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Intern. J. of Research in Marketing 17 Ž2000. 281–305
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Market share response and competitive interaction: The impact of
temporary, evolving and structural changes in prices
Shuba Srinivasan a,) , Peter T.L. Popkowski Leszczyc b, Frank M. Bass c
a
The A. Gary Anderson School of Management, UniÕersity of California, RiÕerside, CA 92521, USA
b
Marketing Department, UniÕersity of Alberta, Edmonton, AB, Canada T6G 2R6
c
UniÕersity of Texas at Dallas, Richardson, TX 75083-0688, USA
Received 1 February 2000; accepted 1 September 2000
Abstract
Managing pricing is a challenging task due to the significant impact on shares and the likelihood of strong consumer and
competitor reaction. The major contributions of this paper are to assess comprehensive share response to temporary,
evolving and structural changes in prices and to determine the level of market share as a function of levels of prices. For the
empirical analysis, we examine two consumer product categories and find that it is valuable to distinguish among temporary,
evolving and structural changes in prices, as their impact on market shares tends to differ. Further, we find that subsequent
competitive reaction will influence predictions of price response. Accordingly, it is important for managers to use
conjectures regarding competitive price reactions in assessing the impact of policy changes. We conclude with the strategic
implications of the findings and discuss a number of opportunities for future research. q 2000 Elsevier Science B.V. All
rights reserved.
Keywords: Cointegration; Structural breaks; Competition; Marketing mix; Vector-Error Correction Models
1. Introduction
Pricing decisions are important to managers due
to the significant and immediate impact of price
changes on shares and profits and the potential for
strong reactions from consumers and competitors.
Pricing ranked third in overall importance among 15
marketing issues in a survey by Davidson and Stacey
)
Corresponding author. Tel.: q1-909-787-6447; fax: q1-909787-3970.
E-mail address: [email protected] ŽS. Srinivasan..
Ž1997. and was cited as Aextremely importantB by
78% of the respondents. Companies continually
change their price strategies and tactics with a view
to increasing sales or profitability. Some price
changes are temporary movements around a fairly
stable level consistent with I Ž0. behavior. For example, a brand that offers a 2-week price discount off a
fairly stable level engages in such a temporary effort.
Other price changes are permanent if there is no
return to the previous level. If a company engages in
a regular practice of discount policies, this would
lead to evolving prices or I Ž1. behavior, an example
of a permanent change. Another type of permanent
0167-8116r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 1 6 7 - 8 1 1 6 Ž 0 0 . 0 0 0 2 3 - 9
282
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
change is a structural change in price in favor of a
new level different from the previous level. If a
company announces a one-time 20% price cut, leading to a new price level, this is an example of a
structural change. As an illustration, Fig. 1 shows the
market share and price series for the Miller brand of
beer over 365 weeks while Fig. 2 shows the market
share and price series for the Blue Bonnet brand of
margarine over 104 weeks. The price series for
Miller indicates there is evolution in price or a
permanent price change, accompanied by an evolution in market share. Fig. 2 shows that Blue Bonnet
brand has a structural change in price along with a
structural change in market share in week 62. Finally, it is evident from the graphs that every 4 to 5
weeks both brands have temporary price promotions.
There are other real-world illustrations for the
three types of pricing behavior. See the following,
for example.
Ž1. Companies adopt short-term promotional tactics ranging from 25 cents off a six-pack of soda to
thousands of dollars off an automobile in order to
gain sales in terms of brand switching, repeat purchase, stockpiling and consumption. These tactics
correspond to temporary price changes.
Ž2. A company offers a price discount that generates immediate positive response resulting in subsequent regular practice of offering discounts. The
airline pricing tactics in 1992 resulting in price wars
is an example of permanent, evolving price behavior.
While this type of price behavior may result in
short-run gains, it would result in sustained losses
due to competitive action and reaction ŽDekimpe and
Hanssens, 1999.. On the other hand, sustained increases in prices can result in substantial long-run
profits.
Ž3. A company reduces prices to a new level by
offering a significant one-time price cut. For exam-
Fig. 1. Ža. Market share series Žin logs. for Miller. Žb. Market share series Žin logs. for Budweiser. Žc. Price series Žin logs. for Miller. Žd.
Price series Žin logs. for Budweiser.
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
283
Fig. 2. Ža. Market share series Žin logs. for Blue Bonnet. Žb. Market share series Žin logs. for Parkay. Žc. Price series Žin logs. for Blue
Bonnet. Žd. Price series Žin logs. for Parkay.
ple, in the dry cereal category, Kellogg’s announced
a 20% across-the-board price cut as a result of
declining shares.
These three scenarios raise important questions
from a managerial standpoint. First, managers are
interested in assessing the comprehensive share response to temporary, evolving and structural changes
in prices. Managers need to know the share elasticities in response to these types of price changes so
that they can make strategic decisions about levels of
prices Žlist prices. vs. tactical decisions on the extent
of price discounts. Second, managers are also concerned with whether or not, and to what extent, the
reaction from competitors will influence predictions
of price response. Omission of competitive reaction
may lead to biased estimates of market response to
price and other marketing variables Žsee, e.g.,
Leeflang and Wittink, 1992, 1996.. Indeed, Bucklin
and Gupta Ž1999. argue that these two managerial
issues are unresolved and point to an immediate need
for academic research in this area.
Our approach is based on two modern empirical
frameworks, Vector Auto-Regressive Models ŽVAR,
hereafter. and Vector Error-Correction Models ŽVEC,
hereafter.. Our model follows the VAR model proposed by Dekimpe and Hanssens Ž1995b, 1999. and
estimates market share, price and competitive prices
as a simultaneous system of equations. We derive the
static equilibrium conditions for the VAR model.
Structural changes in prices and shares are determined using the structural break tests. Once a structural break is identified, the stability of the model is
crucial to the task of evaluating the impact of structural changes within the system. Parameter stability
is tested using diagnostic tests such as the plot of
recursive residuals and the CUSUM test. If the parameters of the data generating process are invariant
to structural shifts, one can then assess the impact of
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S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
structural changes in prices on market shares using
the static equilibrium conditions of the VAR model.
To assess the impact of temporary changes in price
and evolving prices on market shares, we use the
time-series technique of cointegration analysis and
impulse–response analysis. These techniques provide us with the long-run price elasticities—that is,
the response to evolution in prices—and the short-run
price elasticities—the response to temporary price
reductions.
We address the following research questions in
this paper: Ž1. What is the impact of temporary
changes in prices, evolving prices and structural
changes in prices on market shares? Ž2. Do competitors and consumers respond to permanent changes
Žboth evolving and structural. in prices in the same
way that they respond to temporary changes in these
variables? Our analysis offers new contributions to
the literature in this area. First, we introduce the
typology of temporary, evolving and structural
changes in prices and discuss the managerial implications of these types of changes. Second, we estimate the comprehensive share Žcomprehensive in the
sense of a multivariate system. and competitive response to these types of price variations. Finally, we
formally gauge the influence of unilateral price-setting behavior in a dynamic-system context.
The paper is organized as follows: in Section 2,
we highlight how our effort differs from and builds
on extant literature in the area. Section 3 discusses
our modeling approach, Section 4 describes the scanner data we use and Section 5 provides the empirical
results. Section 6 presents conclusions and offers
directions for future research.
2. Background and overview
2.1. Long-run effectiÕeness of marketing-mix strategies
A number of recent articles are closely related to
our work. Table 1 summarizes the key findings of
these studies; we emphasize the main points in the
following discussion.
Research has found that for frequently purchased
consumer goods, market shares are predominantly
stationary ŽBass and Pilon, 1980; Ehrenberg, 1994;
Dekimpe and Hanssens, 1995a; Lal and Padmanabhan, 1995.. While Dekimpe and Hanssens Ž1995a.
observed that a majority of sales series is evolving,
Srinivasan and Bass Ž2000. demonstrate, theoretically and empirically, that this finding is consistent
with market shares being stationary, provided brand
sales and category sales are cointegrated. These studies’ findings are consistent with the view that marketing-mix variables have only a temporary effect on
shares.
Some studies, however, have documented long-run
effects of marketing activity on shares. Focusing on
markets with evolving shares, Bronnenberg et al.
Ž2000. observed that distribution has a positive impact on market share through the early growth stage,
but this effect diminishes over time. These results
indicate that distribution has a long-run impact in the
early stages of the product life cycle but not in the
later stages. Franses et al. Ž1999. are primarily concerned with aberrant observations that hamper the
quality of data and find different results across robust and non-robust methods for the long-run marketing effects. They find that distribution, advertising
and promotions have long-run impact on sales.
