Analyses of Generic Dairy Advertising, 1984-97. By Noel Blisard, Don

Blayney, Ram Chandran, and Jane Allshouse. Food and Rural Economics

Division, and Market and Trade Economics Division, Economic Research

Service, U.S. Department of Agriculture. Technical Bulletin No 1873.

Abstract

Generic advertising raised fluid milk sales about 6.0 percent, or 18.1 billion

pounds, between September 1984 and September 1997. Sales of cheese rose by

about 6.8 billion pounds (milk equivalent) in the same period because of

increased generic advertising. An assessment of 15 cents per hundredweight of

milk sold commercially, mandated by the Dairy and Tobacco Adjustment Act of

1983, funded the advertising. Activities of the National Fluid Milk Processor

Promotion Board also contributed to increased milk sales over the past year.

Gross returns to dairy farmers between September 1984 and September 1997

were estimated to increase by $3.44 for each dollar spent on generic advertising. 

Keywords: Cheese, fluid milk, advertising, demand, entry, exit, distributed lag,

econometrics, simulation, elasticities, Milk Processor Education Program.

Acknowledgments

The authors appreciate the valuable comments of the U.S. Department of

Agriculture’s Evaluation Committee on Dairy Promotion and the input from

many other individuals and organizations, including the National Dairy

Promotion and Research Board, United Dairy Industry Association, Wisconsin

Milk Marketing Board, and the California Milk Advisory Board. A special

thanks goes to Carrie Ingoglia who edited and prepared the camera copy for this

manuscript.

1800 M Street, NW

Washington, DC 20036-5831 February 1999
ii

Contents

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

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

Background on Advertising ........................................ . 3

The Theory of Demand With Advertising ............................. 4

Entry and Exit in Commodity Demand ............................... 5

Empirical Fluid Milk Demand Model ................................ 7

 Lagged Distribution of Advertising Expenditures ..................... 7

 Structural Change Over Time ..................................... 8

 Data ......................................................... 9

Estimation and Empirical Results .................................. . 12

Simulation of Fluid Milk Advertising Effects ........................ . 13

 Gains from Advertising........................................ . . 14

Simulation of Fluid Milk Price and Income Effects .................... . 16

Specification of the Cheese Models ................................ . 16

 Time-Varying Parameter Estimation .............................. . 19

 Data ........................................................ . 19

Estimating Returns to Producers ................................... . 19

 The Dairy Industry Model ...................................... . 20

 The Econometric Model and Its Estimates ......................... . 22

 An Estimate of Producer Returns ................................ . 23

Future Work .................................................. 28

Study Limitations .............................................. . 29

References ................................................... . . 30
iii

Summary

Generic advertising raised fluid milk sales an estimated 1.1 billion pounds, or 4.9

percent, during September 1996-August 1997. Assessments of 15 and 20 cents

per hundredweight of milk sold commercially by producers and processors,

respectively, provided funds for such advertising, as well as for research and

nutrition education for fluid milk and milk products. 

This report presents the results of econometric demand models that examined the

effect of advertising and other factors (market prices, income, and demographic

characteristics) on milk and cheese sales.

Since passage of the Dairy Production Stabilization Act of 1983, fluid milk sales

are estimated to be 6.0 percent (almost 18.1 billion pounds) above what they

would have been without the advertising. Fluid milk advertising expenditures for

September 1984-September 1997 equaled $481.6 million, of which $239.6

million is attributed to the act. The gain for each act-increased advertising dollar

is about 75 pounds of milk.

Advertising expenditures due to the act are estimated to have increased cheese

sales by 6.8 billion pounds (1.0 percent) during September 1984-September

1997. 

Blacks, rural households, single-person households, and people with higher

education levels drink less milk than the national average. Studies have shown

blacks to have a higher level of intolerance to lactose, which may account for

their consuming less milk than average. Rural consumers may have milk supply

sources other than commercial channels, which may also have negative effects

on commercial sales. Single-member families may routinely consume other

nondairy beverages. Education may be linked to a concern about fat, thus

limiting consumption among more educated consumers.

The study’s advertising simulations indicated that declining real fluid milk prices

during September 1984-September 1997 increased fluid milk sales by 814

million pounds. Increasing real incomes raised fluid milk sales by 8.1 billion

pounds. 

It is important to assess how the Dairy Act has affected producers’ returns from

the increased generic advertising. This is a particularly complex evaluation

because one must account for the economic link between the consumers at the

retail level, to the processors, manufacturers, and producers. One must also

account for the myriad other market factors that continue to change and

influence decisions at each market level. Some factors will directly affect one

market level, while other levels are affected only indirectly.

A model developed at Cornell University was the first attempt to explicitly

model government price support and simultaneously examine the issue of
iv

generic advertising effectiveness. It includes supply and demand equations for

wholesale and retail fluid and manufactured milk products (cheese, butter, and

frozen products) and farm-level supply. The model also provides information on

advertising effects at three market levels: retail, wholesale, and farm.

The U.S. Department of Agriculture’s Economic Research Service carefully

examined, updated, and re-estimated the Cornell model. The results of the

estimation were similar to model parameters reported earlier by Cornell using

data through 1993. The stability of the parameter estimates lend credibility to

using the model for simulating the effectiveness of advertising. Model estimates

were used to simulate dairy industry conditions with and without the additional

advertising attributed to the dairy and fluid acts.

Generic advertising under the acts boosted demand for fluid milk and cheese, but

demand for butter and frozen products remained about the same. The advertising

also caused higher farm-level milk prices. Over the simulation period, farm

prices averaged 2.3 percent higher than they would have in the absence of the

programs. A gross rate of return of approximately $3.44 per additional

advertising dollar was found.
1

 Analyses of Generic Dairy Advertising, 1984-97

Noel Blisard

Don Blayney

Ram Chandran

Jane Allshouse

Introduction

This report is an updated analysis of the effectiveness of generic advertising on fluid milk and cheese. As

stipulated in The Dairy and Tobacco Adjustment Act of 1983, this annual report fulfills one requirement

that the USDA evaluate the effectiveness of the dairy promotion program. To do this, the Agricultural

Marketing Service (AMS) contracts with USDA’s Economic Research Service (ERS) to conduct an

econometric evaluation of the effectiveness of the dairy promotion programs. The last report covered

1978-96 (Blisard, Blayney, Chandran, Smallwood, and Blaylock, 1997).

The Dairy and Tobacco Adjustment Act of 1983 (Dairy Act) authorized a national producer program for

dairy product promotion, research, and nutrition education as part of a comprehensive strategy to

increase human consumption of milk and dairy products. This self-help program is funded by a

mandatory 15-cent-per-hundredweight assessment on all milk produced in the contiguous 48 States and

marketed for commercial use. Dairy farmers can direct up to 10 cents per hundredweight of the

assessment for contributions to qualified regional, State, or local dairy product promotion, research or

nutrition education programs. Producers fund the program and the commercial marketing. The program is

administered by Dairy Management Incorporated (formed in 1995 by the management staff for National

Dairy Promotion and Research Board and the United Dairy Industry Association).

The Fluid Milk Promotion Act of 1990 authorized the establishment of a national processor program for

fluid milk promotion, similar to the 1983 Act’s producer program. The Processor program is carried out

by the National Fluid Milk Processor Promotion Board (Fluid Milk Board) whose mission is to establish

a fluid milk promotion and consumer education program funded by fluid milk processors. Those who

process and market more than 500,000 pounds of milk per month in the United States are subject to a 20-

cent-per-hundredweight assessment to fund the program. The program is designed to strengthen the

position of the dairy industry in the marketplace and to maintain and expand markets and uses for fluid

milk products in the United States through consumer education and promotion. The consumer education

aspect of the program uses public relations, advertising, or other means to educate consumers about the

desirable characteristics of fluid milk products. They are intended to increase the general demand for

milk. The advertising campaign of the Fluid Milk Board was launched in early 1995. The combined

effect of both the Dairy Board and the Fluid Milk Board are presented in this report.

Specifically, the objectives of this study were to determine what, if any, effect generic advertising had on

the demand for fluid milk and natural and processed cheese. In addition, we wanted to determine the

gross returns to producers from generic dairy advertising.

This year, the cheese results were estimated from the industry model, which is used to calculate the

returns to producers from generic advertising. In the past, ERS has been supplied with data for two

cheese models (natural and processed cheeses), but ERS did not receive data this year. 
2

The advertising analysis for fluid milk is based on a 12-region, pooled, cross-sectional time-series model

originated by Ward and Dixon (1989a, 1989b) then modified by Blisard, Chandran, and Smallwood. The

12-region sales database enables the fluid milk model to encompass variations of price and quantity

among various regions. The fluid milk model incorporates a second-order polynomial distributed-lag

structure for the carryover effects of advertising. The model also hypothesizes primary (advertising) and

secondary (time-trend and intercept shift) structural changes after the 1983 Act. Advertising is also

allowed to interact with the variable that represents the U.S. population 18 years old and under, the

primary target group of generic dairy advertising, and the group which consumes the most fluid milk.

Accounting for time-series autocorrelation within each region and missing variables correlated across

regions in their effect on the dependent variable, we estimated the model with Parks’ (1967) generalized

least-squares procedure. Data for the analysis extends from December 1978 through September 1997.

Results indicate that current and lagged effects of advertising are distributed over a 12-month period for

fluid milk. The shortrun advertising effect is highest after a 6-month period. To examine the dynamics of

the advertising effect, we separated the period after the act into 13 time intervals. Gains in sales due to

the act are fairly constant from year to year and fluctuate with changes in aggregate spending on generic

dairy advertising. 

The total increase in advertising expenditures since the act is $481.6 million. If we assume that real

advertising expenditures were fixed at the level of the 12-month interval immediately preceding the act

and compare this simulated result with the result from actual advertising expenditures, the increase in

milk consumption resulting from the act for September 1984-September 1997 is 18.1 billion pounds. If

deflated per capita price or income is the same as that in the 12-month interval immediately before the

act, simulated gains because of lower prices are 814 million pounds of fluid milk, and gains because of

higher income are 8.1 billion pounds. These simulations are based on the 12 regions, which represent 43

percent of U.S. fluid milk consumption. 

