Demographic Preferences and Income Segregation*
Victor CoutureJonathan I. Dingel

Allison Green§

Jessie Handbury¶
September 2024
Preliminary Draft
Abstract
We investigate whether demographic preferences explain income segregation inshared spaces. To distinguish preferences over demographic composition from preferences over other venue characteristics, we study venue choices within business chains.
We find two notable regularities: racial homophily does not vary by income, and preferences for high-income co-patrons are similar across racial groups. These demographic
preferences are economically large, explain much of the cross-group variation in exposure to high-income co-patrons, and correlate with movers’ neighborhood choices.
Keywords: homophily, preference estimation, segregation, smartphone data
JEL codes: C55, D12, J1, L8, R2, R4* We are grateful to Andrea Cerrato, Hongrui He, Shih-Hsuan Hsu, Luigi Roncoroni, John Ruf, andSerena Xu for excellent research assistance. We thank Drew Breunig, Nicholas Shailas, Stephanie De Leon,
Elizabeth Cutrone, and the team at Precisely PlaceIQ for data access and helpful conversations. The views
expressed herein are those of the authors and do not necessarily reflect the views of Precisely PlaceIQ,
NBER, nor CEPR. This material is based upon work supported by the National Science Foundation underGrant No. 2030056. This research was approved by the University of Chicago Institutional Review Board
under protocol IRB20-0967. Dingel thanks the Cohen and Keenoy Faculty Research Fund at the Universityof Chicago Booth School of Business for support. Handbury thanks the Zell/Lurie Real Estate Center at
Wharton and the Dean’s Research Fund for financial support.
 University of Toronto
University of Chicago Booth School of Business, NBER, and CEPR
§ Princeton University¶ University of Pennsylvania and NBER

1Introduction

American life is segregated by income and race in domains ranging from residences to media diets. Because social contacts are important for economic mobility, segregated social
interactions likely contribute to racial and income inequality. In this paper, we study how
different demographic groups are exposed to high-income individuals in shared commercial
spaces. We estimate individuals’ preferences over the racial and income composition of copatrons and use them to quantify sources of cross-group differences in experienced income
segregation.
To measure exposure to high-income co-patrons, we use data on the movements of millions of smartphones in the United States in 2018 and 2019. Joining these movement datawith building-level residential demographics, we measure the socioeconomic composition ofeach venue’s patrons and characterize eight groups’ exposure to different co-patron mixes.
The eight demographic groups are four racial-ethnic categories (Asian, Black, Hispanic, andWhite) interacted with two income categories (split by median income).1
We find large differences across groups in exposure to high-income co-patrons. Unsurprisingly, within each racial group, high-income individuals have greater high-income exposure.
Within income groups, Black and Hispanic individuals have lower high-income exposure than
Asian and White individuals. High-income Black individuals, for example, experience nearly
the same high-income exposure as low-income White individuals.
We consider three explanations for these demographic differences in experienced income
segregation. First, differences in preferences for product attributes: groups might vary intheir price sensitivity or taste for particular services. Second, differences in proximity tovenues: low-income individuals may live far from venues with high-income patrons. Third,
preferences over the demographics of co-patrons: these preferences encompass all the ways
co-patron mix may affect an individual’s enjoyment of a venue. This includes their affinities
for certain groups but also, for instance, how a concentration of young professionals working
in a coffee shop might create a productive ambiance.
To distinguish between these explanations, we estimate preferences over co-patron demographics using variation across venues within chain businesses. Because chains offer standardized service across venues, we can isolate preferences over co-patron mix from tastes
for product attributes. Our baseline estimation sample contains restaurants, the businesscategory with the greatest number of chain venues. Relative to other choice settings likehome purchases or school enrollments, the frequent nature of consumption trips allow us to1

Following US government and social science conventions, the four racial/ethnic groups we observe are
non-Hispanic Asian, non-Hispanic Black, Hispanic, and non-Hispanic White. For the sake of brevity, we
often omit the adjective “non-Hispanic” and simply refer to these racial-ethnic groups as racial groups.

1estimate preferences, and their evolution over time, with greater precision and detail.
To estimate demographic preferences, we introduce a model of venue choice. Our research
design exploits the trade-off between the cost of a longer trip and the benefit of a preferredco-patron composition, yielding estimates of preferences expressed in terms of willingness totravel. We specify preferences as a flexible function of the share of own-race co-patrons andthe share of high-income co-patrons. This flexibility allows us to capture complementarities
between race and income composition and to distinguish preferring perfectly homogeneousco-patrons from a mere aversion to being unlike everyone else.
Our estimates reveal notable regularities across demographic groups in their preferences
over co-patron composition. High- and low-income individuals exhibit similar levels of
racial homophily (a preference for one’s own race). Black, Hispanic, and White individuals have similar levels of racial homophily (with that of Asian individuals being somewhat
stronger).Members of different racial groups have broadly similar preferences for high-income
exposure (with those of White individuals being somewhat weaker). Only high-income individuals, however, exhibit monotone preferences over the share of co-patrons who are highincome. Low-income individuals prefer establishments with an integrated mix of low- andhigh-income co-patrons.
These preferences for demographic exposure are economically large. Individuals are willing to travel two to three additional kilometers to visit a venue in the 95th percentile of either
the own-race or high-income distribution rather than a venue at the 5th percentile.2 Thistranslates into willingness to pay of a few thousand dollars per year, close in magnitude towillingness to pay for schools with high test scores (e.g., Black, 1999; Bayer, Ferreira, andMcMillan, 2007).
Given these regularities in demographic preferences, why does demographic exposure differ across groups? Differences in tastes for product attributes, the first of three possible
explanations, are small: little income segregation arises from high-income individuals visiting different chains. In fact, within-chain income segregation resembles income segregation
across all non-residential venues, including public and commercial venues. For example,
both high- and low-income individuals visit McDonald’s restaurants but tend to choose different locations.3 We use our estimated model to show that neighborhoods of residence
and demographic preferences are the factors that largely explain cross-group differences in
exposure.
Racial differences in high-income exposure stem from how preferences over co-patron2

The exception to this 95th -5th comparison is low-income individuals’ preference for high-income exposure,because they prefer economically integrated venues.
3This aligns with Cook (2023), who notes the widespread popularity of large chains, with McDonald’s
being the restaurant individuals across all income groups are most willing to travel to.

2

demographics interact with the joint distribution of income and race across venues. For
instance, high-income Black and White individuals share similar willingness to travel tohigh-income venues. Black individuals, however, visit venues with much smaller sharesof high-income co-patrons, even conditional on the distances between their residences andhigh-income venues. This reflects the role of racial homophily. Because heavily Black (orHispanic) venues generally have lower-income co-patrons, Black (or Hispanic) individuals
face a trade-off between visiting heavily high-income venues and visiting heavily own-racevenues that Asian and White individuals do not face.
The gap in high-income exposure between low- and high-income individuals within racial
groups reflects differences in residential sorting and preferences. Low-income people bothlive in poorer neighborhoods and have weaker preferences for high-income co-patrons. Highincome individuals tend to live in neighborhoods near venues with many high-income copatrons. Conditional on where they live, their stronger income preferences lead them tochoose venues with more high-income co-patrons. Overall, demographic preferences explain
observed income exposure more for high- than low-income people.
To further examine how demographic preferences relate to neighborhood choice, we estimate how demographic preferences vary across neighborhoods with different demographic
mixes. We find that people live in neighborhoods that match their preferences for demographic exposure: within demographic groups, individuals living in higher-income neighborhoods have stronger income preferences, and individuals living in more heavily own-raceneighborhoods have stronger racial preferences. This alignment of individuals’ demographic
preferences and the dominant demographics of their residential neighborhoods suggests that
demographic preferences might, in addition to determining venue choice, be a determinantof neighborhood choice.
Our model is agnostic on how individuals choose residential neighborhoods, but we cantest for such sorting patterns by examining movers. Specifically, we estimate the preferencesof individuals before and after they move between neighborhoods in different metropolitan
areas. The estimated preferences are consistent with people sorting into neighborhoods basedon their preferences over co-patron demographics. For instance, those moving to integrated
neighborhoods show lower racial homophily before the move, even when controlling for their
origin neighborhood’s demographics.4 Consistent with contact theory, preferences for the
local demographic mix strengthen after the move.5 This mover analysis also validates ourmodel specification: the estimated preferences do not shift discontinuously when an individ4

This pattern is consistent with social connections predicting residential moves (Büchel et al., 2020).
The intergroup-contact hypothesis was originally formulated by Allport (1954). Recent work testing thishypothesis includes Lowe (2021) and Bursztyn et al. (2024).
5

3

ual’s choice set changes. While this investigation of movers’ preferences is limited by sample
sizes and a short time horizon, it demonstrates the potential for mobility data to advanceour understanding of demographic preferences.
This paper’s main contribution is to a debate over the existence and importance of demographic preferences. These preferences are thought to play an important role in explaining
residential segregation (e.g., Schelling 1971; Card, Mas, and Rothstein 2008), but a key
challenge is to distinguish preferences over neighbors’ income and racial demographics fromtastes for other neighborhood amenities (Caetano and Maheshri, 2021; Bayer et al., 2022;
Davis, Gregory, and Hartley, 2023; Schönholzer, 2023; Li, 2023).6 By studying venue choice
instead of neighborhood choice, we offer the first estimate of demographic preferences inshared spaces. The business chains we study also have considerably more uniform attributes
than residential neighborhoods, and we observe many more choices.7
We also contribute to a literature documenting segregation in non-residential domains.
Economists have documented racial segregation of friendship networks (Echenique and Fryer,2007), gender segregation of retail venues (Caetano and Maheshri, 2019), and income segregation of universities (Chetty et al., 2020). Closer to this paper is recent work documenting
segregation in the places people visit by race (Davis et al., 2019; Athey et al., 2021; Baldenius et al., 2023), socioeconomic status (Moro et al., 2021; Xu et al., 2019; Abbiasov et al.,2022; Cook, 2023; Massenkoff and Wilmers, 2023; Nilforoshan et al., 2023; Yabe et al., 2023),
or student status (Cook, Currier, and Glaeser, 2022).8 We document experienced segregation within commercial spaces by income and race jointly, using building-level demographic
information.
Finally, our paper complements growing evidence on the economic benefit of social connections to higher-income people. Social connections help workers find jobs through referrals
6

In addition to demographic preferences and differences in tastes for neighborhood amenities, persistent
racial segregation of residences has also been attributed to wealth differences, prejudice, and housing-market
discrimination (Charles, 2003; Rothstein, 2017).
7
In related work, Davis et al. (2019) show that Yelp users in New York City are more likely to travel
to restaurants in neighborhoods with a racial composition similar to their own. Backstrom and Woodward(2021) show that anglers are less likely to fish from a site with larger Black and Hispanic populations thanin their home neighborhood.
8
In this vein, Wang et al. (2018) show that residents of Black and Hispanic neighborhoods travel similar
distances as others but visit high-income neighborhoods less. We use the trade-off between travel distanceand demographic composition to identify individuals’ preferences over co-patron demographics. We findthat Black and Hispanic individuals would prefer both high-income and own-race co-patrons, but few suchvenues exist. In the housing market, racial differences in socioeconomic status mean that racial minorities
face a trade-off between sorting into high own-race and high-income neighborhoods (Sethi and Somanathan,2004; Bayer, Fang, and McMillan, 2014; Reardon, Fox, and Townsend, 2015). We show that minorities face
the same trade-off when choosing social spaces throughout their daily lives. Like some studies of residential
decisions (Bayer and McMillan, 2005; Aliprantis, Carroll, and Young, 2022), we find that racial homophily
plays an important role in this setting.

4

(Bayer, Ross, and Topa, 2008; Barwick et al., 2023). One’s number of high-socioeconomicstatus Facebook friends correlates with social mobility (Chetty et al., 2022a,b). Our smartphone movement data, however, measure social exposure, not social connections. Atkin,
Chen, and Popov (2022) use similar smartphone data to show that serendipitous encounters in Silicon Valley in the kind of venues we study produce more patent citations between
the connected employers. Choi, Guzman, and Small (2024) show that the introduction ofStarbucks into US neighborhoods with no coffee shops increases entrepreneurship. Beyondcommercial gains, Anderson (2011) argues that overlapping visits to shared spaces by peopleof different backgrounds may be a basis for building understanding and tolerance.

2

Data

To measure income segregation and estimate demographic preferences, we need to know thedemographic characteristics and consumption trips of a large sample of individuals. Thissection describes the construction of our estimation sample from smartphone movement data
and building-level demographic data. Appendix A offers more details on each data source.

2.1

Data sources

Our smartphone movement data are from Precisely PlaceIQ, a location data and analytics
firm. Precisely PlaceIQ aggregates pings from applications that request locational services
from the smartphone’s operating system.9 Pings originating from different applications on
the same smartphone are linked to a unique advertising identifier, which we denote a “device.” These pings are intersected with a two-dimensional map of polygons corresponding
to buildings, which we denote “venues.” A spatial and temporal cluster of pings by a given
device in or close to a venue constitutes a “visit” to that venue. Precisely PlaceIQ uses thetiming of the first and last ping in the visit ping cluster to compute a lower bound for visit
duration.
The demographic characteristics of each device come from building-level data that include the income bracket and race of individuals living at an address. Precisely PlaceIQ does
not disclose the third-party provider of these data, so we discuss the reliability of this demographic information later in this section. These demographic data are aggregated acrossall units within a building. Thus, for single-family houses we observe the demographics ofthe household, while for multi-unit buildings we observe building-level averages. We assign
9

We do not know the set of applications contributing data. Some applications collect location data only
when in active use, while others collect location data while running in the background.

5

demographics to devices based on their inferred residence, which is the residential building
where the device regularly spends time at night (Couture et al., 2021).

2.2

Estimation sample

This subsection describes the selection of devices, venues, and visits in our analysis. To estimate preferences, we create a restricted sample of devices and visits for which we confidently
know demographic information and trip purpose. To measure the demographic compositionof each venue, we use a sample of devices and visits that is as broad as possible. Our sample
covers the 100 largest metropolitan areas from June 1, 2018 to December 31, 2019.
Device selection criteria Around 66 million devices have exactly one inferred residence
over our 19-month sample period.10 Around 46 million of these devices live in buildings forwhich we have demographic data. We classify building-level demographics in terms of two
income groups and four racial groups: the share of a building’s residents with householdincome above $75,000 (the bracket cutoff closest to the national median in 2019) and theshares of a building’s residents who are Asian, Black, Hispanic, and White. We use visitsby all devices for which we have building-level data to measure the demographic mix of copatrons in each venue, applying device demographics probabilistically. To estimate groupspecific preferences, we limit our estimation sample to the 36 million devices that reside inbuildings in which at least two-thirds of the residents belong to the same income and racial
group. 93% of buildings are racially homogeneous and 99% of buildings are economicallyhomogeneous, consistent with most Americans living in single-family homes and significant
sorting by residents of multi-family dwellings.
Venue selection criteria Our baseline estimation sample uses trips to restaurants, whichhave by far the largest number of chains, establishments, and visits. We also characterize
co-patron exposure and estimate demographic preferences in banks, big box retail stores,
convenience store & gas stations, grocery stores, gyms, and pharmacies.
Table A.2 compares the number of venues we observe in the 10 largest restaurant chains
to counts from external sources. We observe on average 87% of venues within these chains,with a low of 70 % for Starbucks. Since the smallest spatial unit of observation in our data is
a building, we exclude venues that contain multiple establishments, such as shopping malls.
To avoid measurement concerns related to venue entry and exit, we only keep venues with atleast one visit prior to the beginning of our estimation window (June 1, 2018) and one visit
10

Around 10% of devices move during this period. We drop these from our baseline estimation sample.
We return to studying these movers in Section 6.2.2.

