How Segregated Is Urban Consumption?

Donald R. Davis
Columbia University and National Bureau of Economic Research

Jonathan I. Dingel
University of Chicago and National Bureau of Economic Research

Joan Monras
Universitat Pompeu Fabra, Barcelona Graduate School of Economics,
and Centre for Economic Policy Research

Eduardo Morales
Princeton University and National Bureau of Economic Research

We provide measures of ethnic and racial segregation in urban consumption. Using Yelp reviews, we estimate how spatial and social frictions influence restaurant visits within New York City. Transit time plays
a first-order role in consumption choices, so consumption segregation
partly reflects residential segregation. Social frictions also affect restaurant choices: individuals are less likely to visit venues in neighborhoods
demographically different from their own. While spatial and social frictions jointly produce significant levels of consumption segregation, we
find that restaurant consumption is only about half as segregated as
residences. Consumption segregation owes more to social than spatial
frictions.
We thank Jesse Shapiro, four anonymous referees, Treb Allen, David Atkin, PierrePhilippe Combes, Victor Couture, Thomas Covert, Alon Eizenberg, Ingrid Gould Ellen,
Mogens Fosgerau, Manuel Garcia-Santana, Marçal Garolera, Robin Gomila, Joshua Gottlieb,
Jessie Handbury, Art O’Sullivan, Albert Saiz, and many seminar audiences for helpfulcomments. We thank Bowen Bao, Luis Costa, Amrit K. Daniel, David Henriquez, Yan Hu,
Electronically published June 11, 2019
[ Journal of Political Economy, 2019, vol. 127, no. 4]© 2019 by The University of Chicago. All rights reserved. 0022-3808/2019/12704-0006$10.00

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I. Introduction
For half a century, the United States has prohibited ethnic and racial discrimination in housing, jobs, and education. Even so, segregation in eachof these domains remains a stubborn feature of modern America (Hellerstein and Neumark 2008; Boustan 2011). Many studies have documented
these facts and examined their consequences for socioeconomic outcomes(Massey and Denton 1993; Cutler and Glaeser 1997; Chetty et al. 2014).
Discrimination in consumption venues has also been prohibited for decades, yet racial and ethnic segregation in this domain has been studiedmuch less. A major achievement of the civil rights movement was the Civil
Rights Act of 1964, prohibiting discrimination based on race or ethnicity
in public accommodations.1 Jim Crow laws had segregated places where
people meet socially in order to maintain segregation of intimate contact(Myrdal 1944, 588). In contemporary America, these shared spaces havethe potential to form what Anderson (2011) calls “a cosmopolitan canopy,” a place where a diversity of people may interact such that “a cognitiveand cultural basis for trust is established that often leads to the emergenceof more civil behavior” (xv).2 Such social capital has potential consequences
for many economic outcomes (Guiso, Sapienza, and Zingales 2009; Smith
2010). Gone are the days of whites-only lunch counters. Yet we do not know
the degree to which consumption venues are integrated and serve as places
where people of different backgrounds encounter each other in everyday
life (O’Flaherty 2015, 236–37).
We cannot say a priori whether segregation along demographic lines isgreater in consumption or residences. If spatial frictions (i.e., the costs oftraversing the city) were arbitrarily high, then consumers would largely
patronize the businesses closest to their residences, making residential
and consumption segregation very similar. The fact that individuals may
Charlene Lee, Rachel Piontek, Anil Sindhwani, Ludwig Suarez, Shirley Yarin, and, especially,
Kevin Dano, Ben Eckersley, Hadi Elzayn, and Benjamin Lee for research assistance. Thanks
to the New York Police Department, and especially Gabriel Paez, for sharing geocoded crime
data. Dingel thanks the Kathryn and Grant Swick Faculty Research Fund at the University of
Chicago Booth School of Business for supporting this work. This work was completed in partwith resources provided by the University of Chicago Research Computing Center. Monras
thanks the Banque de France Sciences Po partnership. Part of this work is supported by apublic grant overseen by the French National Research Agency (ANR) as part of the Investissements d’Avenir program LIEPP (ANR-11-LABX-0091, ANR-11- IDEX-0005-02). Morales
thanks the University of Wisconsin–Madison and the Cowles Foundation at Yale University for
their hospitality and support. Code and data are provided as supplementary material online.
1
Examples of public accommodations include hotels, restaurants, and entertainment
venues.
2
There is a large social psychology literature on the mechanisms that may reduce intergroup prejudice. Specifically, the literature on the “contact hypothesis” focuses on the potential for intergroup contact to reduce such prejudice (Allport 1954). Study results in thisliterature have generally been consistent with the hypothesis that appropriate intergroupcontact reduces intergroup prejudice (Pettigrew and Tropp 2006), including some laboratory and field experiments (Paluck and Green 2009).

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move around the city for consumption purposes could mitigate the effectof the spatial separation of residences if diverse consumers choose common destinations. Conversely, mobility may allow them to segregate evenmore if their choices diverge. Divergent choices could result from social
frictions, such as aversion to consuming in areas with different population demographics or racial segregation of social networks that influence
choices (e.g., family or friends). Consumers from different backgrounds
may also choose different venues because of differences in tastes. The interactions of spatial frictions, social frictions, and heterogeneous tastes
make the degree of consumption segregation an empirical question.
In this paper, we estimate a measure of consumption segregation along
racial and ethnic lines for the residents of New York City and quantify thecontributions of spatial and social frictions to it. We do so by estimatinga discrete-choice model of restaurant visits using information on morethan 18,000 consumption decisions by individuals living in New York City
who review on Yelp.com, a website on which users review local businesses.
We use our estimated parameters to predict the consumption decisionsof all New York City residents. We find that consumption choices aremuch less segregated than residential locations. Dissimilarity indices contrasting the consumption destinations of different demographic groups
are about half the value of dissimilarity indices for residential locations.
Both spatial and social frictions have quantitatively large influences on
the geography of consumption, with social frictions contributing relatively
more to consumption segregation along ethnic and racial lines.
Inferring spatial and social frictions from consumption behavior requires
controlling for other determinants of consumers’ choices. We exploit several advantages of our data set, described in Section II, to identify thesefrictions. First, we locate Yelp reviewers’ residences and workplaces, allowing us to measure spatial frictions that account for the fact that consumption may originate at home, at work, or on the commute between them andthat both automobile and public transit may be used. Second, we combine
data from Yelp and the US Census Bureau to characterize reviewer demographics, restaurant characteristics, and neighborhood demographics. This
allows us to distinguish demographic differences in tastes from social frictions and to measure the contributions of both individual-level homophilyand demographic differences across neighborhoods to social frictions.
However, our data set is not without limitations. First, Yelp reviewers are
not representative of the general population. In terms of observable characteristics, reviewers in our estimation sample are more likely to be Asianand female. They live in neighborhoods with higher incomes and more
residents between the ages of 21 and 39. We allow our estimates of preference parameters to vary with these observable individual characteristics
but cannot do the same for unobservable characteristics. Our results forthe whole population of New York City residents thus necessarily predict

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the level of consumption segregation that prevails if everyone behaves as
the observationally equivalent reviewers in our estimation sample. Second, we have limited ability to distinguish between Hispanic and white reviewers in our sample, so we cannot separately identify these two groups’
preference parameters. However, we exploit tract-level information on
demographics to capture social frictions between Hispanics and whites.
Third, we observe every review written by Yelp users, not every restaurant
visit. Identifying consumer preferences therefore involves assumptions onreview-writing behavior, which we discuss in Section III.
We model consumers’ behavior using a conditional-logit specificationin which a consumer’s valuation of a restaurant may depend on spatial frictions, social frictions, and a large set of observable characteristics of theconsumer and the restaurant. All preference parameters are allowed tovary flexibly by race. Our estimation procedure makes use of the McFadden (1978) choice set construction technique to address the computationalburden arising from consumers choosing among the thousands of restaurants in New York City.
We present our parameter estimates in Section IV. Our quantification of
consumers’ aversion to incurring longer travel times reveals a first-orderrole for spatial frictions in determining the geography of consumption.
Depending on the origin of the trip and the mode of transport used, halving the minutes of travel time to a venue implies that a consumer would be
two to nearly four times more likely to visit the venue from that origin bythat mode. These spatial frictions will cause consumption patterns to partly
inherit residential patterns of segregation.
Consumption segregation also reflects the influence of social frictions.
These frictions make consumers’ decisions depend on the contrast between
the residential demographics of the restaurant’s location and either the
residential demographics of the consumer’s home location or the consumer’s own racial or ethnic identity. All else equal, a consumer is more
likely to visit a venue in a census tract that is more demographically similar
to her home tract. Individuals are also more likely to visit restaurants intracts with a larger share of residents of their own racial group. While consumption may be integrated de jure, these social frictions make consumption less integrated de facto.
Importantly, our estimates of both spatial and social frictions are obtained after controlling for race-specific tastes for observable features ofrestaurants and areas of the city. For example, we incorporate cuisine category fixed effects. Thus, for example, our finding that Asian consumers
are more likely to visit restaurants (of any type) located in neighborhoods
with more Asian residents is conditional on the fact that Asian consumers
are more likely to visit restaurants serving Asian cuisines. Similarly, we allow consumers’ valuations of restaurant prices and ratings to depend on
the income level of their home census tract and control for income differ-This content downloaded from 128.135.098.037 on July 30, 2019 08:19:12 AM
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ences when estimating social frictions associated with racial demographic
differences. In robustness checks, we introduce restaurant fixed effects thatvary by race and allow for correlation in consumer-specific preferences
across restaurants of similar characteristics in nested-logit specifications.
These specifications yield similar estimates of spatial and social frictions.
Our estimated model fits the data well. Race-specific preference parameters are key to capturing the level of consumption segregation that we
observe in the estimation sample: a specification that assumes that preference parameters are common across all consumers cannot replicate thein-sample isolation of consumers of different races. The specifications
that introduce race-specific restaurant fixed effects yield only very modest
improvements in fit. This is consistent with the fact that there is little segregation of Yelp reviewers between pairs of restaurants that are observationally equivalent.
Using our estimated model of the restaurant visit decision, we computemeasures of consumption segregation for the entire residential population of New York City in Section V. Specifically, we characterize the ethnic
and racial segregation of the predicted consumption choices using dissimilarity indices. A dissimilarity index describes the fraction of the population belonging to a group—Asian consumers, for example—that would
have to alter their consumption choices in order to match the distributionof predicted restaurant choices made by the remainder of the population.
Despite the magnitude of the estimated spatial and social frictions, consumption dissimilarity is notably lower than residential dissimilarity for
all ethnic and racial groups.
To quantify the contribution of spatial frictions, social frictions, and demographic differences in tastes to consumption segregation, we recompute the dissimilarity indices using the consumption decisions predictedby our estimated model when the coefficients capturing the corresponding friction are set to zero. Social frictions make a larger contribution to
consumption segregation than spatial frictions. Eliminating spatial frictions entirely would reduce consumption dissimilarity indices by 8–20 percent, whereas eliminating social frictions would reduce dissimilarity by
22–41 percent.
We also use our estimated model to examine the impact on consumption segregation of counterfactual changes in transportation policy andin the preference parameters determining the degree of social frictions.
Consistent with the modest role of spatial frictions overall, major changes
in transportation infrastructure have only small effects on consumption
dissimilarity. Reductions in social frictions would integrate consumption.
Finally, we use our estimates to measure the welfare consequences ofneighborhood change for incumbent residents in Section VI. Gentrification is associated with changes in both restaurants’ and residents’ characteristics that affect the value of consumption. We compute the changeThis content downloaded from 128.135.098.037 on July 30, 2019 08:19:12 AM
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in welfare that the residents of a census tract in the middle of Harlem
would experience if the surrounding census tracts were to exhibit the residential and restaurant characteristics of high-income, majority-white census tracts of the Upper East Side. We find a significant reduction in thevalue of their consumption opportunities. This is attributable to the increase in social frictions associated with the change in racial demographics. The change in restaurants’ characteristics would have very modest effects on their welfare.
Our findings relate to a recent literature on the geography of urban
consumption. Studies have documented cross-city variation in the tradable goods available for consumption (Handbury 2013; Handbury andWeinstein 2015), and geographic variation in the supply of nontradables
has been posited to shape the relative attractiveness of cities (Glaeser, Kolko,
and Saiz 2001; Schiff 2015). Waldfogel (2007) documents that restaurant
entry in different cuisine categories is correlated with local demographic
composition. This dimension of economic life has grown increasingly important in recent decades.3 Prior studies of the geography of consumption
within the city include Katz (2007), Houde (2012), Couture (2015), andEizenberg, Lach, and Yiftach (2017). Relative to this prior work, we build
a unique data set that combines information on individuals’ home andwork locations, their demographics, and characteristics of the restaurants
they patronize, and we use it to separately identify the effect of spatial andsocial frictions on consumer decisions.
We study urban consumption using online user-generated content,
which is increasingly exploited by social scientists. Among others, Anderson and Magruder (2012) and Luca (2016a) examine Yelp’s effects on restaurant outcomes. Harrison et al. (2014) use information disclosed in reviews to detect outbreaks of food poisoning unreported to New York City
health authorities. Edelman and Luca (2014) infer racial identities fromprofile photos to study discrimination on Airbnb.com. Caetano and Maheshri (2019) document consumption segregation by gender using Foursquare data. Luca (2016b) surveys this growing body of research on usergenerated content and social media.
We contribute to the large literature on social and economic fragmentation related to demographic differences by measuring consumption segregation in restaurants. The prior literature has largely focused on residential segregation, though there are studies documenting the segregation of
workplaces (Hellerstein and Neumark 2008), students’ friendship networks

