DO HOUSEHOLDS SUBSTITUTE AMONG LUXURY GOODS?

THE IMPACT OF THE GREAT RECESSION ON FRAGRANCE CONSUMPTION

Submitted to Princeton University Department of Economics

In Partial Fulfillment of the Requirements for the A.B. Degree

13 April 2016

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Abstract

The 2008 Global Financial Crisis fueled a steep decline in the U.S. luxury goods

market. According to Euromonitor, retail sales fell from a record $62.8 to $52.5 billion

over 2007-2009. Interestingly, the Lipstick Effect suggests that small, affordable

luxury goods fare better during an economic downturn since consumers seek to maintain

wellbeing and enhance attractiveness. The study employs a subset of the Kilts-Nielsen

Consumer Panel Dataset (KNCPD) for the period 2004-2011, data on households that

consume a particular good of affordable luxury, fragrances. Evidence reveals that

perfumes do not adhere to the lipstick effect during the recession, perhaps because

fragrances are purchased as gifts more frequently than most beauty products. The study

further shows how households alter buying behavior among perfume qualities. Findings

imply that during the crisis, households substitute towards premium and private-label

fragrances and away from mass-market perfumes. Compared to wealthy households,

those of low-income exhibit relatively higher price sensitivity, lower opportunity cost of

time, and greater shopping effort. Low-income households benefit more from price

reductions on premium goods. The recession-induced, heightened preference for

affordable, private-label fragrances explains the latter shift in purchasing patterns.

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Acknowledgements

First and foremost, I would like to thank my adviser, Professor Greg Kaplan, for whose

guidance I am grateful for. Thank you for coaching me and providing feedback

throughout the process.

Thank you to Oscar Torres-Reyna, without whose expertise, skills, and knowledge I

would not have learned so much. Thank you for guiding me, always answering my many

questions with a smile, and most importantly, supporting me along the way.

I would like to thank Michael Markovics and his colleagues at Euromonitor for providing

access to the dataset with information on premium and mass-market perfumes.

I would also like to extend thanks to Professor Ilyana Kuziemko and Felipe Goncalves

for constructive feedback and insights. Professor Kuziemko - I consider myself blessed to

have taken your course and learned from you. Thank you for your encouragement,

academic guidance, and mentorship throughout my junior and senior years.

Many thanks to the best of friends Andrea, Joan, Joanna, You-You for your

optimism, good humor, and invaluable support throughout the past four years and the

thesis journey we did it!

Finally, thank you to my parents and my grandmother for your countless sacrifices,

unconditional love, support, and prayers. Thank you for your words of wisdom and for

always keeping me learning, laughing, and most importantly, appreciating all that makes

life sweet.

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Table of Contents

I. Introduction ...................................................................................................................... 1 i. Background .................................................................................................................. 1 ii. Research Questions ..................................................................................................... 3 iii. Narrative .................................................................................................................... 5 iv. Structure ..................................................................................................................... 7

II. Literature Review ........................................................................................................... 8 i. Overview of Consumption Theory ............................................................................... 8 ii. Discussion of Related Empirical Research ................................................................. 9 iii. Motivations for Purchasing Luxury ......................................................................... 13

III. Data Description .......................................................................................................... 18 i. Kilts-Nielsen Consumer Panel Dataset Description .................................................. 18

i.i. Descriptive Statistics ............................................................................................ 21 i.i.i. Household Panelist Demographics ................................................................ 21

i.ii. Methods of Quality Differentiation .................................................................... 25 i.ii.i. Quality Differentiation By Price ................................................................... 26 i.ii.ii Quality Differentiation By Price: Graphical Analysis .................................. 26 i.ii.iii. Quality Differentiation by Brand ................................................................ 35

IV. Methodology ............................................................................................................... 38 i. Presentation of the Regression Model ........................................................................ 38

V. Results and Discussion ................................................................................................. 43 i. Graphical Analysis and Regression Results ............................................................... 43

i.i Robustness Checks ................................................................................................ 60 i.i.i. Sample of Households ................................................................................... 60 i.i.ii Definition of Premium Quality ...................................................................... 64

VI. Implications and Future Research ............................................................................... 68 i. Implications ................................................................................................................ 68 ii. Limitations and Future Research .............................................................................. 70

VII. Conclusion ................................................................................................................. 72

A. References .................................................................................................................... 75

B. Appendix ...................................................................................................................... 78

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I. Introduction

i. Background

Following the 2008 Global Financial Crisis, the luxury goods sector has recovered

swiftly. Reuters (2013) reports that sales rapidly returned to pre-crisis levels by 2012, and

global leading luxury conglomerates Louis-Vuitton-Mot-Hennessy (LVMH) and

Pinault-Printemps-Redoute (PPR), re-named Kering, experienced higher sales growth in

the U.S. than in China. In 2015, consumers in the United States alone, boosted by a

strong dollar, shelled out a total of $79.3 billion on luxury goods, outperforming those in

Japan, Italy, France, and China combined (see Bain & Company 2015; Euromonitor

2016). Wealthy households clearly have deep pockets when it comes to spending on

luxury goods. Leading market research firm Euromonitor International (2016) reports

that despite faltering growth in U.S. sales of luxury goods to foreigners, largely due to

currency appreciation, heightened domestic demand drives luxury expenditures.

Improved economic conditions, as seen by the increased availability of employment and

the rise in wages, prompt forecasts of strong growth in sales of luxury goods.

The largely positive outlook is but a recent phenomenon. The Global Financial

Crisis of 2008 may have subsided, yet not without leaving significant destruction in its

immediate aftermath. The recent downturn did not discriminate in its impact, bringing

hardship to businesses and consumers across most sectors and socio-economic

backgrounds. In particular, much debate continues to surround the effect of the Great

Recession on the luxury goods market. Prior to the crisis, the luxury goods and services

sector was characterized by high margins and strong growth. Sales of luxury goods

collapsed, as the U.S. experienced a steep decline in luxury retail revenues from a record

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$62.8 to $52.5 billion over the period 2007-2009 (Euromonitor 2016). Euromonitor

(2016) divides the luxury goods sector into eight strategic business units: Designer

Apparel and Footwear, Wines and Spirits, Accessories, Electronic Gadgets, Jewelry and

Timepieces, Leather Goods, Paper Goods, and Super Premium and Personal Care.

Despite the overall decline in luxury spending, evidence suggests that certain sub-

categories within luxury actually fared better than others during the recent recession.

Before exploring idiosyncrasies driving luxury goods expenditures, it is important

to briefly acknowledge the paradox characterizing luxury purchases. In her 2001

interview with Chairman and CEO of LVMH, Bernard Arnault, Suzy Wetlaufer, the

former editor of the Harvard Business Review, introduces the idea responsible for the

success of Arnault and LVMH. She asserts that, no one needs [luxury items], of course,

yet millions desire them (Wetlaufer 2001). These products have little utilitarian purpose,

serving only to achieve dreams or selfish desires, as perceived by consumers (Wetlaufer

2001). Luxury spending is largely driven by the brands power, prestige, and ability to

portray a particular aura that elicits an emotional response.

Consumption of luxury goods is characterized by desire, rather than need. The

challenge with desire is that it exhibits volatility, either falling prey to harsh economic

conditions or preying on selfish motives of the individual to maintain or increase

wellbeing, in other words, to treat themselves (Biciunaite 2013). Economists

acknowledge that recessions are associated with heightened spending on both inferior

goods, those that increase in demand when income declines, and goods or services that

boost morale. The whole luxury industry is propelled by womens desire to spoil

themselves, and the sales of small, affordable luxury goods often benefit the most from

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this desire. Leonard Lauder, Chairman Emeritus of Este Lauder, coined the term,

Lipstick Effect, or the principle that sales of beauty products are inversely correlated

with the health of the economy (see Biciunaite 2013; Schaefer 2008). Although evidence

conveys that lipstick retail sales did not actually withstand the recent recession, other

small luxury products, including nail polish, did fare well (see Allison and Martinez

2010; Biciunaite 2013). From a psychological standpoint, Hill et al. (2012) maintain that

during an economic downturn, spending on most beauty products remains fairly stable, or

increases, as women engage in cheap indulgences to enhance attractiveness and promote

male desirability. From an economic perspective, Gao, Kim, and Zhang (2013) show that

consumers re-allocate resources towards leisure spending (e.g., spending on

entertainment) to maintain wellbeing. Research strongly suggests that affordable, luxury

goods perform well during an economic downturn.

ii. Research Questions

This study examines buyer behavior in the periods prior to and during the Great

Recession, with respect to one luxury good, womens fragrances. Motivations for

choosing perfumes are multifold. Like cosmetics, womens fragrances exist under the

Euromonitor (2016) luxury category of Super Premium and Personal Care and are

characterized as small, affordable luxuries without clear generic, or store brand,

alternatives. For example, fragrances differ from pharmaceutical drugs, as none bear the

name of a local drugstore (Bronnenberg et al. 2015). Even affordable perfumes have a

brand label. Motivations for examining consumption patterns with respect to perfumes

stem from the unique position of fragrances within the luxury sector.