6

after the end of our estimation window (December 31, 2019). This excludes around 10% of
chain venues from the estimation sample.
Visit selection criteria Our estimation sample is restricted to trips to a venue thatoriginate and end at home.11 Considering only these direct trips ensures that their sole
purpose is visiting a venue. This selection eliminates confounding factors due to trip chaining
and allows us to identify preferences within a venue-choice model like that we introduce inSection 4. It means the estimation sample only includes devices that ping frequently enoughto track direct trips. We also exclude visits with duration longer than three hours, as these
are likely by venue employees.12 Overall, the sample of restaurant visits we use to estimate
preferences includes more than 14 million direct trips to more than 27,000 restaurant chain
venues by almost 4 million devices who live in homogeneous buildings. To estimate thedemographic composition of these venues, we use the sample of all 1.5 billion trips to theserestaurants. While we rely on a narrower sample of visits to estimate preferences, we latershow that visit patterns in our estimation sample mirror those from much broader samples.

2.3

Data quality and representativenessIn this subsection, we first assess whether our device selection criteria bias our estimation
sample. We then evaluate the reliability of the demographic information in the building-level
data.
Couture et al. (2021) show that devices active in the smartphone data are broadly representative spatially, and they make visits that resemble what travelers self-report in the
National Household Travel Survey. Figure 1 shows that the additional selection criteria we
impose on our estimation sample involve only limited spatial biases. Panel A plots the number of devices residing in a county for three device samples against the 2019 Census county
population estimates. The “All Devices” sample includes all devices that have exactly one
home assignment. The “Building” sample includes only devices with building-level demographic data. The “Homogeneous Building” sample includes only devices living in buildings
in which one income-race group constitutes more than 67% of residents. Regressing thedevice count of a county on its Census population count yields an R2 of at least 0.87 for each11

We define a direct trip as a visit to a venue where the preceding and succeeding visits were to a device’s
home and within the same “activity day” (a 24-hour period starting at 3:00 AM). Since not every stop at avenue is observed, some trips may be mis-categorized as direct. Davis et al. (2019) and Miyauchi, Nakajima,
and Redding (2021) study consumption trips that can originate at workplaces.
12
Visit duration is measured with error, but can be reasonably interpreted as a lower bound for actual
duration. A visit is registered when a smartphone application collects a ping in a venue, not when they firstenter the venue, so a device may spend more time at a venue than we observe.

7

Figure 1: Comparing estimation sample to full smartphone data
Panel A: County population
All Devices

Building-Level Demographics

Homogeneous Buildings

Panel B: Population by within-county deciles

Notes: Panel A compares the number of devices residing in a county (vertical axis) to the Census’s estimated2019 residential populations using three different device selection criteria: (1) all devices residing in exactly
one residential building between June 1, 2018 and December 31, 2019; (2) among those devices in (1), thedevices whose building-of-residence has demographic data available; (3) devices whose building-of-residence
is comprised of at least 67% one income group and racial group. We exclude counties with a Census
population of less than 10,000 people. Panel B depicts the share of devices living in block groups withineach within-county population decile for four characteristics: population density, population share of highincome (> $75, 000) residents, population share of White residents, and population share of residents who
have obtained a bachelor’s degree. Panel B depicts these decile shares for the three populations of devices
8
depicted in Panel A.

of the three device samples. Our estimation sample is nearly as representative of county
population as all smartphone data.
Panel B of Figure 1 examines within-county representativeness of residential demographics across block groups. It shows the share of devices living in within-county population
deciles (computed using 2015-2019 American Community Survey block group data) alongfour different demographic dimensions: population density, share high-income, share White,and share college-educated. If device samples were exactly proportionate to populations in
the American Community Survey, each bar would be of equal height (0.10). We show theseresults for the three different device samples above.
The bar heights are very similar in the “All” and “Building” device samples. This alleviates concerns over spatial bias in the building-level data that we use to compute thedemographic composition of venues. When we restrict attention to homogeneous buildings,
as we do in the estimation sample of devices, we see a more substantial bias away from thedensest block groups, with about six percent of devices living within the top density decile,and a slight bias towards more heavily White and high-income block groups. Thus, our estimation sample of devices living in homogeneous buildings is broadly spatially representative,
with the exception of devices living in the top density decile (i.e., in multi-unit buildings)
being somewhat underrepresented.
While Figure 1 demonstrates that our smartphone samples are spatially representativeof residences using Census characteristics, Appendix A.4 shows that the building-level data
overrepresents White and high-income households. That said, the cross-county correlation
between the share of devices within a given demographic group in the Census and that sharein the building-level data remains above 0.8 for all demographic groups (Figure A.1). These
deviations from perfect representativeness are expected in smartphone samples, but they
warrant some caution when measuring the demographic composition of co-patrons within
restaurant venues. We therefore follow Cook, Currier, and Glaeser (2022) by reporting differences in demographic exposure across groups instead of absolute levels that may overstate
exposure to high-income devices.
Finally, we validate the building-level demographic information by showing it predicts
differences in the venue choices of residents of neighboring buildings. For instance, residentsof high-income buildings are more likely than their low-income neighbors to visit chainspreferred by high-income people, such as Starbucks. To make such comparisons, we first
calculate each restaurant chain’s relative popularity among high-income versus low-income
patrons within the same tract, using block-group-level income assignments. We then compute each chain’s relative popularity at the building level, contrasting high- and low-income
buildings in the same block group. These block-group-level and building-level measures of9

a chain’s relative popularity with high-income patrons have a rank correlation of 0.8 (Figure A.2). We find similarly high correlations for racial instead of income groups and forconvenience stores & gas stations (the second largest establishment category).
This exercise delivers two conclusions that we leverage in our empirical analysis. First,
behavior predicted from the building-level demographic data is consistent with behavior
predicted using Census demographic data. Second, the building-level data provides more
information than the Census, because it allows us to predict variation in behavior within thesmallest geographic unit available in the Census.

3

Demographic exposure in shared spaces

This section documents how exposure to different types of co-patrons varies by demographic
group. We first report, for each demographic group, the full distribution of visits to chain
restaurant venues by racial and income co-patron mix. We then show how these differencesin visit patterns translate into disparities in income exposure across groups. Finally, we show
other categories of venues and visits have similar levels of income segregation.
Figure 2 shows the racial and income composition of venues and the propensity of eachgroup in our estimation sample to visit venues by co-patron demographics. Each dot in theplot represents a restaurant within the 100 largest metropolitan statistical areas (MSAs). We
compute visit propensity using a non-parametric kernel regression of a demographic group’s
share of visits to a venue on its co-patron characteristics.
These plots show notable patterns in visit propensity across demographic groups. For
all groups, visit propensity is increasing in own-race share. Within each race, higher-income
individuals visit venues with greater shares of high-income co-patrons than their low-income
peers. However, the distribution and availability of venues varies starkly across racial groups.
Unsurprisingly, many more venues are predominately White than Asian, Black, or Hispanic.
Heavily White venues vary significantly in their income composition, whereas heavily Blackand Hispanic venues tend to be low-income. There are very few venues with a high shareof Asian co-patrons, regardless of co-patron income. These visit patterns echo familiar
patterns of residential segregation by race and assortative matching by income (e.g., Bayerand McMillan 2005).

10Figure 2: Exposure to (and Availability of) Co-Patron Mix
Visits of White Visitors

High-Income

80%

60%

40%

20%
2.09
0%
0%

100%

Visits per 100,000
13.15

20%

40%

60%

80%

100%

Share of White Co-Patrons

Share of White Co-Patrons

100%

1.23
0.64
0.07

60%

40%

20%
2.09
0%
0%

Visits of Black Visitors

80%

60%

40%

20%
2.09

60%

80%

40%

60%

80%

100%

1.23
0.64
0.07

100%

Low-Income

100%

Visits per 100,000
13.15

Share of Black Co-Patrons

Share of Black Co-Patrons

100%

40%

20%

Share of High Income Co-Patrons

High-Income

20%

Visits per 100,000
13.15

80%

Share of High Income Co-Patrons

0%0%

Low-Income

1.23
0.64
0.07

Visits per 100,000
13.15

80%

60%

40%

20%
2.09
0%
0%

20%

40%

60%

80%

100%

1.23
0.64
0.07

Share of High Income Co-Patrons

Share of High Income Co-Patrons

Notes: This figure presents the results of a kernel regression of visit shares on co-patron race and income characteristics. Eachpanel displays the visit propensities for a specific demographic group, with each dot representing an individual restaurant. The
color gradient is consistent across groups and follows a log-linear scale. This gradient is chosen to match the group with thegreatest range in visit propensities between the most and least visited venues. An Epanechnikov kernel with a bandwidth of
0.05 is used, and visit propensities are winsorized at the 99th percentile for each group. Continues on next page.

11

Figure 2: Exposure to (and Availability of) Co-Patron Mix (continued)
Visits of Hispanic Visitors
High-Income

Low-Income

80%

60%

40%

20%
2.09
0%
0%

100%

Visits per 100,000
13.15

20%

40%

60%

80%

100%

Share of Hispanic Co-Patrons

Share of Hispanic Co-Patrons

100%

1.23
0.64
0.07

80%

60%

40%

20%
2.09
0%
0%

Visits of Asian Visitors

High-Income

80%

60%

40%

20%
2.09

40%

60%

80%

40%

60%

80%

100%

100%

Low-Income

100%

Visits per 100,000
13.15

Share of Asian Co-Patrons

Share of Asian Co-Patrons

100%

20%

20%

1.23
0.64
0.07

Share of High Income Co-Patrons

Share of High Income Co-Patrons

0%0%Visits per 100,000
13.15

1.23
0.64
0.07

Visits per 100,000
13.15

80%

60%

40%

20%
2.09
0%
0%

20%

40%

60%

80%

Share of High Income Co-Patrons

Share of High Income Co-Patrons

12

100%

1.23
0.64
0.07

Table 1: Exposure to High-Income Co-Patrons

Low Income

Estimation sample
All chain-restaurant visits
All chain-venue visits
All non-residence venue visits
All McDonald’s restaurant visits
Census tracts

High Income

Asian Black Hispanic
(1)
(2)
(3)

White Asian Black Hispanic
(4)
(5)
(6)
(7)

White
(8)

-0.06
-0.04
-0.05
-0.03
-0.08
0.04

-0.02
-0.02
-0.03
-0.03
-0.03
-0.01

0.14
0.12
0.11
0.17
0.11
0.11

-0.16
-0.13
-0.16
-0.15
-0.16
-0.12

-0.15
-0.13
-0.14
-0.14
-0.16
-0.08

0.14
0.12
0.12
0.18
0.09
0.20

-0.00
-0.00
-0.01
0.04
-0.02
0.03

0.05
0.04
0.04
0.09
0.02
0.06

Notes: This table reports, for different visit samples, the share of high-income co-patrons that each demographic groups (eight columns) is exposed to, relative to a baseline in which all venues in that sample arevisited with uniform probability. The first row shows those high-income shares for visits in the estimation
sample. The second through fourth rows shows those shares for broader visit samples. The fifth row shows
those shares only for visits to McDonald’s restaurants. In the sixth row, those shares are computed as if
each Census tract is a venue and individuals only visit the census tract that they live in.

How do these differences in visit propensity and venue availability translate into differences in income exposure? Table 1 reports exposure to high-income co-patrons for eachdemographic group across different environments. Appendix B.1 shows an analogous tablefor own-race exposure. The first row reports high-income exposure computed using visitsfrom our estimation sample. We chose this sample to suit our empirical strategy, not for its
representativeness, so in subsequent rows we expand our sample to larger sets of visits andvenues. The last row reports a measure of residential income exposure, computed using only
Census tables, as if people’s exposure equaled the income composition of their census tractof residence. This last row offers a useful benchmark to evaluate how income segregation inshared spaces compares with residential segregation.
Each column reports the income exposure of a given demographic group relative to abaseline in which these venues are visited uniformly. For example, column 5 of row 1 showsthat high-income White individuals in our estimation sample choose restaurants with a shareof high-income co-patron that is 14 percentage points higher than it would be if they visitedrestaurants at random. We report our results in relative terms because absolute exposurelevels are more sensitive to definitions of income group and sample biases.13
13

The patterns in Table 1 are robust to different ways of weighting each device that correct for biases inthe smartphone sample. There may also be differences in exposure at the extensive margin, from variation in
the number of visits that each demographic group makes. Smartphone samples with a partial history of eachdevice’s movements are not well-suited to study these differences. The National Household Transportation

13

Table 1 yields two main results. First, there are substantial differences in exposure tohigh-income co-patrons across incomes and races. Within each race, high-income individuals
have more high-income exposure: differences in mean exposure between high- and low-income
individuals are typically 15 to 20 percentage points. Within each income group, Asian andWhite individuals have more income exposure than Black and Hispanic individuals. In fact,
the average high-income exposure of a low-income White individual is only about 2 percentage points lower than that of a high-income Black individual. Second, we observe similarcross-group disparities in income exposure in a variety of settings. In particular, income
segregation experienced within chain restaurants, based solely on visits in our estimation
sample, resembles that experienced within all non-residential venues, both commercial andpublic. These broad income segregation patterns even manifest within a single chain like
McDonald’s. There is an approximately 25 percentage point difference between the highincome share in McDonald’s locations visited by low-income Black or Hispanic individuals
and those visited by high-income Asian or White individuals. These disparities in income exposure within shared spaces also mirror disparities in residential income exposure, computed
using census tract demographic shares.
A number of factors may explain this variation in exposure to high-income co-patrons
across demographic groups. First, different groups may have different tastes for productattributes like cuisine and ambience. Second, groups are distributed differently across cities
and neighborhoods and thus might have to incur higher travel costs to patronize venues withlarger high-income shares. Third, different groups may have different preferences for exposure
to high-income co-patrons. Preferences for own-race co-patrons may generate differences inhigh-income exposure because venues with high-income co-patrons tend to be heavily White(Figure 2). In what follows, we investigate the relative importance of these explanations for
demographic differences in high-income exposure.

