Abstract

The study examines supermarket retailer behavior in the selling and buying of Washington

apples, California oranges, California grapes, and Florida grapefruits. The study finds that

retailer prices respond more rapidly to shipping-point price increases than to decreases; and

that retailer prices are fixed relative to the variations that occur at the shipper level. The

study also suggests that retailers set (consumer) prices in excess of the perfectly

competitive level for all four commodities. Retailers' ability to hold shipper prices below

the competitive level was not consistent. For two commodities —Washington apples and

Florida grapefruits—retailers did pay shippers prices below the competitive level.

Keywords: Fresh fruits and vegetables, fresh produce, fresh produce marketing channels,

supermarket, market power, competition, trading practices.

Contractor

and Cooperator

Report No. 2

September 2003

Grocery Retailer Behavior in

the Procurement and Sale of

Perishable Fresh Produce

Commodities

Richard Sexton, Mingxia Zhang, and

James Chalfant*

• Sexton and Chalfant, University of California, Davis. Zhang, the California

Independent Operator.

This report was prepared under a research contract from the

Economic Research Service. The views expressed are those of

the authors and not necessarily those of ERS or USDA.
ii Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Table of Contents

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

Prior Research on Food Retailer Market Power...............................................................................2

Data Sources and Data Issues.......................................................................................................... 5

How Market Power Affects Producers and Consumers .................................................................. 7

Analysis of Farm-Retail Price Spreads...........................................................................................11

CA-AZ Iceberg Lettuce.....................................................................................................13

Vine-Ripe Tomatoes..........................................................................................................17

Mature-Green Tomatoes....................................................................................................20

Summary of Margin Analysis ...........................................................................................25

Analysis of Retailer Oligopsony Power in Fresh Produce Procurement........................................29

CA-AZ Iceberg Lettuce.....................................................................................................31

California Vine-Ripe Tomatoes ........................................................................................34

California Mature-Green Tomatoes35

Florida Mature-Green Tomatoes .......................................................................................37

Summary of Oligopsony Analysis.....................................................................................40

Retailer Pricing to Consumers: Constant or Stabilized Prices........................................................40

Estimates of the Impact of Constant Prices on Producer Welfare.........................43

Retailer Pricing to Consumers: Markups of Price Over Costs ..............................44

Store-Level Demand Functions .........................................................................................48

CA-AZ Iceberg Lettuce .....................................................................................................49

Vine-Ripe Tomatoes..........................................................................................................52

Mature-Green Tomatoes....................................................................................................53

Summary of Analysis of Pricing Behavior to Consumers.................................................54

Retailer Pricing Behavior for Packaged Salads .................................................................55

Conclusions........................................................................................................................64

References..........................................................................................................................66
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 iii

List of Tables

Table 1: Summary of FOB and Retail Prices by Commodity ..................................................... 5

Table 2: Farm-Retail Price Spread Equations for CA-AZ Iceberg Lettuce................................ 11

Table 3: Farm-Retail Price Spread Equations for California Vine-Ripe Tomatoes ................... 14

Table 4: Farm-Retail Price Spread Equations for California and Florida

Mature-Green Tomatoes............................................................................................................. 17

Table 5: Means and Variances for Farm-Retail Price Spreads................................................... 18

Table 6: Correlation Coefficients for Farm-Retail Price Spreads in

SelectedCities ............................................................................................................................. 19

Table 7: Elasticities of the Margin with Respect to Shipment Volume...................................... 20

Table 8: Estimation Results for CA-AZ Iceberg Lettuce ........................................................... 22

Table 9: Estimation Results for California Vine-Ripe Tomatoes............................................... 24

Table 10: Estimation Results for California Mature-Green Tomatoes....................................... 25

Table 11: Estimation Results for Florida Mature-Green Tomatoes............................................ 26

Table 12: Simulation Results for Analysis of Constant/Stabilized Retail Prices ....................... 30

Table 13: Implied Oligopoly Power for CA-AZ Iceberg Lettuce............................................... 34

Table 14: Implied Oligopoly Power for Vine-Ripe Tomatoes ................................................... 36

Table 15: Implied Oligopoly Power for Mature-Green Tomatoes ............................................. 37

Table 16: Retailer Mean Price and Variance of IBB Packaged Salads ...................................... 38

Table 17: Price Correlations for IBB Salads, Iceberg Head Lettuce,

and FOB Price ............................................................................................................................ 41

Table 18: Price Equations for Fresh Express Bagged Salads ..................................................... 44
iv Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

List of Figures

Figure 1: The Short-Run Produce Pricing Model............................................................................ 6

Figure 2: Produce Market Equilibrium Under Retailer Oligopoly/Oligopsony

Power................................................................................................................................................7

Figure 3: CA-AZ Iceberg Lettuce: Farm-Retail Price Spreads for Selected

Retail Chains ..................................................................................................................................10

Figure 4: Vine-Ripe Tomatoes: Farm-Retail Price Spreads for Los Angeles

Chains.............................................................................................................................................12

Figure 5: Vine-Ripe Tomatoes: Farm-Retail Price Spreads for Selected

Retail Chains ..................................................................................................................................13

Figure 6: California Mature-Green Tomatoes: Farm-Retail Price Spreads

for Dallas ........................................................................................................................................15

Figure 7: Florida Mature-Green Tomatoes: Farm-Retail Price Spreads ........................................16

Figure 8: CA-AZ Iceberg Lettuce FOB Price and Harvest Cost: 1998-1999.................................22

Figure 9: Estimates of the Pricing Regime for CA-AZ Iceberg Lettuce:

1998-1999.......................................................................................................................................23

Figure 10: California Vine-Ripe Tomato FOB Price and Harvest Cost:

1998-1999.......................................................................................................................................23

Figure 11: California Mature-Green Tomato FOB Price and Harvest Cost:

1998-1999.......................................................................................................................................24

Figure 12: Estimates of the Pricing Regime for California Mature-Green

Tomatoes: 1999 ..............................................................................................................................25

Figure 13: FOB Price, Price Floor, and Per-Unit Harvest Costs for Florida

Mature-Green Tomatoes: 1998-1999 .............................................................................................26

Figure 14: Estimates of the Pricing Regime for Florida Mature-Green

Tomatoes: 1998-1999.....................................................................................................................27

Figure 15: Impact of Constant Selling Price on Producer Welfare ................................................28

Figure 16: Inferring the Implied Exercise of Oligopoly Power......................................................32

Figure 17: Alternative Retailer Pricing Strategies for Fresh-Cut Salads........................................39
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 1

Introduction

This study is part of an investigation by the U. S.

Department of Agriculture, Economic Research

Service (ERS) into competition and pricing

practices in the fresh fruit and vegetable (produce)

industries. We focus on iceberg lettuce shipped

from California and Arizona (CA-AZ), maturegreen and vine-ripe tomatoes shipped from

California and Florida, and lettuce-based fresh

salads. The companion report by Richards and

Patterson (RP) analyzes similar issues for

Washington apples, California fresh grapes,

California fresh oranges, and Florida fresh

grapefruit.

A key factor motivating the investigation is the

wave of mergers in food retailing that have led to

increasing concentration in the sector. 1 A concern

is that this concentration may manifest itself both

in terms of retailer oligopoly power, in selling to

consumers, and oligopsony power, in buying from

commodity shippers and food manufacturers. The

concerns about oligopsony power are magnified in

the produce sector, because the selling side of these

markets is in most cases unconcentrated, relative to

the buying side. In addition, most produce

commodities are highly perishable, meaning that

supply at any point in time is very unresponsive

(inelastic) to price (Sexton and Zhang (SZ), 1996).

The disparity between numbers of sellers and

buyers and the need to move product to avoid

losses from spoilage limits shippers’ bargaining

power in dealings with retailers.

Understanding retailer market power is critical to

the assessment of various emerging practices in the

produce sector, such as retailers’ requests that

shippers pay slotting fees or provide various

services (see Calvin et al., 2001). If retailers

possess little or no market power, then fee and

service requests must have an efficiency

motivation, and are not an appropriate focus of

policy concern. If market power exists, fees and

services may be a symptom of that market power,

but the appropriate policy remedies do not

necessarily focus on mitigating use of fees and

services. Rather, the focus of policy should be on

the market power itself. If retailers possess

oligopsony power in dealings with growershippers, banning the use of particular fees and/or

services would most likely simply cause the market

power to be manifested in other dimensions, such

 

1 See Kaufman (2000) and Kaufman et al. (2000) for recent summaries

of mergers and acquisition activity in U.S. grocery retailing.

as lower acquisition prices, and at the cost of

reduced efficiency.

Our investigation of retailers’ behavior in the

procurement and sale of iceberg lettuce, maturegreen and vine-ripe tomatoes, and lettuce-based

packaged salads focuses on 20 retail grocery chains

in six U. S. metropolitan markets over the two-year

period from January 1998 through December 1999.

The market areas include Albany, NY (two chains),

Atlanta (three chains), Chicago (three chains),

Dallas (five chains), Los Angeles (four chains), and

Miami (three chains). In several instances, the

same retail chain was studied in multiple cities. By

agreement with the data vendor, we are unable to

reveal the chain names. Thus, retail chains are

identified by their city and by number, e. g.,

Chicago 1, Chicago 2, Miami 1, etc.

The methods utilized in this study differ in several

respects from those employed in the RP companion

study due in part to differences in types of

commodities analyzed in each study. Whereas

fresh lettuce and tomatoes are highly perishable,

the fruits investigated by RP can generally be

stored for some months with proper refrigeration.

Thus, the supply of the highly perishable

commodities can be treated as perfectly inelastic

for all prices in excess of harvest costs, but the

quantity supplied of RP’s semi-perishable fruits

will depend upon current period price, given the

opportunity to move product into and out of

storage.

RP chose to work with an integrated econometric

model that enables them to simultaneously estimate

the degree of oligopsony (buyer) market power and

oligopoly (seller) market power exercised by

retailers. RP’s model also allows the data to reveal

discrete shifts in the pricing regimes, based upon a

theory that retailers’ behavior in some periods may

be characterized by collusive pricing and, in other

periods, by more competitive pricing intended to

“punish” deviations from the collusive agreement.

The methods utilized in this study are somewhat

simpler, due, in part, to the perishable nature of the

commodities investigated here. We look separately

at retailers’ behavior as buyers from growershippers and as sellers to consumers for the

aforementioned commodities. To examine possible

oligopsony power, we utilize the switching

regression model developed by Sexton and Zhang

(SZ, 1996) to investigate pricing for perishable

commodities. The analysis of retailers’ behavior as

sellers relies upon the observation that any seller’s

markup of a commodity’s price over its marginal
2 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

cost for acquisition and selling is determined by the

elasticity of demand the seller faces for the

commodity and a parameter to indicate the extent

to which the retailer is exercising the market power

indicated by the demand curve. We are able to

observe selling price and most aspects of selling

and acquisition cost, and can estimate the price

elasticities of demand from the data. This

information can be used to infer the underlying

pricing behavior.

Throughout the analysis, the focus is on the

implications of retailers’ behavior for price and

economic welfare of producers. The impact of

retailer behavior on consumer welfare is also an

interesting and important question. However, this

question must be analyzed in the context of a broad

cross section of items in the store, not merely for a

few produce items.

Prior Research on Food Retailer

Market Power

Rising concentration and consolidation of sales

among large supermarket chains in the United

States have made retailer market power in the food

industry a topical issue. At a conceptual level, there

should be broad agreement that two basic factors

give grocery retailers some degree of market

power, in the sense of being able to influence

prices. First, as several authors have noted, the

spatial dimension of retail food markets is

important, because consumers are distributed

geographically and incur nontrivial transaction

costs in traveling to and from stores. 2

 This

condition leads to a spatial distribution of grocery

stores, and gives a typical store a modicum of

market power over those consumers located in

close proximity to the store and, hence, the ability

to influence prices at least somewhat. 3 Second,

retailers have the ability to differentiate themselves

through the services they emphasize, advertising,

and other marketing strategies. The question, thus,

is not whether retailers have the ability to influence

price, but, rather, the extent and implications of

that influence.

 

2

For discussions of food retailing from a spatial economics

perspective, see Faminow and Benson (1985), Benson and Faminow

(1985), Walden (1990), and Azzam (1999).

3

Market power due to location is inevitable when consumers are

distributed geographically and incur nontrivial transportation costs.

Even when large numbers of sellers exist in a market, any one seller

competes actively with only its nearest rival(s). In the absence of

barriers to their doing so, retailers will enter a geographic market until

economic profits are driven to zero. Prices will exceed marginal costs

on average, however, based upon the fixed costs of entry.

Oligopoly power in food retailing is not amenable

to the application of some methods used by

economists to examine market power questions,

because modern groceries sell a vast number of

different products – an average of 30,000 or more

items for U. S. supermarkets. The structureconduct-performance (SCP) approach is useful,

however, because prices can be observed readily

and aggregated into indices. 4 These studies seek to

explain grocery prices as a function of demand,

cost, and market structure variables. Studies such

as Hall, Schmitz, and Cothern (1979), Lamm

(1981), Newmark (1990), Marion, Heimforth, and

Bailey (1993), and Binkley and Connor (1998)

have examined average retail food price

relationships, using cities as the unit of

observation.

Marion et al. (1979), Cotterill (1986), Kaufman and

Handy (1989), Cotterill and Harper (1995), and

Cotterill (1999) focused upon the behavior of

individual stores, giving them the opportunity for

increased precision and relevance in construction of

explanatory variables relative to earlier studies.

Cotterill (1986) studied food retailer monopoly

power in Vermont, a sparsely populated state, which

provided an almost ideal setting to delineate relevant

geographic markets for identifying concentration.

Concentration variables (four-firm and one-firm

concentration rates and the Herfindahl index) were

positively associated with price and were statistically

significant. 5

 A parallel study of Arkansas

supermarkets by Cotterill and Harper (1995) and

Cotterill (1999) reached similar conclusions as to the

impacts of retailer concentration on food prices. 6

MacDonald (2000) argues that observed pricing

patterns at retail for food items with a strong seasonal

component are consistent with models of oligopoly

rivalry among retailers.

However, not all studies of grocery retailing have

found a positive association between concentration

and price. Kaufman and Handy (1989) studied 616

supermarkets chosen from 28 cities selected at

random. Both firm market share and a four-firm

Herfindahl index were negatively but insignificantly

 

4

The structure-conduct-performance approach is an empirical

methodology based upon a loose conceptual framework which posits

that conduct and, in turn, performance in an industry are determined

by structural conditions in the industry, such as degree of

concentration, entry barriers, and extent of product differentiation.

5 Four-firm concentration ratio is the share of market sales made by the

four largest sellers, one-firm concentration ratio is the share for the

market leader, and the Herfindahl index is the sum of the squares of

market shares for all sellers in the market.

6

Studies conducted at the city level finding a positive structure-price

relationship include Hall, Schmitz, and Cothern (1979), Lamm (1981),

and Marion, Heimforth, and Bailey (1993).
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 3

correlated with price. Newmark (1990) also obtained

a negative and insignificant coefficient on four-firm

concentration in a study of the price of a market

basket of goods for 27 cities. Binkley and Connor

(1998) suggest one explanation for the conflicting

results in terms of the product coverage in the price

variable. They found a positive and significant

concentration-price correlation for dry groceries, but

a negative and insignificant correlation for fresh and

chilled food items.

Cotterill's (1993) part 5 contains a debate on the

issue of market power in grocery retailing, and

Connor (1999) and Wright (2001) provide recent

critiques of research into the concentration-price

relationship in grocery retailing. To the extent that

the positive correlation between pricing and

concentration found in the majority of studies is a

robust conclusion, it lends credence to the

aforementioned concerns that the recent wave of

grocery mergers is apt to cause adverse price effects

on producers and/or consumers.

Other investigations into food retailer pricing have

focused on the transmission of prices from the farm

to retail for commodities. This research has

emphasized two primary issues: the “stickiness” of

retail prices relative to farm prices, and potential

asymmetries in the transmission of price from farm

to retail. Of particular concern is the allegation that

retail prices tend to respond more quickly and fully

to farm price increases than to farm price

decreases. To the extent that such behavior occurs,

it is harmful to producer interests. If the FOB price

decreases due to a large harvest, but the decrease is

not transmitted to consumers, the additional sales

needed to absorb the increased production are not

achieved, exacerbating the decrease in the FOB

price.

The empirical evidence on asymmetry in price

transmission is mixed. Kinnucan and Forker (1987)

for dairy products, Pick, Karrenbrock, and Carman

(1990) for citrus, and Zhang, Fletcher, and Carley

(1995) for peanuts found evidence that retail prices

and margins were more responsive to farm price

increases than decreases. More recently, Powers

and Powers (2001) found no asymmetry in the

magnitude or frequency of price increases, relative

to price decreases, for CA-AZ lettuce, based on a

sample of 40 grocers for 317 weekly observations

from 1986-92.

The implications for competitiveness of food

retailing from the research on rigidity of retail

prices and asymmetry of transmission of farm-level

price changes are not clear. Rotemberg and Saloner

(1987) have shown that sellers with market power

are more likely to maintain stable prices in

response to changing costs than are competitive

firms. The incentives are reversed for price

changes due to demand shifts, but Rotemberg and

Saloner showed that the cost effect dominates,

when both cost and demand are subject to

fluctuations. 7 Re-pricing or menu costs also

contribute to explaining retail price rigidities.

Changing prices is costly for retailers, so a

product’s price will be fixed unless its marginal

cost or demand changes by a sufficient amount to

justify incurring the cost of re-pricing. Carlton

(1989) summarizes research on this topic, and

Azzam (1999) presents an application to food

retailing.

Moreover, from a marketing strategy perspective, a

plausible pricing strategy in grocery retailing is to

stabilize prices to consumers by absorbing shocks

in farm-level and wholesale prices for certain

frequently-purchased, staple commodities. For

example, 6 of the 20 retailers in our sample did not

change the chain’s price for iceberg lettuce over

the entire sample period of 104 weekly

observations. As we demonstrate subsequently, this

type of pricing behavior by retailers is probably

harmful to grower/shippers, but viewed in

isolation, it can hardly be construed as evidence of

market power, as opposed to simply representing a

marketing strategy by the retailer to attract and

retain customers.

Asymmetry of price transmission, wherein farm

price increases are passed on to consumers more

quickly than farm price decreases, is less readily

explained. In a standard model of monopoly or

oligopoly pricing, the optimal price change in

response to a given increase or decrease in

marginal costs may not be symmetric, and depends

upon the convexity/concavity of consumer demand

(Azzam, 1999). This consideration, however, does

not explain a delay in responding to a price

decrease, relative to a price increase.

To date, very little research has been conducted on

the topic of food retailers’ oligopsony power as

buyers from food shippers and manufacturers. To

an important extent, the issue has surfaced only

recently, in response to concerns over slotting and

related fees charged by retailers. The issue is quite

difficult to address because prices paid by retailers

 

7

The fundamental intuition is that as the extent of competition

increases, individual sellers perceive an increasingly elastic demand.

This makes price changes more beneficial because some of the

benefits are derived at the expense of competitors.
4 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

to shippers or manufacturers are typically not

revealed. Retailers’ selling costs are also generally

confidential and, moreover, almost impossible to

apportion to individual products, given the

multitude of products sold in the store. Produce

commodities provide one of the better

opportunities to examine retailer buying power

because farm-level prices are typically reported, as

are shipping costs to major consuming centers, and

sales are often direct from grower-shippers to

retailers. SZ (1996) examined pricing for CA-AZ

iceberg lettuce from January 1988 to October 1992

and concluded that retailers were successful in

capturing most of the market surplus generated for

that period, essentially consigning growershippers’ economic profits to near zero over the

time period analyzed.