To assess how the act, with its increased advertising, has affected producer returns, one must account for

the economic link between the consumers at the retail level, to the processors, manufacturers, and

producers, as well as control for the myriad other market factors that continue to change and influence

decisions at each market level. Some factors will directly affect one market level, while others may be

affected only indirectly. A model developed at Cornell University was the first attempt to explicitly

model government price support and simultaneously examine the effectiveness of generic advertising.

The model includes supply and demand equations for wholesale and retail fluid and manufactured milk

products (cheese, butter, and frozen products) and farm-level supply. It also provides information on

advertising effects at three market levels: retail, wholesale, and farm.

ERS examined, updated, and re-estimated the Cornell model. The results of the estimation were similar

to model parameters reported earlier by Cornell using data through 1993. The stability of the parameter

estimates lends credibility to using the model for simulating the effectiveness of advertising. Model

estimates were used to simulate dairy industry conditions with and without the additional advertising

attributed to the dairy and fluid acts.

Generic advertising under the acts boosted demand for fluid milk and cheese, although demand for butter

and frozen products remained relatively flat. The advertising programs under the acts also caused higher

farm-level milk prices. Over the simulation period, farm prices averaged 2.3 percent higher than they

would have in the absence of the programs. Our analysis found a gross rate of return of approximately

$3.44 for each additional advertising dollar. In addition, we simulated the cheese equation in order to
3

determine the effect of generic cheese advertising on national cheese disappearance. We found that, over

the study period, generic cheese advertising increased cheese disappearance (sales) by 1.0 percent, or

approximately 6.8 billion pounds (milk equivalent).

Background on Advertising

Advertising is directed toward existing and potential consumers of a product with the objective of

increasing sales. Branded advertising promotes the particular characteristics of a given brand of the

commodity. Generic advertising promotes consumption of the general commodity by a cooperative effort

of producers.

Sheth (1974) identifies four separate mechanisms through which advertising produces potential changes

in consumer demand: precipitation, persuasion, reinforcement, and reminder. Precipitation encourages

consumers to become buyers of a product. Persuasion encourages consumers to choose among alternative

brands within a product category. Reinforcement continually directs consumers’ attention to a particular

brand or product. A reminder encourages consumers to become repeat purchasers of the product. Ward,

Chang, and Thompson (1985) note that generic advertising is intended to precipitate and remind, and

branded advertising is intended to persuade and reinforce. The reminder and precipitation functions are

more likely to increase total industry sales, while persuasion and reinforcement are generally associated

with maintaining or increasing market shares.

Evidence for a few commodity groups suggests that generic advertising increases aggregate demand or at

least reduces the rate of decline in consumption (Ward and Myers, 1979; Thompson, 1975; Ward, 1984).

The empirical evidence that branded advertising helps increase aggregate demand is less persuasive.

Generic advertising, in theory, is brand-neutral, but this neutrality may not exist if generic promotion

emphasizes the common characteristics of a product group, and a concurrent branded advertising

campaign stresses differences. Also, if one firm dominates the branded advertising for a particular

product (such as in the processed cheese market), branded advertising may be serving both as a form of

branded and generic promotion. Concurrent generic and branded advertising campaigns can have both

complementary and competitive aspects, depending on the commodity and the nature of the promotion

activities.

Ward, Chang, and Thompson (1985, p. 275) attribute the following traits to generic advertising:

(1) It encourages consumption and repeat purchases of a product category.

(2) It provides information about product groups and is generally expected

by consumers to be less persuasive (and less deceptive) than branded

messages.

(3) It likely has more factual information than branded advertising, but it is

still oriented to high recall, unlike the kinds of messages one would

expect from promoting infrequently purchased goods.

(4) It may have a negative effect on product differentiation, thus reducing

barriers to entry and excessive profits (and margins) among first

handlers beyond the farmgate.
4

(5) It may force brand advertisers to concentrate on product attributes

(whether real or fancied) that are more difficult for the consumer to

verify.

(6) It may provide producers and smaller firms with a mechanism for benefiting 

from any economies of scale.

The Theory of Demand With Advertising

The classical theory of consumer demand is based on the assumption that individual consumers allocate

expenditures on commodities as if they had a fixed, ordered set of preferences described by an

indifference map or by an ordinal utility function. Consumers maximize this utility function subject to

restraints imposed by the money income they receive and prices they must pay. The result of this process

is a set of demand relations, one for each commodity, that are functions of all prices, income, and other

demand factors. Few empirical analyses have attempted to estimate a complete system of consumer

demand functions for food. Notable exceptions include Brandow (1961), George and King (1971), and

Huang (1985). Most analyses use weakly separable utility and multiple-stage maximization, where the

utility function is partitioned into separate subsets or branches for the commodity product groups (Pollak,

1971). The empirical implication of the multiple-stage utility maximization hypothesis is that the demand

functions for individual commodities within a branch can be specified as a function of the prices of the

goods in that branch and total expenditures for goods in the branch. Such demand functions are called

conditional to highlight the fact that the effects of total income and prices of goods outside the branch

enter the group demand functions through the budget allocation for goods in the branch. An advantage of

the conditional demand function formulation is that, once the budget allocation to goods within the

branch is known, prices of goods outside the branch can be ignored.

The above theory of consumer demand does not explain the consumption behavior of individuals when

their preferences are changed, either autonomously or by advertising and other sales efforts. Two

approaches for incorporating advertising into the neoclassical theory of demand have dominated

economic literature: one that sees advertising as a way to alter utility and one that views advertising as a

way to disseminate information. Neither of these approaches has reached a refined state of theoretical or

empirical development. To the extent that advertising enters into and alters the utility function, the issue

revolves around how to treat that entry. In other words, should advertising itself be an object of

preferences (thus a direct generator of utility) or does it shift preferences? Tintner (1952) and Ichimura

(1950-51) defined a change in preferences by a change in the form of the ordinal utility function.

Basmann (1956) chose to treat advertising as not entering the utility function directly, but rather as

uniquely controlling a set of parameters that determine the form of the utility function. Dixit and Norman

(1978) envision utility functions with goods and any advertising of these goods as arguments. As Rosen

(1980) pointed out, because no economic theory exists that systematically explains the process by which

advertising affects consumers’ tastes and preferences, modeling the effects of advertising via the utility

function lacks theoretical objectivity.

The advertising-as-information approach, refined by Verma (1980) and summarized by Rosen (1980), is

grounded in household production theory, where utility is a function of product characteristics rather than

the products directly. Under such a theoretical concept, the demand for observed goods (market products)

is derived from the demand for commodity attributes. Efficient matching of desired attribute bundles to
5

q ' 1/r j qi (1)

Q ' q(r (2)

Q ' q(Pr(N (3)

market products requires information about attributes embodied in various products and about the

corresponding prices. The process of gathering, analyzing, and producing information relevant to the

household production function means that information and time are supplied in the same manner as

product attributes in the household production function. Because advertising provides information, it

plays the role of an exogenous shift variable in the household’s production functions for information and

ultimately for commodities (product attributes).

The outcome of this line of reasoning is that advertising variables, in addition to the usual price and

income variables, are arguments of the consumer’s demand functions for market goods. The appealing

aspect of this approach is that it views advertising as increasing the endowment of a productive factor,

which makes purchased market goods and time more productive in generating ultimate commodities

(product attributes). Thus, consumers are logically more prepared to sacrifice some income or are willing

to pay higher prices for advertised goods.

Entry and Exit in Commodity Demand

Entry and exit theory deals with the effects of individual consumers or households beginning or ceasing

to purchase a given commodity. Not all consumers will purchase a given commodity at all prices. Rather,

some consumers will choose not to purchase any of a given good at certain relative prices. Advertisers

may try to increase consumption by persuading more consumers to enter the market, and convincing

those already in the market to increase their purchases, or both. The influence of other variables in the

demand function, such as prices and income, may also change over time, thus inducing some individuals

to enter and others to exit the market.

Haidacher (1964) developed a technique for analyzing the effects on the demand for a given good due to

consumers’ entering and exiting the market. The method focuses on decomposing the conventional

aggregate market demand Q with respect to the entry-exit phenomenon. Let the maximum number of

potential consumers in the market be fixed as N. At prices above some minimum level, there may be r

(less than N) consumers actually purchasing the product. The proportion, Pr, of consumers purchasing at

a given price is r/N. If qi

 is the purchase of individual i, the average quantity, q, purchased by individuals

in the market is then:

The summation of qi

 over all consumers in the market is the aggregate market demand Q:

Substitute r = Pr*N into the above equation, and we have:
6

EQ ' Q/ Pi(Pi /Q (4)

EQ ' ( q/ Pi

) ( Pi /q % ( PrN)/ Pi ( Pi /(PrN) (5)

EQ ' q/ Pi ( Pi /q % Pr/ Pi ( Pi /Pr (6a)

EQ ' E

q % EPr (6b)

Let the market price elasticity of demand for good I with price Pi be:

Using equation 3 for Q and applying the product differentiation rule, the price elasticity of demand for

good I expressed in terms of the entry-exit phenomenon is:

Because N is constant, the equation may also be written as:

or

Equation 6b indicates that the own-price elasticity of demand for a good consists of two components: the

price elasticity of average quantity purchased by consumers in the market and the price elasticity of the

proportion of total consumers in the market. 

Thus, to examine consumer behavior with respect to market entry and exit for a good, two additional

demand schedules need to be examined: an average quantity demand equation where the average

quantity bought by consumers is related to price and other demand factors, and a demand equation

relating the percentage of consumers in the market to demand determinants. As demonstrated, these two

equations are components of the ordinary demand curve. The same variables that enter into the ordinary

demand curve are expected to enter into the average quantity purchased curve and the proportion of

consumers in the market curve. In a log-linear demand framework, the sum of the estimated coefficients

for a given variable from the two curves should equal the corresponding estimated coefficient in the

ordinary demand curve.

