Veblen Goods and Urban Distinction:

The Economic Geography of Conspicuous Consumption,

A Survey of 21 Cities

Elizabeth Currid-Halkett*,

University of Southern California

Ralph and Gold Lewis Hall 301B

Los Angeles, CA 90089-0626

E-mail: currid@usc.edu

Hyojung Lee,

University of Southern California

Verna and Peter Dauterive Hall 207

Los Angeles, CA 90089-3331

E-mail: hyojung.lee@usc.edu

and

Gary D. Painter

University of Southern California

Verna and Peter Dauterive Hall 207

Los Angeles, CA 90089-3331

E-mail: gpainter@usc.edu

Correspondence concerning this article should be addressed to: 

Gary Painter

University of Southern California

Ralph and Goldy Lewis Hall 301B

Los Angeles, CA 90089-0626

Phone: (213) 740-4012

Fax: (213) 740-1801

E-mail: gpainter@usc.edu
ABSTRACT 

A fundamental observation of 21st century cities is that they have become great centers of 

consumption. Some argue that their ability to generate consumer options is part and parcel of 

their success, particularly with regard to attracting highly-skilled human capital (Glaeser et al. 

2001; Clark 2004; Handbury 2012; Diamond 2016). While extant studies of metropolitan 

consumer options lump them together as a large “amenities” variable associated with growth and 

skills, previous work has not highlighted the relationship between different types of consumer 

goods and the geographical location of where they are most consumed. These distinctions have

implications for the relationship between consumerism and economic development both as an 

extension of the literature and in its practical application.

In this paper, we seek to understand the geographic variations of consumer behavior. Using

Consumer Expenditure Survey (CEX), we analyze how consumption differs across 21 major 

U.S. metropolitan areas, and the association between urban characteristics and consumption. We 

expand previous geographic analysis of consumption to include luxury goods that are socially 

visible (conspicuous consumption) and luxury goods that are not visible (inconspicuous 

consumption). Our analysis shows that conspicuous consumption is more sensitive to an urban 

context and the variables associated with cities influence status spending. Overall, results 

suggest that the conspicuous consumption of a household is positively associated with size,

density, residential segregation, and regional specialization of eating and drinking places of the

metropolitan area in which the household is located; it is negatively associated with the 

metropolitan area’s income inequality, income level of reference group, and housing cost. The 

results indicate that the importance of surrounding urban context in understanding what people 

consume, especially on status goods which often attain value through social visibility.

Keywords: Conspicuous Consumption; Consumer City; Metropolitan Distinction; Veblen Good
1

I. Introduction

A fundamental observation of 21st century cities is that they have become great centers 

of consumption. Within economic geography, consumption has been explored through three 

frameworks: that of amenities (Glaeser et al. 2001; Diamond 2016), that of high-end, luxury 

retail consumption that is primarily associated with urban living (Wrigley and Lowe 1996; 

Handbury 2012; Clark 2004; Currid-Halkett 2013), and that of the commodification of cultures 

(Zukin 1989, 1998, 2008; Fainstein and Judd 1999). Some argue that cities’ ability to generate 

consumer options is part and parcel of their success (Glaeser et al. 2001; Clark 2004; Handbury 

2012; Diamond 2016), while others suggest that amenities are a key driver in the attraction of

skilled human capital (Florida 2002). Still others observe the coopting of culture and its 

translation into a commodity as a part of urban economic development (Zukin 1989). While 

extant quantitative studies of metropolitan consumer options lump them together as a large 

“amenities” variable associated with growth and skills, we argue that there is a much greater 

distinction across cities in their consumer behavior, and the distinctive patterns are closely 

related with socio-economic and industrial mix of cities. In short, the relationship between 

consumption of individual households and surrounding urban context must be unbundled.

This study will focus on understanding both the individual determinants and the 

contextual determinants of the consumption of luxury goods and those that reveal status. In so 

doing, this study will be the first to focus on the consumption of two classes of luxury goods that 

might be differentially affected by a metropolitan areas attributes. Conspicuous consumption or 

the consumption of visible luxury goods for the purposes of revealing status (Veblen 1899; 

Charles et al. 2009; Heffetz 2011, 2012) and inconspicuous consumption or the consumption of 
2

hidden luxury, expensive goods such as education, retirement and travel are posited to vary

across places due to socially motivations and other factors. Heffetz (2011, 2012) and Charles et 

al. (2009) find distinctions in conspicuous consumption across race and class. While we establish 

inconspicuous consumption as a new unique category of goods, others have looked at nonvisible 

luxury spending patterns. Most famously, Bourdieu (1984) found symbolic types of 

consumption that suggested social positon. More recently, Chetty et al. (2017) find that 

socioeconomic position is significant in intergenerational mobility, particularly for admittance to 

Ivy League universities. While these studies do not address inconspicuous consumption, per se, 

the results are indicative of a type of luxury spending that is not ostensibly for status but implies 

social position nonetheless. For example, Chetty et al. (2017) find that children with parents in 

the top 1% income distribution are 77 times more likely to attend an Ivy League university.

In this analysis, we use data from the Consumer Expenditure Survey in 21 metropolitan 

areas to test what are the individual- and the metropolitan-level characteristics that are associated 

with the consumption of visible and hidden luxury goods and consumption of other goods. The 

analysis uses both metropolitan level fixed effects and random effects models to identify the 

areas and the characteristics that are linked to conspicuous and inconspicuous consumption. In 

short, we find that metropolitan level amenities, income inequality, and segregation, are 

influences for conspicuous consumption, but do not influence other forms of consumption

suggesting that the consumption of status goods is influenced by social context. 
3

II. Literature Review

Since Adam Smith’s The Theory of Moral Sentiments (1759) and Thorstein Veblen’s The 

Theory of the Leisure Class (1899), scholars have sought to understand how consumer behavior 

reveals socio-economic position (Leibenstein 1950; Galbraith 1958; Deaton and Muellbauer 

1980; Deaton 1992; Heffetz 2011; Rank et al. 2014, among others). In the past several decades, 

however, the study of consumption has begun to unpack the role of myriad different variables in 

shaping consumer choices, including race (Charles et al. 2009), age (Cook and Settensten 1995; 

Lee et al. 1997), gender (De Ruijter et al. 2005), generational position (Norum 2003), and even 

food consumption patterns (Yen 1993; Zan and Fan 2010). Others have sought to tease out the 

influence of children (Lino and Carlson 2010) and marriage (Walden 2002) on consumption 

practices. In the tradition of Deaton and Muellbauer (1980) and Piketty (2014), a number of 

scholars have looked at consumer behavior as it relates to inequality (Krueger and Perri 2006; 

Aguilar and Bils 2015; Rank et al. 2014; Chetty et al. 2016, 2017). 

Within the extant literature on consumption, a seminal line of inquiry is the study of why 

people buy what they do for reasons that transcend practicality. What Thorstein Veblen called 

“conspicuous consumption” is the purchase of goods that do not exhibit additional utility or 

functionality but offer status and reveal socio-economic position (Veblen 1899). Drawing from 

Veblen, some of the earlier work in this area focused on the role of price in influencing 

consumers. The phenomenon that Leibenstein (1950) and Bagwell and Bernheim (1996) termed 

“Veblen effects” suggests that increased price reveals luxury and encourages conspicuous 

consumption. The larger body of research on conspicuous consumption argues that while, as a 

general rule, rich households spend more on these socially visible goods than poor households,

other variables also influence status spending. For example, Charles et al. (2009) find that, 
4

controlling for income levels, blacks and Hispanics spend more than whites on conspicuous 

consumption and argue that this pattern is explained by relatively lower income among racial 

minorities. The utility from spending more on conspicuous goods depends on with whom people 

compare themselves. For example, Heffetz (2011, 2012) shows that the demands for 

conspicuous goods are coming from visibility of items and how the visibility differs across 

demographic groups. Heffetz argues that wealthier people gain greater utility out of conspicuous 

consumption due to the greater number of socially visible milieus in which they are able to 

display these items. As one important aspect of status consumption is revealing one’s economic 

position vis-à-vis others, the role of negative peer effects has also been explored (Rayo and 

Becker 2007; Luttmer 2005; Bertrand and Morse 2016). Being friends with or in close 

geographic proximity to wealthier households both increases spending and financial duress and 

decreases happiness (Easterlin 2007; Kahneman and Deaton 2010).

While much work studying consumer behavior considers the expense of goods as a proxy 

for conspicuous consumption, there is an important line of research that studies the more 

nebulous, “inconspicuous” aspects of status. Bourdieu’s (1984) study of taste and the role of 

what he called “habitus”, suggests that many markers of status are contextual construed and rely 

on information more so than simply price or materiality. In Bourdieu’s analysis, much of status 

is derived from prosaic activities and consumption habits embedded into daily life not simply 

expensive material objects. Lamont’s (1992) study of “symbolic capital” expands this idea, 

where she argues that norms and practices create boundaries across income and social groups

(What Khan and Jerolmack (2013) call a “learned form of capital”). These boundaries are often 

cultural and vis-à-vis one’s peer group rather than for the purposes of revealing status to a wider 

population. Holt (1998) takes Bourdieu’s framework and applies it to the United States, 
5

concluding that consumer behavior and class do not always adhere to Veblen effects, but rather 

we make consumer decisions that rely on socio-economically exclusive information. While this 

literature does not use the term “inconspicuous” specifically, the analyses suggests that status is 

not always about conspicuous consumption, but rather it can take immaterial forms and rely on 

tacit social knowledge.

The geography of consumer behavior and its implications has been studied through the 

framework of the “consumer city” (Glaeser et al. 2001). From this perspective, the supply of 

certain goods and services can play an important role in attracting and retaining certain group of 

people as they will ultimately move to community that can maximize their personal utility 

(Tiebout 1956, Hirschman 1970). Indeed, Glaeser et al.’s (2001) pioneering work finds that 

metros with greater bundles of consumer amenities are more productive and attract greater stocks 

of high human capital. Also through a geographic lens, Clark (2004) looks at how different 

bundles of amenities, which he calls “scenes” within a “consumer city”, draw different types of 

human capital and labor pools. Handbury (2012) and Handbury and Weinstein (2015) take this 

line of inquiry into a more detailed study of particular consumer items. They find that luxury 

goods are cheaper in urban areas, suggesting economies of scale for affluent populations, while 

the bigger cities provide the greater product availability as well. Others have considered the 

standardization of the luxury consumer experience across metro areas (Crewe and Lowe 1995; 

Wrigley and Lowe 1996). Diamond (2016) argues that urban “hidden amenities” such as public 

space and human capital, rather than material goods, drive increases in city real estate prices. 