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During recessions, individuals seek to decrease spending and often do so by

consuming more affordable, yet generally lower quality, alternatives of expensive goods.

(see Jaimovich, Rebelo, and Wong 2015; Nevo and Wong 2015). The consumer

purchases three fragrance types, each associated with different levels of price and quality.

According to Euromonitor (2015), premium fragrances are characterized by high price

and quality (e.g., Chanel, Gucci, Yves Saint Laurent), and mass-market fragrances (e.g.,

Avon, Stetson, The Body Shop) are more affordable, of lower quality, and rarely carry a

Designer label. Of the three types, private-label is the cheapest, and demand for private-

label rises during recessions (Dub, Hitsch, and Rossi 2015). Nonetheless, buying a

private-label fragrance is still considered a luxury transaction.

Given the aforementioned attributes, the purpose of this study is to examine the

performance of fragrances during the recent recession. Euromonitor (2015) reports that

U.S. fragrance retail sales decrease from approximately $7.5 to $6.5 billion over 2007-

2009, and this study uses a particular dataset of household perfume transactions to either

confirm or refute fragrance adherence to the lipstick effect. Secondly, this paper provides

further insight on how certain household demographics, notably annual income, influence

fragrance expenditures throughout the crisis. Exactly how household income determines

shifts in consumption behavior, with respect to various qualities of one luxury good,

remains largely unexplored. Substituting towards more affordable fragrance alternatives,

reducing the quantity purchased, or delaying transactions to a later date, represent a sub-

sample of the various means by which household members adjust to business cycle

fluctuations. Results reveal differences between the magnitude of shifts in shopping

behavior for wealthy households and those of lower socio-economic standing.

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Based on the motivations presented, the research questions, with respect to the

years of the Great Recession, are as follows:

(1) Do fragrance expenditures adhere to the lipstick effect?

(2) Do households substitute among the three qualities of fragrances? If so, how

does income affect household substitution behavior?

iii. Narrative

Findings suggest that during the recession, womens fragrances do not, in fact,

follow the positive trend implied by the lipstick effect. A possible explanation for the

deviation is that fragrances are purchased as gifts more frequently than most beauty

products. The study then goes on to illustrate how households alter buying behavior by

substituting within luxury, among three types of fragrances. Graphical analyses and

corresponding regression results imply that during the recession, households of low- and

high-income substitute towards premium and private-label fragrances and away from

mass-market perfumes. A possible reason for this puzzle is that households benefit from

crisis-driven price reductions on premium fragrances (see BCG 2010; Nevo and Wong

2015). A heightened preference for affordable, private-label fragrances explains the latter

shift in consumption (Dub, Hitsch, and Rossi 2015). Findings support conclusions of

Hammerbeck (2008) and Quelch and Harding (1996) that private-label spending and

market share increase during the crisis and decline under strong economic conditions.

Despite the overall drop in fragrance expenditures during the recession,

interestingly, both wealthy and low-income households experience relative increases in

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their average shares of premium fragrance expenditures.1 Recession-driven income

shocks, and changes in employment status, trigger declines in household opportunity cost

of time (Nevo and Wong 2015). Kaplan and Menzio (2015) support that households of

unemployed shoppers spend less on the same goods than do households of employed

shoppers. Griffith, OConnell, and Smith (2015) and Nevo and Wong (2015) assert that a

recession-induced lower opportunity cost of time influences households to re-allocate

time towards shopping effort to lower spending (e.g., searching for special deals by using

coupons, frequenting discount stores, or purchasing on sale).

Compared to low-income households, wealthy households exhibit a greater

opportunity cost of time and allocate less effort to searching for discounts when shopping

(Griffith et al. 2009). BCG (2010) conveys that retailers slash prices of premium goods

during recessions. Low-income households, compared to those of high-income, exhibit

larger increases in their average share of premium fragrance expenditures. Increases in

shopping effort amount to realized benefits from deals on premium fragrances (Nevo and

Wong 2015). The behavioral change by price sensitive, low-income households leads to a

significant increase in their average share of premium fragrance expenditures.

In turn, substitution towards premium perfumes dampens spending on mass-

market fragrances. Low-income households benefit from discounts on premium

fragrances and significantly decrease mass-market fragrance consumption. Wealthy

households follow a similar pattern, yet to a lesser extent. The higher opportunity cost of

time of high-income households also explains the relatively larger increase in their

average share of private-label fragrance expenditures, as compared to that of low-income

1 The Average Share of Premium Fragrance Expenditures is the average of the share of premium fragrance expenditures, divided by the total fragrance expenditures in U.S. dollars, for U.S. households within a given year. The creation of this term is discussed in Section IV, since it constitutes the dependent variable of regression Model II (i)-(iii).

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households. The wealthy perceive increasing private-label expenditures as a more time

efficient means, compared to increasing shopping effort, for lowering expenditures. The

narrative implies that opportunity cost of time and income specific buying behavior act as

drivers of recession-induced substitution among the three types of fragrances.

iv. Structure

The remainder of the paper proceeds as follows: Section II outlines consumption

theory that sets the stage for the discussion of prior empirical research on shopping

behavior during an economic downturn. Section III describes The Nielsen Consumer

Panel Dataset (KNCPD), provided by the Kilts Center for Marketing at the Chicago

Booth School of Business, presents descriptive statistics of household demographics, and

conveys methods of fragrance quality differentiation. Section IV introduces the

regression models and defines key variables. Section V reports the graphical and

regression results of the benchmark model, offers potential explanations for findings, and

discusses results from robustness checks and extensions. Finally, Section VI and Section

VII present outcome implications for both the luxury consumers and the luxury

corporations, as well as some concluding thoughts and future research suggestions.

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II. Literature Review

i. Overview of Consumption Theory

The contribution of this study is to determine the impact of the Great Recession

on household buying behavior, with respect to an affordable, luxury good. Recent

empirical work focuses on the shifts in buying behavior for decreasing spending during

an economic downturn. Several other theoretical and empirical studies hypothesize as to

motivations driving luxury good expenditures and measure willingness to pay for a

brand. Before presenting related empirical work, this section provides an overview of

consumption theory to establish the framework for the discussion to follow.

Nobel Laureate and Princeton University professor, Angus Deaton (1992)

evaluates the Permanent Income Hypothesis, developed in 1957 by Milton Friedman,

which examines the propensity of the individual to smooth consumption over time. In

anticipation of an economic bust that triggers negative shocks to income, consumers

prioritize saving over spending. Before an economic boom, households generally borrow,

rather than save. The Deaton Paradox (1992) implies that large shocks to income are not

followed by consumption shocks of similar magnitude. Deaton resolves the contradiction

by explaining that individual income is more variable than that of the aggregate economy.

A certain individuals income may increase, while the income of another falls. Only after

acknowledging how individual consumption fluctuates with respect to income, do

economists reconcile aggregate patterns with those at the individual level.

Should consumers anticipate and discount future shocks to income, consumption

may not, in fact, be as smooth as Friedmans Permanent Income Hypothesis suggests.

The Deaton (1992) framework of individual consumption behavior establishes a basis for

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analysis of how individual purchasing patterns fluctuate during an economic downturn.

