Competitive Effects of Vertical Restraints and Promotional Activity

Home , Gleem, Good Humor, Mentadent, Surf (detergent)

COMPETITIVE EFFECTS OF VERTICAL RESTRAINTS AND PROMOTIONAL ACTIVITY

DISSERTATION

Presented in Partial Fulﬁllment of the Requirements for

the Degree Doctor of Philosophy in the

Graduate School of The Ohio State University

Kirk W. Kerr, M.A.

*****

The Ohio State University

2008

Dissertation Committee: Approved by

Howard P.Marvel, Adviser

James Peck Adviser Matthew S. Lewis Graduate Program in Economics ABSTRACT

This dissertation empirically analyzes ﬁrm distribution and promotion strategies. Chapter 2 examines the distribution of products through exclusive territory arrangements by developing a model in which manufacturers, producing for uncertain retail demand, utilize exclusive territories to ensure that all demand states, and retailers serving particular demand states, are served. Here exclusive territories result in higher prices, greater consumption, and the entry of small retailers such as convenience and drug stores to the retail market. Analyzing a natural experiment afforded by Indiana’s legalization of exclusive territories in beer distribution, I estimate the effect of exclusive territories on price and consumption using a difference-in-differences model. I ﬁnd that the legalization of exclusive territories in Indiana results in no change in prices or consumption.

I also analyze a unique dataset of all licensed beer sellers in Indiana and ﬁnd that exclusive territories did not cause signiﬁcant entry by convenience or drug stores.

In Chapter 3, I argue that retail promotions arranged by manufacturers offer researchers a window into the competitive interactions of oligopolistic manufacturers. Utilizing scanner data on sales and promotions at a major grocery store,

I estimate the long-term effect of promotions on the sales of leading brands in

10 consumer packaged-goods categories. By testing the sales time series for unit

ii roots, I ﬁnd that promotions may have long-term effects on the sales of several brands in each category. By estimating the persistent impact of a brand’s own promotions and the impact of competitors’ promotions, I ﬁnd that in some cases promotions have persistent positive impacts on sales, but these effects are small and greatly diminished by competitor promotions.

The final chapter utilizes retail promotions and prices to analyze the competitive interactions of leading manufacturers in 10 consumer packaged-good categories. Variables on competitor activity in other shared markets are included in reaction functions to evaluate whether firms respond across as well as within markets. Reactions to out of market promotions and price changes are small or zero, indicating that firms do not respond across markets.

iii c Copyright by

Kirk W. Kerr

2008 To Kathy, who believed in me when I did not

v ACKNOWLEDGMENTS

I wish to thank my advisor, Howard Marvel, for his constant intellectual support and patience. I am particularly grateful to him for helping me form general ideas about economics into more precise research questions. It was his introduction to Industrial Organization and Antitrust Economics that inspired my interest in the ﬁeld, and his guidance as an advisor that has led to completion of this research project. He has provided the intellectual push to improve and reﬁne my ideas that is necessary for the completion of any research project.

I would like to thank Matt Lewis for his help and insight into empirical

Industrial Organization. After teaching one of the most interesting classes I have ever taken, he has helped me to understand how to conduct empirical research. Many thanks to James Peck, for his patience in helping me to learn

Game Theory, a topic of great importance in this ﬁeld, and great difﬁculty to me.

The entire faculty and staff of the Ohio State University Economics Depart- ment deserve a great deal of praise in helping me negotiate the graduate program. I would like to especially mention our Director of Graduate Studies,

Hajime Miyazaki, a tireless advocate for Economics Graduate students. I would like to thank Ana Shook for her constant good humor and encouragement and

vi John-David Slaughter for ensuring that the Department and its computers operate smoothly.

I wish to thank Joseph Haslag of the University of Missouri. Without his example and encouragement, I would not have pursued my graduate studies in Economics. Also, Michael Podgursky, who ﬁrst exposed me to economic research.

I have many friends who have made the path to completion brighter. Leonard

Kiefer, Karl Meeusen, Mark Longbrake, Kate Snipes, and Carlos Alpizar made surviving the difﬁcult ﬁrst year possible. Nels Christensen, for our many discus- sions about life and Economics, and for Starbucks runs. Kathleen Deloughery and Jennifer Shand Pitzer, for good humor in the computer lab.

I wish to make special mention of Tom Dolan, who befriended me soon after

I arrived in Columbus. Tom has offered advice, perspective, and friendship throughout my time at Ohio State.

I thank my parents, Wendell and Debra Kerr, for supporting me through the difficult ordeal of graduate school. Graduate School has been full of chal- lenges and they have supported every step of the way, building me up after each disappointment. They have helped me remember why I started and given me the confidence that I could finish. Lastly, I wish to thank my wife, Kathy. She has provided the encouragement and example that I needed to complete this research.

vii VITA

October 10, 1977 ...... Born - Granada Hills, CA

2001 ...... B.S. Economics, University of Missouri- Columbia 2002 ...... M.A. Economics, University of Missouri- Columbia 2003 ...... M.A. Economics, The Ohio State Uni- versity 2002-present ...... Graduate Teaching and Research Asso- ciate, The Ohio State University

FIELDS OF STUDY

Major Field: Economics

viii TABLE OF CONTENTS

Page

Abstract ...... ii

Dedication ...... v

Acknowledgments ...... vi

Vita ...... viii

List of Tables ...... xi

List of Figures ...... xix

Chapters:

1. Introduction ...... 1

2. Exclusive Territories in Beer Distribution: The Impact of Indiana’s Beer Baron Rule ...... 4

2.1 Introduction ...... 4 2.2 Theory and Evidence on Exclusive Territories ...... 6 2.3 The Model ...... 10 2.4 The Data ...... 15 2.5 Results ...... 18 2.6 Conclusion ...... 24

3. Do Retail Promotions have Persistent Effects? An Anaylsis of Dominick’s Finer Foods Scanner Data ...... 29

3.1 Introduction ...... 29 3.2 Economics of Promotions ...... 30

ix 3.3 Methodology ...... 34 3.4 Data ...... 37 3.5 Results ...... 39 3.6 Conclusion ...... 48

4. Do Firms React across Markets? An Anaylsis using Dominick’s Finer Foods Scanner Data ...... 51

4.1 Introduction ...... 51 4.2 Literature Review ...... 52 4.3 Data ...... 57 4.3.1 Price Index ...... 61 4.3.2 Out of Market variables ...... 61 4.4 Model & Results ...... 63 4.4.1 Promotions reaction functions ...... 65 4.4.2 Price Reaction Functions ...... 67 4.5 Conclusion ...... 68

5. Conclusion ...... 69

Appendices:

A. Tables for ’Do Retail Promotions have Persistent Effects?’ ...... 72

B. Figures for ’Do Retail Promotions have Persistent Effects?’ ...... 118

C. Tables for ’Do Firms React Across Markets?’ ...... 153

x LIST OF TABLES

Table Page

2.1 License and Store types, 2002 ...... 17

2.2 License and Store types, 2004 ...... 17

2.3 Explanation of variables and sources ...... 19

2.4 Summary Statistics ...... 20

2.5 Indiana Off-Premises Beer Licenses:1992-2007 ...... 21

2.6 Store types: 2001- 2007 ...... 21

2.7 Entry and Exit for Indiana Retail Beer Market, 2002-2007 . . . . . 22

2.8 Entry of Convenience and Drug Stores, 2002-2007 ...... 23

2.9 Supply and Demand regressions ...... 25

2.10 Reduced-form Regressions ...... 26

3.1 Augmented Dickey-Fuller Test: Toilet Paper ...... 42

3.2 Brands with a Unit Root, by Category ...... 44

3.3 Univariate Impulse Responses: Toilet Paper ...... 45

3.4 Persistent Impulse Response to Promotional Shocks: Charmin toilet paper ...... 47

3.5 Cumulative Response to Promotional Shocks ...... 49

xi 4.1 Categories and Manufacturers ...... 60

A.1 Manufacturers and Brands by Category ...... 73

A.2 Manufacturers and Brands by Category ...... 74

A.3 Augmented Dickey-Fuller Test: Paper Towels ...... 75

A.4 Augmented Dickey-Fuller Test: Liquid Dish Soap (Hand) ...... 76

A.5 Augmented Dickey-Fuller Test: Liquid Dishwasher Soap ...... 76

A.6 Augmented Dickey-Fuller Test: Powder Dishwasher Soap . . . . . 76

A.7 Augmented Dickey-Fuller Test: Fabric Softener Sheets ...... 77

A.8 Augmented Dickey-Fuller Test: Liquid Fabric Softener ...... 77

A.9 Augmented Dickey-Fuller Test: Liquid Laundry Detergent . . . . . 77

A.10 Augmented Dickey-Fuller Test: Powder Laundry Detergent . . . . 78

A.11 Augmented Dickey-Fuller Test: Toothpaste ...... 78

A.12 Univariate Impulse Responses: Paper Towels ...... 79

A.13 Univariate Impulse Response: Liquid Dish Soap (Hand) ...... 80

A.14 Univariate Impulse Response: Liquid Dishwasher Soap ...... 81

A.15 Univariate Impulse Response: Powder Dishwasher Soap ...... 81

A.16 Univariate Impulse Response: Fabric Softener Sheets ...... 82

A.17 Univariate Impulse Response: Liquid Fabric Softener ...... 82

A.18 Univariate Impulse Response: Liquid Laundry Detergent ...... 83

A.19 Univariate Impulse Response: Powder Laundry Detergent . . . . . 84

A.20 Univariate Impulse Response: Toothpaste ...... 85

xii A.21 Persistent Impulse Response to Promotional Shocks: Charmin toilet paper ...... 86

A.22 Persistent Impulse Response to Promotional Shocks: Dominick’s toilet paper ...... 87

A.23 Persistent Impulse Response to Promotional Shocks: Hi Dri paper towels ...... 88

A.24 Persistent Impulse Response to Promotional Shocks: Dominick’s paper towels ...... 89

A.25 Persistent Impulse Response to Promotional Shocks: Brawny paper towels ...... 90

A.26 Persistent Impulse Response to Promotional Shocks: Ajax liquid dish soap ...... 91

A.27 Persistent Impulse Response to Promotional Shocks: Dawn liquid dish soap ...... 92

A.28 Persistent Impulse Response to Promotional Shocks: Ivory liquid dish soap ...... 93

A.29 Persistent Impulse Response to Promotional Shocks: Joy liquid dish soap ...... 94

A.30 Persistent Impulse Response to Promotional Shocks: HH liquid dish soap ...... 95

A.31 Persistent Impulse Response to Promotional Shocks: Palmolive liquid dishwasher soap ...... 96

A.32 Persistent Impulse Response to Promotional Shocks: all powder dishwasher soap ...... 97

A.33 Persistent Impulse Response to Promotional Shocks: Cascade powder dishwasher soap ...... 98

A.34 Persistent Impulse Response to Promotional Shocks: Dominick’s powder dishwasher soap ...... 99

xiii A.35 Persistent Impulse Response to Promotional Shocks: Snuggle fabric softener sheets ...... 100

A.36 Persistent Impulse Response to Promotional Shocks: Downy fabric softener sheets ...... 100

A.37 Persistent Impulse Response to Promotional Shocks: Bounce fabric softener sheets ...... 101

A.38 Persistent Impulse Response to Promotional Shocks: Downy liquid fabric softener ...... 101

A.39 Persistent Impulse Response to Promotional Shocks: Surf liquid laundry detergent ...... 102

A.40 Persistent Impulse Response to Promotional Shocks: all liquid laundry detergent ...... 103

A.41 Persistent Impulse Response to Promotional Shocks: Purex liquid laundry detergent ...... 104

A.42 Persistent Impulse Response to Promotional Shocks: Cheer liquid laundry detergent ...... 105

A.43 Persistent Impulse Response to Promotional Shocks: Era liquid laundry detergent ...... 106

A.44 Persistent Impulse Response to Promotional Shocks: YES liquid laundry detergent ...... 107

A.45 Persistent Impulse Response to Promotional Shocks: Surf powder laundry detergent ...... 108

A.46 Persistent Impulse Response to Promotional Shocks: all powder laundry detergent ...... 109

A.47 Persistent Impulse Response to Promotional Shocks: Arm & Ham- mer powder laundry detergent ...... 110

A.48 Persistent Impulse Response to Promotional Shocks: Tide powder laundry detergent ...... 111

xiv A.49 Persistent Impulse Response to Promotional Shocks: Oxydol powder laundry detergent ...... 112

A.50 Persistent Impulse Response to Promotional Shocks: Dominick’s powder laundry detergent ...... 113

A.51 Persistent Impulse Response to Promotional Shocks: Close Up toothpaste ...... 114

A.52 Persistent Impulse Response to Promotional Shocks: Arm & Ham- mer toothpaste ...... 115

A.53 Persistent Impulse Response to Promotional Shocks: Colgate toothpaste ...... 116

A.54 Persistent Impulse Response to Promotional Shocks: Brite toothpaste ...... 117

C.1 Key to symbols and abbreviations in tables ...... 153

C.2 Toilet Paper - Promotions Reaction Functions ...... 154

C.2 Toilet Paper - Promotions Reaction Functions ...... 155

C.2 Toilet Paper - Promotions Reaction Functions ...... 156

C.3 Toilet Paper - Promotions Reaction Functions continued ...... 156

C.3 Toilet Paper - Promotions Reaction Functions continued ...... 157

C.3 Toilet Paper - Promotions Reaction Functions ...... 158

C.4 Paper Towels - Promotions Reaction Functions ...... 158

C.4 Paper Towels - Promotions Reaction Functions ...... 159

C.4 Paper Towels - Promotions Reaction Functions ...... 160

C.5 Paper Towels - Promotions Reaction Functions continued . . . . . 160

C.5 Paper Towels - Promotions Reaction Functions continued . . . . . 161

xv C.5 Paper Towels - Promotions Reaction Functions continued . . . . . 162

C.6 Liquid Dish Soap - Promotions Reaction Functions ...... 162

C.6 Liquid Dish Soap - Promotions Reaction Functions ...... 163

C.6 Liquid Dish Soap - Promotions Reaction Functions ...... 164

C.8 Powder Dish Soap - Promotions Reaction Functions ...... 165

C.8 Powder Dish Soap - Promotions Reaction Functions ...... 166

C.9 Fabric Softener Sheets - Promotions Reaction Functions ...... 166

C.9 Fabric Softener Sheets - Promotions Reaction Functions ...... 167

C.7 Liquid Dishwasher Soap ...... 168

C.11 Liquid Laundry Detergent - Promotions Reaction Functions . . . . 169

C.11 Liquid Laundry Detergent - Promotions Reaction Functions . . . . 170

C.11 Liquid Laundry Detergent - Promotions Reaction Functions . . . . 171

C.12 Liquid Laundry Detergent - Promotions Reaction Functions continued ...... 171

C.12 Liquid Laundry Detergent - Promotions Reaction Functions continued ...... 172

C.12 Liquid Laundry Detergent - Promotions Reaction Functions continued ...... 173

C.13 Powder Laundry Detergent - Promotions Reaction Functions . . . 173

C.13 Powder Laundry Detergent - Promotions Reaction Functions . . . 174

C.13 Powder Laundry Detergent - Promotions Reaction Functions . . . 175

C.14 Toothpaste - Promotions Reaction Functions ...... 175

C.14 Toothpaste - Promotions Reaction Functions ...... 176

xvi C.14 Toothpaste - Promotions Reaction Functions ...... 177

C.15 Toothpaste - Promotions Reaction Functions continued ...... 177

C.15 Toothpaste - Promotions Reaction Functions continued ...... 178

C.15 Toothpaste - Promotions Reaction Functions continued ...... 179

C.16 Toilet Paper - Price Reaction Functions ...... 179

C.16 Toilet Paper - Price Reaction Functions ...... 180

C.16 Toilet Paper - Price Reaction Functions ...... 181

C.17 Toilet Paper - Price Reaction Functions continued ...... 181

C.17 Toilet Paper - Price Reaction Functions continued ...... 182

C.17 Toilet Paper - Price Reaction Functions continued ...... 183

C.10 Liquid Fabric Softener ...... 184

C.18 Paper Towels - Price Reaction Functions ...... 185

C.18 Paper Towels - Price Reaction Functions ...... 186

C.18 Paper Towels - Price Reaction Functions ...... 187

C.19 Paper Towels - Price Reaction Functions continued ...... 187

C.19 Paper Towels - Price Reaction Functions continued ...... 188

C.19 Paper Towels - Price Reaction Functions continued ...... 189

C.20 Liquid Dish Soap - Price Reaction Functions ...... 189

C.20 Liquid Dish Soap - Price Reaction Functions ...... 190

C.21 Liquid Dishwasher Soap - Price Reaction Functions ...... 191

C.22 Powder Dishwasher Soap - Price Reaction Functions ...... 192

xvii C.22 Powder Dishwasher Soap - Price Reaction Functions ...... 193

C.23 Fabric Softener Sheets - Price Reaction Functions continued . . . . 193

C.23 Fabric Softener Sheets - Price Reaction Functions continued . . . . 194

C.24 Liquid Fabric Softener - Price Reaction Functions ...... 195

C.25 Liquid Laundry Detergent - Price Reaction Functions ...... 196

C.25 Liquid Laundry Detergent - Price Reaction Functions ...... 197

C.25 Liquid Laundry Detergent - Price Reaction Functions ...... 198

C.26 Liquid Laundry Detergent - Price Reaction Functions continued . . 198

C.26 Liquid Laundry Detergent - Price Reaction Functions continued . . 199

C.26 Liquid Laundry Detergent - Price Reaction Functions continued . 200

C.27 Powder Laundry Detergent - Price Reaction Functions ...... 200

C.27 Powder Laundry Detergent - Price Reaction Functions ...... 201

C.27 Powder Laundry Detergent - Price Reaction Functions ...... 202

C.28 Toothpaste - Price Reaction Functions ...... 202

C.28 Toothpaste - Price Reaction Functions ...... 203

C.28 Toothpaste - Price Reaction Functions ...... 204

C.29 Toothpaste - Price Reaction Functions continued ...... 204

C.29 Toothpaste - Price Reaction Functions continued ...... 205

C.29 Toothpaste - Price Reaction Functions continued ...... 206

xviii LIST OF FIGURES

Figure Page

3.1 Time Series of Sales: Angel Soft toilet paper ...... 40

3.2 Time Series of Sales: Charmin toilet paper ...... 41

B.1 Time Series of Sales: Angel Soft toilet paper ...... 119

B.2 Time Series of Sales: Cottonelle toilet paper ...... 119

B.3 Time Series of Sales: Charmin toilet paper ...... 120

B.4 Time Series of Sales: Dominick’s toilet paper ...... 120

B.5 Time Series of Sales: Northern toilet paper ...... 121

B.6 Time Series of Sales: Scott toilet paper ...... 121

B.7 Time Series of Sales: Green Forest toilet paper ...... 122

B.8 Time Series of Sales: Hi Dri paper towels ...... 122

B.9 Time Series of Sales: Bounty paper towels ...... 123

B.10 Time Series of Sales: Dominick’s paper towels ...... 123

B.11 Time Series of Sales: Brawny paper towels ...... 124

B.12 Time Series of Sales: Viva paper towels ...... 124

B.13 Time Series of Sales: Scott paper towels ...... 125

B.14 Time Series of Sales: Mardi Gras liquid dish soap ...... 125

xix B.15 Time Series of Sales: Green Forest liquid dish soap ...... 126

B.16 Time Series of Sales: Dove liquid dish soap ...... 126

B.17 Time Series of Sales: Sunlight liquid dish soap ...... 127

B.18 Time Series of Sales: Ajax liquid dish soap ...... 127

B.19 Time Series of Sales: Palmolive liquid dish soap ...... 128

B.20 Time Series of Sales: Dawn liquid dish soap ...... 128

B.21 Time Series of Sales: Ivory liquid dish soap ...... 129

B.22 Time Series of Sales: Joy liquid dishwasher soap ...... 129

B.23 Time Series of Sales: HH liquid dishwasher soap ...... 130

B.24 Time Series of Sales: Sunlight liquid dishwasher soap ...... 130

B.25 Time Series of Sales: Palmolive liquid dishwasher soap ...... 131

B.26 Time Series of Sales: Cascade liquid dishwasher soap ...... 131

B.27 Time Series of Sales: Sunlight powder dishwasher soap ...... 132

B.28 Time Series of Sales: all powder dishwasher soap ...... 132

B.29 Time Series of Sales: Cascade powder dishwasher soap ...... 133

B.30 Time Series of Sales: Dominick’s powder dishwasher soap . . . . . 133

B.31 Time Series of Sales: HH powder dishwasher soap ...... 134

B.32 Time Series of Sales: Electrasol powder dishwasher soap . . . . . 134

B.33 Time Series of Sales: Snuggle fabric softener sheets ...... 135

B.34 Time Series of Sales: Downy fabric softener sheets ...... 135

B.35 Time Series of Sales: Bounce fabric softener sheets ...... 136

xx B.36 Time Series of Sales: HH fabric softener sheets ...... 136

B.37 Time Series of Sales: Cling fabric softener sheets ...... 137

B.38 Time Series of Sales: Downy liquid fabric softener ...... 137

B.39 Time Series of Sales: HH liquid fabric softener ...... 138

B.40 Time Series of Sales: Surf liquid laundry detergent ...... 138

B.41 Time Series of Sales: all liquid laundry detergent ...... 139

B.42 Time Series of Sales: Purex liquid laundry detergent ...... 139

B.43 Time Series of Sales: Cheer liquid laundry detergent ...... 140

B.44 Time Series of Sales: Tide liquid laundry detergent ...... 140

B.45 Time Series of Sales: Era liquid laundry detergent ...... 141

B.46 Time Series of Sales: Dominick’s liquid laundry detergent . . . . . 141

B.47 Time Series of Sales: YES liquid laundry detergent ...... 142

B.48 Time Series of Sales: Surf powder laundry detergent ...... 142

B.49 Time Series of Sales: all powder laundry detergent ...... 143

B.50 Time Series of Sales: Dutch HD powder laundry detergent . . . . . 143

B.51 Time Series of Sales: Arm & Hammer powder laundry detergent . 144

B.52 Time Series of Sales: Cheer powder laundry detergent ...... 144

B.53 Time Series of Sales: Tide powder laundry detergent ...... 145

B.54 Time Series of Sales: Bold powder laundry detergent ...... 145

B.55 Time Series of Sales: Ivory Snow powder laundry detergent . . . . 146

B.56 Time Series of Sales: Oxydol powder laundry detergent ...... 146

xxi B.57 Time Series of Sales: Dominick’s powder laundry detergent . . . . 147

B.58 Time Series of Sales: Pepsodent toothpaste ...... 147

B.59 Time Series of Sales: Close Up toothpaste ...... 148

B.60 Time Series of Sales: Pearl toothpaste ...... 148

B.61 Time Series of Sales: Arm & Hammer toothpaste ...... 149

B.62 Time Series of Sales: Colgate toothpaste ...... 149

B.63 Time Series of Sales: Brite toothpaste ...... 150

B.64 Time Series of Sales: Crest toothpaste ...... 150

B.65 Time Series of Sales: Gleem toothpaste ...... 151

B.66 Time Series of Sales: Dominick’s toothpaste ...... 151

B.67 Time Series of Sales: Aqua Fresh toothpaste ...... 152

xxii CHAPTER 1

INTRODUCTION

This research empirically analyzes the effects of exclusive territories and retail promotions in order to study the underlying economics of these strategies to understand how they increase firm profits. With regard to exclusive territories, there is disagreement among economists as to whether such a strategy adversely affects consumer welfare. By combining data from public and private sources, the competitive effects of exclusive territories are analyzed using a natural experiment afforded by a change in Indiana’s Beer distribution laws. Retail promotions are often organized and funded by the product manufacturer rather than the retailer, offering a glimpse of how manufacturers respond to each others actions. Using grocery store scanner data from 10 consumer-packaged good categories. Also, the impact of firms’ promotions on their own and their competitors’ sales outcomes is measured.

Chapter 2 revisits the issue of exclusive territories by offering a new model of exclusive territories in markets with demand uncertainty. Previous theoretical explanations of exclusive territories either justify the practice as a demand enhancing mechanism that aligns distributor and manufacturer incentives or

1 indict it as an anti-competitive device intended to soften manufacturer competition by insulating them from competitor price cuts. A market in which demand uncertainty results in, from the manufacturers perspective, insufﬁcient retail inventory. Manufacturers establish exclusive territories to ensure that retail inventory is sufﬁcient to satisfy all demand states. In this model, exclusive territories result in a higher retail price and increased product sales. Under the additional assumption that different stores serve different demand states, exclusive territories facilitates the entry of small inventory stores to the retail market.

The hypotheses of the model are tested using the natural experiment afforded by Indiana’s legalization of exclusive territories in beer distribution in

2002. Data on price, consumption, and other variables for state-wide beer markets in most US states are obtained through various public and private sources.

Additionally, a unique dataset detailing the composition of the Indiana retail beer market from 2001-2007 is compiled from lists of all Indiana licensed beer retailers. Estimations of beer supply and demand equations show that legalization of exclusive territories had no effect on beer supply or demand in Indiana.

Evaluation of the Indiana retail beer industry composition data before and after legalization of exclusive territories reveals that retail composition in the Indiana beer market was not effected by the policy change.

Chapter 3 estimates the impact of retail promotion on long-run product sales. Theories of retail promotions such as Varian (1980) and Lal and Villas-

Boas (1998) argue that promotions have no long run effects, but impact select customer groups. Conversely, models of experience good advertising argue that

2 effective advertising will cultivate repeat customers, resulting in increased long- run sales. This chapter utilizes grocery store scanner data to examine the possibility that promotions have long-run effects. Many brands have non-stationary sales time series, which allows the possibility that promotions may have long- run (persistent) effects on sales. Estimation of the impulse response of sales to promotional sales shocks show that the persistent effect of promotional sales on total brand sales is small and easily countered by competitor promotions.

Chapter 4 examines how firms respond to competitor promotions when firms compete in multiple markets. Bernheim and Whinston (1990) show that firms competing in multiple markets, either product or geographic, may be able sus- tain a collusive price by engaging in punishment strategies across several markets. While previous studies focused on industries with known examples of multi-market collusion or aggregate data, this study estimates reaction functions with grocery store scanner data. The estimated reaction functions include as an independent variable competitor activity in other shared markets as well as competitor activity in the market in question. The results showed no systematic out-of-market reactions, indicating that multi-market contact does not increase the likelihood of collusion in these markets.

3 CHAPTER 2

EXCLUSIVE TERRITORIES IN BEER DISTRIBUTION: THE IMPACT OF INDIANA’S BEER BARON RULE

2.1 Introduction

Enacted in 1979, Rule 28 gave Indiana the peculiar distinction of being the only state to prohibit brewers of malt beverages from assigning exclusive territories to their wholesalers. The so-called Beer Baron rule, in reference to the large beer distributors who are thought to proﬁt from exclusive territories, expired in

January 2002 caused a great deal of controversy in the Indiana beer market.

Small Indiana beer distributors opposed to the legality of exclusive territories argued that “...services will go down and prices will go up”1 were Indiana to end its ban on exclusive territories and challenged the legalization of exclusive territories 2. The Indiana Alcohol and Tobacco Commissioners, who allowed Rule

28 to expire, claimed that ending Indiana’s ban on exclusive territories would allow sufﬁcient competition, but prevent excessive competition which can lead to “disorderly markets. 3”

1“Commission to discuss beer sales.” Purdue Exponent Online. August 28, 2001 2“ Beer Baron expiration challenged in court.” Modern Brewery Age, January 7, 2002 3“Commission to discuss beer sales.” Purdue Exponent Online. August 28, 2001

4 Economic disagreement on the competitive effects of exclusive territories is longstanding and on-going. To illustrate the economic puzzle regarding exclusive territories, consider a simple example of an upstream monopolist selling to downstream retailers. If the downstream retailers are perfect competitors and retailing has a marginal cost of zero, the retail price will equal the monopoly wholesale price set by the producer. The upstream firm earns a monopoly profit while the downstream firms earn zero profit. If the upstream firm were to assign downstream retailers exclusive territories, downstream firms would charge a higher than competitive price because they now have a monopoly position within their territories. The result of exclusive territories is double marginalization as shown by Spengler (1950), with the upstream and downstream firms charging prices greater than marginal cost. The increase in downstream price reduces total sales and upstream profit. The continued implemen- tation of territorial distribution arrangements by manufacturers in the face of double marginalization is a paradox that economists have attempted to resolve.

Some argue that assigning exclusive territories is a cartel facilitating device or, alternatively, a method of strategically softening competition, and should be prohibited as an anti-competitive practice. Competing explanations of exclusive territories focus on the use of such arrangements to prevent retailer’s from free- riding on the provision of demand-enhancing services by other retailers and serve an efﬁciency-enhancing role in industries with tiered distribution struc- tures.

We use the expiration of Indiana State Rule 28 to empirically analyze the effects of exclusive territories on the price and consumption of beer in Indiana as

5 well as the structure of Indiana beer market. We present a model that explains exclusive territories as an attempt by brewers to expand distribution and prevent retailer shirking. Under competitive conditions with demand uncertainty, neither retailers nor wholesalers find it profitable to rotate stock sufficiently. As- signing wholesalers exclusive territories makes stocking large inventories profitable and can ensure that old product is removed from store shelves. Uncertain demand may make it unprofitable for wholesalers to sell to some retailers without the “protection” of an exclusive territory. In this model, exclusive wholesale distribution territories can spark entry into the retail beer market. To test this model, we collect a unique dataset on the composition of the retail beer market in Indiana from 2001-2007. We find that entry is not significantly altered in the period immediately following the legalization of exclusive territories. We also estimate supply and demand equations to examine the effect of exclusive territories on price and consumption. We find that price and consumption have not been significantly affected by exclusive territories.

2.2 Theory and Evidence on Exclusive Territories

Rey and Stiglitz (1995) model exclusive territories as a mechanism for reducing interbrand as well as intra-brand competition. They show that if retail prices are strategic complements, then under exclusive territories the elasticity of demand perceived by manufacturers is altered from the elasticity of demand under retail competition. The perceived elasticity of demand reﬂects changes to the retail price response to changes in wholesale price and to the retail price response to changes in the competing producer’s price caused by the exclusive

6 territories reduction of retail competition. When the goods are close substitutes or the change in own price elasticity to changes in the producer’s price is less than 1, this alteration in the perceived elasticity of demand allows both retailers and competing manufacturers to match each others price increases. The authors contend that by increasing equilibrium price (and consequently, reducing equilibrium quantity and consumer welfare), producers can increase proﬁts, though it is possible that equilibrium quantity will decrease so much that proﬁts are reduced.

Klein and Murphy (1988) offer a pro-competitive explanation for exclusive territories. They contend that exclusive territories are a contract enforcement device used by producers to prevent shirking on dealer-provided services.

Dealer-provided services may be undetectable to consumer, but can impact overall demand of the product. Though these services impact the demand of specific products, they do not affect the retailer demand. When services are undetectable by the consumer and do not impact retail demand, services will be under-provided from the producer’s point of view. By assigning retailers exclusive territories, the producer allows dealers to earn a portion of the monopoly profit, a quasi-rent, on the retail sale of the producer’s good. This quasi-rent removes dealers’ incentive to shirk in the performance of demand-enhancing services by providing dealers with profits in excess of those possible through under-provision of services. The assignment of exclusive territories results in an increase in retail price as retailers’ exercise their monopoly power in their territory. This increase in price is accompanied by an increase in equilibrium quantity, as the provision of services increases demand for the product.

7 Deneckere et al. (1996) model a vertical distribution market with demand uncertainty to show that resale price maintenance can improve manufacturer and consumer welfare. In their model, the manufacturer sets a wholesale price and sells to retailers, who chose their retail price and inventory before demand is realized. Here, retailers may be left with unsold inventory if actual demand is less than the inventory they purchased. Retailers choose retail prices that equate expected proﬁts to zero because of the possibility of excess supply. Price differences lead to niche competition, where competitive niches are distinguished by the prices at which they sell. Highest price niches sell only in periods of peak demand while lower price niches sell in more frequently occurring demand states.

In this model, it may be unprofitable for a manufacturer to sell to all competitive niches. Niche competition is contrasted with a resale price maintenance game in which the manufacturer set both the wholesale and retail prices. Retailers again choose their inventory prior to the resolution of demand uncertainty, but after demand is revealed, consumer purchases are evenly distributed across retailers because all retailers have the same price. Resale price maintenance increases manufacturer profits and consumer welfare by increasing inventory holding. In a related paper, Deneckere et al. (1997) show that resale price maintenance increases manufacturer profits, as well at total and consumer profits, when retail prices are determined by market clearing, but retail inventories are determined prior to demand resolution.

Empirical examinations of exclusive territories have produced mixed results.

Jordan and Jaffee (1987) took advantage of Indiana’s dual market for wholesale beer to examine the impact of exclusive territories on beer prices in the

8 mid-1970s. The dual market developed when some wholesalers sold beer to retailers outside of their brewer-assigned “area of primary responsibility.” These wholesalers typically agreed to sell beer off of their loading dock to anyone will- ing to transport it, and so became known as dock sellers. Jordan and Jaffe found that wholesalers who restricted their sales to their area of primary responsibility charged higher prices than dock sellers, even when transportation costs are added to the dock price. Culbertson and Bradford (1991) examined the price of beer using interstate variation in exclusive territories. Using reduced-form

OLS regressions of price on various control variables, they found that exclusive territories increased beer prices and that Indiana’s ban on exclusive territories reduced beer prices. However, neither effect was statistically signiﬁcant at a

95% level.

This empirical work was incomplete because both the Rey & Stiglitz and the

Klein & Murphy models predicted that exclusive territories would lead to higher prices. The key difference between the models is that Klein & Murphy argue that exclusive territories increase demand through the provision of services, while

Rey & Stiglitz argue that the sole purpose of exclusivity is to soften upstream competition, resulting in increased prices and reduced consumption. Therefore, empirical work differentiating these theories must analyze the effect exclusive territories policies have on quantity as well as price. Recognizing this, Sass and

Saurman (1993) utilized inter-state longitudinal data to estimate supply and demand equations and measure the effect of exclusive territories. They found that exclusive territories decreased supply and increased demand. The size of these changes was such that the equilibrium price increased and equilibrium

9 quantity was unchanged, weakly supporting pro-competitive theories of exclusive territories. Sass and Saurman (1996) employed time series data to analyze the impact of Indiana’s ban on exclusive territories on consumption. They found that Indiana’s ban on exclusive territories signiﬁcantly reduced consumption, a result supporting anti-competitive explanations of exclusive territories.

In a related paper worth mention, Asker (2004) examines the Chicago retail and wholesale beer markets to measure the cost effects of exclusive dealing.

Many brewers, notably Anheuser-Busch, aggressively pursue obtaining exclusive dealing contracts with their distributors. Using scanner data from a local grocery store, Asker estimates consumer demand and firm behavior to measure any cost savings that brewers or distributors may obtain from an exclusive relationship. Asker is able to link distributors to retailers by mapping the location of retailers to the state-mandated, and therefore publicly recorded, exclusive territory assigned to each distributor. He finds that exclusive distributors have significantly lower costs than non-exclusive distributors, but that exclusivity does not foreclose competition. Since all distributors are assigned exclusive territories, the paper does not comment on the effects of exclusive territories in this market. Also, Asker focuses on the market of a single city, Chicago, whereas the empirical portion of this paper uses interstate variation to identify the effect of exclusive territories.

2.3 The Model

We model the beer distribution chain as consisting of three distinct links: brewers, wholesalers, and retailers. The brewer produces beer and sells it to a

10 wholesaler. The wholesaler distributes beer to retailers with positive marginal cost of distribution. Since each state closely regulates the transportation of beer into and out of the state, it is reasonable to assume that wholesalers operate within a single state. The wholesale market for beer may be competitive or divided into exclusive territories. We assume that retailers are perfect competitors that sell beer to consumers with a marginal cost of retailing assumed to be zero. Retailers may or may not be required to return unsold beer to wholesalers for a refund of the wholesale price. Beer is assumed to deteriorate and be of lower quality after each period.4 Demand for beer is uncertain and not revealed until after wholesalers and retailers choose inventories and prices. Consumers purchase from the retailer with the lowest price.

In retail competition with uncertain demand and sunk inventory costs (i.e. unsold inventory may not be returned to wholesalers), retailers will choose an inventory and price such that expected proﬁts equal zero. Demand uncertainty implies that some levels of high demand may not occur in every period. Re- tailers holding large inventories to serve these peaks in demand will set a retail price greater than the wholesale price to compensate for the risk of unsold inventory. However, demand uncertainty also allows retailers a proﬁtable deviation. A retailer may stock only enough inventory to satisfy the lowest demand state (which always sells out) and charge a price close to the wholesale price.

This retailer would avoid carrying unsold inventory and earn a positive proﬁt.

Since this deviation is available to all retailers, competition for the lowest demand state will quickly drive the proﬁt from serving it to zero. Retailers will

4On its website, Anheuser-Busch claims that Budweiser expires 110 days after packaging. Miller claims, on its website, that heat pasteurization of its beer extends shelf life to 120 days.

11 compete for customers at each level of demand, holding inventories and setting

prices such that every demand state is served and expected retailer proﬁts equal

zero. The end result is niche competition of the type described by Deneckere

et al. (1996). In niche competition, each retailer serves a particular demand

niche and retailers’ expected proﬁts are zero. The lowest-priced retailers serve

the lowest, but most frequently occurring, demand states. When the lowest

price retailers sell-out, any excess demand is satisﬁed by retailers with higher

prices. These retailers only sell when demand is high, thus satisfying higher,

but less frequently occurring, demand states. Under niche competition, prices

vary across stores serving different demand niches, but all demand states are

satisﬁed.

A simple example may help clarify niche competition. Suppose there are

three equally likely demand states: d1(p), d2(p), and d3(p). For a given a supply

function, these states correspond to equilibrium prices and quantities (p1, q1),

(p2, q2), and (p3, q3), such that p1 < p2 < p3 and q1 < q2 < q3. Although

q1 is the equilibrium quantity sold when demand equals d1(p), it is sold with

probability 1 because it is sold in all demand states (since q3 > q2 > q1). Sellers

of quantity q1 are the low-demand niche and sell at p1 = w, the wholesale price.

The intermediate niche is comprised of those sellers who sell q2 q1. Since − the intermediate niche occurs 2 of the time, retailers in the intermediate niche 3

charge price p2 > p1, but still earn expected proﬁt of zero. The high-demand

niche sells only when demand equals d p . Since d p is realized only 1 of 3( ) 3( ) 3

the time, high-demand retailers charge price p3 > p2, sell q3 q2, and earn zero − expected proﬁts.

12 Though niche competition under demand uncertainty satisﬁes all demand states, it does not produce the optimal outcome from the brewer’s perspective.

In addition to satisfying all demand states, the brewer wants inventories to be rotated each period so that all unsold beer is removed from store shelves after each period and replaced with fresh beer. This will not occur in a niche competitive market because each competitive niche finds it profitable to retain beer until sold rather than replace old beer. This reduces retailers’ inventory costs and increases their profits. Brewers want their products rotated because when old beer is consumed, the consumer will attribute its poor taste to the brewer’s brand and not purchase the brand in the future. Since optimal product rotation is not observable by consumers, they will be unaware that the poor taste is due to improper product rotation5. Since the consumer presumably still purchases from the same store, improper product rotation is demand-reducing for the manufacturer, but not the retailer. To ensure proper product rotation, the brewer can implement a returns policy requiring old beer be returned to the wholesaler from which it was purchased for a refund of the wholesale price. Un- der this policy, the retail market reverts to perfect competition. By removing the risk of unsold inventory, the returns policy eliminates the profitable deviations that gave rise to niche competition at the retail level.

The returns policy does not solve the unsold inventory problem, rather it transfers the cost of unsold inventories to wholesalers. Wholesalers could cover these additional costs through higher prices, but once again demand uncertainty allows wholesalers a proﬁtable deviation. By selling only to retailers serving the

5An exception is Anheuser-Busch’s practice of printing a ‘born on’ date on its beer which allows consumers to verify the freshness of beer before its purchase.

13 low demand niche, wholesalers avoid having to accept returns. Alternatively, wholesalers could sell small quantities to all retailers at low wholesale prices.

Retailers will place these items at the front of the shelf, so they are the ﬁrst sold in any demand state because retailers earn a higher margin on these items. By selling quantities that are certain to be sold, a wholesaler can avoid the costs of accepting returns and increase proﬁts. This strategy leaves the burden of accepting returns from retailers serving higher demand niches to other wholesalers.

However, serving high-demand niches is not a profitable endeavor for wholesalers because of transportation costs, low sales quantities, and the high likelihood of being left with unsold inventory. Hence, demand uncertainty causes wholesalers to compete for low demand niches and avoid selling to retailers catering to peak demand niches. By assigning wholesalers exclusive territories, the brewer prevents wholesale competition from devolving into competition for low demand niches. A wholesaler with an exclusive territory does not face competition for low demand niches and so is able to charge a price such that when the lowest demand state is realized, profit from serving all demand states is zero. This price allows the wholesaler to earn at least a normal profit in all demand states and ensures that all demand niches are served.