Data Sources and Data Issues

The source of all retailer data used in this study is

Information Resources International (IRI). IRI

provided detailed weekly data on a wide selection

of produce commodities and packaged salads for

the 2-year period from January 1998 through

December 1999, 104 observations in total, for 20

retail chains in 6 U. S. cities. 8 These cities were

selected strategically (not randomly) to obtain

broad geographic coverage and size diversity. The

data are organized by universal product

classification (UPC) code or price lookup (PLU)

code. For each retailer and each product code, IRI

provided weekly sales volume, listed selling price,

and the number of stores within the chain selling

the product in the given metropolitan area. Note

that the reported selling prices are the same for all

of a chain’s, that stores in a given city. Weekly

farm-level data on production and FOB price,

weekly terminal (wholesale) price data for major

terminal markets, and transportation costs from

producing areas to the six consuming regions in the

study were provided by the USDA Federal-State

Market News Service (F-SMNS). 9

We encountered some fundamental problems in

working with the IRI data. The fresh tomato and

lettuce categories feature PLU codes, rather than

the more standardized UPC codes. The PLU is

 

8

As noted, in many cases, a particular retail chain is represented in

multiple cities. For example, Chain J in City X may have the same

corporate ownership as Chain K in City Y. We are precluded from

making this type of connection when discussing results, because to

reveal the cities where a particular corporate chain is operating, in

many cases would result in revealing the chain’s identity.

9

We were not able to obtain shipping cost information for all of the

metropolitan areas. When data were unavailable, we substituted data

for a nearby city.

typically a four-digit code that is punched into the

register as the product is checked at retail. PLU

codes were originally not standardized among

retailers, but a standardization program has been

pursued through the auspices of the Product

Electronic Identification Board. However, usage

was not fully standardized during the time period

investigated here. The problem was particularly

acute for tomatoes, where reliable industry sources

indicated, for example some retailers classified

vine-ripe tomatoes in the code (4064) reserved

supposedly for mature-green tomatoes, instead of

the normal (3151) vine-ripe code.

A further problem for both the iceberg lettuce and

fresh tomato data is that retailers may assign

multiple PLU codes for essentially the same

product, based on differences in point of origin,

size, or variety, but there is no standardization

among retailers as to this practice. Through

conversations with personnel in the industries and

with are the data vendor, and through careful

analysis of the data, we were able to resolve many

of the issues concerning the PLU codes.

Ultimately, however, we cannot have the degree of

confidence working with the PLU codes as with

standardized UPC codes.

A second concern in working with the IRI data was

unexplained, large shifts from period to period in

sales volume for several chains. Investigation

revealed various possible explanations. In cases

where multiple PLU codes were being used for the

same basic commodity, sales may vary extensively

for particular codes based simply upon how the

product is classified. Proper aggregation across

PLU codes addresses this issue. A low reported

sales volume might be due to “stock outs” in a

particular code for some time periods. IRI

generates unit sales by dividing sales revenue by

per-unit price. Thus, any errors in reporting the

per-unit price due, for example, to failure to reflect

discounts to consumers who purchase with

membership cards, or failure to update computers

to reflect sale prices, will cause variations in sales

revenue that will be attributed incorrectly to

variations in volume.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 5

Ultimately, if we were not confident in the

reliability of the data for a commodity in a given

chain, that commodity-chain was omitted from the

analysis. Thus, for each commodity the analysis is

based on considerably fewer than the original 20

retail chains for which IRI provided data.

Table 1 provides summary information on FOB

and retail prices. Notice that the average variance

in the retail prices among the chains in our sample

is larger than the variance in the FOB price for

each commodity. The relative variability, measured

in terms of the coefficient of variation, is, however,

always greater for the FOB price. 10 Conventional

wisdom, as noted, is that retail prices are less

variable. Three observations worth making,

however. First, retail prices sometimes may vary

because the product is being heavily promoted by

retailers, and used as a loss leader. The variance is

not necessarily the right measure of stability when

the time series of retail prices is very stable with a

few observations considerably lower. Second,

although we have tried to omit instances where the

wrong PLU code was used, we may still

occasionally be overestimating the variance of

retail prices, due to incorrect definition of the

product. Third, the tendency of retailers to follow a

variety of strategies calls into question the

conventional wisdom concerning relative stability

of retail prices. The prices at the FOB and retail

levels compared in many of the studies already

noted are averages across many firms; relative

variances in averages are not necessarily

informative about the same comparison when

individual retailers are used. As already noted, the

price series from some of our retailers exhibit no

variation at all, and the remaining retailers thus

have average variances somewhat higher than the

combined averages reported in the table. Thus, it is

not clear how closely individual retailer data for

 

10The coefficient of variation is the sample standard deviation divided

by the sample mean.

perishable commodities should comport with the

conventional wisdom.

How Market Power Affects

Producers and Consumers

SZ (1995, 1996) developed a model of price

determination for perishable produce commodities

that allows for imperfect competition. When a

commodity is perishable, it must be sold in the

current market period. Thus, total supply is fixed (i.

e. , perfectly inelastic) for all prices in excess of the

per-unit cost of harvesting. This scenario is

depicted in Figure 1, for per-unit harvest cost C0

.

Aggregate demand for the commodity is depicted

as PT

 = DT(Ht); final-product value, PT, is a

decreasing function of the total harvest volume, Ht

.

The function PF

 = DF(Ht) is final demand less perunit shipping and handling costs. It seems

reasonable to treat these per-unit costs as constant

with respect to total volume shipped; hence the

shift from DT

 to DF is parallel. Under conditions of

perfect competition in procurement, DF

 represents

the farm-level demand for the commodity. DF

intersects C0 at the harvest volume H*. Under any

form of competition, the farm price, PF

, must fall to

the level of per-unit harvest costs for crops of

magnitude H*

 or greater. Thus, C0 represents a floor

below which the farm price will not fall. For all

harvests when Ht

 < H*, a per-unit “surplus” exists,

St

 = PF – C0, which is inversely related to the size of

the harvest.

Under perfect competition, the surplus is captured

entirely by the grower-shippers as owners of the

asset in fixed supply, namely the available harvest.

Thus, for example, harvest H1

 generates per-unit

surplus F PC – C0

, which is captured entirely by

growers through market price F PC , where the C

subscript denotes the competitive outcome. Under

imperfect competition in procurement, the surplus

will be divided between grower-shippers and

purchasers (e. g. , retailers and food service

Table 1—Summary of FOB and Retail Prices by Commodity

FOB Price Retail Price1

Commodity Mean Variance Mean Variance

CA iceberg

Lettuce (per head)

$0.2820 0.1297 $1.1322 0.1313

CA vine-ripe

tomatoes (per lb.)

 0.3065 0.0745 1.5818 0.3353

CA mature-green

tomatoes (per lb.)

 0.2597 0.1196 1.8311 0.3516

FL mature-green

tomatoes (per lb.)

 0.4261 0.1597 1.6223 0.3775

1

The values reported in the table are the averages across the chains.
6 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

buyers), based on relative bargaining power. The

less aggressive the competition among buyers to

procure the product, the greater the share of the

surplus they will capture, resulting in a lower farmlevel price.

Note from Figure 1 that the magnitude of per-unit

surplus is decreasing in the size of the harvest.

Thus, under an arrangement where buyers captured

a constant percentage of the market surplus, the

farm-retail price spread would be a decreasing

function of the harvest. SZ (1995, 1996), however,

hypothesized that grower-shippers’ share of the

available surplus would also be a decreasing

function of the harvest, because a large harvest of a

perishable commodity should diminish sellers’

bargaining power relative to buyers’. In that case,

the farm-retail price spread could increase with

increases in harvest, if grower-shippers’ share of

the surplus fell sufficiently. SZ quantified their

hypothesis by expressing farmers’ share, (t

, of the

market surplus through the function Ht

t e−α γ = 0

[0,1], so that farm price is given by the function

under imperfect competition, farm price given

harvest H1

 is F PM , and the market surplus is shared,

with growers capturing F 0 P C M − and buyers

capturing F F P P C M − .

Because supply of the commodity in any period is

inelastic at prices in excess of the per-unit harvest

cost, oligopsony power in the short run affects only

the distribution of the surplus between buyers and

sellers. Any increase in the degree of oligopsony

power will depress returns to growing the

commodity, and, in the long run, this result will

cause some resources to exit the industry. Thus,

oligopsony power will have the long-run effect of

reducing supply, which will cause higher retail

prices and a reduction in consumer welfare.

Consider now the specific behavior of food

retailers in procuring and selling produce

commodities. Denote retail prices and quantities

with the superscript ‘R’. Then aggregate inverse

retail demand is PR

 = DR(QR), where QR is the

volume of the product sold at retail. Denote

demand for all other uses of the commodity as QS

 =

DS

(PS). 11 We assume that these users (e. g.,

processors and institutions) procure and sell the

commodity competitively. 12 Then we can define

a residual supply function, QR

(P), for the

commodity to food retailers as the total harvest less

the secondary-market demand:

(1)

R S

t t t t0

R

t

Q (P ) H D (P ) if P C

Q (P ) 0 otherwise.

= − ≥

=

Effective oligopoly power is exercised when a

retailer marks up the commodity in excess of its

full marginal costs of acquisition and sale, i. e.,

farm price, shipping cost, and selling cost. By

raising price above marginal cost, a retailer reduces

sales of the commodity in its stores. Because the

total supply is fixed in any given market period, the

result of oligopoly power in selling the commodity

at retail is to force the diversion of a greater share

of the volume into secondary and lower-value

market outlets, such as the food service, institution,

and processing sectors.

Similarly, oligopsony power is exercised when

retailers, as buyers, recognize their ability to

influence the acquisition price for the commodity.

Under oligopsony power retailers pay less to

acquire the product than its marginal value at retail

less marginal shipping and selling costs and,

thereby, capture a share of the aforementioned

market surplus.

 

11Based upon conversations with industry sources, we estimate that 60-

65 percent of lettuce sales are to retail and 35-40 percent to the food

service sector. For fresh tomatoes, the approximate percentages are 45-

50 percent to retail and 50-55 percent to food service.

12This assumption is not central to the analysis but seems quite

reasonable. None of these buyers are large relative to the market, and

sales to secondary purchasers are often made through various terminal

markets, long regarded as quintessential competitive markets.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 7

Figure 2 illustrates an oligopoly-oligopsony

equilibrium. Technical details on the model

illustrated in Figure 2 are provided in Alston,

Sexton, and Zhang (1997) and Sexton and Zhang

(2001). The retail industry marginal revenue curve

is MRR

 = ∂ [PRQR]/ ∂ QR, and the curve labeled

PMRR

 = ξ MRR + (1-ξ )PR represents the

“perceived marginal revenue” curve for the food

retailing industry (Melnick and Shalit, 1985), with

the parameter ξ ∈[0,1] indicating the extent of

oligopoly power exercised by the industry. For

example, if the retailing industry sells the

commodity competitively, then PMRR

 = PR and ξ

= 0, or if the retailing industry acts as a pure

monopolist or cartel, then PMRR

 = MRR, i. e., ξ =

1. Within the interval (0,1), higher values of ξ

represent greater levels of retailer oligopoly power.

PMRR

 = >MRR + (1-ξ )PR thus represents the gross

value of an incremental unit of the farm

commodity to the industry. To obtain the net value,

we subtract per-unit shipping and handling costs,

CR

, as illustrated in Figure 2 by the curve PMRR –

CR

.

If retailers procured the commodity competitively,

then grower-shippers would be paid a price based

on the net PMRR

 schedule: PF(QR) = PMRR(QR) -

CR

, depending upon the volume of product

marketed at retail. If retailers exercise oligopsony

power in procurement, the actual farm price will be

less than indicated by the PMRR

 – CR function. The

function PF

(QR) in figure 2 denotes the inverse form

of the residual supply function derived in (1).

Applying Melnick and Shalit’s logic to the market

for acquisition of the farm commodity, we define

the industry’s marginal cost of procuring the

commodity as MCF

= ∂ [PFQR]/ ∂ QR and the

perceived marginal acquisition cost function as

PMCF

 = θ MCF + (1-θ )PF. Similar to the

interpretation afforded the oligopoly power

parameter,ξ , the parameter θ denotes the degree

of oligopsony power and ranges in the unit interval,

with θ = 0 denoting perfect competition in

procurement, i. e. , PMCF

 = PF, and θ = 1 denoting

pure monopoly or a perfect buyer cartel, i. e. ,

PMCF

 = MCF. Within the interval (0,1), larger

values of θ denote greater levels of oligopsony

power.

If the retailing industry both procured and sold the

produce commodity competitively, the market

equilibrium would occur at point A in figure 2,

with volume of sales R QC and producer price F PC .

The difference between the harvest volume, H, and

the volume, R QC , sold at retail is diverted to

processing and foodservice uses. When retailers

exercise both oligopoly and oligopsony power in

procurement and sale of the commodity to the

degree illustrated by the PMR and PMC curves in

figure 2, the market equilibrium occurs at point B

and involves sales of R QM and producer price F PM .

As noted, market power exercised by retailers will

cause relatively more of the total production, R QC -

R QM , to move through processing and food service

outlets.

It is important to emphasize that either oligopoly or

oligopsony power reduces the welfare of

producers.13 Producer welfare is an increasing

function of the volume of product moved through

retail channels, and either type of market power

reduces this movement and diverts more product

into alternative outlets.

 

13It also reduces the welfare of consumers, considering the particular

commodity in isolation. From consumers’ perspective, reduced

movement of product at retail results in higher volumes moved

through alternative market channels. Nonetheless, consumer welfare is

diminished, because oligopoly and/or oligopsony in one of the market

channels results in a failure to equate consumers’ marginal valuation

of the product across the alternative market channels, a necessary

condition for consumer welfare maximization. However, as noted,

examining only a single or a few commodities is an inappropriate basis

to evaluate the effects of retailer behavior on consumers.
8 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Analyses of Farm-Retail Price

Spreads

In this section, we report on some statistical

analyses of the behavior of the farm-retail price

spread (or margin) for the commodities under

investigation. Under any theory of pricing from

farm to retail, variations in the margin over time

will be due, at least partly, to changes in the

marginal costs of transporting the product from

shipping points to the retail location and selling it.

Indeed, under conditions of perfect competition,

variations in the margin should be explained fully

by variations in the seller’s marginal cost.

However, under a more general model of pricing

from farm to retail, additional factors may help to

explain the margin.

Of particular interest is the effect of shipments and

sales volumes on the margin, given the hypothesis

that, under oligopsony power, large shipments

reduce producers’ relative bargaining power and,

hence, share of the market surplus. Conversely,

under perfect competition in the procurement of a

produce commodity, the volume of shipments

would have an effect on the margin only to the

extent that marginal costs of marketing and selling

the commodity were related to the volume shipped

and sold. The margin would be an increasing

(decreasing) function of shipments if industry

marginal costs were an increasing (decreasing)

function of shipments. Although we lack hard

evidence on this point, it is not likely that industry

marginal costs would rise with the volume sold.

Most produce commodities are shipped by

refrigerated truck, and no single industry is a major

user of refrigerated shipping, to the point where its

actions would affect shipping rates. A similar

conclusion applies to retailing costs. A retailer’s

handling costs likely rise with the volume handled,

but the increase would, at most, be proportional to

the increase in volume handled. Thus, based upon

consideration of marketing costs, we would expect,

under conditions of competition, that the total

volume of shipments would have no effect on the

margin, or, possibly, an inverse effect, if greater

movement brought about handling economies for

retailers.

Under the SZ hypothesis discussed previously,

producers’ relative bargaining power is an inverse

function of the magnitude of the harvest. This

hypothesis presents the possibility that the margin

will be an increasing function of the harvest. This

outcome will result only if the impact of the larger

harvest on sellers’ relative bargaining power

dominates its negative effect on the magnitude of

per-unit surplus that is available. Thus, a finding

that the farm-retail price spread is increasing as a

function of the harvest volume represents rather

strong evidence that buyers are exercising market

power in procurement of that commodity.14

We estimated the following models of the farmretail price spread for the chains in the sample:

(2) M P P H S T i,t

1

i,t

R

t

F

i 0 i 1 t i 2 i,t i 3 i,t i,t, ( ) 

(2'), M P P P C b b H b S

bT e

i,t

2

i,t

R

t

F

i,t

R 0

i 0 i 1 t i 2 i,t

i 3 i,t i,t,

 

 

( )/( ) 

where j Mi,t , j=1,2, is measured in $/unit (heads of

lettuce and lbs. of tomatoes), Ht

 is the volume in

ten million lbs. of the farm commodity shipped

during week t, Si,t is the cost per truckload ($000)

of shipping the product from the producing

location to chain i’s city in week t, Ti,t is a time

trend, and ε i,t and ei,t are error terms to capture

unexplained variation in 1 Mi,t and 2 Mi,t ,

respectively.15 The time trend is included to

capture secular changes in the margin, due to

changes over the sample period in retailer selling

costs or the extent of market power.16

Equation (2) is the traditional price-spread

formulation, which expresses the absolute mark- up

of the farm price as a function of total harvest

volume, shipping costs, and trend. We

experimented with alternative specifications for the

farm production variable, including decomposing

Ht

 by points of origin (e. g. , California vs. nonCalifornia production) to determine whether the

 

14 RP set forth a related hypothesis in their work on this project.

They argue that retailers’ bargaining power is inversely related

to the amount of the commodity that the retailers need to

procure in a given time period. For example, if a large

percentage of retailers is promoting a particular commodity

and, thus, anticipating higher sales, the retailers’ relative

bargaining power is diminished and, thus, the margin may be a

declining function of the total volume of sales of the product.

As RP note, this hypothesis makes more sense in the context

of the semi-perishable commodities they examine, where

higher prices can stimulate movement of product from storage,

than for the highly perishable products analyzed here.

15 The USDA F-SMNS reports daily FOB prices for lettuce and fresh

tomatoes. Prices are typically reported in a high-low range. The FOB

price, F Pt , utilized for purposes of this study is the weekly average of

the midpoint of the daily price range.

16 We conducted tests for unit roots in the three price series, FOB,

wholesale, and retail, and for the price-spread series utilized in the

study. Unit roots were rejected in all cases, implying that the series are

stationary and justifying analysis in the levels of the data.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 9

source of production, as opposed simply to the

overall level of production, had an effect on the

margin.17

Equation (2’) expresses the markup as the retailers’

share of the total market surplus. Equation (2’)

directly embodies the Zhang-Sexton hypothesis

regarding the effect of harvests on growers’

relative bargaining power. For parsimony of

presentation, only the results from estimating

equation (2) are presented in detail. Detailed results

from estimation of (2’) do not differ appreciably

and are available from the authors.

Equations (2) and (2’) were estimated for each of

the basic commodities in the study (iceberg lettuce,

mature-green tomatoes, and vine-ripe tomatoes)

using seemingly unrelated regressions (SUR). 18

Note that equations (2) and (2’) are not subject to

simultaneity bias, because Ht

 is determined by

planting decisions made months previously.

Notably we lack data on retailers’ selling costs.

This omission introduces a specification error only

if retailers’ costs were nonconstant over the twoyear period analyzed in this study. Given the low

rate of overall inflation during this time and the

short time period, retailers’ selling costs likely

were very stable. The omission of retailing costs

causes the estimated coefficients to be biased only

if retailing costs are correlated with the explanatory

variables included in the model, a prospect that we

consider unlikely, especially given that shipments

in (2) and (2’) are measured in aggregate, not at the

chain level.

CA-AZ Iceberg Lettuce

Figure 3 illustrates the farm-retail price spread for

iceberg lettuce for a sample of the retail chains

included in the study. The margin is expressed as

dollars per head and plotted by week, for the 104

weeks in the sample. Panels (a) and (b) exhibit

margins for a chain in Miami and Dallas,

respectively. These figures illustrate the volatility

in the margin that is typical. By way of contrast,

panel (c) illustrates the margin for a Los Angeles

chain. The price spread in this case is stable for

 

17For example, a plausible hypothesis to test when examining the price

spread for a California commodity is whether the spread is increasing

in the magnitude of production emanating from outside of California.