Franses et al. Ž2000. propose unit root tests that take
into account the logical consistency constraint on
market shares. Dekimpe and Hanssens Ž1995b, 1999.
have shown that when marketing-mix variables influence an evolving series, it is possible for temporary changes in a marketing-mix variable to have
permanent effects. On the other hand, in stationary
markets, Dekimpe et al. Ž1999. have shown that
when both market shares and marketing-mix variables are stationary, permanent offsetting competitive effects due to temporary changes in marketing
effort are unlikely. Studying the phenomenon of
hysteresis, Hanssens and Ouyang Ž2000., have developed optimal spending rules in terms of both trigger
and maintenance marketing. The over-time impact of
price promotions on category sales was analyzed for
over 500 categories in Nijs et al. Ž2000., who Ža.
found limited evidence of persistent effects, and Žb.
tested the moderating impact of marketing intensity,
competitive conduct and competitive structure on the
short- and long-run primary-demand elasticity of
price promotions. Pauwels et al. Ž2000. investigated
the long-term impact of price promotions on three
components of brand sales, i.e., category incidence,
Table 1
Studies looking at the long-run salesrshare effects of marketing mix variables
Model
Findings
54 yearly observations
Weekly grocery data 1991–1996
Ž257 weeks. of ready-to-drink tea
VECM
VAR
Dekimpe and Hanssens Ž1995a.
Data base of 400 prior analyses
Unit root tests
Dekimpe and Hanssens Ž1995b.
Dekimpe and Hanssens Ž1999.
Monthly advertising and sales data.
Ž1. Monthly pharmaceutical data
for 5 years. Ž2. BRANDAID data
VAR Persistence analysis
VECM
Dekimpe et al. Ž1999.
113 Weeks scanner data four categories
VAR Impulse response analysis
Franses et al. Ž1999.
Weekly scanner data for grocery product
Cointegration analysis
Franses et al. Ž2000.
Weekly scanner data for 2 years
for three categories
Unit root tests
Hanssens and Ouyang Ž2000.
Printer sales for 4 years
Theoretical and econometric analysis
Jedidi et al. Ž1999.
Scanner panel data, 691 households
from 1984 to 1992
Individual-level choice model
and quantity model.
Lal and Padmanabhan Ž1995.
Yearly data from IRI Factbook
Regression analysis
Mela et al. Ž1998.
Scanner panel data, 691 households
from 1984 to 1992
Dynamic market structure and
cluster-wise logit model
Mela et al. Ž1997.
Scanner panel data, 691 households
from 1984 to 1992
Two-stage approach
Nijs et al. Ž2000.
Data for 4 years for 560 categories
VAR Persistence analysis
Pauwels et al. Ž2000.
Scanner data for 2 years for two categories
VAR Persistence analysis
Srinivasan and Bass Ž2000.
Weekly scanner data and store-movement
data for grocery products
Cointegration analysisrVECM
Advertising and sales are cointegrated.
Distribution influences long-run share, through
early growth and diminishes during later stages
of product life cycle.
Sales series are mostly evolving while shares
are stationary.
Advertising has a long-term effect on sales.
Ž1. Sales calls, advertising and price differential
have long-run impact on sales. Ž2. Advertising,
promotion and prices have no long-run impact
on sales.
Promotions only have temporary effect in
stationary market. In non-stationary markets,
small lasting effect, positive for private label,
and negative for two national brands.
Distribution, advertising and promotions
have long-run impact on sales.
Propose unit-root and cointegration tests that
take the logical-consistency properties of
market-share series into account.
Quantifies effect of hysteresis on sales and
derives optimal spending rules.
Advertising has long-run positive effect
on sales; promotions have a negative
long-run effect.
Relative promotional expenditures have
no impact on long-run shares.
Increases in price promotions and reductions
in advertising have led to decreased
brand differentiation.
Increases in price promotions and reductions
in advertising have led to increased consumer
price and promotion sensitivity.
Limited persistent effect of promotions
on category sales.
Quantifies the long-term effect on category
incidence, brand choice and purchase quantity.
A majority of sales series being evolving
is consistent with market shares being
stationary, provided brand sales and category
sales are cointegrated.
285
Data
Baghestani Ž1991.
Bronnenberg et al. Ž2000.
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
Study
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S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
brand choice, and purchase quantity, and found the
relative importance of these components to differ
from the short-term categorization.
Mela et al. Ž1998., studying dynamic market
structure, showed that increases in price promotions
and reductions in advertising led to decreases in
brand differentiation. Jedidi et al. Ž1999. found that
promotions have a negative effect while advertising
has a positive effect on brand equity in the long
term. Mela et al. Ž1997. observed that increases in
price promotions and reductions in advertising have
led to increases in consumer price and promotion
sensitivity.
In summary, while some of the above-mentioned
studies report the absence of long-run effects on
shares, others find positive long-run effects on shares.
Thus, no conclusive findings are currently available
on the long-run effects of marketing-mix variables.
Given the potential for strong reactions to pricing
decisions from competitors, we explore several important studies on competitive reaction.
2.2. CompetitiÕe reactions to marketing-mix strategies
Empirical work on the nature of competitive reactions has been broadly based on two approaches: a
non-cooperative game theory approach and a timeseries causal approach. Non-cooperative game theory
based approaches have taken a variety of forms. For
example, the AmenuB based approach requires specifying a priori the alternative forms of competitive
interaction and using non-nested hypothesis tests to
ascertain which type of interaction fits the data Že.g.,
Gasmi et al., 1992; Kadiyali et al., 1996.. Alternatively, conjectural variation approaches estimate
competitive interaction directly without the need to
specify the form of the interaction a priori Žsee e.g.,
Bresnahan, 1989; Putsis and Dhar, 1998, 1999.. One
potentially valuable use of a game theory based
approach is it provides a broad-based understanding
of the nature of competitive interaction across categories, marketing instruments and strategic groups.
The interested reader is referred to papers by Bresnahan, 1989, and Cotterill et al., 1999.
Time series approaches employ time series data to
infer the reactions of firms across marketing instru-
ments. The most comprehensive studies on competitive reactions are those by Leeflang and Wittink
Ž1992, 1996.. Following earlier studies by Lambin et
al. Ž1975., Leeflang and Reuyl Ž1985., and Leeflang
and Wittink Ž1992. ŽLW, hereafter. have studied
competitive reaction functions of changes in price
and promotion variables in response to changes in
these variables by competitors. In general, when
reactions occur within four periods, they are thought
to be retailer-dominated, while longer-duration responses are thought to be manufacturer-dominated.
In a subsequent study, LW Ž1996. examined competitive interactions among marketing variables and their
impact on market shares and derived equilibrium
conditions. The empirical reaction function was used
to assess the nature and extent of competitive reaction in the marketplace. LW Ž1996. investigated 664
cases over different brands and different marketing
variables, reporting that competitors generally overreact to price changes. Brodie et al. Ž1996. have
replicated the study by LW Ž1996., examining three
product categories with data from New Zealand.
Jedidi et al. Ž1999. used the same response functions
as LW Ž1996. to estimate competitive reactions.
Our study builds on these important studies assessing the long-run effectiveness of and competitive
reactions to marketing-mix strategies. A critical point
of departure between our study and these important
studies is that our approach Ži. makes a distinction
among share responses to temporary changes in
prices, evolving prices and structural changes in
price and Žii. assesses how competitors and consumers respond to these three types of price variations.
3. Methodology
Our analysis proceeds sequentially. First, we test
for non-stationarity of the different time series. We
use the Perron and Vogelsang Ž1992. procedure to
control for structural breaks in the series. If the series
are stationary, we estimate a VAR model in levels.
We test for parameter constancy of the VAR model
with shift dummy variables using several diagnostic
tests ŽMaddala and Kim, 1998.. If the parameters are
constant, we can then assess the impact of structural
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
changes in prices on market shares using the static
equilibrium conditions of the VAR model. If the
series are evolving, we assess whether there is a
long-run cointegrating relationship among market
share and prices. On the one hand, if there is a
cointegrating relationship, we estimate a VEC model.
On the other hand, if there are unit-roots but there is
no cointegrating relationship, we estimate a VAR in
differences since regressions on the levels of evolving variables may produce spurious results ŽGranger
and Newbold, 1974.. Finally, to assess the impact of
temporary changes in prices and evolving prices on
share and on each other, we use impulse–response
analysis. We describe these steps below.