As stated previously, the brewer wishes to ensure that each niche is stocked with fresh beer each period in order to maximize proﬁt. Assigning wholesalers exclusive territories and requiring wholesalers to accept returns of old beer ensures that peak demand, low volume retailers are stocked with fresh beer every period. The brewer sells to wholesalers at a price that allows the wholesalers to earn a share of the monopoly proﬁt in the highest demand states. This makes

14 wholesaler incentives compatible with brewer goals of providing fresh product to consumers. By allowing wholesalers to earn a share of the monopoly proﬁt, the brewer-assigned exclusive territory also serves as a device to prevent wholesaler shirking, since losing the exclusive territory would mean losing the rents it afforded them.6

This model predicts that the average retail price under wholesaler exclusive territories will be higher than under a competitive wholesale market, since wholesalers with exclusive territories raise the price to protect against losses due to unsold inventory. It also predicts that overall quantity consumed will increase because peak-demand niches are now satisﬁed rather than ignored.

Furthermore, if different types of retailers serve different demand niches, then markets with exclusive territories should have more retailers that sell to high- demand niches than competitive wholesale markets.

2.4 The Data

To test the hypotheses of the model, we collected data on the number of retailers of beer from lists containing information on all Indiana alcohol license holders7. These lists contained information on license holders from 2001 to

2007 including a unique license number, the license type, the name of license holder, the name of the establishment (the “Doing Business As” name), location of the establishment (street address, city, county, and ZIP code), and the expiration date of the license. From these raw data a comprehensive list of all

6Klein and Murphy (1988) 7My thanks to Debbie Scott of the Indiana Association of Beverage Retailers for providing me with these data

15 license holders in each year from 2001 to 2007 was compiled. License types are determined by the type of store (e.g. grocery store, drugstore, package liquor store), the variety of products permitted to be sold (i.e. beer; beer and wine; beer, wine, and spirits), and the location of consumption (off-premises or on-premises). We focus on holders of off-premises (“dealers”) licenses for beer, beer and wine, and beer, wine, and spirits, since these “dealers” are more likely to have stock rotation that is at the center of pro-competitive theories of exclusive territories. In order to distinguish between high and low demand serving retailers, each store is classified according to the North American In- dustry Classification System (NAICS) guidelines for grocery stores, convenience stores, drugstores/pharmacies, supercenters/warehouse clubs, specialty food stores, package liquor stores, and mass merchandisers. We verified store type by internet search and telephone contact, since the store type indicated on the license type could be misleading. For example, convenience stores in Indiana hold grocery store licenses since there is no separate license type for convenience stores. Also, many grocery stores and super-centers, such as Kroger’s,

Wal-Mart, and Sam’s Club, have in-store pharmacies which allow them obtain a pharmacy license. Pharmacy licenses are considered advantageous because they allow a retailer to sell spirits in addition to the beer and wine sales permitted with a grocery store license. Table 2.1 illustrates, with data from 2002, how grocery stores and super-centers take advantage of this legal loophole. Note that

31 grocery stores and 39 super-centers and warehouse clubs possessed drugstore licenses, presumably because they featured in-store pharmacies. Table

16 Store type Permit type Grocery Conv. Store Drugstore Super-centers Grocery, Beer 2 19 0 0 Grocery, Beer and Wine 561 543 0 57 Drugstore, Beer, Wine, and Spirits 31 0 360 39

Table 2.1: License and Store types, 2002

Store type Permit type Grocery Conv. Store Drugstore Super-centers Grocery, Beer 6 22 0 0 Grocery, Beer and Wine 609 563 0 55 Drugstore, Beer, Wine, and Spirits 37 0 349 100

Table 2.2: License and Store types, 2004

2.2 shows that the number of grocery stores and super-centers with pharmacy licenses had grown to 37 and 100, respectively, by 2004.

The average price of beer was obtained from the ACCRA Cost of Living Index

(COLI), which includes the average price of a six-pack of beer 8 in its basket of goods. These data are collected quarterly by member Chambers of Commerce in cities and towns across the United States. Since participation is voluntary, it is not uncommon for the sample of cities reporting each quarter to vary somewhat.

To reduce problems associated with variation in sample composition, the sample is limited to cities that report prices in the third quarter of each year from 1990 to 2004. This sample composition ﬁx was used in Sass & Saurman (1993) and

8The brand priced in these reports was Budweiser until 2000, when the brand priced was changed to Heineken

17 allowed for maximum sample size without variation in sample composition. The price data may not be representative of prices in the entire state since the data is collected by chambers of commerce in metropolitan areas and omits prices from small cities and rural areas. Such potential bias should be considered when evaluating empirical results. Variation in prices due to inﬂation was controlled by converting dollar values for prices, income, and taxes to year 2000 dollars using the CPI. Table 2.3 describes each variable and the source of the data.

Table 2.4 provides summary statistics for all variables.

2.5 Results

A survey conducted by the National Association of Convenience Stores (NACS) found that 23.1 percent of consumers report that they purchase beer “most often” at convenience stores. This lags behind both grocery stores (40.2%) and liquor stores (24.9%).9 Based on these ﬁndings, it is not unreasonable to assume that grocery stores, super-centers and warehouse clubs, and mass merchandisers typically serve frequently occurring low demand states. Convenience stores and drugstores10 to serve peak demand states. If exclusive territories facilitates the expansion of service to outlets serving peak demand, then under these ad- mittedly strong assumptions, the number of convenience and drug stores selling beer should increase once exclusive territories become legal.

Table 2.5 presents data on the number of retail outlets in Indiana from 1992-

2007. We can see that the total number of off-premises beer licenses has been

9from www.nacsonline.com PRToolkit (July 2005 version). The website cites as its source the NACS 2000-2005 Future Study 10I place drugstores in the high demand outlet class with convenience stores due to the large number of drugstores, such as Walgreens, CVS, Rite-Aid, etc, that offer a number of consumer goods in addition to pharmaceuticals and over-the-counter medical supplies.

18 Variable Description and Source Consumption Beer consumption (in thousands of gallons) by each state Source: Brewer’s Almanac Price Average price of six pack of Budweiser(pre-2000) or Heineken(2000 and later) Source: ACCRA and PRC Statewide Database Tax State and federal excise tax per six pack of beer Source: Brewer’s Almanac and PRC Statewide Database Income Median household income (in thousands) Source: PRC Statewide Database and US Census Bureau Distanc Distance from most populous city in state to nearest brewery operated by brewers with largest market share (A-B, SAB Miller, Coors) Source: Mapquest.com Population State population over age 20(in thousands) Source: PRC Statewide Database and US Census Bureau Retailers Number of licensed beer retailer by state Source: PRC Statewide Database Indnoban Indicator variable for period after Indiana’s exclusive territory ban ended (post-2002) S18 Indicator variable for the state of Indiana E x Terr Indicator variable for Indiana after its exclusive territory ban ended Mandate Dummy variable for states mandating exclusive territories Source: Sass & Saurman (1993)

Table 2.3: Explanation of variables and sources

19 Variable Observations Mean Std Dev Consumption 510 147764.24 142025.40 Price 510 5.472 1.160 Tax 510 0.502 0.147 Income 510 55.26 6.333 Distance 510 309.32 505.63 Population 510 4727.04 4678.15 Retailers 301 13448.33 15218.28 Indnoban 510 0.2 0.4 S18 510 0.029 0.169 E x Terr 510 0.0059 0.077 Mandate 510 0.471 0.4996

Table 2.4: Summary Statistics

remarkably consistent over time. This is not entirely surprising given that In- diana limits the number of permits available using a quota based on city population. Attempts to obtain exact data on the quota levels across the state were unsuccessful. Table 2.6 shows the number of retail outlets of each store type from 2001 to 2007. Here we see that the number of convenience stores and super-centers holding alcohol permits increased during the sample period.

These results do not support the hypothesis that exclusive territories facilitate increases in the number of high-demand serving outlets. In the year following the expiration of Rule 28 (2002), the total number of peak demand serving outlets (convenience stores and drugstores) decreases, while the total number of low demand outlets remains the same. Examination of entry and exit of retailers in the Indiana beer market in Table 2.7 reveals that 2002 had a somewhat higher than average amount of entry, but these gains were almost entirely offset

20 Year Number of Beer Licenses 1992 2,862 1993 2,874 1994 2,887 1995 2,887 1996 3,162 1997 2,799 1998 2,782 1999 2,800 2000 2,800 2001 2,822 2002 2,824 2003 2,849 2004 2,958 2005 2,911 2006 2,871 2007 2,967

Table 2.5: Indiana Off-Premises Beer Licenses:1992-2007

Source: PRC Statewide Database and Indiana ATC

Store type Year Grocery Conv. Store Drugstore Super-centers Mass Merchandiser 2001 617 547 392 74 61 2002 598 562 368 96 57 2003 622 577 353 100 51 2004 655 585 356 155 49 2005 668 558 349 142 77 2006 603 603 349 124 78 2007 576 691 358 133 75

Table 2.6: Store types: 2001- 2007

Source: Indiana ATC list of license holders

21 Year Entrants Exits 2002 192 187 2003 113 82 2004 214 79 2005 135 108 2006 144 115 2007 246 ...

Table 2.7: Entry and Exit for Indiana Retail Beer Market, 2002-2007

Source: Indiana ATC list of license holders.

by exits. Entry by convenience and drug stores was above the average of subsequent years, particularly 2006 and 2007, as shown in Table 2.8. This could be a much delayed response to legalization of exclusive territories, however there is not sufﬁcient data at this time to rule out alternative explanations. The increase in convenience stores in 2002 is not an abrupt departure from what appears to be a trend of convenience stores entering the beer market. Without data from the period prior to 2002, it is impossible to examine any possible change in the trend in convenience store entry caused by the exclusive territories. However, the data on the overall number of beer licenses are not consistent with the exclusive territory ban restricting entry. These data suggest that changing from a competitive to an exclusive territorial wholesale market had little or no effect on the number or type outlets entering the Indiana beer market.

In order to test the price and quantity predictions of the model, supply and demand for beer are modeled as:

S Q = f (Price, Tax, Distance, Retailers, Mandate, S18, Indnoban, Ex Terr) (2.1)

D Q = f (Price, Income, Population, Mandate, S18, Indnoban, Ex Terr) (2.2)

22 Year Entrants 2002 77 2003 32 2004 55 2005 40 2006 103 2007 174

Table 2.8: Entry of Convenience and Drug Stores, 2002-2007

Source: Indiana ATC List of license holders.

The distance variable is included as a proxy for cost and expected to reduce QS.

Taxes are expected to have a similar effect on consumption. Price is expected to increase QS and decrease QD. Population should increase consumption. The effect of income could be positive or negative depending on whether consumers regard beer as a normal or inferior good. According to the model presented in this paper and Klein and Murphy’s model, mandated exclusive territories should increase QD and decrease QS. The ExTerr variable is a difference-in-differences estimator of the effect of exclusive territories in Indiana on the supply and demand for beer. A positive ExTerr coefﬁcient in the demand equation would be consistent with the model. Regressions were performed using logged values of all continuous variables.

We estimate the supply and demand equations using two-stage least squares, instrumenting for Price. Tables 2.9 presents the results of the supply and demand regressions. The standard errors are obtained using White’s consistent covariance matrix estimator, as a Breusch-Pagan test indicated that both models had heteroscedastic residuals. The distance and retailers variables are signiﬁcant

23 and have the predicted sign in the supply equation. Price has the predicted sign in both the supply and demand equations, but is puzzlingly insigniﬁcant. Man- dated exclusive territories signiﬁcantly decrease demand, in contrast to Sass &

Saurman’s findings. The ExTerr coefficient is insignificant in both equations, suggesting that exclusive territories had no effect on supply or demand. This is in contrast to previous findings that found exclusive territories to be demand enhancing and supply decreasing.

We also estimate reduced-form price and quantity equations and present the results of these regressions in Table 2.10. The results of these regressions are similar to the supply and demand estimations. Retailers, Population, and Tax are signiﬁcant and have the expected effects on consumption. Indnoban has a signiﬁcant, positive effect on price. However, this is likely because the end of

Indiana’s exclusive territory ban roughly coincided with ACCRA’s change in the sample six pack from the domestic Budweiser to the premium import Heineken.

Note that again, the difference-in-differences variable ExTerr is insigniﬁcant, indicating that the end of Indiana’s exclusive territories ban had no effect on price or quantity consumed. Also, mandated exclusive territories appear to reduce consumption, again contrary to previous ﬁndings.

2.6 Conclusion

In this paper, we sketched a new model of exclusive territories using De- neckere, Marvel, and Peck’s model of niche competition to show that exclusive territories may be used to maintain large retail inventories and wide distribution, and ensure stock rotation. This model predicted that exclusive territories

24 Independent Supply Demand Variable Coefﬁcient Coefﬁcient (Std Error) (Std Error) Price 0.067 -0.00643 (0.1737) (0.0590) Tax 0.1451 ... (0.0944) ... Distance -0.03754** ... (0.0107) ... Retailers 0.802** ... (0.0269) ... Income ... 0.034 ... (0.073) Population ... 0.96** ... (0.00835) Mandate 0.0376 -0.0913** (0.0403) (0.0160) S18 0.137** -0.167** (0.0356) (0.0163) indnoban 0.00263 -0.01702 (0.0817) (0.0276) E x Terr 0.0485 -0.00276 (0.0559) (0.0250) Intercept 4.392** 3.711** (0.431) (0.2836) N 301 301 Adjusted R2 0.8552 0.9811

Table 2.9: Supply and Demand regressions

25 Independent Price Consumption Variable Coefﬁcient Coefﬁcient (Std Error) (Std Error) Tax -0.001 -0.092** (0.0476) (0.0304) Distance 0.005 0.0032 (0.00537) (0.00358) Income 0.305** -0.131 (0.0823) (0.0697) Retailers -0.0297 0.1228** (0.0192) (0.0697) Population 0.011 0.848** (0.021) (0.0144) Mandate -0.026 -0.0752** (0.0183) (0.0152) S18 -0.0981 -0.147** (0.0512) (0.0156) Indnoban 0.3322** -0.0253 (0.0168) (0.0181) E x Terr 0.0211 0.0089 (0.051) (0.00237) Intercept 0.534* 4.068** (0.3202) (0.2786) N 301 301 Adjusted R2 0.4993 0.9842

Table 2.10: Reduced-form Regressions

** - indicates signiﬁcance at .05 level.

26 should cause an increase in the price and consumption of beer as well as an increase in the number of outlets serving peak demand states (i.e. convenience and drug stores). Data on the number and type of retail outlets selling beer in Indiana from 2001 to 2007 show that there was not an appreciable increase in the number of convenience stores and drugstores selling beer. Estimation of supply and demand equations and reduced-form price and quantity equations showd that exclusive territories in Indiana had no effect on supply, demand, price, or consumption. Also, the effect of mandated exclusive territories on price and quantity in this study contradicts previous work. The robustness of this result should be tested with improved data, particularly improved price data. The price data contains prices from metropolitan areas in the 3rd quarter of the year only. Limiting the price to third quarter data allows for consistent sample composition at the cost of artiﬁcially reducing price variation and po- tentially masking the true effect of exclusive territories on prices. Additionally, if exclusive territories differentially impact prices in rural and urban areas, the exclusion of rural prices from our data could prevent detection of an exclusive territory effect. The insigniﬁcance of exclusive territories on quantity is more useful in arguing that exclusive territory legalization had no effect on the beer market. The impact of exclusive territories on quantity is what differentiates the pro- and anti-competitive models and the results here suggest that exclusive territories was a null event.

The results support neither the pro- nor the anti-competitive explanations of exclusive territories, leaving open the question of why legalization of exclusive territories was so persistently pursued by brewers and opposed by small

27 distributors and retailers. It is possible that ﬁrms found a way around Indiana’s

exclusive territory ban, enabling them to obtain the ends of exclusive territories

without contractually assigning them. This would explain why ending the ex-

clusive territories ban had no signiﬁcant impact on the Indiana beer market. In

1995, Bartholomew County Beverage Company sued Miller Brewing, arguing

that Miller’s pricing incentives circumvented Indiana’s exclusive territory ban.

Modern Brewery Age reported that Miller paid wholesalers more for beer sold

within a distributors territory than beer sold elsewhere11. Legalization of exclusive territories would enable brewers to assign and enforce exclusive territories directly without the risk of costly litigation.

With respect to exclusive territories impact on retail composition, it is possible that Indiana’s quotas on beer licenses were binding, and prevented the entry of new stores. License quotas are based on the population of the city or town in which it will be located. Requests to the Indiana Alcohol and Tobacco Commis- sion for information on the exact quotas were rebuffed. Had new stores entered the market, they would have facilitated increased consumption in Indiana. The increase in convenience stores with licenses in 2006 and 2007 could represent the delayed increase in convenience store predicted by the model, implying that increased consumption is right around the corner. Continued monitoring of beer consumption is necessary to test this hypothesis.

11“Brewers, wholesalers renew 10-year-old battle”. Modern Brewery Age, February 20, 1995

28 CHAPTER 3

DO RETAIL PROMOTIONS HAVE PERSISTENT EFFECTS? AN ANAYLSIS OF DOMINICK’S FINER FOODS SCANNER DATA

3.1 Introduction

Economists have long been closely concerned with the effects of advertising as a promotional device. Measured media such as television, radio, and news- papers are paid for principally by advertisers who place messages to stimulate sales of their products. But important as such spending is, it is falling as a pro- portion of the spending of leading national advertisers12. Procter & Gamble, the perennial top spender in advertising, reports that its marketing mix is “shifting from measured media to in-store, to internet, and to trial activity [i.e., product

13 sampling] .” Can economic models of media advertising be applied to the understanding of sales promotions? This paper provides empirical evidence on the effects of promotions at the grocery store, the traditional forum for promotional spending.

12See Bradley Johnson, “100 Leading National Advertisers: Top 100 Spending up 3.1%; Big Shift: Traditional media feel the pinch as No. 1 P&G and other marketers extend their reach via internet, promotions,” Advertising Age, June 25, 2007, vol. 78, no. 26. Advertising Age reports that most of the growth of spending by the 1000 leading national advertisers was in the form of unmeasured media such as “marketing services such as direct marketing, sales promotion, and digital communications (including unmeasured forms of internet media such as paid search).” 13Id.

29 The approach here is predominately empirical. We provide estimates of the durability of the effects of promotions, together with measures of the oligopolistic interactions of promotional expenditures. The estimates are constructed using an dataset that provides information on pricing, sales, and promotional sales of various consumer packaged-goods products as recorded by a Chicago retailer, Dominick’s Finer Foods. The data are from the early 1990’s, a period that is particularly interesting for evaluating the impact of promotional spending. While Procter & Gamble today is increasing promotional spending relative to other forms of marketing, in 1992, it moved in the opposite direction, cutting promotions in favor of an everyday low pricing (EDLP) approach.

The principal goal of this research is to measure the lasting impact of promotional spending. Since Nelson’s introduction of the concept of an experience good, economists have thought of advertising as a device to generate sales today that are linked to repeat purchases occurring at some point in the future. The question addressed here is whether promotional spending generates permanent sales increases through the development of repeat customers. We ﬁnd that for some brands, promotions do generate small long-term beneﬁts. However, these gains are eliminated by competitor promotions.

3.2 Economics of Promotions

Sales promotions have been extensively studied in the marketing literature, where several empirical generalizations have gained acceptance. Blat- tberg et al. (1995) note that sales promotions have been shown to increase sales in the short-run, establishing a sensible, if not surprising, rationale for

30 sales promotions. However, Blattberg and his co-authors point out that there is still disagreement regarding the long-term effects of promotions, with some researchers ﬁnding that promotions had negative long-term effects on brand health.

Varian (1980) provides an economic explanation of price promotions in which stores or brands reduce prices as a form of inter-temporal price dis- crimination. In his model, consumers are either informed or uniformed about the existing price distribution. Informed consumers purchase the brand with the lowest price, while uninformed consumers know only their own reservation price and purchase a brand chosen at random, as long it’s price is less than the reservation price. In this type of market, monopolistically competitive brands compete for informed consumers (uninformed consumers are divided equally among brands) by choosing a probability distribution from which they will draw a price. Varian shows that the probability distribution of brands with zero marginal cost and positive fixed cost is U-shaped. This means that prices close to the reservation price are chosen with high probability and prices close to the minimum average cost are chosen with low probability. The Varian model implies that brands should have high prices during most periods, but occasion- ally will sell at a low price to attract informed customers. Sales during the high price periods should be constant, since the brand always sells to the uninformed consumers. Sales during low price periods should be higher since the firm has attracted the informed customers. When the price increases sales should return to the original sales level as the firm is once again selling only to uninformed consumers.

31 Lal and Villas-Boas (1998) extend Varian’s model of price promotions to a game where two manufacturers sell their products to retailers, who in turn sell to heterogeneous consumers. In this model, consumers differ in their sensitivity to prices and their preferences for manufacturers and retailers. The most price sensitive consumers are “pure switchers” and will purchase the product with the lowest retail price regardless of manufacturer or retailer. Some consumers have a preferred brand (“brand loyal”) and will purchase that brand at the retailer that offers it at the lowest price. Other consumers will purchase the cheapest brand at their preferred retailer (“store-loyal”). The ﬁnal group of consumers will purchase only their preferred brand at their preferred store (“pure loyal”).

The relative sizes of these different groups structure the price-setting incentives of retailers and manufacturers. For example, if the ratio of switching consumers to loyal consumers for a high-priced brand is greater than the ratio of switching to loyal consumer for the low-price brand, then the retailer will ﬁnd it advantageous to lower the price of the higher-priced brand. For the manufacturer, the switching segments are the store-loyal and price sensitive segments. In this situation, price promotions should be positively correlated; a increase in promotional pricing (decrease in price) for one brand should lead to a corresponding decrease in price for the competing brand. Under these consumer preferences, manufacturers should increase wholesale price discounts (or trade deals) to retailers because the value of trade deals increases in the size of the “switching” segments. When the relationship between the “switcher” to “loyal” ratios is re- versed, price promotions are negatively correlated and in some circumstances

32 the retailer may ﬁnd it most proﬁtable to promote the product with the higher wholesale price.

In both the Varian model and its extension by Lal & Villas-Boas, promotions have no long term effects. Promotions boost sales by attracting the switching or informed consumers and gains in sales or market share due to promotions are lost as soon as the product promotion ends. However, economists have argued that marketing activity may produce lasting effects, particularly for experience goods. Nelson (1974) argues that advertising could provide consumers with information that will boost long term sales. In the case of search goods, goods whose characteristics are discernible before purchase, advertising easily informs consumers of product characteristics. For experience goods, goods whose characteristics are not discernible prior to purchase, advertising serves as a signal of product quality. The manufacturer’s willingness to expend money to promote the product signals to consumer’s that the product is of high quality. These advertising signals allow consumers to purchase the product that best matches their preferences, increasing the likelihood that they will purchase the same product in the future. In an earlier paper, Nelson (1970) argues that, for experience goods, advertising is a sensible strategy for manufacturers to un- dertake when it generates repeat customers. Bagwell and Ramey (1994) show that “cheap talk” and other forms of advertising that do not increase product demand, can improve consumer-product match. When consumers make several sequential searches, this type of informative advertising cultivates repeat customers and that the economic return to deceptive advertising is reduced. In all

33 of these models, advertising increases long-term sales of the advertised product by matching consumers to the products that ﬁt their preferences.

By improving consumer-product match, sales promotions of experience goods could be an effective device to cultivate repeat customers and increase long- term sales. Assume that consumers are imperfectly informed about the characteristics of products in a category. Consumers will try different products until the expected beneﬁt of a trying a new product is equal to the expected cost.

A sales promotion, by reducing product prices, reduces the cost of trying new products and encourages consumers to search for their ideal product. The inﬂux of new consumers purchasing the product will obviously increase sales during the promotion period. If these new consumers, after experiencing the product,

ﬁnd that the product it matches their preferences better than other products they have tried, they will become repeat customers and product sales will increase in the long-run.

3.3 Methodology

We adopt a methodology developed in Dekimpe and Hanssens (1995) for measuring the persistence of shocks on market outcomes. First, the time series of the market outcomes is examined for stationarity. If the series is stationary, then shocks to the process have only short-run effects that diminish over time.

However, if the process is non-stationary, it has a unit root and shocks have persistent effects that do not diminish over time. We illustrate how shocks persist with an example using a simple AR(1) process, (1 ρL)yt = εt . We can convert −

34 this process to the inﬁnite order MA process,

X∞ t 2 yt = ρ εt = εt + ρ εt 1 + ρ εt 2 + . . . (3.1) − − t=0 to obtain what is known as the impulse response function of the process. The impulse response shows how the process responds to an unexpected shock. In a stationary process where ρ < 1, the impact of a shock diminishes as time | | passes. However, if the process yt has a unit root, then rho = 1 and random | | shocks have permanent effects on yt as shown by the inﬁnite order MA process,

X∞ yt = εt = εt + εt 1 + εt 2 + . . . (3.2) − − t=0 .

Having established that a univariate process has a unit root, we can measure the size of the persistent effect. Campbell and Mankiw (1987) show that measuring the permanent or persistent effect of a shock is possible by summing the coefficients of impulse response function of the first-differenced process. Con- sider a first-differenced univariate ARMA process Φ(L)∆yt = Θ(L)εt , with an infinite order MA representation

1 2 ∆yt = [Φ(L)]− Θ(L)εt = A(L)εt = (1 + a1 L + a2 L + ...)εt . (3.3)

From the MA( ) representation we see that a unit change in εt k has an effect ∞ − of size at k on the change in yt (∆yt ), but this does not tell us the effect of a − change in εt k on the original process yt . We are able to derive the effect of −

εt k on yt by noting that ∆yt = (1 L)yt = A(L)εt and so − −

1 1 yt = (1 L)− A(L)εt = (1 L)− (εt + a1εt 1 + a2εt 2 + ...) (3.4) − t − t 1 − t k − X X− X− = εi + a1 εi 1 + ... + ak εi k + . . . (3.5) − − −∞ −∞ −∞ 35 From [5], we can see that

∂ yt = 1 + a1 + a2 + ... + ak + . . . (3.6) ∂ εt k − Here, the effect of a shock is measured by the ratio of the sum of the coefﬁcients

of the impulse response function.

In this study, we will examine the impulse responses of various consumer

packaged-goods brands’ sales to promotional sales of these brands. We estimate

the following ARX(p) model for each brand:

i i j ∆St = Σp at p ∆St p + Σj bj Promot + εS1,t (3.7) − −

1 j where ∆St is the ﬁrst-differenced market share of brand i at time t, and Promot is promotional sales of brand j at time t. We can derive the impulse response

function and accumulated impulse response in a manner similar to the univari-

ate accumulated impulse response. We begin with the ARX process: (Φ(L)∆yt =

Θ(L) x t + εt where x t is an exogenous variable. With some algebra we see that

1 1 ∆ yt = (Φ(L))− Θ(L) x t + (Φ(L))− εt (3.8)

(1 L)yt = β(L) x t + α(L) εt (3.9) − 1 1 yt = (1 L)− β(L) x t + (1 L)− α(L) εt (3.10) t −t 1 t −k X X− X− yt = xi + β1 xi 1 + ... + βk εi k + ... + (3.11) − − −∞ −∞ −∞ (3.12)

And so, the persistent effect of a change in x t k on yt is −

∂ yt = 1 + β1 + β2 + ... + βk + . . . (3.13) ∂ x t k −

36 Thus, the persistent effect of an exogenous variable on our process can be found by summing the coefﬁcients of the impulse response of the process to the changes in the exogenous variable.

3.4 Data

We use scanner data from Dominick’s Finer Foods (DFF) to investigate the long-run effects of promotions on brand sales. The DFF dataset, available from the University of Chicago’s Kilts Center for Marketing14 contains weekly item sales and promotional activity data from 90 Dominick’s Finer Foods grocery stores in the Chicago area from September 1989 to March 1996. The Dominick’s data report sales and promotional activity at UPC-Store-Week level in multiple categories. Using data on the size of each UPC in terms of the number of ounces or units (rolls of toilet paper or paper towels, sheets of fabric softener sheets) contained in an item, we converted sales and promotional sales to the relevant units for that product category (e.g. ounces of laundry detergent, rolls of paper towel sold). Logs of the unit sales and unit promotional sales were taken and are used in all subsequent ﬁgures and estimations. Product categories were deﬁned using the designation assigned by Dominick’s (using the com_code variable), though some minor adjustments were necessary. Some UPCs that were erroneously listed in multiple categories were placed in a single category based on the brief description of each UPC provided in the data. Additionally, some items in the data were not similar to the other products in their category. Most notably, Niagara brand spray starch and Shout brand stain stick were included in the Fabric Softener Sheets category. Such out-of-place items were removed

14 http://research.chicagogsb.edu/marketing/databases/dominicks/index.aspx

37 from the data. Lastly, certain categories appeared to have signiﬁcant reductions

in data reporting late in the sample. The later periods of these categories were

dropped to prevent reporting irregularities from biasing our results.

To obtain a clearer picture of promotional activity, items were aggregated

into “brands” in the following manner. First, items with the same brand name

in the Dominick’s provided description were identiﬁed and grouped as a brand.

Each brand name has under its umbrella a variety of products differentiated by

size and product type. An example from the Liquid Laundry Detergent category

illustrates. Procter & Gamble operates the the Tide brand name with a number of varieties such as Tide, Tide with Bleach, Tide Free, and Tide Unscented. Each of these variations of Tide is available in sizes ranging from 32 ounces to 150 ounces. We included in each brand aggregate those items which were found to have more that 300 weeks in the data so that the brand aggregates would have consistent composition throughout the sample period. We also limited the brand aggregates to items that were leading sellers within the brand. This eliminated seasonal items as well as new products. Some items were found to have unique

UPCs even though they were identical to other products in the dataset, and appeared in the data only for a short time. These UPCs were included in the brand aggregate if the identical item was included. Manufacturers were also identified using the first five digits of the UPC codes. On two occasions the same brand name was listed as produced by two different manufacturers. Cottonelle toilet paper was listed as produced by both Kleenex and Kimberly-Clark15; Arm

& Hammer toothpaste was listed as having two different manufacturers in spite

15This in spite of the fact that Kimberly-Clark own the Kleenex brand

38 of having the same brand name as the manufacturer. The various strains of

each brand were combined to form single Cottonelle and Arm & Hammer brands.

Tables A.1 and A.2 present the manufacturers and brands in each category as well as the share of total brand dollar sales included in our brand aggregates.

The data record various types of promotional activity that were undertaken during the sample period, identifying the item and promotional device. For this study, we are interested in manufacturer-led promotions, so the promotional device that we study is of key importance. We consider Dominick’s “Bonus Buy” promotion as a manufacturer-led promotion in this paper. Bonus Buys are items advertised in Dominick’s bi-weekly newspaper advertisement and featured as in-store specials with a shelf tag or some other type of prominent shelf placement. During these promotions, Dominick’s “passes through the promotional allowance penny for penny.16” A promotional allowance is a ﬂat fee manufacturers pay to retailers for various promotional services such as prominent shelf space, or fund price reductions while maintaining the retailer margin as appears to be the case with Bonus Buys. Thus, Bonus Buys are funded with manufacturer dollars and it is reasonable to assume that they are arranged by the manufacturer and not merely a retail inventory control device.

3.5 Results

We ﬁrst attempt to determine whether the sales of each consumer packaged- good brand is stationary or non-stationary. If a brand’s sales are stationary, promotions cannot have long-term effects as sales have a constant mean across

16Natschke, Pat. “Dominick’s ’Bonus Buys’ key to Marketing Program.” Supermarket News, 11/6/89

Figure 3.1: Time Series of Sales: Angel Soft toilet paper

time. If a brand’s sales are non-stationary, promotions could have a persistent effect on sales and further investigation of this brand is required. A ﬁrst step when considering the stationarity of brand sales is to simply plot the time series of sales over the sample period. Inspection of the plot provides some idea of whether a series will be stationary. Consider ﬁgures 3.1 and 3.2. Sales of An- gel Soft toilet paper show a substantial amount of variance, but this variance not time-dependent and about a constant mean. Contrast this with the sales of

Charmin which have a downward trend. Based on these graphs, sales of An- gel Soft are likely stationary, while sales of Charmin are likely non-stationary.

Figures B.1-B.67 in Appendix B plot the sales of each brand over the sample period.

Figure 3.2: Time Series of Sales: Charmin toilet paper

For further evidence regarding stationarity, we perform augmented Dickey-

Fuller tests on each series. Augmented Dickey-Fuller (ADF) tests are performed by regressing

∆ salest = f (salest 1, ∆ salest 1,..., ∆ salest k) (3.14) − − − and comparing the t-statistic of the salest 1 coefficient to the Dickey-Fuller crit- − ical value (-2.89). If the t-statistic is smaller than the critical value then the unit-root null hypothesis is rejected. The ADF test results for the toilet paper category are presented in table 3.1. The T-stat presented is that of the first lag to register an insignificant F-statistic. In some cases, no lag registered an insignificant F-statistic in which case the 12th lag is presented. The results of the remaining ADF tests are presented in tables A.3 to A.11 in appendix A.

41 Brand Lag Rho T-Stat Unit Root Angel Soft 12 -108.701 -4.12 No Cottenelle 12 -39.3838 -3.03 No Charmin 12 -28.6914 -2.87 Yes Dominicks 10 -24.6538 -2.56 Yes Northern 12 -30.5476 -3.17 No Scott 12 -57.9028 -3.46 No Green Forest 12 -14.3951 -3.43 No

Table 3.1: Augmented Dickey-Fuller Test: Toilet Paper

Brands with unit roots in each category are presented in table 3.2. It is interesting to note that of the 34 non-stationary brands, almost one-third (11) are Procter & Gamble brands. This is more that half of the Procter & Gamble brands in the sample, a greater percentage than any other manufacturer. This is notable because P & G adopted value pricing out of the belief that shocks, such as promotions, would not have lasting effects. However, the data here indicate that shocks to P & G brand sales were more likely to have persistent effects than shocks to other brand sales. We have little information about how bonus buys are funded beyond Dominick’s assertion that they “passed through the promotional allowance penny for penny.” This suggests that a 100% of a wholesale price discount was passed on to consumers, contrary to the typical practice of only passing a percentage of a wholesale price discount to consumers

(imperfect pass-through). However, promotional allowances are also used to describe ﬁxed payments by manufacturers to retailers for various promotional activities, such a prominent shelf placement. If the promotional allowance that funds bonus buys is a lump-sum payment, then P&G, as the largest ﬁrm and

42 often the market leader in these categories, may have expected to get as large or larger return from promotional allowances than its smaller competitors. If

P&G found that it did not get a larger return per dollar spent than its smaller competitors, it may have sensibly decided that there were better

Having determined the brands for which promotions may have a persistent effect, we quantify the short-run and persistent effects of unexpected shocks to brand sales. This is done by estimating the appropriate AR(p) model of the ﬁrst- difference of the sales of each brand, converting the AR(p) model to the impulse response form, and summing the coefﬁcients of the impulse response model. For each non-stationary brand’s sales series, we estimate the AR(p) model that best

ﬁts the data according to the Akaike Information Criterion (AIC).

Table 3.3 presents the simple and accumulated (persistent) univariate impulse response functions for sales of each brand in the toilet paper category.

The simple impulse response shows how the impact of a shock to the series diminishes as the shock recedes into the past. The accumulated impulse response measures the persistent change in expected sales due to a one unit shock by summing the impulse response of the previous lag. For example, a one unit shock to Charmin toilet paper will raise expected sales of Charmin by approxi- mately 0.17. We can also interpret this to mean that 17% of a shock in Charmin sales will persist in the long run. The accumulated univariate impulse response functions for the remaining non-stationary brands are presented in tables A.12 to A.20.

43 Category Brand Toilet Paper Charmin Dominick’s Paper Towels Hi Dri Dominicks Brawny Liquid Dish Soap Ajax Dawn Ivory Joy HH Liquid Dishwasher Soap Palmolive Powder Dishwasher Soap all Cascade Dominicks Fabric Softener Sheets Snuggle Downy Bounce Liquid Fabric Softener Downy Liquid Laundry Detergent Surf all Purex Cheer Era YES Powder Laundry Detergent Surf all Arm & Hammer Tide Oxydol Dominicks Toothpaste Close Up Arm & Hammer Colgate Brite

Table 3.2: Brands with a Unit Root, by Category

44 Impulse Response Accumulated Impulse Response Lag Charmin Dominicks Charmin Dominicks 0 0.72218 0.42992 0.72218 0.42992 1 -0.68751 -0.23768 0.03467 0.19224 2 0.02441 -0.08698 0.05908 0.10526 3 -0.02400 0.02625 0.03508 0.13151 4 0.02706 -0.01646 0.06214 0.11505 5 0.05217 0.02575 0.11431 0.14080 6 -0.04096 0.04826 0.07335 0.18906 7 -0.07603 -0.02818 -0.00268 0.16088 8 0.04868 -0.01684 0.04600 0.14404 9 0.04384 0.00274 0.08984 0.14678 10 0.02938 -0.00171 0.11922 0.14507 11 -0.10265 0.00570 0.01657 0.15077 12 0.15342 0.00615 0.16999 0.15692

Table 3.3: Univariate Impulse Responses: Toilet Paper

The persistent impact of sales shocks varies substantially between brands and product categories, however some generalizations are possible. The univariate impulse response is typically between 0.05 and 0.12, however some brands have responses as high as 0.6 (Purex liquid laundry detergent). Impulse responses in the paper towel and liquid laundry detergent were higher than most other categories. The univariate impulse response of brands is negatively correlated with brand market share, indicating that smaller brands are more responsive to shocks. This explains why the impulse response of Procter & Gamble brands tend to be smaller than those of other manufacturers’ brands.

The persistent effect of promotions is found by estimating an ARX(p) model of ﬁrst-differenced sales of non-stationary brands on promotional sales of all

45 brands. From the ARX(p) model, standard statistical packages calculate the impulse response of sales to shocks in promotional sales. The accumulated impulse response measures the change in brand sales due to a change in promotional sales of competing brands. Consider the impulse response of Charmin sales to promotional sales in tables 3.4. Here, a one unit increase in promotional sales of Charmin results in a persistent 0.01 unit increase in Charmin sales. Likewise, a one unit increase in promotional sales of Angel Soft causes a 0.004 unit decrease in Charmin sales. The accumulated impulse response of each brand to promotional shocks by competing brands in the category can be found in the tables A.21 to A.54 in appendix A.

From these tables, it is evident that a brand’s promotions do have an small, but immediate effect on it sales. A one unit shock in a brand’s own promotional sales typically results in an immediate 0.02 to 0.03 increase in brand sales. The persistent effect of a brand’s own promotions is negligible. Few brands have a persistent own promotion impulse response greater than 0.01, meaning that

1% of a promotional sales shock transfers into a permanent brand sales increase. The effect of competitor promotions is typically negative as one would expect, though there were a number of occasions when the impulse response to competitors’ promotions was positive. A number of these positive responses are by brands of the same manufacturer. In the short run, it is possible that one brand’s promotion could boost all brands’ sales by attracting consumers to the category in a particular period. This is particularly true for brands produced by the same manufacturer, who may expect the promotion of one of their product to increase the sales of all products in that category. However, it is unlikely

46 Brand Persistent Impulse Response to shocks in promotions by Charmin Lag Angel Soft Cottonelle Charmin Dominick’s 0 -0.01758 -0.02359 0.04328 -0.00907 1 -0.00005 -0.00007 0.00013 -0.00003 2 -0.00119 -0.0016 0.00294 -0.00062 3 -0.00081 -0.00109 0.00199 -0.00042 4 -0.00118 -0.00158 0.0029 -0.00061 5 -0.00224 -0.00301 0.00552 -0.00116 6 -0.00149 -0.002 0.00367 -0.00077 7 0.00046 0.00062 -0.00114 0.00024 8 -0.00127 -0.0017 0.00312 -0.00065 9 -0.00258 -0.00346 0.00635 -0.00133 10 -0.00324 -0.00434 0.00797 -0.00167 11 -0.00051 -0.00068 0.00125 -0.00026 12 -0.00463 -0.00621 0.0114 -0.00239 Charmin Lag Northern Scott Green Forest 0 -0.00717 -0.00508 -0.00283 1 -0.00002 -0.00002 -0.00001 2 -0.00049 -0.00034 -0.00019 3 -0.00033 -0.00023 -0.00013 4 -0.00048 -0.00034 -0.00019 5 -0.00091 -0.00065 -0.00036 6 -0.00061 -0.00043 -0.00024 7 0.00019 0.00013 0.00007 8 -0.00052 -0.00037 -0.0002 9 -0.00105 -0.00074 -0.00041 10 -0.00132 -0.00093 -0.00052 11 -0.00021 -0.00015 -0.00008 12 -0.00189 -0.00134 -0.00074

Table 3.4: Persistent Impulse Response to Promotional Shocks: Charmin toilet paper

47 that this sort of promotional spillover would result in long-term gains for the secondary brands. Our results are consistent with type of positive spillover explanation as the persistent positive sales response to competitors’ promotions is always very small (less than 0.01), although the initial response is often larger.