More broadly, Zukin (1989, 1995, 1998, 2008) has studied the role of culture as a commodity in 

shaping cities and their identities, particularly looking at how consumerism creates “authenticity” 

and fuels gentrification processes. The trickle-down consumption, or expenditure cascades, 
6

describes how consumption patterns of elites may influence consumption behaviors of middle 

income families. This type of behavior is particularly pronounced in conspicuous goods

consumption, which reshapes community identification (Bertrand and Morse 2016; De Giorgi et 

al., 2016; Charles and Lundy 2013; Frank et al. 2014). Recent work suggests that conspicuous 

consumption affects real estate prices, which will ultimately influence mobility and distribution 

of workers (Zahirovic-Herbert and Chatterjee 2011; Lee and Mori 2015). Currid-Halkett (2014) 

finds that metro areas engage in significantly different consumer behavior, particularly around 

status goods and argues that these differences help explain distinctions in urban identity.

More generally, however, our understanding of the observed differences in consumer 

behavior across cities and regions and their implications is an understudied area of research in 

economic geography. While there is an emerging line of inquiry studying the relationship 

between consumption and geography there is limited if any work done on the relationship 

between status consumption and geography. Given that the “value” of conspicuous consumption 

is derived significantly from the physical and social contexts in which it is consumed, it is 

significant to explore these interactions within the urban milieu and the possible geographical 

variations of the phenomenon. In this paper we seek to unpack the geographic variations of 

consumer behavior across metros and how this may inform our understanding of urban 

differences and economic development. We study these relationships looking at both 

conventional and status goods, and seek to understand the interaction between consumer 

behavior and economic geography. What discrete variables might explain differences in 

consumerism across metro areas? How might our understanding of these dynamics illuminate 

our understanding of cities and their differences more generally?
7

III. Method and Data

To understand how consumption is associated within surrounding urban context in which

an individual household is located, we estimate both metropolitan fixed-effects and randomeffects models that include metropolitan level characteristics as regressors. The fixed-effects 

models enable us to control for time invariant characteristics of metropolitan areas and to 

highlight which metropolitan areas have higher or lower levels of unexplained consumption. We 

highlight how spending patterns differ across cities, even after controlling for demographic and 

socio-economic characteristics of individual households. The random-effects models enable us 

to examine how and what natural and urban amenities are associated with the consumption 

patterns across cities, while controlling for unobserved random effects.

The metropolitan fixed-effects model is of the form:

yijk = βXijk + αj + τt + εijt (1)

where yijt is the log of consumption of a household i in a metropolitan area j in a year t. The 

models are estimated for three dependent variables: conspicuous consumption, inconspicuous 

consumption, and other expenditures. Xijt is a vector of demographic and socio-economic 

characteristics of a household i in a metropolitan area j in a year t, αj is a time-invariant city fixed 

effect, and τt is a year fixed effect. The city fixed-effects are of particular interest as they absorb 

any systemic differences in consumption patterns across cities, holding other factors constant. 

We estimate models separately for conspicuous consumption, inconspicuous consumption, and 

other spending to determine how much city differences account for differences in each category 

of consumption. The sampling weights are used in the regression to account for sampling 

design, and robust standard errors are used to correct for heteroscedasticity.
8

Although the fixed-effects model enables us to determine to what extent spending 

patterns differ across metropolitan areas, we cannot infer what explains such differences across 

metropolitan areas from the model. To explore what explains the geographic variations of 

consumer behavior, we estimate random-effects models that include metropolitan area-level 

urban characteristics as well as household-level individual attributes:

yijt = Xijtβ + Njtγ + τt + ζj + εijt (2)

where Njt is a vector of metropolitan area characteristics of city j in year t. The random intercept 

ζj represents unobserved heterogeneity at the metropolitan area level. All other individual 

household level variables are the same as before. Here, we assume that the random intercept is 

uncorrelated with independent variables.1 

The primary source of data used in this study is the 2007–2013 Consumer Expenditure 

Survey (CE) from the Bureau of Labor Statistics (BLS), a quarterly survey of the consumer 

habits of Americans categorized by age, race, marital status, income, and educational attainment, 

among other variables. The data set contains the most comprehensive and reliable source of 

information on consumption expenditures (Li et al. 2010; Bee et al. 2015) and has long been 

used in studying consumers and their buying behaviors in the United States (Charles et al. 2009; 

Heffetz 2011; Charles and Lundy 2013). The Consumer Expenditure Survey Interview PublicUse Microdata (PUMD) provides individual consumer unit-level consumption behaviors with 

hundreds of standardized expenditure item codes (referred to as the Universal Classified Codes, 

 

1 The Breusch-Pagan’s Lagrange multiplier test strongly rejects the null hypothesis of a zero metropolitan

area level variance and favors the fixed- or random-effects model over ordinary least squares (OLS)

analysis. While the Hausman test statistics favors the fixed-effects model over the random effects model,

the random effects model allows for testing the impact of metropolitan level characteristics directly.
9

UCC), and its detailed information on households enable us to analyze consumption 

expenditures across cities while controlling for household characteristics, including geographic, 

demographic, and socio-economic status.2 Annualized expenditures are adjusted for inflation to 

2015 dollars.

The dependent variables in this study are specified as the log of the amount of money 

spent on conspicuous consumption, inconspicuous consumption, and other spending.3 We 

characterize luxury goods as conspicuousness and inconspicuousness based on designations in 

past literature (Bourdieu 1984; Lamont 1992; Charles et al. 2009; Heffetz 2011, 2012; Bagwell 

and Bernheim 1996). While conspicuous consumption is a well-established term in the literature, 

we also study the relationships between inconspicuous consumption and demographic and 

geographic variables. Using the UCC items, we apply the conventional definition for 

conspicuous goods as those luxury goods that are visible and portable. We categorize luxury 

goods that are immaterial and not directly visible as inconspicuous. Other expenditures are 

defined as total expenditures less conspicuous and inconspicuous consumption. The resulting 

consumption categories are shown in Table 1.

[ insert Table 1 about here ]

To test the robustness of the results, we define conspicuous consumption based on the

“visibility” index developed by Heffetz (2011).4 Heffetz (2011) measured visibility of 31 

 

2

In this paper, the terms consumer unit, family, and household are used interchangeably.

3 The log terms were used to accounts for skewness in expenditures. Alternative specifications of the

dependent variable did not alter the primary findings in the study.

4 Heffetz (2011)’s survey and visibility index are based on the consumption categorization that was

proposed by Harris and Sabelhaus (2005). To be consistent with Heffetz (2011)’s visibility index, we

aggregated the consumption data at the UCC code level into the Harris and Sabelhaus (2005)’s

consumption categories.
10

consumption categories through a nationally representative survey of 480 U.S. adults and 

examined the relationship between the visibility and income elasticities. The proposed index 

measures how long it would take for respondents to notice others spend more than average on a 

certain item (1.0 = almost immediately; 0.75 = a short while after; 0.5 = a while after; 0.25 = a 

long while after; and 0). Based on the visibility index and total expenditure elasticity of each 

consumption category presented in Heffetz (2011), we classified the items with visibility index 

of 0.6 or greater and total expenditure elasticity above unity as conspicuous. In the same way, 

the expenditure categories with visibility index of 0.4 or less and total expenditure elasticity 

above unity are classified as inconspicuous consumption.5

The variables of interest in this paper are the metropolitan characteristics that influence 

households’ consumption behavior. Starting in the second quarter of 2006, the Consumer 

Expenditure Survey Interview Public-Use Microdata began providing identifiers for 21 major 

metropolitan areas, referred to as the primary sampling units (PSUs) in the CE, with a population 

greater than 1.5 million.6 Using the 21 city identifiers, we are able to study how consumption 

differs across the major cities in the United States. Also, based on the counties contained in each

metropolitan area, we link additional data to determine associations between what cities offer 

and how it links to what its inhabitants tend to consume. 

 

5 For example, although tobacco products are the most visible item among the 31 categories, we do not

include it to conspicuous consumption category because it is an inferior good. We also tested the

robustness of the results by varying the number of items for grouping, and the results were qualitatively

unchanged.

6 The primary sampling units (PSUs) consist of counties (or parts thereof) or groups of counties. The

boundaries have been consistent from 2006 to 2014. The list of counties for those PSUs are provided in

Table B in the Appendix. 
11

Among the metropolitan characteristics, annual population estimates and population 

density per square mile are drawn from the Census Bureau’s the decennial Census and the 

National Population Estimates. The dissimilarity index was calculated at census tract level for 

the metropolitan areas using the 2009–2015 American Community Survey (ACS) 5-Year 

Estimates. The index measures the degree of residential segregation between households in the 

top income quintile in each city in each year and others.

7

 The median household income by 

racial/ethnic group are calculated based on the 2007–2013 American Community Survey (ACS)

1-Year Public-Use Microdata Sample (PUMS). Based on the householder’s race/ethnicity, we 

assigned the median income of own racial/ethnic group and the one of other groups to examine 

the role of reference group income.8 

The association between urban amenities and consumption, suggested by Glaeser et al. 

(2001), is examined by using location quotient (LQ) of food service and drinking places industry 

(NAICS 722). The index was used to represent relative concentration or specialization of 

restaurants and drinking places of a city based on the 2007–2013 County Business Pattern. 

Following Glaeser et al. (2001) and Albouy (2008), we use the mean temperature in January and 

number of days with greater than or equal to 0.1 inch of precipitation to capture natural amenities 

of metropolitan areas based on the National Climatic Data Center’s the Monthly/Annual 

Climatological Summary data. 

 

7 We also tested other income thresholds such as top 10% and absolute dollar term ($200,000 in 2015

dollars). The results were consistent across those variables.