This outcome could occur if retailers used the prospect of obtaining

the product elsewhere to force price concessions from the California

shippers. However, in most cases we could not reject the hypothesis

that the effect of shipments volume on the margin was the same

regardless of the source of the shipment.

18 Using SUR, the store-level margin equations for each commodity are

estimated as a system of equations. SUR gains efficiency, relative to

single-equation estimation, by taking into account correlation in the

error terms across equations.

most periods, but does experience occasional, wide

fluctuations. Finally, panel (d) illustrates the price

spread for a chain that maintained a constant retail

price throughout the study period. Because the

FOB price varied widely through this time period,

a constant selling price at retail results in a price

spread that exhibits periodic wide fluctuations.

The results of estimating margin equation (2) for

CA-AZ iceberg lettuce are contained in table 2. Six

of the 20 chains in the sample maintained a

constant retail price throughout the sample period.

These chains were excluded from the lettuce

margin analysis.19 Two other chains were excluded

because we had serious questions about the

reliability of the data.20

The coefficient on the total volume of iceberg

lettuce shipments is positive for 11 of the 12 cases

and statistically significant at the 90% level in

seven of those cases.21 The only negative coefficient

is not statistically significant. These results support

the notion that the farm-retail price spread

increases with the volume of shipments, and are

thus consistent with the Sexton-Zhang hypothesis

that large shipments of a perishable commodity

diminish sellers’ relative bargaining power.22

 

19 Given that PR

 is a constant, estimating the margin for these stores is

equivalent simply to estimating an equation to determine the level of

the farm price. The implications for producer welfare from retailers

maintaining constant or stabilized retail prices are investigated later in

the paper.

20 Some of the equations included in the system for iceberg lettuce

exhibit autocorrelation, when single-equation results are examined.

The same is true for results we report subsequently for vine-ripe and

mature green tomatoes. We obtained a separate set of results for every

equation in the system, correcting for autocorrelation on a singleequation basis. The results are comparable to what we report;

particularly, the relationships between the margins and the harvested

volume are unaffected. The other results are somewhat weaker, and

the signs are not consistent across all equations, as is the case with the

results we do report. A test for contemporaneous correlation of the

residuals does support the SUR system estimator. That evidence,

combined with the fact that no qualitative conclusions depend on the

choice of estimator, led us to prefer reporting the systems results.

Given occasional missing observations for which we would have to

correct, there seemed to be no payoff from attempting to incorporate

autocorrelation corrections into our SUR results. The single-equation

results corrected for autocorrelation are available upon request from

the authors.

21 Similarly, the shipment variable in (2’), the margin-share equation

was positive and significant for each of the 12 stores, meaning that the

retailer’s share of the market surplus was an increasing function of the

shipments volume in each instance.

22 A plausible alternative specification of the margin is as the difference

between a retail price at time t and the farm price in the previous week,

period t-1, i.e., R F M PP i,t i,t t 1 = − − . This specification allows a one-week lag

in transmission of farm prices to retail. Re-estimating equation (1) for

iceberg lettuce with this definition of Mi,t weakens, but does not

eliminate, the impact of shipments volume on the margin. Eight of 12

coefficients were positive under this specification, with five of them

being statistically significant. Only one of the four negative

coefficients was statistically significant.
10 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 11

Increases in shipping costs are associated with an

increase in the margin in eight of the 12 cities,

although the effect is significant in only five of the

cases. None of the four instances where the margin

was negatively related to shipping costs was

significant. Higher shipping costs should lead to

higher margins under either competition or buyer

market power. However, whereas increases in

shipping costs are reflected fully in the margin

under perfect competition, buyers with oligopsony

power rationally absorb a portion of this type of

cost. Thus, the general failure of shipping costs to

have a significant effect on the margin may reflect

retailers’ absorption of a large portion of those

costs. In addition, shipping costs comprise a rather

small part of total acquisition and selling costs for

lettuce. Retailers who prefer, as part of an overall

marketing strategy, to offer relatively stable prices

to their customers may elect simply to not pass on

most changes in shipping costs.

Finally, the regressions do not indicate any

consistent underlying trend across chains in the

margin over the short, 2-year period of the data set.

Seven of the 12 trend coefficients are negative, and

5 of them are statistically significant. Three of the

five positive trend coefficients are statistically

significant.

Vine-Ripe Tomatoes

Equations (2) and (2’) were estimated for vine-ripe

tomatoes for 9 of the 20 chains contained in the

sample. In all cases, the FOB price is for

California, the primary source of domestically

supplied vine ripes. Chains were excluded when

we were unable to discern definitively the PLU

codes pertaining to sales of vine-ripe tomatoes.

USDA F-SMNS began reporting FOB prices for

California vine-ripe tomatoes in 1999. This factor

plus seasonality in shipments resulted in only 26

weekly observations being available to estimate the

vine-ripe tomato margin equation. F-SMNS does

not separate tomato shipments by vine-ripe and

mature-green categories. Thus, the shipments

variable, Ht

, is total domestic shipments of fresh

tomatoes, as reported by F-SMNS.

Figure 4 illustrates the price spread for vine-ripe

tomatoes for three Los Angeles retail chains, and

figure 5 provides the same information for selected

chains from other cities. Figure 4 illustrates how

pricing strategies for produce commodities can

vary among chains even within the same city.

Chains 2 and 3 in Los Angeles maintained stable

but high margins through the summer and most of

the fall in 1999, but then each experienced a sharp

 Table 2—Farm-Retail Price-Spread Equations for CA-AZ Iceberg Lettuce

City/Chain Constant1 Total volume1 Shipping cost1 Trend1

Atlanta 1 0.082

(0.460)

 0.0765

(3.453)*

 0.0394

(1.109)

 0.0021

(3.329)*

Chicago 1 0.352

(2.611)*

 0.0503

(3.042)*

 0.0546

(1.860)*

 0.0013

(2.817)*

Albany 1 0.822

(4.869)*

 0.0240

(1.191)

-0.0357

(1.468)

 -0.0011

(1.976)*

Dallas 1 0.807

(3.986)*

-0.0013

(0.052)

 0.0652

(1.121)

 0.0005

(0.758)

Dallas 2 0.446

(3.959)*

 0.0172

(1.240)

 0.0541

(1.757)*

-0.0009

(2.199)*

Dallas 3 0.3831

(2.025)*

 0.0299

(1.282)

 0.1989

(3.826)*

 -0.0031

(4.671)*

Miami 1 0.1003

(0.545)

 0.0854

(3.791)*

0.0528

(1.642)

 0.0012

(1.899)*

Miami 2 0.5588

(5.190)*

 0.0082

(0.615)

 0.0477

(2.253)*

 -0.0005

(1.371)

Los Angeles 1 0.6817

(3.993)*

 0.0355

(1.725)*

 -0.106

(0.457)

-0.0019

(2.229)*

Los Angeles 2 0.2135

(1.539)

 0.0619

(3.702)*

-0.130

(0.674)

 0.0007

(0.969)

Los Angeles 3 0.2680

(1.953)*

 0.0815

(4.926)*

 -0.265

(1.375)

 -0.0005

(0.668)

Los Angeles 4 0.1125

(0.690)

 0.0470

(2.386)*

0.634

(2.625)*

-0.0019

(2.254)*

Absolute t-statistics are in parentheses.

*Denotes statistical significance at the 90% level.
12 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

drop in the margin. Chain 4 conversely exhibited a

slowly rising margin throughout the time period

and did not join the sharp decline in margins

initiated by its competitor chains. In figure 5, note

that Dallas chain 2 has a rather stable margin and

one that is much smaller than exhibited by any of

the three LA chains illustrated in figure 4.

Conversely, the margin is larger absolutely and

more volatile for chain 2 in Atlanta and chain 2 in

Miami. Even in these cases, however, the margins

are smaller than for the LA chains, despite the

likelihood that costs are lower for the LA chains,

given their proximity to California production.

Table 3 summarizes the estimation results. The

volume of shipments has a positive effect on the

margin in seven of the nine cases, although the

effect is statistically significant in only four of

those cases. 23 Neither of the two negative

coefficients is significant at the 90 percent level.

Similar to the case for iceberg lettuce, shipping

costs have no consistent effect on the margin – the

effect is positive in four instances and negative in

the other five. Except for two cases, the effect is

not statistically significant. Similarly, there is no

 

23 Shipments volume had a positive and statistically significant effect

on the retailers’ share of the market surplus (equation (2’)) in each

case.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 13

 overall pattern to the trend over the short time

series at hand. The trend coefficient is positive in

five cases and negative in the other four. The effect

is statistically significant in five of the nine total

instances.

Mature-Green Tomatoes

Mature-green tomatoes are produced domestically

in both California and Florida. Florida tomatoes

compete seasonally with imports from Mexico.

Florida tomatoes are shipped predominantly to

eastern and midwestern markets, with the western

half of the country served predominantly by

tomatoes from California and Mexico. The

California marketing season runs from May to

December and complements the Florida season,

which runs from October through June. See the

ERS report by Calvin et al. (2001) for additional

description of the industry.

We estimated the price-spread equation separately

for shipments emanating from California and

Florida. Satisfactory data were available to

estimate the model for 11 chains for California

tomatoes and 3 chains (all in the southeast) for

Florida tomatoes. Fifty-two weekly observations

were available for California, and 69 observations

were available for Florida. Figures 6 and 7

illustrate the farm-retail price spread for maturegreen tomatoes for selected chains. Figure 6

presents a comparison of the behavior of the price

spread for California mature greens for three Dallas

chains, while figure 7 presents the price spreads for

Florida mature greens. Figure 6 further illustrates

the differences in produce-pricing practices even
14 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

within a city. Chain 2 exhibits a rather stable price

spread of about $0. 70/lb, except for two brief

spikes in the spread. Chain 3’s margin is higher on

average (about $1. 10/lb) and much more volatile.

Chain 4’s margin is also quite stable, but it

averages $1. 58/lb, more than double the margin

for chain 2. Conversely, the pricing behavior for

the chains selling Florida mature greens is quite

comparable. The mean spread ranges from $1.

03/lb (Miami 1) to $1. 40/lb (Atlanta 1), and the

degree of volatility in the spread is quite similar

across all three chains.

Table 4 reports the results from estimation of the

margin equation. Mature greens provide an

interesting comparison, because Florida growershippers are organized, whereas their California

counterparts are not. In particular, Florida tomatoes

are marketed through the auspices of a Federal

marketing order, and most grower-shippers belong

to a marketing cooperative, which handles over 90

percent of fresh tomatoes sold in Florida and

whose activities are coordinated with the marketing

order. An example of the industry’s coordination in

marketing was its attempt to enforce a price floor

during the 1998 and 1999 marketing seasons, in

conjunction with voluntary export restrictions

implemented by Mexico, as part of an agreement to

suspend the U. S. Commerce Department’s

investigation into dumping allegations lodged by

Florida against Mexican tomato exporters.

The volume of shipments increases the price spread

for California mature greens in all 11 cities. The

effect is statistically significant in seven of those

cases. Conversely, the volum of shipments has no

consistent impact on the price spread for Florida

mature greens.24 The coefficient is negative in two

of the three cases, and the one positive coefficient

is small and not statistically significant. Although it

is hard to state a definitive conclusion based on

these limited results, the evidence does suggest that

perhaps the Florida grower-shippers’ organization

and coordination in marketing affords them a

degree of protection against retailers’ efforts to bid

farm prices down during periods of relatively high

supply.25

As in the previous cases, shipping costs have little

consistent effect on the margin. The direction of

the effect is (paradoxically) negative in 11 of the

14 total cases, but is significant in only four of

those cases. Again, failure of shipping costs to play

an important role in determining the margin may

reflect rational absorption of a portion of any

changes in shipping costs by retailers with market

power and/or retailers’ wish to stabilize consumer

prices for staple produce commodities.

 

24The retailers’ share of the market surplus from equation (2’) was an

increasing and statistically significant function of the shipment volume

in each instance for California mature greens. The shipments

coefficient was positive in all cases for Florida mature greens as well,

but the effect was statistically significant in only one instance.

25Specifying the margin with a one period lag (see footnotes 22 and 24)

had little effect on the Florida results. The effect of shipments on the

California margin continued to be positive in 10 of the 11 cases, but

the effect was significant statistically in only three of those cases.

Table 3—Farm-Retail Price-Spread Equations for California Vine-Ripe Tomatoes1

City/Chain Constant Total volume Shipping Cost Trend

Atlanta 2 -0.828

(0.494)

 0.0583

(1.844)*

0.0978

(0.450)

 0.0157

(1.333)

Chicago 1 1.759

(3.429)*

 0.0066

(0.694)

-0.0797

(0.983)

-0.0035

(1.047)

Albany 1 -1.746

(1.168)

-0.0257

(1.166)

0.3296

(1.795)*

 0.0188

(2.049)*

Dallas 2 -0.220

(1.036)

 0.0238

(4.104)*

-0.0322

(0.783)

 0.0071

(4.657)*

Dallas 4 2.498

(2.958)*

-0.0381

(1.684)

-0.0377

(0.225)

-0.0054

(0.904)

Miami 2 -2.308

(2.308)*

 0.0214

(1.067)

 0.3299

(2.603)*

 0.0225

(3.173)*

Los Angeles 2 4.884

(3.341)*

 0.0275

(0.687)

-0.894

(0.820)

-0.0363

(4.141)*

Los Angeles 3 1.316

(0.441)

0.2813

(3.378)*

 0.1034

(0.047)

-0.0252

(1.377)

Los Angeles 4 0.071

(0.124)

0.0384

(2.390)*

-0.416

(1.024)

 0.0188

(5.313)*

1

Absolute t-statistics are in parentheses.

*Denotes statistical significance at the 90% level.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 15

The trend coefficient, interestingly, was negative in

all 11 cases for California mature greens and was

significant in six of those cases. The trend in the

margin was negative and significant in one of the

three cases for Florida mature greens. The

coefficient was positive but not significant in the

other two cases. A possible explanation for the

trend in California is the increasing consolidation

among California tomato grower-shippers. Calvin

et al. estimated that the top four shippers controlled

43 percent of the market in 1999 and the top eight

controlled 70 percent. As the larger shippers

increase their market share, their relative

bargaining power may also increase.

Summary of Margin Analysis

Tables 5, 6, and 7 summarize the main results of

the analysis of farm-retail price spreads. Table 5

contains the means and variances for the price

spreads analyzed in this section. Table 6 is a

correlation matrix of the price spreads for selected

cities, while table 7 reports the elasticity of the

price spread with respect to the total volume of

shipments.

Tables 5 and 6 combine to demonstrate the

considerable independence that retailers have in

setting prices for produce commodities. Because

the farm price for a given commodity is assumed to

be identical across retailers, differences in the price

spread among retailers are due to differences in

setting prices at retail. Table 5 shows that, even
16 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

 within a city, there is considerable variation in the

markup for a commodity. For example, the Dallas

1 retail price for a head of CA-AZ iceberg lettuce

reflected on average about a $0. 95 markup,

whereas Dallas 2 had an average $0. 64 markup.

The markups for California mature-green tomatoes

among the Dallas chains ranged from $0. 68 in

Dallas 2 to $1. 58 in Dallas 4. The differences in

markups are somewhat less among the Los Angeles

chains. Iceberg lettuce average markups ranged

only from $0. 67 to $0. 80, while mature green

markups ranged from $1. 01 to $1. 50 per lb. There

is, however, a wide difference, $1. 35 vs. $2. 29, in

how the Los Angeles 2 and Los Angeles 3 chains

marked up a pound of vine-ripe tomatoes.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 17

The variances of the price spread in parentheses in

table 5 also present an interesting story. A chain

that applied a constant markup to a commodity and

merely passed along changes in its acquisition

costs would exhibit a low variance in the margin.

Chains that varied prices strategically, e. g. , for

sales, would exhibit a higher variance. Variances in

the price spreads for iceberg lettuce were quite

similar, ranging from 0. 015 for Dallas 2 to 0. 051

for Dallas 3. The price spreads for fresh tomatoes

were more variable on balance, and differences in

price-spread variances were more pronounced

among the chains. For example, Chicago 1 had a

variance of only 0. 006 in its price spread for

California vine-ripe tomatoes, whereas variance in

the vine ripe price spread for Los Angeles 3 was 0.

704.

The correlation coefficients in table 6 measure the

extent to which the price spreads move together

over time. Such coefficients range from –1. 0

(perfect negative correlation) to 1. 0 (perfect

positive correlation). Correlation coefficients for

chains within a particular city are highlighted in

boldface type. In a prototypical competitive

market, we would expect to see a high positive

correlation among the price spreads, because all

chains face similar shocks in the costs of procuring

and marketing a commodity. The reality, however,

is that the price spreads are not highly correlated,

even within a city. For iceberg lettuce, the

correlations for the three Dallas chains range from

0. 14 to 0. 26. Correlation in lettuce price spreads is

somewhat higher for the Los Angeles chains,

ranging from 0. 31 to 0. 68. There tends to be even

less co-movement among the price spreads for

fresh tomatoes. The two Dallas chains exhibit a

negative correlation (-0. 32) in their spreads for

California vine ripes, and, similarly, two of the

three coefficients are negative for the three Los

Angeles chains analyzed for vine ripes. Negative

correlations are also present for both Dallas and

Los Angeles chains for mature-green tomatoes.

Table 4—Farm-Retail Price-Spread Equations for California and Florida

 Mature-Green Tomatoes

City Constant1 Total volume1 Shipping Cost1 Trend1

--California Tomatoes--

Atlanta 1 1.821

(5.959)*

0.0367

(1.650)

-0.171

(2.273)*

-0.0068

(4.621)*

Atlanta 2 1.474

(3.737)*

0.0701

(2.603)*

-0.181

(1.773)*

-0.0015

(0.827)

Dallas 1 1.076

(7.180)*

0.0430

(4.143)*

-0.0354

(0.675)

-0.0029

(4.269)*

Dallas 2 0.644

(4.670)*

 0.0395

(4.589)*

-0.1593

(3.058)*

-0.0005

(0.958)

Dallas 3 1.544

(6.053)*

 0.0299

(1.910)*

-0.0030

(0.030)

-0.0047

(4.578)*

Dallas 4 1.471

(3.404)*

0.0223

(0.875)

-0.1190

(0.710)

-0.0058

(3.466)*

Miami 2 0.588

(1.859)*

0.0617

(2.446)*

-0.0169

(0.244)

-0.0014

(0.832)

Los Angeles 1 1.098

(3.004)*

 0.0422

(2.083)*

 0.5611

(0.975)

-0.0068

(3.431)*

Los Angeles 2 0.818

(3.199)*

 0.0217

(1.389)

 0.0690

(0.179)

-0.0013

(0.923)

Los Angeles 3 0.156

(0.388)

 0.0300

(1.313)

 2.089

(3.347)*

-0.0057

(2.581)*

Los Angeles 4 1.602

(5.139)*

0.0281

(1.686)*

-0.524

(1.050)

-0.0015

(0.909)

--Florida Tomatoes--

Atlanta 1 1.976

(4.756)*

0.0183

(0.803)

-0.870

(2.222)*

-0.0034

(2.488)*

Atlanta 2 2.138

(4.138)*

-0.102

(0.361)

-0.766

(1.573)

 0.0005

(0.311)

Miami 1 2.010

(4.733)*

-0.0551

(2.366)*

-0.622

(1.554)

0.0011

(0.823) 1

Absolute t-statistics are in parentheses.