The empirical application of theoretical demand models is dependent on data and other empirical

restrictions. In the demand analysis for cheese, the data include both information on the average quantity

of cheese purchased by consuming households and the proportion of households buying cheese. Thus, we

can examine the entry and exit relations for cheese demand by the proportion of consumers entering the

market, and the average quantity purchased by those already in the market. Data for the fluid milk market

model, on the other hand, are obtained from selected regional time-series data. A cross-sectional timeseries model is thus used for fluid milk, and entry/exit cannot be examined.
7

Empirical Fluid Milk Demand Model

The pooled cross-sectional time-series model for fluid milk uses data from 12 different regions that

encompass over 43 percent of U.S. consumption. Because of the wide range of regional demographic

characteristics, in addition to price, income, and advertising, we specify demand for fluid milk to depend

also on seasonality, demographic characteristics, and a time trend.

Recently, the demographic variables were updated to reflect values contained in the 1990 census data. In

addition, the structure of the milk model was respecified. There were individual slope shifters for generic

dairy advertising for each year since the creation of the National Dairy Promotion and Research Board

(NDB), but they have been eliminated because they were highly collinear. The model now has a slope

variable for advertising (lagged), and a slope shifter (lagged) that begins with the first month of

advertising of the NDB and continues through the most recent month of data. In addition, the advertising

variables and the variable representing the proportion of the population 18 years old and under were

allowed to interact. This last variable acknowledges the fact that those 18 years old and under are the

largest consumers of fluid milk and are a primary target for generic advertising. This year, dummy

variables were added to account for a large increase in advertising expenditures by the Fluid Dairy

Board. This dummy variable for the last year of data interacted with the advertising variable and with the

variable that represents the interaction of advertising with the proportion of the population 18 years old

and younger. 

Lagged Distribution of Advertising Expenditures

One may regard advertising expenditures as affecting demand with some sort of distributed lag. To a

certain extent, advertising is viewed as a capital investment in goodwill, which has a cumulative effect on

sales and depreciates over time. The probable factors that cause a distributed lag in the effect of

advertising in one period on the sales over a succession of periods are (Palda, 1965; Jastram, 1976):

(1) The type of advertising copy and the media used. Not all advertising and media choices by

an advertising agency are designed to produce immediate purchases. Some are meant to

build up favorable impressions upon which to capitalize later (a capital investment in

goodwill).

(2) The germination period for a purchase decision. Several advertisements may be necessary

before a buyer finally purchases. Even if potential customers are persuaded by an ad, they

may not immediately be in the market for the product. The longer the germination period,

the longer a specific advertisement will take to show its result in increased sales.

(3) The marketing level where advertising is initiated. If a firm’s advertising is aimed at

ultimate consumers, while it sells in an intermediate market, an increased sales effect will

be delayed in reaching the firm.

However reasonable the assumption of lagged effect, it gives us no clue about the lag’s distribution. The

form of the lag structure depends on the duration (or longrun multiplier) and the shortrun time

coefficients of the lag distribution. These characteristics empirically depend on the price policies,

promotion policies, and competitive environment embodied in the product.
8

Lnadverit ' j {[log(advergi (t&j ) % advbrd(t&j )%K)] ( Wj} (7)

j ' 0 % 1 ((j%1)/13) % 2 ((j%1)/13)2 (8)

j ' 1[(j%1)/13][(12&j)/13] (9a)

j ' 1 Wj (9b)

For fluid milk advertising, a reasonable lag structure is the 12-month, second-degree polynomial

distributed lag used by Ward and Dixon (1989a). The log of current and lagged advertising for region I at

time t, Lnadverit, has the form:

where j = 0,1,..11, adverg is deflated per capita regional radio and television milk advertising

expenditures, advbrd is deflated per capita national television milk expenditures (including 75 percent of

calcium advertising expenditures before October 1991), and K is a goodwill constant of 0.0015. The W

are weights based on a second-order polynomial of the form:

Substituting the end points j = -1 and j = 12 in the above equation, one obtains the condition 0 = 0 and 2

= - 1, and 

or

The coefficient 1 is the model estimate of the advertising expenditure variable Lnadver. If we let j =

0,1,2,...11, the W’s can be directly estimated to be:

W0 = W11 = 0.071007; W1 = W10 = 0.130178; W2 = W9 = 0.177515;

W3 = W8 = 0.213018; W4 = W7 = 0.236686; W5 = W6 = 0.248521.

Structural Change Over Time

A major hypothesis of the fluid milk demand analysis is that changes in advertising expenditures have

also led to structural changes in consumption habits. As stated by Jastram (1976), through a distributedlag formulation, the effect of each new advertising expenditure builds on the residual contributions of

advertising outlays in preceding periods. Thus, additional consumption generated over time may not be

due to advertising expenditures in a single period, but may be the cumulative effect of advertising due to

continuous increments of advertising outlays. The effect from a continuous increment of advertising

outlay is also called the multiplier effect of advertising. 

The milk model allows the intercept term to shift up or down over the life of the producer and processor

advertising boards. Given the nature of the fluid milk market and the results of research conducted by

ERS, we expect the intercept term to shift upward over the life of the producer and processor advertising

boards, because we hypothesize that the advertising campaigns have been particularly successful in

persuading drinkers of fluid milk to consume more milk or slow the well-known decline in milk

consumption as consumers get older. This outcome may be partially reflected by an increase in the

intercept term.
9

Data

Fluid milk data encompass December 1978 - September 1997. The period before the act is December

1978-August 1984. The period after the act is September 1984-September 1997. The United Dairy

Industry Association (UDIA), California Milk Marketing Board, and National Dairy Research and

Promotion Board provided the regional consumption, income, advertising, and related deflators. USDA’s

Agricultural Marketing Service provided regional prices. Given that the milk model is based on pooling

regional data, it is useful to have an understanding of both the average and regional differences in these

data. In the following discussion, reference is made to the pre- and post-act periods.

Fluid Milk Consumption

Total fluid milk sales are recorded in pounds of milk sold per month within each of the 12 regions.

California has the highest share of the 12-region total sales, about 29 percent. Kansas has the lowest

share, about 2 percent. However, after adjusting for differences in population and monthly calendar days,

the Upper Midwest area ranks the highest in per capita fluid milk consumption (about 10.6 daily ounces),

and California ranks sixth (8.1 ounces). Generally, per capita fluid milk consumption demonstrates

significant seasonal cycles with peaks in the early fall months and troughs in June and July (Ward and

Dixon, 1989b; Sun, Blaylock, and Blisard, 1993). Average consumption of the 12 regions showed a

declining trend before August 1984, following the Dairy Promotion Act. However, consumption tended

to stay higher than the 1984 level until 1992. Average daily consumption for the 12 regions was 8.1

ounces in 1997. 

Fluid Milk Prices

Fluid milk prices are from representative cities within the 12 regions. Before 1993, prices were reported

in both gallon and half-gallon units, and the prices selected for the fluid milk model were in cents for

each half-gallon unit, deflated by regional consumer price indexes (base = 1975). Beginning in 1993, the

half-gallon fluid milk prices were discontinued. Thus, the price series for 1996 was projected from

historical price data. Regionally, Georgia has the highest average price, and New England the lowest.

Comparisons of milk price and average per capita consumption present mixed results. For instance,

California has a low price (32.0 cents per half gallon) but also a low average consumption (8.1 ounces).

Conversely, the upper Midwest has a high price (37.2 cents per half gallon) and also a high average

consumption (10.6 ounces). On the average, the real fluid milk price for the 12 regions demonstrated a

declining trend before 1989. It increased in 1990 but later decreased slightly. The average milk price for

all regions increased to 36.1 cents per half gallon in 1997. 

Income

The New England region has the highest average per capita real income (1975 = 100), followed by

Kansas and the Middle Atlantic region. Average real income for the 12 regions increased from December

1978 to September 1997. The rate of increase was slower before 1984. During December 1978-August

1984, average annual real income for the 12 regions increased by 2.38 percent. For September 1996-

August 1997, average annual real income increased by 1.9 percent.
10

Fluid Milk Advertising

Advertising is measured in terms of expenditures for each month. These expenditures take several forms

depending on the controlling agent, types of media used, and message content. Before the start of the

NDB programs in September 1984, all fluid milk advertising was the responsibility of separate regional

organizations. With the establishment of the NDB, a checkoff from dairy farmers of 15 cents per

hundredweight of commercial milk sales has funded the NDB promotional programs. NDB reverts 10

cents of the checkoff to qualified regional programs and uses the remaining 5 cents for national research,

promotion, and educational programs. Thus, beginning in 1984, generic fluid milk advertising has

included both regional and national promotional expenditures. In addition, because calcium promotion

indirectly increases fluid milk consumption, on advice from the NDB staff, 75 percent of expenditures

for calcium advertising is added to fluid milk advertising. Thus, in the model, real advertising

expenditures are composed of regional radio and television expenditures before September 1984, and

additional national television advertising expenditures with 75 percent of national calcium advertising

(when applicable) after September 1984. Note, that the NDB and UDIA have merged their operations for

national advertising in 1994. The new entity is known as Dairy Management Incorporated (DMI). Hence,

from September 1994 onward, DMI is responsible for national dairy advertising. In addition, the Fluid

Milk Board has funded its own advertising campaign with a 20-cent-per-hundredweight checkoff

program on fluid milk processors (completely separate from the producer checkoff).

To prorate the national advertising expenditures to each region, the national expenditures are expressed

on a per capita basis and multiplied by the regional populations. Thus, the prorated national expenditures

at the regional level differ across regions because of differences in regional populations. Total 12-region

advertising expenditures increased considerably in 1984-85. Later, advertising expenditures declined

because regional shares declined. In 1997, total advertising increased from $55.1 million to about $70.5

million, due mainly to an increase in spending by the Fluid Milk Processor Board. There were substantial

increases in advertising expenditures in the early months following establishment of the Dairy Board.

Total milk advertising has ranged from about $30.0 million just after passage of the act to about $24.5

million in 1992. Advertising expenditures will probably remain in the $60-$70 million range due to a

major campaign by the Fluid Milk Processor Board.