8 Some metropolitan areas have borders that do not align with the Public Use Microdata Area (PUMA),

which is the geographic unit used in the PUMS files. In these cases, we used the adjusted weights for the

households within those PUMAs using the numbers of housing units as weights. Similar approach can be

also found in Albouy and Lue (2015).
12

Lastly, cost of living differences across cities are addressed by using the 2008–2013 

Regional Price Parities (RPPs) provided by the Bureau of Economic Analysis.9 Because the 

index is comprised of the data on goods/services and rents, we include two cost of living 

variables, on goods and rents, in the model. The descriptive statistics of all metropolitan arealevel variables used in this study are presented in Table 2.

[ insert Table 2 about here ]

We also include household-level covariates that have been considered as major 

determinants of consumption behaviors; namely, age, sex, race/ethnicity, marital status, and 

occupation of reference person, family size, whether having a child or children, log of current 

family income (in 2015 dollars), log of total expenditure, log of financial wealth, the highest 

education between reference person and his/her spouse, number of earners in household, units in 

structure, and housing tenure status.

IV. Results

a. Descriptive statistics

Table 3 presents descriptive statistics on income and expenditures for the sample of 

consumer households from 2007 to 2013 by 21 cities, or primary sampling units. As is evident 

in Table 3, there are substantial differences in income and consumption across the metropolitan 

 

9 The Regional Price Parities (RPPs) were used as a cost-of-living index rather than the Consumer Price

Index, since the latter one cannot be used for comparison among the areas. The time periods not covered

by the RPPs were estimated by using the percentage changes in the CPI-U index in each metropolitan

area since the CPI measures how much prices change over time in an individual metropolitan area.
13

areas. For example, on average, residents in Washington, D.C. spent the most on conspicuous 

goods ($13,056 in 2015 dollars); the lowest average conspicuous consumption could be found 

among the New Yorkers ($7,152 in 2015 dollars). These differences may simply reflect the

levels of income and total expenditures in a metropolitan area. In contrast to the substantial gap 

in average expenditure on conspicuous consumption, the share of total expenditure spent on 

conspicuous consumption by residents in New Yorkers was 11.9 percent of total expenditures on 

all goods, and residents in Washington allocate 13.0 percent of their total expenditures to 

conspicuous consumption. The greatest share of conspicuous consumption is among residents of 

Phoenix, who spend about 15.4 percent; the lowest share is among residents of New York City at

11.9 percent. The highest inconspicuous consumption was again reported in Washington, D.C. 

($11,108 in 2015 dollars); the lowest was found in Miami ($4,254 in 2015 dollars). As the share 

of total expenditures, residents in Seattle reported the highest inconspicuous consumption share 

(12.4%), while residents in Miami had the lowest share (6.7%). The share of expenditures spent 

on other items ranges from 73.9 percent (Seattle) to 81.1 percent (Miami).

[ insert Table 3 about here ]

Notably, the cities with lower housing costs tend to have a larger share of conspicuous 

consumption. For example, the top 5 metropolitan areas by conspicuous consumption share are 

Phoenix (15.4%), Dallas (14.9%), Detroit (14.7%), Houston (14.6%), and Chicago (14.0%), 

where have relatively lower housing costs, which might suggest the ability to spend a larger 

share of household income on luxury items. However, the same relationship does not apply with 

the consumption of non-visible luxury goods. The top 5 cities ranked by the inconspicuous 

consumption share is Seattle (12.4), Minneapolis (12.0), Washington, D.C. (11.9%), Connecticut 
14

cities (11.2%), and Boston (11.1%), which includes metropolitan areas that have high housing 

costs.

On the other hand, the variation in conspicuous and inconspicuous consumption might be

partially explained by the characteristics of residents, such as age and educational attainment. 

For example, the metropolitan areas with younger heads of household (e.g. Phoenix, Houston, 

and Dallas) tend to spend more money on conspicuous goods compared to those cities with more 

seniors (e.g. Cleveland, Connecticut cities, and Philadelphia). The cities with relatively well 

educated people (e.g. Washington D.C., Baltimore, and San Francisco) tend to have lower share 

of expenditures on conspicuous goods, and cities with opposite characteristics (e.g. Dallas, 

Houston, and Phoenix) tend to have the higher share. In the analysis below, we estimate models 

to determine what characteristics are associated with the level of consumption. 

b. Individual determinants of consumption

We first present the impacts of individual determinants of conspicuous consumption 

across three models in Table 4. Model 1 contains no fixed effects or random effects. Model 2 

contains the metropolitan fixed effects, and model 3 contains the metropolitan level 

characteristics and random effects. Table 5 presents the same models with inconspicuous 

consumption as the dependent variable.

[ insert Table 4 and 5 about here ]

Most results on the determinant of individual household characteristics conform with 

expectations and what has been found in previous studies. The amount of money spent on 
15

conspicuous consumption, which consists of many durable goods, tends to decrease with age, 

while that on inconspicuous consumption, which includes housekeeping and healthcare services, 

exhibits a U-shaped curve, suggesting the impacts of age. On average, Hispanic households 

spend 9.2 percentage more money on conspicuous goods and services, while other racial/ethnic 

groups are not statistically different from non-Hispanic Whites. However, racial/ethnic minority 

groups spend less money on inconspicuous consumption, ranging from 5.7 percent (African 

American) to 16.9% (Hispanic). These difference in consumption between groups might be at 

least partially explained by the differences in preferences and in status-seeking behavior (Heffetz 

2012; Charles et al. 2009).

The relationship between education and the consumption of both conspicuous and

inconspicuous consumption is consistent with other literature on conspicuous consumption 

(Currid-Halkett 2017). Invisible luxury goods (Table 5) are much more likely to be consumed 

by those with high levels of education. At the same time, levels of conspicuous consumption 

(Table 4) decline with education. This suggests that education may alter decisions of households 

to save more for retirement or purchase more insurance, which do not reveal their social status 

(Currid-Halkett 2017), but that have long term impacts.

Finally, we note that both the coefficient on total expenditures in both Table 4 and Table 

5 are above unity. This estimated elasticity is consistent with the conclusion that both 

conspicuous and inconspicuous goods are luxurious ones.
16

c. Metropolitan Fixed Effects

Despite the large set of household-level controls and year fixed effects, the city where 

people are living in remains a strong predictor for explaining conspicuous consumption (Table 

6). For example, Bostonians on average spend 15.6 percent less on conspicuous goods compared 

to their counterparts in Chicago, controlling for other covariates. The cities that have the greatest 

unexplained effect on conspicuous consumption are Detroit, Dallas, and Cleveland, and the

metropolitan areas with the smallest effect are Boston, Washington, D.C., and San Francisco. 

The gap (31 percent) between the city with the highest unexplained consumption (Detroit) and 

the lowest (Boston) is quite substantial. The gap in conspicuous consumption across cities 

becomes even more dramatic when it is compared to the gap in inconspicuous consumption. The 

magnitudes of estimated coefficients across cities for inconspicuous consumption are typically 

smaller than those for conspicuous consumption.

10

 The places with the greatest fixed-effects on 

inconspicuous consumption are Seattle, Minneapolis, and Phoenix, while the least fixed-effects 

are found in New Jersey cities, Miami, and San Diego.

[ insert Table 6 about here ]

In sum, the results of the household-level analysis indicate that consumption does differ 

across cities, accounting for individual household characteristics, and that these differences 

among cities are larger for conspicuous consumption. The latter part can be explained by the 

fact that the value and meaning of conspicuous goods are created and determined within a social 

 

10 Additional statistics also support this finding. For example, the estimate of the between-subject

standard deviation, measuring variances between cities, from the conspicuous consumption model is

0.093, while that from the non-conspicuous consumption model is 0.087. The interclass correlation,

which indicates how much variances in overall consumption are explained by between-city component,

from the conspicuous consumption is 0.0129, while that from the non-conspicuous consumption is 0.007.
17

context and the interactions that occur in situ. These results suggest that conspicuous 

consumption is an inherently urban feature (Veblen 1899; Simmel 1903). The less dramatic 

results for inconspicuous consumption is a corollary to this result: inconspicuous consumption 

has less visible impact and the motivations for spending may be less oriented around social 

positioning than quality of life (Currid-Halkett 2017; Chetty et al. 2017). Thus, the particular 

social and urban context may matter less for inconspicuous consumption. In the next section, we 

examine how the much of the unexplained variation across cities in conspicuous consumption 

can be explained by the urban context and variables within.

d. Random-Effects Models

To determine which metropolitan characteristics are correlated with consumption

patterns, we estimate the random-effects models with controls for various city attributes. In the 

model, we include four types of metropolitan characteristics that may influence consumer 

behavior based on previous research: urban size and population density (Handbury 2012; 

Handbury and Weinstein 2015; Diamond 2016; Simmel 1903), socio-economic metropolitan 

area characteristics (Charles et al. 2009; Charles and Lundy 2013), urban amenities (Glaeser et 

al. 2001; Clark 2004; Florida 2002), and natural amenities (Glaeser et al. 2001; Albouy 2008). 

The results of the random-effects models are shown in Table 7. The first column in 

Table 7 indicates that city size (population) and population density are positively associated with

conspicuous consumption. This result corroborates Glaeser and Gottlieb (2006) who find that 

consumer options are strongly associated with urban density. As Simmel (1903) noted more 

than a hundred years ago, greater anonymity in the bigger and denser cities may paradoxically 
18

heighten the need to accentuate individual differences. Economies of scale might enable those 

bigger cities to provide more diverse conspicuous goods and services at cheaper prices, which 

make not only top earners but also middle class spend more money on those items (Handbury 

2012; Handbury and Weinstein 2015; Bertrand and Morse 2016; Di Giorgi et al. 2016; Frank et 

al. 2014).

[ insert Table 7 about here ]

Holding other covariates constant, including city size and population density, income and 

population distributions within a city are also strong predictors of conspicuous consumption. 