*Denotes statistical significance at the 90 percent level.
18 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

The elasticities in table 7 represent the estimated

percent change in the margin due to a one-percent

increase in the level of shipments. All elasticities

are evaluated at the means of the data. Elasticities

based upon significant coefficient estimates are

denoted with an asterisk. For the CA-AZ

commodities, the elasticity is positive in 29 of 32

cases and is based upon a significant coefficient in

18 of those cases. Thus, the evidence is quite

strong that larger shipment volumes are associated

with a widening of the margin for the CA-AZ

commodities. The margin for Florida mature-green

tomatoes appears to behave differently based upon

our limited observations. Shipments volume is

associated with an increase in the margin in only

one of the three cases, and the effect is not

statistically significant. Specifying the margin to

allow a one-week lag in transmission of farm

prices to retail tended to weaken but not eliminate

the impact of shipment volume on the margin.

Although the preceding results do not speak

directly to the issue of retailer market power in

procuring and selling perishable produce

commodities, they do indicate the considerable

independence among the retailers in setting prices

and margins for these commodities—independence

that does not exist in a quintessential competitive

market. Similarly, the pervasive widening of the

margin in response to higher shipment volumes

supports the hypothesis that large volumes of these

perishable commodities are used as a tool to bid

down FOB prices and, thus, widen the margins.

Analysis of Retailer Oligopsony

Power in Fresh Produce

Procurement

In this section we report on an analysis of buyer

power in procurement of iceberg lettuce and fresh

tomatoes based upon the methodology reported in

Sexton and Zhang (SZ, 1996). Figure 1 gave a

graphical summary of the model. The SZ model

applies to perishable produce commodities, in

which supply in any market period can be regarded

as perfectly inelastic for all prices in excess of the

per-unit harvest costs. If the available harvest is

sufficiently large, relative to demand, the farm

price will fall to the level of harvest cost (C0

 in

Table 5—Means and Variances for Farm-Retail Price Spreads

City CA Iceberg Lettuce1 CA Vine-Ripe

Tomatoes1

CA Mature-Green

Tomatoes1

FL Mature-Green

Tomatoes1

Atlanta 1 0.9114 1.2957 1.3979

(0.0434) (0.1016) (0.1484)

Atlanta 2 1.5266 1.5991 1.1939

(0.0661) (0.1181) (0.1031)

Chicago 1 1.0038 1.3051

(0.0235) (0.0057)

Albany 1 0.8287 0.9401

(0.0310) (0.0377)

Dallas 1 0.9495 1.2854

(0.0437) (0.0212)

Dallas 2 0.6409 0.6119 0.6840

(0.0148) (0.0494) (0.0156)

Dallas 3 0.8367 1.1019

(0.0509) (0.1066)

Dallas 4 1.5029 1.5780

(0.0318) (0.0458)

Miami 1 0.9881 1.1148 1.0252

(0.0445) (0.0949) (0.1098)

Miami 2 1.0038 0.9543

(0.0217) (0.0275)

Los Angeles 1 0.8048 1.4815

(0.0353) (0.0686)

Los Angeles 2 0.6711 1.3527 1.0144

(0.0217) (0.1695) (0.0324)

Los Angeles 3 0.7437 2.2910 1.3998

(0.0249) (0.7038) (0.0708)

Los Angeles 4 0.7470 1.9082 1.4971

(0.0314) (0.0317) (0.0412) 1

Variances are indicated
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 19

Table 6—Correlation Coefficients for Farm-Retail Price Spreads in Selected Cities

(a) CA Iceberg Lettuce

Dallas 1 Dallas 2 Dallas 3 LA 1 LA 2 LA 3 LA 4

llas 1 1.0000

llas 2 0.1368 1.0000

llas 3 0.1577 0.2577 1.0000

A 1 -0.0253 0.1765 0.3969 1.0000

A 2 0.1960 0.2781 0.2768 0.3143 1.0000

A 3 0.0669 0.1704 0.4294 0.5862 0.6460 1.0000

A 4 0.1380 0.1060 0.3432 0.5039 0.5230 0.6806 1.0000

(b) CA Vine-Ripe Tomatoes

Dallas 2 Dallas 4 LA 2 LA 3 LA 4

Dallas 2 1.0000

Dallas 4 -0.3221 1.0000

LA 2 -0.2372 -0.1024 1.0000

LA 3 0.1144 -0.3197 0.4797 1.0000

LA 4 0.5716 -0.2508 -0.3790 -0.0376 1.0000

(c) CA Mature-Green Tomatoes

Dallas 1 Dallas 2 Dallas 3 Dallas 4 LA 1 LA 2 LA 3 LA 4

Dallas 1 1.0000

Dallas 2 0.5384 1.0000

Dallas 3 0.2956 -0.1138 1.0000

Dallas 4 0.2172 -0.0835 0.5040 1.0000

 LA 1 0.4827 0.2424 0.1931 0.0762 1.0000

 LA 2 0.5446 0.3422 0.0643 0.1803 0.2625 1.0000

 LA 3 0.1026 0.1388 -0.2183 -0.0966 -0.0492 0.4023 1.0000

(d) FL Mature-Green Tomatoes

Miami 1 Miami 2 Atlanta 1

Miami 1 1.0000

Miami 2 0.0281 1.0000

Atlanta 1 0.4135 0.4244 1.0000
20 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Figure 1) under any form of competition, but will

not normally fall lower, because price must be

sufficient to cover the marginal costs of harvesting.

A correctly specified econometric model of price

determination for produce commodities must

incorporate the fact that in some periods farm price

will be determined by the level of per-unit harvest

costs.

When the harvest-cost constraint does not bind (i.

e. , for harvest levels less than H*

, in Figure 1), the

farm price is determined by (a) consumer demand

for the commodity in its various final-product

forms, (b) costs of shipping, handling, and selling

the product, and (c) the extent of competition in the

market. If buyers compete aggressively, the farm

price will be bid up to the level of final product

value less all costs of marketing and selling—the

schedule DF

(H) in Figure 1. However, if buyers

have market power, they will be able to capture

some of the market surplus, S, for the commodity,

defined as the final product value, PR

, minus perunit costs for harvest (C0

) and for marketing and

selling (CR

): R0R

tt tt S = P - C - C .

Economic theory provides little guidance as to how

St

 will be shared between buyers and sellers, under

conditions of imperfect competition. The

hypothesis advanced by SZ was that buyers’

bargaining power would be positively related to the

size of the harvest, because large harvests of a

perishable commodity diminish sellers’ bargaining

power, through the pressure created to move the

perishable crop to market. Let (t

 denote the share of

market surplus captured by producers in period t.

The hypothesis of perfect competition in

procurement is then H0

: γ t = 1. The SZ (1996)

hypothesis of an inverse relationship between γ t

and Ht can be depicted through a variety of

functional forms. They found that a simple

exponential relationship best fit the data: 0 ≤

Ht

t e−α γ = ≤ 1, where α = 0 corresponds to γ t =

1 and perfect competition, and α > 0 indicates the

presence of buyer market power.

Let 8 denote the probability that the FOB price is

constrained in any period by the level of harvest

costs, 0 Ct . Then the process describing FOB price

determination for a perishable produce commodity,

under possible imperfect competition, is

summarized as follows:

(3) t

F 0

t t

F0 RR H

tt tt

P C with prob

P C e (P C ) with prob (1 ). −α

= =λ

= + − = −λ

For estimation purposes, functional forms must be

chosen for the retail demand, marketing cost, and

harvest-cost functions. Following SZ (1996), we

specified the following linear system with additive

error terms:

(4) 1

,

R

i,t P (1/ )i Hi t t = a − b + ε , ~N(0, ),i 1,....,n, 2

1

1

εt σ =

(5)

,

2

, i,t t

R

i t C = c + dT + ε ~ N(0, ),i 1,....,n, 2

2

2 εt σ =

(6) 3

t

0 0 Ct = C + ε , ~ N(0, ). 3

t

3 εt σ

Equation (4) specifies inverse demand in n

consuming markets, with final product price in

market i, R Pi,t , specified as a linear function of

consumption, Hi,t ,in market i. All other

determinants of demand are assumed to be constant

Table 7—Elasticities of the Margin With Respect to Shipment Volume

City CA Iceberg

Lettuce

CA Vine-Ripe

Tomatoes

CA Mature- Green

Tomatoes

FL Mature- Green

Tomatoes

Atlanta 1 0.672* 0.303* 0.138

Atlanta 2 0.429 0.474* -0.067

Chicago 1 0.422* 0.052

Albany 1 0.232 -0.307

Dallas 1 -0.011 0.358*

Dallas 2 0.215 0.437* 0.619*

Dallas 3 0.285 0.217

Dallas 4 -0.285 0.203*

Miami 1 0.692* 0.592* -0.485*

Miami 2 0.091 0.252

Los Angeles 1 0.353* 0.305*

Los Angeles 2 0.739* 0.229 0.229

Los Angeles 3 0.879* 1.378* 0.230

Los Angeles 4 0.501* 0.226* 0.201*

* indicates that the elasticity was computed from a coefficient that was statistically significant at the 90 percent level.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 21

over the two-year sample period, and their effects

are contained within the intercept, a.26 In particular,

equation (4) presumes the absence of important

substitutes for iceberg lettuce. Our analysis of

chain-level demand functions for iceberg lettuce,

discussed later in this report, investigated greenleaf and romaine lettuce as possible substitutes for

iceberg lettuce and found their effect to be

generally insignificant.

In equation (5) all costs of marketing, R Ci,t , except

for shipping costs Ti,t are assumed to be constant

over the sample period, and their effects are

reflected in the intercept term, c. Finally, per-unit

harvest costs are assumed to have a constant mean

C0

 over the sample period. Given the short sample

period and relatively stable prices during the time,

these assumptions seem to be quite reasonable. SZ

(1996) describe aggregation of (4) – (6) to the

industry level to obtain the following empirical

specification of the price-determination model in

 

 

( ):

.

max , .

3 Y1 P C with prob =

(3') Y2 P C e A - H dT

with prob =1-

Y Y1 Y2

t t

F 0

t

3

t t

F 0

t

3 - H

t t

t

3

t t

1

t

2

t

3

t tt

t

 

 

 

ε λ

ε

ε εεε λ

α

In (3’), Ht

 is the total harvest, Tt is a quantityweighted average of shipping costs across the n

consuming markets, and Yt

 is the farm price net of

mean per-unit harvest costs.27 In the absence of the

constraint that price cannot fall below the harvest

cost, farm price would be determined by the

equation for Y2. However, since no harvest would

occur in that event, farm price net of mean harvest

cost is the greater of Y1t

 and Y2t. Based on (3’), in

weeks in which the potential supply, Ht

, exceeds

H*

, the harvest-cost price, Y1, exceeds Y2, and

farm price is determined by the level of harvest

 

26 The specification of final product demand further assumes that the

price elasticities of demand, evaluated at a given price level, are

identical across consuming markets. This assumption facilitates

aggregation of the model across markets.

27Although the consumption levels in individual markets, Hi,t, are

clearly endogenous, the aggregate production, Ht

, can be considered

exogenous for all prices greater than the level of per-unit harvest costs.

Expected price contributes to determining the acreage committed to a

produce commodity. However, once acreage is committed, the

distribution of Ht

 depends primarily upon weather shocks. Except

when price falls to the level of per-unit harvest costs, it seems unlikely

that weekly demand fluctuations determine the corresponding weekly

harvests. As long as the variation from week to week, due to demand

and weather, dominates the variation from year to year in acreage

commitments, our approach should produce the most reliable

estimates, given the inherent difficulty in estimating any model of

supply response based upon two years of data.

costs. When Ht < H*, a per-unit surplus exists in the

market, and Y2 > Y1 determines the farm price.

The system in (3’) defines a nonlinear switching

regression model with heteroskedastic errors. The

model was estimated via maximum likelihood

using GAMS. See SZ (1996) for construction of

the likelihood function. Data for the model include

farm price net of mean per-unit harvest costs, Yt

= F Pt - C0. Farm price was measured for each

commodity in $10/carton as the weekly average of

daily FOB prices reported by the USDA, F-SMNS.

Estimates of per carton harvest costs were obtained

from either the University of California or the

University of Florida Cooperative Extension

Service. Weekly shipments were measured in units

of 40,000,000 lbs. , as reported by the F-SMNS.

For shipments emanating from California or

Arizona, the weighted average shipping cost, Tt

, in

$1,000 per truck load, was derived as the

population-weighted average of truck rates

reported by F-SMNS to ship CA-AZ lettuce to five

U. S. cities: Atlanta, Chicago, Dallas, New York,

and Los Angeles. For shipments emanating from

Florida, Tt

 was based on shipping costs from

Florida to Atlanta, Chicago, and New York. The

parameters to be estimated include A = a – c – C0,

∀, ∃, d, 2 2 0.5

1 2 ( ) σ +σ , and Φ3

.

CA-AZ Iceberg Lettuce

Figure 8 shows the weekly FOB price per carton

and the estimated per-carton harvest cost, 0 C =$4.

45/carton, for CA-AZ iceberg lettuce. Noteworthy

is that the FOB price is near the harvest-cost

minimum for a rather large number of the 104

weekly observations.

Estimation results for the general model, and a

restricted model with ∀ constrained to equal zero

(i. e. , perfect competition in procurement) are

reported in table 8. The point estimate of ∀ for the

general model is αˆ = 0. 833, with standard error 0.

322. The estimate is statistically significant, and

the restricted model of perfect competition is thus

rejected in favor of the general model. Consistent

with the results reported by SZ (1996), this result

supports a conclusion that buyers are able to

capture a large share of available market surplus

for CA-AZ iceberg lettuce and that, in many

periods, the FOB price is constrained to the

harvest-cost minimum. Producers’ estimated share

of the surplus is t t γ= − ˆ exp{ 0.833H }. For the

sample period from 1998-99 the range of t γˆ is [0.
22 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

138, 0. 330], with a mean value of 0. 194, i. e. ,

producers capture on average approximately 20%

of the market surplus. These surplus estimates are

somewhat higher than were obtained by SZ for the

period from January 1988–October 1992, which

ranged from 0. 031 to 0. 145, with a mean value of

0. 0649.

Both the aggregate demand slope parameter, β ,

and the shipping cost parameter, d, have the

hypothesized positive signs, but neither parameter

was estimated with much precision. The estimated

flexibility of the FOB price with respect to the total

volume of shipments evaluated at the sample

means is –2. 301 for the general model, implying

an elasticity of farm-level demand of

approximately –1/2. 301 = -0. 433. 28

 

28 The price flexibility is the percent change in price due to a one

percent increase in shipments. Thus, at the data means, a one- percent

increase in shipments is estimated to cause about a 2.3 percent

decrease in FOB price, when price is not constrained to the harvest

cost regime. The ratio of one over the estimated flexibility is a

consistent, although biased, estimate of the price elasticity of demand.

Following Kiefer (1980), the probability that each

observation is derived from the harvest-cost regime

is Ρt

 = prob{Y1t>Y2t∗Yt} (i. e. , Ρt is the probability

that we are observing Y1t

, given knowledge of Yt).

Figure 9 shows the estimated values of Ρt

. The rule

that minimizes the probability of misclassifying

observations between regimes is Yt

 = Y1t if the

estimated ψt is greater than 0. 5. Based upon this

rule, 38 of 104 or 36. 5 percent of the observations

of the FOB price for 1998-99 (the ones above the

ψt = 0. 5 line in figure 9) are estimated to have

resulted from the harvest-cost regime.

California Vine-Ripe Tomatoes

Analysis for California vine-ripe tomatoes was

limited to the 1999 marketing year (26 weekly

observations) because USDA F-SMNS did not

report FOB prices for vine ripes prior to 1999.

Figure 10 shows the path of 1999 FOB prices and

the per-unit harvest cost, estimated to be $4.

25/carton. F-SMNS does not disaggregate fresh

Table 8—Estimation Results for CA-AZ Iceberg Lettuce

General Model Restricted Model: α = 0

Parameter Estimate Std. Error Estimate Std. Error

α 0.833 0.322

A 4.549 2.526 1.607 0.403

β 0.780 0.923 0.509 0.195

D 0.895 0.781 0.182 0.113

(σ1

2

+σ2

2

)

0.5 1.831 1.174 0.387 0.039

σ3 0.035 0.007 0.041 0.011

log likelihood 26.458 22.945
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 23

tomato shipments between vine-ripe and maturegreen categories. Thus, Ht

 is the total weekly

shipment of fresh tomatoes in the U. S. Because of

the close substitutability between vine-ripe and

mature-green tomatoes, use of the combined

harvest is probably appropriate, irrespective of the

data problem.

Estimation results for the general and restricted

model are reported in table 9. The estimated value

of α is αˆ = 0. 054, with standard error 0. 013.

The estimate is statistically significant, and the

restricted model is again rejected in favor of the

general model. Although αˆ is statistically greater

than zero (0), it is quantitatively small, and

producers’ share of the estimated market surplus is

large, ranging from 0. 828 to 0. 902, with mean 0.

861. It appears that retailers exhibited relatively

little oligopsony power in pricing vine-ripe

tomatoes from California.

The estimates of β and d each have the

hypothesized positive signs, although ˆ

d is not

statistically significant. The estimated flexibility of

the FOB price for California vine ripes with respect

to total shipments of fresh tomatoes (i. e. , vine

ripes and mature greens) is -1. 067 based on the

general model, i. e. , demand is slightly inelastic at

the data means. None of the 26 observations for

F Pt is estimated to have resulted from the harvestcost regime.

California Mature-Green Tomatoes

Figure 11 depicts the FOB price for California

mature-green tomatoes relative to the estimated

harvest cost of $4. 25/carton, represented by the

dotted line. We experienced some difficulties in

estimating the switching regression model for

California mature greens. Estimation was

successful when the data set was limited to only

1999 observations (26 in total). The estimation

results are contained in table 10.
24 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Although the estimate of α is quantitatively large,

(αˆ = 0. 339), it is imprecise (std. error = 0. 505)

and not statistically different from zero (0).

Accordingly, the competitive model is not rejected

in favor of the general model for California mature

greens. The estimates of the producers’ shares of

the market surplus range from 0. 307 to 0. 475,

with a mean value of 0. 404. However, these

estimates must be interpreted with caution because

of the imprecision associated with the estimate of

α. Neither the demand slope nor the transportation

cost parameter was estimated with precision, and

the transportation cost parameter does not have the

anticipated positive sign. Based on the estimate of

β , the flexibility of the FOB price with respect to

total fresh tomato shipments is estimated to be –12.

86 at the data means. This estimate seems

implausibly large, corresponding to a very inelastic

demand.

Figure 12 contains estimates of Ψ t

. Based upon

the classification rule, 18 of the 26 total

observations are estimated to have come from the

harvest-cost regime (i. e. , FOB price was

constrained to the level of harvest costs for roughly

two-thirds of the 1999 observations). Referring to

figure 11, we see that the price declined early on

during the 1999 season and hovered near the

estimated per-unit harvest costs for the remainder

of the season. In essence then, the market power

parameter α and the demand and transportationcost parameters are estimated from only the eight

observations that did not result from the harvestcost ratio. Thus, the imprecision in their estimation

is not surprising.

Florida Mature-Green Tomatoes

Florida mature-green tomatoes are marketed under

the auspices of a federal marketing order and most

grower-shippers are affiliated with a marketing

cooperative, which coordinates with the marketing

order. The Florida mature-green tomato industry

established a voluntary price floor for the 1998-99

and 1999-2000 marketing seasons at F P = $5. 85

per carton, well in excess of harvest costs,

Table 9—Estimation Results for California Vine-Ripe Tomatoes

General Model Restricted Model: α = 0

Parameter Estimate Std. Error Estimate Std. Error

α 0.054 0.013

A 0.697 0.064 0.189 0.469

β 0.131 0.027 0.179 0.052

d 0.003 0.027 0.677 0.267

(σ1

2

+σ2

2

)

0.5 0.001 0.001 0.126 0.048

σ3 0.509 0.115 0.001 0.071

log likelihood 21.031 8.990
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 25

estimated to be $3. 57/carton.29 Figure 13 depicts

the FOB price, the $5. 85 price floor, represented

by the unbroken lines, and the estimated $3. 57

per-unit harvest cost, represented by the dotted

lines.30 The figure demonstrates that the price floor

was quite successful during the sample period.