3
US households’ share of food spending devoted to food prepared away from homegrew from less than 26 percent in 1970 to more than 43 percent in 2012 (US Departmentof Agriculture 2014). Analogously, while the number of daily commuting trips has stayed
relatively constant for decades, trips for social/recreational purposes have steadily grown(Pisarski 2006).

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journal of political economy(Echenique and Fryer 2007), and media consumption (George and Waldfogel 2003; Oberholzer-Gee and Waldfogel 2009). We study racial segregation of consumption in a setting in which consumers travel to consume
and come face-to-face with each other. Everyday encounters between people of different backgrounds in shared public spaces may be a basis forbuilding understanding and tolerance (Anderson 2011), though consumption venues are also sometimes sites of racial and ethnic discrimination(Labaton 1994; Lee 2000; Ayres 2001; Antecol and Cobb-Clark 2008;
Schreer, Smith, and Thomas 2009). We provide the first quantificationof the segregation of these consumption choices.
II. Data
We combine data from Yelp and other sources to estimate our model ofthe restaurant visit decision and compute measures of consumption segregation. Section II.A describes the Yelp data and Section II.B describes
the other sources of data we use. Section II.C presents evidence suggestive of the influence of spatial and social frictions on consumers’ restaurant choices.
A.

Yelp Data

Yelp.com is a website on which users review local businesses, primarily restaurants and retail stores (Yelp 2013). It describes a venue in terms of its
address, average rating, user reviews, and a wide variety of other characteristics. Yelp’s coverage of restaurants is close to comprehensive (seeapp. B.1). In addition to assigning a rating of one to five stars, reviewers
describe their personal experience with the business. Crucial for our purposes is that users sometimes disclose information in their reviews about
their residential and work locations.
We use data on Yelp users who reviewed a New York City (henceforth,
NYC) restaurant venue between 2005 and 2011. As described in detail inappendix B.2, we identify reviewers’ residential and work locations fromthe text of their reviews. We locate reviewers using reviews of all venues,
not only restaurants. Specifically, we first search the text of a large number of reviews for 26 key phrases related to location, such as “close to me,”“block away,” and “my apartment.” Then we read the reviews containing
these phrases to infer whether the reviewer’s home or work is proximate
to the reviewed business. Finally, we estimate the residential and work locations of a reviewer as the average of the latitude-longitude coordinatesof the sets of venues identified as being close to this reviewer’s home andwork locations, respectively. Restricting our sample to users whose reviews
do not reveal a change in residence or workplace within NYC and whose
home and work locations are in census tracts with demographic and in-This content downloaded from 128.135.098.037 on July 30, 2019 08:19:12 AM
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TABLE 1
Estimation Sample and NYC Summary Statistics
Estimation
SampleYelp Rviewers
Reviewer appearance/tract demographics:
Female
Male
Asian
Black
White or Hispanic
Hispanic
White
Reviewer race indeterminate
Home tract characteristics:
Median household income (thousands)
Age 21–39 residents share
Asian isolation index
Black isolation index
White/Hispanic isolation index
Observations

.609
.343
.243
.075
.418

Manhattan
Tracts

NYC
Tracts

.531
.469
.112
.129
.740
.254
.481

.526
.474
.126
.227
.620
.286
.334

73.9
.368
.273
.382
.787
279

55.1
.306
.326
.569
.731
2,110

.264
75.6
.423
.197
.280
.778
440

Note.—This table summarizes characteristics of the 440 Yelp reviewers in our estimation
sample and all census tracts in Manhattan and New York City. Reviewer demographics areinferred from Yelp profile photos. Tract demographics are from the 2010 Census of Population and tract incomes from the 2007–11 American Community Survey. Tracts are weighted
by residential population. Isolation indices are as defined in Massey and Denton (1988).

come information, we obtain an estimation sample of 18,015 reviews written by 440 distinct reviewers.4
Yelp reviewers typically post a profile photo, which we use to infer theirapparent gender and race.5 Mayer and Puller (2008) compare measuresof ethnicity and race inferred from photos with administrative data andfind a high degree of accuracy in partitioning subjects into three racial
groups: Asian, black, and white or Hispanic. Consequently, when inferring each reviewer’s race from her profile picture, we limit ourselves toclassifying individuals into these three groups.
Table 1 reports summary statistics for the 440 reviewers included in ourestimation sample and the broader NYC residential population. Sixtyone percent of estimation sample reviewers are female, and only 5 per4
We could use a considerably larger sample if we required information only on the reviewer’s home location. Table A11 shows that estimates of models that do not require information on individuals’ workplaces are similar whether we use the estimation sample or
the sample of individuals for whom we have information only on home locations. However,this specification underestimates social frictions compared to our specifications that incorporate workplace information.
5
While users may choose “male” or “female” for their gender on their Yelp profile, thisinformation is not publicly displayed. Thus, we classify reviewers on the basis of their gender presentation in their profile photo. Reviewers with profile photos for which we could
not classify the gender (e.g., cartoon graphics, photos of animals) have both male and female dummy variables equal to zero.

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cent of reviewers are of unidentified gender. We could not infer race for26 percent of reviewers. Asian reviewers constitute 24 percent of the estimation sample, while white or Hispanic reviewers are 42 percent of thesample. Asians are thus overrepresented in our sample, as Asian residents
constitute only about 11 percent of the population of NYC. Although
only 10 percent of the reviewers with an inferred race were identified as
black, these individuals wrote more than 1,000 reviews.
Figure 1 depicts the home and work locations of the reviewers in ourestimation sample. Consistent with patterns in the broader populationof NYC, these reviewers’ workplaces are concentrated in Manhattan below Fifty-Ninth Street, while their residences are more dispersed. The average reviewer in our estimation sample lives in a census tract with median household income near $75,600, which is typical of Manhattan but
higher than NYC as a whole ($55,100). The reviewers in our estimation
sample tend to live in census tracts with a share of the population between the ages of 21 and 39 (42 percent) that is higher than that of both
Manhattan (37 percent) and NYC as a whole (31 percent). These patterns are consistent with statements that Yelp’s global user base is younger and higher-income than the population as a whole (Yelp 2013).
Asian and black reviewers in our estimation sample are less residentially segregated than Asian and black residents of NYC as a whole. Table 1
reports “isolation indices” as defined in Massey and Denton (1988),
ok ðpopgk =popg Þ  ðpopgk =popk Þ, where popg is the population of groupg, popk is the population of tract k, and popgk is the population of group

F IG . 1.—Locations of Yelp reviewers in the estimation sample. This figure depicts the distribution of home and work locations of the 440 reviewers in our estimation sample.

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how segregated is urban consumption?

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g in tract k. These characterize the average group g (e.g., Asian) share oftract residents experienced by members of group g. White/Hispanic reviewers in our estimation sample live in census tracts with a white/Hispanic
share of residents that is typical of that experienced by white/Hispanic
residents of NYC. By contrast, Asian and black reviewers in our estimation
sample live in census tracts that have lower Asian and black shares, respectively, than is typical. The isolation indices for Asian and black reviewers in
our estimation sample are about three-quarters of their values for Manhattan residents and half their values for NYC residents as a whole.
Figure A1 displays all the restaurants reviewed by users in our estimation sample. These venues are concentrated in Manhattan below FiftyNinth Street, but our estimation sample contains venues in many partsof NYC. Table A1 summarizes the distribution of reviews across NYC restaurants in terms of venues’ prices, ratings, cuisine types, and boroughsfor both our estimation sample and all Yelp reviewers. The reviewers in
our estimation sample exhibit review patterns similar to those of thebroader Yelp population reviewing NYC restaurants.
B. NYC Transit, Demographic, and Crime Data
To measure spatial frictions, we use car and public transit times between
the centroids of census tracts from Google Maps. In addition to direct
travel from home or work, we compute the additional transit time an individual would incur by incorporating a visit to a venue as part of her commute. Denote the transit time from location x to location y by time(x, y).
For reviewer i living in hi and working in wi, the travel time associated withvisiting venue j in tract kj from her commuting path pi is computed astimeð pi , j Þ 5







1
max time hi , kj 1 time wi , kj 2 timeðhi , wi Þ, 0 ,2where the maximum operator imposes the triangle inequality on transit
times.
To measure social frictions associated with racial and ethnic demographics, we use data from the 2010 Census of Population that describe
each census tract’s residential population in terms of five groups: Asian,
black, Hispanic, white, and other.6 These population counts are depicted
in figure 2.7 Using these data, we measure ethnic and racial differences

6
To be precise, we use the population counts of non-Hispanic Asians, non-Hispanic blacks,
all Hispanics, and non-Hispanic whites to respectively define the groups we call “Asian,”“black,” “Hispanic,” and “white.” The “other” group includes Native Americans, Hawaiians,
other races, and mixed-race categories; it constitutes about 3 percent of the NYC population.
7
This map was inspired by a New York Times 2010 project, “Mapping America: Every City,
Every Block (http://projects.nytimes.com/census/2010/explorer).

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F IG . 2.—New York City population by race or ethnicity, 2010. This figure depicts the residential NYC population in terms of four demographic categories that cover 97 percent of
the population. Each dot represents 200 people. Tract-level population data are from the2010 Census of Population.

between two tracts as the Euclidean distance between the vectors containing the two tracts’ five residential population shares. Specifically, defining
sharestract as the five-element vector containing these population shares,
the “Euclidean demographic distance” (henceforth, EDD) between origin and destination tracts is

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how segregated is urban consumption?