Neither is the positive spillover restricted to brands produced by the same manufacturer. Though there are a number of cases in which promotions of a brand increase sales of a brand with the same manufacturers, this is not true of all positive spillovers. Consider the Procter & Gamble brands Dawn, Ivory, and Joy liquid dish soap. Promotional sales of Joy have a positive effect on Dawn sales but a negative (but insigniﬁcantly small) effect on Ivory sales.

We calculate a cumulative persistent response of sales to promotions by summing the persistent effect (at lag 12) of promotions for each brand. By adding the response of sales to each promotion we can see the ﬁnal effect of a promotion after a competitor has responded with their own promotion. These cumulative persistent responses are presented in table 3.5. The effects are gen- erally quite small (less than 0.01) indicating that competitor promotions are quite effective at preventing a brand’s promotions creating long-term increases in sales.

3.6 Conclusion

This paper estimates the durability of promotional effects using time series data. We ﬁnd that promotional shocks do have long term impacts, but that these impacts are more than offset by competitor promotions. Under these circumstances, it is reasonable to ask why ﬁrms run promotion? One explanation is

48 Category Brand Cumulative Response Toilet Paper Charmin -0.00819 Dominick’s 0.00054 Paper Towels Hi Dri 0.00261 Dominicks -0.0121 Brawny 0.00315 Liquid Dish Soap Ajax 0.00153 Dawn 0.0047 Ivory 0.005 Joy 0.00009 HH 0.00157 Liquid Dishwasher Soap Palmolive 0.00131 Powder Dishwasher Soap all -0.0002 Cascade 0.00302 Dominicks 0.00163 Fabric Softener Sheets Snuggle 0.00788 Downy 0.00039 Bounce -0.00374 Liquid Fabric Softener Downy 0.0017 Liquid Laundry Detergent Surf 0.02157 all 0.01689 Purex -0.02063 Cheer -0.00313 Era 0.00457 YES 0.01014 Powder Laundry Detergent Surf -0.00595 all -0.00574 Arm & Hammer 0.00956 Tide 0.00194 Oxydol 0.00791 Dominicks 0.01187 Toothpaste Close Up 0.01036 Arm & Hammer 0.00952 Colgate -0.00814 Brite 0.01701

Table 3.5: Cumulative Response to Promotional Shocks

49 that brand managers are not as concerned with long-term market share growth as they are with short-term performance. Given that promotions are effective at boosting short-term sales, they would seem an ideal instrument to do so as argued in Leeﬂang and Wittink (1996). Alternatively, the market may be viewed as a prisoner’s dilemma game in which the no promotion equilibrium is not sustainable in spite of repeated play. In this case, Procter & Gamble’s effort to establish EDLP could be viewed as a large brand committing to the optimal equilibrium (from the manufacturer’s point of view) in the hope that other brands would follow suit. The failure of EDLP can be attributed to the inability of other brands to match P&G’s commitment (see Ailawadi et al. (2001)) and may been seen as evidence of how challenging it is to establish and maintain a tacitly collusive equilibrium.

Another explanation of value pricing lies in P&G’s close relationship with

Wal-Mart, its largest customer. Wal-Mart is unique in the consumer packaged- goods and grocery industry in that it does not require lump-sum promotional payments. Wal-Mart asks manufacturers to provide their lowest possible wholesale price continuously rather than provide periodic wholesale price discounts, facilitating the discount retailer’s practice of everyday low pricing. If maintaining separate pricing schemes for Wal-Mart and traditional retailers is costly, then

P&G may have believed, at the time, that switching to value-pricing would be proﬁt-maximizing even if competitors did not reduce promotions. The possibility that manufacturer pricing strategies could be imposed by a downstream retailer is deserving of greater attention in future work.

50 CHAPTER 4

DO FIRMS REACT ACROSS MARKETS? AN ANAYLSIS USING DOMINICK’S FINER FOODS SCANNER DATA

4.1 Introduction

Firm actions and reactions are fundamental to the study of industrial organization and firm behavior. Assumptions regarding firm behavior are the basis of models of market structure and competitive interaction. For example, Cournot and Stackelberg firms are assumed to best respond with particular decision variables (quantity and price respectively) to the actions of their competitors. Firm actions determine the competitiveness of an industry. An incumbent firm in a market can accommodate or fiercely compete with a rival entrant. Either action has obvious implications for the competitive environment of the market.

Additionaly, conglomerate ﬁrms that operate in multiple product markets may have different competitive incentives than ﬁrms that operate in only one market

(Bernheim & Whinston, 1990).

This paper investigates the ﬁrms’ reactions to competitors’ promotional activities. We estimate competitive reaction functions for manufacturers in 10 consumer packaged-goods categories using scanner data from a major Chicago-area

51 grocery store. These functions measure ﬁrm responses to competitors’ promotional activity in the category in question, as well as competitors’ promotional activities in other categories. The next section brieﬂy reviews several papers in competitive response and multi-market contact literature. Section 3 describes the data used to estimate competitive reaction functions. Section 4 outlines the empirical model and presents results from the estimation. Section 5 concludes.

4.2 Literature Review

Bernheim and Whinston (1990) show that firms that compete in several markets simultaneously may be able to more easily collude on prices. They show that multi-market contact may, under certain conditions, enable firms to support a collusive price that would not be sustainable in a single market situation. Multi-market contact enables firms to increase the severity of punishments for deviating from a cartel agreement by punishing in multiple markets. In- creasing the punishment for deviating from a collusive agreement makes it less profitable to defect from collusion in any or all markets in which it operates with other cartel members, and improves the ability of firms to enforce and maintain a collusive agreement. The authors also show that multi-market contact facilitates collusion in industries characterized by “spheres of influence.” In such industries, firms possess market-specific cost advantages or scale economies, which encourage competing firms to concede advantaged markets. This allows each firm to operate within its sphere of influence, the market in which it is advantaged, and earn higher profit levels than possible under competition. Ad- ditionally, if firms compete across markets that have inherent differences, either

52 in the number of competitors or in ﬁrms’ perceived discount rate (how much they value future gains from the market), multi-market contact may facilitate collusion in one or more markets.

Evans and Kessides (1994) explore the “Golden Rule” aspects of multi-market contact. Early arguments about the collusive dangers of multi-market contact argued that the repeated interaction in many markets would lead to firm strategies of mutual forebearance. The guiding principle of a mutual forebearance strategy is similar to the famous “Golden Rule” of doing unto others as you would have them do to you. In terms of business, this meant not competing vigorously through price cuts, because you do not want your competitors to cut prices against you. Evans & Kessides argue that the airline industry is an excel- lent test case for theories of mutual forebearance through multi-market contact because antitrust proceedings have uncovered that the “Golden Rule” was an in- ternally stated practice in the airline industry. Additionally, the airline industry’s hub-and-spoke configuration leads to the development of spheres of influence that Bernheim and Whinston (1990) argue facilitate collusive outcomes. The authors estimated a model of route prices as a function of route and airline characteristics as well as an index of the degree of multi-market contact of the airlines flying a particular route. They found that multi-market contact results in significantly higher prices and that this effect is more pronounced on high price tickets. They argue that these results confirm that the “Golden Rule” strategy is still in effect in the airline industry.

53 Ailawadi et al. (2001) estimate competitive reaction functions to measure the effect of Procter & Gamble’s (P&G) Value Pricing Strategy. Under Value Pric- ing, P&G reduced promotional activity such as trade deals and coupons, opting instead to offer a consistent wholesale price and increasing the national advertising budget. By compiling data on advertising expenditures and share of sales from deals and coupons, Ailiwadi et al. estimate the effect that changes in P&G’s marketing mix had on its competitors’ market share. They find that value pricing tended to reduce P&G’s, rather than its competitors, market share. Their analysis concluded that this was primarily due to a decrease market penetration, the percent of consumers purchasing P&G products, arguing that promotions have a stronger impact on penetration than advertising. The authors also estimate the competitive reactions of P&G’s major competitors’ responses to P&G’s promotional activity. They find that when Value Pricing reduced a competing market share, competitor’s responded with increased promotions, but firms that gained market share when P&G went to value pricing decreased promotions. This “following” effect was more pronounced for firms that competed with P&G in multiple markets. The authors argue that this is evidence of a mutual forbearance strategy as described by Bernheim and Whinston (1990).

Leeﬂang and Wittink (1992) develop an empirical strategy using aggregated scanner data to identify competitive reactions in promotions. Since promotions can be implemented by both retailers and manufacturers, the authors classify price and promotion reactions into three categories. Parallel reactions occur when two brands are offered on a promotion at the same time. The authors argue that this most likely happens because the promotions are planned some

54 time in advance and are not a true competitive reaction. When competing brands are placed on promotion in consecutive or nearly consecutive weeks, it is likely that the retailer is running the second promotion in response to the

first. These retailer-dominated promotions most likely occur with little time in between promotions. Leeflang and Wittink assume that reactions to promotions from 1 to 4 weeks in the past are retailer-dominated. A manufacturer’s response to a competitor promotion is likely to require more time to organize because it would likely require the adaptation of an annual promotion schedule and making supply-chain adjustments necessary for a promotion. The authors assume that this requires five to ten weeks and so reactions to competitor promotions

ﬁve to ten weeks ago are considered to be manufacturer-led. Leeﬂang and

Wittink estimate reaction functions of seven brands of non-food, non-durable consumer goods in which a brand’s promotional activity is a function of all of its competitors’ previous 10 weeks of promotional activity. They include in their functions price promotions, sampling, refunds, features (retailer advertising), and bonus offers. The large number of variables included in the reaction functions (10 lags for each promotional variable for each of six competing manufacturers) leaves the models under-identiﬁed with the scanner data available.

Unable to properly estimate their model, the authors use Granger causality tests to estimate the causal relation between promotional variables. They argue that while simple reactions (reactions with the same promotional instrument) ac- count for a disproportionate number of reactions, they do not capture all of the effects of promotions because competitive responses can occur with other marketing instruments.

55 This paper builds on this existing literature by examining the effects of multi- market contact in 10 consumer packaged-goods markets in grocery store scanner data. Our approach is to examine local data for evidence of ﬁrm responses to competitor promotional activities across product categories. Here we differ from Ailawadi et al. (2001) as they used nationwide quarterly sales, promotion, and advertising expenditure data. While Evans and Kessides (1994) developed and index of multi-market contact to determine if increases in the amount of contact impacted price, we look directly at responses to price changes and promotional sales in other markets. We build largely on the work of Leeﬂang and

Wittink (1992) in estimating competitive reaction functions, but incorporate multi-market contact variables into their framework and make some other adjustments that will be discussed later. Competitive reactions should be timely, most likely occurring within the space of a several weeks rather than quarterly.

This type of reaction is best observed with frequently reported, local data, such as scanner data, rather than aggregate data. Additionally, reactions should take the form of changes in marketing activity such a price reductions or increases in promotions that can be observed directly at the disaggregate level. Reactions in aggregate data are shown in terms of expenditures which can reﬂect not just reactions but changes in other unobserved factors. Scanner data from multiple products enables the examination of multi-market reactions directly rather than through measurement of a multi-market contact effect using an indicator variable or index of multimarket contact.

56 4.3 Data

We use scanner data from Dominick’s Finer Foods (DFF) to investigate the long-run effects of promotions on brand sales. The DFF dataset, available from the University of Chicago’s Kilts Center for Marketing17 contains weekly item sales and promotional activity data from 90 Dominick’s Finer Foods grocery stores in the Chicago area from September 1989 to March 1996. The Dominick’s data report sales and promotional activity at UPC-Store-Week level in multiple categories. Using data on the size of each UPC in terms of the number of ounces or units (rolls of toilet paper or paper towels, sheets of fabric softener sheets) contained in an item, we converted sales and promotional sales to the relevant units for that product category (e.g. ounces of laundry detergent, rolls of paper towel sold). Logs of the unit sales and unit promotional sales were taken and are used in all subsequent figures and estimations. Product categories were defined using the designation assigned by Dominick’s (using the com_code variable), though some minor adjustments were necessary. Some UPCs that were erroneously listed in multiple categories were placed in a single category based on the brief description of each UPC provided in the data. Additionally, some items in the data were not similar to the other products in their category. Most notably, Niagara brand spray starch and Shout brand stain stick were included in the Fabric Softener Sheets category. Such out of place items were removed from the data. Lastly, certain categories appeared to have significant reductions in data reporting late in the sample. The later periods of these categories were dropped to prevent reporting irregularities from biasing our results.

17 http://research.chicagogsb.edu/marketing/databases/dominicks/index.aspx

57 To obtain a clearer picture of promotional activity, items were aggregated

into “brands” in the following manner. First, items with the same brand name

in the Dominick’s provided description were identiﬁed and grouped as a brand.

Each brand name has under its umbrella a variety of products differentiated by

size and product type. An example from the Liquid Laundry Detergent category

illustrates. Procter & Gamble operates the the Tide brand name with a number of varieties such as Tide, Tide with Bleach, Tide Free, and Tide Unscented. Each of these variations of “Tide” is available in sizes ranging from 32 ounces to 150 ounces. We included in each brand aggregate those items which were found to have more that 300 weeks in the data so that the brand aggregates would have consistent composition throughout the sample period. We also limited the brand aggregates to items that were leading sellers within the brand. This eliminated seasonal items as well as new products. Some items were found to have unique

The various brands were then combined to form an aggregate for each manufacturer to provide measures of manufacturer sales, prices, and extent of product line (number of UPCs available in a category). Manufacturers were identi-

ﬁed using the ﬁrst 5 digits of the UPC codes. On two occasions the same brand name was listed as produced by two different manufacturers. Cottonelle toilet paper was listed as produced by both Kleenex and Kimberly-Clark18; Arm &

Hammer (A&H) toothpaste was listed as having two different manufacturers in

18This in spite of the fact that Kimberly-Clark owns the Kleenex brand

58 spite of having the same brand name as the manufacturer. The various strains

of the A&H brand were combined to form single Arm & Hammer brand with a

single manufacturer. Cottonelle was treated as having two manufacturers, con-

sistent with the manufacturer portion of Cottonelle UPCs. YES laundry detergent is currently produced by Reckitt Benckiser but the manufacturer portion of the

UPC is not the same as other Reckitt Benckiser products and was treated as though it was produced by a separate manufacturer.

Table 4.1 presents the manufacturers and brands that are in each category.

The data record various types of promotional activity that were undertaken during the sample period, identifying the item and promotional device. For this study, we are interested in manufacturer-led promotions, so the promotional device that we study is of key importance. We consider Dominick’s “Bonus Buy” promotion as a manufacturer-led promotion in this paper. Bonus Buys are items advertised in Dominick’s bi-weekly newspaper advertisement and featured as in-store specials with a shelf tag or some other type of prominent shelf placement. During these promotions, Dominick’s “passes through the promotional allowance penny for penny19.” A promotional allowance is a ﬂat fee manufacturers pay to retailers for various promotional services such as prominent shelf space, or fund price reductions while maintaining the retailer margin as appears to be the case with Bonus Buys. Thus, Bonus Buys are funded with manufacturer dollars and it is reasonable to assume that they are arranged by the manufacturer and not merely a retail inventory control device.

19“Dominick’s ’Bonus Buys’ key to Marketing Program.” Pat Natschke, Supermarket News, 11/6/89

59 Category Manufacturer Category Manufacturer Toilet Angel Soft (Georgia Pacific) Fabric Unilever Paper Kleenex (Kimberly-Clark) Softener Procter & Gamble Procter & Gamble Sheets Dominick’s Dominick’s Reckitt Benckiser Georgia Pacific Kimberly-Clark Liquid Unilever Green Forest Fabric Procter & Gamble Softener Dominick’s Paper Kleenex (Kimberly-Clark) Towels Procter & Gamble Liquid Unilever Dominick’s Dishwasher Dial Georgia Pacific Detergent Procter & Gamble Kimberly-Clark Dominick’s Green Forest Reckitt Benckiser Liquid Unilever Powder Unilever Dish Colgate-Palmolive Laundry Dial Soap Procter & Gamble Detergent Arm & Hammer Dominick’s Procter & Gamble Dominick’s Liquid Unilever Dishwasher Colgate-Palmolive Toothpaste Mentadent Soap Procter & Gamble Pearl Arm & Hammer Powder Unilever Colgate-Palmolive Dishwasher Procter & Gamble Procter & Gamble Soap Dominick’s Dominick’s Reckitt Benckiser GlaxoSmithKline

Table 4.1: Categories and Manufacturers

60 4.3.1 Price Index

Because we are creating aggregate manufacturer goods from items sold un-

der different brand names at different stores, we need some index of the price of

this aggregate manufacturer good based on the prices of its constituent goods.

We adopt the price index developed and used with this data by Chevalier et al.

(2003) with some minor changes. First, we calculate a price index for each

brand aggregate in the dataset, where Chevalier et al. index for individual

items aggregates. Our brand price index is:

Pi jkt = Σs Σu i ωui jkst Pui jkst (4.1) ∈

where ωui jkst is the dollar share of UPC u (which sells under brand name i by

manufacturer j in category k) in store s in week t. Pui jkst is the log of the price per unit (ounces, rolls, etc) of UPC u in store s in week t. Next, we aggregate to the manufacturer level by combining brand price indexes into a manufacturer price index as follows:

Pjkt = Σi ωi jkt Pi jkt (4.2)

ωi jkt is the dollar share of brand i in manufacturer j’s sales in category k during week t. Note that we use time variable weights as do Chevalier et al to avoid problems of composition bias.

4.3.2 Out of Market variables

We consider firms that compete in multiple categories to have multi-market contact and assume that competitor actions are relevant to the firm’s decision making only when the actions occur in a market in which the firm operates.

For example, both Procter & Gamble and Unilever compete in the liquid and

61 powder laundry detergent categories. Under multi-market contact theories, P &

G’s actions in the powder laundry detergent category should have some effect

on Unilever’s actions in the liquid laundry detergent category and vice versa.

However, Procter & Gamble’s promotion activity in the paper towel category,

where Unilever does not compete, should have no effect on Unilever’s actions

in any other category, insofar as they are independent of P&G actions in other

P&G-Unilever categories. Variables that calculate the out-of-market activity of a

competitor were constructed such that they only include sales from categories

in which both the ﬁrm of interest and the competitor operated. Out of market

sales and promotional sales are calculated by:

Out Mkt Salesi jkt = Σm M Salesjmt (4.3) ∈ Out Mkt PromotionalSalesi jkt = Σm M PromotionalSalesjmt (4.4) ∈ where i is the manufacturer in question, j is the competing manufacturer, k is the category, t is the week, and M is the set of all categories in which both i and

j compete other than category k. The fraction of out of market sales created by promotions is PromotionalSalesOut Mkt Out Mkt i jkt Frac PromotionalSalesi jkt = Out Mkt (4.5) Salesi jkt An out of market price index is calculated as a weighted average of a competitor’s indexed price in other markets in which it competes with the ﬁrm. The out of market price is calculated by

Out Mkt Pi,j,k,t = Σm M ωi,m Pjmt (4.6) ∈ where ωi,m is the category m’s share of manufacturer i’s total revenue, M is again the set of all categories in which i and j compete other than category k.

62 4.4 Model & Results

To measure the competitive reaction of manufacturers to competitors’ pro-

motions and prices, we estimate a simpliﬁed version of Leeﬂang & Wittink’s

competitive reaction functions. These functions model a ﬁrm’s decision variable

as a function of a ﬁrm’s own actions and observable competitor actions. The

model suggested by Leeﬂang and Wittink (1992) uses weekly observations of

a brand’s market share and other promotional variables to study brand’s reac-

tion to competitors’ promotional actions. They assume that manufacturers take

anywhere from ﬁve to 12 weeks to develop a promotional response and have

that response reach the retail market20, and model manufacturer actions today

as a response to competitor actions in the preceding 12 weeks. Their estimating

equation is:

Market Share α t 1 Frac PromotionalSalesIn Mkt ε (4.7) ikt = + Σj J Σl=−t 12 jkt + ikt ∈ − where j denotes the manufacturers in category k in week t and J is the set of

all manufacturers in . Clearly, with even a moderate number of competing man-

ufacturers, this model requires a substantial number of observations to estimate

with sufﬁcient degrees of freedom. Indeed, this identiﬁcation problem limited

the Leeﬂang & Wittink’s initial analysis.

We remedy the identiﬁcation problem by aggregating the past promotional

activity into months. This aggregation reduces the number of independent vari-

ables, solving the identiﬁcation problem, and has the added bonus of elimi-

nating some of the noise of weekly observations. Our aggregation consists of

20Leeﬂang & Wittink assumed that manufacturer responses would require 5 to 10 weeks to develop and put into effect.

63 combining lagged sales and prices into 3 periods of 4 consecutive weeks. We

generically dub these periods months. Thus, month lag 1 (denoted m1 in most

tables), refers to the 4 weeks immediately prior to the current week, month 2

(m2) the period 5 to 8 weeks prior to the current week, etc. The “monthly”

lagged share of promotional sales were calculated by: t 1 Promotional Sales M1 Σl=−t 4 ikl Frac Promotional Salesikt = − (4.8) t 1 Sales Σl=−t 4 ikl t 5 Promotional− Sales M2 Σl=−t 8 ikl Frac Promotional Salesikt = − (4.9) t 5 Sales Σl=−t 8 ikl t 9 Promotional− Sales M3 Σl=−t 12 ikl Frac Promotional Salesikt = − (4.10) t 9 Sales Σl=−t 12 ikl − where i indicates the manufacturers in category k. For the price equations, the average price over each 4 week period was used as the price for that “month”.

We also differ from previous work in our choice of firm decision variable (dependent variable in the model). Previous work has used promotional sales or brand market share as the firm’s reaction variable. However, these measures are inherently dependent on the effect of a decision variable on actual sales. Retail prices are somewhat less problematic, but are still subject to retailer action. We use both the price, as done in other studies, and the share of a manufacturer’s products, as defined by UPC, promoted during a given week as decision variables. The share of items promoted is directly controlled by the manufacturer, and is independent of consumer or retailer actions. For each manufacturer in each category, we estimate the following equations using ordinary least squares:

F rac Promoted I tems α 3 β Frac Promotional SalesIn Mkt(4.11) ikt = + Σj J Σm=1 jkm jkm ∈ 3 θ Frac Promotional SalesOut Mkt ε(4.12) +Σj J Σm=1 jkm jkm + ikt ∈ Price α 3 β PriceIn Mkt 3 θ PriceOut Mkt ε(4.13) ikt = + Σj J Σm=1 jkm jkm + Σj J Σm=1 jkm jkm + ikt ∈ ∈ 64 where J is the set of all manufacturers in category k. Coefficients from the promotions regressions give us the percent change in share of a manufacturer’s product line promoted in response to a 1% change in the manufacturer’s or competitors’ share of sales from promotions. We would expect firms to increase promotion activity (share of product line promotes) in response to increases in competitor promotional activity (share of promotional sales), and so would expect positive coefficients on competitor in and out of market promotional activities variables. Coefficients from the price regressions give us the percent change in a manufacturer’s price in response to a 1% change in the manufacturer’s or a competitor’s price (in or out of the category).

4.4.1 Promotions reaction functions

Results from estimation of the promotions reaction functions are presented in tables C.2 to C.15. Most models have an adjusted R2 greater than 0.2. Most categories have several regressions with statistically signiﬁcant out of market promotional variables, liquid dishwasher soap being the lone exception. How- ever, the results are decidedly mixed. The coefﬁcients of all statistically significant promotional variables, both in market and out of market, are less than

1. This indicates that ﬁrms are not very responsive to changes in their competitors’ promotional activities, but rather make promotion decisions independently.

Furthermore, ﬁrms do not appear to be responding to competitor promotional activity in other markets in a consistent manner, indicating that ﬁrms are likely not coordinating strategies across categories.

65 A closer look at the reaction functions of Kleenex and Kimberly-Clark in tables C.2 - C.5 is instructive in understanding the independence for ﬁrms promotion decisions. As noted above, the Kleenex brand is owned and operated by

Kimberly-Clark, however through a quirk in the Dominick’s data they are treated as being separate manufacturers here. If ﬁrms are coordinating promotional activities across markets, then a manufacturer’s two brand in a market should react similarly to competitor promotions. Examination of the data shows that in the toilet paper category, Kleenex and Kimberly-Clark both have signiﬁcant reactions to the out-of-market promotional activity of Procter & Gamble, though

Kleenex reacts to P&G promotions from two months ago, while Kimberly-Clark reacts to P&G promotions of only one month ago. Both have within-market reactions to last months’ Georgia-Paciﬁc promotions and different months’ promotions by Dominick’s brand. Similarities are few in the Paper Towel category as well. Here Kleenex and Kimberly-Clark share no common out-of-market reactions and react to last month’s Green Forest and Kleenex promotions and dif- fering months’ promotions by Georgia-Paciﬁc in the paper towel category. The few common out-of-market reactions of Kleenex and Kimberly-Clark indicate that there is no manufacturer strategy to respond to competitors’ promotions in other markets, and thus not multi-market collusive agreement on promotions.

The lack of identical common response (same competitor, same lag) suggests that there is little coordination between brands of the same manufacturer. Such lack of coordination makes it unlikely that a coordinated promotions equilibrium or punishment strategy was implemented.

66 4.4.2 Price Reaction Functions

Estimation of the price reaction functions also ﬁnds little evidence of ﬁrms reacting across markets. The results of the estimation of the price reaction functions are presented in tables C.16 to C.29. The adjusted R-squared of these models is lower than the promotion reaction functions, with some models’ adjusted

R2 less than zero. Also, fewer statistically significant out of market reactions are found. Statistically significant out of market price coefficients are typically negative implying that firms lower prices when they see their competitor rais- ing prices in other markets. Thus, multi-market firms are not colluding to raise prices across markets. The results show that the manufacturer’s own in-market price in previous months is the most significant determinant of its current price.

Examination of the Kleenex - Kimberly-Clark relationship in tables C.16 -

C.19 indicates that little coordinated action took place in these markets. In neither the paper towel nor the toilet paper category do Kleenex and Kimberly-

Clark both respond to the same competitor prices. Kleenex fails to respond to any competitor prices in either category, indicating that it sets its price independently. At the same time, non-Kleenex Kimberly-Clark brands responded to a number of competitors in- and out-of-market promotions. All of these responses are to last month’s promotions making it unlikely that Kimberly-Clark organized them as a true response. As with the promotions reaction functions, these result imply that ﬁrms, or at least Kimberly-Clark, do not respond across markets or even coordinate between brands in the same market.

67 4.5 Conclusion

The competitive effects of multi-market contact have been analyzed theoret- ically but empirical work on multimarket contact has been sparse. This paper has attempted to use scanner data to measure the effect of multi-market contact. The results presented in this paper argue that multi-market contact does not facilitate collusion in consumer packaged-goods industries. The results also indicate that manufacturers may not effectively coordinate pricing and promotions across brands in the same product market. This lack of coordinated promotion could be due to compartmentalization of category management within the manufacturer. Bernheim & Whinston argue that multi-market contact will facilitate a collusive outcome in industries where the market differences allow

ﬁrms to pool the incentive constraints across markets. The markets in the consumer packaged-goods industry likely lack those market distinctions, such as

ﬁrm cost advantage or differences in market discount rates, that make multi- market collusion across markets possible. Compartmentalization of category management would also make multi-market collusion difﬁcult.

Though the empirical results presented indicate that ﬁrms’ actions are to a great extent independent of both within category and out-of-category competitor promotional activity, there is still a great deal of work to be done. A more sophisticated model, based on a more complete theoretical speciﬁcation of reaction functions, should be employed.

68 CHAPTER 5

CONCLUSION

This dissertation empirically examines the effects of exclusive territory contracts in the Indiana beer market and how consumer-packaged goods ﬁrms compete through retail promotions. The results on exclusive territories shows that exclusive territories had no effect on the Indiana beer market. The evidence also indicates that promotional competition takes the form of a prisoner’s dilemma game where the equilibrium outcome is not collusive and that competition does not take place across markets.

Assigning beer distributors exclusive territories appears to have little effect on the retail beer market or its composition. Indiana’s legalization of the use of exclusive territories in beer distribution provides a natural experiment on the competitive effects of exclusive territories. Examination of data on the number and type of licensed beer retailers in Indiana reveals that exclusive territories do not impact the composition of the retail market. Estimation of supply and demand equations show that legalization had little effect on the market price or quantity consumed. This is contrary to previous studies which showed increases in price and little impact on consumption. It is possible that beer brewers found ways to circumvent Indiana’s exclusive territories ban which would result in

69 legalization having no effect and the possibility that exclusive territories were anti-competitive. However, the results here argue that competition under exclusive territories remained robust.

Retail promotions have little or no persistent effect on product sales and any persistent effect is easily countered by competitor promotions. Examina- tion of the time series of sales shows that sales of many goods are stationary and not impacted by promotions. Estimation of the impulse response of sales to promotional sales shows that the long-run effect of promotions is small or non-existent. The cumulative, persistent effect of a brand’s and it’s competitor promotions is very small and sometimes negative. These results are consistent with previous work in the marketing literature that argue that retail promotions have no long-run effects.

Results from the estimation of competitive reaction functions show that

firms do not respond across markets in a systematic way. Aggregate scanner data enables the direct observation of the promotional activity of manufacturers in multiple consumer-packaged goods categories. This is preferred to previous methods that relied on indexes and indicator variables because it provides a picture of how competition takes place at the retail level. The findings indicate that promotional decisions are made independently of competitor promotions within the market and competitor promotions in shared markets. This suggests that multi-market contact of consumer-packaged goods firms does not facilitate collusion. This result stands in contrast to previous studies that found that multi-market contact facilitated higher prices and emphasizes the importance

70 of inter-market differences, such as inter-market cost advantages, in the theory of mutual forebearance in multi-market contact.

71 APPENDIX A

TABLES FOR ’DO RETAIL PROMOTIONS HAVE PERSISTENT EFFECTS?’

72 Category Manufacturer Brand Percent of dollar Category sales included Share Toilet Angel Soft Angel Soft 9.6 4.3 (Georgia Pacific) Paper Kleenex Cottonelle 83.6 10.6 (Kimberly-Clark) Procter & Gamble Charmin 85.2 24.1 Dominick’s Dominick’s 78.4 5.3 Georgia-Pacific Northern 89.4 20.7 Kimberly-Clark Scott 96.0 23.0 Green Forest Green Forest 100 3.1 Paper Kleenex Hi Dri 87.7 5.0 (Kimberly-Clark) Towels Procter & Gamble Bounty 92.8 38.3 Dominick’s Dominick’s 77.3 11.5 Georgia-Pacific Brawny 84.9 6.6 Kimberly-Clark Viva 72.9 11.9 Scott 81.0 17.8 Green Forest Mardi Gras 80.9 6.5 Green Forest 100 3.5 Liquid Unilever Dove 99.7 8.0 Dish Soap Sunlight 99.3 11.0 (Hand) Colgate-Palmolive Ajax 95.5 6.3 Palmolive 71.5 18.1 Procter & Gamble Dawn 77.7 24.4 Ivory 90.3 13.3 Joy 91.0 12.7 Dominick’s HH 99.9 6.2 Liquid Unilever Sunlight 84.8 34.3 Dishwasher Colgate-Palmolive Palmolive 68.2 32.3 Soap Procter & Gamble Cascade 88.3 33.4 Powder Unilever Sunlight 97.4 16.2 Dishwasher all (dishwasher) 93.4 7.4 Soap Procter & Gamble Cascade 84.3 56.8 Dominick’s Dominick’s 100 7.7 HH 99.9 3.6 Reckitt Benckiser Electrasol 86.4 8.4

Table A.1: Manufacturers and Brands by Category

73 Category Manufacturer Brand Percent of dollar Market sales included Share Fabric Unilever Snuggle 81.5 17.7 Softener Procter & Downy 83.5 13.6 Sheets Gamble Bounce 81.8 44.9 Dominick’s HH 100 15.6 Reckitt Benckiser Cling 86.0 8.2 Liquid Procter & Gamble Downy 72.5 80.4 Fabric Dominick’s HH 100 5.3 Softener Liquid Unilever Surf 72.5 6.2 Laundry all 78.1 16.0 Detergent Dial (Purex) Purex 94.7 7.7 Procter & Cheer 75.6 10.0 Gamble Tide 72.9 41.8 Era 78.3 7.6 Dominick’s Dominick’s 59.7 1.7 Reckitt Benckiser YES 86.1 3.4 Powder Unilever Surf 85.7 5.7 Laundry all 80.4 6.4 Detergent Dial Dutch HD 100 2.1 Arm & Hammer Arm & Hammer 83.3 11.8 Procter & Cheer 75.0 12.5 Gamble Tide 84.1 48.5 Bold 95.4 1.6 Ivory Snow 89.7 1.1 Oxydol 96.0 3.3 Dominick’s Dominick’s 84.4 2.6 Toothpaste Mentadent Pepsodent 98.1 5.3 Close Up 92.3 6.5 Church & Dwight Pearl 88.1 0.2 Arm & Hammer Arm & Hammer 89.1 8.8 Colgate-Palmolive Colgate 69.2 24.9 Brite 80.4 2.6 Procter & Crest 75.9 38.4 Gamble Gleem 94.5 0.9 Dominick’s Dominick’s 94.9 1.8 GlaxoSmithKline Aqua Fresh 74.9 10.8

Table A.2: Manufacturers and Brands by Category

74 Brand Lags Rho T-Stat Unit Root Hi Dri 1 -5.5503 -1.27 Yes Bounty 7 -22.1095 -2.98 No Dominicks 1 -14.4125 -2.49 Yes Brawny 3 -17.8259 -2.86 Yes Viva 12 -44.1248 -3.30 No Scott 6 -24.1380 -2.89 No Mardi Gras 12 -38.1886 -3.24 No Green Forest 12 -25.7428 -4.95 No

Table A.3: Augmented Dickey-Fuller Test: Paper Towels

75 Brand Lags Rho T-Stat Unit Root Dove 9 -21.0972 -2.91 No Sunlight 12 -27.7639 -2.88 No Ajax 12 -14.0707 -2.50 Yes Palmolive 12 -55.8387 -3.55 No Dawn 9 -28.0650 -2.80 Yes Ivory 8 -23.2656 -2.75 Yes Joy 7 -16.5920 -2.55 Yes HH 4 -17.7735 -2.81 Yes

Table A.4: Augmented Dickey-Fuller Test: Liquid Dish Soap (Hand)

Brand Lag Rho T-Stat Unit Root Sunlight 6 -17.9160 -2.91 No Palmolive 3 -15.5784 -2.60 Yes Cascade 12 -49.5043 -3.59 No

Table A.5: Augmented Dickey-Fuller Test: Liquid Dishwasher Soap

Brand Lag Rho T-stat Unit Root Sunlight 12 -54.4548 -3.81 No all 4 -19.1993 -2.83 Yes Cascade 11 -18.5971 -2.58 Yes Dominicks 3 -14.3684 -2.37 Yes HH 5 -19.8069 -2.98 No Electrasol 12 -54.2883 -3.86 No

Table A.6: Augmented Dickey-Fuller Test: Powder Dishwasher Soap

76 Brand Lag Rho T-stat Unit Root Snuggle 6 -16.3686 -2.57 Yes Downy 8 -19.5501 -2.38 Yes Bounce 8 -20.0954 -2.63 Yes HH 7 -21.6536 -2.95 No Cling 12 -14.3010 -3.47 No

Table A.7: Augmented Dickey-Fuller Test: Fabric Softener Sheets

Brand Lags Rho T-Stat Unit Root Downy 5 -16.6749 -2.72 Yes HH 7 -24.8841 -2.88 No

Table A.8: Augmented Dickey-Fuller Test: Liquid Fabric Softener

Brand Lags Rho T-Stat Unit Root Surf 1 -4.6186 -1.16 Yes all 3 -12.1559 -2.40 Yes Purex 2 -12.3917 -2.45 Yes Cheer 8 -18.4120 -2.77 Yes Tide 11 -30.4327 -3.03 No Era 5 -16.5279 -2.76 Yes Dominicks 12 -68.0378 -3.80 No YES 4 -15.2120 -2.59 Yes

Table A.9: Augmented Dickey-Fuller Test: Liquid Laundry Detergent

77 Brand Lags Rho T-Stat Unit Root Surf 7 -13.9817 -2.37 Yes all 4 -15.1541 -2.62 Yes Dutch HD 11 -25.9212 -2.91 No Arm & Hammer 2 -13.9022 -2.59 Yes Cheer 4 -19.3512 -2.94 No Tide 3 -17.4325 -2.70 Yes Bold 12 -77.6471 -3.94 No Ivory Snow 12 -24.2668 -3.43 No Oxydol 3 -15.5830 -2.64 Yes Dominicks 11 -20.2210 -2.76 Yes

Table A.10: Augmented Dickey-Fuller Test: Powder Laundry Detergent

Brand Lag Rho T-Stat Unit Root Pepsodent 11 -24.8104 -2.95 No Close Up 7 -18.2911 -2.84 Yes Pearl 10 -21.8697 -2.95 No Arm & Hammer 11 -28.7338 -2.76 Yes Colgate 6 -19.0890 -2.81 Yes Brite 10 -23.0264 -2.86 Yes Crest 4 -18.1188 -2.88 No Gleem 1 -13.0945 -2.87 No Dominicks 12 -42.7286 -3.20 No Aqua Fresh 12 -29.8675 -3.28 No

Table A.11: Augmented Dickey-Fuller Test: Toothpaste

78 Accumulated Impulse Response Lag Hi Dri Dominicks Brawny 0 0.61391 0.68487 0.50283 1 0.33144 0.34169 0.19162 2 0.24660 0.33372 0.12055 3 0.26979 0.38834 0.09422 4 0.34157 0.38288 0.06644 5 0.31628 0.37173 0.08831 6 0.29847 0.37560 0.12457 7 0.30211 0.37691 0.09083 8 0.31139 0.37588 0.07340 9 0.30917 0.37583 0.06020 10 0.30626 0.37605 0.10883 11 0.30664 0.37601 0.09085 12 0.30790 0.37597 0.15799

Table A.12: Univariate Impulse Responses: Paper Towels

79 Accumulated Impulse Response Lag Ajax Dawn Ivory Joy HH 0 0.55280 0.44597 0.33224 0.31853 0.35860 1 0.15500 0.10158 0.05700 0.09707 0.18553 2 0.04952 0.08296 0.00638 0.04916 0.10839 3 0.01085 0.09130 0.02443 0.06164 0.05179 4 0.04767 0.10022 0.03583 0.06233 0.06211 5 0.11397 0.10596 0.01231 0.05221 0.08313 6 0.08175 0.14213 0.04600 0.06294 0.05511 7 0.04202 0.14808 0.06209 0.05287 0.06338 8 0.07703 0.11393 0.03639 0.07870 0.09819 9 0.05120 0.11254 0.03036 0.11073 0.10041 10 0.06571 0.11589 0.08249 0.07821 0.04482 11 0.07218 0.11851 0.04698 0.06813 0.05359 12 0.07059 0.12227 0.11354 0.07211 0.05146

Table A.13: Univariate Impulse Response: Liquid Dish Soap (Hand)

80 Accumulated Impulse Response Lag Palmolive 0 0.24648 1 0.13897 2 0.08452 3 0.05700 4 0.08939 5 0.08954 6 0.09461 7 0.08435 8 0.09624 9 0.09404 10 0.09262 11 0.08842 12 0.09075

Table A.14: Univariate Impulse Response: Liquid Dishwasher Soap

Accumulated Impulse Response Lag Dominicks Cascade all 0 0.21907 0.24115 0.22672 1 0.09871 0.06756 0.14745 2 0.06971 0.03355 0.12267 3 0.06145 0.03101 0.07625 4 0.08045 0.05871 0.05959 5 0.04582 0.01391 0.05930 6 0.05673 0.03135 0.06973 7 0.06513 0.05966 0.08992 8 0.08454 0.02883 0.09077 9 0.08331 0.03024 0.08288 10 0.07241 0.04349 0.08337 11 0.07064 0.04656 0.08305 12 0.07246 0.06304 0.08401

Table A.15: Univariate Impulse Response: Powder Dishwasher Soap

81 Accumulated Impulse Response Lag Snuggle Downy Bounce 0 0.32616 0.34280 0.24271 1 0.15026 0.13640 0.08176 2 0.07171 0.05165 0.02587 3 0.07707 0.02656 0.02472 4 0.08013 0.05587 0.05084 5 0.01689 0.03729 0.03445 6 0.01256 0.01170 0.02683 7 0.06924 0.02669 0.05550 8 0.04447 0.04869 0.03542 9 0.05907 0.06714 0.05969 10 0.03162 0.07358 0.05138 11 0.09099 0.07693 0.01981 12 0.09766 0.08582 0.06578

Table A.16: Univariate Impulse Response: Fabric Softener Sheets

Accumulated Impulse Response Lag Downy 0 0.27846 1 0.06223 2 -0.00073 3 0.00995 4 0.02042 5 0.00178 6 0.02220 7 0.04744 8 0.04495 9 0.04846 10 0.03912 11 0.05695 12 0.05485