3.1. Testing uniÕariate equilibrium share models
When a series may be appropriately modeled as
depending on a constant plus a coefficient times a
lag of the series plus a random term, testing whether
a series is stationary or evolving may be accomplished by means of the well-known Dickey–Fuller
ŽDF. test for the presence of a unit root. When more
than one lag is involved, the appropriate test is the
Augmented Dickey–Fuller test ŽADF. ŽBanerjee et
al., 1993.. We use these tests in examining the
stationarity of market shares and marketing-mix variables. We apply the testing procedure in Perron and
Vogelsang Ž1992. to test for structural breaks in the
evolving series. Dekimpe et al. Ž1997, 1999. have
used structural break unit root tests Že.g., Perron and
Vogelsang, 1992. to control for effect of new-product introductions.
3.2. The multiÕariate Õector autoregression model
Our model follows the Vector Auto-Regressive
Model proposed by Dekimpe and Hanssens Ž1995b,
1999.. A five variable VAR is constructed, consisting of a brand’s market share, its price and the prices
of the major competitive brands. As exogenous variables, we included own and competitive features,
displays and price specials. Since these variables are
zero-one type, we treat them as exogenous variables.
We apply logarithmic transformations to market share
and prices, providing a constant elasticity model
ŽFranses et al., 1999.. As an example for the mar-
287
garine market, the following VAR model was estimated for the Blue Bonnet brand:
MS BB ,t
PBB ,t
PPK ,t
P FL ,t
P PL ,t
i
i
i
i
i
a11
a12
a13
a14
a15
a10
i
i
i
i
i
a 21
a 22
a23
a24
a 25
a 20
N
i
i
i
i
i
a 32
a 33
a34
a 35
s a 30 q Ý a 31
is1
i
i
i
i
i
a 40
a 41
a 42
a 43
a44
a 45
a50
i
i
i
i
i
a51
a52
a53
a54
a55
D D D D
a11
a12 a13 a14
D D D D
a 21
a22 a23 a24
D D D D
a32 a33 a34
q a 31
D D D D
a 41
a42 a43 a44
D D D D
a51
a52 a53 a54
F F F F
a11
a12 a13 a14
F F F F
a 21
a22 a23 a24
F F F F
a32 a33 a34
q a 31
F F F F
a 41
a42 a43 a44
F F F F
a51
a52 a53 a54
S S
S S
a11
a12 a13
a14
S
S
S
S
a 21
a22
a23
a24
S
S
S
S
a32
a33
a34
q a 31
S
S
S
S
a 41
a 42
a 43
a 44
S S
S
S
a51
a52 a53
a54
MS BB ,tyi
PBB ,tyi
P PK ,tyi
PFL ,tyi
PPL ,tyi
D BB ,t
D PK ,t
D FL ,t
D PL ,t
FBB ,t
FPK ,t
FFL ,t
FPL ,t
e BBS ,t
S BB ,t
e BBP ,t
S PK ,t
q e PKP ,t ,
S FL ,t
e FLP ,t
S PL ,t
e PLP ,t
Ž 1.
with endogenous variables being market share and
price of Blue Bonnet ŽBB. and prices of Parkay
ŽPK., Fleischman ŽFL. and Private Label ŽPL. brands;
FBB , D BB and SBB indicate the feature, display and
price specials variables of Blue Bonnet Žsimilar defi-
288
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
nitions apply for the other feature, display and price
specials variables., e jt Ž j s BBS, BBP, PKP, FLP
and PLP. denotes the error terms, and N is the order
of the model determined using the log-likelihood
criterion. To avoid potential degrees-of-freedom
problems when estimating extended VAR models
Že.g., when the market shares of several competitors
and marketing-mix variables are included in a simultaneous system., we estimated separate models for
each brand. We can write the VAR in matrix notation as:
yt s A 0 q A1 yty1 q A 2 yty2 q PPP qA N ytyN
q A F x1 t q A D x 2 t q AS x 3 t q et ,
Ž 2.
where yt is a 5 by 1 vector containing market share
and prices at time t; x 1 t , x 2 t and x 3 t are 4 by 1
vectors containing the feature, display and special
variables; and A F , A D and A S are the 5 by 4
matrices of the respective coefficients.
Some features of this model should be noted.
First, it is a comprehensive share and price response
model incorporating feedback effects ŽBass, 1969.,
purchase reinforcement effects ŽGivon and Horsky,
1990., competitive reactions ŽLW, 1992., lagged response effects ŽBass and Clarke, 1972. and decision
rules. Since lagged market shares may influence
prices, we treat price and competitive prices as endogenous. The rationale behind this is that managers
may adjust their prices based on market performance
in past periods ŽVillas-Boas and Winer, 1999; Winer,
1986..1 Second, we use market share rather than
sales; the use of market share as a dependent variable enables us to make relative comparisons across
brands with regard to the nature of competitive
activity ŽLW, 1996.. Finally, when the variables are
evolving, omitting the long-run relationship will underestimate the effects of marketing mix variables on
market share ŽVanden Abeele, 1994.. Thus, when
variables are evolving and cointegrated, it is appropriate to estimate a VECM model rather than a VAR
model. Incorporating the long-run relationship among
variables allows us to estimate long-run elasticities
as well as short-run elasticities ŽEnders, 1995; Johnston and Dinardo, 1996..
3.3. Impact of a structural change in prices on
market share using structural break analysis
To gauge the influence of unilateral price-setting
behavior in a dynamic-system context, we first establish the conditions for the existence of static equilibrium for the one-lag VAR and then extend it to the
more general case. Recall from Eq. Ž2. that the
reduced form of the first order VAR with no exogenous variables is:
yt s A 0 q A1 yty1 q e t .
Eq. Ž3. becomes:
yt s A 0 q A1 Ž A 0 q A1 yty2 q e ty1 . q e t
s Ž I q A1 . A 0 q A21 yty2 q A1 e ty1 q e t .
With successive lagging and using the identity
Ž I y A1 .Ž I q A1 q A21 q PPP qA1ny1 . s Ž I y A1n ., the
preceding equation can be written as:
yt s Ž I y A 1 .
While we account for this in the model, our empirical results
in Table 3 indicate that these effects are not significant for the two
product categories we studied.
y1
Ž I y A1n . A 0 q A1n ytyn q e t
q A1 e ty1 q A21 e ty2 q PPP qA1ny 1 e tyŽ ny1. .
Thus, yt will go to a constant matrix plus a
matrix consisting of a weighted sum of past random
shocks provided that A1n goes to zero as n becomes
large. Further, we require Ž I y A1 . to be nonsingular.
The preceding equation may be written as:
yt s Ž I y A 1 .
1
Ž 3.
y1
A 0 q Ž I y A1 L .
y1
et ,
Ž 4.
assuming A1n goes to zero when n is large. This will
be true if each of the eigenvalues Žcharacteristic
roots. of A1 has an absolute value less than 1. This
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
is the condition for the stability of the VAR model.2
The static equilibrium is:
yeq s Ž I y A1 .
y1
A 0 s Py1
1 A0 .
Ž 5.
Using a similar approach, we can derive the static
equilibrium for the VAR model of order N with
exogenous variables x 1 t , x 2 t and x 3 t . Starting with
Eq. Ž2., we take the expected value of both sides of
the equation and set y’s equal to the equilibrium
value:
yeq s A 0 q A1 yeq q A 2 yeq q PPP qA N yeq
q A F x 1 t q A D x 2 t q A S x 3t .
Ž 6.
We solve for yeq to find:
yeq s Ž I y Ý A i .
y1
289
Once a structural break is identified, the stability
of an estimated model is crucial to the task of
evaluating the impact of structural changes within
the system. If a model is not invariant to structural
interventions, no meaningful discussion AWhat would
happen if . . . B could be undertaken since the Lucas
Ž1976. critique would apply with full force and
simulation analyses would become problematic. A
number of tests are available to examine whether the
parameters of the model are stable, such as the Chow
test, and tests based on recursive estimates such as
the plot of recursive residuals, the CUSUM test, the
CUSUM-of-squares test and the recursive coefficient
estimates ŽCharemza and Deadman, 1997; Wolter et
al., 1998.. If the parameters of the data-generating
process are invariant to structural shifts, one can
determine the impact of structural changes in prices
on market shares.
= Ž A 0 q A F x 1eq q A D x 2 eq q A S x 3eq .
s Py1 Ž A 0 q A F x 1eq q A D x 2 eq q A S x 3eq . .
Ž 7.
The eigenvalues of P are the complements of the
eigenvalues of Ý A; therefore, the static equilibrium
will exist when each of these eigenvalues has an
absolute value less than 1.