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pﬃﬃﬃ
k sharesorigin 2 sharesdestination k = 2,
where ∥⋅∥ indicates the L2 norm. This measure ranges from zero to one.
Figure 3 illustrates the EDD for two origin tracts and many destination
tracts. The Morningside Heights origin in the left panel has a diverse population that is similar to that of most NYC tracts, and thus its EDD to mostdestinations is low. The Manhattan Chinatown origin in the right panel isoverwhelmingly Asian and thus quite demographically distant from mosttracts, with the exception of the Flushing Chinatown in Queens.
To allow social frictions to depend not only on the demographic composition of the tract in which the restaurant is located but also on the surrounding demographic composition, we calculate the Echenique andFryer (2007) spectral segregation index (henceforth, SSI) for the modal
residential race or ethnicity in each census tract. This index measuresthe degree to which a census tract borders census tracts of the same residential demographic plurality and the further degree to which thosetracts themselves border tracts of the same plurality, ad infinitum. For example, in figure 2, the black census tracts at the center of the cluster ofblue dots in Queens, on the right edge of the map, will have higher SSI
values than those at the edge of the cluster.

F IG . 3.—Euclidean demographic distances from two census tracts. These maps depict EDD
from an origin tract to other NYC tracts. In the left panel, the origin tract is in Morningside
Heights; in the right panel, in Manhattan’s Chinatown. Demographic data are from the 2010
Census of Population.

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We also allow income and crime levels in a restaurant’s tract to influence consumer choices. The data on median household incomes comefrom the 2007–11 American Community Survey 5-Year Estimate. To measure crime rates by location, we compute tract-level robbery statistics for
2007–11 using confidential, geocoded incident-level reports provided to
us by the New York Police Department.8 We use robberies as our crime
measure because these are likely the most common and relevant threat
to individuals visiting a restaurant. All tract-level characteristics are summarized in tables 1 and A2.
C. Observed Behavior and Frictions
Individual users’ reviews suggest that both proximity and venue characteristics influence their behavior. Figure 4 maps home, work, and restaurant review locations for two individuals in our sample. The reviewer inthe left panel lives and works in midtown Manhattan. The other reviewer
works in midtown Manhattan and resides in a southeastern Manhattan
development called Stuyvesant Town. Both individuals primarily review
venues that are near their home or work locations. At the same time,
both reviewers visit more downtown venues than uptown venues, which
may reflect differences in the quantity or quality of venues in these areas.
The choices made by all reviewers in our estimation sample suggest
the importance of spatial and social frictions. Figure 5 plots, for all reviewers in our estimation sample, the density of three covariates for theset of venues they reviewed and for a random sample of venues that they
did not review. The top-left panel depicts transit times from home, thetop-right panel transit times from work, and the bottom panel the EDD
between the home census tract and the venue’s tract. The plots show that,
unconditionally, Yelp reviewers are more likely to review restaurants thatare closer to their residential and workplace locations and located in tracts
with demographics more similar to those of their home tract.9
III. Empirical Approach
To measure the relative importance of tastes and spatial and social frictions in determining the restaurant choices of consumers of different
races or ethnicities, we introduce a discrete-choice model of restaurant
visits. Section III.A describes the assumptions we impose on consumers’
preferences. Since we observe restaurant reviews, not all restaurant visits,
8
Fewer than 3 percent of the Yelp reviews in our estimation sample were posted outsideof 2007–11.
9
The fact that both reviewed and unreviewed venues have shorter travel times fromwork than from home reflects the fact that most venues and workplaces are in Manhattan.

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F IG . 4.—Two reviewers’ locations and restaurant reviews. These two maps display two reviewers’ home and work locations and the Yelp restaurant venues they reviewed. Dots denote Yelp venues reviewed by this user. The H denotes the average coordinates of those venues identified as home locations in the text of this user’s reviews. The W denotes the similarly
defined work location.

assumptions on the review-writing behavior of Yelp reviewers are necessary for identification of their preferences. Section III.B introduces theseassumptions. Section III.C describes the steps we follow to estimate themodel using the data introduced in Section II. In Section III.D, we introduce several extensions that relax the key identifying assumptions in ourbaseline model.
A.

Demand Specification

Individuals decide whether to visit any venue and, if they do, which venue
to visit. We index individuals by i, venues by j, and by t the occasions on
which i needs to decide on whether to visit a venue. In our empirical application, we assume that the set J of potential venues that a consumer may
visit is the set of all NYC restaurants listed on Yelp and located in a census
tract for which information on residents’ median income is available.10 We
denote the outside option of not visiting any venue by j 5 0.

10Specifically, we restrict the set of restaurants to only those with price and rating information listed on Yelp in June 2011, when we collected our data.

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F IG . 5.—Travel times, demographic differences, and consumer choice. These plots are
kernel densities for three distributions of reviewer-venue pairs: those venues chosen by reviewers in our estimation sample and a random sample of venues not chosen by these reviewers. The top-left panel plots the densities of travel time from home by public transit;
the top-right panel shows travel time from work by public transit; the bottom panel shows
EDDs. These plots use Epanechnikov kernels with bandwidths of 3, 3 and 0.1, respectively.

When visiting a venue, individuals must choose whether to visit it from
home, from work, or by deviating from their commuting path andwhether to travel via public transit or car. We index pairs of origin locations and transportation modes by l and assume that a trip to a venue
may be one of six types: from home via car (l 5 hc), from home via public transit (l 5 hp), from work via car (l 5 wc), from work via public transit(l 5 wp), from the commuting path via car (l 5 pc), or from the commuting path via public transit (l 5 pp). We denote the set of these six potential origin-mode pairs as L ; fhc, hp, wc, wp, pc, ppg.
We allow preferences for restaurants, trip origin locations, and transportation modes to differ across racial or ethnic groups. We index
groups by g, which may take three values: white or Hispanic (g 5 w),
Asian (g 5 a), and black (g 5 b). We denote the set of these three potential groups as G ; fw, a, bg.
For an individual i belonging to the racial or ethnic group g(i), we assume that her utility of visiting restaurant j on occasion t from originmode l is
Uijlt 5 g1g ði Þl Xijl1 1 g2g ði Þ Xij2 1 b1g ði Þ Zj1 1 b2g ði Þ Zij2 1 nijlt ,(1)This content downloaded from 128.135.098.037 on July 30, 2019 08:19:12 AM
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how segregated is urban consumption?

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where Xijl1 measures the spatial frictions that i incurs when visiting j from l,the vector Xij2 measures the social frictions that may affect the appeal that
restaurant j has to individual i, and Zj1 and Zij2 control for other observed
venue and individual-venue-specific characteristics, respectively. Specifically, the variable Xijl1 is the log of the number of minutes it takes individual
i to travel to restaurant j using the origin-mode pair indexed by l.11 The vector Xij2 contains the EDD and SSI measures introduced in Section II andthe residential population share of each racial and ethnic group in the restaurant’s tract. The venue characteristics in Zj1 are the restaurant’s priceand Yelp rating, the log median household income of the tract in which
the venue is located, 28 area dummies, and nine cuisine dummies.12 Finally,
the vector Zij2 includes the restaurant’s price and Yelp rating interactedwith the reviewer’s home census tract’s median household income, as wellas the percent difference and absolute percent difference in median incomes between the home and restaurant tracts.
The variable nijlt is a scalar unobserved by the econometrician. We allow all preference parameters (g1, g2, b1, b2) to vary across demographic
groups g, and since the coefficient g1 is l specific, we additionally allow
the marginal disutility of a trip to flexibly depend on both its origin
and the mode of transit.
Although our data set describes reviewers’ home and work locations, itdoes not indicate the origin-mode l of each trip. We address this data limitation by assuming that consumers jointly optimize the restaurant they
patronize and the origin-mode from which they do so, choosing thus thejl combination that maximizes their utility. Accordingly, defining adummy variable dijlt that equals one if individual i travels to venue j fromorigin-mode l at period t, we assume that(2)dijlt 5 1 Uijlt ≥ Uij l t ; 8 j 0 ∈ J , l 0 ∈ L ,0 0

where 1{A} is an indicator function that equals one if A is true. We also
define a variable dijt that is one if individual i chooses venue j at periodt, dijt 5 ol∈L dijlt , irrespective of the origin-mode of the trip.
In our benchmark specification, we assume that the vector of unobserved utilities for individual i at period t, nit 5 fnijlt ; 8 j ∈ J , l ∈ Lg,
is independent across individuals and time periods and has a joint typeI extreme value distribution: its cumulative distribution function is11
The disutility of travel time, g1g ðiÞl , may vary with l because the direct pecuniary cost ofan additional minute of travel time differs across modes of transportation (positive for taxis,
zero for the subway). These coefficients may also vary because of heterogeneity acrosstransportation modes or origin locations in nonpecuniary costs (e.g., cleanliness or convenience).
12
The restaurant’s price is captured by dummy variables corresponding to Yelp’s four
price categories. The area dummies are aggregates of NYC community districts, and thenine cuisine dummies aggregate Yelp’s more detailed cuisine categories; see app. B.3.

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journal of political economy

F ðnit Þ 5 expð2oj∈J ol∈L expð2nijlt ÞÞ. This distribution yields a conditionallogit discrete-choice model of restaurant visits.
B. Review-Writing Behavior
Let dijt* be a dummy variable that equals one if individual i writes a review
of restaurant j at time t. The fact that we observe reviews rather than restaurant visits (i.e., we observe dijt* but not dijt) implies that estimating thepreference parameters in equation (1) requires making assumptions on
the review-writing behavior of Yelp reviewers. We impose three assumptions. First, users do not review restaurants they have not visited. Second,
they write at most one review per restaurant (independently of how
many times they visit a restaurant). Third, conditional on having visiteda restaurant and not having previously reviewed it, they write a review
with a probability pit* that is independent of the restaurant’s characteristics and the origin-mode of the trip.
C. Estimation Procedure
We estimate the preference parameters in equation (1) using a maximum likelihood estimator. To derive the relevant likelihood function,
we implement the following five steps. Additional details of the mathematical derivations appear in appendix C.1.
Step 1: Derive restaurant visit probability. According to the assumptionsin Section III.A, the probability that individual i visits venue j from originmode l at period t is(3)P ðdijlt 5 1jXi , Zi , J ; ðg, bÞÞ 5 expðVijl Þ=o o expðVij l Þ ,0 0

j 0 ∈Jl 0 ∈L

where Xi, Zi, g, and b are vectors that collect their respective terms and13
Vijl ; g1g ði Þl Xijl1 1 g2g ði Þ Xij2 1 b1g ði Þ Zj1 1 b2g ðiÞ Zij2 :(4)

The probability that individual i visits venue j at period t is thenP ðdijt 5 1jXi , Zi , J ; ðg, bÞÞ 5 P

od 5 1jX , Z , J ; ðg, bÞ
ijlt

i

i

l∈L

5 oP ðdijlt 5 1jXi , Zi , J ; ðg, bÞÞ
l∈L

(5)



5 o expðVijl Þ=o o expðVij l Þ :
0 0

l∈L

j 0 ∈Jl 0 ∈L13
Formally, Xi ; fðXijl1 , Xij2 Þ; 8 j ∈ J , l ∈ Lg, Zi ; fðZj1 , Zij2 Þ; 8 j ∈ J g, g ; fðg1gl , g2g Þ; 8 g ∈G,l ∈ Lg, and b ; fðb1g , b2g Þ; 8 g ∈ Gg.

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how segregated is urban consumption?