Although theoretical in nature, when applied to the Great Recession, findings predict that

consumption falls in the periods shortly before and during the crisis. This study will

utilize the theoretical basis to analyze consumption behavior of U.S. household panelists

from various socio-economic backgrounds, with respect to a particular luxury good.

ii. Discussion of Related Empirical Research

More recently, Aguiar and Bils (2015) employ the Deaton (1992) framework to

examine individual purchasing behavior during the Great Recession. Changes in

consumption inequality are determined by analyzing expenditure trends of high- and low-

income households. Using the Consumer Expenditure Surveys Interview Study (CE) and

a demand system test, Aguiar and Bils (2015) first explore patterns in after-tax income

inequality, which rises by 33 percent over 1980-2007. Consumption inequality increases

by 11 percent over the same time period. The study focuses on how consumption

inequality moves in response to income shocks. Luxury is defined as non-durable

entertainment, and grocery goods are considered necessities. On average, high-income

households shift consumption towards luxury goods and away from necessities more than

low-income households do. Evidence suggests that high-income households shift

expenditures away from food, towards entertainment, over 2008-2010, while low-income

households shift spending away from entertainment, towards food, over the same period.

Total expenditure inequality rises. Aguiar and Bils (2015) then prove that consumption

inequality, defined as the high-income households greater propensity to consume

luxuries, instead of necessities, mirrors income inequality more than total expenditure

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patterns imply. Like Aguiar and Bils (2015), this study will narrowly focus on recession-

induced expenditure shifts by high- and low-income households.

Several empirical studies analyze the effects of the Great Recession on

consumption patterns. Gao, Kim, and Zhang (2013) focus on the consumption of leisure

goods, specifically nondurable entertainment. Consumer Expenditure Survey (CES) data

for select years within the 2003-2010 period and the Probit model are used to examine

the impact of changes in income on the likelihood of making an entertainment purchase.

While the recession exerts a statistically significant negative effect on income, leisure

spending does not decline substantially. Gao, Kim, and Zhang (2013) explain the puzzle

by suggesting that rational consumers, wrought with financial hardship, re-allocate

resources to maintain wellbeing. From a consumer psychology standpoint, a study by Hill

et al. (2012) supports the lipstick effect and suggests that recessions trigger increases in

spending on beauty products driven by womens need to enhance attractiveness.

Consumption of small, luxury goods may, in fact, increase during economic downturns.

Dub, Hitsch, and Rossi (2015), Jaimovich, Rebelo, and Wong (2015), and Nevo

and Wong (2015) find that consumers experience shifts in shopping behavior during the

Great Recession. Jaimovich, Rebelo, and Wong (2015) introduce the concept of trading

down, and show that consumers are more likely to purchase low quality goods and

services during an economic downturn. Although documented as having ended in June

2009, the study acknowledges that the recession continued to negatively affect average

and median household income until 2012. Consistent with the aforementioned, this study

will define the Great Recession years as 2008-2011. Jaimovich, Rebelo, and Wong

(2015) determine the quality of the good by the individuals willingness to pay for it.

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Price is used as a proxy for quality, as consumers generally associate high price with high

quality. This study operates under the same assumption. The total price paid, and

corresponding price measures, are obtained from Yelp!, and The U.S. Census of Retail

Trade provides information on firm employment and sales for insight on labor intensity

and market share. Price data from Yelp! are merged with Compustat data containing

information on employees, sales, operating expenses, and cost of goods sold. The study

reveals that expenditures at low quality restaurants, those offering limited service, fall by

4.7 percent, while expenditures at restaurants of high quality, those offering full service,

fall by 10.2 percent, thus confirming the trading down hypothesis. Trading down

behavior is then related to employment, as substitution towards goods of lower quality

accounts for one-third of the heightened unemployment during the Great Recession.

Further supporting the narrative of trading down, Dub, Hitsch, and Rossi (2015)

utilize the Nielsen Homescan Dataset, with information on shopping trips, and find a

negative causal effect of income on private-label expenditures. Furthermore, estimates

show a positive trend in private-label shares in the period prior to the Great Recession.

Quelch and Harding (1996) imply that growth in private-label expenditures increases in

times of economic hardship. Evidence of strong demand for private-label during an

economic downturn supports conclusions of Jaimovich, Rebelo, and Wong (2015).

Nevo and Wong (2015) examine recession-induced household substitution

behavior between goods and time. Similar to Dub, Hitsch, and Rossi (2015), the study

uses the Nielsen Homescan Dataset to test the propensity of the household to buy items

on sale or in bulk, purchase generic goods, increase coupon usage, and shop at more

affordable retail stores. A crisis-driven negative income shock triggers a decline in

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household opportunity cost of time. Consumers re-allocate time towards shopping for

household goods and services, with the aim of achieving lower expenditures. Griffith,

OConnell, and Smith (2015) confirm the aforementioned by using Kantar Worldpanel

data on British households over 2005-2012 to show that households increase shopping

effort to lower the average per calorie price of the grocery basket. Shopping activities, as

a share of total expenditures, increase during the recession.

Griffith, Leibtag, Leicester, and Nevo (2009) analyze similar shopping behavior,

emphasizing the effect of consumer demographics on activities such as purchasing on

sale, buying larger quantities, shopping at discount stores, or purchasing the generic

version of a good. Household savings are conditional on demographics, which include

household income, composition, and size, as well as the age and the employment status

of household members. Employing data from the TNS Worldpanel, containing

information on 25,000 households across Great Britain, Griffith et al. (2009) show that

households shopping by car purchase about 2 percentage points more food on sale than

those shopping by foot, by public transportation, or less frequently by car. High-income

households exhibit low marginal utility of income and high opportunity cost of time; they

generally do not allocate as much time towards seeking out deals, and purchasing on sale,

as do their low-income counterparts. Similarly, households of retirees, childless couples,

and single adults save less than do households of families. Griffith et al. (2009) conclude

by exploring preliminary findings of a study of U.S. households and convey the

importance of geography and household demographics on purchasing habits and realized

savings. Similarly, this study will focus on the household demographic of income and its

effect on substitution among three types of womens fragrances.

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Krueger and Mueller (2010) offer further support for the impact of demographics

on shopping behavior with the aim of lowering expenditures. Unemployment spiked

during the recession, peaking at 10 percent in October 2009 (Bureau of Labor Statistics

2012). Krueger and Mueller (2010) find that unemployed people, compared to the

employed, allocate more time towards shopping, which presumably includes activities

cited by Griffith et al. (2009) and Nevo and Wong (2015). Using the KNCPD for years

2004-2009, Kaplan and Menzio (2015) assert that households with fewer employed heads

spend less on the same goods, on average, than do households in which all heads are

employed. The formers ability to lower expenditures is attributed to shopping at more

stores. Differences in shopping behavior between employed and unemployed, and high-

and low-income, household members, set the stage for the narrative of this study.

Finally, Civi (2013) supports that growth opportunities exist for companies that

identify changing consumption patterns in anticipation of and during a recession, and in

doing so, adapt strategies to match shifts in buyer behavior. Companies may find that

recent crisis-induced shifts in purchasing patterns and overall consumer perception of

luxury, when capitalized upon, provide business growth opportunities.

iii. Motivations for Purchasing Luxury

Given the ability of consumers to shift purchasing habits during the recession, it is

important to first consider the motivations that drive luxury expenditures. Several

theoretical and empirical studies, in conjunction with market research reports, explain

classical and changing incentives behind luxury spending.

In his social critique on the leisure, or wealthy, class, economist Thorstein Veblen

(1902) claims that consuming luxury goods is an effective means for seeking status, a

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behavior known as the Veblen Effect. Consumers generally exhibit a higher willingness

to pay for a luxury good with cheaper, functionally equivalent alternatives. Veblen argues

that luxury transactions are characterized by conspicuous consumption, purchasing to

advertise affluence and status.2 An individual buys a luxury good to flaunt material

wealth or influence public image. Bagwell and Bernheim (1996) further describe the

source of the Veblen Effects and find that consumers demonstrate a higher willingness

to pay for a luxury good to ensure social acceptance into an exclusive class.

Miller (1975) and Creedy and Slottje (1991) build on Veblen (1902), asserting

that wealthy consumers derive utility from price. The consumer gathers utility not only

from the high quality, but also from the price premium associated with the brand label.

Creedy and Slottje (1991) convey that price increases are believed to raise consumer

utility, which Miller (1975) supports by asserting that luxury goods are purchased for

superficial reasons associated with brand image and status. A review of Simon Fans

Vanity Economics: An Economic Exploration of Sex, Marriage, and Family by Bertocchi

(2015) best encapsulates theoretical conclusions; a fixation with signaling status drives

irrational choices that oppose basic economic assumptions.