We use Eq. Ž7. to derive the market share response to unilateral price setting behavior in a dynamic-system context. It is important to note that the
share level depends on a simultaneous equation system in which the level of market share depends upon
the levels of prices and vice versa. Prices are determined by managerial policies that reflect a general
level Žthe intercept. and by responsiveness to the
other variables. If the managerial policy is changed,
the level will also change, and this change will
influence the level of market share. Therefore, we
can use the static equilibrium relationships to examine the effects on market share of structural changes
in prices.
2
Each sequence in the stationary VAR has a finite and time-invariant mean and variance after controlling for the structural shift,
and the stability condition holds Žsee Enders, 1995.. We confirm
that this is the case by a check of eigenvalues of the matrix, A t , in
the empirical analysis.
3.4. Impact of eÕolÕing prices and temporary changes
in prices on market shares
If the market share series and price series’ are
evolving, we investigate if there is a long-run equilibrium or cointegrating relationship among the variables ŽBanerjee et al., 1993.. Since own and competitive features, displays and price specials are
zero–one type variables, we treat them as exogenous
and abstain from a long-run analysis for these variables. As an example, if there is a long-run cointegrating relationship in the beer market among the
market share of Miller ŽML. and prices of Miller
ŽML., Budweiser ŽBD., Busch ŽBH., Old Milwaukee
ŽOM. and Milwaukee’s Best ŽMB., then they are
related by an equilibrium relationship such as:
MS ML ,t s b 0 q b 1 P ML ,t q b 2 PBD ,t q b 3 PBH ,t
q b4 POM ,t q b5 PMB ,t q e MLS ,t .
Ž 8.
The existence of a long-term relationship implies
that equilibrium error e ML S,t represents a stationary
process. One rationale for this kind of long-run
cointegrating relationship is that different price levels correspond to different long-run demand or sales
levels, and these, in turn, are associated with different levels of shares.
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
290
Following Johansen Ž1995., we can rewrite Eq.
Ž2., where yt is a k-vector of non-stationary I Ž1.
variables as:
Ny1
D yt s A 0 q P yty1 q
Ý
Gi Dytyi
is1
q A F x1 t q A D x 2 t q AS x 3 t q et
Ž 9.
N
where P s Ý is1
A i y I and Gi s yÝ Njsiq1 A j .
Engle and Granger Ž1987. assert that if the coefficient matrix P has reduced rank r - k, then there
exists k = r matrices a and b with rank r such that
P s ab X and b X yt is stationary; r is the cointegrating rank, and each column of b is the cointegrating
vector. The elements of a are known as the adjustment parameters. Johansen’s Ž1995. method is to
estimate the P matrix in an unrestricted form, then
test whether we can reject the restrictions implied by
the reduced rank of P . We will apply the FIML
approach of Johansen to test for cointegration and to
construct the lagged error terms Žsee Johansen, 1988;
Johansen and Juselius, 1990.. A major problem when
testing for cointegration among N I Ž1. series is that
one can have up to N y 1 cointegrating vectors.
Thus, a direct interpretation of the cointegrating
coefficients is difficult ŽLutkepohl and Reimers,
1992.. Since impulse–response functions do not have
this ambiguity, we use them to estimate the long-run
elasticities.
The long-term dynamics can be analyzed by tracing the impact of an innovation on a single endogenous variable on the evolution of the entire system.
A shock to the k th variable directly affects the k th
variable and is also transmitted to all of the endogenous variables through the dynamic structure of the
VAR ŽVEC. model. The results of such an analysis
are called impulse–response analysis. ŽIn the interests of space, we do not discuss this in detail and
refer the interested reader to Lutkepohl and Reimers,
1992; Dekimpe and Hanssens, 1999; Bronnenberg et
al., 2000, for examples.. Using impulse–response
analysis, one can incorporate both the direct effect of
price on share and the indirect effect of price on
share through competitive prices. Hence, we use
impulse–response analysis to estimate comprehensive share and competitive response to temporary
changes in prices and evolving prices.
4. Data description
To estimate our models, we use data supplied by
Information Resources and AC Nielsen. The former
consists of aggregate weekly store level scanner data
for beer in a test market in the US from 1989 to
1996. This results in 365 weekly observations. Information is available on weekly unit sales, price, price
specials, local print advertising and display activity.
We used AC Nielsen household scanner data on
purchases in the margarine category to construct
time series of shares, prices and promotional variables. This data set has been used extensively in the
recent marketing literature Že.g., Chintagunta, 1993..
The brand-level variables are created from skulevel variables. This aggregation from sku- to brandlevel may be subject to aggregation bias due to
possible heterogeneity among promotional variables
ŽChristen et al., 1997; Pesaran and Smith, 1995.. To
control for this bias, we performed pooling tests to
determine whether we can pool the different varieties
for a brand; over 95% of sales could be pooled.3
However, our estimates of elasticity are consistent
with prior estimates Že.g., Tellis, 1988., thus alleviating, to a certain extent, the likelihood of substantial
aggregation bias. Further, as pointed out by Kopalle
et al. Ž1999. and Bucklin and Gupta Ž1999., there are
also some tradeoffs in using a sku-level specification, such as collinearity and large state space. For
the beer category, we considered the top five brands:
Budweiser, Miller, Busch, Old Milwaukee and Milwaukee’s Best. For the margarine category, we considered four major brands: Blue Bonnet, Parkay,
Fleischman and a major private label brand. Market
share of a brand is calculated as the share of category volume, while price is the inflation-adjusted
average weekly price per ounce. Price specials, displays and advertising are indicator variables. However, since not all stores promote a brand uniformly,
we specified the promotional variables based on the
3
Christen et al. Ž1997. point out that the aggregation bias is
likely to be quite small in data characterized by three conditions:
frequent promotions, frequent price cuts, and small own priceelasticities. We note that all three conditions are met for the
margarine data while the first two conditions are met for the beer
data.
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
percentage of units sold on deal, advertising and
display in a given week Žsee, e.g., Wittink et al.,
1988..
5. Empirical results
5.1. UniÕariate stationarity tests and testing for
structural breaks
Fig. 1 shows the market share and price series for
Miller and Budweiser brands Žto preserve space, we
only include graphs of the major brands, but we
summarize the findings below.. Table 2 displays the
results of the unit-root tests. To assess the stable or
evolving character of market shares and marketingmix variables, we applied several versions of the
Augmented Dickey–Fuller ŽADF. test. Table 2 lists
the test statistics under the column labeled ADF.4
Results for the beer category indicated that the market shares and prices of the four major brands of
beer—Budweiser, Miller, Busch and Milwaukee’s
Best—were evolving while Old Milwaukee’s share
and price were stationary. All advertising, display
and price special variables were stationary for the
five major brands of beer. The Perron and Vogelsang
Ž1992. procedure was used to control for structural
breaks in the data with evolving series Žsee column
labeled Min ta ., but our substantive conclusions
were not affected for the beer category.
Fig. 2 shows the market share and price series for
Blue Bonnet and Parkay brands of margarine. ŽAgain,
to preserve space, we only include graphs of the two
major brands, but we summarize the findings below..
For the margarine category, results shown in Table 2
indicated that the market shares of Blue Bonnet and
Fleischman were evolving while the market shares of
Parkay and the private label brand were stationary.5
4
We started by including 12 lags, determined the greatest lag
with a significant t-value and retested using this number of lags.
A constant was included, and we used both the Schwarz criterion
and the log-likelihood ratio test. All results were consistent.
5
The remaining brands consisted of smaller brands and other
private labels and were not included in the analysis; given that
shares sum to one, it is conceptually impossible to have one share
series evolving while the other four shares are evolving.
291
However, the results showed that the prices of all the
brands were evolving. All advertising, display and
price special variables were stationary except for the
price specials of Parkay and Blue Bonnet. Our substantive conclusions were affected as described below when the Perron and Vogelsang Ž1992. procedure was used to control for structural breaks Žsee
column labeled Min ta ..
The market share and price of Blue Bonnet were
stationary with a break point occurring in week 62 as
seen in Fig. 1c and d. The market share and price of
Fleischman were found to be stationary with a level
shift occurring in week 74. The price of Parkay was
stationary with the break point occurring in week 75.
The Perron and Vogelsang Ž1992. procedure indicated the presence of structural breaks in these series; nevertheless, we additionally conducted F-tests
to test for equality of means before and after the
structural break for these series. These test results
shown in Table 2 were consistent with the results
obtained from the structural break tests in that we
rejected the null hypothesis of equality of means of
these series before and after the break point.
In week 62, Blue Bonnet reduced its price, resulting in a significant increase in market share Žsee Fig.