4

Model

This section introduces a model of individuals’ decisions to patronize venues within businesschains as a function of transit costs and co-patron composition. The model delivers an
estimating equation for each demographic group’s preferences over co-patron demographics
and travel distances. We also describe how to compute counterfactual venue visit sharesusing the estimated model. We use counterfactual scenarios to quantify the contributions ofSurvey, for instance, shows that, despite differences in high- and low-income individuals’ propensity to visit
different destination types – high-income people make more trips to restaurant – high- and low-income people
make similar numbers of consumption trips and similar numbers of non-work trips.

14

various mechanisms to cross-group variation in exposure to high-income co-patrons.

4.1

Nested-logit preferences

We develop a nested-logit model of consumers’ decisions to visit venues. We index decision
makers by i, venues by j, and chains by c. A decision maker is an individual at a point intime. Denote the set of venues from which a decision maker chooses by J . The utility thatdecision maker i would obtain from choosing venue j is
Uij = Vij + ϵij ,where Vij is a scalar that depends on preference parameters and observed covariates and ϵij
is a random component. Decision maker i chooses the venue j ∈ J that has the highest
value of Uij .
We assume that ϵ has an extreme-value distribution such that consumers have nestedlogit preferences over business chains. We partition the set of venues into C disjoint subsets
denoted by Bc (chains). Following Train (2009, Ch 4.2), we denote the similarity of idiosyncratic preferences for establishments in nest Bc by 1 − λc , so that λc = 1 ∀c is the canonicallogit case. The probability that decision maker i chooses venue j is
λc −1
P
′
exp
(V
/λ
)
exp (Vij /λc )ijcj ′ ∈Bc.
Pj|i =
λc′

P
PC
′
′
exp
(V
/λ
)
ij
c
c′ =1
j ′ ∈Bc′

4.2(1)

Within-chain choice probabilities

If the utility shifter Vij depends on preference parameters Γ, the log likelihood function
associated with the choice probability (1) is
LL(Γ) =XXi

Iij ln Pj|i ,jwhere Iij = 1 if i chooses j.
Following Train (2009, p.82), the choice probability (1) can be rewritten as the productof within-chain and between-chain components: Pj|i = Pj|ic × Pc|i . Thus, we can rewrite thelog likelihood function as
LL(Γ) =

XX
i

XX
 XX
Iij ln Pj|ic + ln Pc|i =Iij ln Pj|ic +Iij ln Pc|i .

j

i

15

j

i

j

While a model of Pc|i must incorporate the parameters appearing in Pj|ic via an “inclusive
P P
value” term, we can maximize the first likelihood component, i j Iij ln Pj|ic , alone. We
estimate preference parameters of interest using only within-chain variation, leveraging theconditional choice probability
exp (Yij /λc ),j ′ ∈Bc exp (Yij ′ /λc )Pj|ic = P

where Yij denotes the component of Vij that varies across venues within chain c.14 We allow
Yij to depend on observable attributes with coefficients that are common across chains.
In order to identify parameters common across nests, we assume a common within-chain
correlation of idiosyncratic preference shocks such that λc = λ ∀c.

4.3

Mean utility specification

In the baseline specification, we assume that the mean utility of patronizing a venue withina chain depends on distance to the venue and its co-patron composition. These preferences
may vary across demographic groups, indexed by g. Preferences over distance and co-patroncomposition are additively separable. In particular, the component of utility that varies
across venues within a chain, Yij , depends on the distance from the consumer’s home to thevenue (distanceij ), the share of high-income co-patrons (shighinc), and the share of own-racejco-patrons (sownrace):jYij = δ g f1 (ln distanceij ) + β g f2 (sownrace, shighinc),jjwhere δ g and β g are group-specific coefficient vectors on travel distance and co-patron composition, respectively, f1 (ln distanceij ) is a polynomial of log distance with unit coefficientsand f2 (sownrace, shighinc) is a polynomial of the two co-patron shares with unit coefficients.
jjChoosing the degrees of the polynomials f1 () and f2 () involves a trade-off between parametric flexibility and statistical power. Our baseline specification uses second-degree poly14

Chain-level attributes Yic that are common across establishments within a chain, such as menu items
and prices, do not affect Pj|ic :exp ([Yij + Yic ] /λc )exp (Yic /λc ) exp ([Yij /λc )exp (Yij /λc )
P
=
=P
.
′
′
′
exp
([Y
+
Y
]
/λ
)
exp
(Y
/λ
)
exp
(Y
/λ
)
ij
ic
c
ic
c
ij
c
j ′ ∈Bcj ′ ∈Bcj ′ ∈Bc exp (Yij /λc )Pj|ic = P

16

nomials:
Yij = δ1g ln distanceij + δ2g (ln distanceij )22ghighincg ownracegownrace 2+ β1g shighinc
+
β
s
+
β
s
+
β
s
+ β5g sownrace
× shighinc
.
2
3 j
4
j
j
j
j
j

(2)

Second-degree polynomials are flexible enough to fit observed choice patterns well and parsimonious enough to be precisely estimated for the demographic groups with modest numbersof observations.
We can express preferences over co-patron composition in terms of willingness to travel.
Let (sownrace , shighinc ) and distance denote the characteristics of the average venue. We definegroup g’s willingness to travel for the co-patron composition (sownrace , shighinc ) as the incremental distance ∆g that equates the mean utility of a venue at distance with that co-patroncomposition and a venue at distance + ∆g with the average co-patron composition:
δ g f1 ln distance + β g f2 sownrace , shighinc= δ g f1 ln distance + ∆g (sownrace , shighinc )+ β g f2 sownrace , shighinc .

(3)

The function ∆g (sownrace , shighinc ) is group g’s willingness to travel for that co-patron composition.15

4.4

Maximum likelihood estimation

We estimate the preference coefficients by maximizing the likelihood component
The optimization problem is

max
g g
δ ,β

i

j

i

j Iij ln Pj|ic .

i highinc
δ g f1 (ln distanceij ) + β g f2 (sownrace
,
s
)
/λ
j
j
h
i  .
Iij ln  Phighinc
ownracegg, sj ′) /λδ f1 (ln distanceij ′ ) + β f2 (sj ′j ′ ∈Bc exp


XX

P P

exp

h

(4)

Since each parameter is g-specific, the model can be estimated separately by demographic
group.

4.5

Empirical implementation

We estimate consumer preferences using 19 months of data on devices in the 100 most populous US metropolitan areas, as described in Section 2.2. We estimate the model separately
15

The equation implicitly defines this function. Given the functional form used in equation (2), there is a
closed-form expression for ∆g (sownrace , shighinc ).

17

by demographic group and business category, so our baseline estimates of δ g and β g are
specific to both demographic group g and the restaurants category. We assume consumers
consider all venues within their metropolitan area, so the nest Bc is the set of venues thatbelong to both the same business chain and metropolitan area. We exclude metro-chain
pairs with very few observations and randomly sample a subset of observations from thosewith very many.16
We estimate consumer preferences using within-chain comparisons in order to distinguish consumer preferences over co-patron composition from other venue characteristics.
Co-patron composition may correlate with other traits in the very broad set of (potentiallyunobserved) characteristics entering Vij , such as service quality, comfort, or product offering. The set of characteristics contributing to Yij , which vary across venues within a chain
business and metropolitan area, is substantially smaller. Venues in the same chain are similar because brand power and economies of scale depend on a standardized offering. In
particular, the food products and quality of service typically do not reflect the local composition of co-patrons within each venue. In robustness checks, we focus on a subset of themost standardized chains based on franchising terms and dispersion in establishment-level
characteristics.
One may worry that co-patron composition might predict venue choice if patrons of
nearby venues are demographically similar because of residential segregation. Our specification addresses this concern, because we control for bilateral distance as an individual-byvenue-specific cost shifter.17 Moreover, our estimation sample of direct trips from home is asmall share of the total visits to venues, so the observed co-patron composition is not driven
by the choice shares in our estimation sample.
For brevity, we refer to all mechanisms that cause co-patron composition to predict consumer decisions as preferences over co-patron composition. Of course, co-patron composition
may predict decisions not because consumers have preferences over co-patron demographics
but because co-patron demographics predict other elements of the decision. For example, aconsumer who is indifferent to strangers’ demographics may choose a venue in order to meetup with their demographically similar friends.18 This behavior could generate the same ob16

In particular, we keep group-MSA-chain triplets that have at least 25 direct visits and When a groupMSA-chain triplet has more than 75 venues, we randomly sample only 75 venues. We randomly sample
20,000 devices from group-MSA pairs that have more than 20,000 devices and only keep group-MSA pairs
with at least 200 visits. From this set, we randomly sample 750,000 observations to reduce computationalburden.
17
We could extend our venue choice model to account for residential choice as in Davis et al. (2019)
Appendix C.6. This shows that residential sorting does not bias estimates of preferences for venues as longas individuals choose a residence based on the expected utility of their venue choice set (and not on theiridiosyncratic preference for a specific venue).
18
We also replicate our preference estimation in categories other than restaurants — like banks and big

18

served outcomes as a consumer who has homophilic preferences over anonymous co-patrons.
We need not separate homophily among strangers and homophily in friendship networks toquantify the importance of homophily in explaining cross-group differences in experienced
income exposure. Similarly, if consumers are not aware of all the venues in their choice set,co-patron demographics could predict consideration sets. Our estimation approach would
infer homophilic preferences if demographically similar venues are more likely to be considered. While this distinction would be important when considering some counterfactual
scenarios, our decomposition of observed exposure to high-income co-patrons will not distinguish preferences over co-patron demographics from consideration sets that vary withdemographics.

4.6

Decomposition of exposure to high-income co-patrons

We decompose exposure to high-income co-patrons by contrasting the distribution of visits
across venues in our fitted model with the distributions resulting from various counterfactual
market shares. We summarize exposure to high-income co-patrons for members of group gby fitting a density f g (·) using kernel K(·) and bandwidth h to the high-income share ineach venue:!
highinchighinc
X

s
−
s
1
j
K
Pj|g .
(5)fˆg shighinc =h j∈JhTo compute our benchmark ‘model-predicted’ distribution of exposure to high-income
co-patrons, we use the model-predicted share of visits to each venue j by group g,
Pj|g = Pj|ic × Pic|g ,(6)where Pj|ic comes from our estimated model of within-chain venue choice and Pic|g comesfrom the observed distribution of visits to each chain by members of a demographic group.
To quantify the contributions of various mechanisms to income exposure, we report the
distributions resulting from various counterfactual market shares Pj|g .19 A simple starting
point is the observed distribution of high-income co-patrons across all venues. This is the density that results from evaluating equation (5) using a uniform probability of visiting venues,
Pj|g = |J1 | . To quantify the contribution of between-MSA variation in demographics to exposure to high-income co-patrons, we use a uniform probability conditional on metropolitan
box stores — where meeting friends is less likely.
19
This is similar to the approach Li (2023) uses to quantify the role of preferences versus constraints ingenerating residential racial segregation in the U.S. and Mayer and Puller (2008) use to quantify the role ofthe exogenous school environment versus preferences in the formation of social links.

19

area m, Pj|g = |J1m | Pm|g . The difference between the nationwide uniform probability |J1 | andthis measure captures the contribution of differences between MSAs to income exposure.
To quantify the contribution of between-chain variation in demographics, we use a uniform
1
P. To quanprobability conditional on metropolitan area and business chain, Pj|g = |Jmc
| mc|gtify the contribution of residential proximity, we compute market shares with counterfactual
probabilities Pj|ic using the estimated distance parameters δ̂ g absent any co-patron preferences (β g = 0). To quantify the contribution of preferences over co-patron composition, we
compute market shares with counterfactual probabilities Pj|ic using the estimated co-patron
preference parameters β̂ g absent any disutility of distance (δ g = 0).20

5

Estimation results

In this section, we estimate preferences over distance and co-patron composition. We document notable regularities in demographic preferences: different income and racial groups
display similar levels of racial homophily. Preferences for high-income co-patrons are alsosimilar across racial groups, but lower-income individuals have less pronounced tastes forco-patron income. These preference patterns are consistent across a number of robustness checks, including restricting our estimation sample to the most standardized restaurant
chains.

5.1

Estimated preference parameters

Table 2 reports estimates of the preference parameters in equation (2) for each of the eightdemographic groups. Panel A reports estimates of the distance coefficients δ g , and Panel B
reports estimates of the co-patron composition coefficients β g .
The estimates of distance coefficients δ g imply distance elasticities around −2.2. These
distance elasticities capture both the cost of longer travel distances and the substitutability ofvenues within a restaurant chain. Higher-income individuals have larger distance elasticities,
consistent with empirical evidence that the value of time spent traveling rises with income(Small and Verhoef, 2007). Our distance elasticities are larger than previous estimates fromstudies that consider venue choice among all restaurants, consistent with venues within the20

The last two counterfactual scenarios in which we set β g = 0 or δ g = 0 are non-nested scenarios.
Nested scenarios, such as the stratified uniform-probability scenarios (e.g., within-MSA to within chainMSA), lend themselves to simple comparisons because they differ in only one respect. Non-nested scenarios,
such as alternatively setting preference parameters for distance or for co-patron composition to zero, must be
interpreted carefully. These alternative scenarios do not provide an additive decomposition of the observed
outcomes, because marginal effects are non-linear functions of the coefficients and covariates. We address
this issue further in our discussion of these results in Section 6.

20

Table 2: Preference estimates
Panel A. Estimates of distance coefficients δ g
Estimates
Distance Elasticity at

Low-Income
Low-Income
Low-Income
Low-Income
High-Income
High-Income
High-Income
High-Income

Linear δ1g

Quadratic δ2g

Mean

25p

75p

-1.29
-1.25
-1.26
-1.11
-1.25
-1.11
-1.22
-1.00

-0.23
-0.23
-0.27
-0.32
-0.26
-0.30
-0.30
-0.36

-2.15
-2.11
-2.24
-2.29
-2.21
-2.24
-2.34
-2.33

-1.59
-1.54
-1.59
-1.51
-1.58
-1.50
-1.60
-1.46

-2.24
-2.20
-2.35
-2.42
-2.32
-2.36
-2.46
-2.48

Asian
Black
Hispanic
White
Asian
Black
HispanicWhite

Panel B. Estimates of co-patron composition coefficients β g
β1g Linear
β2g Quadratic β3g Linear β4g Quadratic β5g Interaction
High-Income High-Income Own race
Own race
Term
Low-Income

Asian

Low-IncomeBlack

Low-Income

Hispanic

Low-Income

White

High-Income

Asian

High-Income

Black

High-Income

Hispanic

High-Income

White

4.13
(0.08)
5.06(0.07)
6.08(0.07)
2.94(0.07)
5.55(0.07)
5.35(0.07)
7.72(0.09)
4.21(0.09)-3.20(0.07)-4.20(0.05)-4.21(0.05)-4.13(0.06)-3.21(0.05)-3.18(0.05)-4.18(0.06)-3.65(0.07)

12.19(0.12)
4.94(0.05)
7.08(0.08)
2.17(0.09)
12.78(0.08)
4.15(0.05)
7.14(0.09)
4.47(0.11)-9.93(0.16)-3.86(0.04)-4.99(0.06)-1.70(0.08)-12.31(0.10)-3.61(0.04)-5.10(0.08)-3.70(0.09)-4.02(0.14)-2.18(0.06)-4.23(0.07)
2.35(0.09)-2.21(0.09)-0.29(0.05)-4.49(0.08)
2.59(0.10)

Notes: This table reports estimates of the preference parameters in equation (2). Panel A reports estimatesof the distance coefficients δ g , and Panel B reports estimates of the co-patron composition coefficients β g .
Panel A reports the distance elasticity at the mean and 25th and 75th percentiles of trip distance, which are
6.4, 1.9, and 7.8 kilometers, respectively.