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The first equality applies the definition of dijt, the second one takes into
account that the origin-mode of a trip is unique (i.e., the joint probability that individual i visits restaurant j at period t from two different origins l and l 0 is zero), and the third equality uses equation (3).
Step 2: Derive restaurant review probability. Equation (5) and the reviewwriting model described in Section III.B imply that the probability of observing a review of venue j written by individual i at period t is
P dijt* 5 1jXi , Zi , Jit , Jit0 ; g, b, pit*

5 pit* 1f j ≠ 0, j ∈ Jit0 gP dijt 5 1jXi , Zi , J ; ðg, bÞ ,(6)

where Jit0 denotes the set of restaurants not previously reviewed by i, that
is, Jit0 ; f j ∈ J : dijt* 5 0 for all t 0 < t and j ≠ 0g.14 Combining equations (5)and (6), we can derive the probability that individual i reviews restaurant
j at period t conditional on i reviewing any restaurant at that period:
P dijt* 5 1jdit* 5 1, Xi , Zi , Jit0 ; ðg, bÞ (7)
1f j ≠ 0, j ∈ Jit0 gol∈L exp Vijl
,
5oj∈J ol∈L exp Vij l
0

0
it

0

where dit* 5 oj51 dijt* is a dummy variable that equals one if i writes a review
at t.
Step 3: Reduce choice set. The cardinality of the choice set Jit0 makes it
computationally burdensome to construct the denominator of the probability in equation (7). As Jit0 equals the set of all restaurants in NYC, J,
minus those reviewed by individual i prior to period t, the large dimensionality of J implies that the set Jit0 will also be very large.
To address this dimensionality issue, we adapt the choice set reduction
procedure from McFadden (1978) to our empirical setting. For every individual i and period t in which we observe a review written by i, we define a set Sit that is a subset of Jit0 . We construct Sit by including the restaurant j for which dijt* 5 1 plus a random subset of all other alternatives in
Jit0 , selecting them from Jit0 with equal probability. As all elements of Sit
other than the actual choice of i at t are selected randomly, the set Sit
itself is random. We denote by pðSit jdijt* 5 1, Jit0 Þ the probability of assigning the set Sit to an individual i who reviewed venue j at t. Our sampling
scheme implies thatJ

14
As reflected in eq. (6), if individuals were to review every restaurant they visit for the
first time, pit* 5 1, the probability of observing a review would equal either the probability
in eq. (5) (for any venue j that user i has not previously visited) or zero (for any previously
visited venue).

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1702

journal of political economy(

kit if j ∈ Sit

pðSit jdijt* 5 1, Jit0 Þ 5(8)0otherwise,

where kit ∈ ð0, 1Þ is a constant determined by our choice of the number ofvenues in Sit and the number of venues in Jit0 .
Given equations (7) and (8), we can write the probability that i reviews
restaurant j at period t conditional on a randomly drawn set Sit and that iwrites a review at t as
 
1f j ∈ Sit gol∈L exp Vijl**
  : (9)P ðdijt 5 1jdit 5 1, Xi , Zi , Sit ; ðg, bÞÞ 5
oj ∈S ol∈L exp Vij l
0

0

it

Importantly, to be able to randomly draw a set Sit from the set of nonreviewed restaurants Jit0 , one needs to observe all reviews previously written by user i.
Step 4: Derive individual i–specific likelihood function. Using jit to denote
the restaurant reviewed by individual i at period t, the joint probability ofobserving an individual i writing the Ti reviews f ji1 , ji2 , : :: , jiT g conditional on observing a review written by i in each of the periods {1, ... , Ti}
and on randomly drawing the sets fSi1 , Si2 , ::: , SiT g is
i

i

T
Y
1f j ∈ Sit g
i

t51



o



exp Vijl  :oj ∈S ol∈L exp Vij l
l∈L

(10)

0

0

it

This joint probability is simply the product of the corresponding marginal probabilities in equation (9). Intuitively, all the dynamic effects
of a review written by individual i at period t are reflected in the subsequent choice sets f Jis0 , 8 s > tg, and the effect of each of these choice sets
is subsumed in the randomly selected subsets fSis , 8 s > tg.
Step 5: Derive the log-likelihood function. Given equation (10) and assuming that we observe a random sample i 5 1, :: : , N of individuals fromthe population of interest, we can write the log-likelihood function as
  !
N T
l∈L exp Vijl
o
*
(11)
o
oo1fdijt 5 1g ln oj ∈S ol∈L expVij l  ,i51 t51j∈S
i

it

0

0

it

where Vijl is the function of the parameter vector of interest (g, b) defined in equation (4).
All the estimates presented in Section IV are computed as the values of(g, b) that maximize the function in equation (11). As proved in McFadden (1978), these maximands are consistent estimators of the preference
parameters defined in equation (1).15 Specifically, the fact that we maxi15
Appendix C.2 presents simulation results that illustrate the asymptotic properties ofour estimator. Section IV.D illustrates its finite-sample properties.

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how segregated is urban consumption?

1703

mize a likelihood function that conditions on the randomly chosen sets {Sit;8i, t} does not affect the consistency of our estimator. However, the varianceof this estimator decreases as we increase the cardinality of each set Sit.
While our benchmark estimates use sets {Sit; 8i, t } with 20 restaurants each,tables A4–A6 show that our conclusions are robust to using sets that include 50 or 100 restaurants. In practice, larger choice sets incur greater
computation times without appreciably shrinking our standard errors.
D. Discussion
The baseline model described in Sections III.A and III.B embeds several
key identifying assumptions. In this section, we discuss why we impose
these assumptions, what they imply in our empirical context, and how
we relax them in several extensions to this baseline model.
Absence of race-specific preferences for restaurants’ unobserved characteristics.—Except for the vector of idiosyncratic errors nit, the utility function
in equation (1) assumes that consumers’ preferences exclusively dependon observed restaurant characteristics (see Sec. IV for a complete description of the covariates (Xi, Zi) entering our demand model). However, individuals from different racial groups may have heterogeneous
preferences for restaurants on the basis of characteristics that we do
not observe. For example, group g consumers may prefer a specific venue
because this venue is frequently patronized by other customers belonging to the same group g. To explore the robustness of our baseline results
to the presence of unobserved venue characteristics that determine consumers’ preferences, we generalize the utility function in equation (1) toallow for race- and restaurant-specific unobserved effects:
Uijlt 5 g1g ði Þl Xijl1 1 g2g ði Þ Xij2 1 b1g ði Þ Zj1 1 b2g ði Þ Zij2
(12)

1 oag ðiÞj 1f j 0 5 jg 1 nijlt ,0

j 0 ∈J

where agj captures the group g–specific component of the utility of visiting restaurant j that is determined by unobserved characteristics.16
16
Incorporating the set of fixed effects fagj ; 8 g ∈ G, j ∈ J g significantly increases thedimensionality of the parameter vector we estimate. To explore computational costs, wehave estimated this specification in two ways. First, we use a procedure similar to that inSec. III.C but with two adjustments: (a) we add the term oj ∈J ag ðiÞj 1fj 0 5 jg to the expression for Vijl in eq. (4); (b) for each individual i and period t, we form the set Sit by drawing
only from those restaurants reviewed by at least one sample reviewer that belongs to thesame group g as individual i. Importantly, as Train (2009, chap. 3, sec. 7) discusses, while
the procedure to sample restaurants described in adjustment b implies that our estimatesof the restaurant fixed effects are biased, it does not affect the consistency of our estimatesof the spatial and social frictions. Second, the Poisson approximation to the conditionallogit model is described in Taddy (2015) and implemented in Gentzkow, Shapiro, and00This content downloaded from 128.135.098.037 on July 30, 2019 08:19:12 AM
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1704

journal of political economy

Independence of irrelevant alternatives.—Our baseline model assumes
that all unobserved determinants of the utility to individual i of patronizing venue j at period t from origin-mode l, as captured in the scalar nijlt,
are independent across venues, possible origins of the trip, and modes oftransport. To illustrate the robustness of our baseline estimates, we relax
this independence assumption in multiple directions.
First, in appendix C.3, we introduce an alternative model in which we
assume that nijlt does not vary with the origin-mode l; that is, nijlt 5 nijl t forany pair (l, l 0 ). An implication of this alternative model is that, conditional
on visiting a restaurant j, every individual i travels to it using the origin andmode of transport that maximizes the term g1g ðiÞl Xijl1 . As discussed in Section III.A, the covariate Xijl1 equals the (log) number of minutes that it
takes individual i to reach venue j from the origin-model l; thus, assumingthat nijlt 5 nijl t and g1g ðiÞl 5 g1g ðiÞl < 0 for any pair (l, l 0 ) is equivalent to assuming that individuals always choose the origin-mode pair that minimizes travel time to each venue.
Second, in Section IV.E.2, we present estimates of nested-logit models
that allow for correlation in the unobserved terms nijlt across restaurants jand origin-modes l. Specifically, we allow the terms nijlt to be correlated
across restaurants that share a number of characteristics. Following Train,
McFadden, and Ben-Akiva (1987), appendix C4 describes how we adapt
the estimation procedure in Section III.C to these nested-logit demandmodels.
Absence of within-group unobserved parameter heterogeneity.—One additional
limitation of the conditional-logit model described in Section III.A is thatit does not allow for within–group g unobserved heterogeneity in the parameters capturing preferences for observable restaurant characteristics. The standard approach to do so is to assume that individual-specificpreferences follow a known distribution in the population of interest. In
our setting, estimating such a model is infeasible: unobserved heterogeneity in the vector (g, b) makes the choice set construction procedure in
McFadden (1978) inapplicable and, therefore, requires estimating a likelihood function that, for each individual i and period t in our sample, depends on the actual choice set Jit0 . This is computationally infeasible in acity with thousands of restaurants like NYC.17
While we do not allow for within–group g unobserved heterogeneity inpreferences, the utility function in equation (1) does allow preferences000

Taddy (2019). In our setting, this approximation is exact if all individuals in the sample
have equal expected utility from each restaurant trip. The results from these two estimation procedures are described in Sec. IV.E.1 and app. D.3, respectively.
17
Katz (2007) and Pakes (2010) show that there is a moment inequality approach thatallows one to handle both large choice sets and unobserved heterogeneity in preferences
for observed choice characteristics. We discuss in app. C.5 the relative advantages and disadvantages of this moment inequality approach for our particular application.

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how segregated is urban consumption?

1705

to vary within groups with the observed characteristics of the home andwork census tracts of each individual. Namely, Xij2 and Zij2 contain interactions of individual i and restaurant j characteristics. For example, weallow individuals living in tracts of different income levels to value restaurants’ prices and ratings differently.
Exogeneity of home and work locations.—Section III.A implicitly assumes
that individuals’ home and work locations are exogenously given. In
practice, individuals choose where to live and work, and these locations
may be determined as a function of restaurant characteristics. However,
the endogenous location of home and work will not bias our estimates ofthe preference parameters (g, b) if the distribution of the vector of unobserved characteristics affecting individuals’ restaurant choices, nit, is
independent of the characteristics determining the optimal selectionof home and work location. Note that this is compatible with the vectorof observed characteristics (Xi, Zi) affecting individuals’ endogenous home
and work locations.18
Restaurant and origin independence of review-writing probabilities.—As described in Section III.B, our baseline model assumes that the probability
that an individual writes a review about a visited restaurant does not depend on the restaurant itself nor on the origin of the trip. This allows thereview-writing decision to depend on the consumption experience in a
number of ways. First, our assumption allows arbitrary variation in thepropensity to write a review across reviewers and time. It can therefore
account for the fact that reviewers are more likely to contribute to an
online platform when they are nearing a reputational reward, such as
Yelp’s “elite” status (Luca 2016b). Second, our model is consistent withindividuals being more likely to write reviews about dining experiencesthat surprised them, either negatively or positively. Surprises are, by definition, independent of the variables that are in the information set of
consumers when deciding which restaurant venue to patronize and, therefore, independent of the consumers’ restaurant choice.
In robustness checks, we address three possible violations of our assumption that the review-writing probability is independent of the patronage choice. First, one could claim that users are more likely to review
restaurants with few prior reviews or that are not chain establishments wellknown by most consumers. As we show in appendix C7, one may control

18
In fact, if individuals’ home and work locations are determined as a function of the
expected utility of restaurant consumption, then our estimates are less likely to be biased
the larger the set of characteristics that we explicitly control for through the vector (Xi, Zi).
Intuitively, the fewer the variables that are accounted for by the unobserved term nit, themore likely it is that this composite is independent of the characteristics determining eachindividual’s choice of home and work location. A detailed discussion of this point is contained in app. C.6.