Bronnenberg, Dub, Gentzkow, and Shapiro (2015) and Jin, Zilberman, Heiman,

and Li (2011) take an empirical approach to measuring willingness to pay (WTP) for

brands. Jin et al. (2011) use consumer stated preferences for purchasing brands and focus

on factors driving consumption of brand name products over corresponding generic

alternatives. Survey data are gathered from Texas, California, and Israel to determine

variations in brand preferences across both geography and four product categories (e.g.,

2 Miller (1975) and Veblen (1902) are briefly discussed in Section II of Chiaroni, C. (2015). The Impact of the Connecticut Luxury Automobile Tax on Consumption Patterns. Unpublished manuscript, Department of Economics, Princeton University.

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electronics, clothing, processed food, and fresh produce). Overall WTP for brands in the

four sectors remains the same, regardless of geography. Brand name consumer

electronics products elicit a higher WTP than brand name clothing, processed food, and

fresh produce. Variations in WTP magnitude for brands lead Jin et al. (2011) to conclude

that business strategies should be tailored to the WTP of local consumers.

While Jin et al. (2011) confirm that consumers exhibit higher WTP for brands,

Bronnenberg et al. (2015) convey that individuals may overestimate benefits of a brand

name product with respect to quality. In an empirical study of WTP for National versus

store brands, Bronnenberg et al. (2015) determine the impact of occupation related

knowledge and expertise on purchasing patterns. In a procedurally similar process to

Nevo and Wong (2015), Bronnenberg et al. (2015) use Nielsen Homescan Data from

2004-2011 and a survey revealing consumer demographics (e.g., product knowledge,

schooling, college major, and occupation). Purchases of headache remedies made by

physicians and pharmacists, and food product transactions made by chefs and food

preparers, are recorded. Nielsen Retail Scanner data and Wholesale Price data from

National Promotion Reports PRICE-TRAK are employed to compute product prices and

retail margins. Consumer information largely impacts choices, as informed consumers

purchase store brand, instead of national brand, pharmaceuticals.

The following market research report by The Boston Consulting Group (2010)

raises two important factors, the access to widespread information through electronic

venues, and the long-term impact of the Great Recession on willingness to pay for brand

name, luxury goods, that call into question the validity of aforementioned conclusions.

BCG (2010) supports that luxury holds a mystique extending beyond its clear

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superiority to the ordinary. Clearly, craftsmanship, service, and image contribute to

the intrigue surrounding a luxury brand (BCG 2010). However, recent devastating

economic and social effects of the Great Recession prompt todays consumer of luxury to

search for further reason to spend. Financial tumult subdues purchasing incentives

characterized by conspicuous consumption (Veblen 1992). Previously, brand image was

more important than quality, as consumers perceived luxury goods in superficial terms

(BCG 2010). The crisis unhinges financial security, inhibits the ability to splurge, and

stigmatizes conspicuous consumption at a time of immense financial hardship.

The Great Recession induces luxury companies, and mass-market competitors, to

alter techniques of pricing, marketing, and branding. Many mass-market companies

imitate premium counterparts in elevating brand status through new marketing initiatives

focused on crafting a premium image for the brand, yet offering it at a mass-market price

(BCG 2010). High-end manufacturers and retailers simultaneously cut prices of luxury

goods to stimulate demand. Consumers perceptions of purchasing luxury change, and

the delineation between premium and mass-market goods becomes blurred as a result of

strategic decisions made by manufacturers and retailers of luxury.

Overall, theoretical research into consumption inequality, income business cycle

variability, and motivations for purchasing luxury, establish a framework for this study.

Luxury consumption may remain stable, or increase, as individuals defend wellbeing or

enhance attractiveness (see Gao, Kim, and Zhang 2013; Hill et al. 2012). On the other

hand, the recession is associated with increases in shopping behaviors such as trading

down, or buying goods of lower quality, buying in bulk or on sale, and heightening

shopping effort in general (see Dub, Hitsch, and Rossi 2015; Jaimovich, Rebelo, and

17

Wong 2015; Nevo and Wong 2015). Much research also exists on the impact of

demographics on shopping behavior (see Griffith et al. 2009; Kaplan and Menzio 2015,

forthcoming). This study examines how the recession affects fragrance expenditures and

how household income impacts crisis-driven substitution among fragrance qualities.

The KNCPD, for years 2004-2011, is utilized. Due to the overall size of the data,

the subset under observation includes only households that engage in fragrance

transactions. Just as Jaimovich, Rebelo, and Wong (2015) employ Yelp! to categorize

restaurants, this study creates a database using Nielsen information on fragrance brands

in conjunction with Euromonitor categorizations of premium and mass-market brands.

Perfumes lack the generic alternative that Griffith et al. (2009) and Nevo and Wong

(2015) cite when referencing shifts in shopping behavior. This study examines trading

down, introduced by Jaimovich, Rebelo, and Wong (2015), or substitution towards more

affordable perfumes. Finally, conclusions from graphical and regression analyses provide

the opportunity to extrapolate implications, much like Civi (2013) and Jin et al. (2011)

do, with respect to trends characterizing luxury good consumption during the recession.

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III. Data Description

i. Kilts-Nielsen Consumer Panel Dataset Description

This section introduces the data, The Kilts-Nielsen Consumer Panel Dataset

(KNCPD) provided by the Kilts Center for Marketing at the Chicago Booth School of

Business, and the method of collection. The dataset includes information on grocery,

food, and home supplies purchased by 125,000 household panelists across 61 geographic

areas, 52 major markets and nine Census Divisions, over the time period 2004-2011.3 The

KNCPD contains a longitudinal study of approximately 40,000 households for 2004-

2007, and 60,000 households for 2007 onwards, who convey information to Nielsen on

household demographics, products purchased and corresponding product attributes, as

well as the retailer from which the goods are purchased. To maintain a representative

sample of U.S. household consumption, Nielsen randomly recruits households through

the mail and Internet, offering sweepstakes, monthly prize drawings, and gift points

awarded for weekly data transmission. Nielsen maintains communication with panelists

through issuing monthly newsletters, personalized computerized tips and reminders, and

letters from the President, all to ensure cooperation. Nielsen depends on households to

join the panel and remain committed, since panelists who participate over the long-term

allow for longitudinal analysis of changes in household consumption. When households

become inactive or fail to report transactions as instructed, Nielsen replaces them.

The in-home scanner system, intended for household use, records the store at

which the product was purchased, the date of purchase (e.g., month, day, and year), the

quantity, or number of units, purchased, and the total price paid for the good at the

3 All Section III information regarding the KNCPD is obtained from the KNCPD Manual (2013).

19

Universal Product Code (UPC) level. Household demographics include, but are not

limited to, household annual income, and the age, education by gender, and occupation

by gender of the heads of the household. Nielsen also reports the presence and age of

children in the household. For each shopping trip, summary information, containing the

trip date, retailer code, store code, store zip code, and total expenditures, is provided.

UPC codes for the ten Nielsen Food and Non-Food Departments are included in the

product files, and these UPC-level data provide the price of the product, the number of

units purchased, and the product attributes, which include the UPC description, Nielsen

brand description, packaging, and size of the product. In these data, the unit of size for

fragrance transactions is not recorded. Households also note whether the purchase was

made using a coupon, as well as the total discount amount from the coupon. Coupon

usage is then taken into account when calculating the final amount spent per good.

The dataset contains approximately 1.5 million individual product UPC codes

present in the consumer panel files. Data referring to household demographics are

updated annually. These data are presented in separate files based on the type of

information provided (e.g., Panelists, Trips, Purchases, Retailers, Products, Product Extra

Attributes, and Brand Variations). The Panelists file includes demographic, geographic,

and product ownership information on the panelists, reported according to Nielsen

specifications. The Trips file offers summary information, such as the retail chain, the

total amount spent during the trip, the retail location of the purchases, and the date of the

trip, for all shopping trips made by each household. The Purchases file presents

information on the products purchased, specifically the quantity of the good purchased

and the total price paid for the good, as well as coupon usage. The Products file presents

20

information for each of the UPC codes in the dataset, providing detail on the UPC

description, Nielsen brand description, and the size in units specific to the product.