21

same chain being closer substitutes.21
Table 2 Panel B reports, for each of the eight demographic groups, estimates of all fivecoefficients in β g , which together govern preferences for the share of high-income co-patrons
and the share of own-race co-patrons. Figures 3 and 4 present two different representationsof these preferences. Figure 3 depicts preferences over both the income and race of copatrons, with the preferences of each demographic group in a separate heatmap. Eachpoint represents a chain restaurant venue in the 100 largest metropolitan areas. The colorof the venue captures willingness to travel to that venue in co-patron composition space,∆g (sownrace , shighinc ) from equation (3). To facilitate comparisons across groups, Figure 4
depicts the preferences of all eight demographic groups over one dimension of co-patroncomposition on the same plot, fixing the other dimension at its median value for each group.
This is akin to looking at variation along one horizontal or vertical slice of Figure 3.
We find that preferences for co-patron income are remarkably similar across racial groups,
but high-income individuals have a stronger taste for high-income co-patrons. Figure 3 showsthat high-income individuals have monotone preferences over high-income co-patron share,
increasing from left to right in each plot, whereas their low-income counterparts have nonmonotone preferences for co-patron income. Figure 4 Panel A quantifies the strength ofincome preferences for each group by depicting variation in willingness to travel for establishments with the median own-race share. Across all racial groups, high-income individuals
are willing to travel 2.3 to 3.1 additional kilometers to visit a venue at the 95th percentile ofhigh-income co-patron share, relative to a venue at the 5th percentile. Low-income individuals have less pronounced income preferences, with the most preferred share of high-income
co-patron between 50 to 60 percent for all racial groups. Low-income Asian, Black and Hispanic individuals are willing to travel around 1.4 additional kilometers to visit a venue with
their most preferred income mix, while low-income White individuals have an even lower
willingness to travel of around 0.7 kilometers.
Turning to preferences for own-race co-patrons, we find that all demographic groups
exhibit substantial racial homophily. In all eight panels of Figure 3, the most preferredvenues have high own-race shares. Figure 4 Panel B shows that the strength of this racial
homophily does not vary by income. Comparing levels of racial homophily across racial
groups is difficult because the observed own-race shares differ greatly. White individuals
are the racial majority in most venues, while non-White people have few venues in theirchoice sets with large own-race shares. That said, Black, Hispanic, and White individuals
have similar own-race preferences, in the sense that all are willing to travel about 2.1 km
21Athey et al. (2018), Davis et al. (2019), and Couture et al. (2023) estimate elasticities between -1.0 and-1.5 when considering substitution between all restaurants or non-tradable services in a given city.

22

farther to visit a venue in the 95th percentile of own-race share rather than one in the 5th
percentile. The racial homophily of Asian individuals appears to be stronger, albeit on very
limited support. Finally, we note that racial and income preferences are roughly similar inmagnitude. This similarity is important for how people trade-off income exposure for racial
exposure, as we show in Section 6.
Our paper emphasizes broad patterns of demographic preferences, but our estimatesoffer some finer insights into what individuals prefer. Here, we briefly discuss two additionalfeatures of these preferences. First, Figure 4 shows that preferences are concave in co-patronshares. People are most willing to travel to avoid being an overwhelming racial minority
or in a venue heavily patronized by low-income people. Second, we estimate significant
interactions between racial and economic composition. Table 2 reports positive interaction
terms for White individuals, indicating that they value high-income co-patrons more whensurrounded by White co-patrons. For other racial groups, the reverse holds: the high-income
share matters less as own-race share increases. This suggests that all four racial groups have
sharper income preferences when co-patrons are White.22
22

Seven out of eight interaction terms are between 2.2 and 4.5 in absolute value. To offer a sense ofmagnitude, the coefficient of -2.3 for low-income Black individuals implies that when visiting an establishment
in the top quartile of the Black co-patron share, they are willing to travel an extra 0.66 kilometers to visit ahigher-income venue (at the 90 percent high-income share compared to those with a 10 percent high-income
share.) When visiting an establishment with fewer Black co-patrons, in the bottom quartile of Black copatron share, low-income Black individuals are willing to travel even longer to visit a higher-income venue,
an additional 1.79 kilometers.

23

Figure 3: Preferences over co-patron demographics
High-Income

High-Income

Preferences of White Visitors

Preferences of Black Visitors

Low-Income

Low-Income

Notes: Each plot visualizes the preference estimates over co-patron composition reported by equation (2)for each demographic group. Preferences are expressed in willingness to travel in kilometers relative to theaverage venue, ∆g (sownrace , shighinc ), as defined in equation (3). Each dot corresponds to an individual venue.
Continues onto next page.

24

Figure 3: Preferences over co-patron demographics (continued)
Preferences of Hispanic Visitors
High-Income

High-Income

Low-Income

Preferences of Asian Visitors

25

Low-Income

Figure 4: Preferences for High-Income and Own-race Co-Patrons
B. Willingness to Travel
for Own-Race Share(at median high-income share)

A. Willingness to Travelfor High-Income Share(at median own-race share)

Notes: This figure depicts the preference estimates reported in Table 2 Panel B along each dimension ofco-patron composition while fixing the other. Panel A shows preferences over the share of high-income copatrons when the share of own-race co-patrons is fixed at the median. Panel B shows preferences over theown-race share while fixing income composition.

Translated to dollars, these parameter estimates imply substantial willingness to pay for
preferred demographic exposure. At typical travel speeds and values of time, an additional
kilometer translates to about one dollar, so high-income individuals traveling 2.5–3.0 km
farther to visit a venue in the 95th percentile of high-income co-patron share rather than one
in the 5th percentile implies a $2.5–$3 difference in willingness to pay per trip.23 The 2km
difference in willingness to travel between the 5th and 95th percentile of own-race share forBlack, Hispanic, and White individuals translates to a $2 difference per trip. The average
US driver makes more than 500 consumption trips per year (Couture, Duranton, and Turner,
2018), so these estimates imply a $1,000–1,500 annual willingness to pay to span the range ofavailable demographic exposure in either income or race. For context, Black (1999) estimatesthat the marginal resident is willing to pay approximately 2.1 percent of the mean house
price to access schools with one standard deviation higher test scores, which amounts to23

Assuming households make roundtrips from home at an average speed of about 40km/hour (Couture,
Duranton, and Turner, 2018) and that they value time at $19 per hour (Goldszmidt et al., 2020), willingness
to travel an additional kilometer is equivalent to about one dollar.

26

$3948, amortized over the years during which one lives in that house. Like school quality,

demographic preferences may therefore be an important determinant of neighborhood choice.

5.2

Robustness

This section addresses two potential sources of estimation bias: within-chain heterogeneity
in venue characteristics and mis-specification of the travel-cost function f1 (ln distanceij ).
Restaurant chains offer standardized settings and products, but there still may be withinchain variation in venue characteristics that correlate with co-patron composition. To address
this, we estimate specifications in which we (i) restrict the estimation sample to the chainswith the most standardized venues and (ii) control for more venue and neighborhood characteristics. Travel costs might also differ from a quadratic polynomial in log distance in a waythat is correlated with co-patron composition. To address this, we estimate specifications inwhich we (i) restrict the estimation sample to cities in which trips are overwhelmingly madeby car and (ii) use more flexible functions of distance.
To facilitate comparisons across samples and specifications, we use a parsimonious spec) is a first-degree polynomial so there is one coefficientification in which f2 (sownrace, shighincjjβyg for high-income share and one coefficient βrg for own-race share:
Yij = δ1g ln distanceij + δ2g (ln distanceij )2 + βyg shighinc+ βrg sownrace
.
j
j

(7)

Broadly, these robustness checks deliver preference estimates that are quite similar acrossthe various samples and specifications.
5.2.1

Within-chain heterogeneity

We select the most standardized chains based on two characteristics. The first is the coefficient of variation in the Google Places star rating across venues within the chain.24 Lessvariation in reviewer ratings across venues suggests a more standardized service. The second
characteristic is ownership structure. Following Williamson (1991)’s argument that franchising facilitates local adaptation, we expect chains with franchisees to be less standardized
than owner-operated chains.25 Out of 76 restaurant chains in our sample, we classify the 10
24

The Google Places data on restaurant venue location and characteristics comes from Akbar et al. (2023).
We were able to match 41 percent of Precisely PlaceIQ venues in our estimation sample to a venue in that
Google Places data. Appendix A.6 provides more details on the Google Places data and variable construction.
25
Krueger (1991) makes the related argument that franchisees may shirk on quality by free-riding on brandreputation. In a meta-analysis of 44 studies on franchising, Combs and Ketchen Jr (2003) find support for
the hypothesis that agency theory explains franchising. For instance, more geographically dispersed chains
have higher franchising rates.

27

chains with fewer than 5 percent franchised venues as “entirely wholly-owned” chains, the 7
chains with between 5 and 20 percent of franchised venues as “almost wholly-owned”, andthe remaining chains as “franchised.”26
These two measures of chain standardization are consistent with one another. The Google
Places star rating variation of a franchised chain is on average twice as large as that of awholly-owned chain. Four of the five chains with the least variation in star ratings arewholly-owned, and all ten entirely wholly-owned chains are among the third of chains withthe least variation in star ratings. None of the largest and perhaps most familiar chains –like McDonalds, Subway, Starbucks, and Burger King – are among the most standardizedusing these metrics.27
Figure B.6 reports estimation results for all eight demographic groups within five different
samples of restaurant chains: the baseline sample with all chains, only entirely whollyowned chains, only almost wholly-owned chains, and the bottom quartiles of chains (weighted
by number of venues) with the lowest coefficient of variation for star rating and square
footage, respectively.28 The preference estimates are qualitatively similar across all thesechain samples, albeit noisy for some groups due to small visit counts. Overall, the preference
patterns highlighted in Section 5.1 hold within the most standardized restaurant chains,which are less subject to concerns about across-venue variation in product attributes andservice quality.
Our second set of robustness checks addresses within-chain heterogeneity by controllingfor more venue and neighborhood characteristics. Figure B.7 depicts the results of adding
three venue characteristics: the Google Places star rating, the Google Places number ofreviews, and the venue square footage from Precisely PlaceIQ. Adding these covariates has
little impact on estimated preferences for own-race co-patrons, but it raises our estimates ofpreference for high-income co-patrons for all groups. Finally, we control for the demographic
composition of the residents of or visitors to the neighborhood in which the venue is located.
The coefficients on the co-patron composition of the venue itself are similar in sign andmagnitude when we add controls for the shares of own-race and high-income residents in the26

We collect the franchise data from multiple sources: annual reports to investors for the 2020 fiscal
year, company websites, franchise disclosure documents, and a franchise database compiled by Entrepreneur
magazine.
27
McDonalds, Subway, Starbucks, and Burger King are all in the top third of chains for star rating
variation, and all are franchised (except Starbucks, which is hybrid with about 45 percent of franchised
venues as of September 2019 based on the company’s 10-K filing). The five chains with the lowest coefficientsof variation are Culvers, Longhorn Steakhouse, Olive Garden, MOD Pizza, and Cracker Barrel. Of these,
only Culvers is franchised.
28
We include variation in square footage as an additional robustness check, but these data are often mismeasured (it sometimes includes parking lots for instance) and we are not confident that it captures truevariation across chains.

28

venue’s census tract or the shares of own-race and high-income co-patrons within all othercommercial venues located in a venue’s census tract (Figure B.8).29
5.2.2

Travel-cost specification

The estimates reported in Section 5.1 could be biased by some component of travel costs
that is not captured by a quadratic polynomial in log distance and correlated with co-patroncomposition. For example, venues with more high-income co-patrons may be in locations
better accessed by walking than driving. Or individuals may have a particular taste forvery short trips, which would tend to be to demographically similar venues, given residential
sorting by income and race. We address these concerns by restricting attention to cardominated cities and using more flexible functions of distance.
To address varying transport-mode choices, Figure B.9 reports preference estimates forthe eight demographic groups for subsets of the 100 largest MSAs based on car usage. The
preference estimates for all 100 MSAs are similar to those obtained when restricting theestimation sample to MSAs in which at least 90% or 95% of trips to commercial venues are
by car.30
Figure B.10 reports preference estimates using two alternative functions of distance.
The first uses a cubic polynomial of log distance, which is more flexible than our baseline
quadratic polynomial. The second introduces a dummy variable indicating the venue closest
to the individual’s residence, which would capture a preference for very short trips or aparticular salience of the nearest establishment. These specifications both yield coefficientson co-patron shares very similar to our baseline specification.
A final piece of evidence suggesting that the travel-cost function is well-specified comesfrom event studies of moves between demographically-distinct neighborhoods reported in Section 6 below. If preference estimates were biased by neighborhood demographics co-varying
with distances to venues, estimated preferences would shift when individuals move between
neighborhoods with different demographics. Figure 6 does not show any discontinuous shift
in preferences around such moves.
29
The only exception are the income preferences of low-income individuals, which weaken after adding
controls for visitors to other venues in the same Census tract. The income preferences of low-income individuals are quadratic and hardest to capture with a single coefficient, so these coefficient estimates are lessstable.
30
These MSA-level statistics come from the 2017 National Household Travel Survey (NHTS). Almost 90%of trips to commercial venues in the United States are by car. Appendix A.7 provides more detail on the
NHTS data and variable construction.

29

5.2.3

Other categories of commercial venues

Our baseline analysis reported results for the largest venue category, restaurants. Restaurants and coffee shops have been singled out as a plausible setting for demographic exposureby other studies (Athey et al., 2021; Atkin, Chen, and Popov, 2022; Massenkoff and Wilmers,2023), and restaurant chains generally strive to provide a consistent experience across venues
within the same city. It is possible, however, to estimate demographic preferences within
other kinds of commercial venues that have chains. A priori, it is unclear whether to expect
weaker or stronger preference estimates in other settings. For instance, preferences for copatrons within retail chains may be weaker if demographic exposure is less salient in thatenvironment. Conversely, preference estimates may be biased upward if stores tailor theirproduct offering to the characteristics of their clientele, for instance by offering higher quality products in richer neighborhoods or more shelf space for Asian food in a predominantly
Asian neighborhood. Figure B.5 reports estimated preferences for each demographic groupfor eight distinct venue categories: banks, big-box stores, convenience stores & gas stations,grocery stores, gyms, pharmacies, restaurants, and all business categories pooled together.31
We find some variation in the magnitude of preferences across categories, but the results confirm that the preference patterns documented in Section 5.1 are not unique to restaurants.
For all business categories, individual exhibit racial homophily and prefer venues with morehigh-income co-patrons, with this inclination being again more muted for low-income people.
A notable exception is banks, where low-income individuals avoid branches with high-income
co-patrons. This exception is unsurprising: given the nature of banking services, different
branches of the same bank must tailor their services and advisory expertise to the incomeof their clientele.

