1.5 118 90.8 34 26.2

More than 50 million 53 2 3.8 74 140.0 31 58.5

Total 366 6 1.6 332 90.7 149 40.7

1 There can be more than one Partial Quality Control (PQC) program per plant, but only one Total Quality Control program. Since large plants

have more operations, it is reasonable that they should have more PQC programs. TQC programs are voluntary, and there were significant outlays. They had to apply for prior approval and then get modified FSIS inspection. PQC programs could be voluntary or required and also

required prior approval but no special FSIS changes in personnel.
Economic Research Service/USDA Managing for Safer Food / AER-817  11

The public health community has known since the

1960s that foodborne bacteria, such as Salmonella, can

cause human illness. This concern led the American

Public Health Association (APHA) to file a lawsuit

asking the court to declare Salmonella an adulterant

and requiring that a warning label giving cooking

instructions be affixed to the package. However, the

Supreme Court in 1972 ruled against the suit, saying

that APHA presented no evidence showing that

Salmonella was any more likely to be present in poultry than in any other food product, and that it is common knowledge to cook meat and poultry adequately.

However, FSIS and the general public became more

cognizant of the virulence of pathogenic bacteria in the

1980s when a series of foodborne illness outbreaks

gained prominent news coverage. The first one, an outbreak of E. coli 0157:H7, occurred in an Oregon

McDonalds in 1982. The incident produced no fatalities, but a number of customers became ill with bloody

stools and other symptoms. Just as publicity over that

incident began to subside and experts began to feel the

incident was unique, a second outbreak occurred in

Michigan and other States. These events and several

additional ones involving one roast beef and four

ground beef incidents convinced the Centers for

Disease Control and Prevention (CDC) that the outbreaks were meat related (Griffen and Tauxe, 1991).

Other pathogens also soon caught the public’s attention.

In 1988, the television program 60 Minutes featured a

segment on the health effects and sources of

Salmonella. Although this naturally occurring organism

is found in many raw and cooked products, the program

focused on the poultry industry. The 60 Minutes program brought the issue to the public’s attention and precipitated consumer demands for change. In response,

the poultry industry promoted the development and testing of counter current scalders, bird washes, chlorine

rinses, and other pathogen-reducing technologies that

significantly reduced Listeria monocytogenes,

Salmonella, and Campylobacter levels in chicken products (Waldroup et al., 1992). These and other newer

technologies were then rapidly and voluntarily adopted

by the industry. However, according to recent FSIS findings, Salmonella still is present on about 20 percent of

all young chickens, and Mead et al. (1999) of the CDC

estimated that Salmonella caused 1.3 million illnesses

and 550 deaths in 1997. The Economic Research

Service estimated the 1997 cost of Salmonella cases at

about $2.4 billion (www.ers.usda.gov, January 2002).

Cost estimates include lost wages and medical expenses.

Salmonella poisoning comes from a variety of foods,

including meat, poultry, and eggs, as well as from pet

handling. The contribution from meat, poultry, and eggs

is uncertain.

The potential health effects of other pathogens also

gained notice. As reported by Farber and Peterkin

(1991), Listeria monocytogenes caused the most deaths

ever recorded for a foodborne illness in Chicago when

142 known cases resulted in 48 deaths in 1985.

Roberts and Pinner (1990) estimated that Listeria

monocytogenes caused 1,350 illnesses and 510 deaths

nationwide in 1986, and Mead et al. (1999) estimated

that the pathogen caused about 2,500 illnesses and 500

deaths in 1997. Evidence of the health effect of

Listeria monocytogenes led FSIS to declare it an adulterant in cooked meat or poultry, assign it a zero tolerance, and begin testing for it in 1989 (Peter Perl,

Washington Post Magazine, January 16, 2000).

Listeria monocytogenes, like Salmonella, is a commonly

occurring bacteria that is killed in the cooking process.

The bacteria finds hospitable surroundings on soft

cheeses and other dairy products from unpasteurized

milk, seafood, dry and semi-dry fermented sausages,

deli meats and poultry, and other ready-to-eat dairy and

meat and poultry products. However, if reintroduced

onto the product from the environment after cooking or

if the product is not thoroughly cooked, it can be deadly,

particularly for fetal/newborns, elderly adults, and

immuno-compromised people. A 1998 outbreak caused

by the presence of Listeria monocytogenes in hot dogs

and deli meats from a Sara Lee plant killed 15 people

and sickened over 100.

Less well known as a source of foodborne illness is

Campylobacter. Epidemiologists had trouble determinChapter 3

The Shift to a New Regulatory Paradigm
ing the public health consequences of this pathogen

until the mid-1980s when scientists were first able to

grow it in laboratories. Today, however, Campylobacter

is recognized as the most common cause of foodborne

illness. Mead et al. (1999) estimated that about 100 people died and 2.0 million people became sick due to

Campylobacter infections in 1997. 

Living in the intestine of the infected animal and

spreading to the surface at slaughter, Campylobacter is

extremely common in poultry. It contaminated about

80 percent of all poultry products at the retail level in

1991 (Skirrow and Blaser, 1992, p. 4), making consumption of it in undercooked poultry or through

cross-contamination the most common way of contracting a sporadic Campylobacter infection (Tauxe,

1992, p. 12). People can also get Campylobacter from

contaminated drinking water, unpasteurized milk, or

raw or undercooked meat. 

Large Outbreak of Foodborne 

Illnesses Focuses Public 

Attention on Food Safety

Public awareness of the threat to human health posed by

pathogenic bacteria skyrocketed when an E. coli

0157:H7 poisoning seized public attention in January

1993. In this incident, 4 people died and more than 500

became sick, mainly in Washington, Idaho, and Nevada.

After studying the epidemic, public health officials in

Washington, DC, and at the CDC identified the consumption of hamburgers at Jack-in-the-Box fast-food

restaurants as the source. In Washington State, where

the largest outbreak occurred, the investigation suggested that Jack-in-the-Box employees cooked hamburgers

below the State standard of 155 degrees Fahrenheit and

in some instances below the 140 degrees Fahrenheit recommended by the Food and Drug Administration

(Federal Register announcement, 1996).

Following the Jack-in-the-Box outbreak, FSIS began

to take a new approach to its public health mission.

Since pathogens are not visible to the human eye, the

visual inspections that prevented diseased animal meat

from entering the food supply proved to be of questionable effectiveness against unseen pathogens. So,

FSIS began to focus more of its attention on pathogen

testing and sanitation and process controls.

FSIS was particularly concerned about the presence of

E. coli 0157:H7. It established a zero tolerance level in

ground hamburger because fewer than 50 organisms

are believed to be able to cause serious illness. To support this policy, FSIS began testing 5,000 1-ounce

samples of raw hamburger per year for E. coli

0157:H7. These tests for E. coli 0157:H7 in raw meat

and those for Listeria monocytogenes in cooked products cannot ensure that all meat is free of either

pathogen, but the tests are intended to encourage firms

to make stringent efforts to prevent pathogens of public health significance from being present and growing

in their products.

The penalties for finding pathogens are severe. FSIS

asks, but cannot mandate, the plant to recall its products

and issues a press release. If the plant refuses to recall

the products, FSIS can seize the product. Although these

options are costly, it could also be very costly not to

recall products. For example, if an outbreak were to

occur and be traced back to the offending plant, then the

plant’s owner could face ruinous legal liability claims

and the plant would risk a loss of reputation and could

possibly be held liable for damages.

As a way to better control pathogens, FSIS began to seriously consider the use of a Hazard Analysis and Critical

Control Point (HACCP) process control system in the

early 1990s and began a pilot program with a limited

number of plants to determine its effectiveness. This was

not a new idea. Many restaurants, such as McDonalds

and Jack-in-the-Box, required HACCP of their suppliers,

and several meat and poultry firms, such as Excel,

already used it in their plants (Ollinger, 1996). Indeed,

Booz-Allen, in its 1977 report, had recommended the

use of a quality control program with many of the elements of a HACCP program. Later, the National

Academy of Sciences (1985, 1987, and 1991), the

National Advisory Committee on Microbiological

Criteria for Foods (1988), and the General Accounting

Office (in a series of reports in the early 1990s) called

for the use of HACCP systems in the meat industry.

The Pathogen Reduction/Hazard Analysis and Critical

Control Point (PR/HACCP) rule, initially proposed by

FSIS on February 3, 1995, incorporated many elements of the HACCP programs recommended by other

organizations. It differed from the traditional inspection and control systems in that it considered the production process in its entirety and focused on prevention rather than merely on detection and adjustment.

FSIS based its plan on these seven criteria: (1) assessing all hazards, (2) finding all critical control points,

(3) setting critical limits for each critical control point

(CCP), (4) developing procedures to monitor each

12  Managing for Safer Food / AER-817 Economic Research Service/USDA
CCP, (5) determining corrective actions, (6) implementing a recordkeeping system, and (7) establishing

verification procedures (Unnevehr and Jensen, 1996).1

Besides having elements consistent with these criteria,

FSIS proposed to hold plants responsible for failure to

implement and maintain their HACCP systems. 

Food Safety Under the Pathogen

Reduction HACCP Rule

FSIS published the final PR/HACCP rule on July 25,

1996. The rule was phased in over a 3-year span starting in January 1998. The largest plants (more than 500

employees) had to comply by the end of January 1998,

small plants (10-499 employees) had until January

1999, and very small plants (fewer than 10 employees

or annual sales fewer than $2.5 million) had to conform by the end of January 2000. All plants had to

have sanitation standard operating procedures (SSOPs)

in place by January 1997, regardless of size.

The principal element of the rule was the development

of a HACCP plan for each FSIS-defined product group

that clearly established and controlled CCPs in the

plant’s production system. There were other important

components, however. First, PR/HACCP required meat

and poultry establishments to develop and implement

written SSOPs. Second, it mandated that slaughter

plants conduct generic E. coli microbial tests in order

to verify that fecal contamination was under control.

Finally, in order to verify that their HACCP systems

were controlling pathogens, the PR/HACCP rule established Salmonella performance standards for slaughter

and ground meat and poultry plants.

In conjunction with the PR/HACCP rule, FSIS eliminated several formerly necessary requirements. For example, FSIS no longer required prior approval for equipment installations or plant construction (Federal

Register, 1996). Changes that did affect the HACCP

plan or food safety, however, did require a revised

HACCP plan.

The PR/HACCP rule requires plants to identify CCPs,

take responsibility for implementation and control of

their HACCP programs, maintain performance records,

and adopt plans for action should processes get out of

control. PR/HACCP also stipulates that each plant

must complete a HACCP plan for each of its manufacturing processes (e.g., raw beef not ground). This plan

contains a flow chart that notes all possible hazards for

each step of the production process. Additionally, these

plans include critical limits, monitoring procedures,

corrective actions, recordkeeping methods, and verification procedures for each CCP. 

HACCP Programs Under PR/HACCP

The new PR/HACCP program shares similar characteristics and features with the TQC program that it superseded. PR/HACCP, like TQC, requires that plants take

responsibility for implementation and control of food

safety process control, maintain performance records,

and adopt a plan for action in the event that a process

gets out of control. TQC plants were also required to

identify control points, while a HACCP plan calls for

identification of critical control points. Additionally,

FSIS inspectors ensure plant compliance by verifying

written records and plant activities. PR/HACCP deviates

from the TQC program in that TQC programs dealt with

aspects of food quality not specific to food safety and

were voluntary, while the HACCP programs required

under PR/HACCP deal only with food safety and are

mandatory. Hence, if a plant did not adhere to the

requirements of the TQC, FSIS could cancel the plant’s

status as a TQC plant, causing the plant to revert back to

the traditional inspection method, but the plant could

continue meat or poultry production as long as FSIS

found the product not to be adulterated. However, since

PR/HACCP requires use of a HACCP program, a plant

can be temporarily shut down for failing to adhere to its

HACCP plan, regardless of whether FSIS found its

products to be adulterated. The plant can resume operations as soon as it adheres to its HACCP plan.

Sanitation Procedures and HACCP 

Under PR/HACCP

FSIS has required plants to perform sanitation and

process control tasks since Congress passed the

Wholesome Meat Act (WMA) of 1967 and

Wholesome Poultry Products Act (WPPA) of 1968.

However, the PR/HACCP rule shifted legal responsibility for adhering to sanitation standard operating procedures (SSOPs) to the plant by requiring that a plant

official with overall site authority accept responsibility

for them. In their SSOPs, plants must: (a) identify

operational and pre-operational procedures that, at a

minimum, include the cleaning of all surfaces that contact meat or poultry; (b) identify individuals responsible for daily sanitation activities, and (c) maintain

Economic Research Service/USDA Managing for Safer Food / AER-817  13

1 CCP refers to any part of the production process where food

safety is at risk.
records showing that the plant is adhering to their

SSOPs. The main difference between these requirements, which were issued within a specific regulatory

scheme alongside the HACCP requirements, and those

under the WMA of 1967 and WPPA of 1968 is that

plant personnel are now legally responsible for maintaining records and adhering to sanitation SSOPs.2 

Salmonella and Generic E. Coli Testing

Under PR/HACCP

The PR/HACCP rule included both pathogen testing

requirements and the development and implementation

of HACCP plans. Pathogen testing marked a sharp

departure from previous practices by establishing tolerance levels for Salmonella and generic E. coli and

then permitting plants to use any means available to

meet the tolerance. Failure to meet the tolerance could

result in a plant shutdown. HACCP plans under

PR/HACCP were enforced in much the same way as

existed for sanitation and process controls under WMA

and WPPA. Under each program, plants have a set of

tasks that they are required to perform and that FSIS

verifies. The main difference is that under PR/HACCP,

there are structures codified for both sanitation and

food safety that previously were not as detailed.

FSIS enforcement actions changed to reflect the implementation of new pathogen performance standards.

PR/HACCP required all slaughter plants to conduct

microbial tests for generic E. coli, and all slaughter and

ground meat plants to adhere to Salmonella standards.

Slaughter plants conduct their own generic E. coli tests.

The number of tests depends on production volume. For

example, cattle slaughter plants have to take one sample

per 300 carcasses, while broiler plants are required to

take one sample per 22,000 birds. Plants failing to meet

the generic E. coli standard must discover and correct

the cause of the failure or face increased FSIS scrutiny

of facilities, products, and plant compliance with their

HACCP plan SSOPs. FSIS may also perform more

product testing. If plant performance is deemed unsatisfactory, FSIS can remove its inspectors.

FSIS conducts Salmonella tests, uses the results as a

measure of overall plant process control, and can deem

a failure to meet the standard as one of the bases for

declaring a product to be adulterated. The testing

process takes a random selection approach that gives

plants several chances to meet the standard before

enforcement actions are taken. If a plant fails the first

test, it must complete a second round of tests after it

modifies its process. If the plant fails that round, then

again, it must undergo another round of testing after it

modifies its processes. Failure to pass on the third

attempt constitutes failure to maintain sanitary conditions and failure to maintain an adequate HACCP plan

and will cause FSIS to suspend inspection services.

The suspension remains in effect until the plant submits a detailed action plan to correct the HACCP plan

and outlines the other measures taken by the plant to

reduce the prevalence of pathogens.

It has been rare for plants to fail Salmonella compliance testing. Only about 100 out of the approximately

2,050 slaughter and grinding plants tested up to 1999

failed to pass the first test and only 22 of these 100

plants failed their first two tests. Failure to comply

after two tests would have led to increased enforcement review, but 19 of these plants passed the third

test and continued production. These 19 plants included 1 for ground turkey and 7 for ground beef, and 4

hog slaughter, 6 broiler slaughter, and 2 cow and bull

slaughter plants. Supreme Beef and one other ground

beef plant and one cow and bull slaughter plant failed

three tests, and FSIS suspended them, meaning that

plants retained the right to inspection services if the

suspension was lifted.

The suspension of inspection services at Supreme Beef

was quite controversial and prompted a lawsuit to

overturn FSIS’s right to suspend inspection services

for failure to comply with the Salmonella standard.

The Fifth Circuit Court ruled that the Salmonella standard was invalid because it constituted regulation of

the characteristics of raw materials and not regulation

of sanitary conditions in the plant, as suggested by

FSIS. Although FSIS has authority to regulate the

characteristics of raw materials, the meat trimmings

contaminated with Salmonella in this case came from

a plant that had passed FSIS inspection. The Supreme

Beef decision led FSIS to modify its enforcement program. A news release published on the FSIS website

(fsis.usda.gov, April 2, 2002) indicated that the

Supreme Beef decision does not prevent FSIS from

suspending inspection services or withholding marks

of inspections for failure to develop and implement

SSOPs and HACCP plans. The decision affects only

enforcement of the Salmonella standard but not FSIS’s

ability to test for Salmonella.

14  Managing for Safer Food / AER-817 Economic Research Service/USDA

2 Under the WMA and WPPA, managers were also responsible for

sanitation in that their plant had to meet appropriate sanitary conditions for the production of meat and poultry products.
Performance Under the WMA of 1967 and

WPPA of 1968 and PR/HACCP

FSIS inspectors have monitored sanitary conditions since

the enactment of the WMA and WPPA and now also

verify performance of HACCP tasks. Under the WMA

and WPPA, FSIS assigned critical deficiencies to plants

that did not perform or had poorly performed essential

sanitation and process control tasks. The data used here

were developed especially for this report by FSIS and

are defined as the number of critically deficient sanitation and process control practices divided by all such

practices. Critically deficient sanitation and process controls practices are either failures to perform or poorly

perform tasks that are most important to reducing health

risks to consumers. There are also minor and major deficiencies not deemed to be as high of a risk to human

health and are not considered in this report.

Table 3.1 shows how percent critical sanitation and

process controls practices (percent critical deficiencies)

vary by type of industry and plant size. Table 3.1

includes the mean critically deficient sanitation and

process control tasks as a share of total sanitation and

process control tasks for selected industries. All plants

in the tables have animal slaughter, processing, or animal slaughter and processing operations, but most do

not derive a majority of their income from the manufacture of meat products or animal slaughter. Plants in

industries with SIC codes that begin with 20 have food

manufacturing as their primary business, those starting

with 51 are mainly distributors, and those leading off

with 54 have retail marketing as their major interest.3

The table shows that poultry slaughter plants have the

highest number of percent critically deficient sanitation

and process controls. Other data in the table include

mean plant sales in 1999, mean plant pounds of meat

produced in 1996 (such data do not exist for 1999) and

the number of establishments. The table does not

include very small plants, those with fewer than 10

employees or sales of less than $2.5 million, because

they had not converted to HACCP by 1999 and, thus,

had not been inspected when the data became available.

Table 3.2 shows how critical sanitation and process

control deficiencies vary by plant size for slaughter

and processing plants. The data indicate that the very

smallest plants had about a third the percentage of critical deficiencies as the largest plants, and there exists a

trend in which larger plants, in general, had a greater

share of critical deficiencies than smaller ones.

Summary

In chapter 2, we discussed food safety regulatory history

up to about 1990. This chapter presented a chronology

of foodborne illness outbreaks that increased the public’s awareness of such illnesses and discussed major

regulatory changes during the 1990s. Current food safety regulation has its roots in the Federal Meat Inspection

Act (FMIA) of 1906 and the amendments to the FMIA

enacted through the WMA and WPPA of 1967 and

1968. Rather than being a complete break from the past,

promulgation of the PR/HACCP rule of 1996 marked an

acceleration in the long-term shift in regulatory focus

away from visual animal and meat inspection and

toward efforts dealing with the threat of foodborne illness posed by harmful pathogens. 

The principal element of the PR/HACCP rule of 1996

was the use of a mandatory HACCP program. Other

aspects of the rule included the use of sanitation procedures, a Salmonella standard to verify the effectiveness

of the HACCP program, and mandatory generic E. coli

testing to ensure compliance with the zero fecal matter

standard. The mandated HACCP plan had been recommended to FSIS by various organizations since the

1970s. It included: (1) assessing all hazards, (2) finding all critical points, (3) setting critical limits for each

critical control point (CCP), (4) developing procedures

to monitor each CCP, (5) determining corrective

actions, (6) implementing a recordkeeping system, and

(7) establishing verification procedures (Unnevehr and

Jensen, 1996). 

Compliance with sanitation and process controls under

WMA and WPPA varied according to product market

and plant size. The share of critically deficient sanitation and process control tasks was much higher for

poultry slaughter than for other plants. Segmenting the

data into five size categories showed that smaller

plants had a lower share of critically deficient sanitation and process control tasks than their larger competitors. The range varied from larger plants having

three times more for red meat animal slaughter to

about twice as many for meat processing. 