Table A.17: Univariate Impulse Response: Liquid Fabric Softener

82 Accumulated Impulse Response Lag Surf all Purex Cheer Era YES 0 0.67319 0.80940 0.93481 0.44244 0.42162 0.83531 1 0.42178 0.22564 0.57614 0.14169 0.17554 0.18435 2 0.45145 0.18941 0.44740 0.08129 0.03757 0.16187 3 0.45296 0.13183 0.68239 0.10048 0.06816 0.17145 4 0.45382 0.36035 0.59691 0.11699 0.05894 0.15727 5 0.45285 0.22138 0.55127 0.08437 0.05534 0.16935 6 0.45310 0.11369 0.61410 0.06400 0.07547 0.18317 7 0.45308 0.15786 0.59537 0.06816 0.11423 0.20746 8 0.45308 0.23098 0.58058 0.06893 0.07110 0.23261 9 0.45308 0.24598 0.59720 0.11289 0.12197 0.22966 10 0.45308 0.19475 0.59337 0.12383 0.11629 0.19735 11 0.45308 0.20815 0.58879 0.10016 0.09165 0.19565 12 0.45308 0.22590 0.59312 0.11747 0.08875 0.19600

Table A.18: Univariate Impulse Response: Liquid Laundry Detergent

83 Accumulated Impulse Response Lag Surf all Arm& Hammer Tide Oxydol Dominicks 0 0.46766 0.46165 0.22021 0.28731 0.28190 0.24154 1 0.18452 0.15572 0.06406 0.05931 0.25265 0.12100 2 0.10650 0.13479 0.06623 0.01051 0.18030 0.09157 3 0.10761 0.07221 0.05359 0.05951 0.18203 0.07205 4 0.10440 0.11561 0.06665 0.07739 0.20261 0.09314 5 0.07777 0.09481 0.04223 0.07291 0.20350 0.09708 6 0.07229 0.09217 0.06430 0.07174 0.19782 0.10555 7 0.05730 0.12161 0.08498 0.09005 0.19718 0.10065 8 0.16521 0.13760 0.06838 0.06590 0.19872 0.09749 9 0.12983 0.14945 0.06597 0.06144 0.19901 0.09588 10 0.08154 0.12599 0.06641 0.07246 0.19860 0.09741 11 0.10221 0.12205 0.06653 0.07511 0.19849 0.09824 12 0.10512 0.11698 0.06200 0.07226 0.19860 0.09861

Table A.19: Univariate Impulse Response: Powder Laundry Detergent

84 Accumulated Impulse Response Lag Close Up Arm & Hammer Colgate Brite 0 0.48303 0.33437 0.35357 0.58671 1 0.27389 0.08352 0.03680 0.15315 2 0.09362 0.00274 0.04535 0.06811 3 0.09444 0.03487 0.01003 0.07485 4 0.08149 0.05211 0.06628 0.09343 5 0.10838 0.01560 0.03609 0.08022 6 0.09075 0.02113 0.03082 0.08714 7 0.08716 0.04847 0.01585 0.07782 8 0.10393 0.03376 0.07569 0.03755 9 0.12582 0.02828 0.04736 0.08566 10 0.17062 0.03913 0.06644 0.09997 11 0.15424 0.04611 0.09558 0.14611 12 0.12286 0.06316 0.05555 0.15336

Table A.20: Univariate Impulse Response: Toothpaste

85 Brand Persistent Impulse Response to shocks in promotions by Charmin Lag Angel Soft Cottonelle Charmin Dominick’s 0 -0.01758 -0.02359 0.04328 -0.00907 1 -0.00005 -0.00007 0.00013 -0.00003 2 -0.00119 -0.0016 0.00294 -0.00062 3 -0.00081 -0.00109 0.00199 -0.00042 4 -0.00118 -0.00158 0.0029 -0.00061 5 -0.00224 -0.00301 0.00552 -0.00116 6 -0.00149 -0.002 0.00367 -0.00077 7 0.00046 0.00062 -0.00114 0.00024 8 -0.00127 -0.0017 0.00312 -0.00065 9 -0.00258 -0.00346 0.00635 -0.00133 10 -0.00324 -0.00434 0.00797 -0.00167 11 -0.00051 -0.00068 0.00125 -0.00026 12 -0.00463 -0.00621 0.0114 -0.00239 Charmin Lag Northern Scott Green Forest 0 -0.00717 -0.00508 -0.00283 1 -0.00002 -0.00002 -0.00001 2 -0.00049 -0.00034 -0.00019 3 -0.00033 -0.00023 -0.00013 4 -0.00048 -0.00034 -0.00019 5 -0.00091 -0.00065 -0.00036 6 -0.00061 -0.00043 -0.00024 7 0.00019 0.00013 0.00007 8 -0.00052 -0.00037 -0.0002 9 -0.00105 -0.00074 -0.00041 10 -0.00132 -0.00093 -0.00052 11 -0.00021 -0.00015 -0.00008 12 -0.00189 -0.00134 -0.00074

Table A.21: Persistent Impulse Response to Promotional Shocks: Charmin toilet paper

86 Brand Persistent Impulse Response to shocks in promotions by Dominicks Lag Angel Soft Cottonelle Charmin Dominicks 0 0.00244 -0.00473 -0.00489 0.03142 1 0.00090 -0.00176 -0.00182 0.01166 2 0.00049 -0.00096 -0.00099 0.00637 3 0.00066 -0.00128 -0.00133 0.00853 4 0.00060 -0.00116 -0.00120 0.00769 5 0.00079 -0.00154 -0.00159 0.01023 6 0.00103 -0.00200 -0.00207 0.01329 7 0.00081 -0.00157 -0.00163 0.01045 8 0.00073 -0.00142 -0.00147 0.00942 9 0.00076 -0.00147 -0.00152 0.00979 10 0.00076 -0.00148 -0.00153 0.00983 11 0.00081 -0.00157 -0.00162 0.01041 12 0.00083 -0.00161 -0.00166 0.01069 Dominicks Lag Northern Scott Green Forest 0 -0.00908 -0.00713 -0.00160 1 -0.00337 -0.00264 -0.00059 2 -0.00184 -0.00144 -0.00032 3 -0.00246 -0.00193 -0.00043 4 -0.00222 -0.00175 -0.00039 5 -0.00296 -0.00232 -0.00052 6 -0.00384 -0.00301 -0.00068 7 -0.00302 -0.00237 -0.00053 8 -0.00272 -0.00214 -0.00048 9 -0.00283 -0.00222 -0.00050 10 -0.00284 -0.00223 -0.00050 11 -0.00301 -0.00236 -0.00053 12 -0.00309 -0.00242 -0.00054

Table A.22: Persistent Impulse Response to Promotional Shocks: Dominick’s toilet paper

87 Brand Persistent Impulse Response to shocks in promotions by Hi Dri Lag Hi Dri Bounty Dominick’s Brawny 0 0.05163 -0.01201 -0.00793 -0.00332 1 0.021 -0.00488 -0.00323 -0.00135 2 0.01563 -0.00364 -0.0024 -0.00101 3 0.01454 -0.00338 -0.00223 -0.00093 4 0.02755 -0.00641 -0.00423 -0.00177 5 0.02277 -0.00529 -0.0035 -0.00146 6 0.02015 -0.00469 -0.0031 -0.0013 7 0.0193 -0.00449 -0.00297 -0.00124 8 0.02246 -0.00522 -0.00345 -0.00144 9 0.02198 -0.00511 -0.00338 -0.00141 10 0.02117 -0.00492 -0.00325 -0.00136 11 0.02077 -0.00483 -0.00319 -0.00134 12 0.02149 -0.005 -0.0033 -0.00138 Hi Dri Lag Viva Scott Mardi Gras Green Forest 0 -0.003 -0.01111 -0.00804 0.00008 1 -0.00122 -0.00452 -0.00327 0.00003 2 -0.00091 -0.00336 -0.00243 0.00002 3 -0.00084 -0.00313 -0.00226 0.00002 4 -0.0016 -0.00593 -0.00429 0.00004 5 -0.00132 -0.0049 -0.00354 0.00004 6 -0.00117 -0.00434 -0.00314 0.00003 7 -0.00112 -0.00415 -0.00301 0.00003 8 -0.0013 -0.00483 -0.0035 0.00004 9 -0.00128 -0.00473 -0.00342 0.00003 10 -0.00123 -0.00456 -0.0033 0.00003 11 -0.00121 -0.00447 -0.00323 0.00003 12 -0.00125 -0.00463 -0.00335 0.00003

Table A.23: Persistent Impulse Response to Promotional Shocks: Hi Dri paper towels

88 Brand Persistent Impulse Response to shocks in promotions by Dominicks Lag Hi Dri Bounty Dominick’s Brawny 0 -0.01836 0.00288 0.01098 0.00029 1 -0.00827 0.0013 0.00495 0.00013 2 -0.00775 0.00122 0.00464 0.00012 3 -0.00858 0.00135 0.00514 0.00013 4 -0.00884 0.00139 0.00529 0.00014 5 -0.00904 0.00142 0.00541 0.00014 6 -0.00875 0.00137 0.00523 0.00014 7 -0.00876 0.00138 0.00524 0.00014 8 -0.00881 0.00138 0.00527 0.00014 9 -0.00881 0.00138 0.00527 0.00014 10 -0.00881 0.00138 0.00527 0.00014 11 -0.0088 0.00138 0.00527 0.00014 12 -0.0088 0.00138 0.00527 0.00014 Dominicks Lag Viva Scott Mardi Gras Green Forest 0 -0.0043 -0.00762 0.00115 -0.01029 1 -0.00194 -0.00343 0.00052 -0.00464 2 -0.00182 -0.00322 0.00048 -0.00434 3 -0.00201 -0.00356 0.00054 -0.00481 4 -0.00207 -0.00367 0.00055 -0.00496 5 -0.00212 -0.00375 0.00057 -0.00507 6 -0.00205 -0.00363 0.00055 -0.0049 7 -0.00205 -0.00364 0.00055 -0.00491 8 -0.00206 -0.00366 0.00055 -0.00494 9 -0.00207 -0.00366 0.00055 -0.00494 10 -0.00206 -0.00366 0.00055 -0.00494 11 -0.00206 -0.00365 0.00055 -0.00493 12 -0.00206 -0.00365 0.00055 -0.00493

Table A.24: Persistent Impulse Response to Promotional Shocks: Dominick’s paper towels

89 Brand Persistent Impulse Response to shocks in promotions by Brawny Lag Hi Dri Bounty Dominick’s Brawny 0 -0.01237 -0.00423 -0.00343 0.02564 1 -0.00437 -0.0015 -0.00121 0.00906 2 -0.00261 -0.00089 -0.00072 0.0054 3 -0.00236 -0.00081 -0.00065 0.0049 4 -0.00188 -0.00064 -0.00052 0.0039 5 -0.00235 -0.0008 -0.00065 0.00486 6 -0.00306 -0.00105 -0.00085 0.00634 7 -0.00213 -0.00073 -0.00059 0.00442 8 -0.00163 -0.00056 -0.00045 0.00337 9 -0.00151 -0.00052 -0.00042 0.00314 10 -0.00282 -0.00096 -0.00078 0.00584 11 -0.00221 -0.00076 -0.00061 0.00458 12 -0.00363 -0.00124 -0.001 0.00752 Brawny Lag Viva Scott Mardi Gras Green Forest 0 -0.00088 -0.00712 0.00834 0.00477 1 -0.00031 -0.00252 0.00295 0.00169 2 -0.00019 -0.0015 0.00176 0.00101 3 -0.00017 -0.00136 0.00159 0.00091 4 -0.00013 -0.00108 0.00127 0.00073 5 -0.00017 -0.00135 0.00158 0.00091 6 -0.00022 -0.00176 0.00206 0.00118 7 -0.00015 -0.00123 0.00144 0.00082 8 -0.00012 -0.00094 0.0011 0.00063 9 -0.00011 -0.00087 0.00102 0.00058 10 -0.0002 -0.00162 0.0019 0.00109 11 -0.00016 -0.00127 0.00149 0.00085 12 -0.00026 -0.00209 0.00245 0.0014

Table A.25: Persistent Impulse Response to Promotional Shocks: Brawny paper towels

90 Brand Persistent Impulse Response to shocks in promotions by Ajax Lag Dove Sunlight Ajax Palmolive 0 0.00713 -0.01378 0.02257 -0.00499 1 0.00189 -0.00366 0.00599 -0.00132 2 0.00052 -0.00101 0.00166 -0.00037 3 0.00005 -0.00009 0.00015 -0.00003 4 0.00061 -0.00118 0.00194 -0.00043 5 0.00155 -0.00299 0.0049 -0.00108 6 0.00107 -0.00206 0.00338 -0.00075 7 0.00067 -0.0013 0.00212 -0.00047 8 0.00112 -0.00216 0.00353 -0.00078 9 0.00082 -0.00159 0.00261 -0.00058 10 0.0007 -0.00136 0.00223 -0.00049 11 0.00091 -0.00177 0.0029 -0.00064 12 0.00095 -0.00183 0.003 -0.00066 Ajax Lag Dawn Ivory Joy HH 0 -0.00062 -0.00571 0.00658 0.00033 1 -0.00016 -0.00152 0.00175 0.00009 2 -0.00005 -0.00042 0.00048 0.00002 3 0 -0.00004 0.00004 0 4 -0.00005 -0.00049 0.00057 0.00003 5 -0.00013 -0.00124 0.00143 0.00007 6 -0.00009 -0.00085 0.00098 0.00005 7 -0.00006 -0.00054 0.00062 0.00003 8 -0.0001 -0.00089 0.00103 0.00005 9 -0.00007 -0.00066 0.00076 0.00004 10 -0.00006 -0.00056 0.00065 0.00003 11 -0.00008 -0.00073 0.00085 0.00004 12 -0.00008 -0.00076 0.00087 0.00004

Table A.26: Persistent Impulse Response to Promotional Shocks: Ajax liquid dish soap

91 Brand Persistent Impulse Response to shocks in promotions by Dawn Lag Dove Sunlight Ajax Palmolive 0 0.0022 0.01044 -0.00733 -0.00697 1 0.00026 0.00123 -0.00086 -0.00082 2 0.00032 0.00153 -0.00108 -0.00102 3 0.00026 0.00124 -0.00087 -0.00083 4 0.0003 0.00143 -0.001 -0.00095 5 0.00035 0.00167 -0.00118 -0.00112 6 0.00051 0.00243 -0.00171 -0.00162 7 0.00057 0.0027 -0.0019 -0.0018 8 0.00066 0.00313 -0.0022 -0.00209 9 0.00038 0.00181 -0.00127 -0.00121 10 0.0004 0.00192 -0.00135 -0.00128 11 0.0004 0.00191 -0.00134 -0.00128 12 0.00044 0.00207 -0.00146 -0.00139 Dawn Lag Dawn Ivory Joy HH 0 0.03069 -0.00511 0.00268 -0.00286 1 0.00361 -0.0006 0.00032 -0.00034 2 0.00451 -0.00075 0.00039 -0.00042 3 0.00363 -0.0006 0.00032 -0.00034 4 0.0042 -0.0007 0.00037 -0.00039 5 0.00493 -0.00082 0.00043 -0.00046 6 0.00714 -0.00119 0.00062 -0.00067 7 0.00794 -0.00132 0.00069 -0.00074 8 0.00921 -0.00153 0.0008 -0.00086 9 0.00533 -0.00089 0.00046 -0.0005 10 0.00564 -0.00094 0.00049 -0.00053 11 0.00563 -0.00094 0.00049 -0.00053 12 0.0061 -0.00102 0.00053 -0.00057

Table A.27: Persistent Impulse Response to Promotional Shocks: Dawn liquid dish soap

92 Brand Persistent Impulse Response to shocks in promotions by Ivory Lag Dove Sunlight Ajax Palmolive 0 -0.00236 0.00309 0.0005 -0.00492 1 -0.00034 0.00045 0.00007 -0.00071 2 0.00003 -0.00004 -0.00001 0.00006 3 -0.00012 0.00016 0.00003 -0.00025 4 -0.0002 0.00026 0.00004 -0.00042 5 -0.00008 0.0001 0.00002 -0.00017 6 -0.00034 0.00045 0.00007 -0.00071 7 -0.00042 0.00055 0.00009 -0.00087 8 -0.00023 0.0003 0.00005 -0.00048 9 -0.00021 0.00028 0.00005 -0.00045 10 -0.00056 0.00073 0.00012 -0.00116 11 -0.00034 0.00045 0.00007 -0.00072 12 -0.00076 0.001 0.00016 -0.0016 Ivory Lag Dawn Ivory Joy HH 0 0.00397 0.01858 -0.00022 -0.00217 1 0.00057 0.00268 -0.00003 -0.00031 2 -0.00005 -0.00022 0 0.00003 3 0.00021 0.00096 -0.00001 -0.00011 4 0.00034 0.00159 -0.00002 -0.00019 5 0.00013 0.00062 -0.00001 -0.00007 6 0.00057 0.00268 -0.00003 -0.00031 7 0.0007 0.00327 -0.00004 -0.00038 8 0.00039 0.00181 -0.00002 -0.00021 9 0.00036 0.00168 -0.00002 -0.0002 10 0.00094 0.00439 -0.00005 -0.00051 11 0.00058 0.00272 -0.00003 -0.00032 12 0.00129 0.00603 -0.00007 -0.0007

Table A.28: Persistent Impulse Response to Promotional Shocks: Ivory liquid dish soap

93 Brand Persistent Impulse Response to shocks in promotions by Joy Lag Dove Sunlight Ajax Palmolive 0 -0.00383 -0.00323 -0.0095 0.00127 1 -0.001 -0.00084 -0.00247 0.00033 2 -0.00057 -0.00048 -0.00141 0.00019 3 -0.00076 -0.00064 -0.00189 0.00025 4 -0.00068 -0.00058 -0.00169 0.00023 5 -0.00064 -0.00054 -0.00158 0.00021 6 -0.00071 -0.0006 -0.00176 0.00024 7 -0.00051 -0.00043 -0.00127 0.00017 8 -0.00088 -0.00074 -0.00217 0.00029 9 -0.00133 -0.00112 -0.00329 0.00044 10 -0.0009 -0.00076 -0.00223 0.0003 11 -0.00079 -0.00067 -0.00197 0.00026 12 -0.00084 -0.00071 -0.00208 0.00028 Joy Lag Dawn Ivory Joy HH 0 -0.00293 0.00537 0.01514 -0.00188 1 -0.00076 0.0014 0.00393 -0.00049 2 -0.00043 0.0008 0.00224 -0.00028 3 -0.00058 0.00107 0.00301 -0.00037 4 -0.00052 0.00096 0.00269 -0.00033 5 -0.00049 0.0009 0.00252 -0.00031 6 -0.00054 0.00099 0.0028 -0.00035 7 -0.00039 0.00072 0.00202 -0.00025 8 -0.00067 0.00123 0.00346 -0.00043 9 -0.00101 0.00186 0.00524 -0.00065 10 -0.00069 0.00126 0.00356 -0.00044 11 -0.00061 0.00111 0.00314 -0.00039 12 -0.00064 0.00118 0.00331 -0.00041

Table A.29: Persistent Impulse Response to Promotional Shocks: Joy liquid dish soap

94 Brand Persistent Impulse Response to shocks in promotions by HH Lag Dove Sunlight Ajax Palmolive 0 -0.00306 -0.00296 -0.00777 0.00053 1 -0.00126 -0.00122 -0.0032 0.00022 2 -0.00066 -0.00064 -0.00167 0.00011 3 -0.00013 -0.00013 -0.00033 0.00002 4 -0.00031 -0.00029 -0.00078 0.00005 5 -0.00055 -0.00053 -0.0014 0.0001 6 -0.00041 -0.00039 -0.00103 0.00007 7 -0.00047 -0.00045 -0.00118 0.00008 8 -0.00068 -0.00066 -0.00173 0.00012 9 -0.00075 -0.00072 -0.0019 0.00013 10 -0.0003 -0.00029 -0.00076 0.00005 11 -0.00048 -0.00047 -0.00122 0.00008 12 -0.00042 -0.0004 -0.00106 0.00007 HH Lag Dawn Ivory Joy HH 0 -0.00435 0.0077 -0.00394 0.0253 1 -0.00179 0.00317 -0.00162 0.01043 2 -0.00094 0.00166 -0.00085 0.00545 3 -0.00019 0.00033 -0.00017 0.00109 4 -0.00043 0.00077 -0.00039 0.00253 5 -0.00078 0.00139 -0.00071 0.00456 6 -0.00058 0.00102 -0.00052 0.00336 7 -0.00066 0.00117 -0.0006 0.00385 8 -0.00097 0.00172 -0.00088 0.00563 9 -0.00107 0.00189 -0.00097 0.0062 10 -0.00043 0.00076 -0.00039 0.00249 11 -0.00068 0.00121 -0.00062 0.00398 12 -0.00059 0.00105 -0.00054 0.00346

Table A.30: Persistent Impulse Response to Promotional Shocks: HH liquid dish soap

95 Brand Persistent Impulse Response to shocks in promotions by Palmolive Lag Sunlight Palmolive Cascade 0 -0.00158 0.00716 -0.00005 1 -0.00084 0.0038 -0.00003 2 -0.00045 0.00205 -0.00001 3 -0.00027 0.00123 -0.00001 4 -0.00044 0.00202 -0.00001 5 -0.0004 0.00179 -0.00001 6 -0.00043 0.00196 -0.00001 7 -0.00026 0.00118 -0.00001 8 -0.00038 0.00174 -0.00001 9 -0.00038 0.00173 -0.00001 10 -0.00029 0.00133 -0.00001 11 -0.0003 0.00137 -0.00001 12 -0.00038 0.0017 -0.00001

Table A.31: Persistent Impulse Response to Promotional Shocks: Palmolive liquid dishwasher soap

96 Brand Persistent Impulse Response to shocks in promotions by all Lag Sunlight all Cascade 0 -0.00071 0.014 -0.00574 1 -0.0004 0.0078 -0.0032 2 -0.0003 0.00582 -0.00239 3 -0.00017 0.00338 -0.00139 4 -0.00014 0.00284 -0.00116 5 -0.00011 0.00211 -0.00087 6 -0.00018 0.00346 -0.00142 7 -0.0002 0.00399 -0.00164 8 -0.0002 0.00395 -0.00162 9 -0.00017 0.00329 -0.00135 10 -0.00016 0.0032 -0.00131 11 -0.00013 0.00263 -0.00108 12 -0.00018 0.00363 -0.00149 all Lag Dominick’s HH Electrasol 0 0.00019 -0.0017 -0.00682 1 0.00011 -0.00094 -0.0038 2 0.00008 -0.00071 -0.00284 3 0.00005 -0.00041 -0.00165 4 0.00004 -0.00034 -0.00138 5 0.00003 -0.00026 -0.00103 6 0.00005 -0.00042 -0.00169 7 0.00005 -0.00048 -0.00195 8 0.00005 -0.00048 -0.00193 9 0.00004 -0.0004 -0.0016 10 0.00004 -0.00039 -0.00156 11 0.00004 -0.00032 -0.00128 12 0.00005 -0.00044 -0.00177

Table A.32: Persistent Impulse Response to Promotional Shocks: all powder dishwasher soap

97 Brand Persistent Impulse Response to shocks in promotions by Cascade Lag Sunlight all Cascade 0 -0.00262 0.00149 0.01461 1 -0.00049 0.00028 0.00273 2 -0.00027 0.00015 0.00151 3 -0.00026 0.00015 0.00147 4 -0.00054 0.00031 0.003 5 -0.00013 0.00008 0.00075 6 -0.00037 0.00021 0.00203 7 -0.0006 0.00034 0.00336 8 -0.00025 0.00014 0.00138 9 -0.00024 0.00014 0.00136 10 -0.00041 0.00023 0.00226 11 -0.00044 0.00025 0.00244 12 -0.00054 0.00031 0.00301 Cascade Lag Dominick’s HH Electrasol 0 -0.00003 -0.0018 0.00301 1 -0.00001 -0.00034 0.00056 2 0 -0.00019 0.00031 3 0 -0.00018 0.0003 4 -0.00001 -0.00037 0.00062 5 0 -0.00009 0.00015 6 0 -0.00025 0.00042 7 -0.00001 -0.00041 0.00069 8 0 -0.00017 0.00029 9 0 -0.00017 0.00028 10 -0.00001 -0.00028 0.00047 11 -0.00001 -0.0003 0.0005 12 -0.00001 -0.00037 0.00062

Table A.33: Persistent Impulse Response to Promotional Shocks: Cascade powder dishwasher soap

98 Brand Persistent Impulse Response to shocks in promotions by Dominick’s Lag Sunlight all Cascade 0 0.00092 0.00069 -0.00472 1 0.00035 0.00026 -0.0018 2 0.00023 0.00017 -0.00118 3 0.0002 0.00015 -0.00102 4 0.00029 0.00022 -0.00148 5 0.00014 0.00011 -0.00074 6 0.00019 0.00014 -0.00098 7 0.00022 0.00016 -0.00112 8 0.00032 0.00024 -0.00163 9 0.00031 0.00024 -0.00161 10 0.00026 0.0002 -0.00134 11 0.00025 0.00019 -0.00127 12 0.00026 0.00019 -0.00133 Dominick’s Lag Dominick’s HH Electrasol 0 0.01612 -0.00635 -0.00088 1 0.00614 -0.00242 -0.00034 2 0.00403 -0.00159 -0.00022 3 0.00348 -0.00137 -0.00019 4 0.00504 -0.00198 -0.00028 5 0.00252 -0.00099 -0.00014 6 0.00335 -0.00132 -0.00018 7 0.00381 -0.0015 -0.00021 8 0.00557 -0.00219 -0.00031 9 0.0055 -0.00217 -0.0003 10 0.00458 -0.0018 -0.00025 11 0.00433 -0.00171 -0.00024 12 0.00455 -0.00179 -0.00025

Table A.34: Persistent Impulse Response to Promotional Shocks: Dominick’s powder dishwasher soap

99 Brand Persistent Impulse Response to shocks in promotions by Snuggle Lag Snuggle Downy Bounce HH Cling 0 0.01902 0.00738 -0.00755 0.00558 0.00391 1 0.00716 0.00278 -0.00284 0.0021 0.00147 2 0.003 0.00117 -0.00119 0.00088 0.00062 3 0.00418 0.00162 -0.00166 0.00123 0.00086 4 0.00441 0.00171 -0.00175 0.00129 0.00091 5 0.00093 0.00036 -0.00037 0.00027 0.00019 6 0.0011 0.00043 -0.00044 0.00032 0.00023 7 0.00398 0.00155 -0.00158 0.00117 0.00082 8 0.00251 0.00097 -0.001 0.00074 0.00052 9 0.00335 0.0013 -0.00133 0.00098 0.00069 10 0.00176 0.00068 -0.0007 0.00052 0.00036 11 0.00526 0.00204 -0.00209 0.00154 0.00108 12 0.00529 0.00205 -0.0021 0.00155 0.00109

Table A.35: Persistent Impulse Response to Promotional Shocks: Snuggle fabric softener sheets

Brand Persistent Impulse Response to shocks in promotions by Downy Lag Snuggle Downy Bounce HH Cling 0 -0.00086 0.03207 -0.00641 -0.00753 -0.01509 1 -0.00021 0.00771 -0.00154 -0.00181 -0.00363 2 -0.00004 0.00163 -0.00033 -0.00038 -0.00077 3 -0.00004 0.00135 -0.00027 -0.00032 -0.00063 4 -0.00013 0.00477 -0.00095 -0.00112 -0.00224 5 -0.0001 0.00373 -0.00075 -0.00088 -0.00176 6 -0.00009 0.0035 -0.0007 -0.00082 -0.00165 7 -0.0001 0.00386 -0.00077 -0.00091 -0.00182 8 -0.00014 0.00532 -0.00106 -0.00125 -0.0025 9 -0.00015 0.00554 -0.00111 -0.0013 -0.00261 10 -0.00016 0.00599 -0.0012 -0.00141 -0.00282 11 -0.00019 0.00688 -0.00138 -0.00161 -0.00324 12 -0.00016 0.00586 -0.00117 -0.00138 -0.00276

Table A.36: Persistent Impulse Response to Promotional Shocks: Downy fabric softener sheets

100 Brand Persistent Impulse Response to shocks in promotions by Bounce Lag Snuggle Downy Bounce HH Cling 0 -0.00703 -0.00901 0.01264 -0.0022 -0.00789 1 -0.00165 -0.00211 0.00296 -0.00051 -0.00185 2 -0.00039 -0.0005 0.0007 -0.00012 -0.00044 3 -0.00059 -0.00076 0.00106 -0.00018 -0.00066 4 -0.00142 -0.00181 0.00254 -0.00044 -0.00159 5 -0.00087 -0.00112 0.00156 -0.00027 -0.00098 6 -0.00075 -0.00097 0.00135 -0.00024 -0.00085 7 -0.0016 -0.00205 0.00287 -0.0005 -0.0018 8 -0.00097 -0.00124 0.00174 -0.0003 -0.00109 9 -0.00153 -0.00196 0.00275 -0.00048 -0.00172 10 -0.00116 -0.00149 0.00209 -0.00036 -0.0013 11 -0.00051 -0.00066 0.00092 -0.00016 -0.00058 12 -0.00195 -0.0025 0.00351 -0.00061 -0.00219

Table A.37: Persistent Impulse Response to Promotional Shocks: Bounce fabric softener sheets

Brand Persistent Impulse Response to shocks in promotions by Downy Lag Downy HH 0 0.01398 -0.00387 1 0.00249 -0.00069 2 -0.0003 0.00008 3 0.00023 -0.00006 4 0.001 -0.00028 5 0.00047 -0.00013 6 0.00151 -0.00042 7 0.00268 -0.00074 8 0.00226 -0.00063 9 0.00235 -0.00065 10 0.00181 -0.0005 11 0.00256 -0.00071 12 0.00235 -0.00065

Table A.38: Persistent Impulse Response to Promotional Shocks: Downy liquid fabric softener

101 Brand Persistent Impulse Response to shocks in promotions by Surf Lag Surf all Purex Cheer 0 0.0382 -0.00493 0.00243 0.00003 1 0.024 -0.0031 0.00152 0.00002 2 0.02373 -0.00306 0.00151 0.00002 3 0.02093 -0.0027 0.00133 0.00001 4 0.02285 -0.00295 0.00145 0.00002 5 0.02295 -0.00296 0.00146 0.00002 6 0.02301 -0.00297 0.00146 0.00002 7 0.02279 -0.00294 0.00145 0.00002 8 0.0228 -0.00295 0.00145 0.00002 9 0.02282 -0.00295 0.00145 0.00002 10 0.02284 -0.00295 0.00145 0.00002 11 0.02283 -0.00295 0.00145 0.00002 12 0.02283 -0.00295 0.00145 0.00002 Surf Lag Tide Era Dominick’s YES 0 -0.01343 0.00726 0.00301 0.00351 1 -0.00844 0.00456 0.00189 0.00221 2 -0.00834 0.00451 0.00187 0.00218 3 -0.00736 0.00398 0.00165 0.00192 4 -0.00803 0.00434 0.0018 0.0021 5 -0.00807 0.00436 0.00181 0.00211 6 -0.00809 0.00437 0.00181 0.00211 7 -0.00801 0.00433 0.00179 0.00209 8 -0.00801 0.00433 0.00179 0.0021 9 -0.00802 0.00434 0.0018 0.0021 10 -0.00803 0.00434 0.0018 0.0021 11 -0.00803 0.00434 0.0018 0.0021 12 -0.00802 0.00434 0.0018 0.0021

Table A.39: Persistent Impulse Response to Promotional Shocks: Surf liquid laundry detergent

102 Brand Persistent Impulse Response to shocks in promotions by all Lag Surf all Purex Cheer 0 0.00126 0.03728 0.00179 0.00528 1 0.00038 0.01126 0.00054 0.0016 2 0.00035 0.01032 0.00049 0.00146 3 0.00029 0.00872 0.00042 0.00124 4 0.00072 0.0212 0.00102 0.003 5 0.00046 0.0137 0.00066 0.00194 6 0.00045 0.01321 0.00063 0.00187 7 0.00041 0.01218 0.00058 0.00172 8 0.00055 0.01629 0.00078 0.00231 9 0.00048 0.01415 0.00068 0.002 10 0.00047 0.01397 0.00067 0.00198 11 0.00046 0.01347 0.00065 0.00191 12 0.0005 0.01481 0.00071 0.0021 all Lag Tide Era Dominick’s YES 0 -0.0064 -0.00552 0.01332 -0.00451 1 -0.00193 -0.00167 0.00402 -0.00136 2 -0.00177 -0.00153 0.00369 -0.00125 3 -0.0015 -0.00129 0.00312 -0.00105 4 -0.00364 -0.00314 0.00758 -0.00256 5 -0.00235 -0.00203 0.0049 -0.00166 6 -0.00227 -0.00195 0.00472 -0.0016 7 -0.00209 -0.0018 0.00435 -0.00147 8 -0.00279 -0.00241 0.00582 -0.00197 9 -0.00243 -0.00209 0.00506 -0.00171 10 -0.0024 -0.00207 0.00499 -0.00169 11 -0.00231 -0.00199 0.00481 -0.00163 12 -0.00254 -0.00219 0.00529 -0.00179

Table A.40: Persistent Impulse Response to Promotional Shocks: all liquid laundry detergent

103 Brand Persistent Impulse Response to shocks in promotions by Purex Lag Surf all Purex Cheer 0 -0.01142 -0.02309 0.02754 -0.00916 1 -0.00688 -0.01391 0.01659 -0.00552 2 -0.00537 -0.01085 0.01295 -0.00431 3 -0.00841 -0.01701 0.02029 -0.00675 4 -0.00651 -0.01316 0.0157 -0.00522 5 -0.00651 -0.01315 0.01569 -0.00522 6 -0.00745 -0.01506 0.01797 -0.00597 7 -0.00671 -0.01356 0.01617 -0.00538 8 -0.00684 -0.01383 0.0165 -0.00549 9 -0.0071 -0.01435 0.01711 -0.00569 10 -0.00683 -0.0138 0.01646 -0.00547 11 -0.00692 -0.01399 0.01668 -0.00555 12 -0.00698 -0.01411 0.01683 -0.0056 Purex Lag Tide Era Dominick’s YES 0 -0.00126 -0.0035 -0.01374 0.00088 1 -0.00076 -0.00211 -0.00828 0.00053 2 -0.00059 -0.00164 -0.00646 0.00041 3 -0.00093 -0.00258 -0.01012 0.00065 4 -0.00072 -0.00199 -0.00784 0.0005 5 -0.00072 -0.00199 -0.00783 0.0005 6 -0.00082 -0.00228 -0.00897 0.00057 7 -0.00074 -0.00205 -0.00807 0.00052 8 -0.00076 -0.00209 -0.00823 0.00053 9 -0.00078 -0.00217 -0.00854 0.00055 10 -0.00075 -0.00209 -0.00822 0.00053 11 -0.00076 -0.00212 -0.00832 0.00053 12 -0.00077 -0.00214 -0.0084 0.00054

Table A.41: Persistent Impulse Response to Promotional Shocks: Purex liquid laundry detergent

104 Brand Persistent Impulse Response to shocks in promotions by Cheer Lag Surf all Purex Cheer 0 -0.00155 -0.01366 -0.00145 0.03363 1 -0.00033 -0.00293 -0.00031 0.00721 2 -0.00029 -0.00253 -0.00027 0.00622 3 -0.00033 -0.00289 -0.00031 0.00711 4 -0.00043 -0.00379 -0.00040 0.00934 5 -0.00037 -0.00329 -0.00035 0.00809 6 -0.00030 -0.00264 -0.00028 0.00649 7 -0.00030 -0.00260 -0.00028 0.00641 8 -0.00028 -0.00247 -0.00026 0.00608 9 -0.00039 -0.00342 -0.00036 0.00842 10 -0.00047 -0.00411 -0.00044 0.01013 11 -0.00036 -0.00313 -0.00033 0.00770 12 -0.00035 -0.00305 -0.00032 0.00751 Cheer Lag Tide Era Dominick’s YES 0 -0.00089 -0.01632 -0.01448 0.00077 1 -0.00019 -0.00350 -0.00311 0.00016 2 -0.00016 -0.00302 -0.00268 0.00014 3 -0.00019 -0.00345 -0.00306 0.00016 4 -0.00025 -0.00453 -0.00402 0.00021 5 -0.00021 -0.00393 -0.00349 0.00018 6 -0.00017 -0.00315 -0.00279 0.00015 7 -0.00017 -0.00311 -0.00276 0.00015 8 -0.00016 -0.00295 -0.00262 0.00014 9 -0.00022 -0.00409 -0.00362 0.00019 10 -0.00027 -0.00492 -0.00436 0.00023 11 -0.00020 -0.00374 -0.00331 0.00018 12 -0.00020 -0.00365 -0.00324 0.00017

Table A.42: Persistent Impulse Response to Promotional Shocks: Cheer liquid laundry detergent

105 Brand Persistent Impulse Response to shocks in promotions by Era Lag Surf all Purex Cheer 0 -0.00028 0.0045 0.00137 -0.00768 1 -0.00009 0.00148 0.00045 -0.00252 2 -0.00001 0.00016 0.00005 -0.00027 3 -0.00004 0.00069 0.00021 -0.00118 4 -0.00003 0.00046 0.00014 -0.00078 5 -0.00002 0.0004 0.00012 -0.00068 6 -0.00004 0.00066 0.0002 -0.00113 7 -0.00006 0.00101 0.00031 -0.00172 8 -0.00002 0.00031 0.00009 -0.00053 9 -0.00007 0.00108 0.00033 -0.00184 10 -0.00004 0.00069 0.00021 -0.00118 11 -0.00008 0.0013 0.00039 -0.00221 12 -0.00007 0.0011 0.00033 -0.00187 Era Lag Tide Era Dominick’s YES 0 -0.0019 0.03284 -0.00309 -0.00698 1 -0.00063 0.0108 -0.00102 -0.0023 2 -0.00007 0.00115 -0.00011 -0.00025 3 -0.00029 0.00506 -0.00048 -0.00108 4 -0.00019 0.00333 -0.00031 -0.00071 5 -0.00017 0.00292 -0.00027 -0.00062 6 -0.00028 0.00482 -0.00045 -0.00103 7 -0.00043 0.00736 -0.00069 -0.00157 8 -0.00013 0.00226 -0.00021 -0.00048 9 -0.00046 0.00786 -0.00074 -0.00167 10 -0.00029 0.00506 -0.00048 -0.00108 11 -0.00055 0.00946 -0.00089 -0.00201 12 -0.00046 0.00799 -0.00075 -0.0017

Table A.43: Persistent Impulse Response to Promotional Shocks: Era liquid laundry detergent

106 Brand Persistent Impulse Response to shocks in promotions by YES Lag Surf all Purex Cheer 0 -0.00011 -0.00213 0.00149 0.00709 1 -0.00003 -0.00048 0.00033 0.00159 2 -0.00003 -0.00048 0.00033 0.00159 3 -0.00003 -0.00054 0.00038 0.00180 4 -0.00003 -0.00057 0.00039 0.00189 5 -0.00003 -0.00062 0.00043 0.00207 6 -0.00004 -0.00069 0.00048 0.00229 7 -0.00004 -0.00069 0.00048 0.00230 8 -0.00003 -0.00061 0.00042 0.00201 9 -0.00003 -0.00062 0.00043 0.00204 10 -0.00003 -0.00063 0.00044 0.00208 11 -0.00003 -0.00063 0.00044 0.00210 12 -0.00003 -0.00064 0.00044 0.00212 YES Lag Tide Era Dominicks YES 0 -0.00277 0.00484 -0.01661 0.04213 1 -0.00062 0.00109 -0.00373 0.00945 2 -0.00062 0.00108 -0.00372 0.00944 3 -0.00070 0.00123 -0.00421 0.01068 4 -0.00074 0.00129 -0.00442 0.01120 5 -0.00081 0.00141 -0.00485 0.01230 6 -0.00089 0.00156 -0.00535 0.01358 7 -0.00090 0.00157 -0.00538 0.01364 8 -0.00079 0.00137 -0.00471 0.01196 9 -0.00080 0.00139 -0.00479 0.01215 10 -0.00081 0.00142 -0.00487 0.01236 11 -0.00082 0.00143 -0.00492 0.01249 12 -0.00083 0.00145 -0.00496 0.01259

Table A.44: Persistent Impulse Response to Promotional Shocks: YES liquid laundry detergent

107 Brand Persistent Impulse Response to shocks in promotions by Surf Lag Surf all Dutch HD Arm & Hammer Cheer 0 0.02759 0.00219 -0.00557 -0.00466 -0.00469 1 0.00923 0.00073 -0.00186 -0.00156 -0.00157 2 0.00538 0.00043 -0.00109 -0.00091 -0.00091 3 0.00541 0.00043 -0.00109 -0.00091 -0.00092 4 0.00609 0.00048 -0.00123 -0.00103 -0.00104 5 0.00556 0.00044 -0.00112 -0.00094 -0.00095 6 0.00546 0.00043 -0.00110 -0.00092 -0.00093 7 0.00417 0.00033 -0.00084 -0.00071 -0.00071 8 0.01084 0.00086 -0.00219 -0.00183 -0.00184 9 0.00874 0.00070 -0.00176 -0.00148 -0.00149 10 0.00666 0.00053 -0.00134 -0.00113 -0.00113 11 0.00649 0.00052 -0.00131 -0.00110 -0.00110 12 0.00690 0.00055 -0.00139 -0.00117 -0.00117 Surf Lag Tide Bold Ivory Snow Oxydol Dominicks 0 -0.00878 -0.01327 -0.01224 -0.00468 0.00028 1 -0.00294 -0.00444 -0.00410 -0.00157 0.00009 2 -0.00171 -0.00259 -0.00239 -0.00091 0.00005 3 -0.00172 -0.00260 -0.00240 -0.00092 0.00005 4 -0.00194 -0.00293 -0.00270 -0.00103 0.00006 5 -0.00177 -0.00268 -0.00247 -0.00094 0.00006 6 -0.00174 -0.00263 -0.00242 -0.00093 0.00005 7 -0.00133 -0.00201 -0.00185 -0.00071 0.00004 8 -0.00345 -0.00521 -0.00481 -0.00184 0.00011 9 -0.00278 -0.00420 -0.00388 -0.00148 0.00009 10 -0.00212 -0.00320 -0.00295 -0.00113 0.00007 11 -0.00206 -0.00312 -0.00288 -0.00110 0.00007 12 -0.00219 -0.00332 -0.00306 -0.00117 0.00007