2a and b.. Parkay responded both with price specials
in the short run and with a structural price change in
week 75. Parkay’s share had a small decline after the
price decrease for Blue Bonnet and a slight increase
after a reduction in Parkay’s price; however, its share
remained stationary as evidenced by the rejection of
the unit-root test. The market shares of Fleischman
and the private label brand were not affected by the
change in the pricing strategy of Blue Bonnet; most
of the increase came at the expense of smaller brands
and the remaining private labels. Fleischman’s break
in week 74 was accompanied by a share increase;
this too was at the expense of smaller brands and the
remaining private labels. Price reductions of national
brands resulted in an asymmetric loss of share of the
small brands and private label brands rather than
other national brands, consistent with the findings of
Blattberg and Wisniewski Ž1989..
Thus, in the short run, there is an immediate
response by major competitors in the form of temporary price specials; therefore, their shares remain
stable. However, it takes 12 and 13 weeks for Fleischman and Parkay, respectively, to respond with a
292
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
Table 2
Non-stationarity tests for beer and margarine data
Variable
ADF
Min ta
ADF outcome
Structural break test
outcome Žbreak point.
Beer
Share Budweiser
Share Busch
Share Miller
Share Old Milwaukee
Share Milwaukee Best
Price Budweiser
Price Busch
Price Miller
Price Old Milwaukee
Price Milwaukee Best
Advertising Budweiser
Advertising Busch
Advertising Miller
Advertising Old Milwaukee
Advertising Milwaukee Best
Display Budweiser
Display Busch
Display Miller
Display Old Milwaukee
Display Milwaukee Best
Special Budweiser
Special Busch
Special Miller
Special Old Milwaukee
Special Milwaukee Best
y2.78
y1.86
y2.46
y3.33
y1.61
y2.58
y2.54
y2.07
y3.98
y1.99
y19.27
y19.14
y18.55
y17.15
y8.61
y11.38
y11.42
y12.02
y6.11
y5.30
y12.02
y5.15
y12.21
y3.46
y3.79
y3.01
y2.09
y2.68
Evolving
Evolving
Evolving
Stationary
Evolving
Evolving
Evolving
Evolving
Stationary
Evolving
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Evolving
Evolving
Evolving
Stationary
Evolving
Evolving
Evolving
Evolving
Stationary
Evolving
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Margarine
Share Blue Bonnet
Share Parkay
Share Fleischman
Share Private Label
Price Blue Bonnet
Price Parkay
Price Fleischman
Price Private Label
Advertising Blue Bonnet
Advertising Parkay
Advertising Fleischman
Advertising Private Label
Display Blue Bonnet
Display Parkay
Display Fleischman
Display Private Label
Special Blue Bonnet
Special Parkay
Special Fleischman
Special Private Label
y2.33
y7.52
y2.54
y6.26
y1.65
y2.34
y2.80
y1.65
y12.76
y10.81
y10.42
y3.17
y6.87
y6.21
y3.74
y7.25
y2.49
y1.57
y10.38
y10.61
y8.74
Evolving
Stationary
Evolving
Stationary
Evolving
Evolving
Evolving
Evolving
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Evolving
Evolving
Stationary
Stationary
Stationary Žweek 62.
Stationary
Stationary Žweek 74.
Stationary
Stationary Žweek 62.
Stationary Žweek 75.
Stationary Žweek 74.
Evolving
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Stationary
Evolving
Stationary
Stationary
Stationary
y1.83
y2.84
y2.91
y2.27
y2.24
y9.60
y3.97
y9.55
y9.93
y1.47
y2.78
y3.35
F-test outcome for
equality of meansa
Reject equality of means
Reject equality of means
Reject equality of means
Reject equality of means
Reject equality of means
Reject equality of means
a
F-test for equality of means before and after the break point for series where unit-root tests indicated a structural break; t-statistic for
the parameter estimate associated with the lagged level in the ADF equation, to be compared with the 5% critical value of y2.93; critical
values for the structural-break tests are from Perron and Vogelsang Ž1992..
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
structural change in prices. In general, when reactions occur within four periods, they are thought to
be retailer-dominated while longer duration responses are manufacturer-dominated Žsee LW, 1992;
Kopalle et al., 1999.. Even though the shares of
Fleischman and Parkay are not significantly affected
Ždue to their immediate response in the form of price
specials., these brands did respond to the structural
price change since their shares relative to Blue Bonnet had decreased.
5.2. Impact of a structural change in prices on
market share using structural break analysis
We included three shift dummy variables I1, I 2
and I 3 in the VAR model for margarine corresponding to the structural breaks. The results of the parameter estimates of the reduced form of the VAR model
for margarine are provided in Table 3. Results in
Table 3 show that only the own structural price
break for a series is significant.6 To guide model
specification with respect to the number of lags,
model testing was conducted using the log-likelihood
criterion and the Schwarz criterion.7 The results
were consistent; a second-order VAR was selected
for the margarine data. Following Dekimpe and
Hanssens Ž1999., we only included the parameter
estimates that had a t-value greater than one.8 The
6
As an example, for Blue Bonnet, we include the dummy
variable for break 1 Žindicating Blue Bonnet’s price break.. To
determine the impact of competitive price breaks, we first estimate
a model that includes a dummy for break 1 and break 2 followed
by a model that includes break 1 and break 3 dummies. This is
done since the dummies for break 2 and break 3 are highly
collinear.
7
Though the likelihood-ratio test and AICrSIC information
criterion approach are equivalent, the real difference is in the
significance levels, which are usually fixed at 5% in hypothesis
testing, and are selected automatically Žtypically starting at more
than 10% in small samples but converging to 0 for larger ones. in
the information criterion approach. Loose significance levels are
not necessarily the best choice and the discussion on this issue
cannot be regarded as closed with a clear guideline. Model choice
by residual correlation statistics Žsuch as Ljung-Box Q. has often
been reported to yield the poorest results.
8
Since lagged market share is not significant in any of the
price equations estimated, the price equations are the same for the
four VARs estimated Žsee Eq. Ž1...
293
results of these analyses are used to estimate the
impact on shares of a change in prices.
However, we first need to check for parameter
constancy. For Blue Bonnet’s market share equation,
the plot of recursive residuals does not indicate
instability, as seen in Fig. 3a. We also performed a
series of Chow forecast tests, the CUSUM test based
on recursive residuals and the CUSUM-of-squares
test and examined the recursive estimates of the
parameters of the VAR; the results were consistent.
These tests conducted for all the remaining brands
also did not indicate instability and therefore are not
included. Thus, the structural shift in price does not
lead to a change in the response coefficients.
To graphically show the impact of the change in
pricing strategy for Blue Bonnet, we forecast market
share from the break in week 62 onward using the
VAR parameter estimates, assuming no price break
in the price series of Blue Bonnet. This gives us a
new market share series that can be compared with
the actual market share series to get a measure of the
effect of the structural shift in prices. This is illustrated for Blue Bonnet in Fig. 3b and c, before and
after the structural break, respectively. There is a
substantial one-time increase in share in response to
a structural price change after which share stabilizes.
We predict market shares from week 74 using the
VAR parameter estimates under two scenarios. Under scenario one, we assume that prices of both
Fleischman and Parkay do not have a structural
change or are at the price level before the structural
break. Under scenario two, Parkay’s price is set to
the new level corresponding to the price level after
the structural break while Fleischman’s price is set at
the price level before the structural break. We compare these scenarios with the actual market share
corresponding to the case where both the prices have
a structural break. We find that Fleischman’s share is
lower under scenario two as compared to scenario
one; subsequent competitive reaction does affect the
prediction of price response and needs to be accounted for by managers. Therefore, it is important
for managers to use conjectures regarding price reaction in assessing the impact of policy changes ŽJeuland and Shugan, 1988..
Using the expression for the equilibrium of the
VAR model in Eq. Ž7. and estimates from Table 3,
we assess the effect of a changed level of price on
294
Table 3
Results of VAR model for margarine
Share Blue
Bonnet
Share Parkay
Share
Fleishman
Share Private
Label
Price Blue
Bonnet
Price Parkay
Price
Fleishman
Constant
Share Žy1.
Break 1
Break 2
Break 3
Price Fleishman Žy1.
Price Fleishman Žy2.
Price Blue Bonnet Žy1.
Price Blue Bonnet Žy2.
Price Parkay Žy1.
Price Parkay Žy2.
Price Private Label Žy1.
Price Private Label Žy2.