6

Determinants of demographic exposureThis section examines how preferences over co-patron demographics contribute to realized
exposure to high-income individuals. The preferences that we estimated in Section 5 imply
that, conditional on where they live, people exhibit substantial willingness to travel forvenues with different co-patrons demographics. In Section 6.1, we show that these choices
impact income segregation directly. For example, high-income Asian, Hispanic, and White
individuals’ preferences for high-income co-patrons are sufficient to single-handedly explain
most of their exposure to high-income co-patrons. Beyond their direct effects, preferences
over co-patron demographics are also informative about residential sorting. Section 6.2
31

Table ?? shows the five largest chains in each category.

30

shows that, within demographic groups, individuals reside in and move to neighborhoods
with demographics that align with their preferences over co-patron demographics.

6.1

Decomposition of exposure to high-income co-patrons

Table 1 documented large differences across demographic groups in their exposure to highincome co-patrons. To quantify the importance of various factors in explaining these disparities, we compute model-predicted visit densities under the different counterfactual scenarios
described in Section 4.6. Our venue-choice model takes some of these factors as given: individuals from different demographic groups visit venues in different metropolitan areas andbusiness chains, which vary in their co-patron composition. Conditional on metropolitan
area and chain, our estimated model links the probability of visiting a venue to proximity
to residences and preferences over co-patron demographics.
Table 3: Mean exposure to high-income co-patrons
Low Income

(1)
(2)

Uniform within MSA
Uniform within MSA-chain pairWithin MSA-chain nests(3) Residential proximity(4) Demographic preferences only
(5)
Income preferences only
(6)
Race preferences only
(7) Model-predicted visits

High Income

Asian
(1)

Black
(2)

Hispanic
(3)

White
(4)

Asian Black(5)(6)

Hispanic
(7)

White
(8)

-0.02
-0.02

-0.01
-0.02

-0.05
-0.06

-0.01
-0.00

0.01
0.04

0.01
0.02

-0.02
-0.01

0.01
0.04

-0.06
-0.00
-0.00
-0.01
-0.06

-0.16
-0.05
-0.01
-0.04
-0.16

-0.15
-0.07
-0.02
-0.06
-0.15

-0.03
0.01
-0.00
0.01
-0.03

0.11
0.09
0.09
0.05
0.14

-0.01
0.04
0.06
-0.01
-0.00

0.04
0.03
0.06
-0.01
0.05

0.11
0.10
0.09
0.06
0.13

(8) Estimation-sample visits-0.06 -0.16
-0.15
-0.02
0.14 -0.00
0.05
0.14
Notes: This table shows the average share of high-income co-patrons an individual of each group would
be exposed to under different counterfactual visit scenarios. All rows are differenced from a “uniform”
counterfactual scenario in which devices visit venues with uniform probability nationwide. Mean exposure
to high-income individuals under that “uniform” counterfactual is 60%. The first row reports the counterfactual scenario in which devices visit venues uniformly within their MSA of residence. while the second
row reports the scenario with uniform visits within their MSA of residence and choice of chain. The third
row reports the counterfactual scenario in which devices consider their distance dis-utility while ignoring
preferences for co-patron characteristics. The fourth row reports the counterfactual scenario in which
devices consider their preferences for co-patron characteristics while ignoring their distance dis-utility.
The fifth row reports the counterfactual scenario in which devices only consider their preferences forhigh-income co-patrons. The sixth row reports the counterfactual scenario in which devices only consider
their preferences for own-race co-patrons. The seventh row reports the counterfactual scenario in which
devices consider both their preferences for co-patron compositions and distance dis-utility. Finally, theeighth row shows the actual exposure based on the home-venue-home visits in our estimation sample.

Table 3 reports how the mean exposure to high-income co-patrons varies across these31

counterfactuals. Appendix B.2 shows an analogous table for own-race exposure. Each cell
describes the visit-weighted average share of high-income co-patrons that a given demographic group experiences in a given counterfactual scenario. All rows show changes inmean exposure relative to a “uniform benchmark”: the exposure each group would receive if
all venues were visited with uniform probability. Our venue-choice model fits the data well,as the model-predicted visits generate exposures very close to those in the estimation sample
and all observed visits to chain restaurants.
The other rows of Table 3 introduce different counterfactual scenarios. The “uniform
within MSA” and “uniform within MSA-chain pair” rows reveal the importance of (unmodeled) variation across MSA-chain nests for explaining differences in exposure. Then, we
compare how the two within-nest factors included in our model of venue choice—residential
proximity and demographics preferences—determine model-predicted visits.
The “uniform within MSA” row depicts the change in mean exposure to high-income copatrons if individuals visited venues uniformly within their MSA of residence, relative to anational uniform exposure benchmark. Accounting for metropolitan differences barely shifts
exposure relative to the national average benchmark. The only substantial shift is for lowincome Hispanic individuals, who tend to reside in poorer cities. For them, differences across
MSAs explain one-third of their experienced income exposure relative to the benchmark (0.05 of -0.15).
The “uniform within MSA-chain pair” row reports the change in mean exposure if individuals visited venues uniformly within the chains that they visit in the MSA where they
reside, again relative to the benchmark of national uniform high-income exposure. Accounting for chains has little effect: most exposure differences arise within MSA-chain pairs. The
largest shifts in exposure are among high-income Asian and White individuals, whose choiceof chain shifts their high-income exposure up by 3 percentage points, less than one-quarter ofthe overall difference between their experienced exposure and the national average. We emphasize that while there are statistically significant differences in the types of chains visitedby different groups—for instance, high-income individuals are more likely to visit Starbucks
than Dunkin’ Donuts—these differences are not large enough to meaningfully impact experienced income segregation.32 In summary, differences in the distribution of groups acrosscities and chains fail to explain most of the differences in income exposure across groups.
32

Chain choice depends on taste for chain product offering, preferences for average co-patron composition
within that chain, and distance to venues within that chain. Given the small importance of chain choice,however, we conclude that most of demographic exposure is determined by venue choice within, not across,chains. Nilforoshan et al. (2023) suggest that the availability of venues catering to richer people is the main
reason why larger cities feature more experienced segregation. We find that this hypothesis does not explain
differences in income exposure across demographic groups, because these demographic differences persist
within business chains.

32

It must be then, that most of these differences in income exposure are explained byeither proximity to venues or demographic preferences. The “residential proximity” and“demographic preferences only” rows of Table 3 summarize the contribution of each factor to
predicted income exposure. Specifically, the “residential proximity” row reports the income
exposure outcomes that would result if individuals chose which of a chain’s venue to visit
based only on distance parameters δ g absent any co-patron preferences (β g = 0). Conversely,
the “demographic preferences only” row reports the income exposure outcomes that would
result if individuals chose which of a chain’s venue to visit based only on preferences forco-patron composition absent any disutility of distance (δ g = 0).
Residential distance dramatically reduces the high-income exposure of low-income individuals from all racial groups. The neighborhoods in which low-income individuals reside are the most important factor in explaining why they are less exposed to high-income
co-patrons. In fact, the “residential proximity” and the “model-predicted visits” rows ofcolumns 1 through 4 in Table 3 are nearly identical.his sense, the income segregation of consumption venues reflects the income segregation of residences. For hgh-income individuals
howevresidential proximity increase high-income exposure, because they live in neighborhoods near high-income venues. The exception is high-income Black individuals, who live inmuch poorer neighborhoods than their high-income counterparts in other racial groups. For
them, residential proximity diminishes income exposure relative to a uniform visit benchmark.
Demographic preferences also play an important role in explaining racial differences inincome exposure. In particular, racial homophily impacts income exposure differently acrossracial groups. As an example, Black and White individuals share similar preferences forhigh-income co-patrons and similar levels of racial homophily, but these same preferences
drive Black individuals to visit venues with much smaller shares of high-income co-patrons.
This is because Black and Hispanic individuals, who have lower income on average, mustchoose between visiting high-income venues or heavily own-race venues.33 Asian and White
individuals do not face this trade-off, and eliminating own-race preferences, in row 5 ofTable 3, considerably narrows the income exposure gap between racial groups.
Overall, residential proximity dominates demographic preferences for low-income individuals, whose experienced income exposure is lower than what their demographic preferences
alone would suggest. For high-income Asian, Hispanic and White individuals, however, either residential proximity or demographic preferences suffice to account for their observedhigh-income exposure. These groups live in neighborhoods where venues that suit their de33

Bayer and McMillan (2005) suggest that the scarcity of high-income majority Black neighborhoods may
explain why Black households live in poorer neighborhoods than White households with similar incomes.

33mographic preferences—those with large shares of high-income and own-race co-patrons—are
located nearby. These results are consistent with either individuals choosing neighborhoods
based on their demographic preferences or with residential experiences in those neighborhoods affecting preferences. The next section investigates this further.

6.2

Residential sorting on demographic preferences

We now use our model of venue choice to study the link between demographic preferences andresidential sorting. We perform two exercises. First, we estimate the model for individuals inthe same demographic group who reside in neighborhoods with different demographic mixes.
We find that individuals are sorted across neighborhoods in a way that is correlated with
their demographic preferences. Second, we study the evolution of demographic preferences
around residential moves. When individuals move, they move to neighborhoods that suit
their pre-move demographic preferences, and, in turn, their demographic preferences partiallyconverge to those of their new neighborhood over the next six months.
6.2.1

The extent of sorting on demographic preferences

Do people living in neighborhoods with more high-income or own-race residents have stronger
preferences for high-income or own-race co-patrons? We divide Census tracts into income
terciles and own-race terciles based on the composition of their residents. Leveraging ourbuilding-level demographic data, we estimate the model of equation (2) separately for devices
residing in each income tercile and race tercile.34 The results reveal preference heterogeneity
within demographic groups. Figure 5 depicts these preference estimates for high- and lowincome White individuals (dashed and solid lines, respectively).35
Figure 5 shows that White individuals’ preferences over co-patron demographics arealigned with their neighborhoods’ demographics. The upper-left panel shows that White residents of higher-income neighborhoods have stronger preferences for high-income co-patrons.
This is true for both high- and low-income individuals. These differences in preferences acrossterciles of neighborhood income are large: a low-income resident of a top tercile neighborhood (solid red) has preferences for co-patron income similar to a high-income resident of a
middle tercile neighborhood (dashed gray).
34

This design requires building-level demographic data. We could not observe variation in preferences
across neighborhoods within demographic groups if these groups were defined using neighborhood characteristics.
35
Analogous figures for other racial groups are in Appendix B.1. They show generally similar patterns,
but are noisier due to smaller samples. For example, few low-income Black individuals live in the upper
tercile of the tract income distribution.

34

Figure 5: Preference heterogeneity across White visitors by residential neighborhoods
Panel A: Preferences by Residential Neighborhood Income
WTT for High-Income ShareWTT for Own-Race Share

Panel B: Preferences by Residential Neighborhood Own-Race ShareWTT for High-Income ShareWTT for Own-Race Share

Notes: These plots are analogous to those in Figure 4 except that each line depicts preferences for highor low-income White visitors who reside in neighborhoods (Census tracts) with different demographics.
Residential tract high-income terciles are defined using tract-level high-income share weighted by highincome tract population. Similarly for own-race terciles. We estimate preferences for a randomly selected
100,000 visits by devices in a given residential tercile. To compare across neighborhood terciles, we evaluate
willingness to travel relative to the same fixed demographic composition in all terciles. On the left, we show
willingness to travel relative to a venue at the 5th percentile of the high-income share distribution across allvenues, holding own-race share fixed at its median value. On the right, we show willingness to travel relative
to a venue at the 5th percentile of the own-race share distribution across all venues, holding high-income
share fixed at its median value.

35

The lower-right panel of Figure 5 shows a similar alignment of own-race preferences withneighborhood demographics: White individuals who reside in heavily White neighborhoods
exhibit stronger preferences for own-race co-patrons. Those residing in the top tercile ofneighborhoods with the highest White share would travel five times farther than those in
the bottom tercile to visit a venue in the 95th percentile of White co-patron share rather
than a venue at the 5th percentile.
Overall, we find that within race-income groups, residential demographics and preferences over co-patron demographics are meaningfully correlated. These patterns could arise
because individuals select neighborhoods with residents matching their preferred co-patroncomposition or because residential experiences affect those preferences (e.g., the intergroupcontact hypothesis). The movers analysis in the next subsection speaks to these links.
6.2.2

Origins of sorting on demographic preferences

To examine the relationship between residential demographics and preferences for venue copatrons, we estimate preference parameters at a monthly frequency before and after moves.
To achieve sufficient sample sizes, we study high-income White individuals who move between
MSAs for five months before and five months after their change in residence.36 We examine
how preference parameters evolve when an individual moves from an origin neighborhoodin own-race tercile o to a destination neighborhood in own-race tercile d. We focus on
moves across own-race tercile because moves across income terciles, shown in Appendix B.3,
may arise from income shocks that could affect preferences.e estimate the parsimonious
linear specification of our model—used for all robustness exercises in Section 5.2—in which
preferences over co-patron composition depend on only the share of own-race co-patrons andshare of high-income co-patrons. We estimate tercile-o-to-tercile-d-specific preferences overboth travel costs (δ g,od ) and co-patron composition (β g,od ) that are specific to month k:
Yijt =

5X

1{t = k}



g,od
g,od
g,od ownraceg,od highincδ1kln distanceij + δ2k
ln distance2ij + βrk
sj
+ βyk
sj



,

k=−5

(8)
g,od
g,od
where βrk
and βyk
are the preference coefficients on own-race share and high-income share,respectively, for group g in month k when moving from tercile o to tercile d.
Figure 6 shows estimated preferences for own-race exposure before and after moves across36

We focus on between-MSA moves so that there is a stark change in the choice set and no scope forvenue-specific habits to drive behavior. We follow devices for only ten months because most devices appear
in the data for less than a year. The estimation sample is an unbalanced panel: not every mover is in thesample for all ten months and not every mover makes a home-venue-home restaurant visit in every month.
High-income White individuals are by far the largest sample of cross-MSA movers.

36

Figure 6: Preferences over own-race share by month by tercile-to-tercile move
Moves from Bottom Own-Race Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Middle Own-Race Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Top Own-Race Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Notes: Each plot in this figure depicts event-month-specific, origin-race-tercile-destination-race-tercileg,od
specific estimates of preference for own-race co-patrons βrk
in equation (8). Each dot depicts a point
estimate and the bands depict 95% confidence intervals. The estimation samples contain home-venue-home
visits to restaurants by high-income White individuals who move between MSAs, split by the own-race tercileof the origin residence and the own-race tercile of the destination residence.