Economic Research Service/USDA Managing for Safer Food / AER-817  15

3 All of the businesses in tables 3.1 and 3.2 process meat or

slaughter animals or do both. However, they may be classified as a

distributor because the plant derives most of its revenue from distribution. For these facilities, meat processing or animal slaughter

operations are secondary businesses.
16  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 3.1—Percent critically deficient SPCPs for selected industries1

Plant mean

Industry SIC2 Total 1996 pounds of Estimated 1992 critically

facilities meat and poultry 1999 sales deficient SPCP

Number Millions Millions $ Percent

Red meat slaughter 2011 201 225.7 198.2 4.0

Meat processing 2013 652 23.8 48.6 2.8

Poultry slaughter and processing 2015 82 203.0 113.6 5.7

Frozen meals, pizza, etc. 2038 85 19.3 51.0 1.6

Grocery distributor 5141 107 15.6 103.6 2.2

Frozen food distributor 5142 25 9.1 49.5 2.1

Poultry products distributor 5144 55 54.4 98.1 3.6

Meat products distributor 5147 462 7.5 34.5 2.3

Meat and fish markets 5421 117 15.3 12.1 2.1

1 Data include only those plants that existed in 1992, had converted to HACCP, and were being inspected by FSIS in 1999.

These include the large and small plants but not the very small plants.

2 SIC codes are based on Enhanced Facilities Database estimates.

Source: U.S. Department of Agriculture, Food Safety and Inspection Service.

Table 3.2—Percent critically deficient SPCPs for selected sizes of meat and poultry plants1

Plant mean

Volume Plants 1992 1999 1999 estimated 1992 critically 

output output sales deficient SPCP

————— Number ———— Million $ Percent

Hoofed animal slaughter plants:

Number of hoofed animals 

per year—

Fewer than 1,000 59 36,8872 399 4.7 2.0

1,000-9,999 76 8,212 3,773 7.5 2.7

10,000-99,000 78 36,403 43,254 23.5 3.1

100,000-1 million 66 194,334 332,274 135.5 4.8

More than 1 million 49 1,864,332 2,477,539 774.0 6.5

————— Dollars ————

Only meat-processing plants:

Value of output in dollars 

per year—

Fewer than 2.5 million 127 15.8 17.2 1.3 2.3

2.5 million - 9.9 million 117 2.8 3.1 6.3 2.4

10 million - 49 million 288 12.6 14.3 23.3 2.7

50 million - 100 million 85 33.0 35.6 70.6 3.6

More than 100 million 73 72.9 89.1 206.6 4.2

————— Number ————

Bird slaughter volume:

Number of birds per year—

Fewer than 2.5 million 61 0.55 0.44 31.9 3.1

2.5 million - 7.4 million 33 4.62 5.42 90.9 2.9

7.5 million - 34.9 million 48 15.1 15.0 115.6 4.6

35 million - 50 million 88 31.7 37.2 156.3 7.0

More than 50 million 59 53.3 69.0 178.7 8.6

1 Data include only those plants that had converted to HACCP and were being inspected by FSIS in 1999, and existed in 1992.

These include the large and small plants but not the very small plants.

2 Some plants made transitions from plants slaughtering thousands of animals in 1992 to plants with miniscule slaughtering operations in 1999.

Source: U.S. Department of Agriculture, Food Safety and Inspection Service.
Several studies show that HACCP requirements comprise a sizeable share of nonmeat input costs for meat

and poultry slaughter and processing plants (Boland et

al., 2001; Antle, 2000; and Knutson et al., 1995).

These findings are not surprising. Process control is a

costly yet necessary component of business operations.

A central element of the PR/HACCP rule enacted in

1996 was the use of sanitation and process controls

practices (SPCPs). As discussed earlier, these SPCPs

were not new to meat and poultry slaughter and processing plants. The Wholesome Meat Act of 1967 and

the Wholesome Poultry Products Act of 1968 mandated that FSIS ensure food safety quality (product

wholesomeness) by establishing a set of best sanitation

and process controls practices, such as disassembling

and sanitizing equipment and preventing rat infestations. These safety operations were not particularly

onerous tasks, forming the basis for some recommended food industry process control programs, such as

Best Management Practices. FSIS enforced compliance by monitoring performance and then backing up

its performance rating with the possibility of a temporary plant closure due to noncompliance.1 However,

enforcement remained weak—the percentage of critical deficiencies still exceeded 30 percent in some

plants in 1992.

FSIS had limited enforcement powers to ensure compliance with SPCPs. Rather than permanently closing

a plant for chronic failure to meet operational sanitation standards, FSIS relied on its inspectors to temporarily shut down production until the plant corrected

deficient sanitary operations and then permitted plants

to resume operations. These actions are similar to

those that a plant’s own quality control manager would

use if the plant encountered quality problems.

The marketplace itself may be a stronger enforcer of

sanitary conditions than FSIS. Most of the time, consumers cannot detect whether there are harmful bacteria

or pathogens in the meat that they consume. However, if

a product causes consumer illness and the producer is

identified, the result could be plant bankruptcy or, at the

least, diminished profitability.2 Recall the industry exit

of Hudson Meats after it sold hamburgers contaminated

with E. coli 0157:H7 or the problems encountered by

Sara Lee after Listeria moncytogenes found in its products killed several people and sickened others (Perl,

2000). Thus, even though sanitation controls impose

costs, plants are likely to incur those costs if they are

necessary to remain profitable and viable. In this

respect, adherence to FSIS’s SPCPs may be thought of

as a proxy for process control effort.

The purpose of this chapter is to evaluate the effect of

percent critically deficient SPCPs on plant costs and to

assess whether the costs vary with plant output. The

analysis follows Antle (2000) who integrated a quality

control supply function into a cost function. It differs

from Antle (2000) in that it uses the percent critically

deficient (poorly performed) SPCPs as a measure of

food safety process control effort, while Antle (2000)

uses a hedonic measure that captures all food quality.

Hedonic measures use product characteristics to provide unbiased estimates.

Christiansen and Haveman (1981) and numerous others

have documented a productivity loss associated with

regulation. More specifically, Klein and Brester (1997)

have described the potential for food safety regulation to

adversely affect productivity. SPCP requirements should

be no different. Unless lax quality control effort leads to

an excessive number of product condemnations and

other production losses, plant costs should rise because

Economic Research Service/USDA Managing for Safer Food / AER-817  17

Chapter 4 

Process Control Effort and Plant Costs

1Recall that meat and poultry products shipped in interstate commerce must pass inspection by the Federal meat inspector. By

denying inspector services, FSIS could force the plant to close

until it complied.

2 This is not to say that a perfect linkage exists between food 

safety and plant survival or profitability. Buzby et al. (2001) found

little evidence that the legal liability system acts as a deterrent to

producing unwholesome food. They state: “The product liability

system provides firms with incentives to control hazards in food

primarily when the hazards are easily identifiable, a foodborne illness can be traced to the firm, and ill people or their families are

compensated by the firms responsible for the contamination.”
18  Managing for Safer Food / AER-817 Economic Research Service/USDA

effort devoted to SPCPs requires inputs of labor and

materials but does not increase output.

Antle (2001) points out that use of percent critically deficient SPCPs likely understates food safety quality control costs because plants undertake measures other than

SPCPs to provide food safety. A plant could perform all

of its SPCPs yet sell products containing harmful contaminants, or it could be very lax in its SPCPs and sell

products free of contaminants. However, most food scientists would agree that SPCPs reduce the likelihood of

selling products contaminated with harmful substances.

Antle (2001) also argues that hedonic measures of food

quality likely overstate food safety quality control costs

because these measures capture all food quality costs.

Antle (2000) controls for some aspects of quality related

to nonfood safety, but it is unlikely that he captures all

such attributes. So, percent critically deficient SPCPs

provide a lower bound estimate of food safety quality

costs, while Antle’s (2000) measure provides an upper

bound estimate of food safety quality costs.

Percent critically deficient SPCPs can be interpreted as

a measure of failure to adequately perform certain

tasks or as an indicator of process control effort.3

Variation of percent critical deficiencies in a cost function analysis provides a measure of the cost of SPCPs.

We proceed by establishing a model of plant costs that

includes a test for the cost of SPCPs for eight meat

and poultry slaughter and processing industries. Then,

we estimate the cost of critical deficiencies and examine the economies of scale in the sanitation and

process control effort.

A Model of Plant Costs

Plants add value to products in order to earn higher

profits from product sales. Perceived value includes

ease of preparation, type of meat cut, product wholesomeness, cooking convenience, and many other factors. We model value-enhancing attributes as a function of plant costs, where:

C = f (Q, Pi

, I, V), (4.1)

and C is total cost of production, Q is meat or poultry

output, P is factor prices, I is the type of animal or

meat input, and V is value-enhancing product attributes (value attributes).

Ignoring value for now, we specify a translog cost

function with output and factor prices in log form:

A commonly prescribed way to accommodate multiple

outputs in cost function analyses is to convert plant

output into a vector of outputs of different products

and then estimate a multiproduct cost function

(Baumol et al., 1982). However, some plants do not

produce some products and the log of zero is undefined. Additionally, this approach is not appropriate for

measuring product wholesomeness. Thus, we did not

use the multiproduct cost function. Rather, we followed an approach used in the analyses of railroads

(Caves et al., 1985), trucking (Allen and Liu, 1995),

airline industries (Baltagi et al., 1995), meat (Antle,

2000, and MacDonald et al., 2000), and poultry

(Ollinger et al., 2000) and modeled costs as driven by

a single output and a vector of product characteristics.

The model most closely follows that of Antle (2000)

who integrated Rosen’s (1974) model of a competitive

industry with differentiated product demand into the

quality-adjusted cost function model developed by

Gertler and Waldman (1992). As mentioned earlier, it

differs from Antle (2000) in that we use a measure of

process control effort (percent critically deficient SPCPs)

as a proxy for food safety, while he used an unbiased

estimator of food quality and then controlled for nonfood

safety quality attributes. Since our measure likely understates food safety quality and the measure that Antle

(2000) used likely overstates food safety quality, the two

measures combined provide a window within which

food safety process control costs likely fall.

We append (V) to the translog cost function as a specific right-hand-side argument. It is described as follows:

3 Although one plant may have adequate process controls yet sell

products with pathogens and another plant may have the opposite

characteristics, a good process control program will, on average,

lead to better control of potentially harmful pathogens.

In ln ln ln

 + ln + ln ln

 + ln ln (4.2)

C = P

1

2 P P

Q 1

2 Q) P

P Q

i ij

Q QQ

2

i

i

i

i

0 0

2

α δ β β

γ γ γ

δ δ ξ

++ +

+ ∑

∑ + +

I

Q

I I

i

i i j ij

iQ i

Q

Σ Σ Σ *

( ln *

* *. 1

V kvk k v k kj v k v j

k j

ik v

k

k

k v

k

k

=

P Q, 

k k

i

i

1 2 2 3

4 5

1

2 αα α

α α

∑ + ∑ ∑ +∑ +

∑ ∑ ∑

(ln ) ln ln

ln ln

 

 + ln (4.3)
Economic Research Service/USDA Managing for Safer Food / AER-817  19

where V is the value associated with producing a particular product mix and taking greater care in producing a wholesome product, α1k measures the value of

producing a particular attribute, α2k indicates how a

value attribute changes with changes in that attribute,

α3kj indicates how value changes with interactions

with other types of value attributes, and α4ik and α5k

capture how the costs of value attributes change with

factor prices and output.

Plants add value by undertaking additional processing

steps, such as increasing processing, using higher

grade animals, or providing greater assurance of product wholesomeness. Empirically, several researchers

(Antle, 2000; MacDonald et al., 2000; and Ollinger et

al., 2000) have found that product mix affects plant

costs. Antle (2000) found that food safety quality

affects plant costs.

All slaughter plants produce animal carcasses. For

some slaughter plants, carcasses are the final product

and are shipped to further-processors for cut-up and

consumer packaging. However, most slaughter plants

had cut-up operations that could produce ground meat

or poultry, meat or poultry parts, or other products by

1992. Further-processing plants, such as sausage-making operations, also offer different degrees of processing. Some provide fully cooked or ready-to-eat products, such as luncheon meats, while others produce

sausage links and other ready-to-cook products.

Animals raised specifically for meat, such as steers,

heifers, and young chickens, typically yield a greater

percentage of higher valued meat cuts and have more

uniform sizes than animals raised for other purposes.

Thus, animal type affects processing costs by changing

production practices and may reflect a different product mix available from the carcass. 

Consumers can distinguish between various meat cuts

and other quality differences (e.g., marbling and fat content), but it is much more difficult for them to discern

food safety quality (e.g., whether pathogens are present). Yet, plants ignore food safety quality at their peril.

Recall the exit from the meat industry of Hudson Meats

in 1998 after its products sickened numerous people,

and the millions of dollars in losses at Jack-in-the-Box

and Sara Lee lost after they sold meat products that

killed several people and sickened many others (see,

e.g., Perl, 2000). Events like these have led Jack-in-theBox, McDonalds, and other restaurants and grocery

chains to demand stringent process control programs at

their meat suppliers (Ollinger, 1996). Other meat and

poultry vendors may not have the resources or may not

see the need to enforce stringent standards and, thus,

may accept a lower level of assurance that the product

was produced in a manner to reduce the potential for

pathogens. Nevertheless, even suppliers to these buyers

must consider food safety quality or potentially be

exposed as a supplier of products with low food safety

quality. Other buyers may not need a stringent process

control program if they use meat or poultry for hightemperature cooking operations.

Food-processing experts assert that SPCPs reduce the

potential for cross-contamination of meat or poultry.

Proper sanitation includes cleaning and sanitizing disassembled equipment and cutting implements and preventing rodent infestations and the mixing of cooked

and uncooked meat, ready-to-eat and unprocessed

meat, etc.

Estimation Issues

Following standard practice, we impose symmetry and

homogeneity of degree one, such that βij=βji; αki = αik;

γQi = γiQ; δ1i= δi1 ; δQ2= δ2Q; α4ik = α4ki; α5kQ = α5Qk;

for all i , j, and k and Σβi

=1, Σβij= ΣγQi=Σα4ik=Σα5kQ=

0. Since all variables are divided by their mean values,

the first order factor price terms ( βi ) can be interpreted

as cost shares at mean values. The other coefficients

capture changes in factor prices, output, plant characteristics, and technology with deviations from sample

mean values.

Differentiating ln (C) with respect to the logs of the

factor prices yields four output-constant factor demand

equations that can be used to estimate input cost shares

(equation 4.4). We estimate the longrun cost function

jointly with the factor demand equations in a multivariate regression system. Since factor shares add to

one, the capital share equation is dropped to avoid a

singular covariance matrix:

The derivative of the cost function with respect to

value attributes yields the cost elasticity with respect to

a value attribute (equation 5). The coefficient for the

first-order output term, α1k, gives the cost elasticity

∂

∂ β δ β

γ α

InC

P = P XC = 

Q . 

i

i i

i i i ij

j

i

Qi 4ik

k

In

 + + In +

+ (4.4)

1 P P

vk

∑

In ∑
with respect to value attribute k at the sample mean.

The coefficient on the second-order output term, α2k,

indicates how the cost of value attribute k changes

with changes in attribute k. Other coefficients show

how attribute k changes with changes in attributes j,

factor prices, and output:

Value-enhancing attributes include process control

effort and the processing of products beyond carcasses

and simple processing. The coefficient α3kj indicates

how the production of the attribute vj affects the cost

of production of attribute vk, i.e., how a change in vj

affects the cost of producing vk. The coefficient α5k

indicates how plant size affects the cost of production

of the value attribute, vk. Economies of scale in the

sanitation and process controls effort occur when the

cost of such effort declines with plant size. Note,

economies of scale take place when larger plants have

lower costs per unit than smaller plants. 

Data

Data come from two FSIS datasets and the

Longitudinal Research Database (LRD) of the Center

for Economic Studies at the Bureau of the Census.

One of the FSIS datasets, obtained in a personal conversation with an FSIS representative, contains information on percent critical deficiencies for all establishments inspected by FSIS in 1992. FSIS inspects all

processing plants for their SPCPs and defines an SPCP

as critically deficient if a major task is poorly performed. If the task is not performed on a repeated

basis, then the inspector discusses the problem with

the plant manager. There are also less severe infractions of SPCPs that an inspector may note, but these

are not deemed major tasks and thus are not considered here as critically deficient tasks.

Many observers of FSIS inspection activities believe

that some variance exists in the way inspectors measure process controls, i.e., a critical deficiency to one

inspector may not be one to another. While this is likely to be the case, we have no reason to believe that

there is a systematic bias in these data. Thus, it

appears unlikely that random reporting differences will

affect statistical results.

The other FSIS dataset, the Enhanced Facilities

Database for 1992, contains detailed information on

the numbers and types of animals slaughtered, SIC

codes, 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 LRD provides detailed records of all individual

manufacturing establishments with more than 20

employees. Although the LRD has data for every year

up to 2002, we use only 1992 data because it was

matched to the FSIS dataset containing percent critical

deficiencies. LRD data provide detailed information on

the physical quantities and dollar amounts of many

different product shipments, physical quantities and

prices paid for materials and employment, energy

costs, the book value of capital, and other detailed

financial microdata. The file also notes ownership and

location information.

Data from the Census of Manufacturers include a

rich set of variables that measure semi-finished and

finished products. Semi-finished products include

animal carcasses, whole birds, cut-up birds, turkey

parts, boxed beef and pork, poultry products in wet

and dry ice bulk containers, and chicken traypacks.

Further-processed products include frankfurters,

cooked and smoked hams, pork sausage links, and

hamburger patties.

The data include the 3,200 meat and poultry plants

reporting in the 1992 Census of Manufacturers.

Products include semi-processed products, such as

boxed beef, from slaughtered animals, and furtherprocessed products, such as bologna, ham, or poultry

frankfurters, from either animals or raw meat or poultry. These plants include Federal- and State-inspected

meat plants. The FSIS datasets have only plants

inspected by the Federal Government, but these plants

produce the vast majority of meat and poultry products

consumed in the United States.

Researchers can use LRD data only for research purposes, may not divulge information on any individual

plant or firm, and may publish only aggregated information. This report, therefore, identifies aggregated

statistical data and the coefficients from regression

analyses covering hundreds of establishment records.

Any references to specific company or plant names are

based on publicly available information and not on any

Census source.

20  Managing for Safer Food / AER-817 Economic Research Service/USDA

 lnCv v

 P Q. 

cv

k

k k k kj j

ki

i

i k

ε

∂

∂ αα α

α α

= = + ln + ln +

 ln + (4.5)

3

5

lnv 1 2

∑ 4
We combined the LRD data with the FSIS data by

matching on ZIP Code and name and verifying the

record based on plant output and product type. The

combined dataset includes all Census establishment

data and FSIS data from the EFD and the dataset containing percent critically deficient SPCPs for each

matched plant. The matching procedure linked 2,579

plants from the LRD to plants from the EFD. The

unmatched plants from the LRD included manufacturing plants inspected by Sate inspectors, egg products

establishments (SIC 20159), and plants that could not

be matched.4 Unmatched plants from the EFD were

mainly nonmanufacturing establishments.

We further reduced the dataset of 2,579 plants by

including only plants that generated at least 50 percent of their revenue from beef (SIC 20111), pork

(SIC 20114, 20116, and 20117), other processed

meat—animal inputs (SIC 20110), cured/cooked

pork (SIC 20136), sausages (SIC 20137), other

processed meat—raw meat inputs (SIC 20130),

chicken slaughter (SIC 20151), and poultry processing (SIC 20155). Additionally, since FSIS does not

report percent critically deficient SPCP data for

slaughter-only operations, we deleted these plants.

From this dataset containing 2,276 plants, we

dropped all other plants that lacked essential data to

yield a final dataset with 1,729 observations.

Variable Specifications

Table 4-1 provides definitions of model variables.

Explanatory variables include factor prices (labor,

meat input, other material, and capital), plant output,

input type, product mix, and process control.

We define labor, meat inputs, and nonmeat material

factor prices (PLAB, PMEAT, and PMAT) and output (Q) as shown in table 4.1. Following Allen and

Liu (1995), we define the price of capital (PCAP) as

the opportunity costs of investing in plant and equipment. This definition is imperfect because existing

machinery and building costs are reported at book,

rather than real, values. Additionally, capacity is a

measure of full capacity; but it is unlikely that all

establishments are producing at full capacity for 

all years.

Input type (INPUT) is a dummy variable defined as

one for specific animal input type for the cattle, hog,

and poultry slaughter plants and zero otherwise.5 For

the other industries, it is defined as one for plants that

slaughter animals and zero otherwise. Input type for

cattle slaughter is one for plants that process cows and

bulls and zero for other types of cattle, such as steers

and heifers; for hogs it is one for sows and boars and

zero otherwise; and for poultry it is one for young

chickens and zero otherwise.

Product mix (MIX) captures the relative value of producing a particular product attribute. We set variable

MIX to one minus the share of boxed beef and hamburger output for cattle slaughter, one minus the share

of carcass outputs for hog slaughter, and one minus the

share of whole bird outputs for poultry. The residual

for the slaughter industries is bulk items for cattle

slaughter and processed products for hog and poultry

slaughter. Bulk items include animal carcasses, while

processed products include meat cuts and ground

meat. Since it is less costly to produce bulk products

than processed products, MIX should negatively affect

total costs for cattle slaughter and have a positive

effect in hog and poultry slaughter. 