Price dropped in several weeks to around the level

of the floor, but it did not drop perceptively below

it. Unlike the cases for CA-AZ iceberg lettuce and

California mature-green tomatoes, the harvest-cost

floor apparently was not a factor at all in

establishing the FOB price.

 

29 Enforcement of this price floor was facilitated by the agreement

negotiated in 1996 between tomato shippers in Florida and Mexico to

suspend the U.S. Commerce Department’s investigation into dumping

charges lodged by the Florida industry against Mexican tomato

exporters. As part of this agreement, Mexican tomato shippers agreed

to a price floor of $5.17 per 25 lb. box. The Mexican floor price was

increased to $5.27 in 1998. The agreement required that exporters

representing at least 85 percent of traded tomato volume be signatories

and was not binding upon non-signatories.

30 A voluntary price floor was attempted in marketing California

mature-green tomatoes for the 1998 season. The floor was set at $3.50

plus a handling charge of $0.50, yielding a total price of $4.00 per

carton. Based on industry sources, only about two-thirds of shippers

participated in the agreement. Since $4.00 is roughly equivalent to the

estimate of harvest costs, it is not clear whether the floor had any

effect independent of the natural floor price established by the harvest

costs (see figure 11).

Because it was clear that the industry-established

price floor dominated the harvest-cost floor as a

relevant factor in establishing price for Florida

mature-green tomatoes, we utilized F P instead of

the harvest-cost floor in estimating the pricing

model. Thus, for Florida mature greens, the model

to be estimated was

 

 

Y1 P P with prob =

Y2 P P A - H dT

with prob =1-

Y Y1 Y2

t t

F F

t

3

t t

F F

t

3

t tt

t

3

t t

1

t

2

t

3

t tt

 

 

 

ε λ

ε

ε εεε λ

 

.

max , .

The estimation results are provided in table 11. The

estimated value for α is small (αˆ = 0. 212), and is

not statistically significant. Thus, the model of

competitive procurement is not rejected for Florida

mature greens. The estimates of both β and d are

positive as expected, but ∃ is estimated very

imprecisely. The estimated price flexibility at the

data means is 0. 498, implying that demand is

Table 10—Estimation Results for California Mature-Green Tomatoes

General Model Restricted Model: α = 0

Parameter Estimate Std. Error Estimate Std. Error

α 0.339 0.505

A -2.992 4.315 -1.285 0.855

β 1.201 1.249 0.633 0.200

d -2.183 2.619 -1.032 0.343

(σ1

2

+σ2

2

)

0.5 0.315 0.342 0.159 0.045

σ3 0.099 0.019 0.101 0.020

log likelihood 20.021 19.778
26 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

elastic for Florida mature greens31. Based upon the

estimated value of α, producers’ share of any

market surplus in excess of the amount created by

the price floor ranged from 0. 514 to 0. 783, with

mean value 0. 627. However, because of the

imprecision in estimation of α, we cannot reject

statistically that producers capture the entire

surplus, as they would under perfect competition in

procurement.

Figure 14 depicts the estimated probabilities that

the voluntary price floor determined the FOB price

in each period. Ten of the 69 total observations are

estimated to have been determined by the price

floor.

 

31 Note that Florida tomatoes compete seasonally with

imports from Mexico. Thus, due to the competition

effect, a relatively elastic demand for Florida mature

greens is not surprising.

Summary of Oligopsony Analysis

Results of the oligopsony analysis were rather

mixed. Results for CA-AZ iceberg lettuce were

consistent with earlier results obtained by SZ

(1996), and parameter estimates were plausible

both with respect to sign and magnitude. Results

for fresh tomatoes were less satisfactory. The

market power parameter, α, was statistically

significant in only one of three instances

(California vine ripes) and was quantitatively small

in that case, suggesting that producers captured the

lion’s share of any market surplus. The estimate of

α was larger for California mature greens than the

estimate for vine ripes, but the former was

estimated imprecisely, and we were unable to

reject a hypothesis of competitive procurement.

Analysis for Florida mature-green tomatoes

demonstrated that the industry’s voluntary price

floor was successful in maintaining prices well in

excess of the harvest-cost minimum. This floor was

Table 11—Estimation Results for Florida Mature-Green Tomatoes

General Model Restricted Model: α = 0

Parameter Estimate Std. Error Estimate Std. Error

α 0.212 0.226

A 3.670 1.697 2.542 0.558

β 0.108 0.243 0.193 0.122

d 1.661 1.008 1.107 0.318

(σ1

2

+σ2

2

)

0.5 0.647 0.329 0.410 0.041

σ3 0.042 0.017 0.047 0.029

log likelihood -17.304 -17.796
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 27

estimated to have determined price in 10 of the 69

total periods. The estimate of α for Florida mature

greens pertains to surplus in excess of the price

floor, rather than the harvest-cost floor. This

estimate was small and not statistically different

from zero, meaning that a hypothesis of

competitive procurement could not be rejected in

this case either. Estimates of the price elasticity or

price flexibility of demand for fresh tomatoes

varied widely among the alternative cases

considered.

The weaker performance of the pricing model

when applied to fresh tomatoes could be due to

several factors. Available observations were

limited in all cases relative to those for CA-AZ

iceberg lettuce. For both California mature greens

and vine ripes, estimation was based on only a

single marketing season. In addition, the

availability of only aggregate fresh tomato

shipments causes lingering concerns about the

appropriateness of the shipments variable in the

analyses for tomatoes.

Although the results for fresh tomatoes must be

interpreted circumspectively for the reasons just

noted, they do tend to suggest that fresh tomato

grower-shippers have fared better on average than

iceberg lettuce grower-shippers in capturing a

larger share of the available market surplus. This

result is consistent with the results reported by

Calvin et al. (2000) that tomatoes was the produce

sector that had most been able to withstand retailer

requests for services and fees. Available evidence

suggests roughly comparable grower-shipper

structures in lettuce and fresh tomatoes, although

tomato grower-shippers are probably better

organized, as we have discussed.32 Another

prospectively important consideration is that

tomato grower-shippers deal with repackers, and

do not directly face grocery retailers. In the context

of the Sexton-Zhang model of price determination

for perishable produce, it is quite reasonable to

think that repackers are a less potent force in

bargaining than are the grocery retailers with

whom lettuce grower-shippers deal directly.

Retailer Pricing to Consumers:

Constant or Stabilized Retail

Prices

We now turn to the analysis of grocery retailers’

pricing practices to consumers. In this section, we

investigate the implications for producers when

some chains hold constant or stabilize retail prices

for produce commodities, despite shifts in

production and prices at the farm level. We show

that, under a rather broad set of conditions, this

behavior is harmful to producers. The fundamental

point is that, if some share of the sellers of a

commodity hold the retail price constant, despite

shifts in production and/or aggregate demand, then

price must fluctuate more widely for all other

sellers, in order for the market to clear. In most

cases, this outcome is harmful to producers relative

to the alternative where all sellers allow price to

fluctuate in response to market conditions. The

logic of this argument applies equally to situations

where a subset of sellers does not maintain a fixed

price per se, but instead stabilizes it relative to

market conditions

 

32 See Sexton and Sexton (1994) for a discussion on the history of CAAZ iceberg lettuce grower-shippers’ attempts to organize.
28 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Figure 15 illustrates the basic point for the case of

two market outlets. The left quadrant depicts the

aggregate retail market, and the right quadrant

depicts the aggregate of all other market outlets,

and is referred to as “food service”. F D (H ) 1 1 is

final demand in the food service market, less all

shipping and marketing costs, and F D (H ) 2 2 is final

demand in the retail market, less all shipping and

marketing costs. For simplicity, F D (H ) 1 1 and

F D (H ) 2 2 are assumed to be identical, and the initial

harvest level, H0

, is divided equally between the

two markets. Under perfect competition in

procurement, F D (H ) 1 1 and F D (H ) 2 2 are demand

curves for the farm product in the respective

sectors. Given total harvest H0

, farm price would be

P0

 in each market under competition. Under buyer

power in procurement, farm price will be less than

P0

 as discussed previously and as illustrated in

Figure 1.

Suppose that production increases to H0

 + ∆ , while

demand remains unchanged. If both markets allow

price to change in response to the increase in

production, each sells 0. 5(H0

 + ∆ ) and price in

each market falls to P1

. The increase in producer

revenue in each market is the area, ABCD.

However, if the retail market maintains a fixed

selling price despite the change in production, sales

at retail remain at 0. 5H0

, and the per-unit farm

value remains P0

 in the retail sector. For the

increase in production to clear the market, it must

move entirely through the food service market,

which now sells 0. 5H0

 + ∆ , with farm value in the

food service market falling to P2

.

33 The marginal

revenue from the new production is now illustrated

by the area ABEF in Figure 15, where ABEF <

2(ABCD).

Figure 15 thus illustrates a market setting where

the revenue from additional sales is less when one

segment of the market maintains a fixed price,

causing the additional volume to be sold entirely

through the market outlet(s) that maintain variable

prices. This result holds broadly. In particular, a

sufficient condition for fixed prices to be harmful

to producer welfare is that marginal revenue is a

 

33Because P0 > P2, standard arbitrage would call for product to move

from food service to retail, but these forces are frustrated if retailers

insist on holding price at P0. Only 0.5H0 can be sold at retail for price

P0.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 29

decreasing function of sales for all market outlets.34

Although Figure 15 illustrates a situation with

elastic demands (i. e. , marginal revenue is positive

in both markets), the conclusion applies equally to

markets with inelastic demands. In this case,

additional output causes a reduction in total sales

revenue, but the reduction is less if all prices are

flexible whenever marginal revenue declines as a

function of output. The logic illustrated in Figure

15 applies equally to decreases in production.

Finally, the presence of imperfect competition in

any of the procurement markets does not alter this

fundamental conclusion. Under the conditions set

forth here, fixing prices in a subset of the market

outlets reduces the total surplus accruing to the

farm commodity. Thus, farm sector income will be

lower due to fixed prices, even if the farm sector

captures only a portion of the market surplus.35

Estimates of the Impact of Constant

Prices on Producer Welfare

How important are fixed retail prices in influencing

farm income for a produce commodity? We

conducted a simulation analysis to gain some

insight into this question. Total demand for the

farm commodity was specified in linear form as Q

= a - αP and divided between two sectors: a sector

that fixes retail price at a mean value and faces

demand

Q1

 = ρ (a - αP1), 0 < ρ < 1,

and a sector that allows price to fluctuate freely,

which faces demand

Q2

 = (1-ρ )(a - αP2).

The parameter ρ measures the share of total

demand that is sold through outlets that employ a

fixed retail price.

The mean harvest is H0

, which is normalized

without loss of generality to be 1. 0: H0

 = Q1 + Q2 =

1. 0. Let ∆ t

 denote a random shock to harvest, and

 

34 Let inverse demand in a market j be denoted as Pj

(Hj), where Hj

denotes sales in market j. Total revenue in market j is TRj = Pj(Hj)Hj.

Marginal revenue is MRj

 = dTRj/dHj = Pj’(Hj)Hj + Pj(Hj), and

dMRj

/dHj = Pj’’(Hj)Hj + Pj’(Hj) + Pj’(Hj). Thus, dMRj/dHj< 0 whenever

2Pj

’(Hj) < -Pj’’(Hj)Hj. This condition holds for all concave demand

curves, including linear, and also mildly convex demands.

35It might be argued that consumers prefer stable prices, so retailers

who hold prices constant despite fluctuations in market conditions

actually increase demand for the product (Okun, 1981). However, as

noted, stabilizing prices in one sector of the market implies even

greater price instability in the other sectors, which, under the same

logic, would have an adverse effect on demand in those sectors.

assume ~ N(0, )

2 ∆t σ , i. e. , ∆ t is distributed

normally with mean zero and variance σ 2

.

Similarly, prices are normalized at the mean

harvest to be P1

 = P2 = 1. 0. Given these

normalizations, the relationship among the demand

parameters is a = 1 + α, and α = ε Q,P,where

Q,P ε =− ∂ ∂ =α ( Q / P)(P / Q) (P / Q)is the

absolute value of the price elasticity of total

demand evaluated at (P1

=P2, H0) = (1,1).

To conduct simulations of the impact of fixed retail

prices for CA-AZ iceberg lettuce based on the

preceding model, we must specify plausible values

for the parameters ∆, Q,P ε , and Φ2

. In our sample

of retail chains 6/20 or 30 percent employed fixed

prices for iceberg lettuce during the 104-week

sample period. In addition, we estimate that

approximately 60 percent of iceberg lettuce is sold

through retail food stores. Thus, a rough estimate

of ρ is ρ = 30 percent x 60 percent = 18 percent,

assuming that all “food service” outlets have

flexible prices. This value for ∆ applies only to

retailers that maintain a constant selling price.

Others, while not fixing price per se, clearly

stabilize it relative to market conditions. To

account in at least an approximate manner for this

additional impact, we also conducted a set of

simulations assuming that ρ = 30 percent.

As for Q,P ε , SZ (1996) estimated that Q,P ε = 0.

164. A somewhat higher estimate, Q,P ε = 0. 433,

was obtained in the present study. Both values

were used in the simulations. The variance, 2 σH , of

actual California weekly harvests of iceberg lettuce

for 1998-99 was used to estimate Φ2

. First, to

normalize harvest so that E[Ht

] = 1, we divide the

actual harvest by its mean, H . Since Var

{Ht

/E[Ht]} = 2 σH /E[Ht]

2

, a reasonable setting for

σ 2, the variance of the normalized shock to harvest

)t, is the variance of the actual harvests divided by

2 H .

We simulated a year of harvests by conducting 52

draws of ∆ t

 from a normal distribution with mean

0 and variance σ 2

. The simulated harvest in week t

is then Ht

 = 1 + ∆t. Given the harvest, price and

grower revenue were computed for each week

under the alternative scenarios where (a) all sellers

had variable prices, or (b) a fraction, ρ , of sellers

fixed price at the mean value. Total revenue under

scenario (a) is
30 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

(a) 2 R 1 ( / )( / ) t t t Q,P t Q,P = +∆ − ∆ ε − ∆ ε .

Under scenario (b), total revenue is

(b) t Q,P t

t

Q,P

(1 )[ (1 ) ] R

(1 )

−ρ+∆ ε −ρ −∆ =ρ+

ε −ρ .

For each set of parameter values, 100 52-week

harvests were simulated. The minimum, maximum,

and mean percent loss in revenue from

constant/stabilized retail prices for each set of trials

are reported in table 12. As was clear from the

theoretical analysis, producers’ welfare is reduced

when a subset of retailers stabilize prices. The

relative loss in revenues from fixed/stabilized retail

prices is larger the more inelastic is demand and

the greater the fraction of product that is sold

through fixed/stabilized-price sellers. When ρ = 0.

3 and Q,P ε = 0. 164, the mean loss in revenues was

about 3. 5 percent but was only 0. 6 percent when

ρ = 0. 18 and Q,P ε = 0. 433. Although not

illustrated in table 12, the adverse effect of

fixed/stabilized prices on revenue will be greater

the more volatile is periodic supply, i. e. , the

greater is σ 2 .

Retailer Pricing to Consumers:

Markups of Price Over Costs

Grocery retailers unquestionably possess some

degree of market power, in the sense of having

ability to influence prices, both due to spatial

differentiation, differentiation in product mix and

service levels, customer loyalty, etc. The

implications of this power for consumer welfare

depend primarily upon conditions of entry into the

food-retailing sector. If entry is unimpeded, then

the presence of supracompetitive profits in a local

market will stimulate entry and lower prices, which

will reduce profits towards the competitive level.36

Long-run equilibrium is attained when no

additional entrant can enter profitably. Incumbents

in this equilibrium will be charging prices to

consumers that exceed marginal costs on average,

 

36 Cotterill and Haller (1992) studied entry into U.S. food retailing

markets. A key result was that entry was inversely related to the

degree of concentration (measured by the four-firm concentration

ratio) in the market, leading the authors to conclude that powerful

incumbent firms were able to use strategic entry barriers to deter entry.

Entry was also inversely related to the presence of strong chains in the

market.

Table 12—Simulation Results for Analysis of

Constant/Stabilized Retail Prices

Q,P ε = 0.433 Q,P ε = 0.164

∆ = 0.18 [0.34, 0.84]

0.63

[0.88, 2.89]

1.78

∆ = 0.30 [0.66, 1.84]

1.24

[1.72, 5.65]

3.48

and some level of supracompetitive profits may

persist due to the fixed costs of entry.37

Because it is not possible to analyze the

implications of retailer behavior for consumer

welfare based upon only a few products, our focus

is on the implications of retailer pricing to

consumers for producer welfare. Consider the

following model setting for food retailing.

Consumers and stores are distributed spatially.

During each market period, a consumer will visit

one or more stores selected from a decision set of

stores. Evaluating each store in the decision set, a

consumer’s decision on whether to visit a store is

based upon his/her perception of (a) the total cost

for the planned market basket of purchases, (b) the

transactions costs associated with the shopping trip,

and (c) additional factors, such as the consumer’s

familiarity with the store, the services and selection

offered by the store, perceptions of quality of the

store’s merchandise (Siroh, McLaughlin, and

Wittink, 1998), etc.38

Define Qj,t = Nj,tQ , as the total sales of a particular

produce item in chain j at time t, where Nj,t is the

number of customers that visit the chain in period t and

Q is the average sales per customer. The partial

elasticity of sales with respect to the store’s price,

Pj

, can be expressed as follows:

j j j j Q ,P N,P Q,P ε =ε +ε ,

where N,Pj

ε is the elasticity of customer visits with

respect to price of the produce commodity, and

Q,Pj

ε is the elasticity of mean sales of the

commodity with respect to its price. Q,Pj

ε is widely

believed to be small in absolute value (demand is

inelastic) for produce commodities. Most

 

37 For example, 10 geographically dispersed supermarkets in a small

city may be able to earn positive economic profits, but entry of an 11th

store would cause losses. Thus, 10 stores and positive economic

profits would represent the long-run equilibrium.

38 This consumer-choice framework is very consistent with the model

utilized by Messinger and Narasimhan (1997) to study consumers’

choice of supermarket formats. Also see Popkowski Leszczyc, Sinha,

and Timmermans (2000) for a recent analysis of consumer grocery

store choice and switching behavior.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 31

consumers view produce, such as lettuce and fresh

tomatoes, as staples in their diets. They are also

complementary to many types of meals, and they

constitute a small part of most consumers’ budgets.

Moreover, their perishable nature means that

consumers cannot accumulate inventories in

response to price promotions.

Not much, however, is known about ε N,P. This

elasticity is presumably negative—any increase in

a commodity price, ceteris paribus, will have a

negative impact on visits to a store, but the effect

of any one price change is most likely very small,

except possibly when an item is on ad, and a very

low promotional price is offered.

Thus, the partial elasticity of demand for produce

items, such as lettuce and fresh tomatoes, facing a

typical retailer is expected to be small in absolute

value, implying that the retailer has considerable

power to raise produce prices above marginal

acquisition and selling costs. However, in terms of

a chain’s overall pricing strategy, it cannot exercise

fully its market power for each commodity it sells.

Charging the monopoly markup of price over full

marginal cost, CT

, (P-CT)/P = -1/,

Q,P, for each

product in the chain’s stores would cause a large

decrease in N, rendering such an action

unprofitable.