Advertising Fleishman
Advertising Blue Bonnet
Advertising Parkay
Advertising Private Label
Display Fleishman
Display Blue Bonnet
Display Parkay
Display Private Label
Special Private Label
y0.593 )
y1.689 )))
0.277 )))
y0.162 )
y1.451)))
0.205 )))
y1.347 )
y1.252 )))
y1.662 )))
y2.139 )))
a
0.277 )))
0.080 )))
y0.083 )))
0.260 )))
y0.091)
y0.085 )))
0.407 )
0.638
y0.184 )
0.171)
0.380 )))
0.516 )))
y0.233 )
0.179 )
y0.619 )))
0.709 )))
y0.051)
y0.210 )))
0.729 )))
y0.249 )))
0.332 )
y0.158 )))
y0.354 )))
y0.186
0.206
0.192 )))
0.336 )))
y0.267 ))
y0.099
y0.210 )))
0.295 )))
First-differenced.
Indicates p- 0.10 Žvia two-tailed tests..
))
Indicates p- 0.05.
)))
Indicates significant at p- 0.01.
)
Price Private
Label a
y0.388 )))
0.057
y0.225 )))
y0.242 )
0.745 )))
y0.202 )))
y0.246 )
y0.494 )))
y0.248 )))
y0.413
y0.420 )))
0.158 )
y0.572 )))
y0.064
0.180 )))
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
Variable
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
295
Fig. 3. Ža. Test of parameter stability: plot of recursive residuals for Blue Bonnet. Žb. Market share for Blue Bonnet brand prior to the
structural break. Žc. Market share for Blue Bonnet brand after the structural break. Žd. Market share for Fleischman brand.
the level of market share. The values of prices for
the brands of margarine are set equal to the new
level in the system of simultaneous equations indicated in the reduced form equations; the system
solved for the new levels of the remaining variables.
The results are shown in Table 4. The predicted
levels of market share for Blue Bonnet are 16.9%
before the structural break in week 62 and 20.3%
after; actual shares are 17.6% and 20.4%, respectively. For Fleischman, the predicted levels of share
are 8.4% before the structural break in week 74 and
10.8% after; actual shares are 8.2% and 10.3%,
respectively. The predicted share levels are quite
good, as evident in Fig. 3. The own-price elasticity
estimates corresponding to a structural change in
price for Blue Bonnet, Parkay and Fleischman brands
Table 4
Impact of structural changes in prices for margarine
Brand
Blue Bonnet
Fleishman
Parkay
Private Label
a
Market share before price break a
Market share after price break
Actual Ž%.
Predicted Ž%.
Actual Ž%.
Predicted Ž%.
17.6
8.2
28.9
7.4
16.9
8.4
29.8
7.6
20.4
10.3
28.9
7.4
20.3
10.8
29.8
7.6
Share response elasticity
y1.37
y1.55
0
–
The price breaks for Blue Bonnet, Fleischman and Parkay occur at week 62, 74 and 75, respectively; market shares do not add up to 1
since smaller brands and other private labels account for the rest.
296
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
Table 5
ŽA. Impact of temporary changes in prices for margarine Žstandard error in parentheses.
Effect of independent variable
Response effects of price on share
Blue Bonnet
Parkay
Fleischman
Private Label
Lagged response effects
of price on share
Feedback effects of
share on price
Purchase reinforcement
effects of share Žon share.
CompetitiÕe price reaction effects
Blue Bonnet
Parkay
Response elasticity Žeffect on market share or price of.
Blue Bonnet
Parkay
Fleischman
Private Label
y0.578 ))
Ž0.114.
0.541
Ž0.369.
n.s.
0.187 )
Ž0.245.
y0.439 ))
Ž0.201.
n.s.
n.s.
n.s.
0.683
Ž0.429.
n.s.
0.194
Ž0.125.
n.s.
y0.119
Ž0.073.
y0.392 )))
Ž0.151.
n.s.
0.213 ))
Ž0.109.
y0.439 )
Ž0.255.
0.133 ))
Ž0.065.
0.305
Ž0.259.
n.s.
n.s.
0.231)))
Ž0.081.
0.119
Ž0.115.
–
0.123
Ž0.123.
–
0.218
Ž0.186.
0.290 )
Ž0.153.
–
n.s.
Private Label
0.076
Ž0.073.
0.056
Ž0.055.
n.s.
Decision rules effects
of price Žon price.
0.447 )))
Ž0.115.
Fleischman
0.159 ))
Ž0.077.
n.s.
0.316 )))
Ž0.089.
y0.495
Ž0.322.
n.s.
y0.223 )
Ž0.132.
n.s.
0.073 )
Ž0.04.
0.337 )))
Ž0.079.
n.s.
n.s.
y0.035
Ž0.029.
–
y0.340 ))
Ž0.087.
ŽB. Impact of temporary changes in prices for beer Žstandard error in parentheses.
Effect of independent variable
Response elasticity Žeffect on share or price of.
Budweiser
Busch
Miller
Old Milwaukee
Milwaukee’s Best
y1.810 )))
Ž0.203.
0.363 )))
Ž0.111.
0.522 )))
Ž0.116.
n.s.
0.430 )))
Ž0.162.
y2.420 )))
Ž0.124.
n.s.
0.658 )))
Ž0.171.
n.s.
0.457 )))
Ž0.157.
0.434 )))
Ž0.107.
n.s.
0.371))
Ž0.166.
n.s.
0.365 )))
Ž0.118.
n.s.
0.521)))
Ž0.120.
y1.233 )))
Ž0.077.
CompetitiÕe price reaction effects
Budweiser
–
n.s.
Busch
n.s.
–
Response effects of price on share
Budweiser
Busch
Miller
Old Milwaukee
Milwaukee’s Best
Lagged response effects
of price on share
n.s.
y1.832 )))
Ž0.144.
n.s.
y0.622 )))
Ž0.088.
y1.857 )))
Ž0.160.
1.030 )))
Ž0.133.
0.212 ))
Ž0.097.
0.466 )))
Ž0.064.
0.268 )))
Ž0.037.
0.174 ) )
Ž0.078.
y0.141)))
Ž0.047.
n.s.
0.436 )))
Ž0.103.
1.025 )))
Ž0.131.
y1.640 )))
0.164.
n.s.
n.s.
n.s.
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
297
Table 5 Ž continued .
Effect of independent variable
Response elasticity Žeffect on share or price of.
Budweiser
Busch
Miller
Old Milwaukee
Milwaukee’s Best
0.274 )))
Ž0.045.
n.s.
–
n.s.
n.s.
–
Milwaukee’s Best
0.212 )))
Ž0.062.
0.109 )
Ž0.060.
n.s.
n.s.
n.s.
Decision rules effects of
price Žon price.
0.222 )))
Ž0.035.
0.527 )))
Ž0.026.
0.545 )))
Ž0.032.
0.193 )))
Ž0.074.
0.320 )))
Ž0.030.
0.119 )
Ž0.064.
0.200 )))
Ž0.07.
–
CompetitiÕe price reaction effects
Miller
Old Milwaukee
0.232 )))
Ž0.029.
n.s. indicates estimates with t-value less than one.
)
Indicates p - 0.10 Žvia two-tailed tests..
))
Indicates p - 0.05.
)))
Indicates significant at p - 0.01.
are y1.37, 0 and y1.55, respectively. Thus, for two
out of the three brands with structural breaks in
price, shares are quite responsive to structural
changes in prices. In Section 5.3, we compare the
relative effect of temporary and structural changes in
price for margarine.
5.3. Impact of temporary changes in prices on market share
To assess the impact of temporary changes in
prices on shares for the margarine and beer markets,
impulse–response analyses are performed. For the
beer data, where shares and prices are evolving,
cointegration analysis is first performed on market
shares, price and competitive prices Žsee Eq. Ž8...
Advertising, display and price specials are included
as exogenous variables. Separate models are estimated for each brand in each product class. Once the
number of lags is determined, the VECM is estimated using the Johansen and Juselius Ž1990.
method. The PcFiml software is used for estimation
purposes ŽDoornik and Hendry, 1996.. This method
determines the cointegration rank, the number of
significant cointegrating vectors and the associated
cointegrating vectors with the long-run parameters.
Due to interpretation problems with cointegration
vectors, we use impulse–response functions to assess
both the short-run and the long-run price elasticities.