Product mix (MIX) for further-processors equals one

minus the share of sausages for the industry designated

as other meat processors. For the other further-processors, MIX is the share of smoked pork products for the

cured/cooked pork products industry, one minus the

share of fresh sausages for the sausage industry, and

one minus the share of poultry frankfurters and poultry

hams and luncheon meats for the processed poultry

industry. There were insufficient data to create a product mix variable for the meat processing from animal

inputs industry. We include MIX variables for processing plants as a control variable for market type and

make no hypotheses a priori regarding signs.

We use the percent critically deficient SPCPs (DEF) as

a measure of process control effort. As noted in the

previous chapter, FSIS has several classes of critically

deficient process control tasks. The percent-deficiencies used in this report refers to percent-critical sanitation and process control deficiencies. A critical deficiency is a failure to adequately perform an operation

that FSIS deems essential to plant sanitation and

process control and is discussed with plant manageEconomic Research Service/USDA Managing for Safer Food / AER-817  21

4 SIC is an acronym for Standard Industrial Classification.

5 Costs would likely differ even if animals and raw meat inputs

were identical. The available mix of products from raw meat

inputs and animals varies, suggesting that animal input type may

also be serving as a proxy for certain types of plant outputs.
ment prior to its assignment. More deficiencies imply

that plants are using fewer resources for SPCPs than

competitors with lower percent-deficiencies. Since

resources are costly, a rise in percent-deficiencies

should negatively affect plant costs.

There are other possible measures of process control

performance. As discussed in chapter 2, plants can

have Total Quality Control (TQC) or Partial Quality

Control (PQC) programs. The adoption of a TQC program does not necessarily imply that the plant will

have a superior process control program over another,

however. Rather, adoption occurs if the potential benefits provided by FSIS, such as reduced inspector overtime costs, outweigh the additional regulatory costs of

program implementation. PQC programs are not satisfactory because they cover only part of a plant’s operations. Moreover, rather than being strictly voluntary,

these programs could be imposed on the plant by FSIS

to correct a particularly persistent process control

problem. Besides TQC and PQC programs, one could

think that product recall data would be a good measure

of process control effort. However, this also is unsatisfactory. The chief drawback is that FSIS does not test

all products. Rather, it takes only a random sampling.

Thus, the absence of a recall could imply that a plant’s

products were not tested or that food safety quality

was satisfactory.

Data on product mix and pounds of output came from

FSIS and the Census of Manufacturers. For each

observation, we used Census of Manufacturers’ data

when those data were available and FSIS data if

Census data were missing. Percent-deficiencies and

animal inputs came from FSIS. The labor costs, number of employees, meat costs, pounds of meat inputs,

value of materials, and value of machinery and buildings came from Census. Each observation had data for

each variable, except for some plants, particularly

those in the industries defined as other meat processors

from animal inputs and meat processors from raw

meat inputs.

Plants with missing data were dropped unless they had

data on plant output and the combined value of meat

and nonmeat input costs. For these plants, we multiplied the industry average meat input share of total

meat and nonmeat material costs times total plant

meat/nonmeat material costs to determine plant meat

input costs. Nonmeat material costs were then defined

as total plant meat/nonmeat material costs minus estimated meat input costs. Similarly, we estimated

pounds of meat inputs by multiplying the industryaverage ratio of meat inputs to meat output times plant

meat output.

Estimation and Tests for 

Model Selection

We use a nonlinear iterative, seemingly unrelated

regression procedure. This approach accounts for likely cross-equation correlation in the error terms (a

change in one cost share affects the others). The capital cost share equation was dropped because the sum

of all cost shares must equal one. All dependent and

explanatory variables are normalized by their sample

means. Thus, first-order coefficients can be interpreted

as elasticities at sample means.

Economists prefer a likelihood ratio test to a test of

statistical significance of a single variable, because in

a translog cost function, each variable has many interaction terms, making any single variable a poor measure of variable importance. We used a GallantJorgenson (G-J) likelihood ratio test to evaluate

whether a selected variable affects production costs. In

this test, a less restricted model containing a variable

of interest (maintained hypothesis) is compared with a

more restricted model lacking the variable of interest

(alternative hypothesis). If the difference in the G-J

statistic (chi-square statistics) exceeded a critical

value, then the maintained hypothesis was rejected,

leading one to conclude that the variable of interest

may affect costs.6 

Table 4.2 provides the maintained and alternative

hypotheses, degrees of freedom between the maintained and alternative hypotheses, and model chisquare test results. The acronyms describing the test

and maintained hypotheses are based on the variable

names from equation 4.2, so P,Q,I,M, and D represent

input prices, output, type of animal input (INPUT),

product mix (MIX), and percent-deficient SPCPs

(DEF). Degrees-of-freedom is the difference in the

number of parameters between the maintained and

alternative hypotheses. The number of model variables

is given in the footnotes to the table.

22  Managing for Safer Food / AER-817 Economic Research Service/USDA

6 The difference in the values of the objective function equals

N*S(α, v)R - N*S(α1, v1)u, where S(α, v)R is the minimum value

of the objective function of the restricted model, S(α1, v1)u is the

minimum value of the objective function of the unrestricted model,

and N is the number of observations. SAS prints out the difference

between the most and least restricted modes.
We started by testing the most restrictive alternative

hypothesis, model PQ—prices (P) and output (Q)—

against the least restrictive hypothesis, PQIM which

has the 12 variables associated with animal input

(INPUT) and product mix (MIX) in addition to prices

and output. This test determined whether INPUT and

MIX affect plant costs. PQIM could not be rejected for

cattle slaughter, hog slaughter, cured/cooked pork, and

sausage. Then, we added INPUT to PQ to create PQI

in order to evaluate the importance of MIX to PQIM.

PQIM still could not be rejected for cattle and hog

slaughter and sausage. Next, we added MIX to PQ to

form PQM and compared PQIM with PQM in order to

determine the importance of INPUT. We could not

reject PQIM for cattle slaughter and cured/cooked

pork. These test results suggested that PQIM was

unambiguously the best model for cattle slaughter

because that model could not be rejected in any case.

We also selected PQIM for cured/cooked pork because

PQIM performed better than PQM and provided a

modestly better, but not significant, explanation of

model variance than did PQI.

For the remaining industries, we added MIX or INPUT

to PQ to form PQI and PQM and repeated the process.

First, we tested PQ, the alternative hypothesis, against

the less restrictive maintained hypothesis (PQM). We

could not reject PQM for hog and poultry slaughter,

sausages, and other processed meat from raw meat

inputs. Then, we tested PQ against the maintained

hypothesis of PQI. We could not reject PQI for hog and

poultry slaughter and sausages. We concluded that PQM

was the best model for processed meat from raw meat

inputs because there was no input variable, INPUT, and

we selected PQ for poultry processing because G-J tests

reject PQI and PQM. Other test results were more

ambiguous, but we chose PQM over PQI for hog and

poultry slaughter and sausages because PQM provided a

modestly better explanation (higher chi-square statistic)

of model variance. Finally, we used PQ for other

processed meats from animal inputs.

Summarizing our selection of a preferred model, we

use PQIM for cattle slaughter, PQM for other meat

processing from raw meat inputs, and PQ for poultry

processing and other meat processing from animal

inputs because they are unambiguously the best fitting

models. For the other industries, we based model

selection on their chi-square statistics. Models chosen

because they provided marginally better fits were

PQIM (versus PQI) for cured/cooked pork processing

and PQM (versus PQI) for hog and poultry slaughter

and sausages.

Finally, we added percent-deficient SPCPs to the preferred model to see if it affected costs. For cattle

slaughter and cured/cooked pork, we added the eight

restrictions from percent-deficiencies (DEF) to PQIM

to form PQIMD. Proceeding similarly for other models, we formed PQMD for hog and poultry slaughter,

sausages, and other processed meat from raw meat

inputs and created PQD for other processed meat from

animal inputs and poultry processing. In pair-wise G-J

tests, PQIMD was tested against the alternative

hypothesis of PQIM for cattle slaughter and

cured/cooked pork; PQMD was tested versus the alternative hypothesis of PQM for hog and poultry slaughter, sausages, and other processed meat from raw meat;

and PQD was tested against the alternative hypothesis

of PQ for other processed meat from animal inputs and

poultry processing. We rejected PQIMD for cattle

slaughter and cured/cooked pork, PQMD for hog and

poultry slaughter, processed meat from raw meat

inputs, and sausages, and PQD for the other industries

at the 99-percent level of significance.

The rejection of DEF means that we cannot have a 99-

percent level of confidence that DEF affects model

costs. However, this does not mean that we cannot

draw implications from parameter estimates of DEF

because regression parameters always provide an estimate of the parameter mean.

Parameter Estimates

Appendix tables 4.A.1 to 4.A.8 contain the first-order

coefficients (first column), own-factor price quadratic

terms (diagonal terms), and the interactions among

factor prices and other factor prices and nonprice

terms (above the diagonal) for slaughter and processing plants. There are no terms below the diagonal

because they are identical to those above it. 

The first-order coefficients and some of the key second-order terms are shown in table 4.3. The coefficients for the first-order input price terms can be interpreted as cost shares at sample means. Plants that

slaughter animals tend to produce a large volume of

bulk products, such as carcasses. Further-processors,

on the other hand, take carcasses and other bulk raw

meat inputs and transform them into sausages, hams,

and other further-processed products. Thus, slaughter

plants should have a higher share of their costs from

Economic Research Service/USDA Managing for Safer Food / AER-817  23
meat inputs and less from materials and labor than the

further-processors. Factor shares (coefficients on the

first-order input price terms) show that this is the case.

Meat dominates other costs for all industries, particularly cattle slaughter, and is greater for slaughter plants

in general than for further-processors. Since meat

processors do more extensive processing of niche

products, they have higher labor and other materials

shares. Hog and poultry slaughter typically process

meat to a greater extent than cattle slaughter, but less

than further-processing and thus have lower (animal)

meat input shares than cattle slaughter and higher

shares than the further-processors (MacDonald et al.,

2000, and Ollinger et al., 2000).

Factor shares for cattle slaughter, hog slaughter, and

poultry slaughter are consistent with those reported by

MacDonald et al. (2000) and Ollinger et al. (2000).

There are no corresponding studies of meat and poultry further-processors to provide a comparison.

The FSIS data enabled us to distinguish between cattle

and cow plants in cattle slaughter and hog and boars

versus barrows in hog slaughter. These data, as reflected

in the variable INPUT, show that cow and bull slaughter

plants have significantly higher costs than steer and

heifer plants. Cows and bulls are typically much older

and a different size than steers and heifers. They are

also more likely than steers and heifers to be converted

into ground beef than boxed beef. Hogs and boars for

hog slaughter was not significant and was dropped.

The signs on the first-order product mix variables are

consistent with the expectations outlined earlier but

were not statistically significant. Output is significant

in all cases, suggesting that the direct effect of output

on plant costs is important. 

Product Mix

We use product mix variables to control for production

costs for particular product markets. Some of these

variables reflect submarkets that have clear cost differences relative to other segments of their general product market, while other variables represent markets

that have less obvious cost differences from their overall market. Thus, we can project costs for some variables ex-ante but not for others. Coefficients for the

product mix variables are shown in table 4.3 and the

appendix tables.

Product mix for cattle slaughter equals the share of

carcasses and other bulk beef products, such as organ

meats. Bulk product producers should have lower costs

than producers of boxed beef and hamburger because

bulk products require little processing beyond slaughtering the animal. Results (table 4.3) are consistent

with this assertion. The negative coefficients on MIX

and the MIX quadratic term (the interaction with

itself) means that bulk product plants have lower costs

than plants that do more processing and that costs

decline at an increasing rate as bulk share increases.

The negative effect of greater bulk processing on plant

costs is consistent with both MacDonald et al. (2000)

and Antle (2000), who found that greater processing

increased plant costs.

Product mix for hog and poultry slaughter is defined as

the share of further-processed products, such as chicken

traypacks, pork sausages, and pork or poultry parts.

Plants with a greater share of these processed products

should have higher costs than other plants doing less

processing. Results (table 4.3) show that this is the case

at sample mean values (the coefficient on MIX is positive). The negative quadratic term for hogs shows that

costs increase at a slower rate as hog slaughter plants

do more processing, while the positive coefficient for

poultry suggests that costs increase at a faster rate as

processing increases. These results differ from Antle

(2000) who found that costs decreased with greater

processing. We attribute this difference to some of the

differences in the data noted in the next subsection.

The product mix variable for the further-processors

controls for particular product markets. Results (table

4.3) show that costs increase with a greater share of

nonsausage products for the industry called “further

meat processors from raw meat inputs” and rise with a

greater share of cooked luncheon meats and frankfurters for the sausage industry. Results (table 4.3) for

cured/cooked pork products show that costs decline as

the share of cooked products rises. The models for the

other industries—processed poultry and processed meat

from animal inputs—do not employ MIX variables.

Economies of Scale

The first-order coefficient on the output term provides

a measure of economies of scale at the sample mean,

while the coefficient on the second-order output term

indicates how returns to scale change as output

increases. First-order coefficient values greater than

24  Managing for Safer Food / AER-817 Economic Research Service/USDA
one suggest decreasing returns to scale, while values

below one indicate increasing returns to scale.

Table 4.3 presents the necessary variables for computing economy of scale estimates. The coefficients

reported on the first- and second-order output coefficients for hog and poultry slaughter (0.96 for hog and

0.82 for poultry first-order terms) are consistent with

MacDonald et al. (2000), Ollinger et al. (2000), and

Antle (2000). Since the first-order term indicates

economies of scale and the second-order term shows

the change in economies of scale with output, results

suggest very strong increasing returns to scale in poultry slaughter that increase with output and near constant returns to scale that are diminishing with output

in hog slaughter.

Results for cattle slaughter indicate greater returns to

scale than those reported in MacDonald et al. (2000)

but are in line with those reported in Antle (2000). Our

results and those of Antle (2000) indicate that returns

to scale become stronger with an increase in output,

while those for MacDonald et al. (2000) report the

opposite. Although all of these studies used the LRD,

there are important differences that may explain the

diverse results. First, the data used in this analysis

includes all cattle slaughter plants, making it about

twice as large as those used by MacDonald et al.

(2000) and Antle (2000). Second, this study covers

only 1992, while Antle (2000) includes 1987 and 1992

data and is stratified by output and MacDonald et al.

(2000) covers 1963-92. Third, access to FSIS data

enabled us to isolate cow from steer/heifer slaughter

plants, while neither Antle (2000) nor MacDonald et

al. (2000) had these data. Results suggest that returns

to scale are much weaker for cow and bull plants than

steer and heifer plants (the sum of the coefficients on

Q and the interaction between INPUT and Q) and are

approximately equal to those reported in MacDonald

et al. (2000) for all slaughter plants and higher than

those in Antle (2000).

Except for sausages, returns to scale for the processing

industries are not as large as for the slaughter industries.

Although there are no other studies for comparison, one

might expect more modest returns to scale because

products are much more specialized and production

runs of any particular product are often limited by market size. Indeed, it is surprising to note that results for

sausages suggest strong returns to scale at the sample

mean. However, these economies of scale diminish

rapidly as output increases and are almost exhausted for

plants three times larger than the average plant.

Percentage of Deficient SPCPs

The key terms for an examination of the effect of percent-deficiencies on plant costs are the coefficients on

the first- and second-order percent-deficiency terms.7 If

the first-order term is negative, then costs drop as percent-deficiencies rises. The estimated coefficient on the

second-order percent-deficiency term indicates the rate

at which costs change as percent-deficiencies change.

Using sample mean values for all variables except percent-deficient SPCPs and then varying the percentdeficient SPCP level from one-half to four times the

sample mean, we calculate an average cost index that

shows how costs vary with deficiency levels for all

industries (table 4.4). Costs declined with an increase

in percent-deficient SPCPs for hog and poultry slaughter and sausages, both categories of other processed

meat, and processed poultry. The decrease in costs at

four times sample mean deficient SPCPs varied from

4.9 percent of costs in hog slaughter to 0.5 percent of

costs in other processed meats from animal inputs

(table 4.4). The increase in costs at four times sample

mean percent-deficient SPCPs for cattle slaughter was

about 1 percent and for cured/cooked pork about 3.5

percent (table 4.4). Note that there are actually very

few plants with four times the mean percent-deficiencies. Plants of this type account for less than 2 percent

of all plants and range from about 6 percent of all

plants in the “other” meat inputs industry to almost

zero for hog slaughter and cured/cooked pork.

Cost differences at four times mean deficiency levels

are quite large compared with the relatively low costs

of labor in meat and poultry slaughter and processing,

suggesting an incentive to underinvest in SPCPs. Yet,

most plants have very low percent-deficient SPCP levels. As shown in table 4.4, percent-deficient SPCPs

range from about 9 percent of all SPCPs in hog slaughter to about 2 percent of all SPCPs in other processed

meat from raw meat inputs. At four times the sample

Economic Research Service/USDA Managing for Safer Food / AER-817  25

7 Recall that percent-deficient SPCPs fail to affect model fit at the

99-percent level of significance because of large standard errors

relative to the parameter mean. The large standard errors means

that percent-deficient SPCPs may have a substantially different

effect on plant costs for some plants than what would be implied

by the parameter mean, which indicates how the average plant may

have fared.
mean, percent-deficient SPCPs would vary from 36

percent of all SPCPs in hog slaughter to 8 percent in

processed meat from raw meat inputs. The average at

four times the sample mean value is 19.6 percent. 

There are two plausible explanations as to why most

plants have lower than 10 percent-deficient SPCP levels. First, FSIS can take actions against plants with

excessively high percent-deficient SPCP levels and

would likely refuse inspection services for extremely

high violation levels. Second, and perhaps more

important, poor performance of SPCPs in a manufacturing plant can reduce product shelf-life and affect

product quality in obvious ways, by discouraging meat

or poultry purchases.

Interestingly, results for cattle slaughter and

cured/cooked pork suggest that costs drop as the percent-deficient SPCP level declines. Although cattle

slaughter costs drop almost imperceptibly, there is a 3-

percent decrease in costs for cured/cooked pork. We

speculate that plants with high percent-deficient SPCP

levels in the cured/cooked pork industry have an

excessive number of product condemnations and products requiring reprocessing, causing an increase in

costs as percent-deficient SPCPs rises.8 In this industry, inadequate process controls can seriously undermine product quality. For example, time and temperature and curing atmosphere controls are critical for a

degree of product cooking and curing that can kill

pathogens and provide other product qualities. If these

controls are not properly monitored, final products

must be scrapped, reworked, or sold at a much lower

price than that possible for consumer products.

Percentage of Deficient SPCPs and 

Plant Output

The coefficient on the interaction of percent-deficient

SPCPs and output (DEF and Q) in the parameter summary table (table 4.3) and appendix tables 4.A.1-4.A.8

shows how the costs of percent-deficient SPCPs varies

with output. The negative coefficient suggests that the

elasticity of costs with respect to output declines as

percent-deficient SPCPs rises for all industries. Since

the parameter is significant only for poultry processing, one should not place a high degree of confidence

in the reliability of parameter estimates. However,

since there is a consistent decline with output across

all meat and poultry industries, we can say that, on

average, there are economies of scale in percent-deficient SPCPs, e.g., diseconomies of scale in sanitation

and the process control effort.9

Consider cattle slaughter at sample mean values. The

elasticity of costs with respect to percent-deficient

SPCPs at sample mean values is 0.006. This means

that a 100-percent increase in percent-deficient SPCPs

leads to a 0.6-percent increase in total plant costs at

sample mean values. However, at two times sample

mean output and all other variables at their sample

mean values, the elasticity of costs with respect to percent-deficient SPCPs is 0.0053 [elasticity = 0.006-

0.001*ln (2)]. In other words, a 100-percent increase

in output means that the larger plant has only a 0.53-

percent increase in total costs relative to its smaller

competitor. Thus, costs decrease at a slower rate as

plant size increases. Since the coefficient on the interaction of percent-deficient SPCPs and output is negative in all industries, our results suggest that all eight

meat and poultry industries experience a reduction in

the rate of cost decrease as size increases for a given

level of percent-deficient SPCPs.

Table 4.5 presents the cost elasticity of percent-deficient SPCPs of plants at the industry mean percentdeficient SPCPs and one-half the industry mean, the

industry mean, and twice the industry mean plant output levels. As shown, the cost elasticities are higher for

smaller plants in all industries. This means that an

increase in percent-deficient SPCPs results in a larger

cost reduction for larger plants than for smaller ones.

Conversely, it means that larger plants will find it more

costly to reduce percent-deficient SPCPs than smaller

plants, i.e., the cost of process control decreases as

plant output decreases or the cost of process control

rises increases as output increases. However, the diseconomies of scale present in food safety process control

effort moderates but does not eliminate the decline in

the cost of producing the next pound of meat that

accrues from scale economies for larger size plants.

26  Managing for Safer Food / AER-817 Economic Research Service/USDA

8 Process control costs increase labor and perhaps material costs but

reduce product condemnations and enhance product appeal to the

consumer. It is likely that the costs of process control effort are

greater than the cost of product condemnations would be in its

absence because the producer also benefits from product appeal.