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journal of political economy

for characteristics that affect the review-writing probabilities of Yelp reviewers by introducing them explicitly as covariates in our conditionallogit model. In Section IV.E.3, we report estimates in which we control
for a restaurant’s total number of reviews and whether it belongs to achain with more than eight NYC locations.
Second, it is possible that users are more likely to review restaurantsthat they want to signal they have patronized. The specifications in which
we introduce race-specific restaurant fixed effects allow us to control forthe possibility that, for example, individuals want to signal that they havevisited a restaurant that is idiosyncratically popular with members ofgroup g. We discuss these estimates in Section IV.E.1.
Finally, users may be more or less likely to review restaurants that they
visited from a particular origin, such as a business lunch near their workplace. To address this possibility, we introduce race-origin-mode-specificfixed effects in specifications reported in Section IV.E.3.
Lack of serial correlation in unobserved preferences.—Our baseline model
assumes that individuals’ unobserved restaurant preferences (captured
in the vector nit) are independent over time. As discussed in detail in appendix C8, if, contrary to our assumption, the preference shocks nit areserially correlated, the fact that we identify users’ preferences from theirreviews and that users do not review a restaurant twice will generate aselection bias in our estimates of consumers’ preference parameters.
Specifically, positive serial correlation would cause attenuation bias: upward bias in the estimates of coefficients on characteristics that consumers dislike (e.g., spatial and social frictions) and downward bias in the estimates of coefficients on characteristics that appeal to consumers (e.g.,
restaurants’ rating). We illustrate the possible size of this bias througha simulation in appendix C8. To reduce this selection bias, we report estimates that use only the first half and first fifth of each user’s reviews in
Section IV.E.3.
IV. Estimation Results
This section reports the results of estimating discrete-choice models of
the form described in Section III using the data introduced in Section II.
The models differ in the set of spatial and social frictions we incorporate.
In Section IV.A, we introduce spatial frictions while omitting social frictions. In Section IV.B, we incorporate these measures. In all cases, we include a set of venue and reviewer-venue characteristics that may influence
consumer demand.
All the specifications presented in the main text are estimated using afixed set of randomly generated choice sets, {Sit, 8i, t }, so that variation in
the estimates across columns and tables is exclusively due to variation in
the included covariates.

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how segregated is urban consumption?
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Spatial Frictions

Columns 1–3 of table 2 estimate spatial frictions while omitting social
frictions. All the coefficients on spatial frictions are negative. There is
only modest heterogeneity by race, with somewhat lower disutilities oftransit times for Asian reviewers.19 They are less precisely estimated forblack reviewers, because of the smaller number of observations. The coefficients on transit times from work are larger than those on travel times
from home. This may reflect a higher cost of time spent away from work.
The magnitude of the coefficients is modestly but nearly always higher
for travel by car than by public transit. This could reflect the fact that
NYC public transit fares are invariant to distance while taxi fares are not.
Columns 1–3 of table 2 deliver a clear finding: spatial frictions play afirst-order role in individuals’ consumption choices within the city. Consumers are less likely to visit venues that, in terms of mass transit and automobile travel time, are more distant from their home and work locations,as well as the commuting path between these. Consider two hypotheticalrestaurants, identical in their characteristics except for the number ofminutes away from the individual’s optimal origin of the trip. The first restaurant is 15 minutes from the individual’s workplace by car; the second
restaurant is 30 minutes away. The estimated coefficients in columns 1–3
of table 2 imply that the individual would be about four times as likely
to visit the more proximate venue from work by car (e.g., for a black reviewer, 22:02 ≈ 4:06). Similarly, if the two restaurants were 15 and 30 minutes from the commuting path by public transit, the individual would be
about twice as likely to visit the more proximate venue.
Finally, note that reviewers’ choices also depend on restaurants’ characteristics in predictable ways. Restaurants with higher ratings and lower
prices are generally more attractive. However, restaurants in the $$ pricecategory are attractive relative to $ restaurants, indicating that prices also
reflect quality.20 Reviewers residing in census tracts with higher incomes
exhibit more willingness to pay higher prices. Asian reviewers’ most preferred cuisine category is Asian cuisine, while white/Hispanic reviewers’
most preferred categories are Latin American, American, and vegetarian/vegan.
B. Social Frictions
Columns 4–6 in table 2 introduce social frictions.
Reviewers exhibit homophily at the individual level. Reviewers are more
likely to visit venues located in tracts with a larger residential population of
19
Table A10 shows that there is little heterogeneity in the coefficients on spatial frictions
along age, gender, and income dimensions.
20
Restaurants in the $ category are often fast-food venues.

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journal of political economy

the same race. Asian reviewers are more likely to visit a restaurant in a tract
with more Asian residents relative to all other residential racial and ethnic
categories (white residents are the omitted category). Similarly, black reviewers are more likely to visit a restaurant in a tract with more black residents. In the case of white or Hispanic reviewers, homophily is less evident,
perhaps because we do not distinguish between whites and Hispanics whenclassifying reviewers’ profile photos. In column 6, there is a positive coefficient on Hispanic population share, but there is also a positive coefficienton Asian residents relative to white residents.
The negative coefficients on EDD reveal a role for environmental similarity. Reviewers are less likely to visit venues located in census tracts with demographics different from those of their home census tract. Since we control for transit times between tracts, this result cannot be attributed to thejoint impact of residential segregation and disutility of travel. Similarly, ourcontrols include income differences between tracts, so this result cannot be
attributed to spatial differences in incomes predicting consumers’ choices.
Thus, the coefficient on EDD likely captures mechanisms linked to racialand ethnic differences. Individuals may have preferences regarding the residential demographics of the neighborhoods in which they reside andthose in which they consume. Alternatively, consumers may be more likely
to visit restaurants located near their friends’ residences, with these social
ties being predicted by neighborhoods’ demographic similarity.
The estimated coefficient on EDD implies an economically significant
role for this social friction. Consider a user who contemplates visiting twovenues that are identical except for their EDDs, which differ by one standard deviation. Our estimates imply that an Asian user would be 25 percent
more likely to visit the venue in the more demographically similar census
tract.21 A black user would be 51 percent more likely to visit the more similar
tract. We can also express the economic significance of demographic differences as a trade-off between demographic distance and transit time. To
hold constant an Asian consumer’s utility visiting a venue from home via
public transit, a venue one standard deviation more demographically distant would have to be about 21 percent closer in terms of travel time.22
For a black consumer, it would have to be 44 percent closer.

21

Comparing two venues j and j 0 that are identical in every covariate except Xij2 ,P ðdij 5 1jVi Þ=P ðdij 5 1jVi Þ 5 exp g2g ði Þ Xij2 2 Xij2 :00Table A2 shows that the standard deviation of EDD across all pairs of census tracts in NYC is
0.226, so the coefficient of 21 in col. 4 of table 2 implies that a venue that has an EDD 0.226
lower than an otherwise identical venue will be visited with 25 percent higher probability:
expð21:00  ð20:226ÞÞ ≈ 1:25.
22
To hold Uijlt constant, a change of DXij2 would be offset by the change DXijl1 52g2g ðiÞ Xij2 =g1g ðiÞl . Since the coefficient g1g ðiÞhp estimated in col. 4 of table 2 is 21.06, the change
required to offset a one standard deviation increase in EDD is 21:00 0:226=1:06 ≈ 20:21.

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Dummy for $$ bin

Share of tract population that is other

Share of tract population that is Hispanic

Share of tract population that is black

Share of tract population that is AsianEDD  SSI

Spectral segregation index (SSI) of k j

Euclidean demographic distance (EDD)

Log travel time from commute by car

Log travel time from commute by public transit

Log travel time from work by car

Log travel time from work by public transit

Log travel time from home by car

Log travel time from home by public transit.645***(.194)

2.996***(.119)
21.24***(.141)
22.16(2.43)
22.02***(.584)
2.997***(.098)
21.43***(.171)

21.07***(.101)
21.19***(.086)
21.27***(.145)
21.69***(.188)
2.955***(.063)
21.08***(.060).309***(.087)

Black
(2)

Asian
(1)

.317***(.082)

21.15***(.058)
21.38***(.059)
21.92***(.298)
22.01***(.181)
21.11***(.042)
21.46***(.056)White/Hispanic(3)

Spatial Frictions

TABLE 2
Spatial and Social Frictions Estimates

21.06***(.107)
21.17***(.091)
21.24***(.149)
21.60***(.176)
2.943***(.067)
21.04***(.061)
21.00***(.121).150***(.051)
2.149(.117)
1.03***(.120).220(.319)
2.251(.235).059(2.07).375***(.087)

Asian(4)
2.938***(.127)
21.19***(.158)
21.85*(1.11)
21.79***(.459)
2.930***(.105)
21.32***(.177)
21.84***(.280).075(.093)
2.171(.239).011(.345)
1.08***(.399).467(.381)
3.56(3.43).771***(.197)

Black(5)

21.13***(.059)
21.36***(.060)
21.87***(.287)
21.95***(.171)
21.10***(.044)
21.43***(.058)
21.19***(.130).045*(.027)
2.068(.083).363***(.138).140(.265).415**(.188).484(1.99).355***(.083)White/Hispanic(6)

Social Frictions

1710

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Middle Eastern cuisine category

Latin American cuisine category

Indian cuisine category

European cuisine category

Asian cuisine category

American cuisine category

African cuisine category

Yelp rating of restaurant

Dummy for $$$$ bin

Dummy for $$$ bin.175(.115).086(.185).583***(.064).271(.297).421***(.054).931***(.054).204***(.059).374***(.091).491***(.070).264***(.100)

Asian(1)

White/Hispanic
(3)
2.075
(.120)
2.398*(.219).335***(.059).319(.259).596***(.050).308***(.054).247***(.056)
2.018(.097).699***(.061).204**(.094)

Black
(2)
2.283
(.334)
2.313(1.18).036(.137)
2.046(.548).542***(.118).201(.132)
2.339**(.153)
2.422(.299)
1.03***(.134).066(.250)

Spatial Frictions

TABLE 2 (Continued ).287**(.116).220(.188).579***(.064).280(.299).432***(.054).886***(.054).195***(.059).370***(.091).517***(.070).280***(.101)

Asian
(4)

2.090
(.341)
2.074(1.22).053(.138)
2.198(.553).523***(.119).255*(.134)
2.326**(.154)
2.451(.301)
1.01***(.136).104(.251)

Black
(5)

2.026
(.120)
2.347(.221).344***(.059).298(.261).591***(.050).307***(.054).235***(.056)
2.039(.097).690***(.062).203**(.094)White/Hispanic(6)

Social Frictions

1711

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6,447.365***(.138).041***(.011).086***(.014).088***(.022).010(.008)
2.218***(.045)
2.233(.292).119(.258)
6,936.596***(.116).049***(.009).089***(.013).105***(.022).017***(.007)
2.350***(.046).791***(.293)
2.694***(.259)

2.041(.408)
2.002(.032).109**(.052)
2.119(.224).007(.023).469***(.114)
1.04(.826)
2.869(.733)
1,079

White/Hispanic
(3)

Black
(2)

Spatial Frictions.392***(.138).034***(.011).075***(.014).074***(.022).011(.008)
2.062(.050).114(.305)
2.109(.267)
23.41***(.676)
6,447

Asian
(4)
.001
(.409)
2.022(.032).077(.053)
2.167(.234).008(.023).850***(.126).619(.853)
2.360(.744)
2.43**(1.20)
1,079

Black
(5)
.587***(.116).042***(.009).081***(.013).095***(.023).016**(.007)
2.100*(.053).719**(.300)
2.625**(.262)
23.74***(.771)
6,936

White/Hispanic(6)

Social Frictions

Note.—Each column reports an estimated conditional-logit model of the decision to visit a Yelp venue. Standard errors are in parentheses. Unreported
controls are 28 area dummies.
* Statistically significant at 10 percent.
** Statistically significant at 5 percent.
*** Statistically significant at 1 percent.