37

Table 4: Average Own-Race Preference of High-Income White Devices Before & After Moves
O1

O2

O3

D1

D2

D3

D1

D2

D3

D1

D2

D3

Pre-Move

0.59(0.17)

0.60(0.20)

0.65(0.27)

0.81(0.19)

0.96(0.20)

0.94(0.26)

1.33(0.27)

1.67(0.25)

2.54(0.29)

Post-Move

0.06(0.15)

0.65(0.18)

1.03(0.25)

0.71(0.19)

1.96(0.21)

2.27(0.27)

0.92(0.23)

0.53(0.22)

2.84(0.26)

g,od
Notes: This table reports pooled estimates of βrk
from equation (8). For each origin-destination
pair, coefficients are pooled for event-months prior to move (t = −5 to t = −1), and post-move (t = 0
to t = 5). The estimation samples contain home-venue-home visits to restaurants by high-income
White individuals who move between MSAs, split by the own-race tercile of the origin residence (withO1 being the origin tercile with the lowest share White) and the own-race tercile of the destination
residence (with D1 being the destination tercile with the lowest share White).

terciles of the neighborhood own-race distribution.37 Given the small samples of movers, themonth-specific parameter estimates are noisy. Table 4 reports the pre-move and post-movefive-month averages of these coefficients for each event study. Figure 6 and Table 4 reveal
three patterns of interest.
First, we find some evidence that individuals move to neighborhoods with demographics
that suit their pre-move demographic preferences. Conditional on the demographic tercile ofthe origin, individuals with stronger pre-move racial preferences tend to move to destinationneighborhoods with higher own-race shares. These differences are not always statisticallysignificant, but they are especially large – a near doubling in the strength of racial homophily– when contrasting individuals moving from a third-tercile origin to a third-tercile destination(O3 to D3) with individuals moving from a third-tercile origin to a first-tercile destination(O3 to D1). In other words, high-income White individuals moving from a high- to a lowshare White neighborhood have substantially weaker own-race preferences before the move.
37

Table B.3 replicates Table 4 for income preferences following moves across neighborhood income terciles.
It reports substantial increases in income preferences following moves to higher income terciles, providing
additional evidence that preferences change in line with the demographic of one’s new neighborhood. As
expected, we do not find that individuals sort across income terciles due to their income preferences. Suchmoves may be explained by income shocks. Appendix B.2 reports the full set of event studies for income
exposure around moves to different own-race terciles, and for own-race and income exposure around moves
to different income terciles. Out of these 36 event studies, we note one unexplained jump in preferences
following moves from the highest tercile to the bottom tercile of the income distribution. These movers to
substantially poorer neighborhoods appear to experience an immediate drop in their preferences for highincome exposure upon moving. This result is difficult to interpret because such movers are rare and may
have experienced a negative income shock.

38

Second, estimated preference coefficients do not jump discontinuously when individuals
move. The nine panels of Figure 6 depict event studies for moves between the nine pairs of
terciles of the own-race neighborhood distribution. The top-right (O1 to D3) and bottomleft (O3 to D1) plots show the greatest contrasts between origin and destination terciles. In
all cases, there are neither pre-trends in preferences prior to a move nor noticeable jumps inpreferences right after moving.
Third, demographic preferences partially converge to the local demographic mix aftera move. The changes in estimated preference parameters are, at least, consistent withneighborhoods shaping preferences. Individuals who move to neighborhood with a higher
own-race share tend to exhibit stronger racial homophily after moving. For instance, the ownrace preferences of movers from O1 to D3 rise by almost 60% post-move, but remain weaker
than the preferences of those already living in O3. Similarly, the preferences of individuals
who move to a neighborhood with a lower own-race share evolve to exhibit weaker racial
homophily. Given the limited time window, we cannot assess whether preferences fully
converge, but the point estimates constitute (not always statistically significant) evidence
for neighborhoods shaping preferences.
Overall, our examination of movers suggests that residential sorting explains some ofthe documented spatial heterogeneity in demographic preferences and that exposure to newneighbors may in turn shape these preferences. Sorting appears relevant because preferences before a move predict the dominant demographic of the destination neighborhood.
Residential experiences appear relevant because after a move preferences for the dominantdemographic of the destination neighborhood strengthen. Our results, while limited by sample sizes and time spans, would be consistent with extended contact with one’s neighbors
early in life influencing preferences for demographic exposure, which then evolve slowly withnew experiences.

7

Conclusion

Our study offers new insights into the demographic fragmentation of American life. We
measure exposure to high-income co-patrons in shared commercial spaces by combining datafrom millions of smartphones with building-level demographic characteristics. We estimate
preferences for the racial and income composition of these spaces by studying visits to large
chains in which variation in venue attributes is limited.
We find large disparities across groups in their exposure to high-income co-patrons. Black,
Hispanic, and low-income individuals experience lower high-income exposure. Demographic
preferences, however, are broadly shared across groups and economically large. For instance,
39

racial homophily translates into a willingness to travel two additional kilometers to visit avenue at the 95th percentile of the own-race distribution rather than the 5th percentile, andthese preferences do not attenuate at higher incomes.
Our findings have important implications for the future of American segregation. Since
demographic preferences alone can explain a substantial portion of income segregation inshared spaces, eliminating differences in product tastes and residential proximity may not
suffice to close the gaps in exposure to high-income individuals across demographic groups.
Moreover, if these demographic preferences influence behavior in other settings, such asneighborhoods and schools, removing structural barriers alone is unlikely to fully integrate
American society.
Our analysis, however, reveals settings in which demographic preferences are less pronounced. In particular, preferences are weaker in more integrated neighborhoods and weaken
following a move to a more integrated area. Consistent with the intergroup contact hypothesis, integrated spaces may therefore promote further integration over time by attenuating
demographic preferences.

40

References
Abbiasov, Timur, Cate Heine, Edward L Glaeser, Carlo Ratti, Sadegh Sabouri, Arianna Salazar Miranda, and Paolo Santi. 2022. “The 15-Minute City Quantified Using
Mobility Data.” National Bureau of Economic Research.
Akbar, Prottoy A., Victor Couture, Gilles Duranton, and Adam Storeygard. 2023. “TheFast, the Slow, and the Congestion: Urban Transportation in Rich and Poor Countries.”
Processed, University of British Columbia.
Aliprantis, Dionissi, Daniel R. Carroll, and Eric R. Young. 2022. “What explains neighborhood sorting by income and race?” Journal of Urban Economics, 103508.
Allport, G.W. 1954. The Nature of Prejudice. The Nature of Prejudice, Addison-Wesley
Publishing Company.
Anderson, Elijah. 2011. The Cosmopolitan Canopy: Race and Civility in Everyday Life.
W.W. Norton & Company.
Athey, Susan, Billy Ferguson, Matthew Gentzkow, and Tobias Schmidt. 2021. “Estimating
experienced racial segregation in US cities using large-scale GPS data.” Proceedings of the
National Academy of Sciences, 118(46): e2026160118.
Athey, Susan, David Blei, Robert Donnelly, Francisco Ruiz, and Tobias Schmidt. 2018.
“Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using
Mobile Location Data.” AEA Papers and Proceedings, 108: 64–67.
Atkin, David, Keith Chen, and Anton Popov. 2022. “The Returns to Face-to-Face Interactions: Knowledge Spillovers in Silicon Valley.”
Backstrom, Jesse, and Richard T Woodward. 2021. “Racial Distance Matters Too: Racialand Ethnic Reference Dependence in Recreation Decision Making.” Available at SSRN
3818265.
Baldenius, Till, Nicolas Koch, Hannah Klauber, and Nadja Klein. 2023. “Heat increases
experienced racial segregation in the United States.” arXiv.org Papers 2306.13772.
Barwick, Panle Jia, Yanyan Liu, Eleonora Patacchini, and Qi Wu. 2023. “Information, Mobile
Communication, and Referral Effects.” American Economic Review, 113(5): 1170–1207.
Bayer, Patrick, and Robert McMillan. 2005. “Racial Sorting and Neighborhood Quality.”
NBER working paper.
41

Bayer, Patrick, Fernando Ferreira, and Robert McMillan. 2007. “A unified framework
for measuring preferences for schools and neighborhoods.” Journal of political economy,
115(4): 588–638.
Bayer, Patrick, Hanming Fang, and Robert McMillan. 2014. “Separate when equal? Racialinequality and residential segregation.” Journal of Urban Economics, 82: 32–48.
Bayer, Patrick, Marcus D Casey, W. Ben McCartney, John Orellana-Li, and Calvin S Zhang.
2022. “Distinguishing Causes of Neighborhood Racial Change: A Nearest Neighbor Design.” National Bureau of Economic Research Working Paper 30487.
Bayer, Patrick, Stephen L. Ross, and Giorgio Topa. 2008. “Place of Work and Place ofResidence: Informal Hiring Networks and Labor Market Outcomes.” Journal of Political
Economy, 116(6): 1150–1196.
Black, Sandra. 1999. “Do Better Schools Matter? Parental Valuation of Elementary Education.” The Quarterly Journal of Economics, 114(2): 577–599.
Büchel, Konstantin, Maximilian V Ehrlich, Diego Puga, and Elisabet Viladecans-Marsal.
2020. “Calling from the outside: The role of networks in residential mobility.” Journal of
Urban Economics, 119: 103277.
Bursztyn, Leonardo, Thomas Chaney, Tarek A Hassan, and Aakaash Rao. 2024. “The immigrant next door.” American Economic Review, 114(2): 348–384.
Caetano, Gregorio, and Vikram Maheshri. 2019. “Gender segregation within neighborhoods.”
Regional Science and Urban Economics, 77: 253–263.
Caetano, Gregorio, and Vikram Maheshri. 2021. “A Unified Empirical Framework to Study
Segregation.”
Card, David, Alexandre Mas, and Jesse Rothstein. 2008. “Tipping and the Dynamics ofSegregation.” The Quarterly Journal of Economics, 123(1): 177–218.
Charles, Camille Zubrinsky. 2003. “The Dynamics of Racial Residential Segregation.” Annual
Review of Sociology, 29(1): 167–207.
Chetty, Raj, John N Friedman, Emmanuel Saez, Nicholas Turner, and Danny Yagan. 2020.
“Income Segregation and Intergenerational Mobility Across Colleges in the United States.”
The Quarterly Journal of Economics, 135(3): 1567–1633.

42

Chetty, Raj, Matthew O Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren,
Robert B Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, et al.
2022a. “Social capital I: measurement and associations with economic mobility.” Nature,608(7921): 108–121.
Chetty, Raj, Matthew O Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert B Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, et al. 2022b. “Social capital II: determinants of economic connectedness.” Nature,608(7921): 122–134.
Choi, Jinkyong, Jorge Guzman, and Mario L Small. 2024. “Third Places and Neighborhood Entrepreneurship: Evidence from Starbucks Cafés.” National Bureau of Economic
Research.
Combs, James G, and David J Ketchen Jr. 2003. “Why do firms use franchising as anentrepreneurial strategy?: A meta-analysis.” Journal of management, 29(3): 443–465.
Cook, Cody. 2023. “Heterogeneous Preferences for Neighborhood Amenities: Evidence fromGPS data.” Available at SSRN 4212524.
Cook, Cody, Lindsey Currier, and Edward L Glaeser. 2022. “Urban Mobility and the Experienced Isolation of Students.” National Bureau of Economic Research Working Paper
29645.
Couture, Victor, Cecile Gaubert, Jessie Handbury, and Erik Hurst. 2023. “Income Growth
and the Distributional Effects of Urban Spatial Sorting.” The Review of Economic Studies,
rdad048.
Couture, Victor, Gilles Duranton, and Matthew A. Turner. 2018. “Speed.” Review of Economics and Statistics, 100(4): 725–739.
Couture, Victor, Jonathan I Dingel, Allison Green, Jessie Handbury, and Kevin R Williams.
2021. “JUE Insight: Measuring movement and social contact with smartphone data: areal-time application to COVID-19.” Journal of Urban Economics, 103328.
Davis, Donald R., Jonathan I. Dingel, Joan Monras, and Eduardo Morales. 2019. “How
Segregated Is Urban Consumption?” Journal of Political Economy, 127(4): 1684–1738.
Davis, Morris A., Jesse Gregory, and Daniel A. Hartley. 2023. “Preferences over the Racial
Composition of Neighborhoods: Estimates and Implications.”43

Echenique, Federico, and Roland G. Jr. Fryer. 2007. “A Measure of Segregation Based onSocial Interactions.” The Quarterly Journal of Economics, 122(2): 441–485.
Goldszmidt, Ariel, John A List, Robert D Metcalfe, Ian Muir, V. Kerry Smith, and Jenny
Wang. 2020. “The Value of Time in the United States: Estimates from Nationwide Natural
Field Experiments.” National Bureau of Economic Research Working Paper 28208.
Klopack, Ben. 2020. “One size fits all? The value of standardized retail chains.” Texas A&M
University working paper.
Krueger, Alan B. 1991. “Ownership, Agency, and Wages: An Examination of Franchising in
the Fast Food Industry.” The Quarterly Journal of Economics, 106(1): 75–101.
Li, Nicholas Y. 2023. “Racial SOrting, Restricted Choices, and the Origins of Residential
Segregation in U.S. Cities”.” mimeo.
Lowe, Matt. 2021. “Types of Contact: A Field Experiment on Collaborative and Adversarial
Caste Integration.” American Economic Review, 111(6): 1807–44.
Massenkoff, Maxim, and Nathan Wilmers. 2023. “Rubbing Shoulders: Class Segregation in
Daily Activities.” https://ssrn.com/abstract=4516850.
Mayer, Adalbert, and Steven L Puller. 2008. “The old boy (and girl) network: Social network
formation on university campuses.” Journal of public economics, 92(1-2): 329–347.
Miyauchi, Yuhei, Kentaro Nakajima, and Stephen J. Redding. 2021. “Consumption Accessand Agglomeration: Evidence from Smartphone Data.” CEPR Discussion Paper 67411613580728.
Moro, Esteban, Dan Calacci, Xiaowen Dong, and Alex Pentland. 2021. “Mobility patterns
are associated with experienced income segregation in large US cities.” Nature communications, 12(1): 4633.
Nilforoshan, Hamed, Wenli Looi, Emma Pierson, Blanca Villanueva, Nic Fishman, Yiling
Chen, John Sholar, Beth Redbird, David Grusky, and Jure Leskovec. 2023. “Human mobility networks reveal increased segregation in large cities.” Nature.
Reardon, Sean F., Lindsay Fox, and Joseph Townsend. 2015. “Neighborhood Income Composition by Household Race and Income, 1990–2009.” The ANNALS of the American
Academy of Political and Social Science, 660(1): 78–97.