Nevertheless, the cost of product condemnations can exceed the cost

of process control effort, particularly if a modest increase in percentdeficiencies leads to a large increase in product condemnations.

9 Note that the small coefficient suggests that these diseconomies

are quite small when compared with scale economies stemming

from greater output.
This finding is consistent with Antle (2000). It is also in

line with Williamson (1985), who asserts that, as plants

grow in size, the bureaucratic structure needed to maintain operations becomes more difficult to control due to

information bottlenecks and that these costs eventually

overwhelm any benefits of economies of scale stemming from further growth. This may be particularly true

for the process control effort, if specialization in process

control functions reduces production worker diligence

toward maintaining product process control.

Conclusion

We examined the effect of a measure of food safety

process control (percent-deficient SPCPs) on plant

costs in the meat and poultry slaughter and processing

industries with a cost function model based on Antle

(2000). Like Antle (2000), who found that food quality

is costly, our results show that SPCPs, on average,

raise plant costs. The results reported here, however,

are not as statistically reliable as we would like

because the explanatory variable, percent-deficient

SPCPs, has large standard errors. Additionally, unlike

Antle’s (2000) measure of food quality, we examine

SPCP performance. Although SPCPs are a component

of most process control programs, plants do undertake

other actions to ensure food safety, suggesting that our

results may be lower than all the costs that plants incur

for food safety, process control.

The SPCPs required by FSIS are not particularly onerous. Rather, they are similar to general manufacturing

principles and would likely be components of any food

safety, standard process control program. Thus, we

viewed percent-deficient SPCPs as a measure of negative process control effort. Our findings suggest that

costs declined with percent-deficient SPCPs, i.e., rose

with process control effort, in six of the eight meat and

poultry industries examined and that one of the

remaining industries had almost no change in costs.

We also found a statistically insignificant but consistently negative relationship between output and the

percent-deficient SPCPs in all eight industries. This

means that an increase in output decreases the cost of

percent-deficient SPCPs and that a decrease in output

increases the cost of percent-deficient SPCPs, implying that it would be more costly for a large plant to

reduce percent-deficient SPCPs than for a small plant.

In other words, the cost of process control effort

increases with output. These so-called diseconomies of

scale in the sanitation and process control effort (the

higher cost of sanitation and process control as plant

size increases) are consistent with Williamson (1985),

who argues that, at some point, the bureaucratic costs

of managing a larger plant operation swamp any

economies of scale accruing to larger plant size and

result in an increase in plant costs.

An FSIS representative (communication of June 13,

2002) offers one plausible explanation for our statistically weak results. He says that FSIS inspector responsibilities shifted from working with frontline production personnel to ensure clean facilities to more of an

inspection-verification system in which the inspector

dealt mainly with management. Under either system, a

deficiency would have been accounted for similarly and

percent-deficiencies would offer a measure of process

control effort. However, various procedures could coexist as FSIS phased in one system to replace the other.

Additionally, different inspectors may have slightly different standards for a critical deficiency. Combined,

these inspection attributes suggest that an alternative

measure of process control effort may be appropriate.

For this measure, percent-deficiencies would be defined

as one or more dummy variables of percent-deficiency

levels rather than a continuous function. This research

is left to the future because access to the LRD at the

Bureau of the Census is not possible at this time.

Economic Research Service/USDA Managing for Safer Food / AER-817  27
28  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 4-1—Cost function variable definitions

Independent variables

PLAB Price of labor = (total plant labor costs) / (total employees).

PMEAT Meat input price = (liveweight animal costs + raw meat input costs) / (liveweight

pounds+raw meat input pounds).

PMAT Cost of other material inputs = (energy costs + packing and packaging cost +

other material costs) / (pounds of liveweight meat + pounds of raw meat).

PCAP Price of capital = (OPPORTUNITY + NEW) / CAPACITY, where OPPORTUNITY

= (machinery rental price) * (machinery book value) + (building rental price ) *

(building book value); NEW is the cost of new machinery and buildings; CAPACITY

is buildings and machinery book value minus all retirements. Machinery (Building)

rental prices (Bureau of Labor Statistics) are costs per dollar of machinery (buildings)

expenditure.

Q Output of meat products, in thousands of pounds.

INPUT One for plants that slaughter cows and bulls, sows and boars, or young chickens

for cattle, hog, or poultry slaughter plants, zero otherwise; one for cured/cooked pork,

sausage, or processed poultry plants that slaughter, zero otherwise. Not used for other

industries.

MIX Cattle: 1- ((boxed beef + hamburger)/meat shipments); hogs: 1-(carcass products/

meat shipments); poultry: 1-(whole birds or parts in bulk containers/meat shipments);

processed meat from live animals: no suitable data; processed meat from raw meat:

1-(sausages/meat shipments); cured/cooked pork: 1-(bacon+ smoked ham+other

smoked pork)/meat shipments; sausages: 1-(fresh sausage/meat shipments); and

processed poultry: 1-(poultry frankfurters + poultry hams and luncheon meats)/(meat

shipments).

DEF Average deficient (poorly performed) sanitation and process control tasks as a

percentage of all such tasks.

Dependent variables

COST Sum of labor, meat, materials, and capital factor costs.

LABOR% (salary and wages + supplemental labor costs) / COST.

MEAT% (purchased poultry costs + packed meat costs) / COST.

MAT% (energy costs + packing and packaging cost + other material costs) / COST.

CAPITAL% (OPPORTUNITY + NEW) / COST. See above for definitions.
Economic Research Service/USDA Managing for Safer Food / AER-817  29

Table 4.2—Hypothesis tests for meat and poultry slaughter and processing1

Hypotheses d.f. Model chi-square

Slaughter plants Further-processing plants 

Maintained Alter- Cattle Hog Poultry Cured, Sausage Other meat- Other meat- Poultry 

native cooked pork animal input raw meat processing

input

PQIM2 PQ 12 43* 61* 19 33* 29* n.a. n.a. 12

PQIM PQI 7 22* 38* 9 9 15+ n.a. n.a. 9

PQIM PQM 6 21* 3 9 15+ -6 n.a. n.a. 8

PQM PQ 6 n.a. 60* 13+ n.a. 35* n.a. 74* 9

PQI PQ 5 n.a. 25* 13+ n.a. 14+ n.a. n.a. 3

PQD PQ 6 n.a. n.a. n.a. n.a. n.a. 7 n.a. 10

PQMD PQM 7 n.a. 8 4 n.a. 9 n.a. 15 n.a.

PQIMD PQIM 8 3 n.a. n.a. 17+ n.a. n.a. n.a. n.a.

Notes: * Reject tested hypothesis at the 99-percent levels; + reject tested hypothesis at the 95-percent level. n.a. = not applicable.

Degrees-of-freedom is abbreviated as d.f. P is factor prices, Q is output; I is animal input; M is output mix; D is percent-deficient SPCPs. 1 PQ has 15 estimated parameters and PQIM, PQI, PQM, PQIMD, PQMD, and PQD have 27, 20, 21, 35, 28, and 22 parameters, 

respectively.

2 P, Q, I, M, and D represent input prices, output, input type, product mix, and percent-deficient SPCPs.
30  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 4.3—First-order and selected second-order parameter estimates from the best cost function 

model in the slaughter and processing industries

Variable Slaughter plants Further-processing plants

Cattle Hog Poultry Pork-cured/ Sausage Other meat- Other meat- Processed

cooked animal input meat input poultry

First-order terms 

INPUT 0.336** n.a. n.a. 0.119 n.a. n.a. n.a. n.a.

(0.150) (0.126)

PLAB 0.105*** 0.130*** 0.195*** 0.176*** 0.217*** 0.132*** 0.178*** 0.236***

(0.016) (0.011) (0.008) (0.008) (0.007) (0.011) (0.006) (0.015)

PMEAT 0.797*** 0.700*** 0.634*** 0.624*** 0.499*** 0.776*** 0.613*** 0.522***

(0.200) (0.012) (0.010) (0.009) (0.006) (0.010) (0.006) (0.016)

PMAT 0.062*** 0.120*** 0.124*** 0.141*** 0.208*** 0.056*** 0.165*** 0.198***

(0.013) (0.009) (0.007) (0.004) (0.004) (0.012) (0.006) (0.018)

PK 0.036* 0.050* 0.047*** 0.059*** 0.076*** 0.036*** 0.044*** 0.044**

(0.020) (0.014) (0.007) (0.009) (0.006) (0.013) (0.006) (0.019)

MIX -0.051 0.009 0.046 -0.081 0.015 n.a. 0.009 n.a.

(0.060) (0.085) (0.047) (0.063) (0.051) (0.023)

DEF 0.006 -0.032 -0.018 0.029 -0.009 -0.003 -0.024 -0.029

(0.030) (0.028) (0.033) (0.032) (0.025) (0.026) (0.020) (0.045)

Q 0.857*** 0.962*** 0.819*** 0.945*** 0.858*** 0.950*** 0.926*** 1.013***

(0.050 (0.021) (0.038) (0.042) (0.024) (0.024) (0.019) (0.042)

Selected second-order terms

PLAB* 0.001 0.005** 0.006* 0.00095 0.0001 0.0014 0.00038 0.002

DEF (0.002) (0.002) (0.003) (0.002) (0.001) (0.001) (0.001) (0.002)

PMEAT* -0.002 -0.005** -0.005 -0.00005 -0.0003 -0.0008 -0.00016 -0.001***

DEF (0.002) (0.002) (0.004) (0.002) (0.001) (0.001) (0.001) (0.003)

PMAT* -0.0002 -0.001 0.001- 0.0001 -0.0002 -0.001 -0.00002 0.001

DEF (0.001) (0.002) (0.003) (0.001) (0.001) (0.001) (0.001) (0.003)

PK* 0.0012 0.001 -0.002 -0.0008 0.0004 0.0004 -0.0002 -0.002

DEF (0.002) (0.003) (0.003) (0.002) (0.001) (0.001) (0.001) (0.003)

Q* -0.001 -0.005 -0.0003 -0.009 -0.002 -0.001 -0.000 -0.014**

DEF (0.004) (0.004) (0.010) (0.008) (0.005) (0.004) (0.003) (0.006)

DEF* 0.001 -0.005 -0.007 0.002 -0.0006 -0.0002 -0.004 0.0002

DEF (0.004) (0.006) (0.010) (0.006) (0.004) (0.004) (0.003) (0.007)

INPUT* *0.080** n.a. n.a. 0.033 n.a. n.a. n.a. n.a.

Q (0.038) (0.059)

Q*Q -0.002 0.006 -0.020 0.029 0.044** 0.021 0.035*** 0.078***

(0.009) (0.007) (0.022) (0.033) (0.021) (0.016) (0.011) (0.015)

MIX* 0.006 0.010 0.002 -0.003 -0.015*** n.a. -0.005 n.a.

Q (0.010) (0.017) (0.007) (0.008) (0.005) (0.003)

Notes: Numbers in parentheses are standard errors.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

n.a. = not applicable.
Economic Research Service/USDA Managing for Safer Food / AER-817  31

Table 4.4—Average cost index for selected percent-deficient SPCP levels relative to sample mean 

percent-deficient SPCP level using industry mean values

Industry Model Plants Mean percent Cost index for plants at these percentdeficient deficient SPCP levels relative to costs at 

SPCPs industry mean percent-deficient SPCP levels

Half mean Mean Twice mean Four times

mean

Number Percent ————-Index relative to mean————

Cattle slaughter PQIMD 230 3.70 0.996 1.00 1.004 1.010

Hog slaughter PQMD 307 9.16 1.021 1.00 0.977 0.951

Poultry slaughter PQMD 155 8.33 1.011 1.00 0.986 0.968

Cured, cooked pork PQIMD 117 5.53 0.985 1.00 1.017 1.035

Sausage PQMD 257 4.25 1.006 1.00 0.993 0.986

Other processed

meat (animal inputs) PQD 288 2.17 1.002 1.00 0.997 0.995

Other processed meat

(raw meat inputs) PQMD 546 2.00 1.016 1.00 0.982 0.963

Processed poultry PQD 129 3.95 1.021 1.00 0.980 0.960

Average 1.007 1.00 0.992 0.983

Notes: Percent-deficient SPCPs = number of sanitation and process control violations divided by the total number of sanitation and 

process control activities. A lower value implies more process control effort. A lower cost index value implies a lower cost for the 

same level of effort devoted to sanitation and process control activities.

Table 4.5—Estimates of the elasticity of costs with respect to percent-deficient SPCPs at sample 

mean percent-deficient SPCPs for selected plant sizes in various slaughter and processing industries

Industry Plant output

One-half mean Mean Twice mean

Elasticities

Cattle slaughter 0.0067 0.006 0.0053

Hog slaughter -0.0285 -0.032 -0.0355

Poultry slaughter -0.0178 -0.018 -0.0182

Cured, cooked pork 0.0352 0.029 0.0228

Sausage -0.0076 -0.009 -0.0104

Other processed meat (animal inputs) -0.0023 -0.003 -0.0037

Other processed meat (raw meat inputs) -0.0219 -0.024 -0.0261

Processed poultry -0.0193 -0.029 -0.0387

Average -0.0070 -0.010 -0.0130
32  Managing for Safer Food / AER-817 Economic Research Service/USDA

Appendix table 4.A.1—Cattle slaughter cost function parameter estimates

Variable First-order PLAB PMEAT PMAT PK MIX DEF Q(lbs)

Intercept -0.350**

(0.140)

INPUT 0.336** 0.050*** 0.060*** 0.013 -0.030 0.024 0.025 0.080**

(0.150) (0.015) (0.020) (0.013) (0.020) (0.041) (0.020) (0.038)

PLAB 0.105*** 0.056*** 0.056*** 0.020*** -0.020** 0.001 0.001 0.022***

(0.016) (0.001) (0.003) (0.006) (0.009) (0.003) (0.002) (0.003)

PMEAT 0.797*** 0.089*** 0.063*** 0.030*** 0.001 -0.002 0.023***

(0.200) (0.010) (0.005) (0.008) (0.004) (0.002) (0.003)

PMAT 0.062*** 0.053*** -0.011 0.001 -0.0002 0.004*

(0.013) (0.005) (0.007) (0.003) (0.001) (0.002)

PK 0.036* 0.001 -0.003 0.0012 -0.005

(0.020) (n.a.) (0.004) (0.002) (0.003)

MIX -0.051 -0.110 0.012* 0.006

(0.060) (0.010) (0.007) (0.010)

DEF 0.006 0.001 -0.001

(0.030) (0.004) (0.004)

Q (lbs) 0.857*** 0.002

(0.050) (0.009)

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can 

be interpreted as elasticities at their sample means. Sample size = 230 observations. n.a. = not available.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

Appendix table 4.A.2—Hog slaughter cost function parameter estimates

Variable First-order PLAB PMEAT PMAT PK MIX DEF Q(lbs)

Intercept 0.332***

(0.040)

PLAB 0.130*** 0.031*** 0.029*** 0.008* 0.010 0.012** 0.005** 0.015***

(0.011) (0.010) (0.008) (0.005) (0.010) (0.006) (0.002) (0.002)

PMEAT 0.700*** 0.106*** 0.098*** 0.021*** -0.015** -0.005** 0.009*** 

(0.012) (0.008) (0.005) (0.005) (0.007) (0.002) (0.003)

PMAT 0.120*** 0.086*** 0.004 -0.003 -0.001 0.003*

(0.009) (0.005) (0.007) (0.005) (0.002) (0.002)

PK 0.050* -0.015 0.006 0.001 (0.003)

(0.014) (n.a.) (0.008) (0.003) (0.003)

MIX 0.009 -0.018 0.010* 0.010

(0.085) (0.020) (0.007) (0.017)

DEF -0.032 -0.005 -0.005

(0.028) (0.006) (0.004)

Q (lbs) 0.962*** 0.006

(0.021) (0.007)

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can be 

interpreted as elasticities at their sample means. Sample size = 307 observations. n.a. = not available.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

Appendix tables
Economic Research Service/USDA Managing for Safer Food / AER-817  33

Appendix table 4.A.3—Chicken and turkey slaughter cost function parameter estimates

Variable First-order PLAB PMEAT PMAT PK MIX DEF Q(lbs)

Intercept 0.181***

(0.036)

PLAB 0.195*** 0.068*** 0.071*** 0.009 -0.006 0.003 0.006* 0.029***

(0.008) (0.014) (0.009) (0.006) (0.012) (0.002) (0.003) (0.004)

PMEAT 0.634*** 0.155*** 0.082*** -0.002 -0.004* -0.005 0.029***

(0.010) (0.011) (0.007) (0.007) (0.003) (0.004) (0.005)

PMAT 0.124*** 0.081*** -0.008 -0.002 0.001 0.005

(0.007) (0.006) (0.006) (0.002) (0.003) (0.003)

PK 0.047*** 0.016 0.003 -0.002 -0.005

(0.007) (n.a.) (0.002) (0.003) (0.004)

MIX 0.046 0.007 0.007 0.002

(0.047) (0.011) (0.007) (0.007)

DEF -0.018 -0.007 -0.0003

(0.033) (0.010) (0.010)

Q (lbs) 0.819*** -0.020

(0.038) (0.022)

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can be 

interpreted as elasticities at their sample means. Sample size = 155 observations. n.a. = not available.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

Appendix table 4.A.4—Translog cost function parameter estimates of producers of processed meat 

products from live animals

Variable First-order PLAB PMEAT PMAT PK DEF Q(lbs)

Intercept 0.067

(0.043)

PLAB 0.132*** 0.071*** 0.081*** 0.037*** -0.026* 0.0014 0.020***

(0.011) (0.014) (0.011) (0.011) (0.014) (0.0010) (0.005)

PMEAT 0.776*** 0.136*** 0.088*** 0.033*** -0.0008 0.020***

(0.010) (0.012) (0.011) (0.011) (0.0010) (0.005)

PMAT 0.056*** 0.033** 0.018 -0.0010 -0.0055

(0.012) (0.014) (0.014) (0.0010) (0.0050)

PK 0.036*** -0.025 0.0004 0.0055

(0.013) (n.a.) (0.0010) (0.0050)

DEF -0.003 -0.0002 -0.001

(0.026) (0.0040) (0.004)

Q (lbs) 0.950*** 0.021

(0.024) (0.016)

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can be 

interpreted as elasticities at their sample means. Sample size = 288 observations. n.a. = not available.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.
34  Managing for Safer Food / AER-817 Economic Research Service/USDA

Appendix table 4.A.5—Translog cost function parameter estimates of producers of processed meat 

products from packed meat

Variable First-order PLAB PMEAT PMAT PK MIX DEF Q(lbs)

Intercept 0.043

(0.036)

PLAB 0.178*** 0.018** 0.029*** 0.030*** 0.019*** 0.006*** 0.00038 0.0137***

(0.006) (0.007) (0.006) (0.006) (0.007) (0.001) (0.00100) (0.0020)

PMEAT 0.613*** 0.140*** 0.144*** 0.033*** 0.0065*** -0.00016 0.010***

(0.006) (0.010) (0.009) (0.006) (0.0010) (0.00100) (0.002)

PMAT 0.165*** 0.129*** -0.015** 0.003*** -0.00002 -0.0003

(0.006) (0.010) (0.006) (0.001) (0.00100) (0.0020)

PK 0.044*** 0.001 0.0035*** -0.0002 0.004

(0.006) (n.a.) (0.0010) (0.0010) (0.003)

MIX 0.009 0.002 0.016 -0.005

(0.023) (0.006) (0.025) (0.003)

DEF -0.024 -0.004 -0.003

(0.020) (0.003) (0.003)

Q (lbs) 0.926*** 0.035***

(0.019) (0.011)

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can be 

interpreted as elasticities at their sample means. Sample size = 546 observations. n.a. = not available.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

Appendix table 4.A.6—Translog cost function parameter estimates of producers of 

cured/cooked pork products

Variable First-order PLAB PMEAT PMAT PK MIX DEF Q(lbs)

Intercept 0.054

(0.039)

INPUT 0.119 -0.0078 0.0032 0.0007 0.0039 0.053 -0.096 0.033

(0.126) (0.0230) (0.0270) (0.0130) (0.0260) (0.262) (0.096) (0.059)

PLAB 0.176*** 0.075*** 0.049*** -0.003 -0.023* 0.005*** 0.00095 0.029***

(0.008) (0.015) (0.012) (0.006) (0.014) (0.002) (0.00200) (0.004)

PMEAT 0.624*** 0.122*** 0.088*** 0.015 0.0055*** -0.00005 0.030***

(0.009) (0.017) (0.009) (0.012) (0.0020) (0.00200) (0.005)

PMAT 0.141*** 0.089*** 0.002 -0.0013 -0.0001 0.006***

(0.004) (0.011) (0.008) (0.0020) (0.0010) (0.002)

PK 0.059*** 0.006 0.0018 -0.0008 -0.007

(0.009) (n.a.) (0.0020) (0.0020) (0.005)

MIX -0.081 -0.010 -0.0004 -0.003

(0.063) (0.009) (0.0020) (0.008)

DEF 0.029 0.002 -0.009

(0.032) (0.006) (0.008)

Q (lbs) 0.945*** 0.029

(0.042) (0.033) 

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can be 

interpreted as elasticities at their sample means. Sample size = 117 observations. n.a. = not available.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.
Economic Research Service/USDA Managing for Safer Food / AER-817  35

Appendix table 4.A.7—Translog cost function parameter estimates of producers of sausage products

Variable First-order PLAB PMEAT PMAT PK MIX DEF Q(lbs)

Intercept 0.036 

(0.039)

PLAB 0.217*** 0.045*** 0.040*** 0.016*** 0.011 0.006*** 0.0001 0.039***

(0.007) (0.012) (0.007) (0.005) (0.001) (0.002) (0.0010) (0.004)

PMEAT 0.499*** 0.1553* 0.124*** 0.0087 0.006*** -0.0003 0.031***

(0.006) (0.0090) (0.007) (0.0060) (0.002) (0.0010) (0.003)

PMAT 0.208*** 0.1407*** -0.0007 -0.002 -0.0002 0.014***

(0.004) (0.0070) (0.001) (0.001) (0.0010) (0.002)

PK 0.076*** -0.019 0.002 0.0004 -0.006**

(0.006) (n.a.) (0.002) (0.0010) (0.003)

MIX 0.015 0.007 -0.003 0.015***

(0.051) (0.011) (0.003) (0.005)

DEF -0.009 -0.0006 -0.002

(0.025) (0.0040) (0.005)

Q (lbs) 0.858*** 0.044**

(0.024) (0.021)

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can be 

interpreted as elasticities at their sample means. Sample size = 257 observations. n.a. = not available.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

Appendix table 4.A.8—Translog cost function parameter estimates of processed poultry producers

Variable First-order PLAB PMEAT PMAT PK DEF Q(lbs)

Intercept 0.002

(0.061)

PLAB 0.236*** 0.083*** 0.107*** 0.052*** -0.028* 0.002 0.023***

(0.015) (0.021) (0.016) (0.010) (0.016) (0.002) (0.005)

PMEAT 0.522*** 0.164*** 0.116  0.059** * -0.001*** 0.010

(0.016) (0.019) (0.010) 0.013) (0.003) (0.006)

PMAT 0.198*** 0.085*** -0.021** 0.001 -0.011**

(0.018) (0.009) (0.010) (0.003) (0.005)

PK 0.044** -0.011 -0.002 0.024***

(0.019) (n.a.) (0.003) (0.006)

DEF -0.029 0.0002 -0.014**

(0.045) (0.0070) (0.006)

Q (lbs) 1.013*** 0.078***

(0.042) (0.015)

Notes: Numbers in parentheses are estimated standard errors. All variables are standardized at their mean, so first-order terms can be 

interpreted as elasticities at their sample means. n.a. = not available. Sample size = 129 observations.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.
36  Managing for Safer Food / AER-817 Economic Research Service/USDA

Several studies (Boland et al., 2001, and Antle, 2000)

show that meat and poultry process control practices

comprise a sizeable share of nonmeat input costs for

meat and poultry slaughter and processing plants.