This study employs a specific subset of the KNCPD data, only households that

engage in fragrance transactions over the period 2004-2011. Time is divided into two

categories. The period prior to the Great Recession is defined as 2004-2007, and the

period during the crisis is defined as 2008-2011. To construct the dataset, the Panelist,

Trips, Retailers, and Purchases files are merged together by quarter, totaling thirty-two

files for the years 2004-2011. Each year contains four individual files, which report

information from the Panelist, Trips, Retailers, and Purchases files, segmented by quarter.

The Product files are manipulated to contain only information relevant to the good

of interest, womens fragrances. The product group code corresponding to womens

fragrances is identified. Several Nielsen product module descriptions relate to the specific

product group code. For example, childrens cologne and gift sets, womens cologne and

perfume, and womens gift sets and skin care packages, are under the same product group

code. However, only the product module code for womens cologne and perfume (e.g.,

eau de toilette, eau de parfum) is relevant. For the purposes of the study, only product

information specific to perfume transactions is retained. Each of the previously merged

files by quarter is appended by year, 2004-2011. Within the append operation, the

product information specific to womens fragrances is also merged using the variable in

common, UPC code. The UPC codes in the truncated dataset refer only to womens

fragrances. Once the aforementioned is repeated in eight steps, one step for each year, the

complete dataset is created by appending files for the period 2004-2011.

21

i.i. Descriptive Statistics

i.i.i. Household Panelist Demographics

The subset of the KNCPD includes information on 17,107 U.S. household

panelists, as shown in Table 1. Households are organized into categories based on

income, and household incomes range from $0 to more than $200,000.4 These data

exclude top-earning households, as Nielsen incentives for participation attract only a

subset of the total population below a certain annual income level.

Table 1. Household Income Status Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011).

This study groups household panelists into categories of low-income, those with

an annual salary less than $65,000, and high-income, those with an annual salary greater

than, and including, $65,000. Table 1 shows that low-income and high-income

households account for approximately 70 and 30 percent of sample U.S. households.

4 The KNCPD defines the highest household income category as annual income greater than $200,000. This definition is gathered from the KNCPD Manual (2013).

Household Income Status Frequency Percent %

Low-Income 12,007 70.19 High-Income 5,100 29.81

Total 17,107 100

22

Table 2. Head of Household Employment Status. Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011).

Table 2 records the employment status of heads of the household and reveals that

the primary consumers of womens fragrances are women themselves. Only 12 percent of

the 17,107 household panelists have a male head. These data include almost as many

household panelists with female heads who are not employed for pay, 3 percent, as those

households with female heads who work fewer than 30 hours per week, 5 percent. About

80 percent of sample households have female heads working at least 30 hours per week.

Table 3. Head of Household Education. Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011).

With respect to education, Table 3 conveys that approximately 54 percent of the

households in the dataset include heads that did not receive a college degree. Households

that contain one head with a college degree constitute 20 percent of the total sample, and

Female Household Head Employment Frequency Percent %

Not Employed for Pay 552 3.23 Male Head of Household 2,021 11.81 Employed < 30 Hours/Week 865 5.06 Employed 3034 Hours/Week 7,206 42.12 Employed 35+ Hours/Week 6,463 37.78

Total 17,107 100

Household Head Education Frequency Percent %

None of the Heads Have a College Degree 9,186 53.70 Only One Head Has a College Degree 3,493 20.42 All Heads Have a College Degree 4,428 25.88

Total 17,107 100

23

households in which all heads received a college degree, and perhaps even additional

certificates of higher education, account for 26 percent of the sample.

Table 4. Head of Household Age.

Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011).

These data include household heads whose ages exist within a very narrow range

(e.g., 29-36), and the mean is 32 years. Table 4 shows that the majority of consumers of

luxury fragrances are women in their late twenties to mid thirties.

Given the household demographic information for the sample of 17,107

households that purchase fragrances within the time period 2004-2011, Table 5 explores

changes over time in the composition of households that report fragrance transactions.

Household Head Age Min Max Mean Standard Deviation 28.99 36.15 31.77 2.12

24

Table 5. Household Composition.

Composition Check 2004-2007 2008-2011 Household Income Status Low-Income (Annual Income < $65,000) 73.77 65.42 High-Income (Annual Income $65,000) 26.23 34.58 Head of Household Average Age 30.53 34.29 Female Head of Household Employment Not Employed for Pay 37.27 39.53 Male Head of Household 3.00 3.63 Female Head Employed < 30 Hours/Week 11.65 11.85 Female Head Employed 3034 Hours/Week 5.30 5.12 Female Head Employed 35+ Hours/Week 42.77 39.87 Head of Household Education None of the Heads Have a College Degree 55.34 50.84 Only One Head Has a College Degree 24.31 28.51 All Heads Have a College Degree 20.36 20.66 Age and Presence of Children

No Children Under 18 69.39 72.23 Children Under 6 Years 3.07 2.97 Children Ages 6-12 7.26 6.35 Children Ages 13-17 10.45 9.19 Children Under 6 & 6-12 2.98 2.53 Children Under 6 & 13-17 0.73 0.71 Children 6-12 & 13-17 5.23 5.18 Children Under 6 & 6-12 & 13-17 0.88 0.84

Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011). Notes: The table presents the set of household demographics that are included in the demographic controls of regression Models I-IV (i)-(iii) presented in Section IV. The table shows how the demographic composition, measured in percentages, of the sample household panelists purchasing fragrances changes over time. From the pre-crisis period to the recession, low-income households within the sample decline by approximately 9 percent, while the high-income households in the sample increase by about 8 percent. The remaining demographics remain roughly consistent for both time periods of interest.

The composition check is to confirm that movements in fragrance expenditures,

and movements in household buying behavior with respect to fragrances of various types,

are to be attributed to the negative impact of the recession rather than to the change in the

sample of households over time. Recession-induced negative shocks to income certainly

25

force low-income households to fail to appropriately report expenditures, or to drop out

of the sample altogether, which Table 5 confirms. In the period before the recession,

approximately 74 percent, compared to 65 percent in the crisis period, of the households

in the sample are low-income. Table 5 does not reveal substantial changes over time in

other demographics. Categorical, independent variables in the regression models,

presented in Section IV, control for compositional changes.

i.ii. Methods of Quality Differentiation

Two distinct processes of differentiating among qualities of fragrances are

considered. Quality can be most clearly determined using either of the following

methods: measuring price per ounce or recognizing the quality behind the brand. The first

method involves distinguishing quality (e.g., high quality, medium quality, low quality)

based on the price per ounce of the fragrance purchased. In the second method,

fragrances are categorized as either premium, mass-market, or private-label, based on the

brand. Cross-referencing information provided by the Nielsen Brand Description term,

with Euromonitor categorizations of premium and mass-market brands, facilitates the

procedure.5 The following section begins by explaining the first method, the

categorization of fragrances by quality using price. Graphical analyses explore the

implications associated with using the first method of quality differentiation in charting

low- and high-income households buying behavior. Only after acknowledging important

limitations of the first method, does the study develop the second method of quality

segmentation by brand.

5 The second method of quality differentiation, particularly the process of cross-referencing the KNCPD Brand Description term with Euromonitor International Passport GMID data, will be discussed in detail in Section III.i.ii.iii.

26

i.ii.i. Quality Differentiation By Price

Evaluating fragrance quality requires determining the price per unit, presumably

the price per ounce in U.S. dollars, of the fragrance purchased. The variable Final Price

Paid is generated by subtracting the Coupon Value, or the dollar value of coupons

applied to the fragrance expenditure, from the Total Price Paid, or the total price paid for

all fragrances before discounts are applied to the transaction. Price Per Unit is then

calculated by dividing the Final Price Paid by the Quantity, or the number of units of the

fragrance purchased.6 The distribution of the Price Per Unit for all fragrance transactions

determines the three price ranges that correspond to categories of low, medium, and high

quality perfumes. Fragrances whose Price Per Unit falls in the top third percentile are

grouped into the high quality category, and those whose Price Per Unit falls within the

bottom third percentile are considered low quality. In absolute terms, low quality

fragrances have a Price Per Unit between $0 to $5, inclusive; medium quality fragrances

have a Price Per Unit between $5 to $10, non-inclusive; high quality fragrances have a

Price Per Unit between $10 to $240, inclusive. The unit of measure for Price Per Unit,

with respect to fragrances, is not provided by Nielsen. Constructed using the method of

quality differentiation by price, the following figures support the established narrative of

shifts in high- and low-income household buying behavior throughout the recession.

i.ii.ii Quality Differentiation By Price: Graphical Analysis

Figure 1 reveals the average shares of fragrances of low, medium, and high

qualities consumed over 2004-2011. The Final Price Paid for Low Quality Fragrances,

for each household within a given year, is determined. The Average Share of 6 Definitions of the italicized terms are either obtained from the KNCPD Manual (2013) or calculated using terms defined in the KNCPD Manual (2013).