In the interests of space, we do not discuss in detail
the coefficients of the five separate VEC models
estimated for each of the five brands but rather
interpret the coefficients of the impulse–response
analysis incorporating both direct and indirect effects. Since the impulse–response functions were
estimated for the transformed data expressed in natural logarithms, these were used to assess the short-run
elasticities or the percentage change in the dependent
variable due to a percentage change in the shocked
variable.9 A potential limitation of the impulse–response analysis is that results may not be invariant to
the ordering of the endogenous variables when the
error terms are correlated ŽLutkepohl and Reimers,
1992.. However, when the residual correlations are
small, results are robust to the causal ordering. Enders Ž1995. suggests a cut-off value of 0.20; if the
correlation coefficient between, for example, e BBP,t
and e PKP,t in Eq. Ž1., N r N is - 0.2, the correlation
is deemed not to be significant. In all instances, we
found correlations below 0.20. Furthermore, we also
9
To see this, let the impulse–response be denoted by n as a
result of a one standard deviation Ž s . shock to price. Since we are
dealing with natural logarithms, we have lnŽMS tq 1 .ylnŽMS t . s n
or lnŽMS tq 1 .rlnŽMS t . s n . With algebra, one can show that
percentage change in market share is DMS t rMS t sexpŽ n .y1.
In a similar manner, the percentage change in price is D Pt r Pt s
expŽ s .y1. Therefore, the price elasticity of market shares
ŽexpŽ n .y1.rŽexpŽ s .y1.. The standard errors are obtained using
the Delta method Žsee Kmenta, 1997..
298
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
performed impulse–response analysis using different
ordering of the variables, and results were very
similar.10
The results of the impulse–response analysis for
margarine and beer data are reported in Table 5a and
b, respectively. The parameter estimates for the
short-run own and cross-price elasticities are consistent with our expectations and with the results reported in the prior literature ŽTellis, 1988; Wittink et
al., 1988.. From Table 5a, the short-run price elasticities of share response for the margarine brands are
smaller and range from y0.22 to y0.57; however,
these results are consistent with Blattberg and Wisniewski Ž1989. for the margarine category. In terms
of the cross-elasticities, temporary changes in price
of the private label brand seems to be more effective
in drawing share from Fleischman and Blue Bonnet
than vice versa. This is consistent with Bronnenberg
and Wathieu Ž1996. whose results suggest that private label brands could promote more effectively
than national brands where the former have advantageous positions in terms of quality relative to price.
With regard to temporary price changes, on the one
hand, consumers may believe that the change is
temporary and hence buy more on deal or accelerate
their purchase. On the other hand, if a consumer sees
a brand’s price that is outside the acceptable range of
prices, it is contrasted with the acceptable range and
becomes noticeable, consistent with AssimilationContrast Theory ŽKalyanaram and Little, 1994.. Thus,
structural price changes could lead to larger sales
increases. Consistent with this, our results suggest
that for two of the three brands with a structural
change in prices, Blue Bonnet and Fleischman, shares
are more responsive to structural price changes than
to temporary price changes.
There is a significant lagged response effect of
price on share for Parkay ŽBass and Clarke, 1972..
The influence of lagged prices on current prices is
positive and significant for Parkay and Blue Bonnet,
consistent with inertia in decision-making. Current
period market shares are positively related to past
market shares for Parkay and the private label brand,
10
Dekimpe and Hanssens Ž1999. utilize the information in the
residual correlations matrix to derive a vector of expected shock
values and to simultaneously shock all error terms in the system.
consistent with purchase reinforcement ŽGivon and
Horsky, 1990.. Only for the private label brand do
we find significant feedback effects; price depends
on market performance in the prior period ŽVillasBoas and Winer, 1999.. The results of competitive
price reactions show that the price reactions are
positive between Parkay and Fleischman, indicative
of manufacturer pricing activity ŽLW, 1992.. Table
5b shows that the short-run price elasticity of share
response for the beer brands ranges from y1.64 to
y2.42 while the cross price elasticity ranges from
0.36 to 1.03. The brands with the highest levels of
share ŽBudweiser and Miller. tend to have lower
sensitivity to price compared to lower-selling brands
ŽBusch and Old Milwaukee., suggesting that temporary price changes Žprice promotions. would be more
effective in the short run for the latter brands than
for the former. There are significant lagged response
effects for three brands; the effect for Old Milwaukee is consistent with alternating promotions. However, we do not observe significant feedback and
purchase reinforcement effects for any of the brands.
It appears that in the nonstationary beer market,
market share is mostly influenced by own and competitive marketing mix variables. With regard to
competitive reaction, there is only one negative price
reaction, indicative of the alternating price promotions of retailers ŽLW, 1992.. For the rest of the
brands, price reactions are positive; this reflects the
effect of competitive manufacturer pricing activity,
as in LW Ž1992.. The estimated price reactions are
close to those obtained by Kopalle et al. Ž1999..
5.4. Impact of eÕolÕing prices on market share
The impact of evolution in prices on market
shares is shown in Table 6. Old Milwaukee’s price
and share are stationary; hence, the results for Old
Milwaukee are not shown in Table 6.
The calculation of the long-run price elasticities is
similar to Dekimpe and Hanssens Ž1999, footnote
11.. For example, a shock to Miller’s price results in
a sustained change in Miller’s price of 0.58%. This
change results in sustained share losses of y0.68%.
Therefore, the long-run price elasticity of Miller is
y1.174. All other elasticities are estimated in a
similar manner, and standard errors are obtained
using the Delta method ŽKmenta, 1997..
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
299
Table 6
Impact of evolving prices for beer Žstandard error in parentheses.
Effect of independent variable
Response elasticity Žeffect on price or share of.
Budweiser
Busch
Miller
Milwaukee’s Best
n.s.
n.s.
n.s.
y2.319 )))
Ž0.024.
n.s.
n.s.
n.s.
Milwaukee’s Best
y0.745 )))
Ž0.084.
y0.384 ))
Ž0.168.
0.288 )))
Ž0.085.
n.s
y1.174 )))
Ž0.022.
n.s.
0.270 )))
Ž0.047.
y1.029 )))
Ž0.367.
CompetitiÕe price reaction effects
Budweiser
–
0.555
Ž0.514.
–
1.570 )))
Ž0.371.
0.382 )))
Ž0.058.
–
1.030
Ž0.715.
0.101
Ž0.085.
0.388 )
Ž0.204.
–
Response effects of price on share
Budweiser
Busch
Miller
Busch
Miller
Milwaukee’s Best
y0.087
Ž0.080.
0.323 ))
Ž0.108.
0.203 )))
Ž0.076.
n.s.
0.378 ))
Ž0.160.
0.178 )))
Ž0.069.
0.465 )))
Ž0.132.
n.s. indicates estimates with t-value less than one.
)
Indicates p - 0.10 Žvia two-tailed tests..
))
Indicates p - 0.05.
)))
Indicates significant at p - 0.01.
With regard to evolving prices, on the one hand,
consumers may become price sensitive over time due
to lowered price levels ŽMela et al., 1997.. On the
other hand, a succession of price discounts may
reduce reference prices of consumers, leading to a
lower level of utility for a given level of discount
ŽKalyanaram and Winer, 1995.. Thus, price reductions occurring after discounts would lead to smaller
sales increases Žprice sensitivity could be lower.. Our
results show that the short-run price elasticities, averaging close to y2, are greater than the long-run
price elasticities, averaging approximately y1.3.
With regard to price promotions, consumers may be
cued that the change is temporary and therefore may
stockpile andror switch to other brands to take
advantage of the deal. With respect to the long run,
the cross-price elasticities are consistent with our
expectations except for Busch’s price; it has a negative impact on Budweiser’s share. Since both Busch
and Budweiser are sister brands of the same parent
company, Anheuser–Busch, there may be a certain
degree of complementarity between them. On the
one hand, we find that Miller’s share is more sensi-
tive to temporary price changes in Budweiser than
vice versa. On the other hand, Budweiser is relatively more responsive to Miller’s long-run price
levels. The long-run own and cross-price elasticities
of Budweiser and Miller are consistent with the
share patterns over time, which indicate that Miller
has gained share relative to Budweiser, while prices
of both brands have decreased over time. Fig. 4a–b
show the short- and long-run share response of market share of Miller to its prices and those of Budweiser. Thus, evolving prices have permanent effects
on market shares in the US beer market. This is
consistent with the results reported by Johnson et al.
Ž1992. in their analysis of the Canadian beer market
province-by-province.
With respect to competitive reaction elasticities,
there are positive price reactions suggesting that
there is Aprice-matchingB behavior in the long run.