There is a negative relationship between income inequality, measured by the Gini coefficient, 

and conspicuous consumption. Using slightly different methods and data, Charles and Lundy 

(2013) found a similar negative association between income inequality and visible goods (e.g. 

vehicles and jewelry). The same authors found a positive association between income inequality 

and expenditures on food and shelter, which they explain by noting that households in highinequality metropolitan areas (e.g. New York, Miami, and Los Angeles) have to allocate more 

money on necessary goods (food and shelter) and thus less on conspicuous goods, compared to 

the families in low-inequality areas (e.g. Minneapolis, Phoenix., and Riverside). On the other 

hand, residential segregation, measured by the dissimilarity index, is positively related to 

conspicuous consumption. The more income-segregated cities (e.g. Houston, Dallas, and Los 

Angeles) may have stronger notions of social class, which may influence their residents to spend 

more money on status goods as a positioning device than people in relatively less incomesegregated cities (e.g. Baltimore, Minneapolis, and Seattle).
19

The coefficients on median income of own race/ethnic group within a metropolitan area 

and median income of others groups are consistent with the theories of status-signaling and 

conspicuous consumption suggested by Charles et al. (2009). In this paper, the authors argue 

that people may have different incentives to spend money on conspicuous goods as individuals 

belong to different group. For example, racial/ethnic minority persons may have greater needs to 

buy status goods to distinguish themselves from their economically disadvantaged group. Our 

results confirm this story: Conspicuous consumption is positively associated with the median 

income of own racial/ethnic group within the same metropolitan area but not with the median 

income of other groups.

Not all urban characteristics are related to conspicuous consumption. Although the 

relative specialization of restaurants and bars are positively associated with conspicuous 

consumption, natural amenities such as average temperature in January and number of rainy days 

are not related with consumption patterns. The cost of housing is found to be negatively 

correlated with conspicuous consumption, which is likely due to households possessing less 

discretionary income. 

While there are many associations between conspicuous consumption and metropolitan 

area characteristics, there are only few urban characteristics that are related to inconspicuous 

consumption (city size and median income of own race/ethnic group) and other consumption 

(income inequality and median income of own race/ethnic group). Metropolitan characteristics 

are most likely to predict conspicuous, status-driven consumption. Our analysis suggests that the 

differences across consumption types in those relationships indicate that a specific urban context 

in important with regard to socially visible status consumption.
20

V. Additional tests

We conduct three additional tests to explore the robustness of the results. First, we use 

the Heffetz (2011)’s visibility index and total expenditure elasticity to determine conspicuous 

and inconspicuous consumption.

11

 Table 8 compares estimated coefficients for the models based 

on our categories and the ones based on the Heffetz (2011). In both fixed-effects model and 

random-effects model, the estimated coefficients have strikingly similar magnitudes and 

statistical significances. For example, when we plot the estimated coefficients for the fixedeffects regression models of conspicuous and inconspicuous consumption, the r-squared was 

0.827 and 0.718, respectively. For the random-effects model, the magnitudes of estimated 

coefficients are quite similar, while the models based on our own consumption categories tend to 

show stronger statistical significances. These give us more confidence in our measure of 

conspicuousness and inconspicuousness.

[ insert Table 8 about here ]

Second, there might be another concern on that large cities such as New York and Los 

Angeles could disproportionately impact the results. Because these cities have some unique

urban characteristics, such as significantly larger city size and population density, some might be 

concerned that the results are mainly driven by these big cities, and thus the findings are not 

generalizable to many other cities. In Table 9, the models are estimated without New York and 

Los Angeles. The differences in estimated coefficients are not large. 

The final issue is that our measures of consumption contain many categories with zero 

consumption. To account for left censorship at zero, we estimate Tobit models and display 

 

11 Heffetz (2011)’s visibility index and consumption categories are shown in Table A in Appendix.
21

marginal effects in Table 9. There are some changes in magnitudes and statistical significance of 

coefficients, but the overall patterns are similar.

[ insert Table 9 about here ]

VI. Conclusion

In this study, we unbundled the relationship between consumption and the metropolitan 

context in which a household resides. We do this by using the Bureau of Labor Statistics’ 

Consumer Expenditure Survey (CEX) to estimate first how individual consumption patterns vary 

across 21 major metropolitan areas. To our knowledge, the CEX has not until now been used to 

study city consumption patterns. Equally, our approach is innovative in its granular specificity to 

the study of consumption in both the analysis of between-cities consumption and the particular 

consumption habits associated with these differences. To our knowledge, this is the first study 

that empirically measures the geographical differences in consumption across an array of 

different types of consumption, and quantifies the variables that might explain these distinctions. 

We find that there are discrete differences in consumer behavior in the three forms of

consumption we study: inconspicuous consumption, conspicuous consumption and general 

expenditures. However, the most interesting story is that of conspicuous consumption. We find 

that metropolitan area strongly influences such spending, but has little effect on the other 

categories of consumption. Our work quantifies and articulates the role of conspicuous 

consumption and social positioning in cities and the variables that influence this type of 

spending. 
22

To understand this result, we then used the unexplained variation within metropolitan 

areas to determine how urban context might explain differences in urban consumer behavior. By 

studying the association between specific urban consumption patterns and city characteristics we 

address the “why” in previous literature on consumption and cities (Glaeser et al. 2001; 

Handbury 2012). Through this approach, we were able to identify both the geographical 

variations in consumption and also the attributes that might help explain these unique patterns. 

We find that the city and its unique characteristics influence how its denizens conspicuously 

consume.

Our analysis goes beyond simply looking at individual household characteristics. We 

study the qualities of the urban milieu itself and its characteristics. While our analysis indicates 

differences in consumption behavior across different types of spending, most significantly, we 

find that conspicuous consumption is influenced by the urban context and specific variables 

within. Our results indicate that while consumer behavior varies across metropolitan areas, the 

differences in spending on conspicuous consumption are the largest. The metropolitan level 

analysis reveals that the variance in conspicuous consumption across cities is closely associated 

with a number of discrete variables: population size and density, age structure, share of top 

earners, urban amenities, and cost of living of those cities. Our work addresses and confirms a

line of inquiry theorized in many of the early 20th century theories of the city and its features of 

density, anonymity, eccentricity and need for denizens to differentiate themselves (Veblen 1899; 

Simmel 1903; Mills 1956; Park et al. 1925; Molotch 1996, 2002). 

Like many other social and economic phenomena, much of what we understand about 

consumption manifests in geography. In short, people consume goods in particular places. With 

this fact in mind, how then might place inform consumption and vice versa? To that end, an 
23

emerging, but limited, line of research in geography looks at how and what we consume reveals 

important differences across cities and regions. This work corresponds to the larger body of 

research on the study of the attributes and amenities that comprise place and what those 

differences across metros might mean (Glaeser and Saiz 2004; Romer 1986; Bell 1973, among 

others). While our work gets closer to the specific differences in consumer habits, we have yet 

to explore what these differences might mean for economic development or human capital 

mobility. For example, Diamond (2016) finds that between 1980 and 2000, the population of 

college-educated New Yorkers increased by almost three-quarters while simultaneously the 

population of non-college educated decreased by 15%. Diamond’s finding corresponds to a 

general trend across the country in metro areas with high concentrations of high human capital 

where the uptick in the educated is rapid and parallels with an erosion of lower skilled workers.

The extant literature suggests that consumption plays an important role in economic 

development through amenities, retail and the transformation of culture into a commodity. These 

processes are thought to be at least partially responsible for how cities attract skilled labor pools

(Florida 2002). For example, Diamond (2016) finds that these luxury cities with high human 

capital offer “hidden amenities”, one of which is the desire for educated human capital to be 

located near each other. Our understanding of the role of consumption thus far has been relegated 

to specific qualitative case studies (particularly of New York City) and quantitative analysis that 

offers broad generalizations around amenities and human capital. In short, the variables 

underpinning this relationship have not been fully explored in comparative detail across a wide 

scale of metro areas. While amenities are thought to explain human capital mobility, our work 

indicates that cities offer significantly different amenities from one another, which suggests 

perhaps different motivations for human capital mobility. In our research, the causal direction is 
24

not yet evident but one worth unpacking in future work. Do we shape consumer options (i.e. 

does the urban market respond to its denizens) or do they shape us? Do we self-select into 

particular types of cities with specific amenities or urban context or do we respond to what is 

already on offer? These are important questions that may help us understand the role of 

consumerism in shaping urbanity and, conversely, urbanity’s role in shaping our consumption 

and how these interactions shape social and economic behavior. 
25

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Table 1. List of conspicuous and inconspicuous consumption items

Consumption categories and UCC codes

Conspicuous Consumption

Food

Meals at restaurants, carry-outs and other (790410)

Alcoholic beverages

Alcoholic beverages at restaurants, taverns (790420)

Housing

Cellular phone and service (270102), Household textiles (280110-280900), Sofas (290210), 

Living room chairs (290310), Living room tables (290320), Kitchen, dining room furniture 

(290410), Infants’ furniture (290420), Outdoor furniture (290430), Floor coverings, 

nonpermanent (320111), Cooking stoves, ovens (300311, 300312), Flatware (320330), 

Dinnerware, glassware, serving pieces (320345), Window coverings (320120), Infants' 

equipment (320130), Outdoor equipment (320150), Lamps, lighting fixtures, ceiling fans 

(320221), Clocks and other household decorative items (320233), Office furniture for home 

use (320901), Indoor plants, fresh flowers (320903), Luggage (430130)

Apparel and services

Men and boys (360110-370904), Women and girls (380110-390902), Children under 2 

(410110-410901), Footwear (400110-400310), Watches (430110), Jewelry (430120)

Transportation

New cars (450110), New trucks (450210), New motorcycles (450220), Car/truck lease 

payments (450350)

Entertainment

Televisions (310140), Personal digital audio players (310314), Stereos, radios, speakers, and 

sound components (310316), Boat without motor and boat trailers (600121), Purchase of boat 

with motor (600132)

Personal care products and services

Wigs and hairpieces (640130)

Inconspicuous Consumption

Alcoholic beverages

Beer and wine (790310), Other alcoholic beverages (790320), Beer, wine, other alcohol 

(790330)

Housing

Property management (230901, 230902), Management and upkeep services for security 