27

Expenditures on Low Quality Fragrances is calculated by dividing the sum of the Final

Price Paid for Low Quality Fragrances by the Final Price Paid and finally, computing

the average. Similar procedures are repeated to create the Average Share of Expenditures

on Medium Quality Fragrances and the Average Share of Expenditures on High Quality

Fragrances. The Average Shares of Fragrance Expenditures for each of the three

perfume qualities within a given year are determined. Due to the vast amount of missing

entries for the Transaction Year in the KNCPD, Panel Year, the term that denotes the

year in which the panel is conducted, appears on the x-axis of the graphs. Panel Year and

Transaction Year are assumed to roughly coincide.

28

Figure 1 presents the average shares of high, medium, and low quality fragrance

expenditures over the period 2004-2011, and the following discussion relates graphical

findings to the overall narrative.

Figure 1. Average Shares of Fragrance Expenditures.

Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011). Notes: The y-axis shows the average shares of fragrance expenditures of each quality, over total fragrance expenditures. The x-axis shows the Panel Year, the year in which the panel is conducted, which, as explained, is assumed to roughly coincide with Transaction Year. The graph reveals the average shares of fragrances of low, medium, and high qualities consumed over 2004-2011. The Final Price Paid for Low Quality Fragrances is determined for each household within a given year. The Average Share of Expenditures on Low Quality Fragrances is calculated by dividing the sum of the Final Price Paid for Low Quality Fragrances by households in a given year, by the Final Price Paid for all fragrances in a given year and determining the mean. Similar procedures are repeated to create the Average Share of Expenditures on Medium Quality Fragrances and the Average Share of Expenditures on High Quality Fragrances. The averages of each of the aforementioned terms are determined. The square, triangle, and diamond patterned lines illustrate the average high, medium, and low quality fragrance expenditures as shares of the total fragrance expenditures.

Average household shares of expenditures on high quality fragrances steadily

increase from 2004-2010. Towards the end of the Great Recession, high quality fragrance

expenditures increase significantly. By contrast, the average share of medium quality

fragrance expenditures declines by more than 10 percent from 2004-2006 and increases

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2004 2006 2008 2010 2012Year

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Average Shares of Fragrance Expenditures, 2004-2011

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immediately before the recession. During the crisis, the average share of medium quality

fragrance expenditures drops and remains well below pre-crisis levels. The average share

of low-quality fragrance expenditures is stable prior to dropping sharply from 32 to about

25 percent from 2006-2007. Over 2007-2009, low quality fragrance expenditures increase

by approximately 10 percent, most likely due to households trading down towards

cheaper goods during the recession (Jaimovich, Rebelo, and Wong 2015). Overall, the

average share of high quality fragrances is largely unaffected. A possible explanation for

this puzzle is that households maintain or increase average shares of high quality

fragrance expenditures to sustain wellbeing or enhance attractiveness (see Gao, Kim, and

Zhang 2013; Hill et al. 2012). As expected, average shares of medium quality perfumes

decline, while those of low quality perfumes increase at the height of the Great Recession

(Dub, Hitsch, and Rossi 2015). Figure 1 supports the narrative that households substitute

away from medium quality perfumes, towards those of high and low qualities.

The following figures examine the behavior of high- and low-income households

with respect to their average shares of high, medium, and low quality fragrances over

time. Figure 2 presents the average share of high quality fragrance expenditures of U.S.

households within both income categories over 2004-2011.

30

Figure 2. Average Share of High Quality Fragrance Expenditures.

Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011). Notes: The y-axis shows the average share of high quality fragrance expenditures. The x-axis shows the Panel Year, the year in which the panel was conducted, which, as explained, is assumed to roughly coincide with Transaction Year. The graph reveals the average share of high quality fragrances consumed over 2004-2011. The Final Price Paid for High Quality Fragrances is determined for each household within a given year. The Average Share of Expenditures on High Quality Fragrances is calculated by dividing the sum of the Final Price Paid for High Quality Fragrances by the Final Price Paid for all fragrances in a given year. The Average Share of Expenditures on High Quality Fragrances is determined. The patterned lines illustrate the average share of high quality fragrance expenditures within each year by high- and low-income households.

As expected, wealthy households show greater average shares of high quality

fragrance expenditures over time, ranging from approximately 30 to 50 percent over

2004-2011. While the share of high-quality fragrance expenditures by wealthy

households slightly diminishes after 2006, low-income households increase their share of

expenditures on high quality fragrances from 2006-2011. Towards the end of the

recession, households of all income levels follow a similar pattern; the share of high

quality expenditures increases sharply as economic conditions improve. Figure 2 findings

are consistent with the narrative that low-income households benefit relatively more than

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Share of High-Quality Fragrance Expenditures, 2004-2011

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do high-income households from recession-induced deals on high quality fragrances.

Low-income households re-allocate relatively more time, than their high-income

counterparts, towards increasing shopping effort to lower expenditures (see BCG 2010;

Griffith et al. 2009, 2015; Nevo and Wong 2015). Low-income households reveal higher

price sensitivity, than do high-income households, and show a steeper increase in their

average share of high quality fragrance expenditures throughout the recession.

Figure 3 analyzes the movement of the average shares of medium quality

fragrance expenditures by low- and high-income households across all years.

Figure 3. Average Share of Medium Quality Fragrance Expenditures.

Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011). Notes: The y-axis shows the Average Share of Medium Quality Fragrance Expenditures. The x-axis shows the Panel Year, the year in which the panel was conducted, which, as explained, is assumed to roughly coincide with Transaction Year. The graph reveals the average share of medium quality fragrances consumed over 2004-2011. The Final Price Paid for Medium Quality Fragrances is determined for each household within a given year. The Share of Expenditures on Medium Quality Fragrances is calculated by dividing the sum of the Final Price Paid for Medium Quality Fragrances by households in a given year, by the Final Price Paid for all fragrances. The Average Share of Expenditures on Medium Quality Fragrances by households is then determined. The patterned lines illustrate the average shares of medium quality fragrance expenditures by high- and low-income households.

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Low-income households, as compared to high-income households, exhibit slightly

greater average shares of medium quality fragrance expenditures in 2004. Households

across all income levels decrease average shares of medium quality fragrance

expenditures until 2006, a behavior attributed to the fact that during stable economic

times, consumers purchase more high, instead of medium, quality goods. Average shares

of medium quality fragrance expenditures experience a steep decline from 2008-2009 for

both income groups. In 2011, the share of medium quality fragrance expenditures is still

significantly lower than in the pre-crisis period.

While maintaining a stable average share of high quality fragrance expenditures

during the recession, high-income households significantly decrease their average share

of medium quality fragrances in 2008-2009. In 2010-2011, the average share of high

quality fragrance expenditures increases, while that of medium quality fragrances

declines for both income groups. Consistent with the narrative, Figure 3 suggests that

although high- and low-income groups decrease average shares of medium quality

fragrance expenditures during the recession, high-income households maintain a greater

share of medium quality fragrance expenditures at the height of the recession than do

low-income households. Opportunity cost of time falls across all income groups during a

recession (Griffith et al. 2009). The relatively higher opportunity cost of time of high-

income households most likely accounts for the fact that they search for deals less

frequently than low-income households. Since low-income households benefit more from

deals on high quality goods, they reduce their average share of medium quality fragrance

expenditures more than high-income households.

33

Figure 4 also supports the established hypothesis by conveying that the average

share of low quality fragrances increases across all income levels during the recession

(see Dub, Hitsch, and Rossi 2015; Jaimovich, Rebelo, and Wong 2015). Low-income

households account for the largest average share of low quality fragrance expenditures,

ranging from 27 to roughly 38 percent, over time. Starting in 2007, all income levels

increase average shares of low quality fragrance expenditures until 2009. Consumers alter

shopping behavior in favor of cheaper goods during the crisis.

Figure 4. Average Share of Low Quality Fragrance Expenditures.

Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011). Notes: The y-axis shows the Average Share of Low Quality Fragrance Expenditures. The x-axis shows the Panel Year, the year in which the panel was conducted, which, as explained above, is assumed to roughly coincide with Transaction Year. The graph reveals the average share of low quality fragrances consumed over 2004-2011. The Final Price Paid for Low Quality Fragrances is determined for each household within a given year. The Share of Expenditures on Low Quality Fragrances is calculated by dividing the sum of the Final Price Paid for Low Quality Fragrances by households in a given year, by the Final Price Paid for all fragrances in a given year. The Average Share of Expenditures on Low Quality Fragrances is then determined. The patterned lines illustrate the average shares of low quality fragrance expenditures by high- and low-income households.

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2004 2006 2008 2010 2012Year

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Following 2009, the average share of low quality fragrance expenditures

decreases for low-income households. During the recession, high-income households

show a larger increase in the average share of low quality fragrance expenditures, as

compared to low-income households. Supporting the narrative, the relative higher

opportunity cost of time, cited by Griffith et al. (2009), most likely prompts the wealthy

to choose low quality fragrances, over seeking out deals on high quality fragrances, as a

time efficient way to reduce expenditures.

Graphical analysis reveals findings consistent with the established hypothesis.

Figure 2 implies that average shares of high quality fragrance expenditures remain

roughly stable in 2008-2009. By contrast, Figure 3 shows that households across all

income levels decrease their shares of medium quality fragrance expenditures, a pattern

that suggests substitution towards the most affordable, low quality perfumes. Figure 4

conveys an increase in the average share of low quality fragrance expenditures at the

height of the recession. Important, observed differences in the magnitudes of changes in

buying behavior of high- and low-income households support the narrative of this study.

Several key limitations arise in using the method of quality differentiation based

on the measure of Price Per Unit. The lack of information provided by the KNCPD on

the unit of measure of the fragrance presents a major challenge. The KNCPD offers a

measure of size, reported as CT or Count, which is vague and does not include details

on the individual bottle size of the fragrance in ounces. Due to lack of information, the

Price Per Unit variable does not accurately represent the price per ounce of a fragrance.

The dearth of adequate information aside, challenges also arise when grouping

fragrances into the three categories of quality segmented by price. Many perfumes

35

considered high quality, grouped as such based on Price Per Unit, may in fact be medium

quality perfumes priced to compete with high quality, Designer brands. Figure 2 most

likely includes medium quality fragrances in the high quality category. Therefore, the rise

in the average share of high quality fragrances from 2010-2011 may be due, in part, to a

possible increase in the average share of expenditures on certain medium quality

fragrances. On the other hand, certain high quality, Designer fragrances may fall into the

medium quality category, depending on the price and the retail channel in which they are

sold. For example, a discount store may sell high quality perfumes at cheaper prices than

would an upscale retailer. In addition, fragrance prices may fluctuate significantly over

time. BCG (2010) finds that in anticipation of, and during, a recession, many high-end

retailers slash prices of high quality goods; therefore, some high quality perfumes may

exist within the medium quality price category during the crisis. While graphical

interpretations largely support the established narrative, insufficient information, coupled

with price discrepancies, challenge the method of quality distinction by price.

i.ii.iii. Quality Differentiation by Brand

Considering the important drawbacks of the first method of quality

differentiation, this section introduces a second method used to segment fragrances.

Similar to reports generated by Euromonitor (2015, 2016) and The Boston Consulting

Group (2010) that categorize the luxury goods market into eight or nine sub-categories

for ease of analysis, this study segments the luxury sub-category of womens fragrances

based on brand. Unlike the price per ounce of a bottle of perfume, the status of a brand as

premium, mass-market, or private-label, is largely time invariant. The KNCPD UPC-

specific Brand Description variable provides either the name of the fragrance brand, the

36

name of the parent company, or both. Euromonitor generates annual market research

reports on fragrances in the United States and segments perfume brands into categories of

premium and mass-market. Table 6 presents a sample of the Euromonitor (2015)

database, which categorizes fragrance brands.

This study generates a new database that cross-references Euromonitor

information, and the relevant KNCPD Brand Description data, to categorize the KNCPD

fragrance transactions as either premium or mass-market. Complications arise since the

KNCPD Brand Description information does not always provide sufficient detail for

clear categorization. The Brand Description variable often lists only the name of the

perfume without accompanying information on the brand (e.g., Poison, instead of

Poison by Christian Dior) or only shows the parent company (e.g., Este Lauder or

Modern Muse, instead of Este Lauder Modern Muse) and does not provide both the

parent company and the perfume brand. The KNCPD information includes, in different

entries, content such as Coty Inc., as well as Davidoff and Calvin Klein, two of the

power or best-selling, high-end brands of the Coty (2014) portfolio. The Coty

Company Website (2014) conveys that Coty manufactures fragrances largely for mass-

market consumption; therefore, this study groups the fragrances with Brand Description

Coty Inc. as mass-market. Coty brands, such as Davidoff and Calvin Klein, which

Euromonitor categorizes as premium, are also considered premium in the newly created

database. Coty mass-market brands (e.g., Aspen) are grouped as such. Coty produces

mostly mass-market fragrances, yet the fragrances bearing a Brand Description that

refers to a premium power brand, are considered premium (Coty 2014). The same

grouping process is repeated for other parent companies and brands.

37

Table 6. Sample of Euromonitor Fragrance Categorization.

Categories Brand Parent Company Name Premium Fragrances Aramis Este Lauder Inc Mass Fragrances Aspen Coty Inc Mass Fragrances Avon Avon Products Inc Premium Fragrances Britney Spears Elizabeth Arden Inc Premium Fragrances Calvin Klein Coty Inc Premium Fragrances Chlo Coty Inc Premium Fragrances Christian Dior LVMH Mot Hennessy Louis Vuitton SA Premium Fragrances Davidoff Coty Inc Mass Fragrances Daytona 500 Elizabeth Arden Inc Premium Fragrances Dolce & Gabbana The Procter & Gamble Co

Source: Euromonitor International. (2015, May). Fragrances in the U.S. Retrieved from Euromonitor Internal Passport database. Notes: The table provides a sample of the database of fragrances, categorized as either premium or mass-market. The table conveys that certain parent companies have brand portfolios that include fragrances of premium and mass-market brands.

Although differentiating fragrances by brand is more effective than segmenting

quality by price, it is still important to acknowledge limitations. All perfume brands of

the parent company LVMH are considered premium. Contrastingly, Coty and Elizabeth

Arden produce premium and mass-market fragrances. However, the consumer may

perceive all Elizabeth Arden brands as premium and all Coty brands as mass-market.

Brand perception often determines whether fragrances are considered premium or mass-

market, yet this intangible cannot be taken into account. A robustness measure, using a

different categorization method, attempts to control for the aforementioned.

Several Nielsen Brand Descriptions contain nbl, an abbreviation corresponding

to no brand label, or private-label fragrances. Private-label products are sold under the

name of a small retailer and are generally more affordable than premium or mass-market

alternatives. An additional category, including private-label fragrances with UPCs

containing Brand Description nbl, is created. Segmenting quality by brand results in 49

premium brands, 26 private-label brands, and the remaining categorized as mass-market.

38

IV. Methodology

This section introduces the structure of the regression models, which are applied

to four distinct dependent variables for a comprehensive analysis of household shifts in

fragrance consumption in the periods prior to and during the Great Recession.

i. Presentation of the Regression Model

A series of regressions, differentiated by the dependent variable, determine the

consumption behavior of low- and high-income households over 2004-2011. The

majority of households do not remain in the dataset for all eight years under observation,

thus the regression analysis presents consumption patterns of households on the

aggregate. As one of the measures of robustness, the regression models are re-examined

using only households present for at least one year within each of the two time periods of

interest, the years 2004-2006, before the crisis, and the years 2008-2011, during the

crisis, to determine whether results and the overall narrative hold.

The first set of regressions measures the effect of independent variables on the

natural logarithm of the total fragrance expenditures in U.S. dollars by households within

a given year. Model I and its variations are as follows:

Model I.