These findings are not surprising. Food scientists

assert that process control practices serve as a foundation for reducing the threat of pathogens in meat

and poultry products and are essential for normal

business operations. If food safety process control is

important to food quality, then plants that reduce

food safety process control actions may face adverse

repercussions in the marketplace. The purpose of this

chapter is to examine the effect of food safety

process controls on longrun profits. We use plant survival as a measure of profitability.

Consumers base their purchasing decisions on a wide

variety of attributes, such as food safety quality, tastiness, cost, and appearance. Some consumers may be

particularly concerned about food safety and might

repeatedly purchase higher priced, branded products

offered by manufacturers that emphasize product quality in their advertising. Attributes that consumers cannot measure directly, such as food safety, require a

brand or another form of certification to denote product quality and consistency. Other consumers, however, may value food safety and consistency less highly

and will choose a nonbranded product with a lower

price. Thus, firms selling similar products at different

quality levels and prices will coexist in the marketplace if they deliver an acceptable level of quality at a

reasonable price. Plants selling similar quality products must have identical prices. If a plant sells a highprice product relative to product quality or a lower

quality product relative to price, then it must eventually exit the industry. In this chapter, we examine the

profit-quality relationship in terms of plant exits and

food safety quality. We use performance of SPCPs as a

measure of food safety quality. 

Food safety is a particularly difficult product attribute

to convey to consumers because it cannot be directly

observed. Consumers learn about this quality by either

eating the food themselves or by observing the consequences of others. Even then, consumers may not

know food safety quality. There may be a lag of days

or even weeks before a foodborne illness exhibits

symptoms, and those symptoms are often “flu-like,”

making it difficult for consumers or health care practitioners to identify the source of their illness. So, consumers often do not go to the doctor for confirmation

of a foodborne illness and mistakenly attribute a foodborne illness to another food, the environment, or, in

the case of unbranded products, to some unknown producer. This imperfect linkage between the source of

foodborne illness and the product enables some producers to invest less in process control than they would

if this attribute were perfectly revealed. This incentive

may be particularly relevant for producers of generic

products whose products are commingled by the buyer

with other purchased products, making it difficult for a

consumer or buyer to identify the seller.

Lawrence et al. (2001) assert that large slaughter

plants are more likely to be one of only a few or even

the only supplier to a buyer, and meat and poultry

processors sell unique or branded products. Moreover,

since their production volume is much higher than that

of smaller plants, the chance of any single consumer

becoming sick is much greater. Thus, we hypothesize

that small animal slaughter plants can gain economic

benefits by reducing effort devoted to SPCPs because

they sell in smaller volumes and may be more likely

than larger plants to sell generic products mixed with

products from other plants. We further hypothesize

that large slaughter plants and further-processors must

more diligently practice SPCPs because their products

can more readily be identified. We use SPCP performance ratings as recorded by the Food Safety and

Inspection Service (FSIS) as a measure of effort devoted to process control.

We consider slaughter and processing as distinct

industries. According to the Bureau of the Census data,

Chapter 5

Sanitation and Process Control

Deficiencies and Plant Exits
Economic Research Service/USDA Managing for Safer Food / AER-817  37

the main products for slaughter plants are carcasses,

bulk meat and poultry parts, ground meat and poultry,

and other, mainly generic, raw meat and poultry products.1 Processing industries produce more distinct

products, such as sausages and smoked hams, which

are often branded.

Economic Framework

Antle (2000) demonstrates that food safety quality is

costly. However, if buyers can easily detect food safety, then meat and poultry firms may find it necessary

to closely monitor product contamination.

Oscar Mayer, Sarah Lee, and other further-processors

make large financial investments in product quality

and brand awareness promotions. Nelson (1970, 1974,

and 1978) has argued and Milgrom and Roberts (1986)

have shown that firms make these long-term investments in order to earn a reputation for producing quality products. In the meat and poultry industries,

Ollinger (2000) and Buzby et al. (2001) provide evidence of reputation effects associated with product

wholesomeness.

Losing a reputation for producing safe products can be

very costly. Customers do not expect to contract a

foodborne illness from products they consume and

may severely punish a plant that fails to provide

wholesome food. For example, Hudson Meats had to

sell its hamburger operations after one of its plants was

found to have produced hamburgers contaminated with

E. coli 0157: H7. Additionally, Sara Lee lost hundreds

of millions of dollars when it was identified as the

source of products contaminated with Listeria monocytogenes that killed several people (Perl, 2000).

A plant could continuously clean its facilities and test

each animal for excessive bacteria and pathogens in

order to verify pathogen control. However, the costs of

maintaining such rigorous standards are extremely high

and may be unnecessary. Holmstrom (1979 and 1982)

reminds us that moral hazard is an asymmetry of information among individuals that results from an inability

to observe individual actions, suggesting that manufacturers know more about their products than consumers.

Moreover, Barzel (1982) argues that buyers do not

learn all of a product’s quality attributes because measurement is costly. And Klein and Leffler (1981) argue

that firms adhere to higher quality standards only if the

expected present value exceeds the expected shortrun

gains from product deception. Thus, plants will invest

only enough resources in product quality to avoid being

detected as a seller of off-quality products.

Lawrence et al. (2001) observed that meat and poultry

processing plants often produce branded, specialty, and

single-source products that can be linked to the supplier through its label or relationship to the retailer. Thus,

these producers must be very diligent about maintaining product quality. Plants that slaughter hoofed animals and produce carcasses, on the other hand, usually

sell nonbranded, generic products, making plant identification much more difficult. As with further-processed

products, consumers must first identify food as a

source of an illness and then recognize the food that

caused the illness. If the product was either branded or

unique, then the source is identified. If not, the consumer must remember the store where the food was

purchased, then the store has to identify the plant that

produced the product. If the store purchases identical

products from different suppliers, then the source of

foodborne illness cannot be determined, but if there is

only one provider, then the supplier is known. 

It may be easier to identify a large rather than a small

slaughter plant. Suppose there are 1,000 consumers of

products from plant A and only one consumer of product from plant B. Only 0.1 percent of the consumers of

plant A production need to become sick from a foodborne illness and then correctly identify food and type

of food as the source of the sickness for the plant to

lose its reputation for producing pathogen-free food.

However, 100 percent of the consumers of plant B production need to make the same connection for it to

lose its reputation.

Tracing the source of a foodborne illness to the plant

may also be more successful for large rather than for

small slaughter plants. Some stores, restaurants, and

wholesalers sell thousands of pounds of meat or poultry per year. These buyers often prefer to lower their

transaction costs by purchasing meat or poultry from

only one slaughter plant because only large plants have

the capacity to meet demand for their products

(Ollinger, 1996). However, if large buyers do purchase

meat or poultry from small plants, then they must co1 Throughout this chapter we define a slaughter plant as a plant

that slaughters an animal and then sells the carcass or cuts the carcass into large components for shipment as boxed beef, trimmings,

ground beef, large cuts of meat, and consumer-ready products. The

essential feature of the slaughter plant is that an animal is slaughtered at the facility.
38  Managing for Safer Food / AER-817 Economic Research Service/USDA

mingle products with products from other suppliers,

making it difficult to identify the source of an

unwholesome product. Summarizing, small slaughter

plants should be less likely than large plants to be

identified as the source of unwholesome products. All

further-processors, on the other hand, can be identified

as a source of unwholesome products if a particular

type of meat or poultry is identified as the source of a

foodborne illness. Thus, longrun profits should be

higher for small slaughter plants with a greater percentage of deficient SPCPs and further-processors and

large slaughter plants with a lower percentage of deficient SPCPs.

An Empirical Model of Plant Exits

Economic theory suggests that a plant will exit its

industry when profits in year t, πt

, are less than the 

discounted value of the plant at the end of the period,

e-rtVt+1, minus the current value of the plant, Vt

. Thus,

a plant will exit an industry when πt <Vt – e-rtVt+1.

We follow Anderson (1998) and Muth (2001) who

modeled profits and a reduced form of the profit function in the following way:

πit = P(PDt, MSijt)*Qt – C(Ti ,k,t, Di, t, Mi ,m,t, Fi ,l,t), (5.1)

and 

π i t

* = π( PDt

, MSijt, Ti ,k,t, Di, t, Mi ,m,t, Fi ,l,t), (5.2)

where PD is product demand, MS is market structure,

T is plant technology, D is percent SPCP deficiencies,

M is plant product market, and F is company effects.

We cannot observe longrun profitability, but we do

know that plants must exit an industry when the discounted value of profits, Πt

, are less than zero.

Consequently, we define Yi = 1 if the plant “i ” existed

in 1992 but did not exist in 1996, and define Yi = 0 if

it existed in both 1992 and 1996. Then, we use a

Probit regression to examine the determinants (Xi )

that may be correlated with the likelihood of a plant

exiting. Since plants with negative profits must exit the

industry, we write:

E(YX) = Prob (Yi = 1) = Prob (Πi < 0) and (5.3)

Πi = β’Xi + εi

, (5.4)

where Xi is a vector of factors affecting profits and εi

is the error term:

Prob (YI = 1) = Prob (β’Xi + εi < 0)

= Prob (εi < -β’Xi )

= Prob (εi > β’Xi ) (5.5)

= 1 – F(B’Xi

), (5.6)

where E is the expectation operator, β is a vector of

parameters to be estimated, and F(B’Xi

) is the cumulative distribution. Marginal effects are estimated separately as:

where f (.) is the standard normal density function that

corresponds to the standard cumulative distribution,

F(.). (For technical details, see Greene, page 643.)

Model Specification

Profits vary with demand conditions. If consumer

demand for meat or poultry products declines, then

industry profits likewise should decline. However, all

plants would be affected equally by a drop in demand

if the market is national. Koontz et al. (1993) argue

that boxed beef prices are determined on a national

rather than a local level. Empirically, Anderson et al.

(1998) provide no evidence that demand conditions

affect plants differently. Hence, we assume competitive

markets exist and that price differences for identical

products do not exist.

Azzam and Schroeter (1995) argue that cattle markets

are subject to imperfect competition, permitting larger

purchasers to earn higher profits. Ward and Bliss

(1989) assert that forward contracting for cattle by

larger packers in concentrated markets can reduce animal availability for smaller purchasers and drive up the

prices for other purchasers. However, unpublished census data show that large meatpackers pay higher prices

for animals, suggesting that forward contracting is

simply a way to guarantee an ample supply of inputs

and that large meatpackers select higher quality animals. Empirically, Morrison-Paul (2000) found no evidence of market power in either the input or output

markets in her study of the cattle slaughter industry,

and Anderson et al. (1998) found that very small

regional market share and market structure and for-

[ ] ( )

( ) = = { (5.7) ( ) ∂

∂

β

β β ββ

EYX

X

dF ' X

d X } = f 'X , 
Economic Research Service/USDA Managing for Safer Food / AER-817  39

ward contracting effects in their model of cattle

slaughter plant exits. Due to these results, we consider

market structure effects for slaughter plants but expect

only a modest effect, if any, on plant survival. Meat

processors purchase generic packed meat products and

compete in a national product market, so they should

not be affected by market structure effects.

MacDonald et al. (2000), Duewer and Nelson (1991),

and Ward (1993) in cattle slaughter, Ollinger et al.

(2000) in poultry slaughter, MacDonald and Ollinger

(2000) in hog slaughter, and our results for meat processing (chapter 4) show that economies of scale is a

key determinant of plant cost structure. Thus, large

plants should be less likely to exit the industry since

they, on average, have lower costs than smaller ones.

Plant capital embodies past and recent technological

change. Since existing knowledge cannot be destroyed

and new knowledge accrues, new technology must, on

average, represent an advancement over older plant

technology. For plant survival, this means that newer

plants should be less likely to exit an industry than

older plants because they should be able to accommodate the most recent technological advancements.

However, Dunn et al. (1988) found that new plants

often fail because they underestimate technological

demands of the market. Thus, there may be a nonlinear

relationship between plant age and plant survival.

MacDonald et al. (2000) found that about half of all

bacon, ham, sausages, and other pork products were

produced in slaughter plants in 1982, whereas only

about 30 percent of these products were produced in

slaughter plants in 1992. This change indicates a shift

toward plant specialization that likely contributed to

the lower costs of production reported by MacDonald

et al. (2000) over the 1963-92 period. Since greater

specialization implies fewer products and processes,

we expect plant exits to rise as the number of plant

processes rises and, in the slaughter industry, as the

share of slaughter production declines.

Percent-deficient SPCPs reflect adherence to good

manufacturing practices and should indicate the

process control effort practiced by the plant. However,

since conforming to such standards is costly (Klein

and Leffler, 1981), firms adhere to higher standards

only if the expected present value exceeds the expected shortrun gains from selling a low-quality product.

Thus, firms making large investments to build brand

awareness must also have a lower percentage of deficient SPCPs, suggesting that a higher percentage of

deficient SPCPs encourages plant exits.

Consumers cannot readily identify the food manufacturer of nonbranded products. Raw beef, pork, and, to

a lesser extent, poultry are usually sold under store

brands and come from multiple suppliers, obscuring

supplier identity. Thus, if consumers are displeased

with a product, they must stop buying all products of

that type, e.g., ground beef from all producers. In

terms of the performance of sanitation and process

control tasks, this means that slaughter plants have a

weaker incentive to comply with SPCPs than do

processors, and slaughter plants that are better able to

avoid detection have less incentive than others. Since

small plants are more likely to be one of many suppliers to a buyer and process controls are costly, an

increase in the percentage of deficient SPCPs should

reduce small plant exits. However, a high percentage

of deficient SPCPs may induce large slaughter plants

to close because large plants are more likely to be a

single supplier to a buyer and, given their greater production volume, more likely to cause a foodborne illness if they fail to produce pathogen-free food.

Different product markets may have different survival

prospects because of unique factors, such as processing technologies and final product demand conditions.

Thus, we control for processing operations.2 We also

control for possible company-wide effects since plants

owned by firms may achieve synergies with companion plants. However, firms may be more likely to close

these plants if demand drops and the firm can reduce

its cost of production by shifting all production to

other facilities. Thus, the effects of being a multiplant

firm cannot be determined a priori.

Data

Data include the 1992 percent-deficient SPCP data and

the 1992 and 1996 Enhanced Facilities Database

(EFD) for all meat and poultry slaughter and processing plants (primarily SIC 2011, 2013, and 2015). Both

of these datasets were discussed in chapter 4. Since

slaughter plants and further-processors have substantially different operations, we split this sample into

2 In 1992, FSIS identified a number of different processing operations conducted in plants. These data are reported in the Enhanced

Research Database and can be interpreted as a fundamental manufacturing process underlying particular types of products and their

associated product markets. 
40  Managing for Safer Food / AER-817 Economic Research Service/USDA

separate data sets for slaughter plants (any plant that

slaughtered animals) and further-processors (plants in

SIC 2011, 2013, or 2015 with no slaughter operations). Slaughter plants that do no processing of animal carcasses were dropped from the sample because

FSIS does not report SPCPs for them. FSIS has a different inspection program for these plants.

The data from the EFD that are useful for this chapter

include the number and type of slaughtered animals,

SIC codes, pounds of meat or poultry produced, plant

age, and categorical data on plant manufacturing

processes. The EFD defines pounds of production as

further-processed products, such as hot dogs, plus

semi-processed raw meat products, such as boxed beef

but not bulk slaughter products, such as carcasses. 

Since some plants produce only carcasses or sell 

some output as carcasses, we defined output as 

equal to pounds of carcasses plus pounds of furtherprocessed and semi-processed products. We converted

number of animals slaughtered to pounds of meat and

poultry carcasses by multiplying the number of slaughtered animals times the average animal liveweight

meat production for that species as reported in the

1992 Longitudinal Research Database (LRD) by a 60-

percent conversion rate from liveweight to raw meat

for cattle, hogs, sheep, and goats and a 100-percent

conversion rate for chicken and turkeys. For each

plant, total meat from slaughtered animals equaled the

total amount of meat coming from all animal species

slaughtered by the plant. The liveweight pounds per

animal species as reported in the LRD in 1992 are

1,128 pounds for cattle, 249 pounds for hogs, 154

pounds for sheep and goats, 4.4 pounds for chickens,

and 21.6 pounds for turkey.

Variable Specifications

The dependent variable (Yi ) was set at one if the plant

existed in the EFD in 1992 but not in 1996 and set at

zero if it existed in both the 1992 and 1996 EFD

datasets.3 OUTPUT is the total amount of semiprocessed and processed meat and poultry and the estimated pounds of meat from slaughtered animals.

Other variables are defined as follows. PLANTAGE

equals 93 (representing 1993) minus the year in which

FSIS issued a meat grant or a poultry grant to the

plant. All plants are at least 1 year old. A meat or poultry grant from FSIS gives meat or poultry plants the

right to be inspected by FSIS and to produce, sell, and

ship meat or poultry in both intrastate and interstate

commerce. MULTSPECIE was set to one for plants

slaughtering more than one animal species and set to

zero otherwise.

The variables PROCESSES and SHSLAUTER reflect

plant specialization. PROCESSES was coded as one if a

slaughter plant used one or more or if a further-processing plant used two or more of the following processes:

sausagemaking, production of ready-to-eat product, production of cured products, production of cooked but

uncured products, or production of dry-cured products.

If PROCESSES was not one, it was coded as zero.

SHSLAUTER was pounds of slaughtered animal meat

divided by the sum of processed and semi-processed

meat and meat from slaughtered animals.

Table 5.1 shows a jump in the exit rates for the larger

slaughter and all processing plants that fall in the 90th

percentile of percent-deficient SPCPs. Below the 90th

percentile, there is little apparent change in exit rates

as the percentage of deficient SPCPs rises. Thus, we

suspect that exit rates are a discontinuous function of

percent-deficient SPCPs and set DEF90 at one for

plants that fall in the 90th percentile of percent-deficient SPCPs and at zero otherwise.