Demographic Variables

The demographic variables used to account for different noneconomic characteristics in the various

regions include: (1) the percentage of a region’s population 18 years of age or under; (2) the percentage

that is female; (3) the percentage that is black; (4) the percentage that is rural; (5) the percentage of

households that contain only one person; and (6) the median number of years of schooling among people

over 25 years of age. The monthly observations were generated by interpolation and extrapolation, using

the growth rate and data observations from Bureau of the Census data (April 1, 1980-April 1, 1990).

The observations of economic factors, demographic characteristics, and assumptions of structural change

in consumption habits enable us to estimate the per capita demand for fluid milk as a function of income,

prices, demographics, advertising, seasonality, and time trend:
11

Lnpcadsit ' B0 % B1 Ta % B2 Lnmaprit % B3 Lndpcinit

% B4 Lnnu18it % B5 Lnfemit % B6 Lnblkit % B7 Lnrurit % B8 Lnhousit

% B9 Lnschlit % B9 Lnadverit % B10 Lnad97it % B11Advit % B12 Lnnu18it ( Lnadver

% B13 Nu18d97it % B13 Lntimeit % B14 Djanit % B15 Dfebit % B16 Dmarit

% B17 Daprit % B18 Dmayit % B19 Djunit % B20 Djlyit

% B21 Daugit % B22 Dsepit % B23 Doctit % B24 Dnovit % eit

(10)

where

Lnpcads = Log of the average daily ounces consumed per capita by region.

Ta = Intercept shifter for September 1984-September 1996. 

Lnmapr = Log of the deflated fluid milk price per half gallon, with price reported by the market administrator for selected

U.S. cities.

Lndpcin = Log of deflated per capita income across regions and over time.

Lnnu18 = Log of the percentage of a region’s population under 18 years of age.

Lnfem = Log of the percentage of a region’s population that is female.

Lnblk = Log of the percentage of a region’s population that is black.

Lnrur = Log of the percentage of a region’s population that lives in rural areas within each region.

Lnhous = Log of the percentage of a region’s households that are single-member families.

Lnschl = Log of the median number of years of education for individuals over 25 years of age.

Lnadver = The advertising variable expressed as a restricted polynomial lagged model, with advertising measured in real

per capita advertising expenditures.

Lnad97 = Advertising slope shifter for October 1996-September 1997. 

Adv = Advertising slope shifter for September 1984-September 1996.

Lnadver*

Lnnu18 = Log of advertising multiplied by the log of the percentage of a region’s population

 under 18 years of age.

Nu18d97= Advertising slope shifter for October 1996-September 1997 for the log of

advertising multiplied by the log of the percentage of a region’s population

under 18 years of age.

Lntime = Log of the variable Time (Time=48-273 for December 1978-September 1997).

Djan = Seasonal dummy variable for January.

Dfeb = Seasonal dummy variable for February.

Dmar = Seasonal dummy variable for March.

Dapr = Seasonal dummy variable for April.

Dmay = Seasonal dummy variable for May.

Djun = Seasonal dummy variable for June.

Djly = Seasonal dummy variable for July.

Daug = Seasonal dummy variable for August.

Dsep = Seasonal dummy variable for September.

Doct = Seasonal dummy variable for October.

Dnov = Seasonal dummy variable for November. 

eit = Equation error for region I (I=1-12) and time t (t=48-273).
12

Estimation and Empirical Results

The pooled cross-sectional time-series econometric model for fluid milk sales is specified in a log-linear

form. Because of the distributed-lag advertising assumption, the error term in each cross section is

assumed to be characterized by first-order autocorrelation. In addition, there can be factors omitted from

the model that affect all regions (contemporaneous errors across the regions are assumed to be

correlated). Parks’ method for the generalized least squares procedure is used in the estimation (Parks,

1967). Table 1 provides the estimation results.

The double-log equation provides a reasonably good fit to the data (R2

 = 0.8). Most parameters possess

theoretically correct signs and are statistically significant at the 5-percent probability level. Fluid milk

demand is inelastic, with respect to milk price and income changes. A 1-percent increase in the price

reduces milk consumption by 0.07 percent. A 1-percent increase in income increases milk consumption

by about 0.26 percent. Milk consumption also changes with the season, declining most in June and July

and increasing in the fall.

Of the demographic effects, younger consumers (lnnu18) and women (lnfem) are expected to consume

less milk than men do. In addition, clinical studies show that blacks (lnblk) have a higher level of

intolerance to lactose (Goodhart and Shils, 1980); thus, a negative effect is expected for all three

variables. Rural consumers (lnrur) may have milk supply sources other than commercial channels, which

may also produce negative effects on commercial sales. The estimated effects of the above variables

consistently confirm these hypotheses, except for the women and young children variables, which have

the opposite sign. However, milk consumption is lower among rural and black consumers. A 1-percent

increase in the proportion of each of those groups reduces total milk consumption by 0.05 and 0.16

percent, respectively. 

The expected effects of family size (lnhous) and schooling (lnschl) are ambiguous. Larger families with

young children may view milk as a low-cost protein source and may use it more often. On the other hand,

single-member families may view milk as a convenience food and consume more per-person than larger

households. Education may increase nutritional awareness, and thus, milk consumption. However,

education may be linked to a concern about fat, thus lowering consumption levels among more educated

consumers. Estimated coefficients indicate that single-member families have a negative coefficient of -

0.31, while schooling has a negative coefficient of -0.68. 

Because the advertising variable “lnadver” in the equation represents a 12-month weighted sum of

current and lagged per capita advertising expenditures, the coefficient of this variable, 0.034, reflects an

average effect for the 12-month cumulative advertising expenditures (the 1 in equation 9b). The

advertising coefficients for adv (0.003) measure changes in the average advertising effect following the

act. This coefficient is rather small but is statistically significant at the 1-percent level. In addition, this

same variable, interacted with a dummy variable for the last 12 months of the data set (Lnad98), when

the Processor Board came fully online with its advertising campaign, is relatively large, 0.04, and

statistically significant at the 1-percent level. Also, the interaction term between advertising and the

proportion of the population under 18 years old (Lnadver*Lnnu18) is positive (0.027) and statistically

significant at the 1-percent level, whereas this same variable, interacted with a dummy variable for the

last 12 months of the data set (Nu18d98), is statistically significant at the 1-percent level.
13

Table 1--Summary of fluid milk model estimates, December 1978-September 1997

 Variable Coefficient Standard error T-test

 Intercept 5.410731 0.301966 17.918317

 Ta .063618 .026599 2.391728

 

Prices, income, and demographics:

 Lnmapr -.071295 .009429 -7.560833

 Lndpcin .257757 .018513 13.922706

 Lnnu18 1.044577 .119787 8.720308

 Lnfem 4.260371 .298994 14.249003

 Lnblk -.159283 .002359 -67.520186

 Lnrur -.050425 .004540 11.106643

 Lnhous -.309945 .036184 -8.565773

 Lnschl -.675266 .059715 -11.308226

Advertising:

 Lnadver .033892 .012039 2.815108

 Lnad98 .044459 .006871 6.470489

 Adv .003240 .001989 1.628633

 Lnadver*Lnnu18 .027048 .009526 2.839373

 Nu18d98 .030563 .004795 6.374511

Trend and monthly shifters:

 Lntime -.030173 .010760 -2.804278

 Djan .030290 .004171 7.261388

 Dfeb .018940 .005110 3.706173

 Dmar .028718 .005521 5.201165

 Dapr .000214 .005717 .037346

 Dmay -.012193 .005813 -2.097567

 Djun -.061419 .005852 -10.494521

 Djly -.070039 .005843 -11.986098

 Daug -.024279 .005769 -4.208689

 Dsep .029427 .005577 5.275950

 Doct .028142 .005193 5.419195

 Dnov .028446 .004257 6.681673

Estimated values of rho:

Cal 0.8334 Mic 0.9135 No. of cross sections = 12

Col .5322 Eng .7351 No. of time series = 226

Fla .8349 Atl .7488 Total observations = 2,712

Gbs .7148 Tex .6654 R2

 MSE 

Geo .4672 Umw .7732 0.8105 1.0011 

Kan .5121 Vir .8022

 Source: U.S. Department of Agriculture, Economic Research Service.

Milk consumption had a distinctly declining trend before the act, and this trend continues. The

coefficient of the time trend variable for 1978-97 is -0.03. However, the intercept shifter is positive

(0.06) and statistically significant at the 5-percent level, indicating that average daily consumption has

increased over the life of the dairy and processor boards. 

Simulation of Fluid Milk Advertising Effects

Analysis of the simulation of advertising on fluid milk consumption examines the marginal changes in

advertising effects.
14

Table 2--Generic advertising expenditures for fluid milk, December 1978-September 1996 

 Regional National Total Total

Monthly intervals programs prorated regions national

 (1) (2) (3) (4) (5)

 Dollars

Before the act:

December 1978-August 1979 8,814,681 0 8,814,681 0

September 1979-August 1980 13,380,032 0 13,380,032 0

September 1980-August 1981 14,769,237 0 14,769,237 0

September 1981-August 1982 16,267,178 0 16,267,178 0

September 1982-August 1983 18,664,497 0 18,664,497 0

September 1983-August 1984 18,547,223 0 18,547,223 0

December 1978-August 1984 90,442,848 0 90,442,848 0

After the act: 

September 1984-August 1985 18,583,198 11,403,812 29,987,010 27,553,015

September 1985-August 1986 12,820,909 10,661,764 23,482,673 25,658,104

September 1986-August 1987 11,229,605 10,535,187 21,764,792 25,281,812

September 1987-August 1988 14,921,175 12,668,785 27,589,960 30,195,400

September 1988-August 1989 16,056,224 8,912,924 24,969,148 21,102,400

September 1989-August 1990 15,591,570 7,660,962 23,252,532 18,155,425

September 1990-August 1991 16,735,898 8,152,273 24,888,171 19,131,375

September 1991-August 1992 17,598,292 6,942,465 24,540,757 16,115,050

September 1992-August 1993 20,349,277 11,490,440 31,839,717 26,725,400

September 1993-August 1994 39,970,010 20,447,352 60,417,362 47,421,400

September 1994-August 1995 28,348,027 28,057,136 56,405,162 64,856,900

 September 1995-August 1996 24,086,603 30,981,019 55,067,622 71,845,500

 September 1996-August 1997 23,879,521 46,596,508 70,476,029 106,521,600

September 1997 2,760,737 4,139,515 6,900,252 9,431,300

September 1984-September 1997 262,931,046 218,650,142 481,581,187 509,994,681

December 1978-September 1997 353,373,894 218,650,142 572,024,035 509,994,681

 Source: U.S. Department of Agriculture, Economic Research Service.