Number of trips

Average annual robberies per resident in kj

Log median household income in kj

Percent difference in median incomes (kj 2 hi)

Percent absolute difference in median incomesYelp rating  home tract median income$$$$ bin  home tract median income$$$ bin  home tract median income$$ bin  home tract median income

Vegetarian/vegan cuisine category

Asian
(1)

TABLE 2 (Continued )1712journal of political economy

The consequences of social frictions vary across individuals of different races because of differences in population sizes. Reviewers of all races
are more likely to visit restaurants that have lower values of the EDD covariate, and black individuals have the most negative coefficient on EDD.
Yet the mean value of EDD for venues visited by black reviewers (0.34) isin fact greater than the mean value of EDD for venues not visited bywhite reviewers (0.29). This finding is consistent with the idea in Anderson (2015, 10) that “white people typically avoid black space, but black
people are required to navigate the white space as a condition of their
existence.”
Do demographic differences between census tracts matter more whenthe venue is located deep within a segregated area? To assess this, we use
a spectral segregation index (SSI) that describes a tract’s demographic
isolation in terms of its racial or ethnic plurality. In table 2, the coefficients on both SSI and the interaction of EDD and SSI are modest in
magnitude. When EDD is close to one, their sum is close to zero. Thus,
a restaurant in a tract near the edge of a racially or ethnically distinct
area is about as likely to be visited as a tract with the same demographic
differences located deep inside that area. Individuals’ choices are therefore mostly predicted by the demographic composition of the area immediately surrounding the restaurant.
Relative to columns 1–3, the coefficients on spatial frictions and cuisine categories in columns 4–6 of table 2 are slightly attenuated toward
zero. Residential segregation means that spatial frictions and social frictions are positively correlated, so spatial frictions will be overestimated if
social frictions are omitted. Similarly, if restaurants in tracts with more
Asian residents are more likely to serve Asian cuisine that appeals to
Asian reviewers, then the cuisine coefficients will be overestimated if social frictions are omitted. Comparing columns 1–3 and 4–6 of table 2
suggests that this occurs, but in the vast majority of cases the estimatedcoefficients differ by less than a standard error.
Since our coefficients are normalized by the standard deviation of thelogit error nijlt, comparisons of the levels of coefficients across columns
implicitly assume that this standard deviation is constant. The change inthe coefficients on spatial frictions and cuisine categories across models
should be compared to the change in a coefficient that is plausibly not biased by the omission of social frictions, such as the Yelp rating of the restaurant. Comparing columns 1–3 and 4–6 of table 2, the coefficient on
Yelp rating is slightly attenuated for Asian reviewers and actually larger
for black and white/Hispanic reviewers. Thus, the attenuation of coefficients on spatial frictions and cuisine categories in columns 4–6 is not
solely attributable to a change in the standard deviation of nijlt across models. Comparing these results to table A11 shows that including the workplace origin is key to our estimates of social frictions.

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how segregated is urban consumption?

1713

This section has documented patterns in consumer behavior that tend
to segregate consumption. We find roles for both environmental similarity and individual homophily. Regardless of the particular mechanisms
underlying how demographic differences shape consumption in the city,
our quantification indicates that these social frictions play an importantrole in shaping consumer behavior. These elements will contribute toour estimates of urban consumption segregation in Section V.
C. Model Fit
In this section, we discuss how well our estimated model fits the data.
1.

In-Sample Isolation

We first compare our model’s prediction of consumption segregation tothat observed in the estimation sample. Following Gentzkow and Shapiro(2011), we compute isolation indices using “leave-out” means to address
finite-sample bias. Denote the number of reviews of venue j by membersof racial group g by vg j 5 oi : g ðiÞ5g ot dijt* , the total number of reviews by
those members by vg 5 oj vg j , the number of reviews of venue j by individuals who are not members of group g by v:g ,j 5 oi : g ðiÞ≠g ot dijt* , and thetotal number of reviews of venue j by vj 5 og vg j . The Gentzkow and Shapiro “leave-out isolation index” measures the extent to which members ofgroup g disproportionately review venues whose other reviewers are also
members of group g:
vgj
vg j 2 1
v:g j
vg j
2o
:

S^g 5 o 
vj 2 1
vj 2 1j vg
j v:g
To generate a model-predicted value of S^g that is comparable to that in
the data, we simulate model-predicted visits to restaurants for the observations in the estimation sample. Our estimated model predicts that eachuser will visit a venue with a probability given by equation (7). One draw
for each observation from this probability distribution generates one simulated value of S^g . We simulate the model 500 times to obtain a distribution of S^g values. The value observed in the estimation sample and the90 percent confidence interval for simulated values are presented in table 3.
The actual data exhibit values of S^g within the 90 percent simulated
confidence intervals. Appendix D shows that allowing preference parameters to vary across races is key to matching the observed consumption
segregation: more restrictive specifications that pool preference parameters across races underpredict the isolation indices observed in thedata.

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1714

journal of political economy
TABLE 3Model Fit: Isolation Indices

Asian isolation index
Black isolation index
White/Hispanic isolation index

Estimation Sample

Model Predictions

.087
.087
.045

[.054, .088][.041, .092][.023, .055]

Note.—The reported leave-out isolation indices are the value for the estimation sample
and the 90 percent confidence interval for model-predicted outcomes from 500 generated
samples of the same size. Isolation indices are as defined in Gentzkow and Shapiro (2011).

2.

Schelling-Style Segregation

There is an additional concern to address regarding our measure of consumption segregation. We cannot observe the complete racial and ethnic composition of the patrons of every restaurant in NYC. Accordingly,
our baseline specification assumes that consumer preferences do not depend on this restaurant characteristic. Thus, our baseline model predicts
that two restaurants with the same observable characteristics (tract, cuisine, price, and rating) will exhibit the same racial composition of patrons. The work of Schelling (1969, 1971) gives reasons why this may
not be the case. He develops models in which neighborhoods may tip
to extreme segregation even if the typical city resident prefers a muchless segregated neighborhood. Card, Mas, and Rothstein (2008) provide
evidence of such tipping in US residential patterns, and Zhang (2011)emphasizes the theoretical robustness of such predictions. The correlate
concern in the present context is that there may be high degrees of segregation among restaurants with the same observables (tract, cuisine,
price, and rating) if there is endogenous racial sorting due to preferences for same-race copatrons.
To examine the plausibility of our assumption that restaurants withthe same observables will exhibit the same racial composition of patrons,
we collect information on the racial composition of all Yelp reviewers for
125 pairs of restaurants that are identical in terms of their cuisine category, price category, Yelp rating, and census tract.23 Define the “race gap”
within a pair of restaurants p to be the Euclidean distancepﬃﬃﬃ
gapp ; k sharej 2 sharej k = 2,0with sharej being a three-element vector of the fraction of users reviewing restaurant j who are Asian, black, and Hispanic/white.24 We compare
the observed distribution of gapp for the 125 pairs of observationally23
See app. D.2 for details. For reasons of feasibility, we restrict our attention to restaurants that belonged to a tract-cuisine-price-rating pair and had between 10 and 40 reviews.
24
We drop reviewers whose race is not determined on the basis of their photos fromthese computations.

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how segregated is urban consumption?

1715

equivalent restaurants to the one that would arise if, consistent with ourmodel, individuals were randomly assigned to one of the two restaurants
within each pair.
Figure 6 depicts the distribution of gapp for both the data and the random draws. The mean of the race gap for the observed data is 0.19. The
mean for the random distribution is 0.175. The p-value for the one-sided
test of equal means is .074. Appendix D.2 reports a similar result for differences in pairs of restaurants’ contributions to the Gentzkow and Shapiro (2011) isolation index.
If consumption were segregated within sets of restaurants that our model
treats as observationally equivalent, one might worry that our model would
underpredict the true degree of consumption segregation. Our examination suggests that this is not the case. Conditional on the observable covariates that we employ to predict consumption segregation, Yelp reviews do
not exhibit much further racial segregation.
D. Parametric Bootstrap
To examine the finite-sample behavior of our estimator, we perform a parametric bootstrap. Using the estimated model reported in columns 4–
6 of table 2, we simulate 500 samples of the same size as our estimation
sample. We then estimate our model on each of these generated sam-

F IG . 6.—Racial gap between pairs of observationally equivalent restaurants. These kernel densities depict the distribution of the Euclidean distance between two restaurants’shares of patrons belonging to three racial categories for 125 pairs of restaurants thatare identical in terms of their cuisine category, price category, Yelp rating, and census tract.
The null hypothesis, in line with our model, is that individuals are randomly assigned to
one of the two restaurants within each pair.

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1716

journal of political economy

ples, obtaining a distribution of our estimator under the estimated datagenerating process. Appendix D.5 reports the results in detail.
Generally, the parametric bootstrap shows that the estimator performs
well. For the covariates describing social frictions and restaurant characteristics, the bootstrapped distributions are close to normal, their meansare close to the point estimates we computed on the original sample,
and their standard deviations are very similar to our estimated (asymptotically valid) standard errors. For the spatial-friction covariates, thebootstrapped samples occasionally produce estimates that are extreme
outliers. The reason seems to be that we identify these six spatial-friction
parameters exclusively from restaurant-reviewing outcomes dij* 5 ol dijl* ,without actually observing the origin-mode-level outcomes dijl* , and thattransit times from the same origin are highly collinear.25 When we assume that the error term nijlt and the disutility of travel do not vary acrossorigin-modes l—implying that there is a single spatial-friction parameterto estimate and that consumers visit each restaurant via the origin-mode
pair with the minimum travel time (see app. C.3)—the standard error wecompute for our estimator of the disutility caused by this spatial friction
is very similar to the bootstrapped one.
When we apply this bootstrap procedure to the isolation indices predicted by our estimated parameters, we find that they fit those associatedwith the estimated data-generating process. Table D4 shows that the confidence intervals for isolation indices predicted by the average of thebootstrapped parameters are very similar to those reported in table 3.
Figure D6 shows that the distributions of the endpoints of these 90 percent confidence intervals are nearly centered around the data-generatingprocess’s values.
E. Robustness Checks
1.

Restaurant Fixed Effects

It is feasible to introduce race-specific restaurant fixed effects into ourmodel. However, as we cannot identify these fixed effects for restaurantsthat are not visited by reviewers in the estimation sample, this generalized model cannot be used to compute citywide measures of consumption segregation and counterfactuals.26 We therefore employ the specification with race-specific restaurant fixed effects only to examine the25
For example, the correlation between travel time from work by car and from work by
public transit exceeds .9 for all three racial groups.
26
Estimating models with restaurant fixed effects is computationally costly: estimation
takes days rather than minutes. This is true in our setting whether we estimate our fixed
effects directly or assume the approximation in Taddy (2015). We report the results of
Taddy’s estimation procedure in app. D.3.

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how segregated is urban consumption?