The market variables for the slaughter plant model

include a dummy variable equal to one for chicken

plants and zero otherwise (CHIK) and other similarly

defined dummy variables for turkey plants (TURK)

and cattle slaughter plants (BEEF). For processors, the

variables SAUSAGE, READY-TO-EAT, CURED,

COOKED-UNCURED, CURED-UNCOOKED, and

DRYCURED were set equal to one if the plant produced sausage, ready-to-eat, cured, cooked but

uncured, cured but uncooked, or dry-cured products,

respectively, and set at zero otherwise.

Three variables are used in the slaughter model to capture market structure effects: SHREGION, HERFREGION, and HERFREGION/SHREGION (see

Anderson et al., 1998, for a similar formulation).

SHREGION is the market share of the plant in the

FSIS regional circuit, HERFREGION is the Herfindahl

Index in the FSIS regional circuit, and HERFREGION/SHREGION provides a measure of a plant’s

relative dominance in the FSIS regional circuit, i.e.,

3 Sometimes plants switch from Federal to State inspection programs. We have no way of identifying these plants and count them

as exiting.
Economic Research Service/USDA Managing for Safer Food / AER-817  41

whether or not it is located on the industry fringe.

Note, the Herfindahl Index is a measure of market

power and equals the sum of the squares of firm 

market shares.

Results for Slaughter Plants

Table 5.1 illustrates how plant exits vary by the percentage of deficient SPCPs and plant size for slaughter

plants. Reading down the table, exit rates are higher

for the smallest slaughter plants with lower percentage

of deficient SPCPs and the larger plants with a higher

percentage of deficient SPCPs. These data are consistent with the hypotheses that: (1) small plants with a

high percentage of deficient SPCPs benefit from the

lower production costs associated with lower quality

control effort, and (2) large slaughter plants with a

high percentage of deficient SPCPs are penalized for

selling poor-quality products.

Table 5.2 contains the results of the Probit regressions

for slaughter plants. The log likelihood of the model is

significant at the 99-percent level and the pseudo R2 is

0.07.4 Variables include technology, market, company,

and market structure effects and interactions of output

with other independent variables. A Wald test (table

5.2) shows that plant technology variables are jointly

significant at the 99-percent level and market effect

have a 95-percent level of significance. Neither company nor the market structure effects are jointly significant.

All the technology, market, and company effect variables are significant except those for plant age and the

beef market. None of the market structure variables are

significant.

Marginal effects are particularly important because they

indicate how a marginal change in a variable affects the

outcome. DEF90 is of particular interest and is consistent with expectations. It and its interaction with output

are significant. These results suggest that very small

plants in the 90th percentile of percent-deficient SPCPs

are less likely to exit than their larger competitors.

However, as plant size increases, the advantage enjoyed

by plants in the 90th percentile dissipates until they

reach about the mean plant size. After the mean plant

size, high deficiency levels make it more likely to exit

the industry. These results make sense. Production from

small slaughter plants often cannot be directly linked to

the supplier because products are co-mingled with identical products from other suppliers, obscuring the source

of off-quality products. As pointed out by Libecap

(1992), these slaughter plants have a strong incentive to

minimize effort devoted to tasks like SPCPs because

these tasks are costly and the consequences of failing to

perform them are borne by the industry. However, this is

not the case for large slaughter plants. As pointed out

earlier, these plants are more likely to be a single supplier to a large restaurant chain or grocery store because

those buyers prefer to minimize their transaction costs

by dealing with a single seller (Ollinger, 1996). This

single-supplier relationship makes it much easier to

trace products to a supplier, forcing producers to perform quality control more diligently. Additionally, small

plants produce much less product per hour than large

plants, so an unsanitary condition that persists for an

hour affects a much smaller volume of output and is

consumed by fewer customers, reducing the likelihood

of causing sickness.

Marginal effects are particularly important because

they indicate how marginal changes affect outcomes.

For DEF90, marginal effects suggest that plants in the

90th percentile of percent-deficient SPCPs are 35 percent less likely to exit. However, the effect diminishes

with plant size. For a plant equal to about the industry

mean plant size, the percent-deficient SPCPs have no

effect on plant survival. For plants larger than the

industry mean, plants in the 90th percentile of percentdeficient SPCPs are more likely to exit the industry. 

We turn now to the other marginal effects. The negative signs on the coefficients for the marginal effects of

output are consistent with MacDonald et al. (2000)

and Ollinger et al. (2000). They indicate that a 10-percent increase in plant size reduces the likelihood of

exiting the industry by about 1 percent. Positive coefficients on the coefficients for the multi-species,

processes, and slaughter interacted with output are also

consistent with results by MacDonald et al. (2000) and

Ollinger et al. (2000). These results suggest that

slaughter of more than one animal species, the use of

more than one further-processing operation by small

slaughter plants, and strict specialization in slaughter

by large plants encourage plant exits. The marginal

effects on share of slaughter suggest that a 10-percent

increase in pounds of meat from slaughtered animals

as a share of output reduces the likelihood of exiting

by about 2 percent. 4 Pseudo R2 = 1 minus the ratio of the log likelihood estimate of

the final model to that of the most restrictive model.
42  Managing for Safer Food / AER-817 Economic Research Service/USDA

Company effects and market structure variables were

not jointly significant. The market structure result is

consistent with Anderson et al. (2000) and MorrisonPaul (2000). We also tested percent-deficient SPCPs as

a continuous variable and found that it was modestly

significant. These results are available from the author.

Finally, we considered vertical and horizontal integration across plants and the share of meat inputs from a

plant’s main animal input as a share of meat from all

animal inputs, but they were insignificant.

Results for Processing Plants

Table 5.1 shows how plant exits vary by the percentage

of deficient SPCPs and plant size in the processing

industries. As shown, exit rates increase with percentdeficient SPCPs for all size categories. These results

are consistent with the hypothesis. Since furtherprocessors typically produce branded and unique products that can readily be traced back to the producer

(Lawrence et al., 2001), buyers can, and do, penalize

plants for selling poor-quality products. 

Table 5.3 contains the results for the Probit regression

of plant exits over the 1992-96 period in further-processing industries. The plant technology variables

include output, plant age, number of processes, and the

dummy variable for plants falling in the 90th percentile of percent-deficient SPCPs. Plant product markets are represented by dummy variables for specific

plant processes used to produce sausage, cured,

cooked and uncured, cured and uncooked, and drycured products. Company effect variables include

whether the plant is part of a firm that owns multiple

plants producing meat or poultry products.

The technology variables—output, output squared,

plant age, and whether or not the plant had more than

two further-processing operations or falls in the 90th

percentile of percent-deficient SPCPs—were jointly

significant at the 99-percent level. Market effects were

jointly significant at the 95-percent level of significance but company effects were not. Output, number

of processes, the 90th percentile of deficient SPCPs,

and the dummy variables for cured and

cooked/uncured products were significant.

Results are consistent with previous research. The negative sign on the marginal effect of output is consistent

with cost function results for the processing industries

that show economies of scale exist in production.

Results suggest that a 100-percent increase in plant

size would lead to a 5-percent decrease in the likelihood of exiting the industry.

There is no current evidence for the cost effectiveness

of specialization in meat processing, but results suggest that it pays to specialize. The marginal effects of

the PROCESS term shows that plants with more than

two processes were about 8.1 percent more likely to

exit the industry.

Of most interest is the DEF90 variable. The magnitude

of the coefficient for the percentage of deficient SPCPs

suggests that plants with a percentage of deficient

SPCPs in the 90th percentile have a 5-percent higher

likelihood of exiting the industry than other plants.

The positive sign on percent-deficient SPCPs for further-processors (in sharp contrast with the negative

sign for small slaughter plants) suggests that plants

falling in the 90th percentile of percent-deficient

SPCPs are more likely than other plants to exit the

industry, regardless of plant size. Why might this be

so? As suggested earlier, further-processors can be

much more easily linked to production of pathogentainted products than slaughter plants because furtherprocessed products are more likely to be either branded or a specialty item (Lawrence et al., 2001).

Slaughter products, on the other hand, are often generic and commingled with products from many suppliers

by a buyer, obscuring the identity of the producer.

We also tested percent-deficient SPCPs as a continuous variable and considered vertical and horizontal

integration dummy variables for a multiple-plant firm.

The continuous percent-deficient SPCPs variable for

further processing was significant. Neither the vertical

nor horizontal integration terms were significant and

were dropped.

Summary

This chapter examined the effect of plant technology,

market effects, company effects, and market structure

effects on plant exits in the meat and poultry slaughter

and processing industries. Results suggest that plant

technology variables and market effects variables in

both industries significantly affect plant survival rates.

Of particular interest was the effect of percent-deficient

SPCPs on the likelihood of plant exits. Results suggest

that large slaughter plants and all processing plants with

a high percentage of deficient SPCPs (the 90th percentile of percent-deficient SPCPs) have a higher likelihood of exiting than other plants, despite any cost sav-
Economic Research Service/USDA Managing for Safer Food / AER-817  43

ings that their competitors may realize from reducing

their sanitation and process control effort (and costs).

Why might this be so? Large slaughter plants produce

very large volumes of meat products; thus, the likelihood of any single consumer’s becoming sick is a lot

greater, all else equal. Moreover, large plant output 

constitutes a larger share of the product stocked by a

retailer, making detection more likely. Processors, on

the other hand, produce unique products that are often

branded, so they have an even stronger incentive to produce wholesome products because their identity is much

more easily revealed, regardless of size.

The second major finding is that the discontinuous

nature of the relationship between exits and percentdeficient SPCPs means that only plants severely lax in

their sanitation and process control effort would likely

exit the industry due to food safety process control

performance. Thus, plants have considerable flexibility

in producing products with various degrees of food

safety quality without being penalized. This finding

has important regulatory implications. If enforcement

of SPCPs were strictly practiced and directed mainly

at the most serious violators, there would be little regulatory effect on most plants, and regulatory actions

would provide a strong incentive for plants to avoid

deviating substantially from the median level of SPCP

performance. Regulatory policy would, no doubt, still

encourage plant exits, but those plants would be the

ones with the poorest process control performance and

least willing to undertake additional process control

effort. In terms of regulation under the Pathogen

Reduction/ Hazard Analysis and Critical Control Point

rule, this means that any increase in regulatory stringency would increase exit rates, particularly among the

most poorly performing plants. However, those plants

that do exit would be the ones that would be more

likely to exit anyway.
44  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 5.1— Percentage of slaughter plant exits by plant size and percentile of percent-deficient 

SPCPs, 1992-96

Percent-deficient SPCP Plant size 

category Less than one- One-half to More than All sizes

half mean size twice mean size twice mean size

Percent exits, 1992-96

Slaughter plants:

Less than 10th percentile 8.3 0.0 0.0 8.2

10-90th percentile 9.6 7.4 2.9 8.5

More than 90th percentile 4.0 15.0 7.1 7.1

All percent-deficient levels 8.9 8.6 4.1 8.3

Processing plants:

Less than 10th percentile 11.8 0.0 0.0 11.4

10-90th percentile 10.0 8.7 4.8 9.2

More than 90th percentile 15.0 14.8 7.3 12.8

All percent-deficient levels 10.7 9.3 5.4 9.9

Number of plants in 1992

Slaughter plants:

Less than 10th percentile 144 1 1 146

10-90th percentile 459 108 69 636

More than 90th percentile 50 20 28 98

All deficiency levels 653 129 98 880

Processing plants:

Less than 10th percentile 220 6 2 228

10-90th percentile 789 172 124 1,085

More than 90th percentile 80 27 41 148

All deficiency levels 1,089 205 167 1461
Economic Research Service/USDA Managing for Safer Food / AER-817  45

Table 5.2—Effect of percent-deficient SPCPs on slaughter plant exits, 1992-96

Variable1 Likelihood effect Marginal effect Mean

Estimate Standard error Estimate Standard error

Plant technology: χ2 (10)=27.7***

INTERCEPT 4.718 3.108 0.607 (0.400) -

Log OUTPUT -0.784* 0.418 -0.100* (0.054) 15.30

Log OUTPUT *0.026* 0.014 0.0033* (0.0018) 241.20

Log OUTPUT

Log PLANTAGE -0.483 0.540 -0.062 0.070 2.632

Log PLANTAGE* -0.047 0.100 -0.006 0.013 7.386

Log PLANTAGE

Log PLANTAGE* 0.032 0.035 0.004 0.005 40.38

Log OUTPUT

MULTSPECIE 0.418* 0.252 0.054* 0.032 0.621

PROCESSES 1.363* 0.831 0.175* 0.106 0.536

PROCESSES* -0.104* 0.057 -0.013* 0.007 8.332

Log OUTPUT

Log (SHSLAUTER) -1.558* 0.833 -0.200** 0.105 -0.596

Log (SHSLAUTER) 0.114* 0.061 0.015** 0.008 -9.037

*Log OUTPUT

DEF90 -2.713* 1.469 -0.350** 0.185 0.112

DEF90* 0.174** 0.088 0.022** 0.011 1.874

Log OUTPUT

Markets: χ2 (10)=7.0**

BEEF -0.267 0.271 -0.034 0.035 0.728

CHICKEN -0.510* 0.324 -0.066* 0.041 0.10

TURKEY 0.055* 0.313 0.071* 0.040 0.057

Company: χ2 (2) = 2.8

MULTFOOD 5.823* 3.372 0.749* 0.430 0.165

MULTFOOD*

Log OUTPUT -0.319* 0.183 -0.041* 0.023 3.183

Market structure: χ2 (3) = 2.2

SHREGION -3.305 3.594 -0.426 0.452 0.016

HERFREGION 0.341 1.103 0.044 0.142 0.117

HERFREGION/ -0.372*10-5 0.596*10-5 -0.478*10-6 0.764*10-6 1970

SHREGION

Log Likelihood -234.2**

Pseudo R2 0.07

Observations 879

Notes: *Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

1 The symbol * used in some variable definitions represents the multiplication function, so Log Output*Log Output is Log Output 

times Log Output.
46  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 5.3—Effect of percent-deficient SPCPs on processing plant exits, 1992-961

Variable2 Likelihood effect Marginal effect Mean

Estimate Standard error Estimate Standard error

Plant technology: χ2 (7)=40.0***

INTERCEPT 1.631 1.104 0.262 0.178 -

Log OUTPUT -0.305** 0.155 -0.049** 0.025 14.00

Log OUTPUT* 0.006 0.006 0.001 0.001 202.00

Log OUTPUT

Log PLANTAGE 0.009 0.384 0.0014 0.061 2.560

Log PLANTAGE* -0.114 0.072 -0.018 0.012 7.128

Log PLANTAGE

Log PLANTAGE* 0.027 0.027 0.004 0.004 36.26

Log OUTPUT

PROCESS 0.501** 0.207 0.081** 0.033 0.247

DEF90 0.321** 0.147 0.051** 0.023 0.101

Markets: χ2 (6)=14.4**

SAUSAGE -0.193 0.155 -0.031 0.025 0.144

READY-TO-EAT -0.127 0.131 0.020 0.021 0.389

CURED -0.220* 0.136 -0.035* 0.022 0.529

COOKED and -0.389*** 0.140 -0.062*** 0.022 0.215

UNCURED

CURED and -0.017 0.140 -0.003 0.022 0.697

UNCOOKED

DRYCURED 0.036 0.199 0.006 0.032 0.062

Company: χ2 (1) =0.6

MULTMEAT 0.163 0.192 0.026 0.031 0.079

Log Likelihood -448.5***

Pseudo R2 0.05

Observations 1461

Notes: *Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.

1 Interactions with output were insignificant and were dropped.

2 The symbol * used in some variable definitions represents the multiplication function, so Log Output*Log Output is Log Output 

times Log Output.
Chapters 4 and 5 provide some evidence that food safety process control is costly, yet necessary for plant survival. While these findings are important, we need to

know whether performance under the Wholesome Meat

Act (WMA) and Wholesome Poultry Products Act

(WPPA) is still applicable to performance under the

Pathogen Reduction Hazard Analysis and Critical

Control Point (PR/HACCP) rule in order to see whether

relationships developed in the earlier analyses are

applicable to current regulatory practices. In this chapter, we examined the statistical relationship between

food safety process control performance under WMA

and WPPA in 1992 to that which occurred under

PR/HACCP in 1998. We use these 2 years because

these and other necessary data are available and they

represent pre- and post-PR/HACCP years.

Table 6.1 illustrates the relationship between plant performance of SPCPs and plant performance of HACCP

tasks. The top row shows the percentile difference

between a plant’s SPCP performance rank relative to

its HACCP task performance rank.1 The first cell

shows that 28 percent of all meat slaughter plants had

a change in rank of less than 10 percentile in performance when the PR/HACCP rule supplanted SPCPs.

To fall into this category, a plant in the 30th percentile

of sanitation and process control tasks would have to

fall within the 20-40th percentile of performance of

HACCP tasks. Similarly, a plant in the second cell

with a 30th percentile ranking for SPCPs would fall in

the 10-20th or 40-50th percentile category under

PR/HACCP, and a plant in the third cell with a 20th

percentile ranking for SPCPs would fall in the 0-10th

or 50-60th percentile under PR/HACCP. Notice that

about one-half the plants realized a change of less than

20 percent in their relative performance ranking under

PR/HACCP from their performance under SPCPs and

that about two-thirds of plants fell within 30 percent of

their former ranking.

Economic Framework

The PR/HACCP rule went beyond the regulatory

framework based on the WMA/WPPA by mandating

that all meat and poultry slaughter and processing

plants establish a HACCP process control plan and

perform the associated tasks while continuing to perform sanitation and process control tasks. As discussed

in chapter 3, each plant’s HACCP plan has to include

critical control points, a plan of action to control those

critical control points, criteria for when a process is

out of compliance, and recordkeeping to gauge operating performance and prove that the plant performed

specified tasks.

The PR/HACCP rule dealt only with food safety issues

and increased the number of the types of tasks that

inspectors monitored beyond those required under

WMA/WPPA, but did not fundamentally change the

nature of the public health tasks. For example, each

plant has at least one critical control point (CCP)

under PR/HACCP and, similarly, had to comply with

process control requirements under WMA/WPPA.

FSIS inspectors do monitor CCPs in order to verify

that the plant completed all tasks outlined in the

HACCP plan, but also monitored performance of

process control tasks. Additionally, it is true that failure to comply with the HACCP plan could prompt the

FSIS inspector to discuss the failure with top management, assign the equivalent of a deficiency to the plant

Economic Research Service/USDA Managing for Safer Food / AER-817  47

Chapter 6

Relationship Between the Performance 

of SPCP and HACCP Tasks

1 HACCP performance is defined as HACCP tasks that are in noncompliance divided by the total performed HACCP tasks. Under

PR/HACCP, inspectors can mark a task as unperformed because:

(1) the plant failed to perform a necessary HACCP task, (2) the

plant was producing a product that did not require the operation, or

(3) the inspector had more pressing duties and did not examine the

task. To avoid counting unperformed tasks that were unnecessary,

we considered only tasks that were in noncompliance (these tasks

are necessary but were not performed correctly) and only those

tasks that were actually performed. For the denominator, we also

used performed scheduled tasks and unscheduled task. Results

with this measure were similar. To compute the difference in the

percentile ranking from WMA/WPPA to PR/HACCP, we first

ranked SPCP and HACCP performance of all plants. Next, we

computed the absolute value of the change.
by assigning a noncompliance record, and, if the problem persisted, motivate a temporary withdrawal of

inspection services. However, those same enforcement

tools were available under WMA/WPPA. Finally, the

PR/HACCP rule did require plants to take responsibility for their HACCP process control programs, but

plants have always paid a price for producing off-quality products. Under either regulatory approach, a failure to meet customer demands for food safety leads to

lost revenues and profits. Thus, taking responsibility

for a HACCP program would likely have little effect

on plant performance in the marketplace.2

Plants respond to market and regulatory incentives to

provide process control. Plants with poorly performing

process control programs may sell products in markets

that require less process control effort, while plants

with more stringent process controls may sell products

in markets that demand more process control effort.

The PR/HACCP rule was prompted by a public outcry

over reports of foodborne illnesses and, as recognized

in the 1996 Federal Register announcement for

PR/HACCP, many plants were moving independently

to adopt HACCP programs. Combined, this apparent

shift in consumer demand for food safety process control and the need of plants to deliver the same relative

quality control effort to their customers lead us to

hypothesize that plants that performed well under the

WMA/WPPA should, likewise, have a superior performance under PR/HACCP. If a plant exceeded minimum compliance under WMA/WPPA, then it would

be more likely to meet or exceed minimum compliance requirements under the PR/HACCP rule.

Conversely, if a plant just barely met compliance under

WMA/WPPA, then it would likely just barely meet

minimum compliance under PR/HACCP.

Model Linking HACCP Compliance to

SPCP Compliance

We hypothesize that, since meat and poultry plants did

not change their product market after FSIS implemented

the PR/HACCP rule, SPCP performance should be correlated with HACCP performance. In the model below,

we regress percent-deficient SPCPs and vectors of plant

technology, plant product market, and company-effect

variables on percent HACCP noncompliance records:

H = f (D, Tk, Mi

, Cj), (6.1)

where H is HACCP noncompliance records as a share

of all performed HACCP tasks relative to the industry

mean value, D is percent-deficient SPCPs relative to

the industry mean value, Tk is a vector of plant technology variables, Mi is a vector of markets served by the

plant, and Cj is a vector of company effects variables.