(340911, 340912), Babysitting and child care (340211, 340212), Care for elderly, invalids, 

handicapped, etc. (340906), Adult day care centers (340910), Day care centers, nursery, and 

preschools (670310), Housekeeping services (340310), Gardening, lawn care service 

(340410), Household laundry and dry cleaning, sent out (340520), Home security system 

service fee (340915), Lodging on out-of-town trips (210210), Mattress and springs (290110)

Apparel

Shoe repair and other shoe service (440110), Alteration, repair and tailoring of apparel 

(440130), Watch and jewelry repair (440150), Apparel laundry and dry cleaning not coinoperated (440210)

Transportation
33

Airline fares (530110), Taxi fares and limousine services on trips (530411, 530412), Ship 

fares (530901), Automobile service clubs (620113)

Healthcare

Physician's services (560110), Dental services (560210), Eyecare services (560310)

Entertainment

Recreation expenses, out-of-town trips (610900), Social, recreation, health club membership 

(620111), Fees for participant sports (620121), Participant sports, out-of-town trips 

(620122), Movie, theater, amusement parks, and other (620211, 620212), Play, theater, 

opera, concert (620213), Movies, parks, museums (620214), Admission to sporting events 

(620221), Admission to sports events, out-of-town trips (620222), Fees for recreational 

lessons (620310), Other entertainment services, out-of-town trips (620903), Musical 

instruments and accessories (610130), Rental and repair of musical instruments (620904), 

Rental of video cassettes, tapes, films, and discs (620912), Pet purchase, supplies, medicine 

(610320), Pet services (620410), Vet services (620420), Toys, games, arts and crafts, and 

tricycles (610110), Stamp and coin collecting (610140), Playground equipment (610120), 

Rental non-camper trailer (520904), Rental of boat (520907, 620906), Rental of motorized 

camper (620909, 620921), Rental of other RV's (620919, 620922), Docking and landing fees 

(520901), Athletic gear, game tables, and exercise equipment (600210), Bicycles (600310), 

Camping equipment (600410), Hunting and fishing equipment (600420), Winter sports 

equipment (600430), Water sports equipment (600901), Other sports equipment (600902), 

Rental and repair of miscellaneous sports equipment (620908), Film (610210), Photo 

processing (620330), Repair and rental of photographic equipment (620905), Photographic 

equipment (610230), Photographer fees (620320), Live entertainment for catered affairs 

(680310), 

Rental of party supplies for catered affairs (680320),

Personal care products and services

Personal care services (650310)

Reading

Newspaper, magazine by subscription (590310, 590410), Books (590220, 590230), 

Encyclopedia and other sets of reference books (660310)

Education

College tuition (670110), Elementary and high school tuition (670210)

Miscellaneous

Legal fees (680110), Accounting fees (680902), Dating services (680904), Vacation clubs 

(680905), Credit card memberships (620112), Shopping club membership fees (620115)

Cash contributions

Cash contributions to charities and other organizations (800821), Cash contributions to 

church, religious organizations (800831), Cash contribution to educational institutions 

(800841), Cash contribution to political organizations (800851)

Note: Among the UCC codes for apparel and services, uniforms were excluded (e.g. men’s uniforms (360901),

boy’s uniforms (370903), women’s uniforms (380902), and girl’s uniforms (390901)).
34

Table 2. Mean values for metropolitan level variables, by primary sampling units (MSAs)

Pop. Pop. Median Household Income

(in 000s) Density White Black Asian & PI Hispanic Other

Atlanta 5,237 693.7 64,684 42,464 67,586 40,154 50,142

Baltimore 2,096 831.7 79,522 46,617 85,371 60,535 55,515

Boston 7,404 739.7 71,561 44,871 78,326 35,732 51,012

Chicago 9,534 1,303.5 71,828 36,708 79,833 49,354 52,135

Cleveland 2,880 800.2 55,176 28,051 68,472 36,860 37,291

CT cities 8,042 1,101.2 86,823 55,105 103,071 53,209 64,385

Dallas 6,532 639.0 67,729 39,671 74,579 41,800 50,628

Detroit 5,338 817.5 58,804 32,197 79,509 41,960 41,104

Houston 5,966 675.8 74,551 40,064 76,321 43,317 53,711

Los Angeles 12,877 2,655.9 78,647 43,470 70,646 48,535 63,340

Miami 4,292 1,381.1 61,821 39,134 69,007 43,632 52,820

Minneapolis 3,481 447.6 66,858 29,537 67,342 44,523 45,288

NJ cities 6,953 1,583.9 83,859 48,515 108,876 51,502 63,925

New York 8,214 27,140.4 75,149 43,305 56,752 36,898 56,186

Philadelphia 6,493 1,101.9 72,058 38,629 71,587 38,557 50,337

Phoenix 4,211 289.1 60,742 42,165 70,498 41,438 42,758

Riverside 5,054 173.7 65,842 51,519 79,832 51,804 56,896

Seattle 4,036 560.8 68,507 43,690 72,442 46,622 54,811

San Diego 3,102 737.3 73,655 50,020 82,027 46,920 62,836

San Francisco 3,102 737.3 87,015 46,987 95,009 55,377 70,526

Washington, DC 5,893 863.7 93,006 64,294 101,814 66,227 80,631

Gini Dissimilarity Restaurants Temp. in No. of Regional Price Parities

Coef. Index & bars LQ Jan (°F) rainy days Goods Rents

Atlanta 0.46 0.36 1.05 44.3 106.4 106.4 97.9

Baltimore 0.44 0.33 1.05 34.5 118.9 118.9 100.7

Boston 0.46 0.34 0.92 30.5 126.9 126.9 98.4

Chicago 0.46 0.37 0.93 25.0 132.6 132.6 103.7

Cleveland 0.45 0.37 0.96 27.7 159.1 159.1 93.7

CT cities 0.47 0.35 0.83 29.3 128.7 128.7 97.7

Dallas 0.46 0.41 1.02 46.7 82.7 82.7 97.9

Detroit 0.45 0.38 1.02 26.0 135.3 135.3 98.7

Houston 0.47 0.42 1.01 53.0 92.9 92.9 97.7

Los Angeles 0.48 0.39 0.98 58.7 33.4 33.4 102.9

Miami 0.49 0.39 1.05 69.0 139.3 139.3 99.2

Minneapolis 0.43 0.33 0.88 15.2 114.0 114.0 100.6

NJ cities 0.46 0.39 0.74 33.6 122.9 122.9 99.9

New York 0.53 0.39 0.81 33.8 126.1 126.1 107.5

Philadelphia 0.46 0.38 0.85 34.8 123.1 123.1 103.4

Phoenix 0.44 0.39 1.05 53.9 33.7 33.7 100.4

Riverside 0.44 0.37 1.19 55.7 30.7 30.7 98.9

Seattle 0.44 0.32 0.96 41.1 164.6 164.6 105.3

San Diego 0.45 0.36 1.12 57.2 37.0 37.0 103.4

San Francisco 0.45 0.36 1.12 57.2 37.0 37.0 103.4

Washington, DC 0.44 0.37 0.94 37.5 115.9 115.9 106.1

Note: All figures are inflation adjusted to 2015 dollars. The location quotients are computed for “Food Services and

Drinking Places (NAICS 722).” The values are averages from 2007 to 2013.
35

Table 3. Descriptive statistics on income and consumption by primary sampling units (MSAs)

Family

Income

Total Amount of Dollars Spent on % of Total Expenditures

Spending Cons Incons Other Cons Incons Other

All 21 PSUs 77,172 60,923 10,315 8,156 42,452 13.2 10.1 76.7

(83,442) (53,839) (23,851) (19,991) (29,734) (12.6) (10.9) (16.2)

Atlanta 73,785 53,308 9,514 6,588 37,206 13.9 9.2 76.9

(73,404) (41,481) (20,692) (12,921) (22,399) (12.4) (10.2) (15.4)

Baltimore 92,673 60,506 9,051 8,065 43,391 12.1 10.1 77.8

(104,900) (48,360) (18,620) (16,979) (29,129) (11.7) (10.9) (15.5)

Boston 83,317 64,418 10,834 9,380 44,203 12.6 11.1 76.3

(90,891) (61,591) (32,625) (21,728) (29,081) (12.6) (11.4) (16.3)

Chicago 76,536 61,085 10,909 8,506 41,670 14.0 10.5 75.5

(77,791) (48,443) (22,023) (16,256) (26,218) (13.3) (11.0) (16.6)

Cleveland 62,977 49,892 8,950 5,821 35,121 13.0 9.1 77.8

(66,947) (40,629) (19,951) (10,526) (23,351) (13.0) (9.7) (16.3)

Connecticut 89,874 74,123 12,192 10,956 50,974 12.9 11.2 76.0

Cities (90,386) (61,342) (25,414) (23,948) (32,906) (12.6) (11.2) (16.4)

Dallas 71,788 56,686 10,945 6,343 39,398 14.9 9.1 75.9

(69,413) (42,508) (22,685) (10,072) (24,505) (13.4) (9.2) (15.6)

Detroit 65,554 53,467 10,346 6,865 36,256 14.7 9.9 75.5

(73,757) (42,263) (21,872) (12,591) (22,253) (13.5) (10.6) (16.6)

Houston 74,241 58,382 11,681 7,030 39,671 14.6 9.3 76.1

(88,361) (50,958) (27,451) (12,696) (26,393) (13.6) (9.3) (16.1)

Los Angeles 67,264 58,595 9,594 8,291 40,711 13.1 9.8 77.1

(74,888) (56,636) (22,809) (29,318) (28,052) (12.2) (12.4) (16.8)

Miami 52,071 47,674 8,205 4,254 35,216 12.3 6.7 81.1

(55,651) (53,657) (25,767) (12,178) (30,200) (12.5) (8.4) (15.3)

Minneapolis 75,904 59,278 10,577 8,679 40,023 13.7 12.0 74.3

(72,753) (51,450) (26,059) (16,904) (23,952) (13.3) (11.6) (16.9)

New Jersey 91,108 67,491 10,488 8,903 48,099 12.4 9.8 77.8

Cities (92,901) (54,963) (25,762) (19,200) (28,363) (11.6) (10.5) (15.3)

New York 59,626 48,706 7,152 6,716 34,838 11.9 8.7 79.4

(72,707) (48,659) (15,855) (23,181) (25,030) (11.1) (11.4) (16.3)

Philadelphia 68,496 56,554 9,081 7,594 39,879 12.1 9.4 78.5

(78,078) (48,602) (20,837) (18,261) (26,557) (12.3) (11.0) (16.3)

Phoenix 63,015 55,227 11,214 6,379 37,634 15.4 9.9 74.7

(68,882) (47,363) (23,779) (9,962) (30,775) (15.1) (10.2) (17.4)

Riverside 67,753 56,597 9,372 5,856 41,370 12.9 8.7 78.4

(66,966) (41,564) (21,402) (9,849) (25,991) (12.4) (9.3) (14.9)

Seattle 75,994 68,088 11,545 10,233 46,309 13.7 12.4 73.9

(65,909) (65,747) (23,941) (33,637) (40,997) (12.7) (11.5) (16.4)

San Diego 75,516 57,713 8,322 6,716 42,676 12.0 9.2 78.9

(69,135) (42,777) (17,877) (12,013) (26,360) (11.1) (10.0) (14.9)

San 75,516 57,713 8,322 6,716 42,676 12.0 9.2 78.9

Francisco (111,916) (67,269) (22,014) (25,988) (42,470) (11.7) (11.6) (15.9)

Washington, 

D.C.