A retailer’s optimal strategy in pricing 30,000 or

more product codes is, accordingly, complicated,

and, fortunately, not necessary to model, given that

our purpose to explore the implications of retailers’

pricing strategies for producers of iceberg lettuce

and fresh tomatoes. The essence of our approach is

illustrated in figure 16. Qj

 denotes sales in a

representative retail store for a given produce

commodity. Pj

(Qj) is the inverse demand function

for the store, indicating the ceteris paribus

relationship between the price, P, charged by the

store and its sales volume.

MR (Q ) [P (Q )Q ]/ Q jj jjj j =∂ ∂ is the store’s

marginal revenue from sales of the commodity. CT

represents the store’s total marginal costs of

acquiring and selling the commodity, which consist

of the FOB price, per-unit shipping cost, and

marginal selling costs. If the retailer elected to set

the price of the commodity competitively, it would

set Pj

 = CT and sell QC units per time period. If the

retailer elected to exercise fully its monopoly

power for this commodity, it would set sales at the

output where MRj

(Qj) = CT and set Pj = PM, selling

QM

 units. As noted, neither strategy is likely to be

optimal for a retailer. Competitive pricing

contributes nothing per se to a chain’s profits,

although a low price may help attract more

customers to the chain’s stores. The monopoly

price, PM

, contributes to the chain’s profits, but the

chain likely cannot fully exploit its market power

for each commodity without driving most

customers away from its stores.

Suppose the price actually observed is P*

, with perperiod sales of Q*

. We then construct the perceived

marginal revenue function, PMRj

(Qj) = ξ MRj(Qj)

+ (1-ξ )Pj

(Qj), consistent with the functional forms

chosen for MRj

(Qj) and Pj(Qj), so that PMRj(Qj)

intersects CT

 at the observed volume of sales, Q*.

The level of market power implied by the retailer’s

pricing decision is then >. The expression for the

relative markup, taking into account the effect of ξ

is the following:

 (7) T

Q,P (P C )/P / − =ξ ε ,

i. e. , the markup is always determined jointly by

the implied market power and the price elasticity of

the demand curve.

As discussed earlier in the paper and illustrated in

figure 2, the range for ξ , viewing the commodity

Q in isolation, is ξ ∈ [0,1], with ξ = 0 denoting

perfect competition and ξ = 1 denoting monopoly.

These bounds on ξ need not apply in the multiproduct context of a grocery retailer. In particular,

ξ < 0 may be observed when a commodity is on

sale and being used as a “loss leader” to attract

customers to the chain’s stores, and ξ > 1 may

represent a rational pricing strategy when substitute

and complement relationships among products

within the stores are considered.39

A retailer’s sales are a decreasing function of the

implied level of market power,ξ , exercised by the

retailer, but producer welfare is an increasing

function of retailer’s sales. Thus, producer welfare

is a decreasing function of ξ because market

power curtails movement of the product within the

retailer’s stores and causes diversion of product to

lower-valued uses.

The implied values of ξ can be estimated for a

retailer and a commodity for each time period,

 

39 For example, suppose a retailer wishes to promote sales of its

private label brand of a particular product. Clearly, the

equivalent national brands will be close substitutes for the

private label. In this case, setting a very high markup on the

national brand (i.e., ξ > 1), rather than a very low markup on

the private label, may represent a rational pricing strategy.
32 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

given (a) price and sales during that period, (b) an

estimate of the demand curve facing the retailer for

the commodity, and (c) the retailer’s aggregate

marginal costs for acquiring, shipping, and selling

the commodity. Price and sales per period are

available from the IRI data, subject to the caveats

about accuracy noted earlier in the paper. Storelevel demand functions can be estimated using

conventional econometric methods, although

problems with accuracy of the data will affect such

estimation. As to retailers’ costs, FOB prices are

observed, as are shipping costs,40 but retailers’

selling costs are not observed, nor is it possible to

disentangle most of the costs for selling one

commodity from the costs associated with the

thousands of other commodities the typical retailer

sells.

Inability to observe retailers’ selling costs was

handled in the following manner. We derived an

upper bound on the implied exercise of a retailer’s

market power by ignoring these costs, and treating

retailer marginal costs simply as the sum of

acquisition and shipping costs. For cities where we

had multiple chains with usable data for a

commodity, we computed a lower bound on the

implied exercise of market power by assuming that

the low-price seller of the commodity in the city

sold the commodity on a competitive basis, i. e. ,

the low-price seller set PL

 = CT.

41 Thus, L Pt was

used as a proxy for CT

 in computing the implied

 

40 Some error must be acknowledged in the observation of FOB prices

and shipping costs. Because FOB prices are typically reported in a

range, with high and low prices averaged to construct our FOB price

variable, the acquisition costs for a particular retailer may deviate

somewhat from what we report.

41 This approach is similar to the use of competitive “benchmark”

prices to measure market power, as discussed by Wann and Sexton

(1992).

exercise of market power for all other sellers of the

commodity in that city.42

An alternative approach is to specify a seller’s

marginal costs as a function of input prices and

estimate a system of equations consisting of an

equation for product demand and an equation for

seller optimization. Under this approach,

information about marginal cost is inferred from

the data. Bettendorf and Verboven (2000) provide

a recent application of this approach. Applicability

of this approach in the present context is very

limited for multiple reasons. First, as noted, a

retailer’s optimization problem involves multiple

products and is not easily specified. Second, it is

not clear that a reasonable specification can be

obtained for the unobserved portion of retailers’

marginal costs, namely selling costs. Variable costs

associated with selling produce include

expenditures for labor and energy (e. g. , for

refrigeration). Neither is likely to have varied much

over the 2-year period covered by the IRI data, nor

do we have access to the prices paid by any of the

chains’ for these inputs, thus necessitating the use

of proxies if this approach were to be utilized.

Store-Level Demand Functions

For each chain and each commodity where we

believed the data to be reliable, a demand function

of the following form was estimated:

 (8) Qt

 = f(Pt, S Pt ),

where Qt and Pt are sales and price,

respectively, of the commodity, and S Pt is the

price of a substitute commodity. Sales revenue

and product volumes are always reported at the

chain level by IRI (i. e. , summed across

reporting stores). Because the number of stores

reporting information varies across weeks,

sales and volume were always converted to a

per-store basis. Thus Qt

 represents the simple

mean of sales for stores in the chain reporting

for that period. (Pt

 and S Pt , recall, are constant

across stores in a chain. ) To the extent that

sales vary significantly among stores within a

chain in a given city, variability in the number

 

42 Although the characterization of L Pt as an upper bound for T Ct will

generally be valid, it may not be true in periods when the low-price

seller offers a promotional price for the commodity and, thus, may set

price below T Ct . Similarly, to the extent that retailers in a given city

differ as to their selling costs, error is introduced into the lower bound

calculations.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 33

of stores reporting information is another

uncontrollable source of error in the analysis.

All other demand determinants (e. g. , consumer

income) were assumed to be constant over the 2-

year period covered by the data series. For iceberg

lettuce, the price of a related lettuce product, either

green leaf or romaine, was used for S Pt . For

mature-green and vine-ripe tomatoes, the price of

roma tomatoes (the largest selling tomato category

after vine ripes and mature greens) was used for

S Pt .

The variables in (8) were specified using the BoxCox transformation to enable testing for the

preferred functional form. The Box-Cox model

nests both the popular and convenient linear and

double log models, enabling tests to be conducted

as to whether the data reject either model. We also

conducted a detailed analysis into the structure of

the error term in estimating (8), in particular its

autoregressive properties. Finally, even among the

data series considered most reliable, we still often

encountered missing observations and observations

on sales that seemed aberrant (see the earlier

discussion of the data). Thus, it was important to

have systematic testing in place for outliers.

Outliers were detected and omitted from the final

estimation using the testing procedure described in

Belsley, Kuh, and Welsch (1980).

CA-AZ Iceberg Lettuce

The oligopoly power implied by retailers’ pricing

decisions was analyzed for 12 chains. As noted, six

chains maintained constant selling prices

throughout 1998-99, making it impossible to

estimate a demand function. The implications of

this behavior are discussed in the preceding

section. Two other chains reported iceberg lettuce

sales that we regarded as implausibly low, and,

hus, those chains were omitted from the analysis.43

Durbin-Watson tests revealed the presence of

autocorrelated residuals for each of the 12 chains.

In four of the 12 cases, there appeared to be

second-order autocorrelation. First-order

autocorrelation corrections appeared adequate in

the other cases. The Box-Cox tests for functional

form generally supported use of the double-log

 

43 The principal PLU codes for iceberg lettuce are 61 and 4061.

However, iceberg lettuce may also be assigned to other PLU codes,

most notably 4641 and 4634. Choice of product codes for the analysis

was done on an individual-chain basis, through detailed analysis of

each chain’s data.

model; it was not rejected in nine of 12 cases.44

Because of the desirability of utilizing a common

model across chains, we elected to work with (8) in

the double-log functional form and an AR(2) error

structure:

 (9) lnQt

 = β 0 + β 1lnPt + β 2ln S Pt + ε t, ε t =

ρ 1 ε t-1 + ρ 2 ε t-2 + ρ t, ~ N(0, )

2 υt σ . 45

Table 13 reports results of the analysis of implied

oligopoly power for CA-AZ iceberg lettuce. The

coefficients in the double log model are elasticities.

Thus, 1

ˆ

β is the estimate of the own-price elasticity

of demand for each chain, and 2

ˆ

β is the estimate of

the cross-price elasticity of demand. The estimates

of the own-price elasticity of demand for lettuce

were statistically significant in 10 of the 12 cases.

In 8 of the 12 cases the absolute value of the

estimated own-price elasticity was less than 1. 0,

indicating an inelastic demand. In three of the

remaining cases, the absolute value was between 1.

0 and 1. 1, suggesting that own-price elasticity is

nearly unitary for those chains.46 The coefficient on

the substitute commodity price had the

hypothesized positive sign in nine of 12 cases,

indicating a substitute relationship, but the effect

was seldom statistically significant.

 

44 Hoch et al. (1995) also used the double log model to estimate

product demand functions for individual grocery retailers.

45Some elaboration on the estimation procedure in the presence

of autocorrelated errors is necessary.When observations are

continuous, estimation methods, such as the Cochrane-Orcutt

iterative procedure, are straightforward. Our data sets were

discontinuous, both due to missing and outlier observations.

Estimation of the models proceeded by first estimating the

regression equation by ordinary least squares (OLS) on 104 –

m observations, with m denoting the number of omitted

observations. 1 ρˆ and 2 ρˆ , were then obtained from the

following regression on the OLS residuals, et

, excluding the

first two observations and the two observations following any

excluded Greene (1990). The model was then re-estimated on

104 – 2m observations using * Qt , * Pt , and S* Pt . A set of new

estimated residuals was then obtained and used to derive

updated values of 1 ρˆ and 2 ρˆ . The process was continued until

successive estimates of 1 ρˆ and 2 ρˆ differed by less than 0.001. 46 Hoch et al. (1995) found considerable variation among price

elasticities in various food-product categories for individual

Dominick’s stores in the Chicago area. They showed that

about two-thirds of the variation could be explained by

demographic and competitive environment variables.

Although our results apply to grocery chains within a city, not

to individual stores, the arguments posed by Hoch et al., as to

why price sensitivities will vary across stores, also apply to our

context.
34 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Table 13 reports both the range across observations

and the mean of the implied oligopoly power

parameter for both the upper-bound and the lowerbound computation. The largest mean implied

oligopoly power is by Chicago 1, with ˆ

ξ = 0. 905,

near the 1. 0 maximum for the single-product case,

as an upper-bound calculation. The lowest implied

oligopoly power is by Miami 1, with ˆ

ξ = 0. 029.

observation: et

 = ρ 1 et-1 +ρ 2 et-2 + υt. 1 ρˆ and 2 ρˆ

were then used to transform Qt, Pt, and S Pt . For

example, * Qt = Qt

 - 1 ρˆ Qt-1 - 2 ρˆ Qt-2. In addition, the

first two observations in each sequence of

continuous observations can be transformed as

described, for example, in The mean across chains

of ˆ

ξ is 0. 360.47 The lower-bound computations

could be made in only six cases. For the Los

 

47 As a comparison to the case of a single-product seller, ˆ

ξ =

0.36 is roughly equivalent to the market power that would be

exercised by a three-firm oligopoly under Cournot

competition.

Angeles chains, LA 2 had the lowest average price

for iceberg lettuce over the sample period. If LA 2

were pricing iceberg lettuce as a perfect

competitor, then the market power implied by the

other Los Angeles chains pricing is very low,

ranging on average over the sample from 0. 011 for

LA 4 to 0. 039 for LA 1. Dallas 2 was the lowprice seller in the Dallas area. If Dallas 2’s price

were the competitive price, then the average

oligopoly power implied by Dallas 1’s and Dallas

3’s behavior are, respectively, ˆ

ξ = 0. 055 and ˆξ =

0. 093, respectively. The average across the six

chains for which we have a mean lower bound on

implied oligopoly power is 0. 040.

Our approach generates conservative bounds on the

implied exercise of market power. Because

marginal selling costs are not zero, the upperbound calculations are unquestionably high.

Similarly, it seems implausible that a retailer would

not attach some markup over full marginal cost for

a commodity with a relatively inelastic demand,

such as iceberg lettuce. Thus, the lower-bound

calculations most likely understate the actual

Table 13—Implied Oligopoly Power for CA-AZ Iceberg Lettuce

Chain Own price

Elasticity1

Cross price

Elasticity1

Oligopoly power:

upper bound

Oligopoly power:

Lower bound

Range Mean Range Mean

Atlanta 1 -1.104

(5.603)*

-0.0702

(0.263)

-0.216, 0.811 0.653 *** ***

Chicago 1 -1.503

(3.479)*

-0.0552

(0.215)

-0.213, 1.119 0.905 *** ***

Albany 1 -1.010

(10.679)*

0.1232

(1.529)

0.218, 0.785 0.557 *** ***

Dallas 1 -0.195

(1.930)*

 0.2422

(1.043)

0.021, 0.158 0.124 -0.306, 0.254 0.055

Dallas 2 -1.025

(10.732)*

 0.3862

(2.607)*

-0.031, 0.828 0.540 *** ***

Dallas 3 -0.602

(4.524)*

 0.0353

(0.269)

0.189, 0.493 0.358 -0.221, 0.602 0.093

Miami 1 -0.040

(0.128)

 0.1523

(0.477)

0.014, 0.034 0.029 -0.009, 0.018 0.008

Miami 2 -0.300

(1.728)*

-0.1722

(0.976)

0.110, 0.234 0.202 *** ***

LA 1 -0.400

(2.655)*

 0.0463

(0.246)

0.154, 0.327 0.278 -0.616, 0.207 0.039

LA 2 -0.420

(1.632)

 0.1582

(1.047)

0.116, 0.335 0.273 *** ***

LA 3 -0.541

(3.542)*

 0.0013

(0.015)

0.115, 0.435 0.310 -0.240, 0.328 0.035

LA 4 -0.164

(1.739)*

 0.0143

(0.508)

0.034, 0.132 0.095 -0.066, 0.078 0.011

1

Absolute t statistics are reported in parenthesis.

2

Substitute commodity is romaine lettuce.

3

Substitute commodity is green-leaf lettuce.

*Denotes statistical significance at the 90% level.

***Denotes the low-price chain or the only reporting chain in the metropolitan area for this commodity.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 35

implied exercise of market power. Thus, we are

confident in concluding that most of the chains are

setting prices for iceberg lettuce in excess of full

marginal costs, but the implied exercise of market

power is not especially high for most chains.

However, the implications of this conclusion for

pricing need to be considered in the context of

equation (7). Even modest market power in pricing

in conjunction with an inelastic demand can cause

a large relative markup of price over full marginal

cost. Consider, for example, a hypothetical retailer

facing a demand elasticity of ε Q,P = -0. 609 (the

mean across the 12 chains) and setting price with

implied oligopoly power of ξ = 0. 360 (the mean

upper bound). These parameters indicate a relative

markup of (P-CT

)/P = 0. 591, or a price that is more

than double full marginal costs.

Vine-Ripe Tomatoes

Data thought to be reliable for vine-ripe tomatoes

were available for nine chains. As for iceberg

lettuce, Durbin-Watson tests generally indicated

the presence of autocorrelated residuals. In five of

the nine cases, analysis supported a specification of

second-degree autocorrelation. Box-Cox tests

supported the double-log specification in seven of

nine cases. Thus, for consistency, we again chose

to work with the demand model specification

indicated in (9)—a double-log model, with second

order autocorrelation.48 In all cases, Roma tomatoes

were used as the substitute commodity.49Unlike

iceberg lettuce, a distinctive wholesaling sector

exists for fresh tomatoes. Wholesalers or

“repackers”, as they are known in the trade, sort

tomatoes and check for spoilage, appropriate

ripeness, etc. From industry sources, we estimated

the repacking charge to be $4. 00 per carton for the

period of the analysis. Thus, the upper-bound

estimates of implied oligopoly behavior are based

on acquisition cost, as measured by the FOB price

and repacking costs, including shipping. Because

repackers perform functions that otherwise would

be performed by retailers, inclusion of these costs

for fresh tomatoes increases the accuracy of the

upper bound calculations relative to what was

possible for iceberg lettuce. When possible, lowerbound calculations on the implied exercise of

 

48 The consequence of working with an AR(2) model when the

data do not reject an AR(1) specification is a minor loss of

efficiency, i.e., an extraneous ∆ parameter is being estimated. 49 Ultimately, Chicago 1 had to be omitted for vine ripes

because, despite attempting a variety of model specifications,

we were unable to fit a downward-sloping demand function to

its data.

oligopoly power were also computed, as described

previously.

Results of the analysis are summarized in table 14.

The estimate of the own-price elasticity of demand

is statistically significant in six of the eight cases,

and the point estimate indicates an inelastic

demand in six of the eight cases. The results

suggest that the Roma tomato is not particularly

important as a substitute commodity. The

coefficient was significant in only two cases, and

the hypothesized positive sign, to indicate a

substitute relationship, emerged in only five of the

eight cases.

The upper-bound estimates of implied oligopoly

power vary widely among the chains. The low

mean estimate is 0. 09 for Miami 2, and the high

mean is 1. 11, i. e. , essentially the equivalent of

simple monopoly pricing, for Los Angeles 2. The

average of the upper-bound means among the eight

chains is 0. 53.50 Given the limited number of

chains in the analysis, we were only able to

compute the lower bound estimates for three cases.

The mean lower bounds on implied oligopoly

power ranged from ˆξ = 0. 057 (Los Angeles 4) to

ˆξ = 0. 333 (Dallas 4).

Mature-Green Tomatoes

Data considered to be satisfactory were available

for 11 chains. We were unable to generate a

downward-sloping demand curve for Dallas 3 and,

accordingly, it was omitted from the subsequent

analysis. The tests for model specification yielded

outcomes similar to those obtained for iceberg

lettuce and vine-ripe tomatoes—the presence of

autocorrelation could not be rejected in all cases

with second-order autocorrelation supported in

eight of 10 cases. The Box-Cox tests supported use

of the double-log model in eight of the 10

instances. Thus, the demand model in (9) was

deemed appropriate.

Results of the analysis are summarized in table 15.

The estimated own-price elasticity was negative for

the remaining 10 chains, and the coefficient was

statistically significant in eight of those cases. As

was true for vine ripes, Roma tomatoes were not

especially effective in the model as a substitute

commodity. The estimated cross-price elasticity

 

50As a comparison to the case of a single-product seller, ˆ

ξ = 0.53 is

roughly equivalent to the market power that would be exercised by a

two-firm oligopoly under Cournot competition.
36 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

was positive in only six of the 10 cases, and was

statistically significant in four of those instances.