Table A.45: Persistent Impulse Response to Promotional Shocks: Surf powder laundry detergent

108 Brand Persistent Impulse Response to shocks in promotions by all Lag Surf all Dutch HD Arm & Hammer Cheer 0 -0.00317 0.02710 0.00199 -0.00053 -0.00097 1 -0.00077 0.00655 0.00048 -0.00013 -0.00023 2 -0.00071 0.00607 0.00044 -0.00012 -0.00022 3 -0.00030 0.00260 0.00019 -0.00005 -0.00009 4 -0.00061 0.00521 0.00038 -0.00010 -0.00019 5 -0.00044 0.00378 0.00028 -0.00007 -0.00014 6 -0.00043 0.00366 0.00027 -0.00007 -0.00013 7 -0.00063 0.00538 0.00039 -0.00011 -0.00019 8 -0.00064 0.00549 0.00040 -0.00011 -0.00020 9 -0.00078 0.00667 0.00049 -0.00013 -0.00024 10 -0.00090 0.00767 0.00056 -0.00015 -0.00027 11 -0.00070 0.00601 0.00044 -0.00012 -0.00021 12 -0.00064 0.00551 0.00040 -0.00011 -0.00020 all Lag Tide Bold Ivory Snow Oxydol Dominick’s 0 -0.00086 -0.01086 -0.01885 -0.01589 -0.00614 1 -0.00021 -0.00262 -0.00455 -0.00384 -0.00148 2 -0.00019 -0.00243 -0.00422 -0.00356 -0.00137 3 -0.00008 -0.00104 -0.00181 -0.00153 -0.00059 4 -0.00017 -0.00209 -0.00362 -0.00305 -0.00118 5 -0.00012 -0.00152 -0.00263 -0.00222 -0.00086 6 -0.00012 -0.00147 -0.00255 -0.00215 -0.00083 7 -0.00017 -0.00216 -0.00374 -0.00316 -0.00122 8 -0.00018 -0.00220 -0.00382 -0.00322 -0.00124 9 -0.00021 -0.00267 -0.00464 -0.00391 -0.00151 10 -0.00024 -0.00307 -0.00533 -0.00450 -0.00174 11 -0.00019 -0.00241 -0.00418 -0.00352 -0.00136 12 -0.00018 -0.00221 -0.00383 -0.00323 -0.00125

Table A.46: Persistent Impulse Response to Promotional Shocks: all powder laundry detergent

109 Brand Persistent Impulse Response to shocks in promotions by Arm & Hammer Lag Surf all Dutch HD Arm & Hammer Cheer 0 -0.00495 -0.00233 0.00223 0.01008 0.00100 1 -0.00118 -0.00055 0.00053 0.00240 0.00024 2 -0.00131 -0.00062 0.00059 0.00268 0.00026 3 -0.00108 -0.00051 0.00049 0.00219 0.00022 4 -0.00142 -0.00067 0.00064 0.00290 0.00029 5 -0.00089 -0.00042 0.00040 0.00182 0.00018 6 -0.00140 -0.00066 0.00063 0.00286 0.00028 7 -0.00184 -0.00087 0.00083 0.00375 0.00037 8 -0.00141 -0.00066 0.00063 0.00287 0.00028 9 -0.00136 -0.00064 0.00061 0.00278 0.00027 10 -0.00139 -0.00065 0.00062 0.00282 0.00028 11 -0.00140 -0.00066 0.00063 0.00286 0.00028 12 -0.00131 -0.00062 0.00059 0.00266 0.00026 Arm & Hammer Lag Tide Bold Ivory Snow Oxydol Dominick’s 0 0.00060 0.00363 0.02763 -0.00196 0.00036 1 0.00014 0.00086 0.00656 -0.00047 0.00009 2 0.00016 0.00096 0.00733 -0.00052 0.00010 3 0.00013 0.00079 0.00601 -0.00043 0.00008 4 0.00017 0.00104 0.00794 -0.00056 0.00010 5 0.00011 0.00065 0.00498 -0.00035 0.00006 6 0.00017 0.00103 0.00782 -0.00056 0.00010 7 0.00022 0.00135 0.01028 -0.00073 0.00013 8 0.00017 0.00103 0.00785 -0.00056 0.00010 9 0.00016 0.00100 0.00761 -0.00054 0.00010 10 0.00017 0.00101 0.00773 -0.00055 0.00010 11 0.00017 0.00103 0.00783 -0.00056 0.00010 12 0.00016 0.00096 0.00729 -0.00052 0.00009

Table A.47: Persistent Impulse Response to Promotional Shocks: Arm & Hammer powder laundry detergent

110 Brand Persistent Impulse Response to shocks in promotions by Tide Lag Surf all Dutch HD Arm & Hammer Cheer 0 -0.00083 -0.00864 0.00005 -0.00050 0.00829 1 -0.00011 -0.00109 0.00001 -0.00006 0.00105 2 0.00002 0.00018 -0.00000 0.00001 -0.00017 3 -0.00012 -0.00127 0.00001 -0.00007 0.00122 4 -0.00013 -0.00138 0.00001 -0.00008 0.00133 5 -0.00011 -0.00114 0.00001 -0.00007 0.00109 6 -0.00012 -0.00124 0.00001 -0.00007 0.00119 7 -0.00013 -0.00138 0.00001 -0.00008 0.00132 8 -0.00008 -0.00081 0.00000 -0.00005 0.00077 9 -0.00018 -0.00189 0.00001 -0.00011 0.00181 10 -0.00019 -0.00192 0.00001 -0.00011 0.00184 11 -0.00016 -0.00167 0.00001 -0.00010 0.00160 12 -0.00011 -0.00118 0.00001 -0.00007 0.00113 Tide Lag Tide Bold Ivory Snow Oxydol Dominick’s 0 0.01162 0.02114 -0.02149 0.00554 -0.00100 1 0.00147 0.00267 -0.00272 0.00070 -0.00013 2 -0.00024 -0.00044 0.00045 -0.00012 0.00002 3 0.00171 0.00311 -0.00316 0.00081 -0.00015 4 0.00186 0.00338 -0.00344 0.00089 -0.00016 5 0.00153 0.00278 -0.00283 0.00073 -0.00013 6 0.00167 0.00305 -0.00310 0.00080 -0.00014 7 0.00185 0.00337 -0.00342 0.00088 -0.00016 8 0.00109 0.00197 -0.00201 0.00052 -0.00009 9 0.00254 0.00463 -0.00470 0.00121 -0.00022 10 0.00259 0.00471 -0.00478 0.00123 -0.00022 11 0.00224 0.00408 -0.00415 0.00107 -0.00019 12 0.00159 0.00289 -0.00294 0.00076 -0.00014

Table A.48: Persistent Impulse Response to Promotional Shocks: Tide powder laundry detergent

111 Brand Persistent Impulse Response to shocks in promotions by Oxydol Lag Surf all Dutch HD Arm & Hammer Cheer 0 0.00513 0.00189 0.00103 0.00056 -0.00267 1 0.00438 0.00161 0.00088 0.00048 -0.00228 2 0.00279 0.00103 0.00056 0.00030 -0.00145 3 0.00187 0.00069 0.00037 0.00020 -0.00097 4 0.00272 0.00100 0.00054 0.00030 -0.00141 5 0.00270 0.00099 0.00054 0.00029 -0.00140 6 0.00282 0.00104 0.00056 0.00031 -0.00147 7 0.00178 0.00065 0.00036 0.00019 -0.00092 8 0.00177 0.00065 0.00035 0.00019 -0.00092 9 0.00211 0.00078 0.00042 0.00023 -0.00110 10 0.00200 0.00074 0.00040 0.00022 -0.00104 11 0.00159 0.00059 0.00032 0.00017 -0.00083 12 0.00168 0.00062 0.00034 0.00018 -0.00088 Oxydol Lag Tide Bold Ivory Snow Oxydol Dominick’s 0 -0.00540 -0.00783 0.01044 0.01880 0.00217 1 -0.00461 -0.00669 0.00892 0.01605 0.00186 2 -0.00293 -0.00425 0.00567 0.01021 0.00118 3 -0.00196 -0.00285 0.00379 0.00683 0.00079 4 -0.00286 -0.00414 0.00552 0.00995 0.00115 5 -0.00284 -0.00412 0.00549 0.00989 0.00114 6 -0.00296 -0.00430 0.00573 0.01032 0.00119 7 -0.00187 -0.00271 0.00361 0.00650 0.00075 8 -0.00186 -0.00270 0.00360 0.00648 0.00075 9 -0.00222 -0.00323 0.00430 0.00774 0.00089 10 -0.00211 -0.00305 0.00407 0.00733 0.00085 11 -0.00168 -0.00243 0.00324 0.00583 0.00067 12 -0.00177 -0.00257 0.00343 0.00617 0.00071

Table A.49: Persistent Impulse Response to Promotional Shocks: Oxydol powder laundry detergent

112 Brand Persistent Impulse Response to shocks in promotions by Dominick’s Lag Surf all Dutch HD Arm & Hammer Cheer 0 -0.00076 -0.00574 0.00337 -0.00143 0.00311 1 -0.00026 -0.00196 0.00115 -0.00049 0.00106 2 -0.00017 -0.00127 0.00074 -0.00032 0.00068 3 -0.00016 -0.00120 0.00070 -0.00030 0.00065 4 -0.00025 -0.00191 0.00112 -0.00048 0.00103 5 -0.00025 -0.00185 0.00108 -0.00046 0.00100 6 -0.00021 -0.00157 0.00092 -0.00039 0.00085 7 -0.00028 -0.00210 0.00123 -0.00052 0.00114 8 -0.00024 -0.00183 0.00107 -0.00046 0.00099 9 -0.00023 -0.00175 0.00103 -0.00044 0.00095 10 -0.00023 -0.00170 0.00100 -0.00042 0.00092 11 -0.00024 -0.00182 0.00107 -0.00045 0.00099 12 -0.00024 -0.00183 0.00107 -0.00045 0.00099 Dominick’s Lag Tide Bold Ivory Snow Oxydol Dominick’s 0 -0.00499 0.00120 0.02133 -0.00148 0.02271 1 -0.00170 0.00041 0.00729 -0.00051 0.00776 2 -0.00110 0.00027 0.00470 -0.00033 0.00500 3 -0.00104 0.00025 0.00444 -0.00031 0.00473 4 -0.00166 0.00040 0.00710 -0.00049 0.00756 5 -0.00160 0.00039 0.00687 -0.00048 0.00731 6 -0.00136 0.00033 0.00583 -0.00041 0.00621 7 -0.00182 0.00044 0.00780 -0.00054 0.00830 8 -0.00159 0.00038 0.00680 -0.00047 0.00723 9 -0.00152 0.00037 0.00650 -0.00045 0.00691 10 -0.00148 0.00036 0.00631 -0.00044 0.00672 11 -0.00158 0.00038 0.00677 -0.00047 0.00721 12 -0.00158 0.00038 0.00678 -0.00047 0.00722

Table A.50: Persistent Impulse Response to Promotional Shocks: Dominick’s powder laundry detergent

113 Brand Persistent Impulse Response to shocks in promotions by Close Up Lag Pepsodent Close Up Pearl Arm & Hammer Colgate 0 0.00469 0.0605 -0.0181 0.00871 -0.00644 1 0.00179 0.02308 -0.0069 0.00332 -0.00246 2 0.00051 0.00654 -0.00196 0.00094 -0.0007 3 0.00075 0.00974 -0.00291 0.0014 -0.00104 4 0.00055 0.0071 -0.00212 0.00102 -0.00076 5 0.0011 0.01422 -0.00425 0.00205 -0.00151 6 0.0009 0.01157 -0.00346 0.00167 -0.00123 7 0.00097 0.01253 -0.00375 0.0018 -0.00133 8 0.00095 0.01223 -0.00366 0.00176 -0.0013 9 0.00107 0.01384 -0.00414 0.00199 -0.00147 10 0.00145 0.01866 -0.00558 0.00269 -0.00199 11 0.00113 0.01458 -0.00436 0.0021 -0.00155 12 0.00096 0.01239 -0.00371 0.00178 -0.00132 Close Up Lag Brite Crest Gleem Dominick’s Aqua Fresh 0 -0.00567 0.00808 -0.00004 0.00219 -0.00333 1 -0.00216 0.00308 -0.00002 0.00083 -0.00127 2 -0.00061 0.00087 0 0.00024 -0.00036 3 -0.00091 0.0013 -0.00001 0.00035 -0.00054 4 -0.00067 0.00095 -0.00001 0.00026 -0.00039 5 -0.00133 0.0019 -0.00001 0.00051 -0.00078 6 -0.00108 0.00155 -0.00001 0.00042 -0.00064 7 -0.00117 0.00167 -0.00001 0.00045 -0.00069 8 -0.00115 0.00163 -0.00001 0.00044 -0.00067 9 -0.0013 0.00185 -0.00001 0.0005 -0.00076 10 -0.00175 0.00249 -0.00001 0.00067 -0.00103 11 -0.00137 0.00195 -0.00001 0.00053 -0.0008 12 -0.00116 0.00166 -0.00001 0.00045 -0.00068

Table A.51: Persistent Impulse Response to Promotional Shocks: Close Up toothpaste

114 Brand Persistent Impulse Response to shocks in promotions by Arm & Hammer Lag Pepsodent Close Up Pearl Arm & Hammer Colgate 0 0.00313 0.00126 0.03649 0.02633 -0.00615 1 0.0006 0.00024 0.00698 0.00503 -0.00118 2 0.00007 0.00003 0.00079 0.00057 -0.00013 3 0.00033 0.00013 0.00388 0.0028 -0.00065 4 0.00051 0.00021 0.00599 0.00432 -0.00101 5 0.00018 0.00007 0.00205 0.00148 -0.00035 6 0.00028 0.00011 0.00332 0.0024 -0.00056 7 0.00051 0.0002 0.00593 0.00428 -0.001 8 0.00034 0.00014 0.00393 0.00284 -0.00066 9 0.00029 0.00012 0.00334 0.00241 -0.00056 10 0.00041 0.00017 0.0048 0.00346 -0.00081 11 0.0004 0.00016 0.0047 0.00339 -0.00079 12 0.00055 0.00022 0.00641 0.00462 -0.00108 Arm & Hammer Lag Brite Crest Gleem Dominick’s Aqua Fresh 0 0.00409 0.00193 0.0004 -0.01075 -0.0025 1 0.00078 0.00037 0.00008 -0.00206 -0.00048 2 0.00009 0.00004 0.00001 -0.00023 -0.00005 3 0.00043 0.00021 0.00004 -0.00114 -0.00027 4 0.00067 0.00032 0.00007 -0.00177 -0.00041 5 0.00023 0.00011 0.00002 -0.0006 -0.00014 6 0.00037 0.00018 0.00004 -0.00098 -0.00023 7 0.00066 0.00031 0.00006 -0.00175 -0.00041 8 0.00044 0.00021 0.00004 -0.00116 -0.00027 9 0.00037 0.00018 0.00004 -0.00098 -0.00023 10 0.00054 0.00025 0.00005 -0.00141 -0.00033 11 0.00053 0.00025 0.00005 -0.00139 -0.00032 12 0.00072 0.00034 0.00007 -0.00189 -0.00044

Table A.52: Persistent Impulse Response to Promotional Shocks: Arm & Hammer toothpaste

115 Brand Persistent Impulse Response to shocks in promotions by Colgate Lag Pepsodent Close Up Pearl Arm & Hammer Colgate 0 -0.01055 -0.01281 -0.02572 -0.00437 0.00808 1 -0.00059 -0.00071 -0.00143 -0.00024 0.00045 2 -0.00135 -0.00163 -0.00328 -0.00056 0.00103 3 -0.00028 -0.00033 -0.00067 -0.00011 0.00021 4 -0.00205 -0.00249 -0.00500 -0.00085 0.00157 5 -0.00110 -0.00133 -0.00268 -0.00046 0.00084 6 -0.00127 -0.00154 -0.00309 -0.00052 0.00097 7 -0.00060 -0.00073 -0.00146 -0.00025 0.00046 8 -0.00237 -0.00288 -0.00579 -0.00098 0.00182 9 -0.00114 -0.00138 -0.00278 -0.00047 0.00087 10 -0.00194 -0.00235 -0.00472 -0.00080 0.00148 11 -0.00272 -0.00330 -0.00663 -0.00113 0.00208 12 -0.00153 -0.00186 -0.00374 -0.00064 0.00118 Colgate Lag Brite Crest Gleem Dominick’s Aqua Fresh 0 0.01008 -0.00660 0.00760 -0.01826 -0.00348 1 0.00056 -0.00037 0.00042 -0.00102 -0.00019 2 0.00129 -0.00084 0.00097 -0.00233 -0.00044 3 0.00026 -0.00017 0.00020 -0.00048 -0.00009 4 0.00196 -0.00128 0.00148 -0.00355 -0.00068 5 0.00105 -0.00069 0.00079 -0.00190 -0.00036 6 0.00121 -0.00079 0.00091 -0.00219 -0.00042 7 0.00057 -0.00038 0.00043 -0.00104 -0.00020 8 0.00227 -0.00149 0.00171 -0.00411 -0.00078 9 0.00109 -0.00071 0.00082 -0.00197 -0.00038 10 0.00185 -0.00121 0.00140 -0.00335 -0.00064 11 0.00260 -0.00170 0.00196 -0.00470 -0.00090 12 0.00147 -0.00096 0.00111 -0.00266 -0.00051

Table A.53: Persistent Impulse Response to Promotional Shocks: Colgate toothpaste

116 Brand Persistent Impulse Response to shocks in promotions by Brite Lag Pepsodent Close Up Pearl Arm & Hammer Colgate 0 -0.00846 -0.00371 0.00763 -0.00422 -0.00517 1 -0.00115 -0.00051 0.00104 -0.00058 -0.00071 2 -0.00054 -0.00024 0.00049 -0.00027 -0.00033 3 -0.00092 -0.0004 0.00083 -0.00046 -0.00056 4 -0.00105 -0.00046 0.00094 -0.00052 -0.00064 5 -0.00097 -0.00042 0.00087 -0.00048 -0.00059 6 -0.00084 -0.00037 0.00076 -0.00042 -0.00051 7 -0.00068 -0.0003 0.00061 -0.00034 -0.00041 8 -0.00017 -0.00007 0.00015 -0.00008 -0.0001 9 -0.00125 -0.00055 0.00113 -0.00063 -0.00077 10 -0.00142 -0.00063 0.00128 -0.00071 -0.00087 11 -0.00198 -0.00087 0.00179 -0.00099 -0.00121 12 -0.00207 -0.00091 0.00187 -0.00104 -0.00127 Brite Lag Brite Crest Gleem Dominick’s Aqua Fresh 0 0.08258 -0.01177 0.00937 0.00252 -0.00349 1 0.01126 -0.0016 0.00128 0.00034 -0.00048 2 0.00529 -0.00075 0.0006 0.00016 -0.00022 3 0.00895 -0.00127 0.00102 0.00027 -0.00038 4 0.01023 -0.00146 0.00116 0.00031 -0.00043 5 0.00945 -0.00135 0.00107 0.00029 -0.0004 6 0.0082 -0.00117 0.00093 0.00025 -0.00035 7 0.00661 -0.00094 0.00075 0.0002 -0.00028 8 0.00166 -0.00024 0.00019 0.00005 -0.00007 9 0.01222 -0.00174 0.00139 0.00037 -0.00052 10 0.01391 -0.00198 0.00158 0.00042 -0.00059 11 0.01936 -0.00276 0.0022 0.00059 -0.00082 12 0.02026 -0.00289 0.0023 0.00062 -0.00086

Table A.54: Persistent Impulse Response to Promotional Shocks: Brite toothpaste

117 APPENDIX B

FIGURES FOR ’DO RETAIL PROMOTIONS HAVE PERSISTENT EFFECTS?’

118

Figure B.1: Time Series of Sales: Angel Soft toilet paper

Figure B.2: Time Series of Sales: Cottonelle toilet paper

119

Figure B.3: Time Series of Sales: Charmin toilet paper

Figure B.4: Time Series of Sales: Dominick’s toilet paper

120

Figure B.5: Time Series of Sales: Northern toilet paper

Figure B.6: Time Series of Sales: Scott toilet paper

121

Figure B.7: Time Series of Sales: Green Forest toilet paper

Figure B.8: Time Series of Sales: Hi Dri paper towels

122

Figure B.9: Time Series of Sales: Bounty paper towels

Figure B.10: Time Series of Sales: Dominick’s paper towels

123

Figure B.11: Time Series of Sales: Brawny paper towels

Figure B.12: Time Series of Sales: Viva paper towels

124

Figure B.13: Time Series of Sales: Scott paper towels

Figure B.14: Time Series of Sales: Mardi Gras liquid dish soap

125

Figure B.15: Time Series of Sales: Green Forest liquid dish soap

Figure B.16: Time Series of Sales: Dove liquid dish soap

126

Figure B.17: Time Series of Sales: Sunlight liquid dish soap

Figure B.18: Time Series of Sales: Ajax liquid dish soap

127

Figure B.19: Time Series of Sales: Palmolive liquid dish soap

Figure B.20: Time Series of Sales: Dawn liquid dish soap

128

Figure B.21: Time Series of Sales: Ivory liquid dish soap

Figure B.22: Time Series of Sales: Joy liquid dishwasher soap

129

Figure B.23: Time Series of Sales: HH liquid dishwasher soap

Figure B.24: Time Series of Sales: Sunlight liquid dishwasher soap

130

Figure B.25: Time Series of Sales: Palmolive liquid dishwasher soap

Figure B.26: Time Series of Sales: Cascade liquid dishwasher soap

131

Figure B.27: Time Series of Sales: Sunlight powder dishwasher soap

Figure B.28: Time Series of Sales: all powder dishwasher soap

132

Figure B.29: Time Series of Sales: Cascade powder dishwasher soap

Figure B.30: Time Series of Sales: Dominick’s powder dishwasher soap

133

Figure B.31: Time Series of Sales: HH powder dishwasher soap

Figure B.32: Time Series of Sales: Electrasol powder dishwasher soap

134

Figure B.33: Time Series of Sales: Snuggle fabric softener sheets

Figure B.34: Time Series of Sales: Downy fabric softener sheets

135

Figure B.35: Time Series of Sales: Bounce fabric softener sheets

Figure B.36: Time Series of Sales: HH fabric softener sheets

136

Figure B.37: Time Series of Sales: Cling fabric softener sheets

Figure B.38: Time Series of Sales: Downy liquid fabric softener

137

Figure B.39: Time Series of Sales: HH liquid fabric softener

Figure B.40: Time Series of Sales: Surf liquid laundry detergent

138

Figure B.41: Time Series of Sales: all liquid laundry detergent

Figure B.42: Time Series of Sales: Purex liquid laundry detergent

139

Figure B.43: Time Series of Sales: Cheer liquid laundry detergent

Figure B.44: Time Series of Sales: Tide liquid laundry detergent

140

Figure B.45: Time Series of Sales: Era liquid laundry detergent

Figure B.46: Time Series of Sales: Dominick’s liquid laundry detergent

141

Figure B.47: Time Series of Sales: YES liquid laundry detergent

Figure B.48: Time Series of Sales: Surf powder laundry detergent

142

Figure B.49: Time Series of Sales: all powder laundry detergent

Figure B.50: Time Series of Sales: Dutch HD powder laundry detergent

143

Figure B.51: Time Series of Sales: Arm & Hammer powder laundry detergent

Figure B.52: Time Series of Sales: Cheer powder laundry detergent

144

Figure B.53: Time Series of Sales: Tide powder laundry detergent

Figure B.54: Time Series of Sales: Bold powder laundry detergent

145

Figure B.55: Time Series of Sales: Ivory Snow powder laundry detergent

Figure B.56: Time Series of Sales: Oxydol powder laundry detergent

146

Figure B.57: Time Series of Sales: Dominick’s powder laundry detergent

Figure B.58: Time Series of Sales: Pepsodent toothpaste

147

Figure B.59: Time Series of Sales: Close Up toothpaste

Figure B.60: Time Series of Sales: Pearl toothpaste

148

Figure B.61: Time Series of Sales: Arm & Hammer toothpaste

Figure B.62: Time Series of Sales: Colgate toothpaste

149

Figure B.63: Time Series of Sales: Brite toothpaste

Figure B.64: Time Series of Sales: Crest toothpaste

150

Figure B.65: Time Series of Sales: Gleem toothpaste

Figure B.66: Time Series of Sales: Dominick’s toothpaste

151

Figure B.67: Time Series of Sales: Aqua Fresh toothpaste

152

APPENDIX C

TABLES FOR ’DO FIRMS REACT ACROSS MARKETS?’

Abbreviation Meaning ** 95% Significant 90% Significant I-M In Market O-M Out Market AS Angel Soft Klnx Kleenex PG Procter & Gamble Dom Dominick’s GP Georgia Pacific KC Kimberly-Clark GF Green Forest UniL Unilever CP Colgate-Palmolive RB Reckitt Benckiser AH Arm & Hammer MentD Mentadent CD Church & Dwight GSK GlaxoSmithKline

Table C.1: Key to symbols and abbreviations in tables

153 Variable Angel Kleenex Procter & Dominicks Soft Gamble Intercept 0.037 -0.02 -0.012 -0.036 (0.045 ) ( 0.02 ) ( 0.022 ) ( 0.03 ) O-M Klnx m1 -0.053 -0.016 0.064 ( 0.04 ) ( 0.04 ) ( 0.054 ) O-M Klnx m2 0.014 0.053 -0.006 ( 0.041 ) ( 0.04 ) ( 0.055 ) O-M Klnx m3 -0.019 0.193 ** 0.013 ( 0.038 ) ( 0.037 ) ( 0.051 ) O-M PG m1 0.084 0.007 -0.029 ( 0.064 ) ( 0.056 ) ( 0.076 ) O-M PG m2 0.123 ** -0.041 0.008 ( 0.061 ) ( 0.057 ) ( 0.076 ) O-M PG m3 -0.102 * -0.051 0.14 * ( 0.057 ) ( 0.057 ) ( 0.077 ) O-M Dom m1 -0.015 -0.009 -0.021 ( 0.033 ) ( 0.044 ) ( 0.06 ) O-M Dom m2 -0.102 ** -0.041 -0.152 ** ( 0.033 ) ( 0.041 ) ( 0.057 ) O-M Dom m3 -0.051 0.095 ** 0.018 ( 0.033 ) ( 0.04 ) ( 0.054 ) O-M GP m1 -0.066 -0.116 ** 0.054 ( 0.053 ) ( 0.05 ) ( 0.069 ) O-M GP m2 -0.043 0.08 0.019 ( 0.052 ) ( 0.05 ) ( 0.068 ) O-M GP m3 -0.057 0.099 ** 0.103 ( 0.048 ) ( 0.047 ) ( 0.064 ) O-M KC m1 0.157 ** 0.153 ** 0.094 ( 0.055 ) ( 0.054 ) ( 0.074 ) O-M KC m2 0.092 -0.033 0.199 ** ( 0.058 ) ( 0.057 ) ( 0.078 ) O-M KC m3 0.044 -0.12 ** 0.013 ( 0.059 ) ( 0.059 ) ( 0.08 ) O-M GF m1 0.014 -0.058 -0.012 ( 0.037 ) ( 0.036 ) ( 0.049 ) O-M GF m2 -0.03 -0.024 -0.044 ( 0.038 ) ( 0.036 ) ( 0.05 ) O-M GF m3 -0.024 0.015 -0.071 ( 0.041 ) ( 0.038 ) ( 0.053 ) I-M AS m1 0.281 ** 0.003 0.079 ** -0.024 continued on next page Table C.2: Toilet Paper - Promotions Reaction Functions

154 continued from previous page Variable Angel Kleenex Procter & Dominicks Soft Gamble ( 0.065 ) ( 0.026 ) ( 0.026 ) ( 0.036 ) I-M AS m2 0.113 * 0.007 0.06 ** 0.06 * ( 0.064 ) ( 0.026 ) ( 0.026 ) ( 0.035 ) I-M AS m3 0.092 0.018 0.011 0.002 ( 0.065 ) ( 0.026 ) ( 0.026 ) ( 0.036 ) I-M Klnx m1 0.124 0.219 ** 0.021 -0.039 ( 0.088 ) ( 0.037 ) ( 0.036 ) ( 0.049 ) I-M Klnx m2 -0.058 0.009 -0.047 0.193 ** ( 0.089 ) ( 0.041 ) ( 0.039 ) ( 0.054 ) I-M Klnx m3 0.009 0.037 0.02 0 ( 0.088 ) ( 0.04 ) ( 0.039 ) ( 0.054 ) I-M PG m1 0.081 -0.025 0.121 ** -0.04 ( 0.093 ) ( 0.04 ) ( 0.038 ) ( 0.053 ) I-M PG m2 -0.042 -0.036 0.025 0.066 ( 0.093 ) ( 0.04 ) ( 0.039 ) ( 0.053 ) I-M PG m3 0.033 0.004 -0.108 ** -0.008 ( 0.091 ) ( 0.037 ) ( 0.036 ) ( 0.05 ) I-M Dom m1 0.031 -0.034 0.065 * 0.244 ** ( 0.085 ) ( 0.036 ) ( 0.035 ) ( 0.047 ) I-M Dom m2 0.108 0.005 0.073 ** 0.08 * ( 0.087 ) ( 0.036 ) ( 0.035 ) ( 0.048 ) I-M Dom m3 0.017 -0.076 ** -0.1 ** -0.074 ( 0.082 ) ( 0.036 ) ( 0.034 ) ( 0.047 ) I-M GP m1 0.039 0.102 ** 0.099 ** 0.089 ( 0.092 ) ( 0.04 ) ( 0.04 ) ( 0.055 ) I-M GP m2 -0.283 ** 0.028 0.016 -0.017 ( 0.098 ) ( 0.042 ) ( 0.041 ) ( 0.057 ) I-M GP m3 0.034 0.033 -0.13 ** -0.074 ( 0.099 ) ( 0.043 ) ( 0.042 ) ( 0.058 ) I-M KC m1 0.12 0.006 0.141 ** 0.006 ( 0.105 ) ( 0.046 ) ( 0.045 ) ( 0.061 ) I-M KC m2 0.016 0.023 0.07 -0.033 ( 0.109 ) ( 0.05 ) ( 0.045 ) ( 0.062 ) I-M KC m3 -0.036 0.036 0.057 -0.031 ( 0.111 ) ( 0.051 ) ( 0.046 ) ( 0.063 ) I-M GF m1 -0.012 -0.01 -0.04 0.026 ( 0.079 ) ( 0.033 ) ( 0.032 ) ( 0.044 ) I-M GF m2 -0.048 0.032 -0.088 ** 0.026 ( 0.086 ) ( 0.035 ) ( 0.034 ) ( 0.046 ) continued on next page Table C.2: Toilet Paper - Promotions Reaction Functions

155 continued from previous page Variable Angel Kleenex Procter & Dominicks Soft Gamble I-M GF m3 0.132 * 0.097 ** 0.034 0.033 ( 0.075 ) ( 0.033 ) ( 0.031 ) ( 0.043 ) N 374 374 374 374 Adj. R2 0.1539 0.3171 0.4986 0.3914 Table C.2: Toilet Paper - Promotions Reaction Functions

Variable Georgia- Kimberly Green Pacific Clark Forest Intercept -0.022 -0.02 0.076 * ( 0.029 ) ( 0.025 ) ( 0.041 ) O-M Klnx m1 -0.112 ** 0.082 * -0.043 ( 0.056 ) ( 0.048 ) ( 0.08 ) O-M Klnx m2 -0.034 0.073 -0.171 ** ( 0.057 ) ( 0.049 ) ( 0.081 ) O-M Klnx m3 0.083 -0.053 -0.03 ( 0.054 ) ( 0.046 ) ( 0.077 ) O-M PG m1 0.015 -0.186 ** -0.458 ** ( 0.09 ) ( 0.077 ) ( 0.129 ) O-M PG m2 0.138 0.128 * -0.081 ( 0.085 ) ( 0.073 ) ( 0.121 ) O-M PG m3 -0.014 0.105 0.123 ( 0.08 ) ( 0.069 ) ( 0.114 ) O-M Dom m1 0.091 ** 0.009 -0.112 * ( 0.046 ) ( 0.039 ) ( 0.065 ) O-M Dom m2 -0.042 0.003 0.066 ( 0.047 ) ( 0.04 ) ( 0.066 ) O-M Dom m3 0.017 0.062 0.079 ( 0.047 ) ( 0.04 ) ( 0.067 ) O-M GP m1 0.17 ** 0.004 -0.123 ( 0.074 ) ( 0.064 ) ( 0.106 ) O-M GP m2 -0.107 0.093 -0.178 * ( 0.072 ) ( 0.062 ) ( 0.103 ) O-M GP m3 0.134 ** 0.051 -0.049 ( 0.067 ) ( 0.058 ) ( 0.096 ) O-M KC m1 0.235 * * -0.05 0.099 continued on next page Table C.3: Toilet Paper - Promotions Reaction Functions continued 156 continued from previous page Variable Georgia- Kimberly Green Pacific Clark Forest ( 0.078 ) ( 0.067 ) ( 0.111 ) O-M KC m2 -0.102 -0.075 0.024 ( 0.081 ) ( 0.07 ) ( 0.116 ) O-M KC m3 0.211 * * -0.008 0.215 * ( 0.082 ) ( 0.071 ) ( 0.117 ) O-M GF m1 -0.092 * -0.047 -0.035 ( 0.052 ) ( 0.045 ) ( 0.074 ) O-M GF m2 0.055 -0.082 * 0.063 ( 0.053 ) ( 0.046 ) ( 0.076 ) O-M GF m3 0.073 0.084 * 0.05 ( 0.057 ) ( 0.049 ) ( 0.082 ) I-M AS m1 0.054 0.006 0.036 ( 0.037 ) ( 0.032 ) ( 0.052 ) I-M AS m2 0.05 0.027 -0.046 ( 0.037 ) ( 0.032 ) ( 0.053 ) I-M AS m3 0.043 -0.011 -0.002 ( 0.037 ) ( 0.032 ) ( 0.052 ) I-M Klnx m1 0.067 -0.053 0.092 ( 0.052 ) ( 0.044 ) ( 0.074 ) I-M Klnx m2 -0.091 0.078 0.225 ** ( 0.057 ) ( 0.049 ) ( 0.082 ) I-M Klnx m3 -0.028 0.036 -0.126 ( 0.057 ) ( 0.049 ) ( 0.081 ) I-M PG m1 0.006 0.135 ** 0.127 ( 0.056 ) ( 0.048 ) ( 0.08 ) I-M PG m2 -0.16 ** -0.018 -0.151 * ( 0.056 ) ( 0.048 ) ( 0.08 ) I-M PG m3 -0.199 ** 0.081 * 0.051 ( 0.052 ) ( 0.045 ) ( 0.074 ) I-M Dom m1 0.097 * 0.103 ** -0.005 ( 0.05 ) ( 0.043 ) ( 0.072 ) I-M Dom m2 0.016 0 0.032 ( 0.051 ) ( 0.044 ) ( 0.072 ) I-M Dom m3 -0.063 -0.06 -0.014 ( 0.05 ) ( 0.043 ) ( 0.071 ) I-M GP m1 0.292 ** -0.139 ** -0.212 ** ( 0.056 ) ( 0.048 ) ( 0.08 ) I-M GP m2 -0.09 0.037 -0.182 ** ( 0.06 ) ( 0.051 ) ( 0.085 ) continued on next page Table C.3: Toilet Paper - Promotions Reaction Functions continued 157 continued from previous page Variable Georgia- Kimberly Green Pacific Clark Forest I-M GP m3 0.129 ** 0.002 0.119 ( 0.061 ) ( 0.052 ) ( 0.087 ) I-M KC m1 -0.027 0.338 ** 0.154 * ( 0.065 ) ( 0.056 ) ( 0.092 ) I-M KC m2 -0.013 0.1 * -0.006 ( 0.07 ) ( 0.06 ) ( 0.1 ) I-M KC m3 -0.071 0.196 ** 0.155 ( 0.071 ) ( 0.061 ) ( 0.102 ) I-M GF m1 -0.003 0.056 0.496 ** ( 0.047 ) ( 0.04 ) ( 0.067 ) I-M GF m2 0.008 -0.051 0.035 ( 0.049 ) ( 0.042 ) ( 0.07 ) I-M GF m3 -0.072 -0.059 -0.018 ( 0.046 ) ( 0.04 ) ( 0.066 ) N 374 374 374 Adj. R2 0.3955 0.5554 0.4360 Table C.3: Toilet Paper - Promotions Reaction Functions

Variable Kleenex Procter& Dominicks Gamble Intercept -0.056 ** -0.005 0.046 ( 0.017 ) ( 0.025 ) ( 0.043 ) O-M Klnx m1 -0.178 ** 0.031 0.125 ( 0.035 ) ( 0.047 ) ( 0.08 ) O-M Klnx m2 -0.016 0.02 0.096 ( 0.039 ) ( 0.051 ) ( 0.088 ) O-M Klnx m3 -0.017 -0.018 0.147 * ( 0.04 ) ( 0.052 ) ( 0.089 ) O-M PG m1 -0.016 0.017 0.195 ( 0.034 ) ( 0.071 ) ( 0.12 ) O-M PG m2 -0.053 -0.024 -0.141 ( 0.036 ) ( 0.07 ) ( 0.118 ) O-M PG m3 0.074 ** 0.003 0.016 ( 0.034 ) ( 0.071 ) ( 0.119 ) O-M Dom m1 0.019 -0.034 -0.009 continued on the next page Table C.4: Paper Towels - Promotions Reaction Functions

158 continued from previous page Variable Kleenex Procter& Dominicks Gamble ( 0.033 ) ( 0.069 ) ( 0.118 ) O-M Dom m2 -0.001 0.035 -0.029 ( 0.034 ) ( 0.075 ) ( 0.128 ) O-M Dom m3 0 -0.167 ** 0.119 ( 0.033 ) ( 0.072 ) ( 0.123 ) O-M GP m1 -0.055 -0.032 0.021 ( 0.037 ) ( 0.05 ) ( 0.085 ) O-M GP m2 0.033 0.063 0.078 ( 0.04 ) ( 0.051 ) ( 0.087 ) O-M GP m3 -0.072 * 0.032 0.002 ( 0.041 ) ( 0.054 ) ( 0.093 ) O-M KC m1 -0.013 0.011 -0.014 ( 0.042 ) ( 0.059 ) ( 0.1 ) O-M KC m2 0.098 ** 0.134 ** 0.17 ( 0.045 ) ( 0.062 ) ( 0.107 ) O-M KC m3 -0.046 0.043 -0.054 ( 0.046 ) ( 0.065 ) ( 0.112 ) O-M GF m1 0 0.031 0.005 ( 0.032 ) ( 0.045 ) ( 0.078 ) O-M GF m2 -0.002 -0.09 ** 0.003 ( 0.032 ) ( 0.043 ) ( 0.074 ) O-M GF m3 0.026 -0.011 -0.067 ( 0.03 ) ( 0.038 ) ( 0.064 ) I-M Klnx m1 0.151 ** 0.073 0.026 ( 0.037 ) ( 0.049 ) ( 0.083 ) I-M Klnx m2 0.015 -0.072 -0.059 ( 0.038 ) ( 0.05 ) ( 0.086 ) I-M Klnx m3 -0.052 -0.053 0.072 ( 0.034 ) ( 0.044 ) ( 0.075 ) I-M PG m1 0.058 0.049 0.27 ** ( 0.058 ) ( 0.077 ) ( 0.131 ) I-M PG m2 0.019 -0.01 -0.029 ( 0.056 ) ( 0.074 ) ( 0.126 ) I-M PG m3 0.018 0.026 -0.098 ( 0.053 ) ( 0.071 ) ( 0.121 ) I-M Dom m1 0.033 0.075 * 0.196 ** ( 0.03 ) ( 0.04 ) ( 0.068 ) I-M Dom m2 0.049 * 0.024 -0.008 ( 0.029 ) ( 0.038 ) ( 0.066 ) continued on the next page Table C.4: Paper Towels - Promotions Reaction Functions