112,449 76,523 13,056 11,108 52,359 13.0 11.9 75.2

(103,029) (60,041) (29,941) (18,333) (30,786) (12.5) (11.4) (16.2)

Note: All figures are inflation adjusted to 2015 dollars. The sampling weight variables in the Consumer Expenditure

Survey data are used to make the estimates nationally representative. The sample includes consumer units in the

Primary Sampling Units from 2007 to 2013.
36

Table 4. Regression results (Dependent variable: log of conspicuous consumption)

Model 1 Model 2 Model 3

Coef. Sig. Coef. Sig. Coef. Sig.

Age of householder (ref: 15 to 24)

25 to 34 –0.151 *** –0.157 *** –0.158 ***

(0.016) (0.016) (0.029)

34 to 44 –0.288 *** –0.290 *** –0.293 ***

(0.016) (0.016) (0.032)

45 to 54 –0.386 *** –0.381 *** –0.384 ***

(0.017) (0.017) (0.035)

55 to 64 –0.444 *** –0.433 *** –0.437 ***

(0.018) (0.018) (0.035)

65 to 74 –0.418 *** –0.405 *** –0.408 ***

(0.020) (0.020) (0.037)

75 and over –0.589 *** –0.570 *** –0.575 ***

(0.024) (0.024) (0.046)

Female householder –0.022 ** –0.018 ** –0.018 *

(0.006) (0.006) (0.008)

Race/Ethnicity of householder (ref: Non-Hispanic White) 

African American 0.020 –0.001 –0.107 **

(0.010) (0.010) (0.041)

Asian and Pacific Islander –0.019 0.008 0.022

(0.012) (0.012) (0.029)

Hispanic 0.093 *** 0.092 *** –0.001

(0.009) (0.010) (0.041)

Other –0.045 –0.031 –0.088

(0.028) (0.028) (0.060)

Marital status of householder (ref: Married couple)

Widowed –0.050 ** –0.045 ** –0.046 *

(0.017) (0.017) (0.019)

Divorced 0.013 0.014 0.013

(0.012) (0.012) (0.018)

Separated 0.016 0.020 0.019

(0.021) (0.021) (0.027)

Never married 0.052 *** 0.065 *** 0.063 ***

(0.012) (0.012) (0.017)

Composition of earners (ref: Single earner)

No earners –0.010 –0.013 –0.011

(0.015) (0.015) (0.020)

Dual earners –0.051 * –0.051 * –0.051

(0.020) (0.020) (0.031)

Other cases –0.015 –0.015 –0.014

(0.017) (0.016) (0.024)

Household size –0.025 *** –0.026 *** –0.026 ***

(0.003) (0.003) (0.006)

Having a child/children –0.066 *** –0.063 *** –0.064 ***

(0.010) (0.009) (0.013)

Highest education attainment (ref: High school dropouts)

High school graduate –0.031 * –0.025 –0.026

(0.014) (0.014) (0.020)

Some college –0.004 0.001 0.001

(0.013) (0.013) (0.019)

Bachelor's degree –0.070 *** –0.059 *** –0.058 **
37

(0.015) (0.015) (0.022)

Master's degree or higher –0.146 *** –0.131 *** –0.131 ***

(0.016) (0.016) (0.024)

Occupation (ref: Manager, professional)

Admin, sales, tech 0.014 0.013 0.013

(0.009) (0.009) (0.009)

Service 0.014 0.017 0.015

(0.010) (0.010) (0.014)

Operator and assembler 0.002 –0.003 –0.003

(0.014) (0.014) (0.019)

Mechanic and mining –0.051 ** –0.047 ** –0.045 *

(0.018) (0.018) (0.021)

Farming and fishing 0.033 0.055 0.049

(0.038) (0.038) (0.069)

Missing occupation info. –0.037 ** –0.035 ** –0.038

(0.013) (0.013) (0.020)

Units in structure (ref: Single-family housing)

Multi-family housing 0.033 *** 0.049 *** 0.051 ***

(0.008) (0.009) (0.013)

Mobile home or other 0.125 *** 0.129 *** 0.129 ***

(0.017) (0.017) (0.022)

Owner –0.015 –0.025 ** –0.024

(0.009) (0.009) (0.017)

log(income) –0.010 ** –0.009 ** –0.008

(0.003) (0.003) (0.004)

log(wealth) –0.003 –0.001 –0.001

(0.003) (0.003) (0.003)

Missing wealth info. –0.006 0.004 0.006

(0.027) (0.027) (0.026)

log(total expenditures) 1.372 *** 1.390 *** 1.390 ***

(0.009) (0.009) (0.024)

MSA Fixed-Effects No Yes No

MSA Characteristics No No Yes

MSA Random-Effects No No Yes

Number of observations 75,433 75,433 75,433

R-Squared 0.5411 0.5464 0.5460

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded. The robust standard errors are used to correct for heteroscedasticity.
38

Table 5. Regression results (Dependent variable: log of inconspicuous consumption)

Model 1 Model 2 Model 3

Coef. Sig. Coef. Sig. Coef. Sig.

Age of householder (ref: 15 to 24)

25 to 34 –0.234 *** –0.229 *** –0.231 ***

(0.023) (0.023) (0.032)

34 to 44 –0.311 *** –0.301 *** –0.308 ***

(0.024) (0.024) (0.039)

45 to 54 –0.269 *** –0.257 *** –0.262 ***

(0.024) (0.024) (0.039)

55 to 64 –0.227 *** –0.212 *** –0.218 ***

(0.025) (0.025) (0.041)

65 to 74 –0.080 ** –0.062 * –0.069

(0.027) (0.027) (0.046)

75 and over 0.110 *** 0.128 *** 0.120

(0.030) (0.030) (0.063)

Female householder 0.097 *** 0.096 *** 0.098 ***

(0.008) (0.008) (0.012)

Race/Ethnicity of householder (ref: Non-Hispanic White) 

African American –0.070 *** –0.057 *** –0.171 **

(0.014) (0.014) (0.065)

Asian and Pacific Islander –0.162 *** –0.161 *** –0.153 ***

(0.017) (0.017) (0.037)

Hispanic –0.184 *** –0.169 *** –0.270 ***

(0.013) (0.013) (0.062)

Other –0.017 –0.030 –0.084

(0.037) (0.037) (0.062)

Marital status of householder (ref: Married couple)

Widowed 0.069 *** 0.076 *** 0.072 *

(0.019) (0.019) (0.032)

Divorced –0.021 –0.022 –0.023

(0.016) (0.016) (0.021)

Separated 0.036 0.034 0.039

(0.027) (0.027) (0.043)

Never married 0.061 *** 0.063 *** 0.060 **

(0.016) (0.016) (0.022)

Composition of earners (ref: Single earner)

No earners –0.168 *** –0.172 *** –0.165 ***

(0.019) (0.019) (0.029)

Dual earners –0.170 *** –0.175 *** –0.168 ***

(0.025) (0.025) (0.035)

Other cases –0.279 *** –0.282 *** –0.277 ***

(0.021) (0.021) (0.028)

Household size –0.069 *** –0.069 *** –0.070 ***

(0.005) (0.005) (0.010)

Having a child/children 0.211 *** 0.213 *** 0.213 ***

(0.013) (0.013) (0.020)

Highest education attainment (ref: High school dropouts)

High school graduate 0.136 *** 0.150 *** 0.142 ***

(0.018) (0.018) (0.035)

Some college 0.323 *** 0.328 *** 0.322 ***

(0.017) (0.018) (0.040)

Bachelor's degree 0.436 *** 0.448 *** 0.440 ***
39

(0.019) (0.019) (0.052)

Master's degree or higher 0.557 *** 0.571 *** 0.565 ***

(0.020) (0.020) (0.049)

Occupation (ref: Manager, professional)

Admin, sales, tech –0.030 * –0.029 * –0.031 **

(0.012) (0.012) (0.011)

Service –0.107 *** –0.099 *** –0.106 ***

(0.014) (0.014) (0.022)

Operator and assembler –0.164 *** –0.168 *** –0.171 ***

(0.020) (0.020) (0.023)

Mechanic and mining –0.111 *** –0.115 *** –0.111 **

(0.024) (0.024) (0.037)

Farming and fishing –0.114 * –0.111 * –0.115

(0.050) (0.050) (0.061)

Missing occupation info. –0.102 *** –0.099 *** –0.103 ***

(0.016) (0.016) (0.024)

Units in structure (ref: Single-family housing)

Multi-family housing 0.090 *** 0.101 *** 0.102 ***

(0.011) (0.011) (0.023)

Mobile home or other 0.072 ** 0.067 ** 0.071 *

(0.022) (0.022) (0.034)

Owner 0.301 *** 0.305 *** 0.302 ***

(0.012) (0.012) (0.031)

log(income) 0.007 0.007 0.008

(0.004) (0.004) (0.006)

log(wealth) 0.032 *** 0.030 *** 0.031 ***

(0.004) (0.004) (0.003)

Missing wealth info. 0.215 *** 0.207 *** 0.212 ***

(0.034) (0.034) (0.034)

log(total expenditures) 1.473 *** 1.477 *** 1.477 ***

(0.010) (0.010) (0.029)

MSA Fixed-Effects No Yes No

MSA Characteristics No No Yes

MSA Random-Effects No No Yes

Number of observations 75,433 75,433 75,433

R-Squared 0.5308 0.5333 0.5319

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded. The robust standard errors are used to correct for heteroscedasticity.
40

Table 6. Summarized results of the fixed-effects regressions

Dependent variable: ln(conspicuous) ln(inconspicuous) ln(other)

Coef. S.E. Sig. Coef. S.E. Sig. Coef. S.E. Sig.