Gains from Advertising 

We simulated the gain due to generic dairy advertising with the following assumptions. First, we

assumed that regional advertising expenditures remained at the September 1983-August 1984 level

(undeflated yearly total of about $18.5 million for the 12 regions). We compared simulated sales under

this advertising scenario with sales simulated from the model using actual data. The difference is the gain

in sales from the act, assuming that regional programs would have continued to advertise at the levels

before the act. Since the simulation is performed in real terms, per capita advertising expenditures in the

assumed scenario are deflated, and real per capita advertising expenditure levels are kept the same as in

September 1983-August 1984.

Tables 2 and 3 provide the fluid milk advertising expenditures and the bootstrap simulation results for the

advertising scenario. In table 2, columns 2 and 5 give total expenditures for the regions and the NDB and

the FMB. Column 3 is the NDB and the FMB expenditures prorated to the 12 regions. Column 4 shows 
15

the total of regional and prorated national advertising efforts for the 12 regions. Estimated total fluid milk

advertising after the act equaled $481.6 million in the 12 regions.

Table 3, column 2 shows actual sales, and column 3 shows predicted sales using the observed data.

Column 4 reports the gains due to advertising under the above assumptions. Column 5 shows the gains in

column 4 as percentages of actual sales (column 2).

Column 4 shows that the simulated sales gain due to the acts is 18.1 billion pounds, about 6.0 percent of

actual total sales. If yearly advertising expenditures had stayed at the 12-month (September 1983-August

1984) level before the act ($18.5 million), total advertising expenditures after the act would have been

only $242.0 million, $239.6 million less than actual post-act expenditures. The comparison of the sales

gains due to the act (18.1 billion pounds) with the gains in advertising expenditures ($239.6 million),

indicate that the gain for each act-increased advertising dollar is about 75.5 pounds.

Table 3--Actual fluid milk sales and simulated sales gains from generic advertising, December 

1978-September 1996

Monthly intervals

Fluid milk sales Advertising gains

due to act

Actual Simulated Amount Percent of

actual

(1) (2) (3) (4) (5)

 ---------------------Million pounds------------------ Percent

Before the act:

 December 1978-August 1979 16,321.2 16,128.2 0 0

 September 1979-August 1980 21,861.6 21,406.3 0 0

 September 1980-August 1981 21,754.7 21,273.7 0 0

 September 1981-August 1982 21,411.6 21,213.9 0 0

 September 1982-August 1983 21,431.1 21,173.2 0 0

 September 1983-August 1984 21,808.5 21,558.1 0 0

 September 1978-August 1984 124,588.7 122,753.4 0 0

After the act:

 September 1984-August 1985 22,152.1 22,248.2 1,354.3 6.11

 September 1985-August 1986 22,406.4 22,363.8 1,379.9 6.16

 September 1986-August 1987 22,619.0 22,475.3 1,370.5 6.06

 September 1987-August 1988 22,944.9 22,793.2 1,400.3 6.10

 September 1988-August 1989 23,340.6 22,794.6 1,405.9 6.02

 September 1989-August 1990 23,569.3 22,861.7 1,405.8 5.96

 September 1990-August 1991 23,747.8 22,692.2 1,398.3 5.89

 September 1991-August 1992 24,008.4 22,965.2 1,420.0 5.91

 September 1992-August 1993 23,380.1 22,978.6 1,425.4 5.10

 September 1993-August 1994 23,331.5 23,058.1 1,423.3 6.10

 September 1994-August 1995 23,267.2 23,167.9 1,453.7 6.25

 September 1995-August 1996 23,528.2 23,211.6 1,411.9 6.00

 September 1996-August 1997 23,338.0 23,264.8 1,136.0 4.86

 September 1997 1,997.6 1,977.4 96.2 4.82

 

 September 1984-September 1997 303,681.1 298,852.6 18,081.6 6.00

 Source: U.S. Department of Agriculture, Economic Research Service.
16

 Simulation of Fluid Milk Price and Income Effects

Table 4 presents simulations of consumption changes when price or income is assumed to remain at the

September 1983-August 1984 level. For the 12 regions, the average real fluid milk price during

September 1983-August 1984 was 36.5 cents per half gallon. It decreased to 34.2 cents per half gallon

during 1988, and rose to 37.8 cents per half gallon during 1990. Prices declined again to about 36 cents

per half gallon in 1993 but rose to 36.1 cents in 1997. Declining prices caused consumption to increase.

The simulated total gain from price decreases from September 1984 to September 1997 is 814 million

pounds, about 0.3 percent of actual sales.

Income has an increasing trend. In September 1996-August 1997, per capita real income was 38 percent

higher than that of the corresponding 1983/84 period, resulting in a simulated consumption increase of

8.1 billion pounds, or 2.7 percent of actual sales.

Specification of the Cheese Models

Branded and generic advertising, the price of cheese, prices of substitutes (such as meat, poultry, and

fish), income, seasonality, trends, and government donations influence the demand for cheese. To isolate

and measure the effects of advertising, we must control for the effects of these variables on quantities

demanded. Purchase patterns, prices, and product characteristics are sufficiently different for processed

and natural cheese to warrant separate analyses of each. Among these differences are the following:

(1) Purchases of natural cheese vary significantly by month and season, with a peak in December

and a trough in July. Purchases of processed cheese vary much less by season.

(2) Government donations of cheese under the Temporary Emergency Food Assistance Program

were chiefly processed cheese. Hence, donations probably had a greater effect on purchases of

processed cheese than on natural cheese.

(3) Natural cheese costs more than processed cheese. Hence, it should have larger price and income

effects.

The cheese advertising data include both generic and branded advertising. In the natural cheese equation,

generic and branded advertising expenditures were entered separately. For processed cheese, a single

company usually dominates the product promotion, with a high percentage of the advertising

expenditures allocated to a few products (Leading National Advertisers). Thus, for processed cheese, we

entered branded and generic advertising as a single variable since branded advertising may have generic

advertising characteristics. Advertising effects in the cheese equations are modeled with a logarithmic

inverse functional form with carryover effects following a gamma distribution.
17

LnQ n

t , Lnq nt , LnP nr ' 0 % 1 LnP nt % 2 LnP pt % 3 LnP mt

% 4 Dt % 5 LnYt % 6 Tt % j

11

j'1

dj Mj

% 1 j

t&1

i'0

(i%1)c/(1&c)L i[1/(K1 % A g

t&i

)]

% 2 j

t&1

i'0

(i%1)s/(1&s)H i[Ln(K2 % A b

t&i

)] % et

(11)

Table 4--Simulated gains in fluid milk sales attributed to price and income changes after passage

of the act, 1984-97 

Fluid milk sales Price and income gains Gains due to--

 

Price Income Price Income Price Income

Monthly intervals fixed1 fixed1

 (1) (2) (3) (4) (5) (6) (7)

------------------------Million pounds----------------------- Percent 

September 1984-August 1985 22,218.4 22,029.0 29.8 219.2 0.13 1.00

September 1985-August 1986 22,283.5 22,028.0 80.4 335.8 .36 1.52

September 1986-August 1987 22,377.6 22,048.1 97.7 427.2 .44 1.94

September 1987-August 1988 22,666.3 22,264.9 126.9 528.2 .56 2.37

September 1988-August 1989 22,696.7 22,193.8 97.9 600.8 .43 2.71

September 1989-August 1990 22,876.6 22,224.2 -14.9 637.5 -.07 2.87

September 1990-August 1991 22,650.2 22,141.3 42.0 551.0 .19 2.49

September 1991-August 1992 22,898.6 22,475.8 66.6 489.4 .29 2.18

September 1992-August 1993 22,925.1 22,335.3 53.5 643.3 .23 2.88

September 1993-August 1994 23,021.1 22,334.9 37.0 723.2 .16 3.24

September 1994-August 1995 23,118.8 22,360.0 49.2 808.0 .21 3.61

September 1995-August 1996 23,136.8 22,229.7 74.8 981.9 .32 4.42

September 1996-August 1997 23,205.3 22,170.1 59.5 1094.7 .26 4.94

September 1997 1,963.6 1,870.3 13.7 107.1 .69 5.73

September 1984-September 1997 298,038.6 290,705.4 814.0 8147.2 .27 2.80

 1Gains measured when price or income were fixed at the September 1983-August 1984 level.

 Source: U.S. Department of Agriculture, Economic Research Service.

Because we are interested in examining the entry and exit effects of advertising in the consumer demand

for cheese, we estimated three demand equations for each type of cheese. These equations are the market

demand for cheese, the average quantity demanded, and demand for the proportion of purchasing

consumers in the market. Aside from advertising expenditures, seasonal dummies, and a trend term, other

variables are in logarithmic form. The two sets of mathematical demand equations, for natural and

processed cheese, are as follows:
18

LnQ p

t , Lnq pt , LnP pr ' 0 % 1 LnP pt % 2 LnP nt % 3 LnP It

% 4 LnP m

t % 5 LnYt % 6 LnDt

% 1 j

t&1

i'0

(i%1)g/(1&g)G i [1/(K1 % Advt&i )] % et

(12)

and

where:

LnQt

n = Log of per capita quantity of natural cheese purchases by U.S. households, in pounds for

each month t (t = 1...189 for January 1982-September 1997).

Lnqt

n = Log of average per capita quantity of natural cheese purchases by U.S. households

purchasing natural cheese, in pounds for each month t (t = 1...189 for January 1982-

September 1997).

LnPr

n = Log of proportion of all U.S. households that purchased natural cheese during month t (t =

1...189 for January 1982-September 1997).

LnQt

p = Log of per capita quantity of processed cheese purchases by U.S. households, in pounds for

each month t (t = 1...189 for January 1982-September 1997).