159 continued from previous page Variable Kleenex Procter& Dominicks Gamble I-M Dom m3 -0.026 0.029 -0.044 ( 0.03 ) ( 0.039 ) ( 0.066 ) I-M GP m1 0.227 ** -0.048 -0.11 ( 0.05 ) ( 0.066 ) ( 0.112 ) I-M GP m2 -0.037 0.098 -0.326 ** ( 0.049 ) ( 0.065 ) ( 0.112 ) I-M GP m3 0.066 0.091 0 ( 0.044 ) ( 0.062 ) ( 0.106 ) I-M KC m1 0.081 0.09 0.259 ** ( 0.05 ) ( 0.067 ) ( 0.114 ) I-M KC m2 0.017 0.033 -0.011 ( 0.054 ) ( 0.071 ) ( 0.122 ) I-M KC m3 -0.058 -0.004 0.084 ( 0.054 ) ( 0.069 ) ( 0.118 ) I-M GF m1 0.176 ** 0.042 -0.006 ( 0.039 ) ( 0.052 ) ( 0.089 ) I-M GF m2 0.001 -0.012 -0.14 ( 0.039 ) ( 0.053 ) ( 0.09 ) I-M GF m3 0.023 0.06 0.088 ( 0.039 ) ( 0.054 ) ( 0.092 ) N 374 374 374 Adj. R2 0.3933 0.3021 0.2615 Table C.4: Paper Towels - Promotions Reaction Functions

Variable Georgia Kimberly- Green Pacific Clark Forest Intercept 0.066 ** -0.041 ** 0.027 ( 0.019 ) ( 0.02 ) ( 0.018 ) O-M Klnx m1 0.033 0.087 ** 0.043 ( 0.039 ) ( 0.042 ) ( 0.037 ) O-M Klnx m2 0.056 0.084 * 0.025 ( 0.044 ) ( 0.048 ) ( 0.042 ) O-M Klnx m3 -0.053 0.043 0.081 * ( 0.045 ) ( 0.048 ) ( 0.043 ) O-M PG m1 -0.092 ** -0.009 0.083 ** continued on the next page Table C.5: Paper Towels - Promotions Reaction Functions continued 160 continued from previous page Variable Georgia Kimberly- Green Pacific Clark Forest ( 0.039 ) ( 0.041 ) ( 0.037 ) O-M PG m2 -0.123 ** 0.056 -0.008 ( 0.04 ) ( 0.043 ) ( 0.038 ) O-M PG m3 -0.097 ** -0.037 0.015 ( 0.039 ) ( 0.041 ) ( 0.037 ) O-M Dom m1 0.043 0.046 -0.054 ( 0.037 ) ( 0.04 ) ( 0.036 ) O-M Dom m2 -0.01 -0.012 -0.068 * ( 0.038 ) ( 0.041 ) ( 0.036 ) O-M Dom m3 -0.045 0.011 -0.023 ( 0.037 ) ( 0.04 ) ( 0.035 ) O-M GP m1 0.087 ** 0.073 -0.073 * ( 0.042 ) ( 0.045 ) ( 0.04 ) O-M GP m2 0.043 -0.086 * -0.022 ( 0.045 ) ( 0.048 ) ( 0.043 ) O-M GP m3 0.044 0.074 0.07 ( 0.046 ) ( 0.049 ) ( 0.044 ) O-M KC m1 -0.075 0.03 0.011 ( 0.047 ) ( 0.05 ) ( 0.045 ) O-M KC m2 -0.024 0.005 -0.016 ( 0.051 ) ( 0.054 ) ( 0.048 ) O-M KC m3 -0.102 ** -0.194 ** 0.055 ( 0.051 ) ( 0.055 ) ( 0.049 ) O-M GF m1 -0.062 * 0.048 0.096 ** ( 0.036 ) ( 0.038 ) ( 0.034 ) O-M GF m2 0.024 0.074 * -0.008 ( 0.036 ) ( 0.039 ) ( 0.035 ) O-M GF m3 -0.071 ** -0.023 -0.047 ( 0.033 ) ( 0.036 ) ( 0.032 ) I-M Klnx m1 0.028 0.158 ** 0.088 ** ( 0.041 ) ( 0.044 ) ( 0.04 ) I-M Klnx m2 -0.086 ** -0.046 0.094 ** ( 0.042 ) ( 0.045 ) ( 0.041 ) I-M Klnx m3 0.005 -0.013 0.028 ( 0.038 ) ( 0.041 ) ( 0.037 ) I-M PG m1 0.051 0.037 -0.119 * ( 0.066 ) ( 0.07 ) ( 0.063 ) I-M PG m2 0.139 ** 0.126 * -0.115 * ( 0.063 ) ( 0.068 ) ( 0.06 ) continued on the next page Table C.5: Paper Towels - Promotions Reaction Functions continued 161 continued from previous page Variable Georgia Kimberly- Green Pacific Clark Forest I-M PG m3 0.002 0.106 * 0.092 ( 0.06 ) ( 0.065 ) ( 0.058 ) I-M Dom m1 0.057 * -0.004 -0.001 ( 0.033 ) ( 0.036 ) ( 0.032 ) I-M Dom m2 -0.135 ** -0.013 0.11 ** ( 0.033 ) ( 0.035 ) ( 0.031 ) I-M Dom m3 0.056 * 0.021 0.068 ** ( 0.033 ) ( 0.036 ) ( 0.032 ) I-M GP m1 0.102 * 0.057 -0.008 ( 0.056 ) ( 0.06 ) ( 0.053 ) I-M GP m2 -0.059 0.188 ** -0.051 ( 0.055 ) ( 0.059 ) ( 0.052 ) I-M GP m3 -0.023 0.087 -0.011 ( 0.05 ) ( 0.053 ) ( 0.048 ) I-M KC m1 0.224 ** 0.235 ** 0.024 ( 0.057 ) ( 0.061 ) ( 0.054 ) I-M KC m2 0.062 0.134 ** -0.045 ( 0.061 ) ( 0.065 ) ( 0.058 ) I-M KC m3 0.247 ** 0.074 -0.05 ( 0.061 ) ( 0.065 ) ( 0.058 ) I-M GF m1 -0.05 -0.107 ** 0.104 ** ( 0.044 ) ( 0.047 ) ( 0.042 ) I-M GF m2 0.017 -0.088 * -0.031 ( 0.044 ) ( 0.047 ) ( 0.042 ) I-M GF m3 0.014 0.108 ** 0.083 ** ( 0.044 ) ( 0.048 ) ( 0.042 ) N 374 374 374 Adj. R2 0.4052 0.6443 0.3875 Table C.5: Paper Towels - Promotions Reaction Functions continued

Variable Unilever Colgate- Procter& Dominicks Palmolive Gamble Intercept 0.016 0.057 ** 0.005 0.029 ( 0.014 ) ( 0.015 ) ( 0.007 ) ( 0.033 ) continued on the next page Table C.6: Liquid Dish Soap - Promotions Reaction Func- tions 162 continued from previous page Variable Unilever Colgate- Procter& Dominicks Palmolive Gamble O-M UniL m1 -0.023 -0.03 0.047 ** -0.117 ( 0.04 ) ( 0.035 ) ( 0.02 ) ( 0.085 ) O-M UniL m2 0.011 -0.041 0.027 -0.085 ( 0.038 ) ( 0.034 ) ( 0.019 ) ( 0.082 ) O-M UniL m3 0 -0.025 0.018 0.334 ** ( 0.037 ) ( 0.033 ) ( 0.018 ) ( 0.079 ) O-M CG m1 0.012 0.102 ** 0.011 -0.066 ( 0.033 ) ( 0.04 ) ( 0.02 ) ( 0.072 ) O-M CG m2 -0.087 ** 0.061 0.001 0.162 ** ( 0.037 ) ( 0.043 ) ( 0.021 ) ( 0.078 ) O-M CG m3 -0.012 -0.066 0.047 ** 0.076 ( 0.034 ) ( 0.04 ) ( 0.02 ) ( 0.077 ) O-M PG m1 0.006 -0.055 -0.041 ** -0.037 ( 0.038 ) ( 0.033 ) ( 0.021 ) ( 0.091 ) O-M PG m2 -0.001 0.035 0.022 0.025 ( 0.038 ) ( 0.034 ) ( 0.021 ) ( 0.093 ) O-M PG m3 -0.035 -0.049 -0.053 ** -0.24 ** ( 0.038 ) ( 0.034 ) ( 0.021 ) ( 0.095 ) O-M Dom m1 -0.017 -0.073 ** -0.001 0.001 ( 0.04 ) ( 0.036 ) ( 0.014 ) ( 0.062 ) O-M Dom m2 0.028 0.002 -0.02 -0.015 ( 0.043 ) ( 0.037 ) ( 0.014 ) ( 0.064 ) O-M Dom m3 -0.054 -0.034 0 0.069 ( 0.041 ) ( 0.036 ) ( 0.014 ) ( 0.063 ) I-M UniL m1 0.176 ** 0.006 0.016 -0.052 ( 0.031 ) ( 0.033 ) ( 0.016 ) ( 0.07 ) I-M UniL m2 0.101 ** 0.07 ** -0.029 * 0.125 * ( 0.033 ) ( 0.034 ) ( 0.017 ) ( 0.074 ) I-M UniL m3 0.084 ** 0.037 0.044 ** -0.108 ( 0.032 ) ( 0.033 ) ( 0.017 ) ( 0.073 ) I-M CG m1 0.073 ** 0.145 ** 0.016 0.042 ( 0.029 ) ( 0.03 ) ( 0.015 ) ( 0.067 ) I-M CG m2 0.088 ** -0.02 -0.006 -0.01 ( 0.03 ) ( 0.031 ) ( 0.015 ) ( 0.067 ) I-M CG m3 -0.029 -0.019 0.001 0.11 ( 0.03 ) ( 0.031 ) ( 0.015 ) ( 0.068 ) I-M PG m1 -0.061 0.027 0.172 ** -0.047 ( 0.044 ) ( 0.044 ) ( 0.022 ) ( 0.1 ) I-M PG m2 -0.017 0.077 -0.044 * -0.061 continued on the next page Table C.6: Liquid Dish Soap - Promotions Reaction Func- tions 163 continued from previous page Variable Unilever Colgate- Procter& Dominicks Palmolive Gamble ( 0.046 ) ( 0.047 ) ( 0.023 ) ( 0.103 ) I-M PG m3 0.083 * 0.063 0.001 0.015 ( 0.043 ) ( 0.044 ) ( 0.022 ) ( 0.095 ) I-M Dom m1 0.05 * * -0.017 0.004 0.394 ** ( 0.024 ) ( 0.023 ) ( 0.011 ) ( 0.05 ) I-M Dom m2 -0.005 0.006 -0.018 -0.041 ( 0.024 ) ( 0.023 ) ( 0.012 ) ( 0.05 ) I-M Dom m3 -0.03 -0.001 0.008 0.103 ** ( 0.024 ) ( 0.023 ) ( 0.011 ) ( 0.05 ) N 392 392 392 392 Adj.R2 0.2564 0.1108 0.3069 0.2423 Table C.6: Liquid Dish Soap - Promotions Reaction Func- tions

164 Variable Unilever Procter& Dominicks Reckitt Gamble Benckiser Intercept -0.025 0.066 ** -0.033 0.079 ** ( 0.015 ) ( 0.019 ) ( 0.025 ) ( 0.026 ) O-M UniL m1 0.203 ** 0.035 0.034 -0.02 ( 0.044 ) ( 0.053 ) ( 0.068 ) ( 0.04 ) O-M UniL m2 0.073 * 0.083 -0.051 -0.134 ** ( 0.043 ) ( 0.051 ) ( 0.064 ) ( 0.04 ) O-M UniL m3 0.09 ** -0.037 0.083 0.047 ( 0.044 ) ( 0.052 ) ( 0.067 ) ( 0.04 ) O-M PG m1 0.028 -0.021 -0.095 -0.067 ( 0.041 ) ( 0.052 ) ( 0.068 ) ( 0.055 ) O-M PG m2 0.068 * -0.031 0.016 0.01 ( 0.041 ) ( 0.053 ) ( 0.071 ) ( 0.058 ) O-M PG m3 0.041 -0.171 ** 0.104 -0.044 ( 0.041 ) ( 0.053 ) ( 0.07 ) ( 0.055 ) O-M Dom m1 -0.043 0.079 ** 0.125 ** 0.037 ( 0.048 ) ( 0.037 ) ( 0.05 ) ( 0.047 ) O-M Dom m2 0.068 -0.01 -0.034 0.092 * ( 0.047 ) ( 0.038 ) ( 0.052 ) ( 0.047 ) O-M Dom m3 -0.09 ** -0.072 * 0.001 -0.08 * ( 0.045 ) ( 0.037 ) ( 0.05 ) ( 0.047 ) O-M RB m1 -0.052 -0.015 0.057 0.089 ( 0.048 ) ( 0.058 ) ( 0.079 ) ( 0.072 ) O-M RB m2 0.068 0.022 0.066 -0.132 * ( 0.048 ) ( 0.059 ) ( 0.079 ) ( 0.07 ) O-M RB m3 0.005 -0.041 0.084 -0.134 * ( 0.049 ) ( 0.057 ) ( 0.078 ) ( 0.07 ) I-M UniL m1 0.287 ** 0 -0.012 0.01 ( 0.039 ) ( 0.046 ) ( 0.06 ) ( 0.054 ) I-M UniL m2 0 0.06 -0.062 -0.081 ( 0.041 ) ( 0.048 ) ( 0.064 ) ( 0.058 ) I-M UniL m3 -0.068 * -0.034 0.104 * 0.19 ** ( 0.039 ) ( 0.046 ) ( 0.06 ) ( 0.055 ) I-M PG m1 -0.084 ** 0.264 ** 0.024 -0.058 ( 0.036 ) ( 0.044 ) ( 0.059 ) ( 0.052 ) I-M PG m2 -0.064 * -0.023 0.044 -0.114 ** ( 0.037 ) ( 0.045 ) ( 0.06 ) ( 0.053 ) I-M PG m3 0.033 0.039 0.032 0.042 ( 0.036 ) ( 0.044 ) ( 0.059 ) ( 0.053 ) I-M Dom m1 0.014 0.034 0.415 ** -0.087 * continued on the next page Table C.8: Powder Dish Soap - Promotions Reaction Func- tions 165 continued from previous page Variable Unilever Procter& Dominicks Reckitt Gamble Benckiser ( 0.038 ) ( 0.039 ) ( 0.053 ) ( 0.051 ) I-M Dom m2 0.024 -0.017 0.072 0.091 * ( 0.04 ) ( 0.042 ) ( 0.057 ) ( 0.055 ) I-M Dom m3 0.036 0.015 -0.007 -0.059 ( 0.038 ) ( 0.039 ) ( 0.052 ) ( 0.05 ) I-M RB m1 0.057 * 0.011 -0.051 0.156 ** ( 0.034 ) ( 0.041 ) ( 0.055 ) ( 0.048 ) I-M RB m2 -0.058 * -0.036 0 0.036 ( 0.033 ) ( 0.041 ) ( 0.056 ) ( 0.048 ) I-M RB m3 -0.03 0.048 -0.048 0.066 ( 0.034 ) ( 0.04 ) ( 0.055 ) ( 0.046 ) N 390 390 390 390 Adj. R2 0.4625 0.1751 0.3120 0.1576 Table C.8: Powder Dish Soap - Promotions Reaction Func- tions

Variable Unilever Procter& Dominicks Reckitt Gamble Benckiser Intercept 0.117 ** 0.037 -0.019 0.003 ( 0.024 ) ( 0.023 ) ( 0.03 ) ( 0.024 ) O-M UniL m1 0.023 0.111 ** -0.135 * 0.012 ( 0.058 ) ( 0.057 ) ( 0.073 ) ( 0.05 ) O-M UniL m2 -0.074 0.226 ** 0.059 -0.075 ( 0.057 ) ( 0.056 ) ( 0.071 ) ( 0.053 ) O-M UniL m3 0.111 * -0.095 0.258 ** 0.174 ** ( 0.059 ) ( 0.058 ) ( 0.075 ) ( 0.051 ) O-M PG m1 -0.168 ** -0.114 * 0.025 -0.064 ( 0.056 ) ( 0.059 ) ( 0.076 ) ( 0.048 ) O-M PG m2 0.142 ** -0.085 -0.035 -0.06 ( 0.057 ) ( 0.06 ) ( 0.077 ) ( 0.049 ) O-M PG m3 -0.144 ** 0.005 0.027 -0.029 ( 0.057 ) ( 0.06 ) ( 0.078 ) ( 0.049 ) O-M Dom m1 -0.135 ** 0.009 -0.037 0.044 ( 0.062 ) ( 0.04 ) ( 0.052 ) ( 0.047 ) O-M Dom m2 0.048 -0.137 ** 0.087 0.014 continued on next page Table C.9: Fabric Softener Sheets - Promotions Reaction Functions 166 continued from previous page Variable Unilever Procter& Dominicks Reckitt Gamble Benckiser ( 0.065 ) ( 0.044 ) ( 0.058 ) ( 0.051 ) O-M Dom m3 0.198 ** 0.029 0.077 0.014 ( 0.061 ) ( 0.044 ) ( 0.058 ) ( 0.047 ) O-M RB m1 -0.056 0.118 ** -0.076 0.008 ( 0.045 ) ( 0.046 ) ( 0.061 ) ( 0.044 ) O-M RB m2 -0.03 -0.044 -0.139 ** 0.012 ( 0.046 ) ( 0.047 ) ( 0.062 ) ( 0.044 ) O-M RB m3 0.017 0.046 0.009 0.145 ** ( 0.046 ) ( 0.045 ) ( 0.059 ) ( 0.043 ) I-M UniL m1 0.213 ** -0.049 0.025 0.013 ( 0.038 ) ( 0.037 ) ( 0.048 ) ( 0.037 ) I-M UniL m2 -0.041 -0.028 0.005 -0.146 ** ( 0.038 ) ( 0.037 ) ( 0.048 ) ( 0.037 ) I-M UniL m3 0.027 -0.052 -0.087 * 0.127 ** ( 0.038 ) ( 0.036 ) ( 0.048 ) ( 0.038 ) I-M PG m1 0.112 ** 0.448 ** 0.107 0.017 ( 0.051 ) ( 0.051 ) ( 0.067 ) ( 0.051 ) I-M PG m2 -0.111 ** 0.157 ** -0.03 0.13 ** ( 0.055 ) ( 0.056 ) ( 0.074 ) ( 0.054 ) I-M PG m3 0.069 -0.1 * -0.018 -0.041 ( 0.05 ) ( 0.051 ) ( 0.067 ) ( 0.051 ) I-M Dom m1 0.064 0.083 ** 0.332 ** -0.006 ( 0.046 ) ( 0.04 ) ( 0.053 ) ( 0.044 ) I-M Dom m2 -0.115 ** 0.017 0.025 0.036 ( 0.046 ) ( 0.04 ) ( 0.052 ) ( 0.044 ) I-M Dom m3 -0.048 -0.045 0.073 -0.081 * ( 0.046 ) ( 0.038 ) ( 0.05 ) ( 0.043 ) I-M RB m1 0.023 -0.159 ** 0.218 ** 0.266 ** ( 0.068 ) ( 0.069 ) ( 0.09 ) ( 0.067 ) I-M RB m2 -0.075 0.08 0.067 -0.031 ( 0.066 ) ( 0.066 ) ( 0.087 ) ( 0.065 ) I-M RB m3 -0.094 0.014 0.058 -0.05 ( 0.065 ) ( 0.063 ) ( 0.083 ) ( 0.065 ) N 390 390 390 390 Adj. R2 0.1781 0.3855 0.3241 0.2217 Table C.9: Fabric Softener Sheets - Promotions Reaction Functions

167 Variable Unilever Colgate- Procter& Palmolive Gamble Intercept 0.009 0.031 ** 0.032 ** ( 0.02 ) ( 0.013 ) ( 0.016 ) O-M UniL m1 0.011 0.005 0.036 ( 0.06 ) ( 0.034 ) ( 0.046 ) O-M UniL m2 0.008 -0.035 0.071 ( 0.062 ) ( 0.035 ) ( 0.048 ) O-M UniL m3 0.074 -0.044 0.008 ( 0.059 ) ( 0.034 ) ( 0.045 ) O-M CG m1 -0.001 -0.027 0.103 ( 0.115 ) ( 0.084 ) ( 0.098 ) O-M CG m2 0.018 0.095 -0.059 ( 0.134 ) ( 0.1 ) ( 0.116 ) O-M CG m3 0.058 -0.051 -0.052 ( 0.115 ) ( 0.085 ) ( 0.099 ) O-M PG m1 0.122 0.122 * -0.042 ( 0.1 ) ( 0.074 ) ( 0.085 ) O-M PG m2 0.034 -0.155 -0.009 ( 0.14 ) ( 0.103 ) ( 0.12 ) O-M PG m3 -0.164 0.013 0.018 ( 0.137 ) ( 0.101 ) ( 0.117 ) I-M UniL m1 0.353 ** 0.042 0.024 ( 0.043 ) ( 0.028 ) ( 0.033 ) I-M UniL m2 -0.026 0.03 -0.003 ( 0.044 ) ( 0.029 ) ( 0.034 ) I-M UniL m3 0.004 -0.008 -0.036 ( 0.044 ) ( 0.029 ) ( 0.034 ) I-M CG m1 0.031 0.28 ** -0.01 ( 0.05 ) ( 0.034 ) ( 0.039 ) I-M CG m2 -0.063 -0.103 ** -0.047 ( 0.054 ) ( 0.036 ) ( 0.042 ) I-M CG m3 0.13 ** 0.044 -0.023 ( 0.052 ) ( 0.034 ) ( 0.04 ) I-M PG m1 -0.037 0.088 ** 0.246 ** ( 0.048 ) ( 0.032 ) ( 0.037 ) I-M PG m2 0.018 -0.037 -0.099 ** ( 0.048 ) ( 0.032 ) ( 0.037 ) I-M PG m3 -0.039 0.037 0.023 ( 0.047 ) ( 0.031 ) ( 0.036 ) N 392 392 392 Adj. R2 0.2538 0.2086 0.1207

Table C.7: Liquid Dishwasher Soap 168 Variable Unilever Dial Arm & Colgate- Hammer Palmolive Intercept 0.027 * 0.028 0.065 ** -0.002 ( 0.016 ) ( 0.023 ) ( 0.018 ) ( 0.016 ) O-M UniL m1 0.129 ** -0.006 0.012 0.013 ( 0.038 ) ( 0.043 ) ( 0.033 ) ( 0.034 ) O-M UniL m2 -0.016 0.025 -0.162 ** 0.026 ( 0.04 ) ( 0.046 ) ( 0.036 ) ( 0.036 ) O-M UniL m3 -0.033 0.011 0.044 -0.017 ( 0.041 ) ( 0.047 ) ( 0.036 ) ( 0.034 ) O-M Dial m1 -0.069 ** -0.023 0.029 ( 0.024 ) ( 0.037 ) ( 0.028 ) O-M Dial m2 -0.007 0.022 -0.004 ( 0.025 ) ( 0.038 ) ( 0.03 ) O-M Dial m3 -0.01 -0.052 -0.037 ( 0.023 ) ( 0.033 ) ( 0.027 ) O-M AH m1 -0.04 0.002 -0.106 ** -0.057 ** ( 0.028 ) ( 0.043 ) ( 0.036 ) ( 0.029 ) O-M AH m2 -0.002 0.036 -0.053 0.016 ( 0.027 ) ( 0.044 ) ( 0.037 ) ( 0.029 ) O-M AH m3 0 -0.045 -0.03 0.053 * ( 0.031 ) ( 0.045 ) ( 0.037 ) ( 0.03 ) O-M CG m1 0.023 0.031 -0.029 ( 0.032 ) ( 0.037 ) ( 0.036 ) O-M CG m2 0.037 -0.036 -0.032 ( 0.031 ) ( 0.038 ) ( 0.034 ) O-M CG m3 0.007 0.045 0.021 ( 0.031 ) ( 0.037 ) ( 0.033 ) O-M PG m1 0.094 * -0.037 -0.129 ** 0.078 ( 0.057 ) ( 0.067 ) ( 0.05 ) ( 0.052 ) O-M PG m2 0.092 -0.032 -0.011 0.069 ( 0.056 ) ( 0.072 ) ( 0.055 ) ( 0.056 ) O-M PG m3 0.077 -0.015 -0.067 -0.043 ( 0.053 ) ( 0.071 ) ( 0.053 ) ( 0.053 ) O-M Dom m1 0.031 0.024 0.021 -0.032 ( 0.034 ) ( 0.039 ) ( 0.033 ) ( 0.024 ) O-M Dom m2 -0.022 -0.056 -0.086 ** 0.024 ( 0.034 ) ( 0.039 ) ( 0.033 ) ( 0.025 ) O-M Dom m3 -0.055 * 0.001 0.032 -0.028 ( 0.03 ) ( 0.039 ) ( 0.033 ) ( 0.023 ) I-M UniL m1 0.059 ** -0.023 0.013 -0.003 continued on the next page Table C.11: Liquid Laundry Detergent - Promotions Reac- tion Functions 169 continued from the previous page Variable Unilever Dial Arm & Colgate- Hammer Palmolive ( 0.023 ) ( 0.039 ) ( 0.03 ) ( 0.023 ) I-M UniL m2 -0.029 0.089 ** 0.005 0.031 ( 0.022 ) ( 0.037 ) ( 0.029 ) ( 0.023 ) I-M UniL m3 -0.015 -0.023 -0.088 ** -0.016 ( 0.024 ) ( 0.037 ) ( 0.029 ) ( 0.024 ) I-M Dial m1 0.048 ** 0.106 ** 0.024 -0.004 ( 0.019 ) ( 0.03 ) ( 0.023 ) ( 0.019 ) I-M Dial m2 0.003 -0.02 0.08 ** -0.03 ( 0.02 ) ( 0.031 ) ( 0.024 ) ( 0.02 ) I-M Dial m3 0.013 -0.023 -0.007 -0.007 ( 0.02 ) ( 0.031 ) ( 0.025 ) ( 0.02 ) I-M AH m1 0.036 0.032 0.281 ** 0.005 ( 0.022 ) ( 0.034 ) ( 0.026 ) ( 0.022 ) I-M AH m2 -0.032 -0.01 0.04 0.01 ( 0.024 ) ( 0.038 ) ( 0.029 ) ( 0.024 ) I-M AH m3 -0.004 -0.038 -0.009 -0.017 ( 0.022 ) ( 0.035 ) ( 0.026 ) ( 0.023 ) I-M CG m1 0.004 -0.047 -0.058 * 0.186 ** ( 0.024 ) ( 0.041 ) ( 0.031 ) ( 0.025 ) I-M CG m2 -0.01 0.004 0.016 0 ( 0.026 ) ( 0.041 ) ( 0.032 ) ( 0.026 ) I-M CG m3 -0.019 0.002 -0.038 -0.015 ( 0.025 ) ( 0.039 ) ( 0.03 ) ( 0.025 ) I-M PG m1 -0.025 0.055 0.114 ** 0.078 ** ( 0.028 ) ( 0.041 ) ( 0.032 ) ( 0.027 ) I-M PG m2 -0.022 0.003 0.065 ** -0.023 ( 0.027 ) ( 0.04 ) ( 0.031 ) ( 0.026 ) I-M PG m3 -0.057 ** 0.045 0.042 0.05 ** ( 0.027 ) ( 0.04 ) ( 0.031 ) ( 0.026 ) I-M Dom m1 0.063 ** -0.018 -0.088 ** 0.018 ( 0.025 ) ( 0.038 ) ( 0.029 ) ( 0.024 ) I-M Dom m2 0.048 * 0.024 0.028 0.008 ( 0.026 ) ( 0.04 ) ( 0.031 ) ( 0.025 ) I-M Dom m3 0.06 ** 0.045 0.065 ** 0.095 ** ( 0.025 ) ( 0.041 ) ( 0.031 ) ( 0.025 ) I-M RB m1 -0.024 -0.005 0.014 -0.072 ** ( 0.021 ) ( 0.034 ) ( 0.027 ) ( 0.021 ) I-M RB m2 -0.026 0.035 0.014 -0.008 ( 0.022 ) ( 0.035 ) ( 0.026 ) ( 0.022 ) continued on the next page Table C.11: Liquid Laundry Detergent - Promotions Reac- tion Functions 170 continued from the previous page Variable Unilever Dial Arm & Colgate- Hammer Palmolive I-M RB m3 0.007 0.047 0.044 -0.003 ( 0.022 ) ( 0.035 ) ( 0.027 ) ( 0.022 ) N 396 396 396 396 Adj. R2 0.3033 0.0732 0.4991 0.3343 Table C.11: Liquid Laundry Detergent - Promotions Reac- tion Functions

Variable Procter & Dominicks Reckitt Gamble Benckiser Intercept 0.041 ** -0.067 * 0.026 ( 0.017 ) ( 0.035 ) ( 0.017 ) O-M UniL m1 -0.11 ** 0.032 ( 0.04 ) ( 0.084 ) O-M UniL m2 0.016 -0.113 ( 0.044 ) ( 0.091 ) O-M UniL m3 0.016 0.117 ( 0.046 ) ( 0.098 ) O-M Dial m1 -0.017 0.069 ( 0.025 ) ( 0.052 ) O-M Dial m2 -0.027 -0.14 ** ( 0.026 ) ( 0.056 ) O-M Dial m3 0.032 0.161 ** ( 0.024 ) ( 0.051 ) O-M AH m1 0.029 -0.171 ** ( 0.033 ) ( 0.07 ) O-M AH m2 0.033 0.103 ( 0.032 ) ( 0.067 ) O-M AH m3 0.053 0.018 ( 0.035 ) ( 0.073 ) O-M CG m1 -0.038 0.061 ( 0.037 ) ( 0.069 ) O-M CG m2 0.092 ** 0.009 ( 0.036 ) ( 0.067 ) O-M CG m3 -0.028 0.032 ( 0.035 ) ( 0.066 ) continued on the next page Table C.12: Liquid Laundry Detergent - Promotions Reac- tion Functions continued 171 continued from the previous page Variable Procter & Dominicks Reckitt Gamble Benckiser O-M PG m1 0.104 0.05 ( 0.07 ) ( 0.149 ) O-M PG m2 -0.062 0.229 ( 0.068 ) ( 0.142 ) O-M PG m3 -0.113 * -0.068 ( 0.064 ) ( 0.137 ) O-M Dom m1 0.006 -0.032 ( 0.027 ) ( 0.058 ) O-M Dom m2 0.036 -0.033 ( 0.028 ) ( 0.058 ) O-M Dom m3 0.025 -0.106 ** ( 0.025 ) ( 0.053 ) I-M UniL m1 -0.052 ** -0.078 -0.002 ( 0.025 ) ( 0.052 ) ( 0.033 ) I-M UniL m2 0.03 0.007 0.053 ( 0.024 ) ( 0.052 ) ( 0.033 ) I-M UniL m3 0.005 0.067 -0.084 ** ( 0.026 ) ( 0.054 ) ( 0.033 ) I-M Dial m1 -0.02 0.071 0.001 ( 0.021 ) ( 0.044 ) ( 0.03 ) I-M Dial m2 0.03 -0.061 -0.009 ( 0.022 ) ( 0.046 ) ( 0.03 ) I-M Dial m3 -0.002 0.005 -0.025 ( 0.022 ) ( 0.047 ) ( 0.03 ) I-M AH m1 0 -0.016 0.031 ( 0.024 ) ( 0.049 ) ( 0.032 ) I-M AH m2 0.047 * 0.052 -0.01 ( 0.027 ) ( 0.056 ) ( 0.037 ) I-M AH m3 -0.019 -0.01 -0.058 * ( 0.025 ) ( 0.052 ) ( 0.033 ) I-M CG m1 0.022 0.043 0.104 ** ( 0.026 ) ( 0.055 ) ( 0.036 ) I-M CG m2 0.083 ** 0.019 -0.095 ** ( 0.028 ) ( 0.058 ) ( 0.039 ) I-M CG m3 0.031 -0.01 0.085 ** ( 0.027 ) ( 0.057 ) ( 0.037 ) I-M PG m1 0.029 0.019 -0.04 ( 0.03 ) ( 0.062 ) ( 0.038 ) I-M PG m2 -0.008 0.06 0.045 continued on the next page Table C.12: Liquid Laundry Detergent - Promotions Reac- tion Functions continued 172 continued from the previous page Variable Procter & Dominicks Reckitt Gamble Benckiser ( 0.029 ) ( 0.061 ) ( 0.037 ) I-M PG m3 -0.035 0.162 ** 0.041 ( 0.027 ) ( 0.058 ) ( 0.036 ) I-M Dom m1 0.027 0.401 ** 0.131 ** ( 0.026 ) ( 0.054 ) ( 0.037 ) I-M Dom m2 0.049 * -0.005 0.042 ( 0.027 ) ( 0.057 ) ( 0.038 ) I-M Dom m3 0.062 ** 0.235 ** 0.027 ( 0.028 ) ( 0.057 ) ( 0.038 ) I-M RB m1 0.05 ** 0.102 ** 0.24 ** ( 0.023 ) ( 0.049 ) ( 0.032 ) I-M RB m2 -0.014 -0.002 0.001 ( 0.023 ) ( 0.049 ) ( 0.032 ) I-M RB m3 -0.044 * -0.188 ** -0.097 ** ( 0.024 ) ( 0.05 ) ( 0.033 ) N 396 396 396 Adj. R2 0.2061 0.3941 0.2980 Table C.12: Liquid Laundry Detergent - Promotions Reac- tion Functions continued

Variable Unilever Dial Arm & Procter & Dominicks Hammer Gamble Intercept 0.065 ** -0.062 0.063 ** 0.006 0.018 ( 0.029 ) ( 0.039 ) ( 0.021 ) ( 0.011 ) ( 0.034 ) O-M UniL m1 0.028 0.071 -0.034 -0.011 0.038 ( 0.056 ) ( 0.05 ) ( 0.028 ) ( 0.022 ) ( 0.064 ) O-M UniL m2 -0.081 0.143 ** -0.057 ** 0.009 -0.125 * ( 0.058 ) ( 0.049 ) ( 0.027 ) ( 0.023 ) ( 0.065 ) O-M UniL m3 0.138 ** -0.042 0.034 0.026 0.105 * ( 0.054 ) ( 0.048 ) ( 0.027 ) ( 0.022 ) ( 0.063 ) O-M Dial m1 0.037 -0.047 -0.051 ** -0.008 0.027 ( 0.03 ) ( 0.041 ) ( 0.022 ) ( 0.012 ) ( 0.035 ) O-M Dial m2 -0.024 0.114 ** 0.046 ** -0.004 -0.056 ( 0.031 ) ( 0.042 ) ( 0.023 ) ( 0.012 ) ( 0.036 ) O-M Dial m3 -0.036 -0.099 ** -0.006 -0.026 ** 0.014 continued on the next page Table C.13: Powder Laundry Detergent - Promotions Re- action Functions 173 continued from the previous page Variable Unilever Dial Arm & Procter & Dominicks Hammer Gamble ( 0.032 ) ( 0.043 ) ( 0.023 ) ( 0.013 ) ( 0.038 ) O-M AH m1 0.054 0 0.048 0.034 ** -0.028 ( 0.035 ) ( 0.048 ) ( 0.029 ) ( 0.015 ) ( 0.047 ) O-M AH m2 -0.02 -0.042 0.066 ** 0.01 -0.023 ( 0.04 ) ( 0.054 ) ( 0.033 ) ( 0.018 ) ( 0.053 ) O-M AH m3 0.029 -0.001 -0.076 ** -0.012 0.064 ( 0.037 ) ( 0.048 ) ( 0.031 ) ( 0.017 ) ( 0.05 ) O-M PG m1 0.035 -0.021 0.007 -0.022 0.01 ( 0.058 ) ( 0.055 ) ( 0.029 ) ( 0.023 ) ( 0.067 ) O-M PG m2 -0.106 * 0.179 ** 0.038 -0.014 0.113 * ( 0.057 ) ( 0.053 ) ( 0.029 ) ( 0.023 ) ( 0.068 ) O-M PG m3 -0.094 -0.155 ** 0.061 ** -0.043 * -0.028 ( 0.058 ) ( 0.053 ) ( 0.029 ) ( 0.023 ) ( 0.069 ) O-M Dom m1 -0.12 ** -0.049 -0.051 * 0.018 -0.02 ( 0.056 ) ( 0.053 ) ( 0.03 ) ( 0.015 ) ( 0.046 ) O-M Dom m2 0.05 -0.006 0.023 0.013 0.079 * ( 0.059 ) ( 0.055 ) ( 0.031 ) ( 0.015 ) ( 0.046 ) O-M Dom m3 0.122 ** 0.126 ** -0.005 0.05 * -0.082 * ( 0.052 ) ( 0.056 ) ( 0.032 ) ( 0.015 ) ( 0.044 ) I-M UniL m1 0.024 ** 0.019 ** -0.003 0.003 -0.002 ( 0.007 ) ( 0.009 ) ( 0.005 ) ( 0.003 ) ( 0.008 ) I-M UniL m2 -0.006 -0.003 0.001 0.005 * 0.002 ( 0.007 ) ( 0.009 ) ( 0.005 ) ( 0.003 ) ( 0.008 ) I-M UniL m3 -0.006 -0.002 0.006 0.005 * -0.005 ( 0.007 ) ( 0.009 ) ( 0.005 ) ( 0.003 ) ( 0.008 ) I-M Dial m1 0.171 ** 0.512 ** 0.006 0.017 0.038 ( 0.039 ) ( 0.048 ) ( 0.027 ) ( 0.015 ) ( 0.045 ) I-M Dial m2 0.017 -0.002 -0.028 -0.035 ** -0.071 ( 0.039 ) ( 0.052 ) ( 0.03 ) ( 0.016 ) ( 0.047 ) I-M Dial m3 0.011 0.194 ** 0.017 -0.013 0.068 * ( 0.036 ) ( 0.046 ) ( 0.026 ) ( 0.014 ) ( 0.041 ) I-M AH m1 -0.005 0.053 0.152 ** -0.044 ** -0.107 ** ( 0.045 ) ( 0.059 ) ( 0.033 ) ( 0.018 ) ( 0.055 ) I-M AH m2 -0.009 0.02 -0.041 -0.05 ** -0.019 ( 0.046 ) ( 0.06 ) ( 0.034 ) ( 0.019 ) ( 0.057 ) I-M AH m3 0.009 0.123 ** -0.01 -0.007 0.057 ( 0.047 ) ( 0.062 ) ( 0.034 ) ( 0.019 ) ( 0.057 ) I-M PG m1 -0.005 0.009 0.046 0.148 ** 0.028 ( 0.065 ) ( 0.091 ) ( 0.049 ) ( 0.025 ) ( 0.076 ) continued on the next page Table C.13: Powder Laundry Detergent - Promotions Re- action Functions 174 continued from the previous page Variable Unilever Dial Arm & Procter & Dominicks Hammer Gamble I-M PG m2 0.032 -0.282 ** -0.128 ** 0.016 -0.024 ( 0.07 ) ( 0.094 ) ( 0.051 ) ( 0.027 ) ( 0.082 ) I-M PG m3 0.051 0.096 -0.029 0.022 0.168 ** ( 0.071 ) ( 0.09 ) ( 0.049 ) ( 0.027 ) ( 0.082 ) I-M Dom m1 0.057 0.008 -0.031 -0.016 0.299 ** ( 0.043 ) ( 0.055 ) ( 0.03 ) ( 0.015 ) ( 0.046 ) I-M Dom m2 -0.016 -0.154 ** -0.071 ** -0.011 0.026 ( 0.043 ) ( 0.053 ) ( 0.029 ) ( 0.016 ) ( 0.047 ) I-M Dom m3 0.128 ** -0.058 0.011 0.01 0.011 ( 0.043 ) ( 0.053 ) ( 0.029 ) ( 0.016 ) ( 0.047 ) N 396 396 396 396 396 Adj. R2 0.2579 0.4942 0.2103 0.4095 0.2154 Table C.13: Powder Laundry Detergent - Promotions Re- action Functions

Variable Mentadent Church & Arm & Colgate- Dwight Hammer Palmolive Intercept 0.053 ** -0.007 0.038 * 0.082 ** ( 0.019 ) ( 0.008 ) ( 0.021 ) ( 0.014 ) O-M AH m1 0.039 0.148 ** ( 0.047 ) ( 0.028 ) O-M AH m2 -0.002 -0.119 ** ( 0.048 ) ( 0.028 ) O-M AH m3 -0.033 0.019 ( 0.047 ) ( 0.028 ) O-M CP m1 0.008 0.025 ( 0.038 ) ( 0.028 ) O-M CP m2 -0.075 * -0.059 ** ( 0.04 ) ( 0.027 ) O-M CP m3 0.014 0.044 ( 0.039 ) ( 0.028 ) O-M PG m1 -0.112 ** -0.033 ( 0.044 ) ( 0.029 ) O-M PG m2 0.071 -0.024 ( 0.045 ) ( 0.03 ) continued on next page Table C.14: Toothpaste - Promotions Reaction Functions