44

Rothstein, Richard. 2017. The Color of Law: A Forgotten History of How Our Government
Segregated America. Liveright.
Schelling, Thomas C. 1971. “Dynamic Models of Segregation.” The Journal of Mathematical
Sociology, 1(2): 143–186.
Schönholzer, David. 2023. “Measuring Preferences for Local Governments.”
Sethi, Rajiv, and Rohini Somanathan. 2004. “Inequality and Segregation.” Journal of Political Economy, 112(6): 1296–1321.
Small, Kenneth A, and Erik T Verhoef. 2007. The economics of urban transportation. Routledge.
Train, Kenneth. 2009. Discrete Choice Methods with Simulation. Cambridge University Press.
USDOT, Federal Highway Administration. 2017. “2017 National Household Travel Survey.”
Waldfogel, Joel. 2008. “The median voter and the median consumer: Local private goods
and population composition.” Journal of Urban Economics, 63(2): 567–582.
Wang, Qi, Nolan Edward Phillips, Mario L. Small, and Robert J. Sampson. 2018. “Urban
mobility and neighborhood isolation in America’s 50 largest cities.” Proceedings of the
National Academy of Sciences, 115(30): 7735–7740.
Williamson, Oliver E. 1991. “Comparative Economic Organization: The Analysis of Discrete
Structural Alternatives.” Administrative Science Quarterly, 36(2): 269–296.
Xu, Yang, Alexander Belyi, Paolo Santi, and Carlo Ratti. 2019. “Quantifying segregationin an integrated urban physical-social space.” Journal of The Royal Society Interface,
16(160): 20190536.
Yabe, Takahiro, Bernardo Garcı́a Bulle Bueno, Xiaowen Dong, Alex Pentland, and Esteban Moro. 2023. “Behavioral changes during the COVID-19 pandemic decreased income
diversity of urban encounters.” Nature Communications, 14(2310).

45

Appendix – For Online Publication
A

Data Appendix

A.1

Smartphone visits data

As described in Couture et al. (2021), each observed visit consists of a device, a venue, atimestamp, and an attribution score. Precisely PlaceIQ’s attribution scores are larger whena device is more likely to have been within a venue, based on the number and density ofpings, data source of pings, and proximity of the pings to the polygon defining the venue.
We retain all visits with an attribution score greater than a threshold value recommended
by Precisely PlaceIQ based on their experience correlating their data to a diverse array oftruth sets, including consumer spending data and foot-traffic counts. Precisely PlaceIQ also
reports a lower bound for the visit’s duration based on the time between consecutive pingsat the same venue.
As described in Couture et al. (2021), when two venues are in close proximity to one
another, a single visit event may have an attribution score for both venues that exceeds thethreshold value recommended by Precisely PlaceIQ. In these cases, we retain only the visit
to the venue with the highest attribution score. In other cases, the polygons of two different
venues overlap.38 When two polygons overlap, we retain polygons with an identified businesscategory over those lacking a category. If both polygons have identified business categories
or neither have identified business categories we drop those visits.
We include all visits between June 1, 2018 and December 31, 2019. On the average day,there were 167 million visits produced by 38 million devices visiting 40 million residential
and non-residential venues. The average device appears in the data for 159 days over the
19-month window, but a notable number appear on only one day. After we restrict attention
to devices in our estimation sample (one permanent home assignment over the 19-monthwindow) there are 104 million visits from 18 million devices visiting 30 million venues on anaverage day.

A.2

Home assignments

We construct home assignments using a procedure introduced in Couture et al. (2021),
which we repeat here for convenience. Residential venues are a distinct category in the38

The modal case of this happening is when the basemap contains one polygon representing a businessestablishment and a second polygon representing both that building and the accompanying parking lot.

Appendix – 1

Precisely PlaceIQ data. This allows us to construct a weekly panel of home locations for asubset of devices using the following assignment methodology:
1. For each week, we assign a device to the residential venue where its total weekly visit
duration at night (between 5pm and 9am) is longest, conditional on it making at least
three nighttime visits to that venue within the week.39 If a device does not visit any
residential location on at least three nights, then on initial assignment that device-weekpair has a missing residential location.
2. After this preliminary assignment, we fill in missing weeks and adjust for noisiness in
the initial panel using the following interpolation rules:
Rule 1: Change “X · X” to “X X X”: If the residential assignment for a week is missing
and the non-missing residential assignment in the weeks before and after is thesame, we replace the missing value with that residential assignment.
Rule 2: “a X Y X b” to “a X X X b” where a ̸= Y and b ̸= Y : If a device has aresidential assignment Y that does not match the assignment X in the week before
or after, we replace Y with X as long as Y was not the residential assignment
two weeks before or two weeks after.40
3. After step 2’s interpolation, for any spells of at least four consecutive weeks where adevice is assigned the same residential venue, we assign that venue as a device’s “home”
for those weeks. Spells of less than four weeks are set to missing.
4. If a device has more than one home assignment and the pairwise distance between
them is less than 0.1 kilometers, we keep the home that appears for the most weeks.
5. If a device has the same home assignment in two non-consecutive periods and noother home assignments in between, then we assign all weeks in between to that homeassignment.

A.3

Building-level demographics

Precisely PlaceIQ provides us with demographic data at the building level for around 36
million residential buildings. This includes information on standard demographic information
39
Since we only observe minimum duration, there are instances where total duration is 0 across all residential locations. In these cases, we assign the residential venue as the venue a device makes the most nighttime
visits.
40
For cases where a device’s residential location is bouncing between two places (“Y X Y X X”) we are
not able to ascertain whether Y or X is more likely to be a device’s residence in a given week.

Appendix – 2

Table A.1: Homogeneous Buildings by Race and Income
CategoryGroup

Buildings

Percent

Race/Ethnicity

All
Asian
Black
Hispanic
White

34,547,538
1,013,998
2,198,715
3,936,421
24,203,483

100
3
6
11
70

IncomeAll
Low Income
High Income

34,547,538
14,369,436
19,839,966

100
42
57

Notes: This table shows the number of buildings for we which we have information on race/ethnicity
and income. The “All” rows show that number for all buildings, and other rows show the number ofbuildings that are “nearly homogeneous” (> 67%) for the four racialgroups and two income groups.

such as education, income, race, gender, and age. Each category is reported in discretizedbuckets, and a building is assigned weights across buckets reflecting the share of people
who live in the building who fall into each bucket. For income, we aggregate the provided
bins to low-income (< $75, 000, the bracket cutoff closest to the national median in 2019),
and high-income (> $75, 000). For racial/ethnic categories, we aggregate the provided bins
to non-Hispanic Asian, non-Hispanic Black, non-Hispanic White, Hispanic, and all otherracial/ethnic groups not covered by our study.41
Table A.1 shows the number of buildings that contain information on race/ethnicity
and income. The table also shows the number of buildings that are “nearly homogeneous”(> 67%) for the four race/ethnicity groups and two income groups. 99% of buildings are atleast 67% low- or high-income. 91% of buildings are at least 67% Asian, Black, Hispanic, orWhite.

A.4

Building-level data representativenessIn the main text, we show that the smartphone sample for which we have building level datais spatially representative, meaning for instance, that within a county, we have about thesame number of devices in block groups with a high share of White residents as in blockgroups with a low share of White residents. Here we show that the building-level demographic
data are highly correlated with publicly available Census demographic data when aggregatedto larger spatial units. This exercise shows, for instance, that we have more high-income
41

Specifically, Asian includes “Central and Southwest Asian”, “Far Eastern”, “Southeast Asian”, Black
includes “African American”, White includes “Eastern European”, “Jewish”, “Mediterranean”, “Scandinavian”, “Western European”, Hispanic includes “Hispanic”, and the remaining groups not covered by ourstudy are “Middle Eastern”, “Native American”, “Other” and “Polynesian”.

Appendix – 3

devices in the building-level data relative to the census. Figure A.1 compares four countylevel demographic shares in the building-level data to those in the 2015-2019 American
Community Survey (ACS): share of non-Hispanic Black residents, share of non-Hispanic
White residents, share of Hispanic residents, and share of residents whose household income
is less than $75,000. The two county-level measures are highly correlated: the R2 exceeds
0.81 for all four demographic shares. Given Panel B of Figure 1 showing that the smartphonesample with building demographics is broadly spatially representative across block groups
within counties, the gaps between observations and the 45-degree line in Figure A.1 largely
reflect differences within, rather than across, block groups.
Overall, we find that aggregating the building-level demographic information to counties
yields more White and high-income households than found in the Census data. Figure A.1
shows that regressing the share of high-income households in the Census on the share of highincome households in the Precisely PlaceIQ building-level data yields a coefficient of 1.36
and an R2 of 0.84. Similar regressions using share of Black, Hispanic, and White households
yield coefficients of 0.84, 0.87, and 0.89, and R2 s of 0.81, 0.97, and 0.92.
These differences also vary in intensity across counties. The top-left plot of Figure A.1
shows that the share of low-income devices in the building-level data is smaller than theshare of low-income residents in the Census, except in counties with the largest low-income
shares. This means that low-income households are under-represented in the building-level
data, but less so in counties with more low-income households. Finally, the three otherplots of Figure A.1 show that, compared to the Census data, Hispanic households are proportionally represented while White households are over-represented and Black households
are under-represented in the Precisely PlaceIQ data. We note that the disparities between
demographics in our smartphone sample and Census data are more pronounced for income
than for race. Given these differences and the arbitrary nature of a dichotomous definitionof “high-income”, we avoid reporting income exposure shares in absolute terms. This decision is based on the notion that, although we can identify venues with higher shares ofhigh-income individuals, we may systematically overestimate the income of individuals inthese venues.

A.5

Building level data reliability

We now seek to validate the accuracy of the demographic information in the building-level
data. Specifically, we demonstrate that it reliably predicts differences in behavior between
residents of neighboring houses (i.e., two households living in the same block group). For
example, we find that residents of high-income buildings are more likely than their neigh-Appendix – 4

Figure A.1: Comparison of county-level demographics in Census and building-level data
Share of Households:

Income under $75,000

Hispanic

β = 1.36, R = 0.84

β = 0.87, R = 0.97

.8
PlaceIQ
.4
.6
.2
0

0

.2

PlaceIQ
.4
.6

.8

1

2

1

2

0

.2

.4

.6

.8

1

0

.2

.4

Census

.6

.8

1

.8

1

Census

White

Black

β = 0.89, R = 0.92

β = 0.84, R = 0.81

.8PlaceIQ
.4
.6
.2
0

0

.2

PlaceIQ
.4
.6

.8

1

2

1

2

0

.2

.4

.6

.8

1

0

Census.2.4.6
Census

Notes: These plots compare county-level demographic composition in the 2015-2019 American Community
Survey and that of devices for which we have building-level demographic data. The diameter of each marker
is proportionate to the county’s population in the ACS. The regression coefficient and R2 reports the resultof regressing Precisely PlaceIQ county shares on ACS shares, weighted by the ACS population.

Appendix – 5bors in low-income buildings to visit chains preferred by high-income individuals, such as
Starbucks.
To achieve this, we compare visit behavior patterns between buildings within the same
block group. This exercise establishes that the building-level data is informative at a finerlevel than the most granular demographic data publicly available from the Census Bureau,
which is at the block group level.
Our methodology is based on the idea that building-level demographic differences should
generate observable differences in visit patterns between people of different demographic
groups living in the same block group (and therefore facing the same choice set). Consistent
with this idea, research byWaldfogel (2008) and Klopack (2020) shows that race and income
correlate with heterogeneity in preferences for different types of venues and chains.
These observable differences in behavior predicted using only building-level demographic
data should, in turn, correspond to similar observable differences in behavior predicted using
only demographic information from the Census. Therefore, we compare how across-building
variation in demographics within a block group predicts chain popularity to how across-blockgroup variation in demographics within a tract predicts chain popularity. If demographics
predict chain patronage, we should find similar rankings of chains using these two different
data sources.
We proceed with this comparison as follows. First, we compute the within-block groupchain popularity ranking using only building-level demographic information. For each chain
in each block group, we compute the ratio of the average number of visits by devices living in
high-income buildings (at least 67% of residents earning more than $75, 000) to the average
number of visits by devices living in all other buildings. We then take a weighted averageof this ratio across block groups to obtain a ranking of the chains by popularity with highincome relative to non-high-income individuals.42
Second, we compute the within-tract chain popularity ranking using only Census block42

Let gi be an indicator if device i belongs to demographic group g, and ḡb be the share of devices in blockgroup b belonging to demographic
group g. We weight using the number of devices living in a block group
P(Nb ) and a variance weight ( (gi − ḡb )2 ):i∈bwb = NbX(gi − ḡb )2 .

i∈bAdding the second variance term produces a statistic that exactly matches the ranking of chains producedby the OLS estimate of γcg for each chain:
log yic = γcg zc gi + δcb zc db(i) + ϵibc ,where yic is the number of visits from device i to chain c, gi is again an indicator if device i belongs todemographic group g, zc is an indicator for chain c, and db(i) is an indicator if a device i lives in block groupb.

Appendix – 6

group demographic information. To do this, we compute, for each chain in each census tract,
the average ratio of visits for devices living in block groups that are at least 67% high-income
to visits from devices living in all other block groups. Finally, we compute an analogous setof ratios for visits by White versus non-White devices..
Figure A.2 depicts the results of these comparisons for restaurants and convenience stores& gas stations, the two business categories with the greatest number of chains. Each observation in the plot is a chain in those categories. Plots on the left-hand side show the
relative propensity of high-income devices to visit a chain, and plots on the right-hand sideshows the relative propensity of White devices to visit a chain. The ranking obtained using
only building-level demographic variation within a block group is very similar to the ranking
obtained using only Census demographic information. We find Spearman rank correlations
between 0.7 and 0.9 for restaurant and convenience stores, for both income and race. For
example, both building-level and Census-block-group-level demographic information suggestthat high-income individuals make relatively more visits to Starbucks than Dunkin’ Donuts.
Table A.2 summarizes information on the size of our venue sample for the five largest chain
in each category of establishment (ten largest for restaurants). The table compares the actual
number of establishments in each chain (gathered from various sources including company
websites and investor reports) with the number of establishments in the Precisely PlaceIQ
data. It also reports the total number of visits to each chain. The Precisely PlaceIQ basemap
of venues is close to comprehensive, and contains upward 80 percent of all venues for mostchains, except for gyms where the basemap is less comprehensive.43 Bank and Gym chainsreceive fewer visitors than other categories, so preference estimates are noisier for thesecategories.

A.6

Google Places Data

We use data from Google Places to assign characteristics to restaurant chain venues for therobustness exercise in section 5. The Google Places venue data was collected by Akbar et al.
(2023) in 2019.44 That Google data is available in 98 of the 100 largest MSAs. The Google
data identifies the name of the venue (e.g., McDonald’s), as well as its exact geo-location,
number of reviews, star rating, and price in four categories ($, $$, $$$, $$$$). We match a
Google venue to a Precisely PlaceIQ restaurant chain venue if its location falls within the43

Three chains have more venues in the basemap than were open circa 2019 according to company records.
For Rite Aid and Walmart, this reflects store closures rather than wrongly identified locations. We are lessconfident about why Safeway has more venues in our basemap, but it could reflect rebranding.
44
That data comes from querying one of 109 keywords (like ”restaurants”) on Google Maps, at a large
number of random locations within city boundaries defined by Akbar et al. (2023). The 109 keywords were
designed to capture the universe of possible trip purpose.