Lnqt

p = Log of average per capita quantity of processed cheese purchases by U.S. households, in

pounds for each month t (t = 1...189 for January 1982-September 1997).

LnPr

p = Log of proportion of all U.S. households that purchased processed cheese during month t (t

= 1...189 for January 1982-September 1997).

LnPt

n = Log of price of natural cheese in dollars for each pound, deflated by the Consumer Price

Index (CPI, 1977 = 100 for all urban consumers).

LnPt

p = Log of price of processed cheese in dollars for each pound, deflated by the CPI.

LnP t

m = Log of price index for meat, poultry, and fish, deflated by the CPI.

LnPt

I = Log of price of imitation cheese in dollars for each pound, deflated by the CPI.

LnYt = Log of U.S. per capita disposable income in month t, deflated by the CPI.

Dt = Monthly dummy variable, Dt

 = 1 if cheese was distributed under the Temporary Emergency

Food Assistance Program.

Tt = Time trend, T = 1...189 for January 1982-September 1997.

Mj = Monthly dummy variables, M1 = 1 if j = January, zero otherwise; M2 = 1 if j = February,

zero otherwise; and so forth. December is omitted to avoid perfect multicollinearity.

At

g

-I = Current and past per capita generic advertising expenditures for cheese, deflated by media

cost index (I = 0 for the current period and I = t-1 for the beginning period).

At

b

-I = Current and past per capita branded advertising expenditures for cheese, deflated by media

cost index (I = 0 for current period and I = t-1 for the beginning period).

Advt-I = Deflated current and past per capita advertising expenditures (branded and generic) for

processed cheese (I = 0 for current period and I = t-1 for the beginning period).

K1,K2 = Goodwill indexes for generic and branded cheese advertising. This value is small (0.0001),

intended to capture the word-of-mouth or other goodwill effect at any given time even if no

advertising took place.
19

The weights (I +1)c/(1-c) Li and (I+1)s/(1-s) Hi in equation 11 represent gamma lag structures for the inverse

of the logarithm of current and past per capita (deflated) generic advertising expenditures, and of per

capita branded advertising expenditures. The time shapes of these gamma lags are determined by

parameters c, L, s, and H. The gamma lag structure for the inverse of both generic and branded

advertising expenditures and goodwill in equation 12 is (I+1)g/(1-g) Gi

. The time shapes of these gamma

lags are determined by parameters g and G.

Direct estimation of the parameters of the gamma distribution is not practical. The estimation strategy

was to set the parameters to fixed values and to estimate the remaining parameters in a given equation by

ordinary least squares. The procedure was repeated for a wide range of values for c, L, s, H, g, and G, and

the equation yielding the best statistical fit with plausible parameter estimates was selected. Thus, the

standard errors for the parameters c, L, s, H, g, and G are not available. This estimation procedure will

also bias downward the standard errors of other parameters in the model.

Time-Varying Parameter Estimation

As with the fluid milk model, the structure of current and lagged advertising effects in the cheese model

is hypothesized to change over time. The advertising multiplier effect changes because as the public

continues to see and read cheese advertisements, the quality of the ad changes, or the overall advertising

strategy is refined. By allowing the advertising parameter to change over time, the model hypothesizes

that there is a structure change in the distribution of advertising effects on sales. In the fluid milk demand

model, such structural change in the advertising effects is captured through the estimates of the

interaction of weighted advertising expenditures and time. In the cheese model, the time-varying

parameter models (tvpm) procedure tests this dynamic multiplier effect. The tvpm estimation procedure

of the cheese model specifically allows the coefficients of advertising to have a time-varying process in

the form of a random walk. In other words, let the advertising coefficients be t

 = t-1 + t. With this

specification, parameter t

 will drift over the course of the data, usually with an obvious trend reflecting

continuing change of the parameter if it does change over the sample period.

Data

ERS received no data this year for the cheese analysis since DMI did not purchase Market Research

Corporation of America (MRCA) data because of the expense. Hence, the above hypothesized models

were not run. We hope to receive data for next year’s analysis that is comparable with data we received

in the past. In the Report to Congress which corresponds to this bulletin, we reported simulation results

from the industry model that is stated below. In summary, we found that advertising expenditures under

the Dairy Act increased total national cheese disappearance by the milk equivalent of approximately 6.8

billion pounds or about 1.0 percent during the period from the fourth quarter 1984 through the third

quarter 1997. During the most recent 1-year period, from the fourth quarter of 1996 through the third

quarter of 1997, generic advertising increased the national sales of cheese by approximately 500 million

pounds (milk equivalent) or 0.9 percent of total sales. Since another model was used this year for the

cheese analysis, the results are not comparable with past reported results.

Estimating Returns to Producers

The July 1, 1996, USDA Report to Congress on the National Dairy Promotion and Research Program

and the National Fluid Milk Processor Promotion Program was the first study to include an estimate of
20

the returns to milk producers from generic advertising administered by the National Dairy and Fluid Milk

Processor Boards. In this section, we present an overview of how that estimate was obtained, along with

discussions of key technical elements of the analysis.

First, the conceptual industry model is described. Second, a statistical model is specified and estimated to

quantify the relationships in the conceptual model. This estimated model is important since it is used as

the basis of simulations of the dairy industry under two situations: with and without generic advertising

activities by the two boards. The simulation results provide the information used to calculate a return to

producers from the generic dairy advertising. 

The Dairy Industry Model

The industry model in this study is an extension of a model initially developed by several researchers at

Cornell University and has been refined over time (Liu and others, 1989; Kaiser and others, 1993;

Kaiser, 1995). The selection of this model was made after considerations of other analytical approaches

and methodological issues had been made (Wohlgenant and Clary, 1993; Cornick and Cox, 1994).

Through its many incarnations, the model has exhibited certain features. It is a structural model of the

dairy industry at the national level, not a model of individual firms or any subgroup of firms. Three

industry levels are identified: farm, wholesale, and retail. The wholesale and retail markets have been

disaggregated into various product submarkets, which are the same at each level. These features and

others are highlighted in the following discussion.

The farm level of the model is represented by a supply relationship written in general as:

 Qmilk = g( Pmf , Smf ) (13)

where:

Pmf is the price the producer receives,

Smf is a vector of milk supply shifters, and 

Qmilk is the quantity of milk produced.

In equation 13, the price is an expected price and the quantity is milk produced. 

The price the producer receives is a weighted average of milk prices used in final products. In simplest

terms, the price can be written as:

Pmf = h( Pfl , Pman ) (14)

where Pfl is the price paid for fluid grade milk and Pman is the manufacturing grade milk price. There is a

relatively small share of manufacturing grade milk (grade B) produced in the United States. Therefore, a

large proportion of the milk in manufactured products is of fluid grade.

It is important to consider economic relationships at the wholesale dairy market level, where raw farm

milk is transformed into various consumer milk and dairy products, for at least two reasons. It reflects the

derived demand of consumers for milk in its various forms and is the point in the marketing chain for

milk and dairy products where public dairy policy has its major effects.
21

Public dairy policymakers in the United States have used prices in the past as the tool to direct the

industry toward desired objectives. The Dairy Price Support Program and the Federal Milk Marketing

Orders are two major policy programs. The U.S. Government supports the farm price of milk at an

announced level through purchases of specific manufactured products, mainly American cheese, butter,

and nonfat dry milk, at announced product purchase prices. The Federal Milk Marketing Orders,

originally designed to move fluid milk to major population centers, establish a classified pricing

structure. The institutional structure of classified prices has been modified over time to incorporate milk

pricing in manufactured product uses and ultimately to affect the price milk producers receive. The

support price program and the Federal Milk Marketing Orders are linked by the basic formula price,

which recently replaced the Minnesota-Wisconsin price as the price mover in the marketing orders.

The wholesale dairy market is disaggregated into four submarkets: fluid milk, frozen products (mainly

ice cream), butter, and cheese. Supply and demand relationships for each submarket are defined so that

the effects of government programs are included where appropriate. The fluid milk and frozen products

submarkets are defined generally as:

Qwhs = e( Pwh , Swh ) (15)

Qwhd = Qr (16)

where:

Qwhs is the wholesale supply, 

Pwh is the own-price of the product at the wholesale level,

 Swh is a vector of wholesale supply shifters, 

Qwhd is the wholesale demand, and 

Qr

 is the retail equilibrium quantity of the product(s).

The wholesale supplies and demands for cheese and butter differ from those of fluid milk and frozen

products and require further attention. Unlike fluid milk and frozen products, cheese and butter can be

stored for relatively long periods. The possibilities for storage make inventory and inventory changes

relevant variables for consideration in models of these two submarket models. Purchases of cheese and

butter through the Commodity Credit Corporation (CCC) for price support imply that the wholesale

product prices are not the only prices that might be in play. When cheese and butter markets are “at

support,” announced CCC purchase prices are applicable. These are prices at which the government will

buy all American cheese and butter that meet a set of specific criteria. By removing the product from the

commercial marketing channels, the government supports milk prices at the announced level. The

wholesale cheese and butter submarkets are specified thus:

Qwhs = d( Pwh, Pccc, Swh, INV ) (17)

Qwsd = Qr (18)

where:

Qwhs, Pwh, Swh, Qwsd, and Qr

 are defined as in equations 15 and 16 (for butter and cheese in this

case), Pccc is the CCC purchase price for cheese or butter, and INV is a vector of inventoryrelated variables.
22

The retail market for each of the four product categories is defined by a supply function, a demand

function, and an equilibrium condition requiring that retail supply equals retail demand. These three

relationships are written as:

Qrs = m( Pr

 , Srs ) (19)

Qrd = n( Pr

 , Drs ) (20)

Qrs = Qrd = Qr (21)

where:

Pr

 is the retail price of the product, 

Srs is a vector of retail supply shifters, and 

Drs is a vector of retail demand shifters. 

Qrs, Qrd, and Qr

 are the retail supply, the retail demand, and the equilibrium retail quantity of the

product(s).

There is likely some limited generic dairy advertising or promotion at the wholesale level, but the vast

majority is aimed at retail product consumers and, in many cases, very specific groups of those

consumers. Thus, generic dairy advertising and promotion programs are viewed as elements of the vector

of retail demand shifters, Drs. 