(i) Ln(Total Fragrance Expenditures)HT7 = 0 + 1(RecessionT) + 0(YearT) + HT

(ii) Ln(Total Fragrance Expenditures)HT = 0 + 1(RecessionT) + 0(YearT) + 2(Income StatusH) + Demographics8 + HT

(iii) Ln(Total Fragrance Expenditures)HT = 0 + 1(RecessionT) + 0(YearT) + 2(Income

StatusH) + 3(RecessionT*Income StatusH) + Demographics9 + HT

7 The dependent variable, Ln(Total Fragrance Expenditures), is formed by taking the natural logarithm of the quantity of one added to the Final Price Paid, or the term denoting the total fragrance expenditures. The value of one is added such that households that abstain from purchasing fragrances of any particular type within certain years are still included. 8 Models I-IV (ii) include the continuous, demographic variables: Age of Head of Household and (Age of Head of Household)^2.

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This study examines the behavior of households of two income categories within

two distinct time periods. In Models I-IV (i)-(iii), RecessionT is a categorical variable

equivalent to zero for the years 2004-2006 and equivalent to one for the period 2008-

2011. In Model I (i), should the coefficient, 1, exhibit negative, significance, then,

compared to total fragrance expenditures in the pre-crisis period, total fragrance

expenditures decline during the recession.10 Although documented as having ended in

June 2009, the recession, as Aguiar and Bils (2015) support, negatively impacted average

and median household income until 2012. YearT accounts for a linear trend in fragrance

expenditures, and should its coefficient, 0, reveal positive, significance, then fragrance

expenditures follow an upward trend over time. Income StatusH is an indicator variable

for households of either low- or high-income status. In Model I (ii), should 2 reveal

positive, significance, then high-income households, compared to low-income

households, contribute to greater increases in total fragrance expenditures. Subscripts H,

Household Code, and T, Panel Year, indicate expenditures by households and the year of

the expenditures, in that order, with Panel Year=T=2004,...,2011. Observations are

collapsed by Panel Year and by Household Code that identifies individual households.

Variation (iii), the most exhaustive of the regression models, builds on (ii) by

incorporating the interaction term RecessionT*Income StatusH and several additional

household demographic control variables. Interacting the RecessionT and Income StatusH

variables results in four distinct combinations of income level and time period: Pre-

Recession, Low-Income, Recession, Low-Income, Pre-Recession, High-Income,

9 Models I-IV (iii) include the demographic variables: Age of Head of Household, (Age of Head of Household)^2, Head of Household Employment, Head of Household Education, and Age and Presence of Children. Age of Head of Household and (Age of Head of Household)^2 are continuous variables; other demographic control variables are categorical. 10 Term coefficients in the log-linear regression Model I (i)-(iii) are associated with percent changes in the dependent variable. Conversely, linear regression Model II-IV (i)-(iii) outcomes are associated with percentage point shifts in the dependent variable.

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and Recession, High-Income. RecessionT*Income StatusH, when considered in relation

to the dependent variable, designates fragrance expenditures by households of a particular

income group (e.g., high- or low-income) in a given period (e.g., before or during the

crisis). Output tables omit combinations containing either Pre-Recession or Low-

Income categories. Pre-Recession, Low-Income constitutes the base condition. In

Model I (iii), a sum of the individual coefficients, 1 and 3, on RecessionT and

RecessionT*Income StatusH that exhibits negative, significance would convey that high-

income households experience a significant percent decrease in fragrance expenditures

during the crisis. Conversely, a negative, significant difference between the RecessionT

and RecessionT*Income StatusH coeffcients, 1 and 3, would indicate that low-income

households significantly decrease total fragrance expenditures during the crisis.11

Regressions in Model I (i)-(iii) are then repeated with respect to the following

dependent variables: Average Share of Premium Fragrance Expenditures, Average Share

of Mass-Market Fragrance Expenditures, and Average Share of Private-Label Fragrance

Expenditures. The Average Share of Premium Fragrance Expenditures is created through

the following process. The Final Price Paid for Premium Fragrances is determined for

each household within a given year. The Share of Expenditures on Premium Fragrances

is calculated by dividing the sum of the Final Price Paid for Premium Fragrances by

households in a given year, by the Final Price Paid for all fragrances in a given year. The

Average Share of Premium Fragrance Expenditures is then determined.

11 A joint linear combination test is used to determine the significance of the combined effect of the RecessionT and RecessionT*Income StatusH terms on the dependent variable. The linear combination test and its results are discussed in Section V.

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Model II.

(i) (Average Share of Premium Fragrance Expenditures)HT = 0 + 1(RecessionT) + 0(YearT) + HT

(ii) (Average Share of Premium Fragrance Expenditures)HT = 0 + 1(RecessionT) +

0(YearT) + 2(Income StatusH) + Demographics + HT (iii) (Average Share of Premium Fragrance Expenditures)HT = 0 + 1(RecessionT) +

0(YearT) + 2(Income StatusH) + 3(RecessionT*Income StatusH) + Demographics + HT

In Model II (iii), the sign and significance of the sum (difference) of the

coefficients on RecessionT and RecessionT*Income StatusH determines the impact, in

percentage points, of the Great Recession on the Average Share of Premium Fragrance

Expenditures by high-income (low-income) households, as compared to their pre-crisis

average share of premium fragrance expenditures.

Model III.

(i) (Average Share of Mass-Market Fragrance Expenditures)HT = 0 + 1(RecessionT) + 0(YearT) + HT

(ii) (Average Share of Mass-Market Fragrance Expenditures)HT = 0 + 1(RecessionT) +

0(YearT) + 2(Income StatusH) + Demographics + HT (iii) (Average Share of Mass-Market Fragrance Expenditures)HT = 0 + 1(RecessionT) +

0(YearT) + 2(Income StatusH) + 3(RecessionT*Income StatusH) + Demographics + HT

A procedure similar to that used to create the Average Share of Premium

Fragrance Expenditures, is repeated to generate the Average Share of Mass-Market

Fragrance Expenditures, the dependent variable presented in Model III. Should the sum

(difference) of the coefficients on RecessionT and RecessionT*Income StatusH in Model

III (iii) prove negative and significant, then high-income (low-income) households reduce

their average share of mass-market fragrance expenditures during the economic crisis.

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Model IV.

(i) (Average Share of Private-Label Fragrance Expenditures)HT = 0 + 1(RecessionT) + 0(YearT) + HT

(ii) (Average Share of Private-Label Fragrance Expenditures)HT = 0 + 1(RecessionT) +

0(YearT) + 2(Income StatusH) + Demographics + HT (iii) (Average Share of Private-Label Fragrance Expenditures)HT = 0 + 1(RecessionT) +

0(YearT) + 2(Income StatusH) + 3(RecessionT*Income StatusH) + Demographics + HT

The final model measures the impact of the same independent variables, as in

Models I-III (i)-(iii), on the Average Share of Private-Label Fragrance Expenditures.

Should the sum (difference) of the individual coefficients on RecessionT and Income

StatusH in Model IV (iii) exhibit positive, significance, then high-income (low-income)

households increase their average share of private-label fragrance expenditures during the

Great Recession. Differences in the magnitudes of the shifts in expenditures by low- and

high-income households reveal the impact of household income on shopping behavior.

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V. Results and Discussion

i. Graphical Analysis and Regression Results

This section engages in a side-by-side discussion of graphical and regression

analyses and offers plausible explanations for results to support the established narrative.

Fragrances are differentiated by brand type, rather than by price, using the method

previously specified. Interpretations of outcomes both from graphs, and corresponding

regression Models I-IV (i)-(iii), offer conclusions relevant for responding to the first and

second research questions.

Figure 5. Total Fragrance Expenditures.

Source: Kilts-Nielsen Consumer Panel Dataset (2004-2011). Notes: The y-axis conveys the total fragrance expenditures, in U.S. dollars, of sample household panelists. The x-axis shows the Panel Year, the year in which the panel was conducted, which, as previously explained, is assumed to roughly coincide with Transaction Year. The plotted line reveals the sum of the Final Price Paid, or total amount spent on fragrances, by U.S. households.

3000

040

000

5000

060

000

7000

0Ex

pend

iture

s in

USD

2004 2006 2008 2010 2012Year

Total Fragrance Expenditures, 2004-2011

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Figure 5 illustrates the total expenditures, in U.S. dollars, on womens fragrances

for 2004-2011. The dependent variable is determined by calculating the sum of the Final

Price Paid, or the total amount, in U.S. dollars, spent on fragrances by households in

each year. In 2004, total household perfume expenditures reach about $60,000. After

2005, exp