The mean upper-bound estimates on implied

oligopoly power varied from 0. 076 (Los Angeles

1) to 1. 156 (Dallas 4). The average of the upperbound means across chains was ˆξ = 0. 482. We

were able to compute the lower-bound estimates

for six chains. They range from a low of 0. 021

(Los Angeles 1) to a high of 0. 748 (Dallas 4), with

a mean value of ˆξ = 0. 203.

Summary of Analysis of Pricing

Behavior to Consumers

The implications for consumers from retailers’

pricing behavior cannot be discerned from analysis

of a few produce commodities. However, the

manner in which retailers set prices for these

commodities does have implications for producers,

and this was the focus of our analysis. Estimation

of chain-level demand functions for iceberg lettuce,

vine-ripe tomatoes, and mature-green tomatoes

revealed inelastic demands in the vast majority of

cases. Retailers who face inelastic demands for

products have the opportunity to exploit those

demands by marking price up in excess of full

marginal costs. The extent of this markup reveals

the market power implied by the retailers’ decision.

Because we lacked information on retailers’ selling

costs, our estimation of the implied market power

cannot be precise. Rather, we generated a set of

upper-bound and lower-bound estimates.

Even based on the upper-bound calculations, it

seems clear that retailers’ are not fully exploiting

consumers’ inelastic demands for produce

commodities in their pricing decision. This result is

consistent with the intuitive model of consumer

shopping behavior set forth here. Consumers’

willingness and ability to consider multiple stores

to conduct a given shopping trip serves as a brake

on retailers’ pricing. On the other hand, the

estimates indicate that most retailers are setting

prices for iceberg lettuce and fresh tomatoes in

excess of full marginal costs. The results were

rather consistent across the three commodities in

the extent of markups indicated. These markups

curtail movement of the commodity within the

chain and increase the amount of a given harvest

that must be diverted to lower-valued uses. Such

retailer markups of price are, thus, harmful to

producer welfare.

Table 14—Implied Oligopoly Power for Vine-Ripe Tomatoes

Chain Own price

Elasticity1

Cross price

Elasticity1

Oligopoly power:

upper bound

Oligopoly power:

Lower bound

Range Mean Range Mean

Atlanta 2 -0.206

 (3.808)*

-1.481

(1.407)

0.12, 0.17 0.15 *** ***

Albany 1 -1.387

 (9.752)*

 0.440

(2.632)*

0.69, 1.01 0.86 *** ***

Dallas 2 -0.971

 (2.547)*

 0.667

(2.401)*

0.30, 0.61 0.48 *** ***

Dallas 4 -0.681

 (1.090)

-0.005

(0.001)

0.45, 0.54 0.51 0.189, 0.413 0.333

Miami 2 -0.145

 (2.538)*

 0.017

(0.330)

0.07, 0.11 0.09 *** ***

LA 2 -1.590

(10.881)*

 0.221

(1.006)

0.58, 1.30 1.11 *** ***

LA 3 -0.874

 (3.414)*

-0.658

(1.571)

0.43, 0.80 0.69 -0.395, 0.688 0.177

LA 4 -0.445

 (1.731)*

 0.019

(0.081)

0.30, 0.37 0.35 -0.146, 0.294 0.057

1

Absolute t statistics are reported in parenthesis.

*Denotes statistical significance at the 90% level.

***Denotes the low-price chain or the only reporting chain in the metropolitan area for this commodity.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 37

Retailer Pricing Behavior for

Bagged Salads

Bagged or packaged salads have been a highgrowth segment of the produce industry. Calvin et

al. (2000) report that 462 lettuce-based bagged

salad items (i. e. UPC codes) existed in 1999,

compared to 202 items in 1993. Sales in the freshcut salad category reached nearly $1. 9 billion in

2000, based on A. C. Nielson statistics. The largest

selling category is iceberg-based blends, with a

cumulative 39 percent market share in 2000. Salad

blends, featuring a combination of lettuce types,

comprised 30 percent of sales at the same time,

with salad kits accounting for 13 percent of sales.

The basic technology to prepare bagged salads is

not complicated, and many lettuce shippers entered

this segment of the industry during its rapid growth

stage during the 1990s.51 The bagged salad sector

has since consolidated. Calvin et al. report that the

top two sellers (Fresh Express and Dole) held a

combined 75. 5 percent share in 1999 of

supermarket sales for fresh-cut salads. A third firm,

Ready Pac, had a market share near 8 percent, with

 

51 Thompson and Wilson (1999) estimated the upfront investment costs

to process fresh-cut salads to be in the range of $20-30 million.

private-label brands jointly comprising 9. 7

percent.

Our analysis focuses on the traditional icebergbased (IBB), fresh-cut salad. It remains the largest

seller, each of the top three manufactures produces

one or more sizes of this product, and we are able

to analyze pricing links to the farm input, iceberg

head lettuce, which comprises the essential

ingredient to producing an IBB salad.

Our analysis for IBB salads necessarily differs

from the preceding analysis conducted for iceberg

head lettuce and fresh tomatoes, due to differences

in the available information. Notably, we lack

information on processing costs and on the pricing

arrangements between processors and retailers,

including fees paid by processors. Thus, we are

unable to conduct formal investigations of

oligopoly or oligopsony power. Nonetheless, some

useful conclusions emerge based on the

information that is available.

Table 16 provides an overview of pricing behavior

for IBB salads for the 20 retail chains. The table

reports mean price per lb. and variance of price (in

parentheses) for each brand of IBB salad carried by

the chain, including its own private label. Most

chains carried multiple sizes of each brand (e. g.,

16 oz, 32 oz, 48 oz), and the price reported is a

Table 15—Implied Oligopoly Power for Mature-Green Tomatoes

Chain Own price

Elasticity1

Cross price

Elasticity1

Oligopoly power:

Upper bound

Oligopoly power:

Lower bound

Range Mean Range Mean

Atlanta 1 -0.883

(7.617)*

 0.020

(0.157)

-0.161, 0.744 0.587 *** ***

Atlanta 2 -0.481

(3.926)*

-0.098

(0.382)

 0.169, 0.410 0.340 -0.566, 0.327 0.039

Dallas 1 -0.396

(2.027)*

 0.091

(0.939)

 0.200, 0.396 0.275 0.048, 0.227 0.152

Dallas 2 -0.854

(6.702)*

 0.537

(4.458)*

 0.080, 0.591 0.403 *** ***

Dallas 4 -1.600

(6.672)*

-0.164

(0.625)

 0.639, 1.368 1.156 0.249, 1.030 0.748

Miami 1 -0.513

(2.791)*

-0.083

(0.508)

 0.050, 0.438 0.321 *** ***

LA 1 -0.107

(0.441)

 0.162

(1.705)*

 0.051, 0.090 0.076 -0.083, 0.079 0.021

LA 2 -1.025

(5.348)*

 1.288

(6.391)*

 0.036, 0.815 0.627 *** ***

LA 3 -1.315

(8.390)*

 0.605

(2.912)*

 0.386, 1.117 0.906 -0.917, 1.040 0.217

LA 4 -0.174

(0.643)

-0.014

(0.044)

 0.070, 0.146 0.125 -0.083, 0.138 0.042

1

Absolute t statistics are reported in parenthesis.

*Denotes statistical significance at the 90% level.

***Denotes the low-price chain or the only reporting chain in the metropolitan area for this commodity.
38 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

sales-weighted average across size categories. The

average and variance for the chain’s iceberg head

lettuce price is also provided for comparison. An

immediate point of interest in table 16 is the variety

of behaviors exhibited among the retailers as to

how many and which brands of IBB salads to

carry. Among our sample chains, only the Los

Angeles chains carried Ready Pac IBB salads,52 and

none carried minor brands outside of the top three.

Only Los Angeles 2 carried all three leading brands

of IBB salads. Seven chains carried two brands—

Fresh Express and Dole in five of those cases, with

Dole and Ready Pac and Fresh Express and Ready

Pac in the others. Six chains carried a private label

brand. Among those chains, three carried only their

private label brand, two carried their private label

and one other brand (Dole, in each case), while the

other carried both Dole and Fresh Express. Finally,

three chains carried Fresh Express IBBs

exclusively, while two carried Dole IBBs

exclusively. 53

 

52 Ready Pac tends to specialize in producing salad blends. Several of

the chains which did not carry Ready Pac IBB salads carried other

product categories by Ready Pac.

53 It is important to emphasize that this information applies only to

IBBs. It was quite common among the sample chains to carry other

salad items from a processor, e.g., Ready Pac, even though the chain

did not carry the processor’s IBBs.

Of similar interest to the decision as to how many

and which brands to carry are the decisions as to

pricing strategy. Figure 17 illustrates price per

pound for 1998-99 for six chains for each IBB

salad brand carried by the chain. The six were

chosen because they illustrate the range of pricing

behaviors practiced by the 20 chains in the sample.

Panel (a) illustrates pricing for Los Angeles 1 for

its private label, the only IBB salad it carries.

While LA 1 charges an average price per lb. that is

lower than the price charged by its Los Angeles

competitors for the national brands in all but one

case (LA 2’s pricing of Ready Pac), the price

varies considerably from week to week. LA 1

follows a strategy of setting its private label price

at about $1. 60/lb for 3-to-5 week intervals, and

then offering a promotional price, often less than

$1. 00/lb for a 1-week period. Albany 2, illustrated

in panel (c), uses a somewhat similar strategy for

pricing Dole’s IBBs, the only brand it carries.

The converse of the preceding behavior is the

every-day-low-price strategy practiced by Miami 3,

illustrated in panel (e). Miami 3 carries Dole and

Fresh Express, in addition to its own private label.

Miami 3 maintained its private label price in the

range of $1. 40/lb below the prices it charged for

either Fresh Express or Dole. It maintained

Table 16—Retailer Mean Price and Variance of IBB Packaged Salads

Private Label1Fresh Express1Dole1Ready Pac1Head Lettuce1

LA 1 1.404 (0.050) 1.087 (0.064)

LA 2 1.526 (0.063) 1.416 (0.029) 1.356 (0.049) 0.953 (0.022)

LA 3 1.486 (0.008) 1.490 (0.007) 1.026 (0.033)

LA 4 1.685(0.005) 1.490 (0.011) 1.027 (0.038)

Dallas 1 1.652 (0.023) 1.233 (0.049)

Dallas 2 1.354 (0.015) 0.923 (0.020)

Dallas 3 1.410 (0.053) 1.121 (0.060)

Dallas 4 1.901 (0.032) 1.253 (0.012)

Dallas 5 1.331 (0.038) 1.943 (0.115) 0.950 (0.000)

Atlanta 1 1.702 (0.041) 1.193 (0.026)

Atlanta 2 1.724 (0.122) 1.252 (0.059)

Atlanta 3 1.636 (0.042) 1.685 (0.121) 1.210 (0.000)

Chicago 1 1.625 (0.044) 1.977 (0.146) 1.237 (0.006)

Chicago 2 1.896 (0.028) 1.931 (0.019) 1.400 (0.000)

Chicago 3 2.035 (0.147) 1.100 (0.000)

Miami 1 1.487 (0.052) 1.270 (0.047)

Miami 2 1.668 (0.080) 1.605 (0.049) 1.004 (0.029)

Miami 3 1.394 (0.003) 1.882 (0.023) 1.520 (0.002) 1.210 (0.000)

Albany 1 1.630 (0.038) 1.619 (0.068) 1.111 (0.044)

Albany 2 1.430 (0.079) 1.290 (0.000)

1

Variances are indicated in parentheses.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 39

 relatively stable prices for these brands as well,

although it set a premium price for Fresh Express,

on average $0. 26/lb more than it charged for Dole.

This price ranking between Dole and Fresh Express

is not, however, consistent among retailers. Of the

six chains that carried both, Dole had the higher

average price in half of the cases. Dallas 5 in panel

(b) illustrates a case where Dole commands the

premium price relative to Fresh Express, and both

items are subject to periodic price promotions.

Atlanta 3 carries its own private label and a single

brand, Fresh Express. As illustrated in panel (d),

Atlanta 3 has chosen to maintain a rather constant

price for its private label of about $1. 60/lb, while

using Fresh Express’ IBB salad as a promotional

item. Although Atlanta 3’s price for Fresh Express

was higher on average than its private label price,

for many weeks during 1998-99 Atlanta 3 set a

promotional price of about $1. 00/lb for Fresh

Express that was much less than its private label

price. Finally, LA 4, depicted in panel (f),

illustrates a strategy whereby Fresh Express is sold

at a relatively constant premium price, and Ready

Pac is offered as a low-price alternative, which is

also featured on periodic price promotions.
40 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Further insight into the coordination or lack thereof

in retailers’ pricing decisions for IBB bagged

salads is provided in table 17. This table is

organized on a city-by-city basis and contains

correlation coefficients for the prices charged by

each chain in the city for the brands of IBB salads

it carries and its iceberg head lettuce price.54

Correlations of these prices with the FOB iceberg

lettuce price are also provided. Several conventions

are utilized in the table to highlight key

information. Shading is used to highlight blocks of

 

54 Albany was omitted from this table because information was

available for only two chains, each of which had limited selection of

IBB salads.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 41

Table 17—Price Correlations for IBB Salads, Iceberg Head Lettuce, and FOB Price

Dallas

FOB

Dallas 1

Private

Label

Dallas 1

Head

Dallas 5

Fresh

Express

Dallas 5

Dole

Dallas 5

Head

Dallas 2

Dole

Dallas 2

Head

Dallas 4

Fresh

Express

Dallas 4

Head

Dallas 3

Fresh

Express

Dallas 3

Head

FOB 1.000

Dallas 1 Private Label 0.056 1.000

Dallas 1 Head 0.388 0.052 1.000

Dallas 5 Fresh Express -0.067 0.019 0.031 1.000

Dallas 5 Dole -0.274 -0.021 -0.118 0.438 1.000

Dallas 5 Head . . . . . .

Dallas 2 Dole 0.078 0.086 -0.088 0.018 0.056 . 1.000

Dallas 2 Head 0.605 0.075 0.283 0.091 -0.208 . 0.084 1.000

Dallas 4 Fresh Express 0.047 0.016 -0.141 -0.051 0.004 . 0.039 0.039 1.000

Dallas 4 Head 0.061 0.009 0.069 0.068 0.148 . -0.242 0.008 -0.193 1.000

Dallas 3 Fresh Express 0.050 0.004 0.078 -0.062 -0.063 . 0.187 0.002 -0.156 0.382 1.000

Dallas 3 Head 0.412 -0.010 0.279 0.030 -0.038 . 0.051 0.417 -0.072 -0.036 -0.141 1.000

Los Angeles

FOB

LA 1

Private

Label

LA 1

Head

LA 2

Fresh

Express

LA 2

Dole

LA 2

Ready

Pac

LA 2

Head

LA 3

Dole

LA 3

Ready

Pac

LA 3

Head

LA 4

Fresh

Express

LA 4

Ready

Pac

LA 4

Head

B 1.000

1 Private Label 0.110 1.000

1 Iceberg 0.688 0.073 1.000

2 Fresh Express -0.133 0.124 -0.035 1.000

2 Dole -0.169 0.015 -0.279 0.389 1.000

2 Ready Pac 0.103 0.021 0.139 -0.083 -0.063 1.000

2 Head 0.446 0.174 0.613 0.005 -0.238 0.125 1.000

3 Dole -0.237 0.015 -0.405 0.179 0.385 -0.330 -0.146 1.000

3 Ready Pac 0.011 0.133 -0.007 0.018 0.216 -0.349 0.072 0.137 1.000

3 Head 0.534 0.029 0.775 -0.047 -0.465 0.122 0.717 -0.332 -0.078 1.000

4 Fresh Express 0.033 0.009 0.027 -0.078 -0.002 0.065 -0.008 -0.155 -0.027 0.014 1.000

4 Ready Pac -0.201 -0.032 -0.280 0.221 0.214 -0.014 -0.178 0.058 0.032 -0.272 0.028 1.000

4 Head 0.456 0.063 0.660 0.063 -0.268 -0.032 0.659 -0.192 0.046 0.733 0.019 -0.232 1.000

Miami

FOB

Miami 1

Private

Label

Miami 1

Head

Miami 3

Private

Label

Miami 3

Fresh

Express

Miami 3

Dole

Miami 3

Head

Miami 2

Fresh

Express

Miami 2

Dole

Miami 2

Head

FOB 1.000

Miami 1 Private Label 0.017 1.000

Miami 1 Head 0.345 0.049 1.000

Miami 3 Private Label 0.036 0.043 -0.040 1.000

Miami 3 Fresh Express -0.198 0.136 -0.086 0.146 1.000

Miami 3 Dole -0.242 0.042 -0.125 0.212 0.710 1.000

Miami 3 Head . . . . . . .

Miami 2 Fresh Express -0.169 0.095 0.085 -0.054 -0.050 0.088 . 1.000

Miami 2 Dole 0.081 -0.138 -0.019 -0.091 -0.264 -0.283 . -0.130 1.000

Miami 2 Head 0.742 -0.009 0.559 0.128 -0.215 -0.230 . -0.007 0.032 1.000
42 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

correlation coefficients for the prices charged by a

given chain. Correlations of prices charged by

different chains for a given brand (i. e. , Fresh

Express, Dole, and Ready Pac) are indicated by

bold face, and correlations of prices for head

lettuce across chains are indicated in italics. Empty

cells reflect retailers who maintained a constant

iceberg head lettuce price throughout the sample

period.

Recall that correlation coefficients fall in the range

of –1. 0 (perfect negative correlation) to 1. 0

(perfect positive correlation), with values near zero

indicating very little correlation between the

movements of the particular price pair. The central

message of table 17 is that prices within a city for

IBB salads tend to exhibit very low levels of

correlation. Indeed, in many cases the correlation is

negative, indicating prices that move on average in

opposite directions.

Correlations of the retail prices with the FOB

iceberg lettuce price are provided in the first

column of each correlation matrix. In many cases,

retailers’ prices for iceberg head lettuce are

positively correlated with the FOB price for

iceberg lettuce. For example, among the Dallas

chains, these correlations range from 0. 061 (Dallas

4) to 0. 605 (Dallas 2). However, the correlations

between the iceberg FOB price and the bagged

salad prices are invariably low, and are often

negative. The clear message is that the link

between the farm-gate price and the retail price for

IBBs is almost completely attenuated, even though

iceberg lettuce is the central ingredient in an IBB

salad. Consider, for example, the Los Angeles

chains. Four of the branded salad prices are

negatively correlated with the FOB price, and the

highest positive coefficient is only 0. 110 (LA 1

private label).

Further consideration of the price patterns in figure

17 helps in understanding the lack of correlation

between movements in the FOB price and the

chains’ prices for IBB salads. Many chains choose

to use IBBs for price promotions. The promotions

tend to follow an imprecise, but regular cycle. The

correlation coefficients suggest that promotions are

not coordinated with movements in the FOB price.

The alternative strategy, to maintain stable prices

for particular brands of IBBs, also, of course,

results in low levels of correlation with the highly

volatile FOB price.

Table 17—Price Correlations for IBB Salads, Iceberg Head Lettuce, and FOB Price—

continued

Atlanta

FOB

Atlanta 1

Fresh

Express

Atlanta 1

Head

Atlanta 3

Private

Label

Atlanta 3

Fresh

Express

Atlanta 3

Head

Atlanta 2

Fresh

Express

Atlanta 2

Head

FOB 1.000

Atlanta 1 Fresh Express 0.102 1.000

Atlanta 1 Head -0.012 0.129 1.000

Atlanta 3 Private Label -0.266 -0.122 0.296 1.000

Atlanta 3 Fresh Express -0.058 0.014 -0.137 -0.005 1.000

Atlanta 3 Head . . . . . .