1717

robustness of the coefficients on observable reviewer-restaurant covariates reported in table 2 and to assess the relative fit of these specifications.
The results suggest that our baseline specification in table 2 is sufficient
to capture the relevant variation in consumers’ choices. Table 4 reports theresult of estimating the specification with restaurant fixed effects. The estimated coefficients on our measures of spatial and social frictions are similar to those reported in table 2. Table 5 reports the result of a likelihood
ratio test comparing the fit of the restaurant-fixed-effects specification to
the specifications in table 2 that use only observable characteristics. For
TABLE 4
Restaurant Fixed Effects
Asian
(1)
21.12***(.111)
Log travel time from home by car
21.23***(.092)
Log travel time from work by public transit
21.29***(.141)
Log travel time from work by car
21.78***(.201)
Log travel time from commute by public transit 21.04***(.073)
Log travel time from commute by car
21.15***(.066)
2.796***EDD between hi and kj(.133)EDD  SSI
2.527***(.182)$$ bin  home tract median income.040***(.011)$$$ bin  home tract median income.080***(.014)$$$$ bin  home tract median income.062***(.022)
Yelp rating  home tract median income.020**(.010)
Percent absolute difference in median incomes
2.200***(hi 2 kj)(.057).053
Percent difference in median incomes (kj 2 hi)(.362)
Number of trips
6,447
Number of fixed effects
2,867
Log travel time from home by public transit

Black
(2)

White/Hispanic(3)

21.07***(.141)
21.28***(.153)
21.87**(.803)
21.92***(.420)
21.03***(.106)
21.40***(.158)
22.40***(.324)
2.533(.433)
2.003(.034).052(.056)
2.200(.259).012(.027)

21.24***(.061)
21.49***(.062)
21.82***(.210)
22.10***(.176)
21.17***(.041)
21.58***(.061)
21.15***(.146)
2.003(.087).050***(.010).076***(.013).082***(.023).025***(.009)

1.54***(.181).505(.995)
1,079
892

2.189***(.061).182(.383)
6,936
3,497

Note.—Each column reports an estimated conditional-logit model of individuals’ decisions to visit a Yelp venue. Standard errors are in parentheses. The unreported covariates
are restaurant fixed effects.
* Statistically significant at 10 percent.
** Statistically significant at 5 percent.
*** Statistically significant at 1 percent.

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1718

journal of political economy
TABLE 5Observables versus Restaurant Fixed Effects
Log Likelihood Values

SampleObservables

Restaurant
Fixed Effects

x2 TestStatisticp -Value

Asian reviewers
Black reviewers
White/Hispanic reviewers

255,257.58
29,185.82
259,121.74

247,449.99
26,759.39
251,591.93

15,615.19***
4,852.87
15,059.63***

.00
1.00
.00

Note.—The observables specifications include 60 covariates, while the fixed-effect specifications include 14 covariates plus the 10,945 restaurant fixed effects. Thus, the x2 test has
10,899 degrees of freedom.
* Statistically significant at 10 percent.
** Statistically significant at 5 percent.
*** Statistically significant at 1 percent.

black reviewers, we do not reject the hypothesis that the observables specification fits the data as well as the fixed-effects specification. For Asianand white/Hispanic reviewers, the fixed-effects specification is superior
in terms of fit, but the estimated coefficients are largely consistent withthose in table 2. The fixed-effects specification exhibits worse fit in termsof isolation indices, predicting values greater than the estimation-sample
values reported in table 3.27
2.

Nested-Logit Specification

We relax the independence of irrelevant alternatives property of theconditional-logit model of Section III.A by specifying a nested-logit
structure. Appendix C.4 derives this estimator in detail. We define nests
by two schemes: (a) restaurants of the same cuisine category, Yelp rating,and area and (b) restaurants of the same cuisine category, price category,and census tract. For this exercise, we define 39 cuisine categories (moredisaggregated than the nine categories shown in table 2) and employ the
28 areas, four price categories, and nine Yelp ratings (from one to five stars)
described in Section II. These two schemes group the 10,945 restaurants
into 3,064 and 7,622 nests, respectively. Table D3 reports the estimates.
The estimated coefficients on spatial frictions, social frictions, and restaurant characteristics are all similar to the values reported in columns 4–6 oftable 2. The within-nest-correlation parameter l is generally near one, consistent with the conditional-logit assumption we imposed in Section III.A.
27
In finite samples, our estimates of the restaurant fixed effects partly reflect the frequency with which a restaurant is randomly sampled in choice sets. Even if all restaurants
are sampled with the probability given by eq. (8), uneven sampling in finite realizations willaffect the estimates of the fixed effects regardless of consumers’ preferences. This may
worsen the fit of the isolation index. As noted in n. 16, this does not threaten our estimatesof the coefficients on observable covariates.

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how segregated is urban consumption?

1719

Table D3 reports the p-values for likelihood ratio tests of the null hypothesis that the true model is a conditional logit versus the alternative hypothesis that the true model is the corresponding nested-logit model. While
the tests formally reject the hypothesis that l 5 1 for three of the six specifications, these nested-logit specifications yield very similar coefficientson observable covariates and deliver very similar predictions of the insample isolation measures (see table D1). Since estimating these nestedlogit models comes at considerably greater computational cost, we employ
the conditional-logit specification when computing consumption segregation and counterfactual outcomes.
3.

Additional Observable Characteristics
and Sample Restrictions

In appendix A, we implement several robustness checks that restrict theset of observations or introduce additional covariates to address concerns discussed in Section III.D. These are reported in tables A4–A6.
We also report these robustness checks for the specification in which
consumers visit each restaurant via the origin-mode pair with the minimum travel time in tables A7–A9. We briefly summarize the robustness
checks here.
To address concerns that the error term nijlt may exhibit serial correlation, we restrict the sample to either the first half or the first fifth of reviews written by each user. These restrictions increase the standard errors and cause one coefficient to be unidentified in the resulting small
sample of black reviewers. However, they do not systematically increase
the absolute values of coefficients, suggesting that there is not substantial attenuation bias caused by serial correlation in the unobserved preference shocks nijlt. For a more detailed discussion, see appendix C.8.
To address concerns related to how we located reviewers, we show thatboth dropping the 5 percent of restaurant reviews used to locate reviewers and splitting the sample on the basis of the number of Yelp reviews
revealing a reviewer’s home location alter the coefficients of interest little. To address concerns that early adopters of Yelp may be less sensitive
to spatial and social frictions, we restrict the sample to reviewers whojoined Yelp later. The estimated coefficients are broadly similar to thosein table 2, without any systematic increase or decrease. Controlling for
39 cuisine categories (rather than nine) slightly attenuates our point estimates of homophily and makes the environmental similarity coefficients slightly more negative. We find that our results change little whencontrolling for tract-level differences in private vehicle ownership rates.
To address concerns that the decision to write a review may depend on
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1720

journal of political economy

whether it belongs to a restaurant chain. Introducing these covariates
substantially alters the coefficients on the restaurant’s rating and price,
but the coefficients on spatial and social frictions change little.
To address concerns that users may be more or less likely to review restaurants visited from a particular origin, table A12 introduces originmode-specific intercepts. This yields broadly similar coefficients on log
travel times, though the accompanying standard errors are considerablylarger. The coefficients on social frictions are modestly attenuated.
In summary, the results in table 2 are broadly unchanged by restricting
the estimation sample or introducing additional covariates.
V. Consumption Segregation
In this section, we use data on the demographic composition of all census tracts in NYC and the estimates presented in columns 4–6 of table 2
to compute NYC-wide measures of segregation in consumption for Asian,
black, Hispanic, and white consumers. In Section V.A, we define the measure of segregation we use. Section V.B presents our estimates of consumption segregation and examines the contributions of spatial and social
frictions to them. In Section V.C, we illustrate the mechanisms underlying
the citywide results by focusing on the consumption patterns observed inparticular neighborhoods within the city. Finally, in Section V.D we examine a number of counterfactual experiments.
A.

Dissimilarity Indices

To measure consumption segregation, we use the “dissimilarity index”
commonly employed in the literature on residential segregation. For eachgroup g, we compute
DissimilarityðgÞ 5


1
Pr dij 5 1jgði Þ 5 gÞ 2 Prðdij 5 1jgði Þ ≠ gÞ ,
o
2 j∈J
(13)

where g is Asian, black, Hispanic, white, or other.28 This index sums,
across all restaurants, the absolute difference between the probability
that a randomly selected individual belonging to group g visits a restaurant and the probability that a randomly selected individual who does
not belong to group g visits the same restaurant. The higher the valueof this index, the larger the differences in consumption choices. Thinkof the probability Prðdij 5 1jgðiÞ 5 gÞ as the fraction of individuals of
28
As discussed in Sec. III, we estimate parameters for three racial groups indexed by g.
However, we compute dissimilarity indices for five racial groups indexed by g. We computePrðdij 5 1jgðiÞ 5 gÞ for Hispanic, white, and other using g-specific data on residential locations and the estimated white/Hispanic (g 5 w) preference parameters.

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how segregated is urban consumption?

1721

group g that would visit restaurant j if they were all going out to dine; thedissimilarity index in equation (13) indicates the share of individuals ingroup g that would need to alter their consumption choices in order tomatch the distribution of predicted restaurant choices made by the remainder of the population. A pairwise dissimilarity measure can also becomputed to compare the consumption choices of any two groups, g1
and g2 :Dissimilarityðg1 , g2 Þ 5
1
P dij 5 1jgði Þ 5 g1 2 P ðdij 5 1jgði Þ 5 g2 Þ :o
2 j∈JThese dissimilarity indices are invariant to the size of the groups being
compared.
If we were to observe all visits to restaurants for a sufficiently large representative sample of NYC residents, we could estimate Prðdij 5 1jgðiÞ 5 gÞ
directly by its sample analogue. To our knowledge, such a large and representative data set does not exist.29 We therefore apply the parameters estimated in Section IV to the broader population of NYC in order to consistently estimate the probability Prðdij 5 1jgðiÞ 5 gÞ for all restaurants j andraces g. Appendix E details how we construct consistent estimates of theseprobabilities. A further advantage of relying on the demand model described in Section III as the basis for computing our measures of consumption segregation is that it can also be used to quantify the contributions ofspatial and social frictions to our overall measure of consumption dissimilarity.
B. Citywide Consumption Segregation
Table 6 reports residential and consumption dissimilarity indices for eachdemographic group. Panel A reports overall dissimilarity indices for eachgroup; panel B reports pairwise dissimilarity indices. We compute residential dissimilarity at the level of census tracts and consumption dissimilarity
at the level of restaurant venues.30 We employ the parametric bootstrap
29
As explained by Gentzkow et al. (2019), computing consistent measures of segregation can be difficult in a sample that is small relative to the dimensionality of the choice
set faced by individuals. In our context, this sample would have to be large relative tothe number of restaurants in NYC. The advantage of the behavioral model introducedin Sec. IV is that it expresses the probabilities Prðdij 5 1jgðiÞ 5 gÞ as a function of a relatively small number of estimated parameters.
30
The choice of spatial unit is nontrivial when computing dissimilarity indices, asEchenique and Fryer (2007) stress. Restaurants are a natural spatial unit within which interactions may occur, while tract-level results may be sensitive to how the Census Bureauchose to partition the city. To facilitate comparison of measures of residential and consumption segregation, table A14 reports consumption dissimilarity indices computed at
the level of census tracts. The resulting indices are broadly similar to those computed at
the level of restaurant venues.