The econometric model is derived from the dairy industry model as presented in equations 13-17. The

relevant farm level price for production decisions is an expected price. As in previous work (Kaiser,

1995), milk producers are assumed to hold what are called “naive price expectations”; the price received

tomorrow will be the same as the price received today, in symbols, E (Pmf(t+1)) = Pmf(t). The farm milk

supply is therefore predetermined and the model of the dairy industry is recursive, with the system of

wholesale and retail market relationships independent of the farm milk supply function.

Each of the retail product markets contains a market equilibrium condition that retail supply equals retail

demand that results in the variable Qr

. Each wholesale product market also includes an equilibrium

condition that wholesale supply equals wholesale demand, but wholesale demand is set equal to Qr

. This

condition implies that a fixed-proportions production technology characterizes the firms providing retail

dairy products to consumers. Some research has suggested that this may be unrealistic (Wohlgenant,

1989), but the data used to estimate the econometric model in part support the assumption. National

demand data is proxied by commercial disappearance data that do not differentiate between wholesale

and retail uses.

The Econometric Model and Its Estimates

The dairy industry model to be estimated comprises 13 equations: 4 retail demand equations, 4 retail

supply equations, 4 wholesale supply equations, and the farm milk supply equation.

 

Each equation is specified in double logarithmic form. The double-log form provides estimated model

coefficients that can be directly interpreted as elasticities. All of the equations were estimated using twostage least square s (TSLS) with quarterly data for the period of first quarter 1975, to fourth quarter

1997.
23

It has been argued (Jastram, 1976) that promotion campaigns have large initial effects and then begin to 

have declining influence or wear out. Advertising expenditures are included in the retail demand

equations with a distributed-lag structure. Second-order polynomial distributed lags of four quarters were

used with end-point restrictions imposed on both ends (Pindyck and Rubinfeld, 1981). This specification

is analogous to that used for the fluid milk demand model presented earlier in this report. Advertising

expenditure data were obtained from various issues of Leading National Advertisers. These data have

been criticized as possibly underestimating actual expenditures, which in turn may bias downward

estimates of advertising coefficients in models. However, they are the best data available for analytical

use. 

The estimated equations with their associated measures of significance and fit are shown in table 5. The

large majority of estimated coefficients are statistically significant and are of the correct sign as expected

in economic theory. Many coefficients with the incorrect sign are statistically insignificant. The

magnitudes of the estimated coefficients appear plausible and are consistent with other empirical

estimates. While they appear in several equations, the insignificant coefficients seem to be more

prevalent in the retail-level equations, particularly the butter and frozen product demands.

The adjusted R-squared is a measure of fit that shows how well the specified right-hand-side variables

explain variation in the respective endogenous (left-hand side) variables. Ten of the equations have

adjusted R-squared values of 0.80 or greater, one has a value just below (0.78). The two equations that

are not as good by this measure are the retail butter and the retail frozen product demand equations, with

adjusted R-squared values of 0.54 and 0.29, respectively.

An Estimate of Producer Returns

The model structure allows one to focus on the industry, on one of the market levels, or on any one of the

four product submarkets. Since producer returns from generic advertising and promotion by the NDB

(and the recent processor program) are the specific interest here, the farm-level relationships are of

primary interest. Highlights from the retail level, in particular the demand relationships, are a secondary

topic. The retail demand relationships are important because they show the first-round effects from

promotion and advertising expenditures.

Simulations of the dairy industry using the estimated econometric model were made for cases with and

without the boards. The simulations are for the period beginning with the third quarter of 1984 and

ending with the fourth quarter of 1997. The with-boards simulation essentially provides the industry

baseline for comparison of the without-boards estimates. The without-boards case is what the industry

would have been like in the absence of NDB and processor actions. How the without-boards case is

simulated plays a major role in the calculation of differences between the two situations.

The simulation of the dairy industry without the boards’ generic advertising expenditures is based on the

assumption that the level of funding for generic advertising and promotion that existed prior to the

implementation of the mandatory assessment would continue. That funding had been provided

voluntarily in some States, or by mandatory assessments in others (Forker, 1996). The expenditures for

media buys in the third quarter of 1983 through the second quarter of 1984 were used as the basis for

estimating an implied national assessment for generic advertising and promotion in the without-boards

case. The implied without-boards assessment was estimated to be about 6.3 cents per hundredweight

(cwt).
24

Table 5--Econometric estimation of the dairy industry model 

Farm level:

Farm milk supply--

LMILK = 1.2286 + 0.1122LAMPFEED - 0.0817LFWAGEFEED + 0.6582LMILK(-1) +

 (5.68) (2.78) (-1.59) (10.04)

0.0581DUMQ1 + 0.1007DUMQ2 + 0.0018T

 (8.25) (15.73) (4.29)

Adjusted R-squared 0.96 DW 1.70

Wholesale level:

Wholesale fluid milk supply--

LWFLS = 2.5205 + 0.0927LWFPP1 - 0.0855LPFEP1 - 0.0143DUMQ1 - 0.0593DUMQ2 -

 (15.27) (0.94) (-2.38) (-5.42) (-17.57)

 0.0512DUMQ3 + 0.0012T + 0.7905AR(1)

 (-16.71) (2.36) (10.28)

Adjusted R-squared 0.93

Wholesale cheese supply--

LWCHS = 1.8884 + 0.1028LWPCH + 0.0175DUMQ1 + 0.09961DUMQ2 + 0.0089T +

(75.84) (1.41) (2.04) (12.01) (20.39)

 0.6155AR(1)

 (7.65)

Adjusted R-squared 0.98

Notes to table 5 

Values in parentheses are t-values

LMILK = Milk production, billion pounds.

LAMPFEED = Ratio of all milk price to ration value.

LFWAGEFEED= Ratio of farm wage rate to ration value.

DUMQ1 = Intercept dummy variable equal to 1 for first quarter of a year, 0 otherwise.

DUMQ2 = Intercept dummy variable equal to 1 for second quarter of a year, 0 otherwise.

DUMQ3 = Intercept dummy variable equal to 1 for third quarter of a year, 0 otherwise.

LWFLS = Wholesale fluid supply measured in billion pounds, milkfat equivalent.

LWFPP1 = Ratio of wholesale fluid price index to class 1 milk price.

LPFEP1 = Ratio of energy price index to class 1 milk price.

LWCHS = Wholesale cheese supply measured in billion pounds, milkfat equivalent.

LWPCH = Wholesale cheese price.

LWBS = Wholesale butter supply measured in billion pounds, milkfat equivalent.

LWPB = Wholesale butter price

T = Time trend variable equal to 1 for first quarter 1975, 2 for second quarter 1975, 3 for third

quarter 1975, and so forth.

LWFZS = Wholesale frozen products supply measured in billion pounds, milkfat equivalent.

LWPFZ = Wholesale frozen product price index.

LRFLS = Retail fluid supply measured in billion pounds, milkfat equivalent.

RFLPWFLP= Ratio of retail price index for fresh milk and cream to wholesale fluid milk price

 index.
25

Table 5--Econometric estimation of the dairy industry model -- continued

Wholesale butter supply--

LWBS = 0.5288 + 0.0473LWPB + 0.0006T + 0.0525DUMQ1 - 0.1973DUMQ2 -

 (4.95) (1.61) (1.84) (2.27) (-5.94)

0.4193DUMQ3 + 0.7730LWBS(-1)

 (-15.31) (10.93)

Adjusted R-squared 0.89

Wholesale frozen product supply--

LWFZS = 0.3634 + 0.1238LWPFZ + 0.0701DUMQ1 + 0.3279DUMQ2 + 0.3499DUMQ3 +

 (1.40) (2.22) (8.90) (36.73) (45.14)

+ 0.6967AR(1)

 (8.77)

Adjusted R-squared 0.95

Retail level:

Retail fluid milk supply--

LRFLS = 2.5922 + 0.2391RFLPWFLP - 0.1156PFEWPF - 0.0166DUMQ1 - 0.0614DUMQ2

 (99.01) (1.10) (-1.62) (-5.25) (-18.15)

 - 0.0520DUMQ3 + 0.9198AR(1) 

 (-19.74) (24.96)

Adjusted R-squared

Notes to table 5 (continued) Continued--

PFEWPF = Ratio of energy price index to wholesale fluid milk price index.

LRCHS = Retail cheese supply measured in billion pounds, milkfat equivalent.

RCPWCP= Ratio of retail cheese price index to wholesale cheese price.

LTREND= Natural logarithm of the trend variable, T.

LRBS = Retail butter supply measured in billion pounds, milkfat equivalent.

RBPWPB= Ratio of retail butter price index to wholesale butter price.

LRFZS = Retail frozen products supply measured in billion pounds, milkfat equivalent.

RFZPWFZP= Ratio of retail frozen products price index to wholesale frozen products price index.

LRFLDEM= Per capita retail fluid demand measured in billion pounds, milkfat equivalent.

RFPBEV = Ratio of retail fluid milk and cream price index to the price index for nonalcoholic 

beverages.

INCBEV = Ratio of personal disposable income to the consumer price index for nonalcoholic

beverages.

LRCHSDEM= Per capita retail cheese demand measured in billion pounds, milkfat equivalent.

RCPMEA= Ratio of retail cheese price index to retail meat price index.

INCMEA= Ratio of personal disposable income to retail meat price index.

TSQ = Time trend variable squared.

LRBDEM= Per capita retail butter demand measured in billion pounds, milkfat equivalent.

RPBFAT = Ratio of retail butter price index to retail fats and oils price index.

INCFAT = Ratio of personal disposable income to retail fats and oils price index.

LRFZDEM= Per capita retail frozen products demand measured in billion pounds, milkfat

equivalent.

RFZPFOO= Ratio of retail frozen products price index to retail price index for food.