Metropolitan area FEs (ref: Chicago)

Atlanta 0.042 (0.018) * –0.013 0.024 –0.018 0.005 **

Baltimore –0.106 (0.022) *** –0.079 0.029 ** 0.013 0.006 *

Boston –0.156 (0.017) *** 0.027 0.020 0.020 0.005 ***

Cleveland 0.075 (0.023) ** –0.048 0.029 –0.022 0.007 **

Connecticut cities –0.151 (0.016) *** –0.063 0.020 ** 0.050 0.005 ***

Dallas 0.102 (0.015) *** 0.037 0.021 –0.012 0.005 *

Detroit 0.113 (0.017) *** 0.007 0.022 –0.027 0.005 ***

Houston 0.040 (0.018) * 0.055 0.023 * –0.018 0.005 **

Los Angeles –0.072 (0.014) *** 0.043 0.019 * 0.017 0.004 ***

Miami –0.037 (0.022) –0.183 0.029 *** 0.025 0.006 ***

Minneapolis –0.066 (0.019) *** 0.137 0.024 *** –0.025 0.006 ***

New Jersey cities –0.137 (0.016) *** –0.192 0.022 *** 0.061 0.005 ***

New York –0.053 (0.017) ** –0.049 0.022 * 0.022 0.005 ***

Philadelphia –0.107 (0.016) *** –0.070 0.020 ** 0.026 0.005 ***

Phoenix 0.051 (0.021) * 0.075 0.027 ** –0.033 0.007 ***

Riverside –0.061 (0.019) ** –0.028 0.024 0.036 0.005 ***

San Diego –0.141 (0.019) *** –0.099 0.026 *** 0.061 0.006 ***

San Francisco –0.196 (0.016) *** –0.021 0.020 0.065 0.005 ***

Seattle –0.086 (0.018) *** 0.156 0.023 *** 0.014 0.006 *

Washington, DC –0.156 (0.016) *** 0.015 0.021 0.038 0.005 ***

Number of observations 75,433 75,433 75,433

R-Squared 0.5464 0.5333 0.8485

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded. The robust standard errors are used to correct for heteroscedasticity.

Table 7. Summarized results of the random-effects regressions

Dependent variable: ln(conspicuous) ln(inconspicuous) ln(other)

Coef. S.E. Sig. Coef. S.E. Sig. Coef. S.E. Sig.

Metropolitan characteristics

log(population) 0.043 (0.012) ** 0.101 (0.041) * –0.016 (0.009)

log(population density) 0.052 (0.012) *** 0.020 (0.030) –0.012 (0.007)

Gini coefficient (%) –0.016 (0.005) ** –0.022 (0.013) 0.006 (0.003) *

Dissimilarity index (%) 0.010 (0.003) ** –0.011 (0.009) –0.000 (0.002)

log(own race’s income) –0.209 (0.055) *** –0.213 (0.075) ** 0.050 (0.019) **

log(others’ income) –0.005 (0.059) 0.004 (0.099) 0.053 (0.032)

Restaurants and bars LQ 0.407 (0.137) ** 0.176 (0.263) –0.079 (0.063)

Avg. temp in Jan (°F) –0.000 (0.001) 0.000 (0.002) 0.000 (0.000)

No. of rainy days –0.000 (0.000) –0.000 (0.000) 0.000 (0.000)

RPP (goods) 0.002 (0.003) 0.006 (0.004) –0.001 (0.001)

RPP (rents) –0.002 (0.000) *** –0.001 (0.001) 0.001 (0.000) **

Number of observations 75,433 75,433 75,433

R-Squared 0.5460 0.5319 0.8480

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded. The robust standard errors are used to correct for heteroscedasticity.
41

Table 8. Summarized regression results, consumption categories based on Heffetz (2011)’s

visibility index and total expenditure elasticity

Dependent variable: ln(conspicuous) ln(inconspicuous)

Our own Heffetz (2011) Our own Heffetz (2011)

Coef. Sig. Coef. Sig. Coef. Sig. Coef. Sig.

Fixed-Effects Model

Metropolitan area FEs (ref: Chicago)

Atlanta 0.042 * 0.034 –0.013 0.070 **

(0.018) (0.022) (0.024) (0.026)

Baltimore –0.106 *** –0.114 *** –0.079 ** –0.011

(0.022) (0.026) (0.029) (0.030)

Boston –0.156 *** –0.153 *** 0.027 –0.054 *

(0.017) (0.019) (0.020) (0.021)

Cleveland 0.075 ** 0.070 ** –0.048 –0.008

(0.023) (0.026) (0.029) (0.032)

Connecticut cities –0.151 *** –0.155 *** –0.063 ** 0.001

(0.016) (0.018) (0.020) (0.021)

Dallas 0.102 *** 0.094 *** 0.037 0.188 ***

(0.015) (0.018) (0.021) (0.023)

Detroit 0.113 *** 0.055 ** 0.007 0.087 ***

(0.017) (0.020) (0.022) (0.023)

Houston 0.040 * 0.009 0.055 * 0.228 ***

(0.018) (0.021) (0.023) (0.025)

Los Angeles –0.072 *** –0.079 *** 0.043 * 0.025

(0.014) (0.017) (0.019) (0.020)

Miami –0.037 –0.154 *** –0.183 *** –0.297 ***

(0.022) (0.028) (0.029) (0.032)

Minneapolis –0.066 *** –0.009 0.137 *** 0.226 ***

(0.019) (0.022) (0.024) (0.025)

New Jersey cities –0.137 *** –0.164 *** –0.192 *** –0.157 ***

(0.016) (0.019) (0.022) (0.022)

New York –0.053 ** –0.039 * –0.049 * –0.036

(0.017) (0.020) (0.022) (0.024)

Philadelphia –0.107 *** –0.109 *** –0.070 ** –0.059 **

(0.016) (0.019) (0.020) (0.022)

Phoenix 0.051 * 0.011 0.075 ** 0.093 **

(0.021) (0.026) (0.027) (0.031)

Riverside –0.061 ** –0.045 * –0.028 0.082 **

(0.019) (0.022) (0.024) (0.026)

San Diego –0.141 *** –0.114 *** –0.099 *** 0.014

(0.019) (0.023) (0.026) (0.027)

San Francisco –0.196 *** –0.153 *** –0.021 –0.050 *

(0.016) (0.018) (0.020) (0.022)

Seattle –0.086 *** –0.034 0.156 *** 0.194 ***

(0.018) (0.021) (0.023) (0.025)

Washington, DC –0.156 *** –0.146 *** 0.015 0.078 **

(0.016) (0.019) (0.021) (0.023)

Number of observations 75,433 68,807 75,433 68,807

R-Squared 0.5464 0.4991 0.5333 0.3835

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded. The robust standard errors are used to correct for heteroscedasticity.
42

Table 8. (Continued)

Dependent variable: ln(conspicuous) ln(inconspicuous)

Our own Heffetz (2011) Our own Heffetz (2011)

Coef. Sig. Coef. Sig. Coef. Sig. Coef. Sig.

Random-Effects Model

Metropolitan characteristics

log(population) 0.043 ** 0.038 ** 0.101 * 0.029

(0.012) (0.014) (0.041) (0.059)

log(population density) 0.052 *** 0.039 * 0.020 0.039

(0.012) (0.016) (0.030) (0.049)

Gini coefficient (%) –0.016 ** –0.013 * –0.022 –0.024

(0.005) (0.005) (0.013) (0.020)

Dissimilarity Index (%) 0.010 ** 0.005 –0.011 0.002

(0.003) (0.005) (0.009) (0.013)

log(own race’s median income) –0.209 *** –0.223 ** –0.213 ** –0.097

(0.055) (0.065) (0.075) (0.127)

Log (other groups’ median income) –0.005 –0.004 0.004 0.141

(0.059) (0.086) (0.099) (0.150)

Restaurants and drinking places LQ 0.407 ** 0.354 * 0.176 0.451

(0.137) (0.143) (0.263) (0.361)

Avg. temperature in January (°F) –0.000 –0.001 0.000 –0.002

(0.001) (0.001) (0.002) (0.003)

Days with 0.1+ in. of precipitation –0.000 –0.000 –0.000 –0.001

(0.000) (0.000) (0.000) (0.001)

RPP Index (goods) 0.002 0.006 0.006 0.008

(0.003) (0.003) (0.004) (0.006)

RPP Index (rents) –0.002 *** –0.002 *** –0.001 –0.002 *

(0.000) (0.001) (0.001) (0.001)

Number of observations 75,433 68,807 75,433 68,807

R-Squared 0.5460 0.4984 0.5319 0.3798

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded. The robust standard errors are used to correct for heteroscedasticity.
43

Table 9. Summarized regression results with different subsample and estimation method

< Conspicuous Consumption >

All cities Exclude NY and LA Tobit model (dy/dx)

Coef. S.E. Sig. Coef. S.E. Sig. Coef. S.E. Sig.

log(population) 0.043 (0.012) ** 0.079 (0.029) ** 0.203 (0.021) ***

log(population density) 0.052 (0.012) *** 0.019 (0.033) 0.075 (0.020) ***

Gini coefficient (%) –0.016 (0.005) ** –0.016 (0.008) * –0.063 (0.008) ***

Dissimilarity Index (%) 0.010 (0.003) ** 0.008 (0.004) * 0.001 (0.004)

log(own race’s income) –0.209 (0.055) *** –0.218 (0.076) ** –0.315 (0.076) ***

log(others’ income) –0.005 (0.059) 0.007 (0.081) –0.044 (0.069)

Restaurants and bars LQ 0.407 (0.137) ** 0.620 (0.216) ** 0.546 (0.107) ***

Avg. temp in Jan (°F) –0.000 (0.001) –0.001 (0.001) 0.001 (0.001)

Rainy Days –0.000 (0.000) –0.000 (0.000) –0.001 (0.000) ***

RPP Index (goods) 0.002 (0.003) 0.002 (0.002) 0.005 (0.002) *

RPP Index (rents) –0.002 (0.000) *** –0.002 (0.001) *** –0.004 (0.001) ***

Number of observations 75,433 52,149 84,253

R-Squared 0.5460 0.5533 0.1242

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded for the first and second column. For the third column, a Tobit model was estimated and the

corresponding marginal effects are reported above. The robust standard errors are used to correct for

heteroscedasticity.