This provides evidence of the Adouble jeopardyB
effect of price promotions. A negative price shock
results in a strong competitive reaction. This, in turn,
leads to a persistent reduction both in price and
competitive prices. This is in contrast to the response
300
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
Fig. 4. Ža. Long-run impact of a negative unit % shock to Miller’s price on Miller’s share. Žb. Long-run impact of a negative unit % shock to
Budweiser’s price on Miller’s share. Žc. Long-run impact of a negative unit % shock to Budweiser’s price on Miller’s price. Žd. Long-run
impact of a negative unit % shock to Miller’s price on Budweiser’s price.
to structural changes in prices where competitors
respond with a lag of 12 to 13 weeks. Structural
price changes elicit delayed competitive response
since strategic decisions concerning price levels tend
to be manufacturer-dominated while temporary
changes in prices and evolving prices elicit immediate response because tactical decisions concerning
temporary promotions tend to be retailer-dominated
ŽLW, 1992.. Moreover, competitive price response is
likely to be delayed when prices have been relatively
stable and thus the price change is unanticipated—
structural change—as opposed to situations where
price movements have been continual-evolving
prices. Fig. 4c–d show the impulse–response function of price due to shocks to competitive price for
Budweiser and Miller. LW Ž1996., for instance, provide empirical evidence for the fact that managers
tend to react to changes in a competitor’s marketing
instrument, even when the brand’s performance is
not affected by that competitive change.
Taken together, the results suggest that due to
competitive interactions of prices, short-run promotions may lead to permanent reductions in price
levels which impact long-run shares due to different
own- and cross-elasticities. Finally, from a strategic
marketing perspective, we now address the issue of
whether a structural decrease in price is preferred to
prices that are evolving downwards.11 First, our empirical results show that evolution in prices causes
evolution in market shares, whereas a structural
change in price results in an immediate shift in level
of market share and hence allows the firm to get to
11
We are thankful to Mike Hanssens and an anonymous referee
for motivating the following discussion.
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
the desired market share level sooner. Second, competitive reaction is likely to be delayed because of
the unexpected nature of a structural change and due
to the fact that structural price changes tend to be
manufacturer-dominated. Finally, consumers tend to
notice and contrast prices that lie outside their range
of acceptance with the acceptable range, and hence,
share response to structural price changes could be
higher. Thus, a structural decrease in price could be
more profitable. As a result, a company that is
profitable in the short run may be able to replicate its
marketing tactics, a potentially beneficial outcome in
the long run.
v
6. Conclusions
We offer novel findings and new contributions on
the three types of price changes—temporary, evolving and structural—to the literature in this area.
Table 7 summarizes the key findings of our study;
we emphasize the main managerial implications, from
both a tactical and a strategic perspective, in the
following discussion.
v
6.1. Managerial implications based on our empirical
analysis
v
Distinguish among temporary changes in prices,
eÕolÕing prices and structural changes in prices,
as their impact on market shares tends to differ.
While temporary price changes only cause a
temporary change in shares, both structural
changes in price and evolving prices have permanent effects on market shares. Structural shifts
to levels of price do shift levels of market share
for the margarine data. On the one hand, in the
margarine category, for two out of the three
brands, share response to structural price changes
is greater than response to temporary price
changes. On the other hand, we find that shortrun price elasticities—response to temporary
changes—are greater than the long-run price
elasticities—response to evolving prices—in the
beer category. These differences may be partly
due to category-specific effects; temporary price
v
301
discounts may be more likely lead to increased
consumption for beer than for margarine.
When should firms use one type of price change
Õs. another? From a tactical perspective, temporary price variations may be more effective at
increasing shares for lower-selling brands rather
than for higher-selling brands in the short run,
as evidenced in the beer category. From a strategic perspective, managers may ask this question: If I am in a price decrease mode, should I
offer a series of small but continuous price
decreases or a single large price decrease?
Structural price cuts allow the firm to get to the
desired market share level sooner and elicit
delayed competitive reaction. Moreover, they
generate greater share response because when
consumers see a brand’s price that is outside the
acceptable range of prices, it is contrasted with
the acceptable range and becomes more noticeable. Hence, our results suggest that a structural
price cut is preferable to a series of continuous
price decreases.
Competitors do not respond the same way to the
three types of price changes. Structural price
changes elicit delayed competitive response
since strategic decisions concerning price levels
tend to be manufacturer-dominated while temporary changes in prices and evolving prices
elicit immediate response since tactical decisions concerning temporary promotions tend to
be retailer-dominated. Moreover, competitors are
likely to respond with a delay when prices have
been relatively stable and therefore the price
change is unanticipated as opposed to situations
where price movements have been continual.
Our results provide evidence of the Adouble
jeopardyB effect of price promotions as a result
of temporary price changes. From a manager’s
point of view, it is clearly not prudent to promote a brand frequently, thus inviting immediate competitive reaction. This, in turn, leads to a
persistent reduction of prices in the category in
the long run.
Anticipate competitiÕe response to price
changes. Subsequent competitive reaction affects predictions of price response. Firms should
try to anticipate their competitors’ behavior, i.e.,
form conjectures regarding competitive price re-
302
Table 7
Pricing behavior: comparison of temporary price changes, evolving prices and structural price changes
Illustration
Empirical treatment
Exemplar
Substantive implications based on our empirical analysis
ŽI. Temporary price
changes
Short-term promotions
Že.g., 50 cents off a
six-pack of beer..
ADF unit-root test
All major brands in margarine
and beer categories
Ži. Only a temporary effect on shares.
Žii. Short-run response elasticities are greater
than long-run elasticities Ževolving prices..
Žiii. Temporary price changes could be more effective
for lower-selling brands than for higher selling brands.
Živ. Strong competitive reaction consistent
with retailer-dominated reactions.
ŽII. Evolving prices
Regular practice of offering
discounts Že.g. airline pricing
tactics in 1992..
ADF unit-root test
Four major brands in the
beer category
Ži. Significant permanent effects on share
could lead to evolution in share.
Žii. Response elasticities lower than those
for temporary price changes.
Žiii. Immediate competitive reactions consistent
with retailer-dominated reactions.
Živ. Long-run price matching behavior Žprice wars..
Žv. Could be less profitable than structural
changes since the latter allow the firm to
get to desired market share level sooner.
ŽIII. Structural changes
in price
A company offers a significant
one-time price cut or increase
to a new level Že.g. Kellogg’s
price-cut of 20% for cereal..
Structural break
test ŽPerron and
Vogelsang, 1992.
Three brands in the
margarine category
Ži. Permanent effects on share could lead to
shift in share level.
Žii. No change in consumer response coefficients.
Žiii. Subsequent competitive reaction does affect
the prediction of price response and needs to
accounted for by managers.
Živ. Share response could be greater than
response to temporary changes.
Žv. A structural decrease in price could be
more profitable since firm gets to desired
share level sooner.
Žvi. Major competitors respond with a 12 to
13 week lag to an unexpected price break
consistent with retailer-dominated reactions.
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
Pricing behavior
S. SriniÕasan et al.r Intern. J. of Research in Marketing 17 (2000) 281–305
actions in assessing the impact of policy changes.
Firms should use competitive intelligence to
understand competitor’s motives, capabilities
and potential reactions.
6.2. Limitations and future research
Our study has some limitations. First, we estimated separate models for each brand due to data
restrictions and degrees-of-freedom problems when
estimating extended VAR models in a simultaneous
system. In terms of future research, it would be
fruitful to develop a model in which all competitors
are considered simultaneously. Second, our analysis
pertains to two categories. Future research concerning the nature of the long-run response to permanent
changes in levels of prices may benefit from systematic collection of data across different conditions and
circumstances so as to provide empirical generalizations.
Third, while these findings are very important to
management, more research is needed to determine
the profitability of these results. However, this issue
has been under-researched due to the unavailability
of suitable data. Jedidi et al. Ž1999. and Dekimpe
and Hanssens Ž1999. have made a first attempt to
address the profitability of different long-run marketing strategies. Finally, some pricing decisions need
attention at the brand manager level while others
need attention at the retail-account level. This, in
turn, leads to a demand for analysis at the store level
rather than the brand level. Academic research has
only now begun to address this topic ŽBucklin and
Gupta, 1999.. These are key issues for future research.
Acknowledgements
We are indebted to Information Resources and
Peter Rossi, University of Chicago, for providing the
data. We thank the Editor, Jan-Benedict Steenkamp
and Bart Bronnenberg, Marnik Dekimpe, Philip Hans
Franses, Dominique M. Hanssens and Dick R.Wittink. We also thank the seminar participants at the
University of California, Riverside, the University of
California, Irvine, the 1999 Marketing Science Con-
303
ference and the Conference on Competition and
Marketing, Mainz, Germany, for their invaluable
comments and suggestions. Funding for this research
was provided by the Academic Senate Research
Fund from the University of California, Riverside
and the Social Sciences and Humanities Research
Council of Canada.
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