INCFOO = Ratio of personal disposable income to retail price index for food.
26

Table 5--Econometric estimation of the dairy industry model -- continued

Retail cheese supply--

LRCHS = 0.7052 + 0.3380RCPWCP +0.0547DUMQ2 + 0.1329LTREND + 0.3035AR(1)

(2.98) (5.35) (5.15) (8.58) (3.62)

Adjusted R-squared 0.93

Retail butter supply--

LRBS = 0.7213 + 0.2829RBPWPB + 0.1339DUMQ1 + 0.0877DUMQ2 - 0.1270DUMQ3 +

 (1.53) (2.65) (9.09) (5.65) (-9.47)

0.7819AR(1)

 (12.06)

Adjusted R-squared 0.83

Retail frozen product supply--

LRFZS = 0.9337 + 0.3856RFZPWFZP + 0.0708DUMQ1 +0.3284DUMQ2 + 0.3503DUMQ3 +

 (52.48) (1.00) (9.16) (37.34) (45.85)

0.7551AR(1)

 (10.49)

Adjusted R-squared 0.95

Retail fluid milk demand--

LRFLDEM = 2.6598 - 0.1202RFPBEV + 0.2079INCBEV - 0.0030T - 0.0128DUMQ1 -

 (2.66) (-2.05) (2.98) (-4.85) (-3.01)

 0.0566DUMQ2 - 0.0503DUMQ3 - 0.0504BST + 0.0092LGFAD + 

 (-9.96) (-11.89) (-3.66) (5.63)

 0.0147LGFAD(-1) + 0.0166LGFAD(-2) + 0.0147LGFAD(-3) +0.0092LGFAD(-4) +

 (5.63) (5.63) (5.63) (5.63)

 0.6239MA(1)

 (6.97)

Adjusted R-squared 0.87

Notes to table 5 (continued)

BST = Intercept dummy variable equal to 1 for the quarters when bST is commercially

available, 0 otherwise.

LGFAD = Generic fluid milk advertising expenditures deflated by the media price index,

measured in $1,000.

LGBAD = Generic butter advertising expenditures deflated by the media price index,

measured in $1,000.

LGCAD = Generic cheese advertising expenditures deflated by the media price index,

measured in $1,000.

LGICAD = Generic ice cream advertising expenditures deflated by the media price index,

measured $1,000.

“L” preceding variable names indicates the natural logarithm of the variable.
27

Table 5--Econometric estimation of the dairy industry model – continued 

Retail cheese demand--

LRCHSDEM = 2.3609 - 0.6175RCPMEA + 0.2353INCMEA - 0.0924DUMQ1 - 0.0536DUMQ2 -

 (4.11) (-3.40) (1.765 (-12.24) (-6.35)

 0.0550DUMQ3 - 0.0743BST+ 0.00006TSQ + 0.0035LGCAD + 

 (-7.22) (-3.26) (8.45) (2.70)

 0.0056LGCAD(-1) + 0.0064LGCAD(-2) + 0.0056LGCAD(-3) + 

 (2.70) (2.70) (2.70)

 0.0035LGCAD(-4) + 0.4184AR(1)

 (2.70) (3.93)

Adjusted R-squared 0.98

Retail butter demand--

LRBDEM = 0.0683 - 0.3741RPBFAT + 0.7065INCFAT - 0.1640DUMQ1 - 0.2151DUMQ2 -

 (0.30) (-1.72) (1.24) (-6.04) (-7.88)

 0.1334DUMQ3 + 0.1721BST -0.0074T -0.0004LGBAD -0.0006LGBAD(-1) -

 (-4.95) (4.63) (-1.69) (-0.87) (-0.87)

 0.0007LGBAD(-2) - 0.0006LGBAD(-3) - 0.0004LGBAD(-4) 

 (-0.87) (-0.87) (-0.87)

Adjusted R-squared 0.53 DW 2.18 

 

Retail frozen product demand--

LRFZDEM = -0.0984 - 0.2934RFZPFOO + 0.5448INCFOO - 0.00004TSQ + 0.0130DUMQ1 +

 -0.23) (-1.37) (6.19) (-10.19) (1.48)

 0.2873DUMQ2 + 0.3174DUMQ3 - 0.0002LGICAD - 0.0003LGICAD(-1) -

 (32.90) (36.23) (-1.67) (-1.67)

 0.0004LGICAD(-2) - 0.0003LGICAD(-3) - 0.0002LGICAD(-4)

 (-1.67) (-1.67) (-1.67)

Adjusted R-squared 0.97 DW 1.25

Simulated average quarterly values of selected price and aggregate quantity variables under the two

scenarios, with and without the boards’ activities, are presented in table 6. A rough measure of the

returns to producers from generic advertising can be obtained using the estimates of the farm milk price

and the board assessments in the with and without simulations. The difference in the assessments is 8.7

cents per cwt. The difference in simulated average farm milk prices is 30 cents per cwt, 2.3 percent above

the price in the without-boards scenario, for a return of $3.44 for each dollar of advertising.

The simulated retail demand quantities reported in table 6 support the contention that generic advertising

and promotion is achieving one of its main objectives: increasing the demand for milk and dairy

products. The clearest positive effects of generic advertising are seen for fluid milk and cheese, the

product groups that receive most of the advertising monies. The quarterly average increase in aggregate

retail demand is estimated to be 5.5 percent for fluid milk and 1 percent for cheese. Both the frozen

product and the butter demands appear to be essentially unchanged in the with-boards case. Recall that

the retail demand equation for butter exhibits statistical shortcomings so the simulation results must be

interpreted with caution. The frozen product demand equation is more difficult to assess. It has a good fit,

but other statistical properties, for instance the Durbin-Watson statistic, suggest that one needs to be

careful in interpreting this equation and its simulated results.
28

Future Work

The empirical results presented here indicate that milk producers receive a positive return from generic

advertising on the order of $3.44 for each dollar assessed to fund the activities. These results are

consistent with previous estimates that ranged from $2.00 to near $7.00 in the literature.

The variation among estimates of producers’ returns from generic advertising points to several key

elements of this and other analyses. First, and not surprising, is that the models used to generate

empirical estimates of economic variables for the analyses condition the results. The empirical estimates

presented here are based on an aggregate industry model developed at Cornell University over several

years, with data updated and coefficients re-estimated. This model has been widely used and continues to

be evaluated and improved (Kaiser, 1996).

There is clearly room for improvement in the model presented here, given sufficient time and resources.

The retail butter supply and demand relationships need to be revisited. There have been few attempts to

estimate butter supply and demand that have given what might be called reasonable results. The

assumption of fixed-proportions technology between the wholesale and the retail levels also needs to be

examined further. There has been work in this area focused on other products that can offer suggestions

as to how to proceed (Kinnucan, Nelson, and Xiao, 1995). A third item to consider is inclusion of

variables for branded advertising efforts in the retail product markets.

Having suggested there is more work to do to improve the analytical base for evaluating generic dairy

advertising and promotion programs, it is important to take stock of recent actions in the agricultural

policy arena and in the courts. Generic advertising and promotion programs have both ardent critics and

supporters. In the new environment of reduced roles for public policy in the agricultural sector,

supporters of the generic dairy promotion and research programs will likely be under increasing pressure

to show why the programs are needed and what benefits they provide to various groups, including the

milk producers and processors who fund them.

Table 6--Simulated values of selected market variables with and without the boards, 1984:3-1997

Variable Unit Without boards With boards Percent difference

 

Farm milk supply Bil. lbs. me.1 34.7 35.6 2.6 

Farm milk price Dol./cwt. 12.91 13.21 2.3

Advertising

 assessment Cents/cwt. 6.3 15.0

Fluid demand Bil. lbs. me. 12.90 13.62 5.5

Cheese demand Bil. lbs. me. 12.76 12.89 1.0

Butter demand Bil. lbs. me. 5.35 5.35 0.0

Frozen product

 demand Bil. lbs. me. 3.20 3.20 0.0

1

 The abbreviation “me.” is milk equivalent milkfat basis.

Source: U.S. Department of Agriculture, Economic Research Service
29

Study Limitations

In their comparative static analysis of optimal advertising policy, Nerlove and Waugh (1961) noted that

without supply control, the elasticities of supply, demand, and longrun marginal revenue of advertising

jointly determine the optimal advertising expenditures. If the model is cast in a dynamic framework,

optimal advertising policy also depends on the expected rates of change in demand and supply shifters,

the temporal distribution of advertising effects, and the discounting rate of investment (Nerlove and

Arrow, 1962). Both the comparative static and dynamic optimization studies, however, deal with

generalized aggregate supply and demand markets. Kaiser and others (1993) examined a disaggregated

industry model at the retail, wholesale, and farm levels with markets for fluid products, frozen products,

cheese, and butter. The multiproduct, multimarket-level model could simultaneously account for the

direct and cross-product effects of concurrent advertising programs for fluid and manufactured products.

Wohlgenant and Clary (1993), on the other hand, examined a farm-to-retail price linkage model using an

industry-derived demand equation for milk linking advertising and government purchases to farm price.

Because we use a single-equation retail demand, supply is implicitly fixed. Thus, advertising effects from

this study could be larger than if we assumed a flexible supply that responds to increased demand.

The other limitations of this study are related to the data. First, the fluid milk model encompasses only 43

percent of national milk consumption. For the cheese analysis, the Market Research Corporation of

America (MRCA) data measure only household purchases of cheese at retail establishments for offpremises consumption. MRCA did not measure cheese consumed away from home or as a component of

a food product. USDA per capita disappearance data suggest that cheese use has increased over time, but

the MRCA data show it generally declining. Such data differences suggest that growth in eating away

from home and the consumption of cheese in food mixtures more than offset the downward trend in

purchases for consumption at home. Generic advertising may affect consumption of cheese away from

home and food mixtures containing cheese not measured with the MRCA data. Thus, our estimates may

understate the total effects of generic advertising.

Another area that requires attention is the data for the advertising variable. Advertising expenditures in

dollars and cents is a convenient measure of the theoretical concept of an advertising variable. However,

these expenditures take several forms depending on the controlling agent, types of media used, and 

message content. In other words, the quality of advertising is not evident from an expenditure

measurement.

The issue of how best to model the effects of past advertising on current consumption should also receive

more attention. This critical issue can significantly affect the simulated effects of advertising

expenditures on consumption.
30

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