< Inconspicuous Consumption >

All cities Exclude NY and LA Tobit model (dy/dx)

Coef. S.E. Sig. Coef. S.E. Sig. Coef. S.E. Sig.

log(population) 0.101 (0.041) * 0.108 (0.026) *** 0.250 (0.026) ***

log(population density) 0.020 (0.030) –0.177 (0.029) *** –0.007 (0.024)

Gini coefficient (%) –0.022 (0.013) –0.009 (0.007) –0.050 (0.010) ***

Dissimilarity Index (%) –0.011 (0.009) –0.004 (0.004) –0.022 (0.005) ***

log(own race’s income) –0.213 (0.075) ** –0.101 (0.124) –0.484 (0.092) ***

log(others’ income) 0.004 (0.099) 0.208 (0.115) –0.028 (0.082)

Restaurants and bars LQ 0.176 (0.263) –0.019 (0.192) 0.045 (0.131)

Avg. temp in Jan (°F) 0.000 (0.002) –0.000 (0.001) –0.004 (0.001) **

Rainy Days –0.000 (0.000) 0.001 (0.000) * –0.001 (0.000) ***

RPP Index (goods) 0.006 (0.004) 0.003 (0.002) –0.002 (0.003)

RPP Index (rents) –0.001 (0.001) –0.001 (0.001) 0.001 (0.001)

Number of observations 75,433 52,149 84,253

R-Squared 0.5319 0.5337 0.1122

Note: *: p < 0.05, **: p < 0.01, ***: p < 0.001. The sample includes consumer units in the Primary Sampling Units

from 2007 to 2013. The consumer units with zero expenditure on conspicuous, inconspicuous, or other consumption

are excluded for the first and second column. For the third column, a Tobit model was estimated and the

corresponding marginal effects are reported above. The robust standard errors are used to correct for

heteroscedasticity.
44

APPENDIX

Table A. Consumption categories and their values of visibility index

Consumption 

Categories Spending Items

Visibility 

Index

Conspicuous/

Inconspicuous

Cig Tobacco products like cigarettes, cigars, and pipe tobacco 0.76

Car The purchase of new and used motor vehicles such as cars, trucks, 

and vans

0.73 Conspicuous

Clo Clothing and shoes, not including underwear, undergarments, and 

nightwear

0.71 Conspicuous

Fur Home furnishings and household items, like furniture, appliances, 

tools, and linen

0.68 Conspicuous

Jwl Jewelry and watches 0.67 Conspicuous

Ot1 Computers, games, TVs, video, audio, musical and sports 

equipment, tapes, CDs

0.66 Conspicuous

FdO Dining out at restaurants, drive-through, etc., excluding alcohol; 

including food at school

0.62 Conspicuous

AlH Alcoholic beverages for home use 0.61

Brb Barbershops, beauty parlors, hair dressers, health clubs, etc. 0.60

AlO Alcoholic beverages at restaurants, bars, cafeterias, cafe ́s, etc. 0.60

Ot2 Cable TV, pets and veterinarians, sports, country clubs, movies, 

and concerts

0.58

Bks Books, including school books, newspapers and magazines, toys, 

games, and hobbies

0.57

Edu Education, from nursery to college, like tuition and other school 

expenses

0.56

FdH Food and nonalcoholic beverages at grocery, specialty, and 

convenience stores

0.51

Hom Rent, or mortgage, or purchase, of their housing 0.50

Cel Mobile phone services 0.47

Air Airline fares for out-of-town trips 0.46

Htl Lodging away from home on trips and housing for someone away 

at school

0.46

Bus Public transportation, both local and long distance, like buses and 

trains

0.45

CMn Vehicle maintenance, mechanical and electrical repair and 

replacement

0.42

Gas Gasoline and diesel fuel for motor vehicles 0.39

Med Medical care, including health insurance, drugs, dentists, doctors, 

hospitals, etc.

0.36

Cha Contributions to churches or other religious organizations, and 

other charities

0.34 Inconspicuous

Lry Laundry and dry cleaning 0.34

Utl Home utilities such as electricity, gas, and water; garbage 

collection

0.31

Tel Home telephone services, not including mobile phones 0.30

Fee Legal fees, accounting fees, and occupational expenses like tools 

and licenses

0.26 Inconspicuous

CIn Vehicle insurance, like insurance for cars, trucks, and vans 0.23

HIn Homeowner’s insurance, fire insurance, and property insurance 0.17 Inconspicuous

LIn Life insurance, endowment, annuities, and other death benefits 

insurance

0.16 Inconspicuous

Und Underwear, undergarments, nightwear, and sleeping garments 0.13

Source: Heffetz (2011)’s visibility index based on Harris and Sabelhaus (2005)’s spending categories.
45

Table B. Geographic areas in the consumer expenditure survey’s primary sampling units

PSU PSU Name Definition (County, State)

A109 New York, NY Bronx, Kings, New York, Queens, Richmond, NY

A110 New York-Connecticut Suburbs Fairfield, Hartford, Litchfield, Middlesex, New Haven, Tolland, CT; 

Dutchess, Nassau, Orange, Putnam, Rockland, Suffolk, Westchester, 

NY

A111 New Jersey Suburbs Bergen, Essex, Hudson, Hunterdon, Mercer, Middlesex, Monmouth, 

Morris, Ocean, Passaic, Somerset, Sussex, Union, Warren, NJ

A102 Philadelphia-WilmingtonAtlantic City, PA-NJ-DE-MDNew Castle, DE; Cecil, MD; Atlantic, Burlington, Camden, Cape 

May, Cumberland, Gloucester, Salem, NJ; Bucks, Chester, 

Delaware, Montgomery, Philadelphia, PA

A103 Boston-Brockton-Nashua, MANH-ME-CTWindham, CT; Bristol, Essex, Hampden, Hampshire, Middlesex, 

Norfolk, Plymouth, Suffolk, Worcester, MA; York, ME; 

Hillsborough, Merrimack, Rockingham, Strafford, NH

A207 Chicago-Gary-Kenosha, IL-INWICook, DeKalb, Du Page, Grundy, Kane, Kankakee, Kendall, Lake, 

McHenry, Will, IL; Lake, Newton, Porter, IN; Kenosha, WI

A208 Detroit-Ann -Arbor-Flint, MI Genesee, Lapeer, Lenawee, Livingston, Macomb, Monroe, Oakland, 

St. Clair, Washtenaw, Wayne, MI

A210 Cleveland-Akron, OH Ashtabula, Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, 

Summit, OH

A211 Minneapolis-St. Paul, MN-WI Anoka, Benton, Carver, Chisago, Dakota, Hennepin, Isanti, Ramsey, 

Scott, Sherburne, Stearns, Washington, Wright, MN; Pierce, St. 

Croix, WI

A312 Washington, DC-MD-VA-WV District of Columbia, DC; Calvert, Charles, Frederick, Montgomery, 

Prince George’s, Washington, MD; Alexandria city, Arlington, 

Clarke, Fairfax, Fairfax city, Falls Church city, Fauquier, 

Fredericksburg city, King George, Loudoun, Manassas Park city, 

Manassas city, Prince William, Rappahannock, Spotsylvania, 

Stafford, Warren, VA; Berkeley, Jefferson, WV

A313 Baltimore, MD Anne Arundel, Baltimore, Baltimore city, Carroll, Harford, Howard, 

Queen Anne’s, MD

A316 Dallas-Fort Worth, TX Collin, Dallas, Delta, Denton, Ellis, Henderson, Hood, Hunt, 

Johnson, Kaufman, Parker, Rockwall, Tarrant, Wise, TX

A318 Houston-Galveston-Brazoria, 

TX

Austin, Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, 

Montgomery, San Jacinto, Waller, TX

A319 Atlanta, GA Cleburne, AL; Barrow, Bartow, Butts, Carroll, Cherokee, Clayton, 

Cobb, Coweta, Dawson, De Kalb, Douglas, Fayette, Forsyth, Fulton, 

Gwinnett, Haralson, Henry, Newton, Paulding, Pickens, Pike, 

Rockdale, Spalding, Walton, GA

A320 Miami-Fort Lauderdale, FL Broward, Miami Dade, FL

A419 Los Angeles-Orange, CA Los Angeles, Orange, CA

A420 Los Angeles Suburbs, CA Riverside, San Bernardino, Ventura, CA

A422 San Francisco-Oakland-San 

Jose, CA

Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, 

Santa Clara, Santa Cruz, Solano, Sonoma, CA

A423 Seattle-Tacoma-Bremerton, WA Island, King, Kitsap, Pierce, Snohomish, Thurston, WA

A424 San Diego, CA San Diego, CA

A429 Phoenix-Mesa, AZ Maricopa, Pinal, AZ

Source: The Bureau of Labor Statistics (BLS). Consumer Expenditure Survey Metropolitan Areas. Retrieved from:

http://www.bls.gov/regions/ce_areadef.pdf

Note: The list includes only “A” size PSUs with a population greater than 1.5 million, which are identified on the

2006–2014 Consumer Expenditure Survey Public Use Microdata.