It is necessary to examine plant technology effects

because plant size, plant age, and other attributes related to plant technology likely affect the ability to perform HACCP tasks. For example, larger plants may be

more difficult to manage and older plants more difficult to clean because they were not designed to conform to modern slaughter and processing plant technology. Additionally, product markets establish acceptable food safety standards to which suppliers must

adhere to win sales. For example, meat purchased for

cooking may have a lower food safety standard than

ready-to-cook products for consumers. Finally, we

consider company effects because companies often

have company policies that affect manufacturing

processes and product quality.

The dependent variable, H, is an index bounded

below by zero, denoting plants for which inspectors

report no HACCP noncompliance records, and

bounded above by one, reflecting plants for which

inspectors report only HACCP noncompliance

records. Statisticians call bounded distributions such

as these censored data. In our case, H has a normal

distribution centered around and truncated at zero. If

the distribution is not truncated, some values would

be less than zero. In theory, negative values have

implications in that they include plants that undertake

quality control measures beyond those required by

FSIS (they overcomply with HACCP standards).

Tobin (1958) was the first to consider regressions with

censored dependent variables. He specified a dependent variable with a distribution centered at zero that

contained a theoretically possible latent variable (y*).

Greene (1993) gives the following general formulation

of the censored regression model, also known as the

Tobit model:

48  Managing for Safer Food / AER-817 Economic Research Service/USDA

2 FSIS inspectors also monitor nonfood safety regulatory requirements. These requirements did not change under the PR/HACCP

rule.

y

y

y

i

i

i

*

 = + 

 = 0 if * 0

 > 0 if * 0. (6.2)

i ′

≤

>

β x

y

y

i

i

i

ε
Applying equation 6.2 to 6.1, the distribution for yi

*

is the percent HACCP noncompliance records.

Theoretically, this distribution contains both positive

(HACCP noncompliance records) and negative (performed HACCP-like tasks) values. HACCP-like

tasks include tasks that may not be required under

the PR/HACCP rule but are performed by the company because they are deemed to be necessary. A

positive coefficient on an independent variable

means that the variable positively affects compliance

with HACCP tasks or discourages a more extensive

quality control program. Negative signs mean that

the independent variable encourages quality control

programs beyond what is required under PR/HACCP.

Marginal effects for this theoretical distribution, a normal distribution, are written as in equation 6.3 and

indicate how much changes in the independent variable affect failure to perform HACCP and HACCPlike tasks:

The marginal effect of adhering to HACCP standards

requires a slightly different specification than equation

6.3 because HACCP tasks occur only in the positive

portion of the distribution. A positive coefficient on an

independent variable means that the variable positively

affects performance of HACCP tasks, i.e., discourages

a plant from complying with HACCP regulation and

vice versa for negative signs. The coefficient indicates

how great the change is. Greene’s (1993) derivation of

the marginal effects follows:

E[yi

xi] = Φ(β′xi /σ)(β′xi + σλi), (6.4)

where

λi = φ(β′xi σ) / Φ(β′xi /σ), (6.5)

and the marginal effect of the independent variables on

yi is:

∂E[yixi] / ∂xi =βΦ(β′xi /σ). (6.6)

Note, σ is the standard deviation, φ is the probability

density function of the standard normal distribution,

and Φ is the cumulative density function of the standard normal distribution.

The empirical representation of equation 6.2 is based

on equation 6.1, where xi equals a group of variables

including D (percent-deficient SPCPs), Tk (technology

variables in addition to percent-deficient SPCPs), Mi

(markets served), and Cj (company effects) is:

where

Hi (share of HACCP noncompliance records) 

= 0, if Hi

* ≤ 0,

Hi (share of HACCP noncompliance records) 

> 0, if Hi

* > 0.

Note that the mean of Hi

*, a theoretically normally

distributed dependent variable, is less than the mean of

Hi because Hi cannot be less than zero. Plants that fall

in the negative portion of the distribution for Hi

* are

plants that believe that the market they serve demands

more quality control effort than required under

PR/HACCP.

Variable Definitions and Data

Hi, the observed dependent variable, is defined as noncompliance records (HACCP tasks inspected and determined to be not in compliance by the FSIS inspector) as

a percentage of all performed HACCP tasks divided by

the mean percentage of HACCP noncompliance records.

We divided by the industry mean to control for differences in scales between percentage of HACCP noncompliance records and percent-deficient SPCPs.

The variable D (equation 6.1) is denoted as DEFICIENCY in table 6.2 and is defined as the percentdeficient SPCPs divided by the mean percent-deficient

SPCPs. As with HACCP noncompliance records, we

divide by the mean percent-deficient SPCPs to control

for scale effects. Plants with lower percent-deficient

SPCPs devote more effort to SPCPs and plants with

higher percent-deficient SPCPs expend less effort. We

hypothesize that less effort devoted to SPCPs should

mean less effort under PR/HACCP (higher percentage

of HACCP noncompliance records). Statistically, we

expect a positive relationship.

The technology variables include the log of plant sales

(OUTPUT), the log of plant age (PLANTAGE), a

dummy variable (PROCESSES) set at one for slaughter plants that also have further-processing operations

and set at zero otherwise, and slaughter meat output

Economic Research Service/USDA Managing for Safer Food / AER-817  49

i 0

1

H

(6.7) 

=+ + +

 +

α 1 β φγδ

ε

i m,i

m=1

n

k,i j,i

j=

n

j,i

i

D C M

, 

Tmi ki

k

p

∑ ∑ , , + ∑

=1

∂ ∂β E y x i i [ . * xi

] / (6.3) =
divided by plant sales (SHSLAUTER). PLANTAGE is

defined as 2000 minus the year in which FSIS awarded

a meat or poultry grant to the plant.3

Larger plants, plants with more than one process, and

plants that slaughter animals have more complex operations than other plants. Williamson (1985) argues that

this complexity can lead to bureaucratic breakdowns.

Thus, we posit that plant size (OUTPUT), number of

plant processes (PROCESSES), and share of slaughter

(SHSLAUTER) positively affect the percentage of

HACCP noncompliance records.

Older plants may have older equipment and facilities not

designed to be compatible with modern cleanliness standards. These plants may require a greater level of maintenance and other servicing than younger plants, resulting in a greater likelihood of not performing some tasks,

so PLANTAGE should positively affect the percentage

of HACCP noncompliance records. However, Dunn et

al. (1988) suggest that young plants have higher exit

rates than older plants because they underestimate the

capital requirements needed to compete in industries.

This inexperience could lead to an increase in the percentage of HACCP noncompliance records. Thus, we

cannot, a priori, project the proper sign for plant age.

We designate market effects variables (M) for both

slaughter and processing industries. These variables

serve as control variables because different markets

likely have different food safety process control needs

and thus different performance ratings. For each

slaughter plant, market variables are set to one if the

plant slaughters the animal and are set to zero otherwise. In particular, we assign a one to BEEF if the

plant slaughters cattle and assign a zero to it if it does

not. Similarly, we set PORK to one if the plant slaughters hogs and set it to zero if it does not slaughter

hogs. We use this same convention for chicken slaughter with the dummy variable CHICKEN and for turkey

slaughter with the dummy variable TURKEY.

Additionally, we assign a one to OTHER if the plant

slaughters goats and other noncattle and nonhog

hoofed animals and assign a zero to it if it does not.

Finally, we set GROUND at one for slaughter plants

that also grind meat and set it to zero otherwise.

For meat-processing plants, the vector M includes several variables representing different markets. If a plant produced fully cooked, not shelf-stable products, such as

bologna, then we set FULCUKNSS to one and set it to

zero otherwise. Similarly, we designated HETTRETNSS

as one if the plant produced heat-treated, not shelf-stable

products, such as chicken nuggets, and designated it as

zero otherwise. Likewise, we set SECINHNSS to one if

the plant produced not shelf-stable products with secondary inhibitors, such as bacon, and set it as zero otherwise. Additionally, we denoted NOHETTRETSS as 

one if the plant produced shelf-stable, not heat-treated

products, such as pepperoni, and denoted it as zero otherwise. Finally, we set HETTRETSS to one if the plant

produced heat-treated, shelf-stable products, such as 

beef jerky.

The vector of company effects (C) includes a dummy

variable set at one for plants owned by firms with

more than one meat or poultry plant and set at zero

otherwise (MULTFOOD). Another company effects

variable is set at one for plants owned by firms with

more than one plant in that plant’s four-digit SIC code

industry and set at zero otherwise (MULTIND).

Data on HACCP noncompliance records and performed

HACCP tasks came from a 1998 FSIS dataset obtained

in a personal communication with an FSIS representative. Since the very small plants had not yet changed to

HACCP by 1998, these data do not include plants with

fewer than five employees or less than $2.5 million in

revenues. The percent-deficiency data are the 1992 data

obtained from an FSIS representative and were discussed

earlier. Data on plant technology, company effects, and

markets served come from the 1999 Enhanced Facilities

Database and were discussed in chapter 4. We separate

the data into slaughter and processing plants. Slaughter

plants are FSIS-inspected plants that slaughter hoofed

animals or poultry, while meat processors are plants that

do not slaughter animals but operate in SIC 2013.

Results

Tables 6.2 and 6.3 contain the parameter estimates for

the slaughter and meat processing industries from the

Tobit regression described in equation 6.2. The parameters are estimates of the effect of a percent-deficient

SPCPs and variables for plant technology and market

and company effects on HACCP noncompliance

records for hoofed animal slaughter, poultry slaughter,

and the processing industries. All models were adjusted for multiplicative heteroskedasticity with the following specification: σi

2= σ2exp (γ’Z) where Z is a

vector of variables that affect the disturbance term, σi

.

50  Managing for Safer Food / AER-817 Economic Research Service/USDA

3 U.S. law requires meat and poultry plants to have grants (licenses) to sell meat or poultry in interstate commerce.
In our case, Z = Log of OUTPUT and γ = [ln σ2, β]. 

A likelihood test shows that the heteroskedastic correction is significant for each model. 

Regression results of equation 6.2 show that the joint

likelihood of the entire model is significant for each

model. A Wald test indicates that the joint likelihood

of plant technology is significant in all industries, but

market variables are not significant, and company

effects variables are significant only for poultry slaughter and meat processing. Note, we consider percent-deficient SPCPs a plant technology variable because

process control is a component of plant technology.

The likelihood effects show whether an independent

variable affects the percentage of HACCP noncompliance records. Percent-deficient SPCPs and output are

significant and positive in all three industries. The

parameter estimate for plant age is negative and significant in the hoofed animal slaughter model, but is positive and insignificant in the poultry slaughter and meat

processing models. The number of processes is negative but insignificant in models for both slaughter

industries. The share of slaughter products is positive

in both slaughter industries but significant only in

poultry slaughter. The market variables BEEF and

FULCUKNSS are the only significant market variables. The dummy variable for plants owned by firms

that own more than one meat or poultry plant is significant and positive in models for the poultry slaughter

and meat processing industries.

Marginal effects show how small changes in independent variables affect percentage of HACCP noncompliance records. The marginal effect of output is significant and positive in all cases. A 10-percent increase in

plant size increases percentage of HACCP noncompliance records by 2 percent in red meat animal slaughter, 0.0002 percent in poultry slaughter, and 0.0085

percent in meat processing. Percent-deficient SPCPs is

also positive and small in all industries, but statistically significant only in poultry slaughter. A 10-percent

increase in percent-deficient SPCPs results in about a

0.05-percent increase in percentage of HACCP noncompliance records in hoofed animal slaughter and a

0.001 and 0.003 percent increase in poultry slaughter

and meat processing. Of the other variables, only

PROCESSES, log (SHSLAUTER), and MULTFOOD

for poultry are statistically significant.

Overall, likelihood and marginal effects results suggest

that large plants and those plants with a high percentdeficient SPCPs will be more likely to be in noncompliance with necessary HACCP tasks. Conversely, small

plants with a low percent-deficient SPCPs will be less

likely to be in noncompliance with HACCP-like or

HACCP tasks. This makes sense, large plants are more

complex, on average, and likely have high transaction

costs that raise overhead costs (Williamson, 1985). So,

large plants may have a relatively greater cost of complying with quality standards than small plants.

However, large plants can spread the costs of new technologies, such as carcass cleaning technologies that kill

harmful pathogens, over more product volume, enabling

them to have lower technological costs. Thus, large

plants may be turning to new pathogen control technologies, as indicated by anecdotal evidence, because they

have a comparative advantage in the use of new mechanical technologies and small plants have a comparative

advantage in the performance of manual tasks. 

Conclusion

In chapter 3, we argued that regulation under the

PR/HACCP rule and that which existed under the WMA

and WPPA were related in the types of tasks performed

and oversight. In this chapter, we hypothesized that plant

performance of food safety tasks under the regulatory

regime associated with the WMA/WPPA should be correlated with performance under the PR/HACCP rule.

Results show a correlation of performance of SPCPs

with plant technology variables and the performance of

HACCP tasks.

The effect of technology variables, particularly the

positive effect of plant size, on percentage of HACCP

noncompliance records is not surprising. Williamson

(1985) asserts that, as plant size rises, so does plant

complexity, making effective management more costly.

Since plant management must drive quality control,

greater complexity positively affects percentage of

HACCP noncompliance records.

The positive effect of percent-deficient SPCPs on percentage of HACCP noncompliance records means, for

example, a plant with a high percentage of deficient

SPCPs would likely have a high percentage of HACCP

noncompliance records. Similarly, plants with a low percentage of deficient SPCPs would likely have a low percentage of HACCP noncompliance records. These

results support the argument that regulation under WMA

and WPPA is closely related to regulation under the

PR/HACCP rule.

Economic Research Service/USDA Managing for Safer Food / AER-817  51
52  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 6.1—Percentage of plants and their change in food safety performance under PR/HACCP and WMA

and WPPA in meat and poultry slaughter and processing

Plant type Change in performance: Absolute value of difference between

percentile of relative percent HACCP noncompliance records

and percentile of relative percent-deficient SPCPs

Change in relative performance rank1

0-10 10-20 20-30 30-40 40-50 50-100 Total plants

Percentage of plants

Meat slaughter 28.0 23.4 19.3 5.9 10.4 13.0 239

Meat processing 27.7 18.7 20.1 5.4 11.5 16.6 1,350

Poultry slaughter

and processing 39.2 16.5 19.6 5.1 9.3 10.3 97

1 Change in relative performance rank captures the change in performance ranking from SPCP ranking to HACCP ranking. The 0-10 means that

the rank under PR/HACCP is within 10 percentiles of plant rank of SPCPs; 10-20 mean rank under PR/HACCP is within 20 percentiles of SPCP

rank but more than 10 percentile; and other categories are similar.
Economic Research Service/USDA Managing for Safer Food / AER-817  53

Table 6.2—Effect of percent-deficient SPCPs on the percentage of HACCP noncompliance records in meat

and poultry slaughter

Cattle and hog slaughter Poultry slaughter and processing

Variable Likelihood Marginal Mean Likelihood Marginal Mean

effect effect effect effect

INTERCEPT -4.900 -0.003 -3.395*** -0.0003***

(3.454) (0.005) (1.151) (0.0001)

Plant technology 

joint likelihood χ2 (5) = 36** χ2 (5) = 38***

DEFICIENCY 0.250*** 0.005 1.00 0.099** 0.10*10-4** 1.00

(0.074) (0.010) (0.048) (0.47*10-5)

Log OUTPUT 0.451*** 0.200*** 16.83 0.221*** 0.22*10-4** 17.97

(0.168) (0.031) (0.066) (0.66*10-5)

Log PLANTAGE -0.010** -0.0002 2.81 0.009 0.87*10-6

(0.004) (0.0007) (0.074) (0.74*10-5) 2.632

PROCESSES -0.022 -0.0004 0.51 -0.127 0.13*10-5* 0.24

(0.393) (0.006) (0.154) (0.75*10-6)

Log (SHSLAUTER) 0.243 0.005 -0.96 0.156*** 0.15*10-4** 0.22

(0.247) (0.009) (0.054) (0.54*10-5)

Markets

joint likelihood χ2 (4) = 6.6 χ2 (3) = 1.0

BEEF -1.209 -0.023 0.67

(0.770) (6.158)

PORK -0.798 -0.015 0.65

(0.850) (0.030)

OTHER -0.998 -0.019 0.04

(1.806) (0.037)

CHICKEN 0.082 0.82*10-5 0.76

(0.242) (0.24*10-4)

TURKEY -0.056 -0.56*10-5 0.29

(0.267) (0.53*10-4)

GROUND -0.150 -0.003 0.69 -0.057 0.57*10-5 0.31

(0.309) (0.005) (0.167) (0.17*10-4)

Company

joint likelihood χ2 (2) = 2.8 χ2 (2) = 8.2**

MULTFOOD -0.708 -0.014 0.21 0.344** 0.34*10-4** 0.60

(0.625) (0.025) (0.178) (0.18*10-6)

MULTIND 0.253 0.005 0.13 -0.023 -0.23*10-5 0.44

(0.538) (0.012) (0.165) (0.16*10-4)

σ 108.6*** 0.002

Model likelihood χ2 (11) = 144*** χ2 (10)=70***

Observations 239 97

Notes: Numbers in parentheses are standard errors.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.
54  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 6.3—Effect of percent-deficient SPCPs on the percentage of HACCP noncompliance records 

in meat processing

Variable Likelihood Marginal Mean

effect effect

INTERCEPT -5.940*** -0.79*10-3

(0.787) (0.14*10-2)

Plant technology

joint likelihood χ2 (3) = 153.6***

DEFICIENCY 0.233*** 0.31*10-4 1.00

(0.030) (0.94*10-3)

Log OUTPUT 0.373*** 0.85*10-3*** 16.73

(0.045) (0.76*10-4)

Log PLANTAGE 0.008 0.10*10-5 2.79

(0.059) (0.10*10-4)

Markets

joint likelihood χ2 (5) = 6.8

FULCUKNSS 0.253*** 0.33*10-4 0.54

(0.097) (0.62*10-4)

HETTRETNSS -0.031 -0.41*10-5 0.19

(0.118) (0.60*10-3)

SECINHNSS -0.429 -0.56*10-4 0.05

(0.305) (0.11*10-3)

NOHETTRETSS 0.328 0.43*10-4 0.04

(0.231) (0.84*10-2)

HETTRETSS 0.082 -0.11*10-4 0.12

(0.178) (0.55*10-2)

Company

joint likelihood χ2 (2) = 5.8*

MULTFOOD 0.307** 0.41*10-4 0.16

(0.127) (0.76*10-4)

MULTIND -0.484 -0.64*10-4 0.01

(0.481) (0.14*10-3)

σ 3.06***

Model likelihood χ2 (10)=212***

Observations 1,350

Notes: Numbers in parentheses are standard errors.

*Significant at the 90% level; ** significant at the 95% level; *** significant at the 99% level.
Economic Research Service/USDA Managing for Safer Food / AER-817  55

To our knowledge, there have been four previous cost

estimates of PR/HACCP: the FSIS cost-benefit analysis published in the Federal Register prior to promulgation of PR/HACCP, Knutsen et al. (1995), and academic reports by Antle (2000) and Boland et al.

(2001). These estimates suggested that costs would

vary from less than 1 cent per pound (FSIS) to a

maximum of about 17 cents per pound for beef, and 5

cents per pound for chicken (Antle, 2000).1 Boland et

al. (2001) provide the only post-HACCP survey evidence of HACCP costs. However, their results are not

nationally representative since they include 50 small

plants in the Great Plains. The FSIS and Knutsen et

al. (1995) cost analyses are ex-ante analyses based on

estimated costs of the effort required to perform mandated tasks and ignore unexpected downtime and production costs for addressing food safety problems.

Antle’s (2000) estimate provides an upper bound

under which HACCP costs likely fall. His cost function approach accounts for food safety-related costs,

such as product condemnations and production downtime, but also includes the costs of producing products with nonfood safety quality attributes.

Ideally, there would be a lower bound cost estimate

of PR/HACCP regulation because that lower bound

combined with Antle’s (2000) upper bound estimate

would provide a window within which we would

expect the costs of PR/HACCP to fall. In this chapter, making several strong assumptions and using a

weighting factor to adjust our cost estimates to those

costs that exist under PR/HACCP, we provide that

lower bound cost estimate.

We use the cost estimates from chapter 4 to project the

cost of PR/HACCP regulation. Although these cost estimates pertain to the cost of SPCPs, the arguments in

chapter 3 and empirical linkage between HACCP tasks

and SPCPs in chapter 6 suggest that food safety regulation under WMA and WPPA is correlated with food

safety regulation under PR/HACCP. Thus, the implications and cost estimates derived in chapter 4 should be

related to the costs of regulation under PR/HACCP. It is

important to understand the nature of these costs

because PR/HACCP is the foundation of Federal food

safety process control and future regulations are likely

to be derived from the current PR/HACCP rules.