Appendix – 7

Figure A.2: Using building-level data to predict visit propensity within block groups
Ratio of high- to low-income visits

Restaurants
Ratio of White to non-White visits

Gas Stations
Ratio of high- to low-income visits
Ratio of White to non-White visits

Notes: The top-left panel depicts visits by high-income devices relative to visits by all other devices forrestaurant chains. The vertical axis is this ratio for devices living in high-income relative to all other blockgroups within the same Census tract. The horizontal axis is this ratio for devices living in high-income
relative to all other buildings within the same Census block group. The top-right panel depicts analogous
ratios for White visits relative to non-White visits. The bottom two panels depict these comparisons using
the “convenience store & gas stations” category rather than restaurants. The largest 20 chains by numberof visits within each category are labeled on each plot. Data on block group income and race is from the2015-2019 American Community Survey. High-income block groups are defined as having at least 67% ofresidents earning more than $75, 000. White block groups are at least 67% White. Data on building-level
income and race is described in this appendix. High-income buildings are defined as having at least 67% ofresidents earning more than $75, 000. White buildings are at least 67% White.

Appendix – 8

Table A.2: Coverage of Chain Venues in PlaceIQ Basemap
Category

Chain

Actual (#)

PIQ (#)(%)

PIQ Visits (M)SourceBankBank Of America
Wells Fargo
Chase
PNC
Citizens Bank

4300
5352
4976
2296
1105

3124
3035
3225
1687
823

73
57
65
73
74

0.68
0.66
0.57
0.51
0.34

®
®
®
®
®

Big BoxWalmartTargetCostcoSams Club
Tractor Supply Co

3947
1868
542
599
1844

4429
1360
505
555
1709

112
73
93
93
93

393.25
66.89
61.00
47.54
18.60

®
®
®
®
®

12846
9267
6339
10830
7892

12635
7712
6061
7633
7742

98
83
96
70
98

280.94204.16163.07
153.78147.02®
®
®
®
®

Grocery

Kroger
Safeway
Ahold Delhaize
Walmart Market
Publix

2757
895
1973
809
1239

2247
1315
1524
692
855

82
147
77
86
69

103.73
35.56
31.50
30.67
28.20

®
®
®
®
®

Gym

Planet Fitness
LA Fitness
Orange Theory Fit.
Anytime Fitness
24 Hour Fitness

1934
623
1150
2469
445

809
472
373
837
380

42
76
32
34
85

0.88
0.69
0.54
0.53
0.39

®
®
®
®
®

Pharmacies

CVS Pharmacy
Walgreens
Rite Aid

9895
9168
2461

8656
8380
2649

87
91
108

187.11135.51
23.40

®
®
®

Restaurant

SubwayStarbucks
McDonalds
ChickFilA
Dunkin’ Donuts
Burger King
WendysTaco Bell
Pizza Hut
Panda Express

24154
15041
13846
2493
9630
7346
5852
6766
7280
2161

21693
10598
13050
2030
7719
6789
5475
6743
6085
1630

90
70
94
81
80
92
94
100
84
75

992.49618.57580.77224.12
218.47153.43139.08138.03129.12101.41

®
®
®
®
®
®
®
®
®
®

Shell
7-Eleven
Convenience Circle K
Exxon
ChevronNotes: This table reports the number of venues within the five largest chains in all commercial
venue categories: “Bank”, “Big Box”, “Convenience Stores & Gas Stations”, “Grocery”, “Gym”,“Pharmacies”, and ten largest chains for “Restaurant”. The ‘Actual (#)’ column reports the number ofU.S. chain locations as reported in various sources, which are hyperlinked in the last ‘Source’ column.
The ‘PIQ (#)’ column reports the total number of venues in the PlaceIQ basemap,including those
excluded from the estimation sample. The ‘PIQ Visits (M)’ column reports all visits to the chain
between June 1, 2018 through December 31, 2019, by all devices in PlaceIQ, in millions.

Appendix – 9

Precisely PlaceIQ polygon for that venue (enlarged by a factor 1.3 to account for uncertainty
in the exact locations of establishments) and if the name of the chain matches. 53 percent
of restaurant chain venues in Precisely PlaceIQ correspond to a Google establishment. In 86
percent of these cases Precisely PlaceIQ and Google establishment agree that the same chain
is at the location. This match rate reflects in part the fact the city boundaries in Akbar
et al. (2023) are smaller than those of MSAs, but also that the Google Places venue sample
in Akbar et al. (2023) does not capture all venues available on Google Places.

A.7

National Household Travel Survey Data

We use the 2017 NHTS (USDOT, 2017) to identify MSAs with large shares of consumption
trips by car for the robustness exercises in section 5. We match each NHTS household to our
MSA boundary using their county of residence.45 We only keep individuals aged 18 and overin the sample. We define consumption trips as including all trips with NHTS trip purpose
code 11 (Buy goods: groceries, clothes, appliances, gas), 12 (Buy services: dry cleaners,banking, service a car, pet care), and 13 (Buy meals: go out for a meal, snack, carry-out).
We only keep the 92 MSAs (out of the 100 largest MSAs) with at least 100 consumption
trips in the NHTS. We define ‘car’ trips as all trips with NHTS transport mode code 3 (Car),
4 (SUV), 5 (Van) and 6 (Pick Up Trucks). We then use NHTS trip-level weight to computethe share of consumption trips by car. Among the MSAs with more than 100 consumption
trips, there are 56 MSAs in which more than 90% of consumption trips are by car and 14
MSAs in which more than 95% of consumption trips are by car.

B

Appendix Tables and Figures

B.1

Patterns of demographic exposure

45This geographic matching requires the confidential geo-coded version of the NHTS. We thank Gilles
Duranton, who has access to it, for producing this list of MSAs for us.

Appendix – 10

Table B.1: Mean exposure to own-race co-patrons
Low Income
Asian Black Hispanic
(1)
(2)
(3)

High Income
White Asian Black Hispanic
(4)
(5)
(6)
(7)

White
(8)

Estimation sample
0.08
0.27
0.25
0.08
0.08
0.19
0.13
0.11
All chain-restaurant visits
0.08
0.23
0.24
0.07
0.08
0.17
0.14
0.10
All chain-venue visits
0.09
0.25
0.24
0.07
0.09
0.18
0.14
0.09
All non-residence venue visits
0.15
0.30
0.32
0.02
0.15
0.21
0.19
0.05
All McDonald’s restaurant visits 0.08
0.26
0.26
0.08
0.08
0.20
0.16
0.11
Census tracts
0.16
0.32
0.30
0.13
0.18
0.24
0.23
0.13
Notes: This table is analogous to Table 1 but reports own-race exposure instead of high-income
exposure. The table reports, for different visit samples, the share of own-race co-patrons that eachdemographic groups (eight columns) is exposed to, relative to a baseline in which all venues in thatsample are visited with uniform probability. The first row shows those own-race shares for visits in theestimation sample. The second through fourth rows shows those shares for broader visit samples. The
fifth row shows those shares only for visits to McDonald’s restaurants. In the sixth row, those sharesare computed as if each Census tract is a venue and individuals only visit the census tract that they
live in.

Appendix – 11

Table B.2: Mean exposure to own-race co-patrons
Low Income

(1)
(2)

Uniform within MSA
Uniform within MSA-chain pairWithin MSA-chain nests(3) Residential proximity(4) Demographic preferences only
(5)
Income preferences only
(6)
Race preferences only
(7) Model-predicted visits

High Income

Asian Black Hispanic
(1)
(2)
(3)

White
(4)

Asian Black Hispanic
(5)
(6)
(7)

White
(8)

0.03
0.03

0.06
0.08

0.15
0.16

0.05
0.05

0.03
0.03

0.06
0.06

0.11
0.11

0.03
0.05

0.06
0.06
0.03
0.06
0.08

0.25
0.11
0.07
0.10
0.27

0.25
0.18
0.15
0.17
0.25

0.07
0.08
0.06
0.08
0.08

0.06
0.06
0.03
0.06
0.08

0.18
0.08
0.04
0.11
0.19

0.13
0.11
0.09
0.12
0.13

0.10
0.09
0.07
0.08
0.11

(8) Estimation-sample visits
0.08
0.27
0.25
0.08
0.08
0.19
0.13
0.11
Notes: This table is analogous to Table 3 but reports mean exposure to own-race co-patrons. It
shows the average share of own-race co-patrons an individual of each group would be exposed to under
different counterfactual visit scenarios. See notes to Table 3 for details.

Appendix – 12

B.2

Determinants of demographic exposure
Figure B.1: Preference heterogeneity for non-White visitors
Asian visitors by residential tract’s own-race tercile
WTT for High-Income ShareWTT for Own-race Share

Asian visitors by residential tract’s high-income tercile
WTT for High-Income ShareWTT for Own-race Share

Notes: This figure is analogous to Figure 5. except that it shows preference estimates for Asian, Black,and Hispanic visitors. Residential tract high-income terciles are defined using tract-level high-income shareweighted by high-income tract population. Same for own-race terciles. The terciles for high-income residents
are consistent across 8 demographic groups. Continus onto next page.

Appendix – 13

Spatial Heterogeneity. contBlack visitors by residential tract’s own-race tercile
WTT for High-Income ShareWTT for Own-race ShareBlack visitors by residential tract’s high-income tercile
WTT for High-Income ShareWTT for Own-race ShareAppendix – 14

Spatial Heterogeneity. contHispanic Visitors by residential tract’s own-race tercile
WTT for High-Income ShareWTT for Own-race Share

Hispanic Visitors by residential tract’s high-income tercile
WTT for High-Income ShareWTT for Own-race ShareAppendix – 15

Figure B.2: Movers Result Across Own-Race Terciles: High income preference
Moves from Bottom Own-Race Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Middle Own-Race Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Top Own-Race Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

g,odNotes: This figure is analogous to Figure 6 except it shows estimates for high-income co-patrons βykinequation (8).

Appendix – 16

Figure B.3: Movers Result Across High-Income Terciles: High income preference
Moves from Bottom High-Income Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Middle High-Income Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Top High-Income Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Notes: This figure replicates the exercise in 6.2.2 for moves between income terciles. It is analogous tog,odFigure 6 except it showing estimates over high-income co-patrons βyk
in equation (8) for cross-MSA moves
between income terciles. Residential tract high-income terciles are defined using tract-level high-income
share weighted by high-income tract population.

Appendix – 17

Figure B.4: Movers Result Across High-Income Terciles: Own-race preference
Moves from Bottom High-Income Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Middle High-Income Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Moves from Top High-Income Tercile
To Bottom Tercile

To Middle Tercile

To Top Tercile

Notes: This figure replicates the exercise in 6.2.2 for moves between income terciles. It is analogous tog,odFigure 6 except it showing estimates over own-race co-patrons βrk
in equation (8) for cross-MSA moves
between income terciles. Residential tract high-income terciles are defined using tract-level high-income
share weighted by high-income tract population.

Appendix – 18

Table B.3: Average Preferences of High-Income White Individuals Before & After Move
Across Income Terciles
O1

O2

O3

D1

D2

D3

D1

D2

D3

D1

D2

D3

Pre-Move

0.58(0.16)

0.12(0.21)

0.01(0.26)

1.42(0.12)

1.23(0.17)

1.10(0.21)

2.36(0.13)

2.30(0.17)

2.61(0.19)

Post-Move

0.09(0.15)

1.11(0.13)

1.78(0.14)

0.43(0.18)

1.03(0.14)

2.01(0.15)-0.45(0.20)

1.29(0.17)

2.37(0.16)

Notes: This table is analogous to Table 4 except it shows estimates for moves between income terciles.
g,od
The table reports pooled estimates of βyk
from equation (8). For each origin-destination pair, coefficientsare pooled for event-months prior to move (t = −5 to t = −1), and post-move (t = 0 to t = 5). The
estimation samples contain home-venue-home visits to restaurants by high-income White individuals who
move between MSAs, split by the high-income tercile of the origin residence (with O1 being the origin tercile
with the lowest high-income share) and the high-income tercile of the destination residence (with D1 being
the destination tercile with the lowest high-income share).

Appendix – 19

B.3

Robustness checks
Figure B.5: Robustness to different categories

Notes: This figure presents preference estimates over co-patron composition using the linear shares
specification from Equation (7) for each demographic group and by business category. Preferences are
expressed in willingness to travel in kilometers relative to the average venue, ∆g (sownrace , shighinc ), as
defined in equation (3). Each point represents the coefficient on the share of high-income co-patrons(left) or share of own race co-patrons (right) for a specific chain category.

Appendix – 20

Figure B.6: Robustness to standardized chains categories

Notes: These figures are analogous to Figure B.5 but show results for restaurant chains restrictedby standardization metrics. Preference estimates over co-patron composition are reported from thelinear shares specification from Equation (7). The baseline sample includes preference estimates over
all restaurant chains. “Entirely wholly owned” restricts to chains with 5% or fewer franchised venues;“Almost wholly owned” restricts to chains with 20% or fewer. “CV Rating” limits to the 25% of chainswith the lowest variation in Google Places star ratings. “CV Square Footage” restricts to the 25% withthe lowest variation in square footage reported by PlaceIQ Precisely.

Appendix – 21

Figure B.7: Robustness to adding venue-level controls

Notes: These figures are analogous to Figure B.5 except they add venue-level controls to the baseline
restaurant sample. Preference estimates over co-patron composition are reported from the linear shares
specification from Equation (7). The venue-level coontrols are Google Places star rating, Google Places
number of reviews, and venue square footage as reported by PlaceIQ Precisely.

Appendix – 22

Figure B.8: Robustness to adding neighborhood-level controls

Notes: These figures are analogous to Figure B.5 but they add neighborhood-level controls to thebaseline restaurant sample. Preference estimates over co-patron composition are reported from thelinear shares specification from Equation (7). Residential characteristics include the share of own race
residents and share of high income residents in the census tract where each venue is located. Visitor
characteristics include the share of own race co-patrons and share of high-income co-patrons to allother commercial venues within the census tract where each venue is located. Commercial venues are
identified by PlaceIQ Precisely.

Appendix – 23

Figure B.9: Robustness to transportation mode

Notes: These figures are analogous to Figure B.5 but restrict the set of MSAs in the baseline restaurant
sample by car usage. Preference estimates over co-patron composition are reported from the linear
shares specification from Equation (7). Using data from the 2017 National Household Travel Survey(NHTS), the sample of venues is limited to MSAs where at least 90% or 95% of trips to commercial
venues are by car. For the 90% threshold, 79 MSAs remain, and for the 95% threshold, 23 MSAs
remain.

Appendix – 24

Figure B.10: Robustness to distance specifications

Notes: These figures are analogous to Figure B.5 but report preference coefficients when varying
the specification on distance in the baseline restaurant sample. Preference estimates over co-patroncomposition are reported from the linear shares specification from Equation (7). The baseline quadratic
specification is the same as reported in equation (2). The cubic specification adds a cubic log-distanceterm. The closest venue specification adds a dummy for the establishment within each chain that isclosest to a visitor’s residence.

Appendix – 25