Atlanta 2 Fresh Express -0.071 0.011 -0.017 -0.116 0.011 . 1.000

Atlanta 2 Head 0.472 0.078 0.243 0.003 -0.021 . -0.083 1.000

Chicago

FOB

Chicago 2

Fresh

Express

Chicago 2

Dole

Chicago 2

Head

Chicago 3

Fresh

Express

Chicago 3

Head

Chicago 1

Fresh

Express

Chicago 1

Dole

Chicago 1

Head

FOB 1.000

Chicago 2 Fresh Express 0.016 1.000

Chicago 2 Dole 0.053 0.249 1.000

Chicago 2 Head . . . .

Chicago 3 Fresh Express -0.153 0.046 0.195 . 1.000

Chicago 3 Head . . . . . .

Chicago 1 Fresh Express -0.028 0.090 0.286 . 0.206 . 1.000

Chicago 1 Dole -0.007 -0.030 0.055 . -0.073 . 0.145 1.000

Chicago 1 Head -0.052 -0.077 -0.188 . 0.055 . 0.017 0.025 1.000
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 43

Consider now the bold-faced coefficients,

indicating correlations between chains’ prices for

the same brand of IBB. They, too, are invariably

low. Three of four instances are negative for the

Dallas chains, three of five are negative for the Los

Angeles chains, and two of three are negative for

Miami. The correlations of prices within brands are

positive, but essentially zero, for both Atlanta and

Chicago. Clearly there is no coordination among

the chains in making pricing decisions for IBB

salads in any of the sample cities. The correlations

for iceberg head lettuce prices (indicated by italics)

within a city are generally higher, but in all cases

they are considerably less than 1. 0. Because

retailers in the same city face similar or identical

FOB prices and shipping costs for iceberg lettuce,

the expectation is that retail prices within a city

should be highly correlated. The fact that the

correlation coefficients are generally in the range

of 0. 5 to 0. 7 indicates that there is considerable

independence among retailers in pricing iceberg

lettuce.

Finally, the shaded portions of table 17 reveal little

correlation of prices within a chain for iceberg

head lettuce and IBB salads. For example, for the

Dallas chains, the correlations of the chain’s head

lettuce price with its IBB salad price range from –

0. 193 (Dallas 4) to 0. 084 (Dallas 2). It is not clear

from a strategic perspective what relationship to

anticipate for these within-chain correlations. If a

chain were passively setting prices based upon its

costs, the prices should all move together, as a

function of the FOB price. As table 17 shows, this

tends not to happen. The most likely explanation

for the near independence of the IBB and head

lettuce price movements within a chain is simply

that each price is set independently, with little or

no attempt made to develop a coordinated pricing

strategy across the iceberg products.

To further investigate the relationship between

price movements at the farm level for iceberg

lettuce and price movements at retail for IBB, we

specified regression equations of the following

form:

(10)

R FF F F ∆ =β +β∆ +β ∆ +β ∆ +β ∆ P PP P P t 0 1 t 2 t1 3 t2 4 t3 −− − .

In (10), R ∆Pt = R Pt - R Pt 1− is the change in retail price

for an IBB salad product, and F ∆Pt = F Pt - F Pt 1− is the

corresponding change in the FOB price for iceberg

lettuce. F ∆Pt j − thus denotes F ∆Pt lagged j times.

Thompson and Wilson (1999) report that bagged

salads have a shelf life of 14-18 days. Allowing up

to a week for product to move from the field to the

shelf, we have a three to four week window of time

over which a price change at the farm level may

influence price at retail. Thus, in addition to the

contemporaneous change in price at the farm level,

we also specify R ∆Pt as a function of 1-period, 2-

period, and 3-period lags of F ∆Pt .

Equation (10) was estimated for each IBB salad

brand carried by each chain. For parsimony of

presentation, we present only the results for Fresh

Express in table 18. The results for the other

national brands and for the private-label brands

were very similar to the Fresh Express results. The

results in table 18 reveal no consistent relationship

between the prices retailers are setting for Fresh

Express IBB salads and contemporaneous and

lagged movements in the FOB price. Signs are not

consistent across chains, most coefficients are not

statistically significant, and total explanatory

power of the regressions, as measured by the

adjusted R2

 statistic is extremely low. The results

from estimating (10) reaffirm the fundamental fact

that pricing at retail for IBB salads bears

essentially no relation to pricing outcomes at the

farm level. Because IBBs retain the closest link

among bagged salads to a farm product input, we

would anticipate that a similar conclusion applies

to other bagged salad products, such as blends and

kits
44 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Table 18—Price Equations for Fresh Express Bagged Salads1

City/Chain Constant {delta}P^F_t {delta}P^F_t1{delta}P^F_t2{delta}P^F_t3Adjusted R2

Atlanta 1 -0.000

(0.003)

0.381

(0.982)

-0.029

(0.073)

-0.065

(0.171)

0.336

(0.953)

-0.025

Atlanta 2 0.003

(0.068)

-0.199

(0.331)

-0.062

(0.102)

-0.096

(0.163)

0.514

(0.937)

-0.027

Atlanta 3 -0.004

(0.087)

0.345

(0.515)

0.351

(0.518)

-0.507

(0.775)

0.854

(1.401)

-0.018

Chicago 1 0.003

(0.122)

-0.180

(0.638)

-0.303

(1.061)

0.172

(0.623)

-0.380

(1.476)

-0.009

Chicago 2 -0.000

(0.016)

0.077

(0.257)

0.315

(1.044)

0.217

(0.745)

-0.127

(0.467)

-0.011

Chicago 3 0.008

(0.254)

0.818

(1.851)*

-0.377

(0.845)

-0.033

(0.077)

0.316

(0.785)

 0.003

Dallas 3 0.000

(0.010)

0.054

(0.141)

0.413

(1.063)

-0.087

(0.232)

0.268

(0.765)

-0.025

Dallas 4 0.007

(0.309)

-0.170

(0.559)

0.205

(0.666)

-0.710

(2.392)*

0.417

(1.506)

 0.024

Dallas 5 -0.006

(0.254)

0.309

(1.019)

0.139

(0.453)

-0.205

(0.694)

0.188

(0.682)

-0.020

Miami 2 0.004

(0.110)

-0.366

(0.833)

0.016

(0.037)

0.594

(1.340)

0.737

(1.654)*

 0.050

Miami 3 0.002

(0.145)

-0.039

(0.198)

-0.089

(0.459)

-0.006

(0.032)

0.106

(0.536)

-0.033

LA 2 0.003

(0.297)

0.156

(1.140)

-0.054

(0.387)

0.066

(0.493)

0.017

(0.135)

-0.027

LA 4 0.000

(0.008)

-0.040

(0.292)

0.129

(0.940)

-0.145

(1.102)

0.068

(0.552)

-0.025

 1Absolute t statistics are indicated in parentheses.

 * Denotes statistical significance at the 90 percent level.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 45

Conclusions

Consolidation among grocery retailers has caused

concern about the state of competition in food

marketing. This study has investigated grocery

retailer behavior in the procurement and selling of

iceberg lettuce, fresh tomatoes, and bagged salads.

The emphasis throughout has been on the

implications of retailers' behavior for the welfare of

producers. Knowing the effects of retailer behavior

on consumer welfare would require a

comprehensive analysis across product categories.

The analysis involved three major components.

First, we conducted a detailed analysis of farmretail price spreads (margins) for CaliforniaArizona iceberg lettuce, vine-ripe tomatoes from

California, and mature-green tomatoes from both

California and Florida. A central point of the pricespread analysis was to investigate the role of total

shipments in influencing the price spread. Under

competitive procurement of the aforementioned

commodities, there is little reason for the shipment

volume to affect the margin. However, under

imperfect competition, the hypothesis offered was

that high shipments volume for a perishable

commodity would diminish the bargaining power

of sellers, relative to buyers, and lead to a widening

of the margin. This effect was confirmed for each

of the commodities studied. An additional result of

note from the margin analysis was that

transportation costs did not have the effect

predicted by a model of perfect competition.

Changes in shipping costs tended to have little

effect on the price spread. This result is also

consistent with imperfect competition in

procurement, because buyers with market power

rationally absorb a portion of shipping costs. It is

also consistent with an objective of retailers to

stabilize prices to consumers.

We conducted formal tests for buyer market power

in procurement of the fresh produce commodities,

based upon the pricing model developed by Sexton

and Zhang (1996). Estimation results for iceberg

lettuce supported the earlier conclusion of Sexton

and Zhang that retailers were able to capture the

lion's share (about 80 percent) of the market

surplus, whereas under competitive procurement,

the entire surplus would go to producers. These

results also lend support to the finding from the

price-spread analysis that large harvest volumes

served to reduce sellers' relative bargaining power.

Application of the model to fresh tomatoes yielded

rather mixed results. Parameters were generally

estimated imprecisely, and a hypothesis of perfect

competition in procurement could not be rejected

for either Florida or California mature-green

tomatoes. Based on the parameter estimates,

producers' share of the market surplus is

considerably higher for tomatoes than for iceberg

lettuce. Florida's mature-green tomato industry, in

particular, appeared to have been effective in

utilizing collective action to maintain a floor on its

selling price and capture a substantial share of the

market surplus in excess of the floor.

Retailer market power in selling to consumers is

also harmful to producers because it curtails

product movement and forces diversion of the

product to lower-valued uses. We used a simple

framework to develop estimates of retailers'

implied oligopoly power in setting prices to

consumers for iceberg lettuce and fresh tomatoes.

The evidence suggested that retailers are setting

prices for these commodities in excess of full

marginal costs, but are not exploiting the

magnitude of market power available to them,

based upon the estimated price elasticities of

demand. Also noteworthy was that several retailers

maintained constant selling prices for iceberg

lettuce throughout our 2-year sample period.

Although such pricing may be part of a rational

retailer strategy to attract and retain customers, we

showed that fixing or stabilizing prices was, in

general, harmful to producer welfare, because it

leads to greater price volatility in the segments of

the market that do not hold prices fixed.

Our analysis for bagged salads revealed a great

diversity among retailers as to strategy for this

segment of the market. Focusing on iceberg-based

salads, we showed that chains differed both in

terms of pricing and product selection, including

whether to carry a private-label brand. The data

revealed no evidence of coordination among

retailers in setting prices. The analysis also

revealed a nearly complete absence of a

relationship between the farm-level price for

iceberg lettuce and the prices set at retail for

bagged salads.

On balance, we believe that the evidence supports a

conclusion that buyers are often able to exercise

oligopsony power in procuring fresh produce

commodities. This result should not be surprising,

given the structural conditions in these markets.

The apparent success of the Florida mature-green

tomato industry in enforcing a price floor and

capturing a significant share of the surplus in

excess of the price floor demonstrates the potential

benefits to producers through the coordinated

behavior allowed them under the law.
46 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

The structure of grocery retailing necessarily gives

large retailers some degree of market power in

terms of an ability to influence price. Ample

evidence of this power is the wide variety of

pricing strategies that were manifest for the

commodities included in this study. However, there

was no evidence of coordinated pricing or

collusion among retailers in a given city. To the

extent that retailers are exercising market power, in

the sense of marking up prices in excess of full

marginal costs, they are exploiting the unilateral

monopoly power they possess through geographic

and brand differentiation. The pricing behavior is

not the result of coordination with other retailers.

Occasional concerns over data quality, and the

wide variety of pricing strategies followed by

retailers both limit our ability to make inferences

concerning market power. As always, it was

necessary to make certain assumptions to facilitate

testing hypotheses concerning market power. On

balance however, retailers' pricing behavior to

consumers probably works to the detriment of

produce grower-shippers. Markups of price above

cost curtail product movement to the retail sector.

Retail prices held constant in the face of fluctuating

farm-gate prices also cause diversion of product to

lower-valued uses. The timing of sales promotions

for produce items bears little or no relationship to

prices at the farm level. Retailers thus appear to be

acting in their own perceived best interest in

making these pricing decisions. Closer

coordination appears to be evolving between

grower-shippers and retailers, and perhaps this

coordination can result in retail pricing decisions

that better serve both retailers’ and producers'

interests.
Economic Research Service/USDA Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 47

References

Alston, J. M., R. J. Sexton, and M. Zhang, 1997, The

effects of imperfect competition on the size and

distribution of research benefits, American Journal

of Agricultural Economics 79, 1252-1265.

Azzam. A. M., 1999, Asymmetry and rigidity in

farm-retail price transmission, American Journal

of Agricultural Economics 81, 525-533.

Belsley, D., E. Kuh, and R. Welsch, 1980,

Regression Diagnostics (John Wiley, New York).

Benson, B. L. and M. D. Faminow, 1985, An

alternative view of pricing in retail food markets,

American Journal of Agricultural Economics 67,

298-306.

Bettendorf, L. and F. Verboven, 2000, Incomplete

transmission of coffee bean prices: evidence from

the Netherlands, European Review of Agricultural

Economics 27, 1-16.

Binkley, J. K. and J. M. Connor, 1998, Market

competition and metropolitan B area grocery

prices, Journal of Retailing 74, 273-293.

Calvin, L., R. Cook, M. Denbaly, C. Dimitri, L.

Glaser, C. Handy, M. Jekanowski, P. Kaufman, B.

Krissoff, G. Thompson, and S. Thornsbury, 2001,

U. S. Fresh Fruit and Vegetable Marketing:

Emerging Trade Practices, Trends, and Issues, U.

S. Department of Agriculture, Economic Research

Service, Agricultural Economic Report No. 795,

January.

Carlton, D. W., 1989, The theory and the facts of

how markets clear: is industrial organization

valuable for understanding macroeconomics? in:

R. Schmalensee and R. Willig, eds. Handbook of

Industrial Organization, (North Holland,

Amsterdam), 909-946.

Connor, J. M., 1999, Evolving research on price

competition in the grocery retailing industry: an

appraisal, Agricultural and Resource Economics

Review 28, 119-127.

Cotterill, R. W., 1986, Market power in the retail

food industry: evidence from Vermont, Review of

Economics and Statistics 68, 379-386.

Cotterill, R. W., 1999, Market power and the

Demsetz quality critique: an evaluation for food

marketing, Agribusiness 15, 101-118.

Cotterill, R. W., 1993, Competitive Strategy Analysis

in the Food System (Westview Press, Boulder,

CO).

Cotterill, R. W. and L. E. Haller, 1992, Barrier and

queue effects: a study of leading US supermarket

chain entry patterns, Journal of Industrial

Economics 40, 427-440.

Cotterill, R. W. and C. D. Harper, 1995, Market

power and the Demsetz quality critique: an

evaluation for food retailing, Food Marketing

Policy Center, Research Report No. 29,

University of Connecticut.

Faminow, M. D. and B. L. Benson, 1985, Spatial

economics implications for food market response

to retail price reporting, Journal of Consumer

Affairs 19, 1-19.

Greene, W. H., 1990, Econometric Analysis,

(MacMillan, New York).

Hall, L., A. Schmitz and J. Cothern, 1979, Beef

wholesale-retail marketing margins and

concentration, Economica 46, 295-300.

Hoch, S. J., B-D Kim, A. L. Montgomery, and P. E.

Rossi, 1995, Determinants of store-level price

elasticity, Journal of Marketing Research 32, 17-

29.

Kaufman, P., 2000, Consolidation in food retailing:

prospects for consumers & grocery suppliers,

Agricultural Outlook August, 18-22.

Kaufman, P. and C. Handy, 1989, Supermarket

prices and price differences, U. S. Department of

Agriculture, Economic Research Service,

Technical Bulletin 1176.

Kaufman, P., C. Handy, E. McLaughlin, K. Park,

and G. Green, 2000. Understanding the dynamics

of produce markets: consumption and

consolidation grow. U. S. Department of

Agriculture, Economic Research Service, AIB758, August.

Kiefer, N. M., 1980, A note on regime

classification in disequilibrium models, Review

of Economic Studies 47, 637-639.

Kinnucan, H. W. and O. D. Forker, 1987,

Asymmetry in farm-retail price transmission for

major dairy products, American Journal of

Agricultural Economics 69, 285-292.

Lamm, R. M., 1981, Prices and concentration in the

food retailing industry, Journal of Industrial

Economics 30, 67-78.

MacDonald, J. M., 2000, Demand, information, and

competition: why do food prices fall at seasonal

demand peaks? Journal of Industrial Economics

48, 27-45.
48 Grocery Retailer Behavior Perishable Fresh Produce /CCR-2 Economic Research Service/USDA/ERS

Marion, B. W., W. F. Mueller, R. W. Cotterill, F. E.

Geithman, and J. R. Schmelzer, 1979, The price

and profit performance of leading food chains,

American Journal of Agricultural Economics 61,

420-433.

Marion, B. W., K. Heimforth, and W. Bailey, 1993,

Strategic groups, competition, and retail food

prices, in: R. W. Cotterill, ed., Competitive

Strategy Analysis in the Food System (Westview

Press, Boulder, CO) 179-199.

Messinger, P. R. and C. Narasimhan, 1997, A model

of retail formats based on consumers’ economizing

on shopping time, Marketing Science 16, 1-23.

Melnick, R. and H. Shalit, 1985, Estimating the

market for tomatoes, American Journal of

Agricultural Economics 67, 574-582.

Newmark, C. M., 1990, A new test of the priceconcentration relationship in grocery retailing,

Economics Letters 33, 369-373.

Okun, A. M., 1981, Prices and quantities: a

macroeconomic analysis (The Brookings

Institute, Washington, DC).

Pick, D. H., J. Karrenbrock, and H. F. Carman, 1990,

Price asymmetry and marketing margin behavior:

an example for California B Arizona citrus,

Agribusiness 6, 75-84.

Popkowski Leszczyc, P. T. L, A. Sinha, and H. J. P.

Timmermans, 2000, Consumer store choice

dynamics: an analysis of the competitive market

structure for grocery stores, Journal of Retailing

76, 323-345.

Powers, E. T. and N. J. Powers, 2001, The size and

frequency of price changes: evidence from grocery

stores, Review of Industrial Organization 18, 397-

416.

Richards, T. J. and P. M. Patterson, 2001, Imperfect

competition in fresh produce markets: an empirical

analysis of channel performance, working paper,

Morrison School of Agribusiness and Resource

Management, Arizona State University.

Rotemberg, J. J. and G. Saloner, 1987, The relative

rigidity of monopoly pricing, American Economic

Review 77, 917-926.

Sexton, R. J. and T. A. Sexton, 1994, Cooperative

market power and antitrust with application to

California information-sharing cooperatives, in: R.

W. Cotterill, ed., Competitive Strategy Analysis for

Agricultural Marketing Cooperatives (Westview

Press, Boulder, CO) 179-199.

Sexton, R. J. and M. Zhang. "Can Retailers

Depress Lettuce Prices at Farm Level?,"

California Agriculture 49(May-June 1995):14-

18.

Sexton, R. J. and M. Zhang, 1996, A model of price

determination for fresh produce with application to

California iceberg lettuce, American Journal of

Agricultural Economics 78, 924-934.

Sexton, R. J. and M. Zhang, 2001, An assessment of

the impact of food industry market power on U. S.

consumers, Agribusiness 17, 59-79.

Sirohi, N., E. W. McLaughlin, and D. R. Wittink,

1998, A model of consumer perceptions and store

loyalty intentions for a supermarket retailer,

Journal of Retailing 74 223-245.

Thompson, G. D. and P. N. Wilson, 1999, Market

demands for bagged, refrigerated salads, Journal

of Agricultural and Resource Economics 24, 463-

481.

Walden, M. L., 1990, Testing implications of spatial

economics models: some evidence from food

retailing, Journal of Consumer Affairs 24, 24-43.

Wann, J. J. and R. J. Sexton, 1992, Imperfect

competition in multiproduct food industries with

application to pear processing, American Journal

of Agricultural Economics 74, 980-990.

Wright, J., 2001, Von’s grocery and the

concentration-price relationship in grocery retail,

UCLA Law Review 48, 743-779.

Zhang, P., S. M. Fletcher, and D. H. Carley, 1995,

Peanut price transmission asymmetry in peanut

butter, Agribusiness 11, 13-20.