175 continued from previous page Variable Mentadent Church & Arm & Colgate- Dwight Hammer Palmolive O-M PG m3 -0.031 0 ( 0.046 ) ( 0.03 ) O-M Dom m1 -0.063 -0.03 ( 0.044 ) ( 0.031 ) O-M Dom m2 0.058 -0.056 * ( 0.045 ) ( 0.033 ) O-M Dom m3 -0.06 -0.007 ( 0.044 ) ( 0.031 ) I-M Ment m1 0.1 ** -0.005 -0.021 0.061 ** ( 0.043 ) ( 0.018 ) ( 0.039 ) ( 0.024 ) I-M Ment m2 0.076 * -0.022 0.041 0.001 ( 0.044 ) ( 0.018 ) ( 0.041 ) ( 0.025 ) I-M Ment m3 0.088 ** 0.082 ** 0.02 -0.109 ** ( 0.044 ) ( 0.018 ) ( 0.041 ) ( 0.025 ) I-M CD m1 -0.09 0.412 ** 0.039 -0.019 ( 0.096 ) ( 0.04 ) ( 0.09 ) ( 0.055 ) I-M CD m2 -0.033 0.027 -0.093 0.133 ** ( 0.105 ) ( 0.044 ) ( 0.093 ) ( 0.058 ) I-M CD m3 0.081 0.047 -0.079 -0.123 ** ( 0.091 ) ( 0.038 ) ( 0.083 ) ( 0.051 ) I-M AH m1 0.019 -0.023 0.214 ** -0.003 ( 0.044 ) ( 0.018 ) ( 0.041 ) ( 0.025 ) I-M AH m2 -0.004 0.044 ** -0.045 0.088 ** ( 0.045 ) ( 0.019 ) ( 0.041 ) ( 0.026 ) I-M AH m3 0.018 -0.046 ** 0.107 ** 0.039 ( 0.047 ) ( 0.02 ) ( 0.042 ) ( 0.026 ) I-M CP m1 0.028 0.014 -0.043 0.076 ** ( 0.053 ) ( 0.022 ) ( 0.049 ) ( 0.029 ) I-M CP m2 -0.044 0.028 0.04 -0.027 ( 0.052 ) ( 0.022 ) ( 0.049 ) ( 0.029 ) I-M CP m3 -0.085 * -0.064 ** -0.065 0.004 ( 0.051 ) ( 0.021 ) ( 0.048 ) ( 0.028 ) I-M PG m1 0.13 ** -0.016 0.145 ** -0.022 ( 0.054 ) ( 0.022 ) ( 0.05 ) ( 0.03 ) I-M PG m2 -0.095 * -0.046 ** -0.011 -0.111 ** ( 0.055 ) ( 0.023 ) ( 0.05 ) ( 0.03 ) I-M PG m3 0.097 * 0.04 * -0.021 0.042 ( 0.052 ) ( 0.022 ) ( 0.047 ) ( 0.029 ) I-M Dom m1 -0.052 0.079 ** 0.041 0.039 continued on next page Table C.14: Toothpaste - Promotions Reaction Functions

176 continued from previous page Variable Mentadent Church & Arm & Colgate- Dwight Hammer Palmolive ( 0.054 ) ( 0.023 ) ( 0.049 ) ( 0.03 ) I-M Dom m2 0.022 0.094 ** -0.071 -0.121 ** ( 0.057 ) ( 0.024 ) ( 0.053 ) ( 0.032 ) I-M Dom m3 0.012 -0.041 0.093 -0.024 ( 0.06 ) ( 0.025 ) ( 0.057 ) ( 0.035 ) I-M GSK m1 0.01 0.065 ** 0.035 0.046 * ( 0.047 ) ( 0.02 ) ( 0.044 ) ( 0.027 ) I-M GSK m2 0.061 -0.103 ** 0.101 ** 0.004 ( 0.047 ) ( 0.019 ) ( 0.042 ) ( 0.027 ) I-M GSK m3 -0.037 0.031 * -0.035 -0.002 ( 0.045 ) ( 0.019 ) ( 0.041 ) ( 0.025 ) N 396 396 396 396 Adj. R2 0.0674 0.5324 0.1845 0.2644 Table C.14: Toothpaste - Promotions Reaction Functions

Variable Procter & Dominick’s Glaxo Gamble SmithKline Intercept 0.01 0.008 0.053 ** ( 0.018 ) ( 0.025 ) (0.013 ) O-M AH m1 0.088 ** -0.067 ( 0.038 ) ( 0.051 ) O-M AH m2 -0.002 0.023 ( 0.039 ) ( 0.053 ) O-M AH m3 -0.008 -0.092 * ( 0.04 ) ( 0.055 ) O-M CP m1 -0.075 ** 0.093 ** ( 0.037 ) ( 0.046 ) O-M CP m2 0.031 -0.025 ( 0.036 ) ( 0.045 ) O-M CP m3 -0.039 -0.014 ( 0.039 ) ( 0.047 ) O-M PG m1 0.003 0.04 ( 0.052 ) ( 0.068 ) O-M PG m2 0.064 -0.162 ** ( 0.051 ) ( 0.069 ) continued on the next page Table C.15: Toothpaste - Promotions Reaction Functions continued 177 continued from the previous page Variable Procter & Dominick’s Glaxo Gamble SmithKline O-M PG m3 -0.021 0.111 ( 0.05 ) ( 0.069 ) O-M Dom m1 0.018 0.044 ( 0.031 ) ( 0.043 ) O-M Dom m2 -0.006 -0.063 ( 0.033 ) ( 0.045 ) O-M Dom m3 0.002 0.036 ( 0.03 ) ( 0.041 ) I-M Ment m1 0.023 0.022 0.029 ( 0.032 ) ( 0.045 ) ( 0.03 ) I-M Ment m2 0.057 * 0.073 0.001 ( 0.033 ) ( 0.046 ) ( 0.031 ) I-M Ment m3 0.032 -0.029 0.039 ( 0.034 ) ( 0.046 ) ( 0.03 ) I-M CD m1 -0.013 0.235 ** -0.149 ** ( 0.073 ) ( 0.1 ) ( 0.067 ) I-M CD m2 -0.037 0.229 ** 0.251 ** ( 0.079 ) ( 0.109 ) ( 0.074 ) I-M CD m3 -0.032 -0.25 ** -0.006 ( 0.07 ) ( 0.098 ) ( 0.064 ) I-M AH m1 0.049 0.058 -0.02 ( 0.033 ) ( 0.046 ) ( 0.031 ) I-M AH m2 0.028 0.002 0.056 * ( 0.035 ) ( 0.048 ) ( 0.031 ) I-M AH m3 0.065 * -0.073 -0.028 ( 0.035 ) ( 0.048 ) ( 0.033 ) I-M CP m1 -0.095 ** 0.068 -0.006 ( 0.04 ) ( 0.055 ) ( 0.037 ) I-M CP m2 -0.003 -0.081 0.004 ( 0.039 ) ( 0.054 ) ( 0.036 ) I-M CP m3 0.032 0.031 -0.039 ( 0.039 ) ( 0.053 ) ( 0.036 ) I-M PG m1 0.124 ** 0.011 -0.029 ( 0.042 ) ( 0.057 ) ( 0.038 ) I-M PG m2 -0.038 -0.008 -0.075 ** ( 0.042 ) ( 0.058 ) ( 0.038 ) I-M PG m3 -0.014 0.152 ** 0.039 ( 0.039 ) ( 0.054 ) ( 0.036 ) I-M Dom m1 -0.001 0.35 ** 0.055 continued on the next page Table C.15: Toothpaste - Promotions Reaction Functions continued 178 continued from the previous page Variable Procter & Dominick’s Glaxo Gamble SmithKline ( 0.041 ) ( 0.057 ) ( 0.038 ) I-M Dom m2 0.035 -0.099 -0.024 ( 0.044 ) ( 0.061 ) ( 0.04 ) I-M Dom m3 0.009 -0.109 * -0.093 ** ( 0.047 ) ( 0.065 ) ( 0.042 ) I-M GSK m1 -0.077 ** -0.067 0.147 ** ( 0.037 ) ( 0.051 ) ( 0.033 ) I-M GSK m2 0.046 0.024 -0.061 * ( 0.036 ) ( 0.05 ) ( 0.033 ) I-M GSK m3 -0.033 0.077 -0.012 ( 0.035 ) ( 0.047 ) ( 0.032 ) N 396 396 396 Adj. R2 0.1101 0.2352 0.0981 Table C.15: Toothpaste - Promotions Reaction Functions continued

Variable Angel Klnx Procter& Dominicks Soft Gamble Intercept 0.008 -0.055 * -0.044 -0.173 ( 0.005 ) ( 0.028 ) ( 0.076 ) ( 0.509 ) O-M Price Klnx m1 0.232 -6.586 7.904 ( 2.572 ) ( 5.898 ) ( 12.058 ) O-M Price Klnx m2 -0.231 -6.16 4.16 ( 2.077 ) ( 6.162 ) ( 12.565 ) O-M Price Klnx m3 0.442 -1.495 6.086 ( 1.768 ) ( 5.182 ) ( 10.551 ) O-M Price PG m1 0.564 -0.012 0.262 ( 1.265 ) ( 0.02 ) ( 0.512 ) O-M Price PG m2 0.323 0.007 0.02 ( 1.282 ) ( 0.02 ) ( 0.513 ) O-M Price PG m3 0.956 0.003 -0.582 ( 1.148 ) ( 0.02 ) ( 0.513 ) O-M Price Dom m1 -0.082 -1.172 14.878 * ( 0.469 ) ( 0.938 ) ( 8.535 ) O-M Price Dom m2 -0.03 0.459 -21.79 ** continued on the next page Table C.16: Toilet Paper - Price Reaction Functions

179 continued from the previous page Variable Angel Klnx Procter& Dominicks Soft Gamble ( 0.373 ) ( 1.078 ) ( 9.088 ) O-M Price Dom m3 0.07 -0.92 10.585 ( 0.427 ) ( 0.951 ) ( 9.124 ) O-M Price GP m1 -0.934 -13.122 44.274 ( 4.024 ) ( 21.383 ) ( 43.514 ) O-M Price GP m2 0.467 20.834 -14.919 ( 4.063 ) ( 23.854 ) ( 48.671 ) O-M Price GP m3 0.97 12.341 -6.771 ( 3.959 ) ( 22.47 ) ( 45.567 ) O-M Price KC m1 -0.992 -11.091 ** -1.711 ( 0.868 ) ( 2.689 ) ( 5.556 ) O-M Price KC m2 -0.456 -4.221 1.283 ( 1.063 ) ( 2.926 ) ( 6.082 ) O-M Price KC m3 -0.531 1.385 -6.325 ( 1.013 ) ( 2.973 ) ( 6.195 ) O-M Price GF m1 2.323 56.198 -99.131 ( 14.242) ( 52.755 ) ( 108.341) O-M Price GF m2 -0.043 -4.776 47.159 ( 12.94 ) ( 58.269 ) ( 120.791) O-M Price GF m3 -1.623 -20.686 22.564 ( 12.977) ( 55.623 ) ( 113.348) I-M Price AS m1 -0.103 -0.838 -1.361 2.259 ( 0.106 ) ( 2.024 ) ( 1.94 ) ( 11.624 ) I-M Price AS m2 0.206 -0.488 -0.065 12.127 ( 0.104 ) ( 1.726 ) ( 1.91 ) ( 11.424 ) I-M Price AS m3 -0.081 -0.509 -0.907 5.46 ( 0.106 ) ( 1.246 ) ( 1.503 ) ( 9.017 ) I-M Price Klnx m1 0.034 0.303 0.893 -1.98 ( 0.016 ) ( 0.813 ) ( 0.675 ) ( 4.028 ) I-M Price Klnx m2 -0.016 0.255 0.613 -2.489 ( 0.017 ) ( 0.654 ) ( 0.702 ) ( 4.19 ) I-M Price Klnx m3 -0.034 0.155 0.202 -1.892 ( 0.016 ) ( 0.554 ) ( 0.587 ) ( 3.501 ) I-M Price PG m1 0.009 -0.125 -0.461 ** 0.043 ( 0.018 ) ( 0.381 ) ( 0.129 ) ( 0.78 ) I-M Price PG m2 -0.046 -0.092 0.305 ** -1.027 ( 0.017 ) ( 0.389 ) ( 0.134 ) ( 0.816 ) I-M Price PG m3 0.07 -0.011 0.25 * -0.92 ( 0.017 ) ( 0.344 ) ( 0.137 ) ( 0.848 ) continued on the next page Table C.16: Toilet Paper - Price Reaction Functions

180 continued from the previous page Variable Angel Klnx Procter& Dominicks Soft Gamble I-M Price Dom m1 0 0.018 0.12 -4.49 ( 0.002 ) ( 0.15 ) ( 0.111 ) ( 2.895 ) I-M Price Dom m2 0.002 0.025 -0.029 7.298 ** ( 0.003 ) ( 0.12 ) ( 0.128 ) ( 3.076 ) I-M Price Dom m3 0 -0.032 0.091 -3.399 ( 0.002 ) ( 0.137 ) ( 0.113 ) ( 3.092 ) I-M Price GP m1 0.018 0.287 1.568 -13.414 ( 0.023 ) ( 1.279 ) ( 2.457 ) ( 14.629 ) I-M Price GP m2 0.001 -0.112 -2.393 3.612 ( 0.021 ) ( 1.29 ) ( 2.736 ) ( 16.349 ) I-M Price GP m3 -0.005 -0.294 -1.565 3.151 ( 0.02 ) ( 1.254 ) ( 2.589 ) ( 15.37 ) I-M Price KC m1 -0.002 0.206 1.804 ** 0.515 ( 0.018 ) ( 0.23 ) ( 0.264 ) ( 1.588 ) I-M Price KC m2 -0.008 0.126 0.482 * 0.927 ( 0.018 ) ( 0.299 ) ( 0.287 ) ( 1.738 ) I-M Price KC m3 0.08 0.071 0.04 1.676 ( 0.019 ) ( 0.291 ) ( 0.297 ) ( 1.802 ) I-M Price GF m1 -0.004 -0.756 -6.461 32.773 ( 0.022 ) ( 4.535 ) ( 6.099 ) ( 36.673 ) I-M Price GF m2 0.053 0.085 0.855 -16.535 ( 0.023 ) ( 4.12 ) ( 6.731 ) ( 40.849 ) I-M Price GF m3 0.007 0.458 2.436 -6.751 ( 0.024 ) ( 4.142 ) ( 6.433 ) ( 38.401 ) N 374 374 374 374 Adj. R2 0.1972 0.3329 0.2903 0.1554 Table C.16: Toilet Paper - Price Reaction Functions

Variable Georgia Kimberly- Green Pacific Clark Forest Intercept -0.003 -0.01 -0.034 ( 0.026 ) ( 0.037 ) ( 0.026 ) O-M Price Klnx m1 -3.667 -3.945 0.485 ( 4.419 ) ( 2.99 ) ( 1.089 ) O-M Price Klnx m2 -2.566 -3.884 0.63 continued on the next page Table C.17: Toilet Paper - Price Reaction Functions continued 181 continued from the previous page Variable Georgia Kimberly- Green Pacific Clark Forest ( 3.569 ) ( 2.415 ) ( 0.879 ) O-M Price Klnx m3 -1.107 -2.796 0.343 ( 3.037 ) ( 2.055 ) ( 0.748 ) O-M Price PG m1 1.317 6.112 ** 0.688 ( 2.174 ) ( 1.471 ) ( 0.536 ) O-M Price PG m2 1.405 1.484 0.185 ( 2.202 ) ( 1.49 ) ( 0.543 ) O-M Price PG m3 2.047 0.638 0.333 ( 1.973 ) ( 1.335 ) ( 0.486 ) O-M Price Dom m1 -0.073 1.742 ** 0.273 ( 0.805 ) ( 0.545 ) ( 0.198 ) O-M Price Dom m2 -0.28 0.042 0.003 ( 0.641 ) ( 0.434 ) ( 0.158 ) O-M Price Dom m3 0.005 -0.412 0.099 ( 0.734 ) ( 0.496 ) ( 0.181 ) O-M Price GP m1 -2.528 -18.473 ** -1.139 ( 6.914 ) ( 4.679 ) ( 1.704 ) O-M Price GP m2 0.264 1.628 -0.415 ( 6.981 ) ( 4.724 ) ( 1.72 ) O-M Price GP m3 1.815 -0.977 0.545 ( 6.802 ) ( 4.603 ) ( 1.676 ) O-M Price KC m1 -2.94 ** 2.952 ** -0.324 ( 1.492 ) ( 1.009 ) ( 0.368 ) O-M Price KC m2 -1.161 -1.111 -0.388 ( 1.827 ) ( 1.236 ) ( 0.45 ) O-M Price KC m3 -1.484 0.372 -0.176 ( 1.741 ) ( 1.178 ) ( 0.429 ) O-M Price GF m1 8.607 10.859 5.859 ( 24.469 ) ( 16.559 ) ( 6.029 ) O-M Price GF m2 -1.765 -7.627 2.752 ( 22.232 ) ( 15.045 ) ( 5.478 ) O-M Price GF m3 -13.776 3.379 -2.434 ( 22.296 ) ( 15.088 ) ( 5.494 ) I-M Price AS m1 -1.452 -6.099 ** 1.905 ( 1.877 ) ( 2.661 ) ( 1.878 ) I-M Price AS m2 -1.02 -3.785 * 1.769 ( 1.6 ) ( 2.268 ) ( 1.601 ) I-M Price AS m3 -1.283 -1.658 0.251 ( 1.155 ) ( 1.638 ) ( 1.156 ) continued on the next page Table C.17: Toilet Paper - Price Reaction Functions continued 182 continued from the previous page Variable Georgia Kimberly- Green Pacific Clark Forest I-M Price Klnx m1 0.723 1.54 -0.355 ( 0.754 ) ( 1.069 ) ( 0.755 ) I-M Price Klnx m2 0.509 1.439 * -0.445 ( 0.606 ) ( 0.859 ) ( 0.607 ) I-M Price Klnx m3 0.206 0.928 -0.274 ( 0.513 ) ( 0.728 ) ( 0.514 ) I-M Price PG m1 -0.201 -2.193 ** -0.48 ( 0.353 ) ( 0.501 ) ( 0.353 ) I-M Price PG m2 -0.118 -0.473 -0.251 ( 0.361 ) ( 0.512 ) ( 0.361 ) I-M Price PG m3 -0.081 -0.231 -0.21 ( 0.318 ) ( 0.452 ) ( 0.319 ) I-M Price Dom m1 0.039 -0.625 ** -0.206 ( 0.139 ) ( 0.197 ) ( 0.139 ) I-M Price Dom m2 0.085 0.002 0.006 ( 0.111 ) ( 0.158 ) ( 0.111 ) I-M Price Dom m3 -0.014 0.147 -0.071 ( 0.127 ) ( 0.18 ) ( 0.127 ) I-M Price GP m1 0.23 6.595 ** 0.737 ( 1.186 ) ( 1.682 ) ( 1.187 ) I-M Price GP m2 -0.173 -0.573 0.261 ( 1.196 ) ( 1.696 ) ( 1.197 ) I-M Price GP m3 -0.464 0.319 -0.436 ( 1.163 ) ( 1.649 ) ( 1.164 ) I-M Price KC m1 0.322 -0.794 ** 0.146 ( 0.213 ) ( 0.302 ) ( 0.213 ) I-M Price KC m2 0.173 0.259 0.343 ( 0.277 ) ( 0.393 ) ( 0.278 ) I-M Price KC m3 0.084 0.229 0.246 ( 0.27 ) ( 0.383 ) ( 0.27 ) I-M Price GF m1 -1.423 -3.885 -3.868 ( 4.205 ) ( 5.962 ) ( 4.208 ) I-M Price GF m2 0.451 2.949 -1.904 ( 3.819 ) ( 5.416 ) ( 3.823 ) I-M Price GF m3 2.584 -1.124 2.069 ( 3.84 ) ( 5.446 ) ( 3.844 ) N 374 374 374 Adj. R2 0.0136 0.0761 -0.0496 Table C.17: Toilet Paper - Price Reaction Functions continued

183 Variable Unilever Procter & Gamble Dominicks Intercept 0.014 0.07 ** -0.018 ( 0.013 ) ( 0.017 ) ( 0.025 ) O-M UniL m1 0.037 0 0.051 ( 0.042 ) ( 0.033 ) ( 0.049 ) O-M UniL m2 -0.087 * 0.04 -0.057 ( 0.045 ) ( 0.034 ) ( 0.051 ) O-M UniL m3 0.05 0.035 0.036 ( 0.043 ) ( 0.032 ) ( 0.049 ) O-M PG m1 -0.069 ** -0.078 * -0.134 ** ( 0.032 ) ( 0.041 ) ( 0.06 ) O-M PG m2 0.043 0.001 0.082 ( 0.033 ) ( 0.044 ) ( 0.063 ) O-M PG m3 -0.043 0.127 ** 0.294 ** ( 0.034 ) ( 0.044 ) ( 0.065 ) O-M Dom m1 0.02 0.012 0.074 ( 0.037 ) ( 0.051 ) ( 0.075 ) O-M Dom m2 -0.001 -0.023 0.045 ( 0.038 ) ( 0.051 ) ( 0.076 ) O-M Dom m3 0.044 -0.032 -0.028 ( 0.038 ) ( 0.051 ) ( 0.076 ) I-M UniL m1 0.293 ** -0.067 0.043 ( 0.035 ) ( 0.044 ) ( 0.066 ) I-M UniL m2 -0.009 0.087 * -0.041 ( 0.037 ) ( 0.048 ) ( 0.072 ) I-M UniL m3 0.101 ** -0.162 ** 0.103 ( 0.034 ) ( 0.044 ) ( 0.065 ) I-M PG m1 0.043 0.289 ** -0.188 ** ( 0.033 ) ( 0.043 ) ( 0.064 ) I-M PG m2 0.013 -0.046 0.157 ** ( 0.032 ) ( 0.042 ) ( 0.063 ) I-M PG m3 -0.081 ** -0.028 -0.146 ** ( 0.032 ) ( 0.042 ) ( 0.063 ) I-M Dom m1 -0.047 * 0.029 0.271 ** ( 0.024 ) ( 0.027 ) ( 0.04 ) I-M Dom m2 0.052 ** -0.025 0.03 ( 0.024 ( 0.027 ( 0.04 I-M Dom m3 0.04 0.037 0.059 ( 0.024 ( 0.026 ( 0.038 N 392 392 392 Adj. R2 0.3892 0.2238 0.3146

Table C.10: Liquid Fabric Softener 184 Variable Klnx Procter & Dominicks Gamble Intercept 0.041 -0.466 -0.057 ( 0.139 ) ( 0.679 ) ( 0.076 ) O-M Price Klnx m1 -3.563 -9.709 28.52 ( 3.941 ) ( 362.773 ) ( 24.234 ) O-M Price Klnx m2 -2.801 -260.038 48.289 ( 5.557 ) ( 492.652 ) ( 34.71 ) O-M Price Klnx m3 0.461 -512.667 36.308 ( 3.797 ) ( 382.3 ) ( 27.249 ) O-M Price PG m1 0.227 -0.095 0.116 ( 0.495 ) ( 0.174 ) ( 0.096 ) O-M Price PG m2 0.126 0.079 -0.027 ( 0.481 ) ( 0.179 ) ( 0.094 ) O-M Price PG m3 -0.052 -0.017 -0.077 ( 0.357 ) ( 0.176 ) ( 0.094 ) O-M Price Dom m1 0.057 -5.999 -2.152 ** ( 0.704 ) ( 6.757 ) ( 0.683 ) O-M Price Dom m2 -0.326 19.438 ** 2.265 ** ( 0.767 ) ( 7.564 ) ( 0.738 ) O-M Price Dom m3 0.182 9.173 0.473 ( 0.689 ) ( 6.614 ) ( 0.607 ) O-M Price GP m1 -0.02 69.303 68.202 ** ( 0.196 ) ( 450.484 ) ( 30.755 ) O-M Price GP m2 0.217 -144.073 31.309 ( 0.2 ) ( 449.721 ) ( 30.806 ) O-M Price GP m3 -0.045 -9.843 0.343 ( 0.169 ) ( 10.96 ) ( 0.733 ) O-M Price KC m1 1.061 -44.223 -6.62 ( 1.588 ) ( 194.927 ) ( 14.093 ) O-M Price KC m2 0.983 208.12 -3.12 ( 1.991 ) ( 188.383 ) ( 13.84 ) O-M Price KC m3 0.132 28.56 -2.582 ( 1.458 ) ( 59.653 ) ( 3.963 ) O-M Price GF m1 4.075 53.111 -8.96 ( 4.554 ) ( 93.271 ) ( 6.324 ) O-M Price GF m2 1.349 -16.717 -8.447 ( 5.217 ) ( 100.607 ) ( 6.658 ) O-M Price GF m3 0.806 50.053 -13.464 ** continued on the next page Table C.18: Paper Towels - Price Reaction Functions

185 continued from the previous page Variable Klnx Procter & Dominicks Gamble ( 4.534 ) ( 50.854 ) ( 3.404 ) I-M Price Klnx m1 2.335 1.364 -7.325 ( 2.688 ) ( 48.44 ) ( 6.262 ) I-M Price Klnx m2 2.387 33.752 -12.504 ( 3.789 ) ( 65.797 ) ( 8.97 ) I-M Price Klnx m3 0.04 67.295 -9.416 ( 2.588 ) ( 51.083 ) ( 7.043 ) I-M Price PG m1 -0.119 -0.47 ** -0.017 ( 0.341 ) ( 0.142 ) ( 0.033 ) I-M Price PG m2 -0.053 -0.461 ** -0.001 ( 0.333 ) ( 0.145 ) ( 0.033 ) I-M Price PG m3 0.034 -0.302 ** 0.047 ( 0.248 ) ( 0.144 ) ( 0.033 ) I-M Price Dom m1 -0.264 1.337 0.886 ** ( 0.431 ) ( 1.078 ) ( 0.177 ) I-M Price Dom m2 0.22 -2.755 ** -0.401 ** ( 0.465 ) ( 1.163 ) ( 0.19 ) I-M Price Dom m3 -0.152 -0.609 -0.153 ( 0.421 ) ( 0.987 ) ( 0.15 ) I-M Price GP m1 0.001 -8.96 -17.616 ** ( 0.148 ) ( 60.125 ) ( 7.943 ) I-M Price GP m2 -0.107 19.407 -8.066 ( 0.149 ) ( 60.025 ) ( 7.957 ) I-M Price GP m3 0.081 0.972 -0.089 ( 0.13 ) ( 1.467 ) ( 0.19 ) I-M Price KC m1 -0.746 6.255 1.684 ( 1.088 ) ( 26.023 ) ( 3.641 ) I-M Price KC m2 -0.646 -27.476 0.776 ( 1.361 ) ( 25.156 ) ( 3.576 ) I-M Price KC m3 -0.078 -3.934 0.673 ( 0.996 ) ( 7.972 ) ( 1.025 ) I-M Price GF m1 -2.897 -6.754 2.215 ( 3.109 ) ( 12.472 ) ( 1.638 ) I-M Price GF m2 -0.806 1.418 2.284 ( 3.55 ) ( 13.448 ) ( 1.723 ) I-M Price GF m3 -0.562 -6.466 3.447 ** ( 3.083 ) ( 6.805 ) ( 0.881 ) continued on the next page Table C.18: Paper Towels - Price Reaction Functions

186 continued from the previous page Variable Klnx Procter & Dominicks Gamble N 370 370 370 Adj. R2 0.0510 0.0726 0.1771 Table C.18: Paper Towels - Price Reaction Functions

Variable Georgia Kimberly- Green Pacific Clark Forest Intercept -0.867 ** -0.469 * 0.038 ( 0.339 ) ( 0.253 ) ( 0.057 ) O-M Price Klnx m1 -7.67 -4.459 -2.69 ( 7.911 ) ( 7.63 ) ( 3.62 ) O-M Price Klnx m2 1.96 14.241 0.225 ( 11.154 ) ( 10.758 ) ( 5.104 ) O-M Price Klnx m3 2.961 0.308 1.069 ( 7.622 ) ( 7.351 ) ( 3.488 ) O-M Price PG m1 -0.243 -0.881 0.086 ( 0.993 ) ( 0.957 ) ( 0.454 ) O-M Price PG m2 -0.188 -0.401 -0.138 ( 0.966 ) ( 0.932 ) ( 0.442 ) O-M Price PG m3 0.494 1.526 ** -0.153 ( 0.717 ) ( 0.692 ) ( 0.328 ) O-M Price Dom m1 0.053 0.607 -0.588 ( 1.414 ) ( 1.364 ) ( 0.647 ) O-M Price Dom m2 0.493 0.522 0.287 ( 1.54 ) ( 1.486 ) ( 0.705 ) O-M Price Dom m3 0.268 -0.763 0.016 ( 1.382 ) ( 1.333 ) ( 0.632 ) O-M Price GP m1 -0.029 0.275 0.016 ( 0.394 ) ( 0.38 ) ( 0.18 ) O-M Price GP m2 -0.283 0.279 0.011 ( 0.402 ) ( 0.388 ) ( 0.184 ) O-M Price GP m3 -0.027 0.023 0.019 ( 0.339 ) ( 0.327 ) ( 0.155 ) O-M Price KC m1 1.442 1.082 0.331 ( 3.188 ) ( 3.075 ) ( 1.459 ) O-M Price KC m2 -0.865 -3.999 0.265 continued on the next page Table C.19: Paper Towels - Price Reaction Functions continued 187 continued from the previous page Variable Georgia Kimberly- Green Pacific Clark Forest ( 3.996 ) ( 3.854 ) ( 1.829 ) O-M Price KC m3 -1.064 -2.633 -0.138 ( 2.928 ) ( 2.824 ) ( 1.34 ) O-M Price GF m1 9.726 1.101 4.808 ( 9.141 ) ( 8.816 ) ( 4.183 ) O-M Price GF m2 -2.599 -11.448 0.952 ( 10.472 ) ( 10.1 ) ( 4.792 ) O-M Price GF m3 -8.53 -9.489 -0.714 ( 9.102 ) ( 8.778 ) ( 4.165 ) I-M Price Klnx m1 6.256 2.965 0.766 ( 6.554 ) ( 4.886 ) ( 1.096 ) I-M Price Klnx m2 -1.44 -8.852 -0.07 ( 9.238 ) ( 6.887 ) ( 1.545 ) I-M Price Klnx m3 -2.7 -0.046 -0.36 ( 6.311 ) ( 4.705 ) ( 1.055 ) I-M Price PG m1 0.306 0.648 -0.036 ( 0.833 ) ( 0.621 ) ( 0.139 ) I-M Price PG m2 0.265 0.469 0.088 ( 0.813 ) ( 0.606 ) ( 0.136 ) I-M Price PG m3 -0.424 -0.96 ** 0.067 ( 0.604 ) ( 0.45 ) ( 0.101 ) I-M Price Dom m1 -0.264 -0.185 0.015 ( 1.051 ) ( 0.783 ) ( 0.176 ) I-M Price Dom m2 0.661 -0.626 0.078 ( 1.133 ) ( 0.845 ) ( 0.189 ) I-M Price Dom m3 -0.206 -0.299 0.028 ( 1.027 ) ( 0.765 ) ( 0.172 ) I-M Price GP m1 -0.273 -0.227 0.006 ( 0.362 ) ( 0.27 ) ( 0.061 ) I-M Price GP m2 0.363 -0.166 0.019 ( 0.365 ) ( 0.272 ) ( 0.061 ) I-M Price GP m3 -0.147 -0.019 0.004 ( 0.318 ) ( 0.237 ) ( 0.053 ) I-M Price KC m1 -1.392 -0.839 -0.111 ( 2.654 ) ( 1.979 ) ( 0.444 ) I-M Price KC m2 0.986 2.489 -0.114 ( 3.319 ) ( 2.474 ) ( 0.555 ) I-M Price KC m3 0.905 1.633 0.055 ( 2.428 ) ( 1.81 ) ( 0.406 ) continued on the next page Table C.19: Paper Towels - Price Reaction Functions continued 188 continued from the previous page Variable Georgia Kimberly- Green Pacific Clark Forest I-M Price GF m1 -7.825 -0.474 -0.734 ( 7.581 ) ( 5.651 ) ( 1.267 ) I-M Price GF m2 3.425 7.273 -0.202 ( 8.656 ) ( 6.453 ) ( 1.447 ) I-M Price GF m3 5.967 6.241 0.277 ( 7.518 ) ( 5.605 ) ( 1.257 ) N 370 370 370 Adj. R2 0.0357 -0.0007 0.7151 Table C.19: Paper Towels - Price Reaction Functions continued

Variable Unilever Colgate- Procter & Dominicks Palmolive Gamble Intercept -0.193 -0.326 0.134 -0.031 ( 0.124 ) ( 0.284 ) ( 0.164 ) ( 0.019 ) O-M Price UniL m1 0.007 0.069 -0.088 -0.108 ** ( 0.029 ) ( 0.211 ) ( 0.07 ) ( 0.047 ) O-M Price UniL m2 -0.047 -0.036 0.066 -0.025 ( 0.03 ) ( 0.219 ) ( 0.073 ) ( 0.049 ) O-M Price UniL m3 -0.059 ** 0.084 0.229 ** -0.001 ( 0.029 ) ( 0.213 ) ( 0.071 ) ( 0.048 ) O-M Price CG m1 0.151 * 0.027 0.052 -0.116 ( 0.092 ) ( 0.583 ) ( 0.217 ) ( 0.154 ) O-M Price CG m2 0.082 0.507 -0.266 -0.052 ( 0.085 ) ( 0.546 ) ( 0.203 ) ( 0.145 ) O-M Price CG m3 -0.05 -0.557 0.082 0.256 * ( 0.087 ) ( 0.563 ) ( 0.209 ) ( 0.149 ) O-M Price PG m1 -0.016 -0.131 -0.015 -0.014 ( 0.017 ) ( 0.121 ) ( 0.039 ) ( 0.014 ) O-M Price PG m2 0.007 0.003 -0.02 -0.018 ( 0.017 ) ( 0.123 ) ( 0.04 ) ( 0.014 ) O-M Price PG m3 0.009 -0.166 -0.031 -0.007 ( 0.018 ) ( 0.125 ) ( 0.041 ) ( 0.014 ) O-M Price Dom m1 0.709 6.74 0.438 -0.028 ** ( 1.096 ) ( 7.823 ) ( 0.275 ) ( 0.014 ) continued on the next page Table C.20: Liquid Dish Soap - Price Reaction Functions

189 continued from previous page Variable Unilever Colgate- Procter & Dominicks Palmolive Gamble O-M Price Dom m2 0.069 -5.999 -0.153 -0.011 ( 1.336 ) ( 8.264 ) ( 0.301 ) ( 0.015 ) O-M Price Dom m3 0.278 10.217 -0.4 -0.003 ( 1.161 ) ( 7.784 ) ( 0.286 ) ( 0.015 ) I-M Price UniL m1 -0.082 -0.066 0.261 * 0.007 ( 0.104 ) ( 0.245 ) ( 0.144 ) ( 0.021 ) I-M Price UniL m2 0.042 0.348 0.12 0.017 ( 0.096 ) ( 0.226 ) ( 0.135 ) ( 0.02 ) I-M Price UniL m3 0.076 -0.596 ** 0.119 0.041 ** ( 0.099 ) ( 0.231 ) ( 0.138 ) ( 0.02 ) I-M Price CG m1 0.039 0.036 0.05 -0.004 ( 0.048 ) ( 0.111 ) ( 0.064 ) ( 0.009 ) I-M Price CG m2 -0.061 -0.076 0.048 0.007 ( 0.049 ) ( 0.113 ) ( 0.066 ) ( 0.01 ) I-M Price CG m3 -0.049 -0.079 0.169 ** 0.016 * ( 0.049 ) ( 0.114 ) ( 0.066 ) ( 0.01 ) I-M Price PG m1 -0.022 0.118 0.018 -0.028 * ( 0.077 ) ( 0.177 ) ( 0.105 ) ( 0.015 ) I-M Price PG m2 -0.031 0.041 0.336 ** 0.02 ( 0.075 ) ( 0.171 ) ( 0.1 ) ( 0.015 ) I-M Price PG m3 0.085 0.09 0.202 ** 0.04 * * ( 0.076 ) ( 0.175 ) ( 0.101 ) ( 0.015 ) I-M Price Dom m1 -0.216 1.77 * 0.376 0.267 ** ( 0.429 ) ( 1 ) ( 0.595 ) ( 0.085 ) I-M Price Dom m2 0.535 -0.432 0.148 0.503 ** ( 0.407 ) ( 0.948 ) ( 0.566 ) ( 0.083 ) I-M Price Dom m3 -0.121 -1.167 0.501 0.134 ( 0.417 ) ( 0.966 ) ( 0.574 ) ( 0.085 ) N 383 383 383 383 Adj. R2 0.0212 0.0219 0.3321 0.5609 Table C.20: Liquid Dish Soap - Price Reaction Functions

190 Variable Unilever Colgate- Procter & Palmolive Gamble Intercept -0.056 -0.062 -0.036 ( 0.038 ) ( 0.04 ) ( 0.039 ) O-M Price UniL m1 0.001 0.029 -0.001 ( 0.008 ) ( 0.025 ) ( 0.014 ) O-M Price UniL m2 -0.013 * -0.031 0.004 ( 0.008 ) ( 0.025 ) ( 0.014 ) O-M Price UniL m3 -0.008 0.029 0.004 ( 0.007 ) ( 0.024 ) ( 0.014 ) O-M Price CG m1 -0.024 0.036 -0.04 ( 0.023 ) ( 0.037 ) ( 0.043 ) O-M Price CG m2 -0.017 -0.025 -0.041 ( 0.021 ) ( 0.036 ) ( 0.039 ) O-M Price CG m3 0.037 -0.012 0.05 ( 0.022 ) ( 0.036 ) ( 0.042 ) O-M Price PG m1 -0.002 -0.013 -0.003 ( 0.004 ) ( 0.014 ) ( 0.008 ) O-M Price PG m2 -0.004 0.006 0.001 ( 0.005 ) ( 0.014 ) ( 0.008 ) O-M Price PG m3 -0.003 0.007 0.008 ( 0.005 ) ( 0.015 ) ( 0.008 ) I-M Price UniL m1 0.504 ** -0.138 0.036 ( 0.088 ) ( 0.093 ) ( 0.093 ) I-M Price UniL m2 0.107 0.065 -0.137 ( 0.097 ) ( 0.102 ) ( 0.101 ) I-M Price UniL m3 0.244 ** -0.152 * 0.228 ** ( 0.087 ) ( 0.091 ) ( 0.091 ) I-M Price CG m1 -0.054 0.547 ** 0.067 ( 0.07 ) ( 0.079 ) ( 0.074 ) I-M Price CG m2 0.112 0.113 0.096 ( 0.078 ) ( 0.085 ) ( 0.082 ) I-M Price CG m3 -0.023 0.162 ** 0.059 ( 0.07 ) ( 0.076 ) ( 0.074 ) I-M Price PG m1 -0.074 0.009 0.472 ** ( 0.078 ) ( 0.082 ) ( 0.082 ) I-M Price PG m2 0.069 0.046 -0.057 ( 0.082 ) ( 0.086 ) ( 0.086 ) I-M Price PG m3 0.057 0.074 0.06 ( 0.075 ) ( 0.081 ) ( 0.081 ) N 383 383 383 Adj. R2 0.3606 0.6267 0.2003