Previously Estimated Costs of SPCPs and

the PR/HACCP Rule

Most plants would perform some SPCPs and some

HACCP tasks in the absence of FSIS regulation because

the markets that they serve demand process controls. The

precise number and comprehensiveness of the tasks may

or may not exceed the number and detail required by

FSIS under the 1996 PR/HACCP rule. We illustrate

three industry-average, cost-per-pound levels for quality

control effort in figure 7.1: no regulation, mandatory sanitation and process control standards, and PR/HACCP

rule. The cost levels are arbitrarily drawn but do illustrate that mean regulated costs are likely to be higher

than the no-regulation case because regulation may

require some tasks that a plant may not otherwise perform. We assume that plants would continue to perform

tasks that they deem essential but are not required under

regulation. PR/HACCP costs are greater than SPCP costs

because sanitation and process control tasks are components of HACCP plans, which also include monitoring

critical control points.

Chapter 7

Projecting the Costs of HACCP from the

Costs of SPCPs

1 Antle (2000) provides several estimates depending on the level of

food safety when HACCP was promulgated. A lower level of food

safety at the time of HACCP enactment leads to higher cost estimates. He also assumes a level of improvement due to the regulation. Based on a previous study, he uses a 20-percent level. For his

upper bound estimate, he uses a 20-percent level of improvement

and a 50-percent level of food safety at the time of HACCP implementation. Cost estimates range from 2.3 to 17 cents per pound for

hog, cattle, and poultry slaughter. Lower bound HACCP cost estimates, based on a 90-percent level of safety at HACCP enactment

and a 20-percent level of improvement, vary from 0.3 to 1.9 cents

per pound for hog, cattle, and poultry slaughter. Antle also reports

average costs of about $1.15 per pound for cattle slaughter, $0.79

per pound for hogs (large plants), and $0.60 per pound for poultry

(large plants).
The stars in figure 7.1 represent individual plant costs

of process control effort per pound and are hypothetical points that are used only to illustrate that different

plants will choose to expend different levels of process

control effort. For example, plant A expends less effort

than the mean level of expenditures that would exist

without regulation, and plants A and B put forth less

effort than the mean level of expenditures put forth by

the industry under the WMA and WPPA, while plants

C and D expend more effort than the mean expenditure

level. Only plant D incurs greater process control

expenses than the mean level of expenditures under the

PR/HACCP rule. Thus, plants A and B would incur

regulatory costs to raise their performance to a level

compatible with SPCPs, and plants A, B, and C would

incur costs to meet the amount of effort required under

PR/HACCP. Plant D never incurs regulatory costs.

In table 4.3, we showed how average costs relative to

industry mean costs vary with differences in percentdeficient SPCPs. We did not and could not estimate the

costs of complying with sanitation and process control

standards because regulation exists for all plants, making an estimate of costs under no regulation impossible.

However, we can estimate the potential costs that a

plant with above the mean percent-deficient SPCP performance would incur to reach the sample mean level (a

benchmark used by FSIS regulators). We can also estimate the potential costs savings that a plant with below

the mean percent-deficient SPCP level would realize by

performing at the sample mean.

Table 7.1 (columns 5, 6, and 7) contains the estimated

costs of performing SPCPs at various performance levels relative to the sample mean, as outlined in chapter

4 and described in table 4.4. The first two columns in

the table show the industry and the associated mean

percent-deficient SPCPs, column 3 indicates the number of plants in the industry, and column four shows

mean plant costs (animal and meat, materials, and

labor costs) as published by the Census Bureau. We

express SPCP costs as costs relative to the mean percent-deficient SPCPs at various percent-deficient

SPCP levels.

Table 7.1 shows that plants with higher-than-average

percent-deficient SPCPs have lower costs in all industries except cattle slaughter and cured/cooked pork

products. On average, plants with twice the mean percent-deficient SPCPs (column 6, last row) would need

roughly $263,000 to improve to the sample mean of

percent-deficient SPCPs. Alternatively, plants above

the sample mean level of percent-deficient SPCPs

could reduce costs by performing only at the sample

mean level.2 For example, a plant at one-half the mean

level of percent-deficient SPCPs (column 5, last row)

could reduce effort to a level compatible with the

industry mean and lower its costs by about $240,000.

Note that only about 10 percent of all plants have more

than twice the mean percentage of deficiencies and

less than 2 percent of plants have more than four times

the mean percentage of deficiencies, suggesting that

few plants actually reduce process control effort to

these levels. 

Projecting the Cost of Performing at the

Mean HACCP Level

The costs of PR/HACCP can be projected from SPCP

costs since the nature of the tasks and enforcement

appear to be similar. Employees perform cleaning and

process control tasks and record information to comply

with SPCP standards and record information and

adjust process controls for critical control points under

PR/HACCP. Similarities also exist for enforcement.

Under both systems, inspectors observe an operation

being performed, a particular site, or a record to verify

56  Managing for Safer Food / AER-817 Economic Research Service/USDA

2 Chapter 5 showed that plants in the 90th percentile of percentdeficient SPCPs were more likely to exit the industry than other

plants, suggesting that plants would benefit little by reducing

process control effort above the 90th percentile. So, we assume

that the 90th percentile (twice the sample mean of deficiencies) is

the level at which no or minimal process controls exist.

Figure 7:1

Hypothetical mean cost per pound expended

for food safety process control under

PR/HACCP, SPCPs, and no regulation

HACCP

Sanitation and

process controls

No regulation

Mean cost ($/lb)

Plant D

Plant C

Plant B

Plant A
compliance with a required task. If a plant improperly

performs a task, the inspector marks the task as either

a deficient SPCP (pre-HACCP) or a HACCP noncompliance record (post-HACCP) and asks the plant to

address the failure. Moreover, under both systems, an

excessive number of serious noncompliances could

cause FSIS to temporarily shut down the plant by

refusing inspector services.

To accurately project the costs of HACCP based on

our SPCP cost estimates, we need comparable estimates of costs under the two regulatory regimes. These

costs need not be precise in absolute value but should

capture cost components associated with compliance.

If cost estimates are comprehensive, then a ratio of the

two estimates can be used to illustrate the relative

change in regulatory requirements. For example,

Ollinger and Cornejo (1999) estimated regulatory

stringency based on ratios of the estimated regulatory

costs announced in the Federal Register by the

Environmental Protection Agency (EPA) for one regulatory regime relative to the estimated costs under a

former regulatory regime. This ratio provided a gauge

of the relative change in costs of the two regimes. The

individual cost estimates from the EPA did not reflect

actual costs, but since the regulatory cost estimates did

capture all aspects of the regulatory change, the ratio

of cost estimates across different regulatory regimes

did measure relative stringency.

Working under contract for FSIS, the Research Triangle

Institute (RTI) assessed the regulatory costs of SPCP

performance standards in 1994. It suggested that costs

amounted to about $12,500 per plant. Our cost estimates

are sharply higher than this estimate because the RTI

estimate considered only the supervisory costs of maintaining SPCPs and dealing with the FSIS inspector,

while our estimates are based on the actual performance

of SPCPs and resulting outcomes. Poorly performing

plants may incur production shutdowns, meat condemnations, and other costs due to failure to perform SPCPs

but will also avoid the costs of performing and monitoring SPCPs. Highly performing plants will incur the

labor and supervisory costs of performing SPCPs and

the costs of voluntarily shutting down production when

there is a threat to product safety.

Under another contract with FSIS, Anderson et al.

(1994) of RTI estimated the costs of HACCP regulation for nine plants. As with the SPCP study, costs

included all supervisory costs, the cost of dealing with

FSIS inspectors, and recordkeeping tasks (a prime

component of HACCP plans) but did not include the

costs of corrective actions, carcass condemnations, or

plant shutdowns for corrective actions.

The ratio of cost estimates of the two RTI studies

should indicate the relative change in stringency of the

costs of PR/HACCP relative to the costs of SPCP standards. This ratio shows HACCP costs to be about 90

percent higher than the costs plants incur for SPCPs.

Table 7.2 (columns 4, 5, 6, and 7) contains the estimated relative changes in HACCP costs at various levels

of percentage of HACCP noncompliance records.

Table 7.2 shows that PR/HACCP sharply increases

food safety process control costs relative to SPCPs.

Plants that have two times the industry mean percentage of HACCP noncompliance records realize about

$500,000 in lower costs—about 2.6 percent of its

costs—than they would if they performed at the industry mean level. Alternatively, plants with one-half the

mean percentage of HACCP noncompliance records

incur about $450,000 more in costs than plants at the

sample mean. 

Cost Estimates of Complying With the

PR/HACCP Rule

It is possible to estimate the costs of HACCP compliance based on our estimate of SPCP costs and the RTI

estimates. However, we must make several strong

assumptions. First, we assume that plants at two times

the mean level of percent-deficient SPCPs (table 7.1)

completely ignore sanitation and process control

requirements to the point that product quality is noticeably affected and likewise ignore PR/HACCP requirements. This assumption appears to be plausible

because plants with about twice the sample mean percent-deficient SPCPs (about the 90-percentile level of

performance) increase their chances of failing to survive in their industry (see chapter 5). Second, we

assume that RTI cost projections accurately reflect the

relative change in regulatory compliance costs across

regulatory regimes. Third, we assume that there are no

private incentives to increase product quality during

the transition from SPCPs to HACCP. This assumption

means that all new costs incurred under PR/HACCP

would not have taken place otherwise and appears to

be conservative because it seems unlikely that consumer demand for greater safety has not changed given

increased media coverage of food safety and a number

of foodborne illness outbreaks since 1992. Finally, we

assume that plants with below the industry-mean level

Economic Research Service/USDA Managing for Safer Food / AER-817  57
of percent-deficient SPCPs would reduce their regulatory costs by redeploying resources such that they performed at the industry’s mean percentage of HACCP

noncompliance records after a regulatory change. 

A plant would incur costs of about $500,000 to

improve its performance to the mean of the percentage

of HACCP noncompliance records if it performed

HACCP tasks at twice the industry mean level of percentage of HACCP noncompliance records (table 7.2).

This is about the same amount that a plant with a

fourth the mean percent-deficient SPCPs could save by

redeploying assets in order to comply with HACCP

regulation. This means that all plants with more than

the mean percent-deficient SPCPs (about a fourth of

all plants) would incur an average cost of regulation of

about $500,000. Additionally, plants with from a

fourth the mean percent-deficient SPCPs to the mean

percent-deficient SPCPs (about a third of all plants)

would incur somewhere between zero and $500,000 in

costs. Plants with less than a fourth the mean percentdeficient SPCPs would have no cost of compliance

under PR/HACCP because they already make expenditures compatible with the requirements under

PR/HACCP. The average cost per plant works out to

about $208,300—about 1.1 percent of all costs—or,

based on Antle’s (2000) costs of production, about 1.2

cents per pound for beef, 0.7 cent per pound for pork,

and 0.4 cent per pound for poultry. These costs are in

line with Antle’s (2000) average estimated cost of

compliance of 1.39 cents per pound on a weightedaverage basis and the Boland et al. (2001) estimate of

about 0.9 cent per pound for small meat plants.3

Estimated costs for processed meat and processed

poultry are about 1.6 cents per pound.

For the plant, regulatory costs are a much higher share

of nonmeat and poultry costs because meat inputs

account for anywhere from 79.7 percent of total costs

for cattle slaughter to about 50 percent of total costs

for sausage products. Thus, the costs of PR/HACCP

relative to all nonmeat and poultry costs varies from

about 5.5 percent of all costs for cattle slaughter to

about 2.2 percent of all costs for sausages.

Summary

Chapters 4, 5, and 6 show that an increase in percentdeficient SPCPs results in a decline in plant costs, leads

to a greater likelihood of plant’s exiting the industry, and

is positively correlated with a relatively high percentage

of HACCP noncompliance records. In this chapter, we

used cost function results from chapter 4 to estimate the

costs of HACCP. We found that the costs of maintaining

a HACCP system is about 1.1 percent of costs, which

amounts to costs ranging from about 0.4 cent per pound

for poultry to 1.2 cents per pound for beef, or about 0.9

cent per pound on average for meat or poultry. This estimate is much higher than that proposed by FSIS in the

Federal Register announcement, which placed the cost at

about 0.12 cent per pound. However, the estimate is

quite close to the weighted-average cost estimated by

Antle (2000) of about 1.39 cents per pound and almost

identical to the Boland et al. (2001) survey costs of about

0.9 cent per pound.4

The 1.1-percent increase in costs is much more substantial to the plant operator than it may appear. Meat and

poultry plants have little control over the price of animals and inputs of raw meat and poultry; but these costs

constitute anywhere from 50-80 percent of input costs.

As a result, the costs of PR/HACCP amount to anywhere

from about 2.2 to 5.5 percent of controllable costs.

The increased costs of PR/HACCP relative to the sanitation and process control standards under the WMA

and WPPA give plants a stronger incentive to reduce

compliance unless buyers increased their food safety

demands. In the absence of increased buyer demand

for greater food safety, FSIS regulators would have to

increase enforcement stringency to maintain the same

compliance level.5

58  Managing for Safer Food / AER-817 Economic Research Service/USDA

3 Antle (2000) reports average meat prices varying from $1.15 per

pound for beef to $0.60 per pound for poultry. Recall that the

SPCP cost of HACCP compliance is biased downward because

there is not a perfect linkage between SPCPs and food safety,

whereas Antle's (2000) estimate is biased upward due to quality

aspects unrelated to food safety. The weighted-average-cost estimate for Antle (2000) comes from Boland et al. (2001).

4 Cost estimates of HACCP reported in each publication can be

criticized, but their consistency suggest that HACCP costs may

approach about 1 cent per pound. The SPCP-based estimate provided here is biased because it measures effort rather than quality;

Antle (2000) estimates total product quality and then controls for

some observable quality attributes so the accuracy of the quality

variable depends on the correlation of the instrument with safety;

and Borland et al. (2001) estimate plant labor and materials costs

but do not consider downtime.

5 It appears likely that heightened media coverage of food safety

and a number of foodborne illness outbreaks since 1992 have

increased demand for food safety over the past 10 years.
The higher cost estimate provided here for PR/HACCP

than estimated by FSIS does not suggest that PR/

HACCP is not cost effective. Depending on the anticipated effectiveness of the regulation and methodology

used, the ERS estimate of the benefits of PR/HACCP

ranged from $1.9 billion - $171.8 billion annually.

Even the lowest benefits estimate, which assumes 20-

percent effectiveness and the most conservative benefits methodology, generates about twice as much in

health cost savings as industry incurs in cost increases.

We did not address the issue of whether HACCP regulation favors large plants over small plants in this

chapter. However, recall that we did examine this

question in chapter 4 dealing with the costs of SPCPs.

In that chapter, we found that large plants did not have

lower costs of performing SPCPs than small plants.

Since the two regulations are related, it appears likely

that, in the long run, PR/HACCP does not favor large

plants. In the short run, a different assessment may be

reached because HACCP costs include many more

fixed costs than the SPCP-based system. Large plants

can more readily spread these fixed costs over their

larger production volumes.

Economic Research Service/USDA Managing for Safer Food / AER-817  59
60  Managing for Safer Food / AER-817 Economic Research Service/USDA

Table 7.1—Estimated costs of various percent-deficient SPCPs relative to the mean level of 

percent-deficient SPCPs1

Industry Percent Plants Plant Costs of SPCP tasks relative Average cost of

deficiency2 costs3 to the costs of a plant performing SPCP

at the mean level of percent- tasks relative to 

deficient SPCPs industry mean level

1⁄2*Mean 2*Mean 3*Mean

Percent Number $ million ————————$1,000————————

Cattle slaughter 3.70 230 32.3 89 -101 -261 -66.5

Hog slaughter 9.16 307 32.3 -957 1,041 1,810 597.4

Poultry slaughter 8.33 155 32.5 -424 548 1,083 327.5

Cured/ cooked pork 5.53 117 12.5 257 -273 -460 -155.2

Sausage 4.25 257 12.5 -79 82 137 46.5

Processed meat

from animals 2.17 288 12.5 -27 28 47 15.9

Processed meat 2.00 546 12.5 -205 227 400 90.8

from raw meat

Processed poultry 3.95 129 12.5 -254 247 390 137.8

Average—all industries 5.00 19.3 -237 263 510 156.2

1 Negative numbers mean that costs are below the costs for plants at the industry mean level of percent-deficient SPCPs.

2 Percent-deficiency is the industry average plant-level number of unperformed or poorly performed SPCPs divided by the total 

number of SPCPs.

3 Mean plant cost is mean values published by Census of Manufacturers for all meat slaughter, meat processing, and poultry 

slaughter and processing plants. It is defined as value added plus meat and material input costs.

Table 7.2—Projected HACCP costs based on estimated costs of various percent-deficient SPCP levels 

relative to the industry mean levels of percent-deficient SPCPs1

Industry Average HACCP Costs of HACCP tasks relative Average cost of

total plant non-compliance to the costs of a plant at the performing HACCP

costs2 records3 mean level of percent HACCP tasks relative to

noncompliance records 4 industry mean level 

1⁄2*Mean 2*Mean 3*Mean

$ million Percent ———————$1,000———————

Cattle slaughter 32.3 2.38 170 -193 -496 -126

Hog slaughter 32.3 1.90 -1,827 1,977 3,439 1,135

Poultry slaughter 32.5 5.45 -810 1,047 2,058 622

Cured/cooked pork 12.5 1.23 491 -521 -874 -294

Sausage 12.5 1.39 -151 157 260 88

Processed meat

from animals 12.5 1.40 -52 53 89 30

Processed meat

from raw meat 12.5 1.40 -392 434 760 172

Processed poultry 12.5 5.45 -485 472 741 262

Average—all industries 19.3 2.13 -450 500 969 297

1 Negative numbers mean that costs are below the costs for plants at the industry mean level of percentage of HACCP 

noncompliance records tasks.

2 Mean plant cost is mean values published by Census of Manufacturers for all meat and poultry slaughter and processing 

plants. It is defined as value added plus meat and material input costs.

3 Percentage of HACCP noncompliance records is the average of the number of HACCP in noncompliance divided by the 

number of scheduled and performed tasks.

4 Research Triangle Institute estimated the costs of compliance with HACCP tasks is about 1.90 times higher than the costs 

of complying with SPCP tasks. We multiplied this level of change in stringency times the estimated costs of SPCPs (table 7.1) 

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List of Tables

Text tables

2.1 Total and Partial Quality Control programs as shares of plants, 1992 . . . . . . . . . . . . . . . . . . . . . . . 10

3.1 Percent critically deficient SPCPs for selected industries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2 Percent critically deficient SPCPs for selected sizes of meat and poultry plants . . . . . . . . . . . . . . . 16

4.1 Cost function variable definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Hypothesis tests for meat and poultry slaughter and processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.3 First-order and selected second-order parameter estimates from the best cost function model 

in the slaughter and processing industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.4 Average cost index for selected percent-deficient SPCP levels relative to sample mean 

percent-deficient SPCP level using industry mean values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.5 Estimates of the elasticity of costs with respect to percent-deficient SPCPs at sample 

mean percent-deficient SPCPs for selected plant sizes in various slaughter and 

processing industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.1 Percentage a slaughter plant exits by plant size and percentile of percent-deficient 

SPCPs, 1992-96. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.2 Effect of percent-deficient SPCPs on slaughter plant exits, 1992-96 . . . . . . . . . . . . . . . . . . . . . . . . 45

5.3 Effect of percent-deficient SPCPs on processing plant exits, 1992-96. . . . . . . . . . . . . . . . . . . . . . . 46

6.1 Percentage of plants and their change in food safety performance under PR/HACCP 

and WMA and WPPA in meat and poultry slaughter and processing . . . . . . . . . . . . . . . . . . . . . . 52

6.2 Effect of percent-deficient SPCPs on the percentage of HACCP noncompliance 

records in meat and poultry slaughter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

6.3 Effect of percent-deficient SPCPs on the percentage of HACCP noncompliance 

records in meat processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

7.1 Estimated costs of various percent-deficient SPCPs relative to the mean level of 

percent-deficient SPCPs.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7.2 Projected HACCP costs based on estimated costs of at various percent-deficient SPCP 

levels relative to the industry mean levels of percent-deficient SPCPs.. . . . . . . . . . . . . . . . . . . . . 60

Appendix table

4.A.1 Cattle slaughter cost function parameter estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.A.2 Hog slaughter cost function parameter estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.A.3 Chicken and turkey slaughter cost function parameter estimates . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.A.4 Translog cost function parameter estimates of producers of processed meat products

from live animals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.A.5 Translog cost function parameter estimates of producers of processed meat products

from packed meat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.A.6 Translog cost function parameter estimates of producers of cured/cooked pork products. . . . . . 34

4.A.7 Translog cost function parameter estimates of producers of sausage products . . . . . . . . . . . . . . 35

4.A.8 Translog cost function parameter estimates of processed poultry producers . . . . . . . . . . . . . . . . 35

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