Table C.21: Liquid Dishwasher Soap - Price Reaction Functions 191 Variable Unilever Procter & Dominicks Reckitt Gamble Benckiser Intercept -0.067 ** -0.105 0.007 -0.096 ( 0.033 ) ( 0.123 ) ( 0.01 ) ( 0.068 ) O-M Price UniL m1 0.006 -0.001 -0.005 0.152 ( 0.006 ) ( 0.044 ) ( 0.018 ) ( 0.259 ) O-M Price UniL m2 0.001 -0.026 0.023 0.003 ( 0.006 ) ( 0.045 ) ( 0.018 ) ( 0.259 ) O-M Price UniL m3 0 -0.034 0.003 -0.237 ( 0.006 ) ( 0.043 ) ( 0.018 ) ( 0.238 ) O-M Price PG m1 -0.006 -0.032 -0.007 -0.028 ( 0.004 ) ( 0.027 ) ( 0.005 ) ( 0.126 ) O-M Price PG m2 -0.004 -0.011 0.004 0.124 ( 0.004 ) ( 0.027 ) ( 0.005 ) ( 0.135 ) O-M Price PG m3 -0.011 ** 0.035 0.009 * -0.416 ** ( 0.004 ) ( 0.027 ) ( 0.005 ) ( 0.127 ) O-M Price Dom m1 0.14 0.064 -0.007 0.844 * ( 0.239 ) ( 0.185 ) ( 0.006 ) ( 0.455 ) O-M Price Dom m2 0.025 0.148 0.007 -0.59 ( 0.283 ) ( 0.188 ) ( 0.006 ) ( 0.458 ) O-M Price Dom m3 0.016 -0.229 -0.002 -0.329 ( 0.238 ) ( 0.182 ) ( 0.006 ) ( 0.464 ) O-M Price RB m1 1.853 1.309 0.716 ** 0.114 ( 1.555 ) ( 7.711 ) ( 0.314 ) ( 0.497 ) O-M Price RB m2 1.345 4.345 -0.381 -0.691 ( 1.559 ) ( 7.824 ) ( 0.33 ) ( 0.516 ) O-M Price RB m3 -3.089 * -0.626 -0.334 -0.2 ( 1.663 ) ( 8.448 ) ( 0.353 ) ( 0.545 ) I-M Price UniL m1 0.12 -0.02 -0.026 0.078 ( 0.097 ) ( 0.387 ) ( 0.035 ) ( 0.213 ) I-M Price UniL m2 0.105 0.471 -0.02 0.061 ( 0.095 ) ( 0.381 ) ( 0.034 ) ( 0.212 ) I-M Price UniL m3 0.257 ** 0.337 0.052 0.324 ( 0.091 ) ( 0.371 ) ( 0.033 ) ( 0.201 ) I-M Price PG m1 0 0.364 ** 0.003 -0.035 ( 0.022 ) ( 0.086 ) ( 0.008 ) ( 0.048 ) I-M Price PG m2 0.018 0.175 * 0.008 -0.005 ( 0.022 ) ( 0.09 ) ( 0.008 ) ( 0.049 ) I-M Price PG m3 0.012 0.117 -0.001 0.086 * ( 0.021 ) ( 0.085 ) ( 0.007 ) ( 0.047 ) I-M Price Dom m1 0.136 1.145 0.673 ** 0.189 continued on the next page Table C.22: Powder Dishwasher Soap - Price Reaction Functions 192 continued from the previous page Variable Unilever Procter & Dominicks Reckitt Gamble Benckiser ( 0.206 ) ( 0.789 ) ( 0.07 ) ( 0.46 ) I-M Price Dom m2 0.225 -0.784 0.204 ** 0.363 ( 0.222 ) ( 0.885 ) ( 0.08 ) ( 0.486 ) I-M Price Dom m3 -0.198 0.508 -0.005 -0.432 ( 0.197 ) ( 0.779 ) ( 0.07 ) ( 0.427 ) I-M Price RB m1 0.022 0.137 0.006 -0.174 ( 0.05 ) ( 0.204 ) ( 0.018 ) ( 0.11 ) I-M Price RB m2 0.018 -0.238 -0.004 0.286 ** ( 0.052 ) ( 0.205 ) ( 0.018 ) ( 0.114 ) I-M Price RB m3 0.069 -0.238 0.02 0.035 ( 0.053 ) ( 0.207 ) ( 0.018 ) ( 0.118 ) N 383 383 383 383 Adj. R2 0.2107 0.2396 0.5827 0.0232 Table C.22: Powder Dishwasher Soap - Price Reaction Functions

Variable Unilever Procter & Dominicks Reckitt Gamble Benckiser Intercept 0.035 -0.335 ** -0.025 0.029 ( 0.055 ) ( 0.109 ) ( 0.025 ) ( 0.033 ) O-M Price UniL m1 0.009 -0.024 -0.084 0.001 ( 0.011 ) ( 0.04 ) ( 0.052 ) ( 0.173 ) O-M Price UniL m2 -0.003 -0.01 0.12 ** -0.006 ( 0.012 ) ( 0.043 ) ( 0.055 ) ( 0.168 ) O-M Price UniL m3 0.009 -0.037 0.01 0.323 ** ( 0.011 ) ( 0.039 ) ( 0.05 ) ( 0.162 ) O-M Price PG m1 -0.013 * -0.056 ** 0.018 -0.01 ( 0.007 ) ( 0.023 ) ( 0.014 ) ( 0.038 ) O-M Price PG m2 0.011 -0.05 ** 0.005 0.065 * ( 0.007 ) ( 0.024 ) ( 0.015 ) ( 0.039 ) O-M Price PG m3 0.003 -0.042 * 0.014 0.005 ( 0.007 ) ( 0.024 ) ( 0.015 ) ( 0.038 ) O-M Price Dom m1 1.102 ** -0.132 0.039 ** 0.083 ( 0.442 ) ( 0.17 ) ( 0.016 ) ( 0.365 ) O-M Price Dom m2 0.213 0.052 -0.022 -0.217 continued on the next page Table C.23: Fabric Softener Sheets - Price Reaction Func- tions continued 193 continued from the previous page Variable Unilever Procter & Dominicks Reckitt Gamble Benckiser ( 0.505 ) ( 0.171 ) ( 0.017 ) ( 0.393 ) O-M Price Dom m3 0.398 -0.157 -0.02 0.479 ( 0.422 ) ( 0.157 ) ( 0.015 ) ( 0.358 ) O-M Price RB m1 1.064 -6.811 -1.781 ** 0.324 ** ( 0.974 ) ( 4.378 ) ( 0.63 ) ( 0.088 ) O-M Price RB m2 1.524 -6.39 -0.535 -0.014 ( 1.024 ) ( 4.555 ) ( 0.65 ) ( 0.091 ) O-M Price RB m3 1.984 * -8.536 * -0.398 -0.041 ( 1.056 ) ( 4.64 ) ( 0.657 ) ( 0.095 ) I-M Price UniL m1 0.289 ** 0.316 * -0.032 0.01 ( 0.086 ) ( 0.165 ) ( 0.046 ) ( 0.05 ) I-M Price UniL m2 0.003 0.138 -0.038 -0.049 ( 0.083 ) ( 0.17 ) ( 0.048 ) ( 0.049 ) I-M Price UniL m3 0.142 * 0.258 0.074 -0.04 ( 0.079 ) ( 0.165 ) ( 0.046 ) ( 0.048 ) I-M Price PG m1 -0.047 0.552 ** 0.005 0.008 ( 0.042 ) ( 0.082 ) ( 0.023 ) ( 0.025 ) I-M Price PG m2 0.088 ** 0.148 * 0.019 0.002 ( 0.043 ) ( 0.088 ) ( 0.024 ) ( 0.026 ) I-M Price PG m3 -0.015 0.122 0.004 0.008 ( 0.04 ) ( 0.084 ) ( 0.023 ) ( 0.024 ) I-M Price Dom m1 -0.042 -0.332 0.442 ** -0.121 ( 0.142 ) ( 0.285 ) ( 0.078 ) ( 0.086 ) I-M Price Dom m2 -0.158 -0.051 0.237 ** -0.147 * ( 0.152 ) ( 0.299 ) ( 0.081 ) ( 0.089 ) I-M Price Dom m3 -0.076 0.406 0.132 * 0.11 ( 0.146 ) ( 0.274 ) ( 0.075 ) ( 0.085 ) I-M Price RB m1 -0.221 0.462 0.004 0.205 ** ( 0.17 ) ( 0.339 ) ( 0.091 ) ( 0.094 ) I-M Price RB m2 -0.244 0.37 0.056 0.342 ** ( 0.171 ) ( 0.341 ) ( 0.093 ) ( 0.097 ) I-M Price RB m3 -0.044 -0.192 -0.118 -0.064 ( 0.173 ) ( 0.335 ) ( 0.09 ) ( 0.1 ) N 384 384 384 384 Adj. R2 0.3656 0.5897 0.5710 0.1957 Table C.23: Fabric Softener Sheets - Price Reaction Func- tions continued

194 Variable Unilever Procter & Gamble Dominicks Intercept -0.305 ** 0.039 -0.044 ** ( 0.154 ) ( 0.22 ) ( 0.022 ) ** O-M Price UniL m1 0.004 0.219 ** -0.031 ( 0.032 ) ( 0.091 ) ( 0.056 ) O-M Price UniL m2 -0.002 -0.148 0.002 ( 0.035 ) ( 0.099 ) ( 0.061 ) O-M Price UniL m3 -0.014 0.028 -0.006 ( 0.031 ) ( 0.088 ) ( 0.054 ) O-M Price PG m1 -0.003 -0.006 0.019 ( 0.019 ) ( 0.052 ) ( 0.016 ) O-M Price PG m2 -0.007 -0.009 -0.003 ( 0.019 ) ( 0.052 ) ( 0.016 ) O-M Price PG m3 -0.046 ** -0.02 0.009 ( 0.02 ) ( 0.054 ) ( 0.016 ) O-M Price Dom m1 1.114 -0.239 0.033 ** ( 1.248 ) ( 0.374 ) ( 0.017 ) ** O-M Price Dom m2 1.091 0.764 ** 0.009 ( 1.546 ) ( 0.389 ) ( 0.017 ) O-M Price Dom m3 0.798 -0.254 -0.011 ( 1.267 ) ( 0.374 ) ( 0.017 ) I-M Price UniL m1 0.296 ** 0.126 -0.012 ( 0.093 ) ( 0.145 ) ( 0.019 ) I-M Price UniL m2 0.264 ** 0.051 -0.017 ( 0.095 ) ( 0.149 ) ( 0.02 ) I-M Price UniL m3 0.156 * -0.062 0.029 ( 0.088 ) ( 0.144 ) ( 0.019 ) I-M Price PG m1 -0.067 0.198 ** -0.004 ( 0.056 ) ( 0.093 ) ( 0.012 ) I-M Price PG m2 -0.077 0.239 ** 0.015 ( 0.057 ) ( 0.089 ) ( 0.011 ) I-M Price PG m3 -0.002 0.299 ** 0.003 ( 0.058 ) ( 0.09 ) ( 0.011 ) I-M Price Dom m1 -1.046 ** 1.462 * 0.088 ( 0.531 ) ( 0.791 ) ( 0.098 ) I-M Price Dom m2 -0.91 * 0.252 0.118 ( 0.54 ) ( 0.818 ) ( 0.104 ) I-M Price Dom m3 0.473 0.706 0.109 ( 0.542 ) ( 0.814 ) ( 0.105 ) N 387 387 387 Adj. R2 0.4626 0.2387 0.0466

Table C.24: Liquid Fabric Softener - Price Reaction Functions 195 Variable Unilever Dial Arm & Colgate- Hammer Palmolive Intercept -2.367 0.131 0.032 0.14 ( 1.533 ) ( 0.501 ) ( 0.043 ) ( 0.419 ) O-M Price UniL m1 -10.296 ** -7.657 * 0.116 -0.512 ( 4.728 ) ( 4.522 ) ( 0.101 ) ( 0.887 ) O-M Price UniL m2 -1.66 0.646 0.153 0.369 ( 4.66 ) ( 5.031 ) ( 0.112 ) ( 0.852 ) O-M Price UniL m3 -10.222 ** -8.834 * -0.026 0.132 ( 4.875 ) ( 5.115 ) ( 0.113 ) ( 0.843 ) O-M Price Dial m1 -65.13 30.8 -3.264 ** ( 153.663 ) ( 69.904 ) ( 1.618 ) O-M Price Dial m2 104.09 73.799 1.28 ( 182.591 ) ( 82.297 ) ( 1.872 ) O-M Price Dial m3 -7.682 -43.561 -3.272 ** ( 155.422 ) ( 70.144 ) ( 1.578 ) O-M Price AH m1 25.813 * 7.045 0.113 8.855 ( 15.569 ) ( 7.053 ) ( 0.153 ) ( 5.935 ) O-M Price AH m2 10.55 1.479 0.054 1.412 ( 15.159 ) ( 6.867 ) ( 0.152 ) ( 5.899 ) O-M Price AH m3 18.267 -3.111 0.01 -9.103 ( 14.995 ) ( 6.858 ) ( 0.152 ) ( 5.855 ) O-M Price CG m1 -4.195 2.286 0.102 -0.217 ( 3.974 ) ( 7.989 ) ( 0.145 ) ( 0.399 ) O-M Price CG m2 4.172 -5.262 0.064 1.282 ** ( 3.91 ) ( 7.896 ) ( 0.144 ) ( 0.401 ) O-M Price CG m3 7.394 * -4.731 -0.149 0.709 * ( 4.011 ) ( 7.702 ) ( 0.141 ) ( 0.398 ) O-M Price PG m1 -1.4 -1.242 0.029 0.268 ( 2.085 ) ( 1.543 ) ( 0.033 ) ( 0.661 ) O-M Price PG m2 -0.328 -0.025 -0.008 0.552 ( 2.012 ) ( 1.604 ) ( 0.035 ) ( 0.623 ) O-M Price PG m3 0.965 2.035 0 -0.304 ( 2.035 ) ( 1.509 ) ( 0.033 ) ( 0.617 ) O-M Price Dom m1 -23.587 26.48 -0.478 0.152 ( 22.201 ) ( 26.271 ) ( 0.588 ) ( 4.01 ) O-M Price Dom m2 -0.571 6.62 0.86 -1.246 ( 21.532 ) ( 28.38 ) ( 0.622 ) ( 3.555 ) O-M Price Dom m3 -12.782 4.384 -0.01 -2.702 ( 21.086 ) ( 26.887 ) ( 0.593 ) ( 3.853 ) I-M Price UniL m1 0.201 0.021 0.009 * -0.064 * continued on the next page Table C.25: Liquid Laundry Detergent - Price Reaction Functions 196 continued from the previous page Variable Unilever Dial Arm & Colgate- Hammer Palmolive ( 0.14 ) ( 0.055 ) ( 0.005 ) ( 0.038 ) I-M Price UniL m2 -0.001 -0.02 -0.005 0.12 ** ( 0.147 ) ( 0.06 ) ( 0.005 ) ( 0.041 ) I-M Price UniL m3 -0.007 -0.042 -0.001 -0.029 ( 0.131 ) ( 0.054 ) ( 0.005 ) ( 0.037 ) I-M Price Dial m1 -0.013 -0.062 0.01 -0.07 ( 0.288 ) ( 0.119 ) ( 0.01 ) ( 0.078 ) I-M Price Dial m2 0.017 -0.06 0.006 0.018 ( 0.283 ) ( 0.117 ) ( 0.01 ) ( 0.076 ) I-M Price Dial m3 -0.014 -0.022 0.008 0.028 ( 0.289 ) ( 0.114 ) ( 0.01 ) ( 0.078 ) I-M Price AH m1 1.375 -0.589 0.841 ** 0.073 ( 2.086 ) ( 0.865 ) ( 0.074 ) ( 0.6 ) I-M Price AH m2 0.543 0.54 0.063 0.314 ( 2.668 ) ( 1.133 ) ( 0.098 ) ( 0.781 ) I-M Price AH m3 -1.384 -0.506 -0.046 0.036 ( 2.057 ) ( 0.857 ) ( 0.073 ) ( 0.581 ) I-M Price CG m1 0.076 -0.017 0.017 -0.287 ** ( 0.422 ) ( 0.168 ) ( 0.014 ) ( 0.119 ) I-M Price CG m2 -0.62 0.053 0.039 ** 0.409 ** ( 0.404 ) ( 0.158 ) ( 0.013 ) ( 0.111 ) I-M Price CG m3 -0.741 * 0.034 -0.004 0.05 ( 0.418 ) ( 0.166 ) ( 0.014 ) ( 0.117 ) I-M Price PG m1 0.124 0.055 * 0.002 0.003 ( 0.078 ) ( 0.032 ) ( 0.003 ) ( 0.023 ) I-M Price PG m2 -0.087 0.07 * * 0.001 -0.027 ( 0.083 ) ( 0.034 ) ( 0.003 ) ( 0.024 ) I-M Price PG m3 -0.086 -0.011 0.004 0.013 ( 0.088 ) ( 0.036 ) ( 0.003 ) ( 0.025 ) I-M Price Dom m1 4.332 0.078 0.594 ** -3.782 ** ( 6.661 ) ( 3.009 ) ( 0.26 ) ( 1.801 ) I-M Price Dom m2 -2.257 -2.193 -0.265 1.75 ( 8.009 ) ( 3.694 ) ( 0.311 ) ( 2.147 ) I-M Price Dom m3 6.85 2.532 0.052 -0.375 ( 7.063 ) ( 2.995 ) ( 0.253 ) ( 1.842 ) I-M Price RB m1 0.852 -0.222 -0.012 -0.201 ( 0.985 ) ( 0.385 ) ( 0.033 ) ( 0.271 ) I-M Price RB m2 0.812 -0.107 0.033 -0.094 ( 0.978 ) ( 0.389 ) ( 0.033 ) ( 0.275 ) continued on the next page Table C.25: Liquid Laundry Detergent - Price Reaction Functions 197 continued from the previous page Variable Unilever Dial Arm & Colgate- Hammer Palmolive I-M Price RB m3 0.018 0.079 0.047 0.251 ( 0.932 ) ( 0.37 ) ( 0.032 ) ( 0.27 ) N 387 387 387 387 Adj. R2 0.0080 -0.0175 0.8580 0.1218 Table C.25: Liquid Laundry Detergent - Price Reaction Functions

Variable Procter & Dominicks Reckitt Gamble Benckiser Intercept -7.839 ** 0.032 ** -0.347 ** ( 2.381 ) ( 0.015 ) ( 0.122 ) O-M Price UniL m1 2.113 -0.119 ( 9.074 ) ( 0.097 ) O-M Price UniL m2 -7.57 -0.007 ( 9.275 ) ( 0.098 ) O-M Price UniL m3 15.029 0.327 ** ( 9.252 ) ( 0.094 ) O-M Price Dial m1 -110.837 14.904 ** ( 190.182 ) ( 6.389 ) O-M Price Dial m2 262.259 6.592 ( 227.939 ) ( 7.321 ) O-M Price Dial m3 79.255 -18.166 ** ( 195.067 ) ( 6.309 ) O-M Price AH m1 3.937 -0.288 ( 19.062 ) ( 0.611 ) O-M Price AH m2 10.727 -0.051 ( 18.621 ) ( 0.602 ) O-M Price AH m3 2.378 0.681 ( 18.727 ) ( 0.6 ) O-M Price CG m1 5.53 0.099 ( 10.197 ) ( 0.082 ) O-M Price CG m2 -3.602 0.036 ( 10.13 ) ( 0.082 ) O-M Price CG m3 -10.314 -0.075 ( 10.701 ) ( 0.086 ) continued on the next page Table C.26: Liquid Laundry Detergent - Price Reaction Functions continued 198 continued from the previous page Variable Procter & Dominicks Reckitt Gamble Benckiser O-M Price PG m1 -3.867 * 0.021 ** ( 2.279 ) ( 0.01 ) O-M Price PG m2 -4.043 * 0.023 ** ( 2.366 ) ( 0.01 ) O-M Price PG m3 -1.625 0.012 ( 2.289 ) ( 0.01 ) O-M Price Dom m1 2.603 0.014 ( 3.996 ) ( 0.008 ) O-M Price Dom m2 -1.669 -0.009 ( 4.078 ) ( 0.009 ) O-M Price Dom m3 -4.392 0.009 ( 4.109 ) ( 0.009 ) I-M Price UniL m1 0.23 0 0.014 ( 0.267 ) ( 0.002 ) ( 0.015 ) I-M Price UniL m2 -0.108 0.001 -0.022 ( 0.274 ) ( 0.002 ) ( 0.017 ) I-M Price UniL m3 0.209 0.001 0.009 ( 0.251 ) ( 0.002 ) ( 0.015 ) I-M Price Dial m1 -0.391 0 0.003 ( 0.533 ) ( 0.003 ) ( 0.031 ) I-M Price Dial m2 0.396 0.006 * -0.021 ( 0.528 ) ( 0.003 ) ( 0.03 ) I-M Price Dial m3 -1.05 ** 0.002 -0.022 ( 0.529 ) ( 0.003 ) ( 0.031 ) I-M Price AH m1 -1.491 0.027 0.526 ** ( 3.885 ) ( 0.024 ) ( 0.228 ) I-M Price AH m2 -4.582 -0.085 ** 0.085 ( 4.937 ) ( 0.031 ) ( 0.307 ) I-M Price AH m3 1.987 0.015 -0.025 ( 3.926 ) ( 0.024 ) ( 0.22 ) I-M Price CG m1 0.296 -0.007 0.037 ( 0.779 ) ( 0.005 ) ( 0.047 ) I-M Price CG m2 -0.364 0.006 0.015 ( 0.743 ) ( 0.005 ) ( 0.043 ) I-M Price CG m3 1.537 ** 0.008 * -0.037 ( 0.76 ) ( 0.005 ) ( 0.046 ) I-M Price PG m1 -0.428 ** -0.001 -0.012 ( 0.15 ) ( 0.001 ) ( 0.009 ) I-M Price PG m2 -0.125 0 -0.006 continued on the next page Table C.26: Liquid Laundry Detergent - Price Reaction Functions continued 199 continued from the previous page Variable Procter & Dominicks Reckitt Gamble Benckiser ( 0.158 ) ( 0.001 ) ( 0.009 ) I-M Price PG m3 -0.121 0 -0.007 ( 0.163 ) ( 0.001 ) ( 0.01 ) I-M Price Dom m1 0.595 0.625 ** 0.222 ( 12.565 ) ( 0.074 ) ( 0.686 ) I-M Price Dom m2 12.329 0.187 ** -0.882 ( 15.158 ) ( 0.088 ) ( 0.874 ) I-M Price Dom m3 -2.503 -0.138 * -0.212 ( 13.353 ) ( 0.077 ) ( 0.721 ) I-M Price RB m1 2.625 0.013 -0.035 ( 1.745 ) ( 0.011 ) ( 0.108 ) I-M Price RB m2 0.029 -0.009 -0.104 ( 1.74 ) ( 0.011 ) ( 0.109 ) I-M Price RB m3 -3.352 ** 0.012 0.211 ** ( 1.645 ) ( 0.01 ) ( 0.104 ) N 387 387 387 Adj. R2 0.0167 0.7546 0.1772 Table C.26: Liquid Laundry Detergent - Price Reaction Functions continued

Variable Unilever Dial Arm& Procter& Dominicks Hammer Gamble Intercept -0.102 -0.004 -0.104 -0.277 -0.017 ( 0.107 ) ( 0.004 ) ( 0.066 ) ( 0.322 ) ( 0.016 ) O-M Price UniL m1 -0.021 0 0.063 ** -0.055 -0.063 ** ( 0.025 ) ( 0.001 ) ( 0.029 ) ( 0.132 ) ( 0.031 ) O-M Price UniL m2 0.021 -0.001 -0.036 -0.077 0.03 ( 0.027 ) ( 0.001 ) ( 0.031 ) ( 0.138 ) ( 0.032 ) O-M Price UniL m3 -0.013 0 -0.004 0.049 0.011 ( 0.024 ) ( 0.001 ) ( 0.027 ) ( 0.128 ) ( 0.03 ) O-M Price Dial m1 -0.041 0.001 -0.024 -0.078 -0.05 ( 0.053 ) ( 0.001 ) ( 0.062 ) ( 0.281 ) ( 0.068 ) O-M Price Dial m2 -0.071 0 -0.051 -0.116 0.088 ( 0.052 ) ( 0.001 ) ( 0.061 ) ( 0.277 ) ( 0.067 ) O-M Price Dial m3 -0.032 0.001 0.023 -0.208 0.053 continued on the next page Table C.27: Powder Laundry Detergent - Price Reaction Functions 200 continued from the previous page Variable Unilever Dial Arm& Procter& Dominicks Hammer Gamble ( 0.052 ) ( 0.001 ) ( 0.059 ) ( 0.278 ) ( 0.068 ) O-M Price AH m1 0.096 -0.001 1.11 ** 2.389 0.068 ( 0.383 ) ( 0.009 ) ( 0.441 ) ( 2.063 ) ( 0.507 ) O-M Price AH m2 -0.36 0.016 -0.042 -4.394 -0.281 ( 0.517 ) ( 0.013 ) ( 0.585 ) ( 2.799 ) ( 0.684 ) O-M Price AH m3 0.261 -0.007 -0.329 0.533 -0.217 ( 0.387 ) ( 0.01 ) ( 0.467 ) ( 2.161 ) ( 0.522 ) O-M Price PG m1 -0.012 0 -0.021 -0.204 ** -0.009 ( 0.015 ) ( 0 ) ( 0.017 ) ( 0.074 ) ( 0.009 ) O-M Price PG m2 -0.018 0 0.002 -0.144 * -0.012 ( 0.015 ) ( 0 ) ( 0.018 ) ( 0.078 ) ( 0.009 ) O-M Price PG m3 -0.009 0 -0.011 -0.03 -0.009 ( 0.016 ) ( 0 ) ( 0.018 ) ( 0.082 ) ( 0.009 ) O-M Price Dom m1 1.311 0.041 2.198 -0.14 -0.037 ** ( 0.979 ) ( 0.032 ) ( 1.479 ) ( 0.541 ) ( 0.009 ) O-M Price Dom m2 1.049 0.019 -2.467 0.216 0.024 ** ( 1.151 ) ( 0.038 ) ( 1.63 ) ( 0.561 ) ( 0.01 ) O-M Price Dom m3 -0.198 0.027 0.533 -1.011 * 0.009 ( 0.994 ) ( 0.032 ) ( 1.404 ) ( 0.575 ) ( 0.01 ) I-M Price UniL m1 -0.006 -0.006 -0.018 -0.045 0.083 ** ( 0.106 ) ( 0.005 ) ( 0.072 ) ( 0.311 ) ( 0.016 ) I-M Price UniL m2 -0.12 0.001 0.144 * -0.646 * -0.03 * ( 0.117 ) ( 0.005 ) ( 0.08 ) ( 0.343 ) ( 0.018 ) I-M Price UniL m3 -0.068 -0.003 0.059 1.411 ** 0.023 ( 0.117 ) ( 0.006 ) ( 0.081 ) ( 0.338 ) ( 0.017 ) I-M Price Dial m1 0.523 0.66 ** 0.423 5.392 -0.077 ( 1.598 ) ( 0.075 ) ( 1.137 ) ( 4.83 ) ( 0.242 ) I-M Price Dial m2 -1.316 -0.01 0.184 -0.802 0.188 ( 1.923 ) ( 0.09 ) ( 1.322 ) ( 5.744 ) ( 0.287 ) I-M Price Dial m3 0.617 -0.004 0.721 2.234 -0.137 ( 1.609 ) ( 0.075 ) ( 1.108 ) ( 4.758 ) ( 0.243 ) I-M Price AH m1 0.24 0.008 0.137 0.201 -0.01 ( 0.164 ) ( 0.008 ) ( 0.111 ) ( 0.484 ) ( 0.024 ) I-M Price AH m2 0.097 0.008 0.264 ** 0.404 0.028 ( 0.159 ) ( 0.007 ) ( 0.106 ) ( 0.469 ) ( 0.023 ) I-M Price AH m3 -0.011 0.002 0.099 0.352 -0.009 ( 0.148 ) ( 0.007 ) ( 0.099 ) ( 0.439 ) ( 0.022 ) I-M Price PG m1 -0.016 -0.001 -0.004 0.234 ** -0.007 ( 0.034 ) ( 0.002 ) ( 0.023 ) ( 0.097 ) ( 0.005 ) continued on the next page Table C.27: Powder Laundry Detergent - Price Reaction Functions 201 continued from the previous page Variable Unilever Dial Arm& Procter& Dominicks Hammer Gamble I-M Price PG m2 0.009 0 -0.022 0.262 ** -0.002 ( 0.035 ) ( 0.002 ) ( 0.024 ) ( 0.101 ) ( 0.005 ) I-M Price PG m3 -0.021 0 0.01 0.203 ** 0.015 ** ( 0.034 ) ( 0.002 ) ( 0.023 ) ( 0.098 ) ( 0.005 ) I-M Price Dom m1 0.344 -0.014 -0.808 ** 5.027 ** 0.39 ** ( 0.608 ) ( 0.028 ) ( 0.411 ) ( 1.919 ) ( 0.097 ) I-M Price Dom m2 -0.355 0.036 0.291 -4.282 ** 0.283 ** ( 0.661 ) ( 0.03 ) ( 0.441 ) ( 2.018 ) ( 0.103 ) I-M Price Dom m3 0.717 0.053 * -0.256 -0.613 -0.008 ( 0.593 ) ( 0.027 ) * ( 0.407 ) ( 1.707 ) ( 0.085 ) N 387 387 387 387 387 Adj. R2 0.1154 0.74 83 0.4638 0.5747 0.3178 Table C.27: Powder Laundry Detergent - Price Reaction Functions

Variable Mentadent Church & Arm & Colgate- Dwight Hammer Palmolive Intercept -0.083 ** 0 0.003 -0.004 ( 0.041 ) ( 0.0001 ) ( 0.015 ) ( 0.067 ) O-M Price AH m1 -0.043 -0.067 ( 0.039 ) ( 0.738 ) O-M Price AH m2 -0.064 -1.614 * ( 0.04 ) ( 0.913 ) O-M Price AH m3 0.001 1.37 ** ( 0.035 ) ( 0.675 ) O-M Price CG m1 0.009 -0.022 ( 0.016 ) ( 0.059 ) O-M Price CG m2 0 0.076 ( 0.016 ) ( 0.059 ) O-M Price CG m3 0.003 0.016 ( 0.017 ) ( 0.058 ) O-M Price PG m1 -0.001 -0.011 ( 0.003 ) ( 0.025 ) O-M Price PG m2 -0.003 0.014 ( 0.003 ) ( 0.026 ) continued on the next page Table C.28: Toothpaste - Price Reaction Functions

202 continued from the previous page Variable Mentadent Church & Arm & Colgate- Dwight Hammer Palmolive O-M Price PG m3 -0.004 -0.026 ( 0.003 ) ( 0.026 ) O-M Price Dom m1 -0.432 ** 0.449 ( 0.152 ) ( 0.624 ) O-M Price Dom m2 0.456 ** -0.145 ( 0.172 ) ( 0.59 ) O-M Price Dom m3 0.131 0.461 ( 0.163 ) ( 0.6 ) I-M Price MentD m1 -0.147 0.0001 -0.002 -0.03 ( 0.107 ) ( 0.0002 ) ( 0.033 ) ( 0.134 ) I-M Price MentD m2 -0.029 -0.0002 0.005 0.083 ( 0.105 ) ( 0.0002 ) ( 0.033 ) ( 0.133 ) I-M Price MentD m3 0.107 0.0001 -0.083 ** 0.142 ( 0.105 ) ( 0.0002 ) ( 0.034 ) ( 0.134 ) I-M Price CD m1 38.007 0.7822 ** 6.865 -51.308 ( 43.59 ) ( 0.0663 ) ( 14.412 ) ( 56.203 ) I-M Price CD m2 35.447 0.2424 ** -6.681 39.159 ( 55.552 ) ( 0.0845 ) ( 16.871 ) ( 68.737 ) I-M Price CD m3 -64.765 -0.1264 * -1.797 47.849 ( 43.772 ) ( 0.0666 ) ( 13.925 ) ( 55.248 ) I-M Price AH m1 -0.709 ** -0.0004 0.118 0.5 ( 0.315 ) ( 0.0005 ) ( 0.103 ) ( 0.425 ) I-M Price AH m2 0.076 0.0003 0.148 0.331 ( 0.306 ) ( 0.0005 ) ( 0.099 ) ( 0.417 ) I-M Price AH m3 0.331 -0.0002 0.223 ** 0.62 ( 0.296 ) ( 0.0004 ) ( 0.093 ) ( 0.407 ) I-M Price CG m1 0.081 0.0001 0 -0.143 ( 0.092 ) ( 0.0001 ) ( 0.029 ) ( 0.118 ) I-M Price CG m2 0.059 -0.0001 0.095 ** 0.115 ( 0.093 ) ( 0.0001 ) ( 0.03 ) ( 0.114 ) I-M Price CG m3 0.117 -0.0001 0.015 0.242 ** ( 0.085 ) ( 0.0001 ) ( 0.028 ) ( 0.105 ) I-M Price PG m1 0.315 ** 0.0001 0.029 0.186 ( 0.086 ) ( 0.0001 ) ( 0.028 ) ( 0.119 ) I-M Price PG m2 -0.156 ** 0 0.01 0.152 ( 0.078 ) ( 0.0001 ) ( 0.028 ) ( 0.112 ) I-M Price PG m3 -0.037 0 0.054 ** -0.037 ( 0.077 ) ( 0.0001 ) ( 0.026 ) ( 0.106 ) I-M Price Dom m1 -0.163 0.0033 ** 0.208 -1.494 continued on the next page Table C.28: Toothpaste - Price Reaction Functions

203 continued from the previous page Variable Mentadent Church & Arm & Colgate- Dwight Hammer Palmolive ( 0.846 ) ( 0.0013 ) ( 0.263 ) ( 1.056 ) I-M Price Dom m2 -0.087 -0.0004 0.16 -1.842 ( 0.924 ) ( 0.0014 ) ( 0.278 ) ( 1.155 ) I-M Price Dom m3 -0.039 -0.004 ** 0.4 -0.64 ( 0.92 ) ( 0.0014 ) ( 0.278 ) ( 1.16 ) I-M Price GSK m1 -0.119 -0.0002 0.052 0.145 ( 0.204 ) ( 0.0003 ) ( 0.065 ) ( 0.272 ) I-M Price GSK m2 -0.263 -0.0003 -0.066 0.307 ( 0.213 ) ( 0.0003 ) ( 0.069 ) ( 0.289 ) I-M Price GSK m3 -0.143 0.0002 -0.121 * -0.03 ( 0.208 ) ( 0.0003 ) ( 0.065 ) ( 0.275 ) N 389 389 389 389 Adj. R2 0.0422 0.7641 0.4952 0.2291 Table C.28: Toothpaste - Price Reaction Functions

Variable Procter& Dominicks GlaxoSmith Gamble Kline Intercept -0.006 0.0006 -0.0531 * ( 0.064 ) ( 0.0051 ) ( 0.022 ) O-M Price AH m1 0.284 0.1314 ( 0.274 ) ( 0.1234 ) O-M Price AH m2 0.323 -0.0481 ( 0.298 ) ( 0.1366 ) O-M Price AH m3 -0.512 ** 0.0319 ( 0.244 ) ( 0.1132 ) O-M Price CG m1 0.036 0.0103 ( 0.07 ) ( 0.0219 ) O-M Price CG m2 0.064 -0.0475 ** ( 0.067 ) ( 0.0216 ) O-M Price CG m3 0.057 0.0132 ( 0.073 ) ( 0.022 ) O-M Price PG m1 0.002 0.0012 ( 0.013 ) ( 0.0028 ) O-M Price PG m2 0.015 -0.0031 ( 0.014 ) ( 0.0028 ) continued on the next page Table C.29: Toothpaste - Price Reaction Functions continued 204 continued from the previous page Variable Procter& Dominicks GlaxoSmith Gamble Kline O-M Price PG m3 0.002 0.0001 ( 0.015 ) ( 0.0029 ) O-M Price Dom m1 -0.139 0.0006 ( 0.097 ) ( 0.0031 ) O-M Price Dom m2 -0.197 * -0.0025 ( 0.104 ) ( 0.0033 ) O-M Price Dom m3 0.103 -0.0085 ** ( 0.103 ) ( 0.0033 ) I-M Price MentD m1 0.261 ** 0.0136 -0.0081 ( 0.132 ) ( 0.0122 ) ( 0.0569 ) I-M Price MentD m2 -0.194 -0.0004 -0.0474 ( 0.129 ) ( 0.012 ) ( 0.0558 ) I-M Price MentD m3 -0.161 -0.013 0.0554 ( 0.131 ) ( 0.0121 ) ( 0.0562 ) I-M Price CD m1 187.028 ** 2.9325 -17.1937 ( 59.72 ) ( 5.0643 ) ( 23.2297 ) I-M Price CD m2 -117.777 * -5.8317 -4.3906 ( 68.833 ) ( 6.1127 ) ( 29.6044 ) I-M Price CD m3 -49.019 -0.2438 6.1714 ( 59.048 ) ( 5.2135 ) ( 23.3266 ) I-M Price AH m1 0.475 0.0451 -0.029 ( 0.435 ) ( 0.0405 ) ( 0.1676 ) I-M Price AH m2 -0.564 0.0854 ** -0.2788 * ( 0.419 ) ( 0.0376 ) ( 0.1633 ) I-M Price AH m3 -0.713 * -0.002 0.0926 ( 0.382 ) ( 0.0343 ) ( 0.1575 ) I-M Price CG m1 0.352 ** -0.0082 0.1243 ** ( 0.116 ) ( 0.0105 ) ( 0.049 ) I-M Price CG m2 0.068 0.0087 0.017 ( 0.115 ) ( 0.0103 ) ( 0.0498 ) I-M Price CG m3 0.06 -0.0211 ** 0.015 ( 0.107 ) ( 0.0097 ) ( 0.0453 ) I-M Price PG m1 -0.061 -0.0003 0.0665 ( 0.116 ) ( 0.0101 ) ( 0.046 ) I-M Price PG m2 0.125 0.0064 0.0433 ( 0.111 ) ( 0.0097 ) ( 0.0418 ) I-M Price PG m3 0.118 0.0058 -0.0197 ( 0.104 ) ( 0.0092 ) ( 0.0411 ) I-M Price Dom m1 0.421 0.2794 ** 0.2527 continued on the next page Table C.29: Toothpaste - Price Reaction Functions continued 205 continued from the previous page Variable Procter& Dominicks GlaxoSmith Gamble Kline ( 1.057 ) ( 0.0944 ) ( 0.4509 ) I-M Price Dom m2 0.864 0.2721 ** -0.1359 ( 1.142 ) ( 0.1024 ) ( 0.4924 ) I-M Price Dom m3 3.511 ** 0.0602 -0.7997 ( 1.139 ) ( 0.1033 ) ( 0.49 ) I-M Price GSK m1 0.672 ** -0.0113 -0.0437 ( 0.269 ) ( 0.0238 ) ( 0.1088 ) I-M Price GSK m2 0.397 0.0053 0.0714 ( 0.284 ) ( 0.0255 ) ( 0.1134 ) I-M Price GSK m3 0.108 0.0483 * 0.0673 ( 0.275 ) ( 0.0248 ) ( 0.1106 ) N 389 389 389 Adj. R2 0.3287 0.3006 0.1608 Table C.29: Toothpaste - Price Reaction Functions continued

206 BIBLIOGRAPHY

Ailawadi, K. L., Lehmann, D. R., and Neslin, S. A. (2001). Market response to a major policy change in the marketing mix: Learning from Procter & Gamble’s value pricing strategy. Journal of Marketing, 65(1):44–61.

Asker, J. (2004). Measuring cost advantages from exclusive dealing: An empirical study of beer distribution. Working Paper.

Bagwell, K. and Ramey, G. (1994). Advertising as information: Matching products to buyers. Journal of Economics & Management Strategy, 2(2):199–243.

Bernheim, B. D. and Whinston, M. D. (1990). Multimarket contact and collusive behavior. RAND Journal of Economics, 21(1):1–26.

Blattberg, R. C., Briesch, R., and Fox, E. J. (1995). How promotions work. Marketing Science, 14(3):G122–G132.

Campbell, J. Y. and Mankiw, N. G. (1987). Are output ﬂuctuations transitory? Quarterly Journal of Economics, 102(4):857–880.

Chevalier, J. A., Kashyap, A. K., and Rossi, P. E. (2003). Why don’t prices rise during periods of peak demand? evidence from scanner data. American Eco- nomic Review, 93(1):15–37.

Culbertson, W. P. and Bradford, D. (1991). The price of beer: Some evidence from interstate comparisons. International Journal of Industrial Organization, 9(2):275–289.

Dekimpe, M. G. and Hanssens, D. M. (1995). The persistence of marketing effects on sales. Marketing Science, 14(1):1–21.

Deneckere, R., Marvel, H. P., and Peck, J. (1996). Demand uncertainty, inventories, and resale price maintenance. Quarterly Journal of Economics, 111(2):885–913.

Deneckere, R., Marvel, H. P.,and Peck, J. (1997). Demand uncertainty and price maintenance: Markdowns as destructive competition. American Economic Review, 87(4):619–641.

207 Evans, W. N. and Kessides, I. N. (1994). Living by the “golden rule”: Multi- market contact in the u.s. ariline industry. Quarterly Journal of Economics, 2:341–366.

Jordan, W. J. and Jaffee, B. L. (1987). The use of exclusive territories in the diestribution of beer: Theoretical and empirical observations. The Antitrust Bulletin, Spring.

Klein, B. and Murphy, K. M. (1988). Vertical restraints as contract enforcement mechanisms. Journal of Law and Economics, 31(2):265–297.

Lal, R. and Villas-Boas, M. (1998). Price promotions and trade deals with mul- tiproduct retailers. Management Science, 44(7):935–949.

Leeﬂang, P.S. and Wittink, D. R. (1992). Diagnosing competitive reactions using (aggregated) scanner data. International Journal of Research in Marketing, 9(1):39–57.

Leeﬂang, P.S. and Wittink, D. R. (1996). Competitive reaction versus consumer response: Do managers overreact? International Journal of Research in Mar- keting, 13(2):103–119.

Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2):311–329.

Nelson, P. (1974). Advertising as information. Journal of Political Economy, 82(4):729–754.

Rey, P. and Stiglitz, J. (1995). The role of exclusive territories in producers’ competition. RAND Journal of Economics, 26(3):431–451.

Sass, T. R. and Saurman, D. S. (1993). Mandated exclusive territories and economic efﬁciency: An empirical analysis of the malt-beverage industry. Journal of Law and Economics, 36(1).

Sass, T. R. and Saurman, D. S. (1996). Efﬁciency effects of exclusive territories: Evidence from the indiana beer market. Economic Inquiry, 34.

Varian, H. R. (1980). A model of sales. American Economic Review, 70(4):651– 659.

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