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1722

journal of political economy
TABLE 6
Residential and Consumption Segregation
Consumption Dissimilarity
Residential
Dissimilarity Estimated(1)(2)

No Spatial(3)

No Social(4)

Neither
Friction(5)

A. Dissimilarity Index
Asian

.521

Black

.653

Hispanic

.486

White

.636

White or Hispanic

.470

.315
.290
.245
.232
[.305, .335] [.280, .314] [.233, .268] [.222, .259]
.352
.322
.273
.260
[.337, .397] [.307, .372] [.258, .320] [.248, .309]
.142
.114
.106
.088
[.134, .162] [.108, .137] [.099, .125] [.083, .109]
.190
.153
.112
.093
[.180, .209] [.143, .174] [.106, .130] [.090, .112]
.205
.189
.150
.156
[.197, .236] [.182, .224] [.143, .182] [.149, .191]
B. Pairwise Dissimilarity

Asian-black.796

Asian-Hispanic.584

Asian-white.519

Black-Hispanic.558Black-white.822

Hispanic-white

.658

.495
.448
.388
.357
[.480, .534] [.429, .491] [.370, .429] [.340, .402]
.288
.273
.220
.217
[.277, .310] [.262, .299] [.208, .246] [.206, .247]
.278
.255
.212
.203
[.268, .298] [.245, .279] [.200, .236] [.193, .233]
.328
.297
.261
.250
[.312, .372] [.284, .348] [.246, .308] [.238, .300]
.354
.324
.263
.255
[.337, .401] [.309, .375] [.249, .310] [.243, .306]
.159
.115
.095
.037
[.144, .176] [.098, .135] [.085, .104] [.028, .047]

Note.—This table reports dissimilarity indices. Panel A reports the index for each demographic group’s residential/consumption locations compared to members of all other demographic groups. Panel B reports the index for residential/consumption locations between
each pair of demographic groups. The demographic group “other” is included in computations but not reported. Col. 1 reports indices based on tracts’ residential populations. The remaining columns report venue-level dissimilarity indices based on the coefficient estimates incols. 4–6 of table 2. Col. 2 uses the estimated coefficients. Col. 3 sets the coefficients on travel
time covariates to zero. Col. 4 sets the coefficients on demographic-difference covariates tozero. Col. 5 sets the coefficients on travel time and demographic difference covariates to zero.
Bootstrapped 95 percent confidence intervals from 496 draws are reported in brackets.

distributions of our estimator from Section IV.D to produce 95 percent
confidence intervals for the consumption dissimilarity estimates.
Column 1 provides the dissimilarity index for residential segregation
while column 2 provides the analogous index for consumption segregation. Comparing these columns, all groups are significantly more integrated
in their consumption than in their residences. The ratio of residential dissimilarity to consumption dissimilarity is 3.4 for Hispanics, 3.4 for whites,
1.9 for blacks, and 1.7 for Asians. NYC’s levels of residential dissimilarity
are similar to the nationwide average level of dissimilarity for black residents

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how segregated is urban consumption?

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in 2010, while the levels of dissimilarity in consumption implied by our estimates are comparable to the levels of black residential dissimilarity observed in America’s most integrated metropolitan areas (Glaeser and Vigdor
2012). The 20–45 percentage point difference between consumption segregation and residential segregation of NYC residents is one to two times
the largest declines in black residential dissimilarity observed from 1970
to 2010 across US metropolitan areas (Glaeser and Vigdor 2012). At themedian historical rate of decline, residential segregation would have tocontinue its decline for nearly a century to reach levels comparable toour estimated levels of consumption segregation.
The fact that all groups are significantly more integrated in their consumption than in their residences is not a necessary consequence of themodel assumptions imposed in Section III. While the willingness of residents to travel outside of their home census tract to consume may tend
to reduce consumption segregation relative to residential segregation, demographic differences in cuisine tastes or demographically linked social
frictions could cause consumption segregation to exceed residential segregation. Our numbers show that social frictions and heterogeneity in tastes
do not overturn the integrating effect of consumers’ mobility.
Across demographic groups, black and Asian individuals exhibit thehighest values of consumption dissimilarity, but in part because we assign
white and Hispanic consumers the same preference parameters as a resultof our inability to differentiate between white and Hispanic Yelp reviewers
in their photos. To the extent that white and Hispanic consumers differ intheir preferences, we will underestimate consumption segregation forthese two groups. In panel B, the largest pairwise dissimilarities are foundbetween Asian and black consumers. Black and white consumers’ choices
are also dissimilar, while Hispanic-white and black-Hispanic consumption
choices are more integrated.31
In order to measure the contributions of spatial and social frictions to
consumption segregation, we again use the estimates in columns 4– 6 oftable 2 and recompute the dissimilarity indices that arise from settingsome of the estimated coefficients to zero. This calculation holds fixedboth the set of restaurants and their characteristics and, thus, should
not be interpreted as capturing the total effect of eliminating spatial orsocial frictions (which would likely generate supply responses). In computing the dissimilarity indices reported in column 3 of table 6, the coefficients on travel time covariates are set to zero, eliminating the role

31
Despite their common preference parameters, white and Hispanic consumers still exhibit pairwise dissimilarity due to observable differences in residential locations that generate
differences in the social friction covariates employing tract-level racial and ethnic demographics (e.g., EDD) and other covariates employing tract-level characteristics (e.g., the interaction of price dummies and median household income in the reviewer’s home tract).

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1724journal of political economyof spatial frictions.32 In column 4, the coefficients on the demographicdifferences covariates are set to zero, eliminating the role of social frictions.33 In column 5, both these sets of coefficients are set to zero, so that
consumers in different groups exhibit different predicted consumption
behavior due only to differences in their residential income levels andincome-linked valuations of venues’ prices and ratings; race-specific valuations of restaurants’ cuisines, prices, and ratings; race-specific responses
to robberies per resident; and race-specific area fixed effects.34
Comparing columns 2 and 3, the elimination of spatial frictions has amild integrating effect, causing consumption dissimilarity to fall by anaverage of 3 percentage points. That is, individuals from different demographic groups value restaurant destinations sufficiently similarly thateliminating spatial frictions would, all else equal, make their choices more
integrated. Because of spatial frictions, consumption segregation at least
partly inherits the pattern of residential segregation. Comparing columns 2 and 4, eliminating the roles of environmental dissimilarity andhomophily (i.e., social frictions) reduces consumption dissimilarity byan average of 6.6 percentage points, or more than twice the effect of eliminating spatial frictions. If consumer behavior did not respond to differences between the residential demographics of the restaurant tract andboth the consumer’s individual identity and home tract demographics,
predicted consumption behavior would be much more integrated.35 Eliminating both spatial and social frictions would reduce consumption dissimilarity by about one-third on average.
The relative contributions of spatial and social frictions are consistentacross demographic groups. For each group, social frictions make a notably greater contribution to the observed level of consumption dissimilarity than spatial frictions. Moreover, the relative overall levels of estimatedconsumption dissimilarity appear to reflect dissimilarity attributable to demographic differences in tastes. In the absence of both spatial and social
frictions, black consumers would exhibit the greatest consumption dissimilarity, and Hispanic consumers the least, just as they do in the estimatedlevels in column 2.
The pairwise dissimilarity indices reported in panel B of table 6 reflect
the rich set of covariates incorporated in our behavioral model. For ex32
This is a ceteris paribus exercise. In reality, spatial frictions and social frictions may not
be entirely independent. For example, if social frictions reflect segregated friendship networks and users visiting restaurants near their friends’ residences, then the elimination of
spatial frictions would eliminate this component of social frictions.
33
Specifically, we set to zero the coefficients on all the EDD, SSI, and share covariates
reported in table 2.
34
For example, Asian reviewers are, all else equal, more likely to visit Asian and Indian
restaurants, and reviewers residing in higher-income tracts are more likely to visit restaurants with higher prices.
35
This decomposition holds residential segregation fixed. However, one could expectthat residential segregation would be different in the absence of social frictions.

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how segregated is urban consumption?

1725

ample, while Asian-black and black-white residential dissimilarity indices
are similar, consumption dissimilarity is notably greater between Asianand black consumers than between black and white consumers. This
partly reflects the magnitude of the Asian-black interactions in table 2.
Column 5 of table 6 reveals that it also reflects divergent choices due
to differences in income levels and tastes. The Asian-black pairwise dissimilarity results, which are not affected by the fact that we pool preference parameters for white and Hispanic reviewers, match the findings inpanel A of table 6: Asian-black residential dissimilarity is much greaterthan Asian-black consumption dissimilarity, and this consumption dissimilarity reflects social frictions more than spatial frictions.
These results are robust to a number of the alternative estimating assumptions discussed in Section IV.E.3. Table A15 shows that we obtain
similar results when using parameter estimates obtained from our nestedlogit specifications, limiting the sample to reviewers’ early reviews, splitting the sample on the basis of the amount of locational information used
to identify a reviewer’s residence, restricting the sample to late adopters,
controlling for vehicle ownership rates, controlling for chain establishments and the restaurant’s number of Yelp reviews, using more disaggregated cuisine categories, and constraining all trips to start at home. We alsofind very similar results when computing dissimilarity using a minimum–transit time specification (see app. C.3), as reported in table A16.
C. Illustrative Examples
We illustrate the mechanisms behind the results in table 6 in two parts ofthe city. First, we examine three neighborhoods in Manhattan: the Upper
East Side, Central Harlem, and East (Spanish) Harlem (respectively, Manhattan community districts 8, 10, and 11). The fact that each of theseneighborhoods is residentially segregated, with a distinct demographic
majority, makes the general process at work easy to visualize in figure 7.36
Panel A in figure 7 captures residential segregation. Each dot represents
5 percent of the tract population. The Upper East Side of Manhattan
stretches from Fifty-Ninth Street to Ninety-Sixth Street, Central Park to
the East River. While NYC is only 33 percent white, the Upper East Sideis 81 percent white. If we restrict attention to the 14 census tracts between
Third Avenue and Central Park, the median tract is 92 percent white.
Among nearly 61,000 residents of these tracts, only 726 were black. In
short, this is a highly segregated area of the city.
Central Harlem comprises Fifth Avenue to Eighth Avenue, Central
Park North (110th Street) to the Harlem River. This is the storied center
36
Table A17 reports shares of residents and predicted consumers by race for the threecommunity districts depicted in these maps.

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F IG . 7.—Residential and consumption segregation in three Manhattan communities. These maps depict community districts 8 (Upper East Side),
10 (Central Harlem), and 11 (East Harlem) in Manhattan. The five maps correspond to the five scenarios reported in table 6. Each dot represents 5 percentof the tract’s residential population or predicted restaurant visitors. Asian residents or consumers are represented by red dots, black by blue dots, Hispanic
by orange dots, and white by green dots. In different tracts, dots represent a different number of people, so the maps depict variation in shares, not levels.

how segregated is urban consumption?

1727

of urban black America. While its black population share has fallen fromits 1990 level of 88 percent, it remains 63 percent black. The next-largest
group is Hispanics at 22 percent, with modest levels of whites (10 percent) and Asians (2 percent). While Central Harlem’s residential population is becoming more diverse, panel A of figure 7 makes it evident thatit remains a highly segregated area.
East (Spanish) Harlem stretches from Ninety-Sixth Street to the Harlem
River, and Fifth Avenue to the East River. The Hispanic fraction of the
population has remained roughly constant in the last 20 years at about
50 percent. There is a large, even if declining, black population in East
Harlem, at roughly 30 percent, located most densely where East Harlem
abuts Central Harlem to the west and in the more northerly areas of the
district. Asians and whites are present in small but growing numbers.
Panel B of figure 7 shows the degree of consumption segregation withinthese areas. Each dot represents 5 percent of the visits to that tract. Two features jump out from this panel. The first is that predicted consumption inpanel B is strikingly less segregated than residences in panel A. This is consistent with a comparison of columns 1 and 2 of table 6. The boundaries
between black and Hispanic consumers are porous, consistent with theblack-Hispanic interactions in table 2 and the pairwise dissimilarity index
in table 6. Asian consumers are more prevalent in the Upper East Side, forexample, than Central Harlem, reflecting both a shorter distance to Asian
residential population centers and smaller social frictions between Asians
and whites than between Asians and the two other groups. The second feature is that, nonetheless, there remains a very high level of segregation. As
summarized in table A17, black consumers dominate consumption in Central Harlem, Hispanics in East Harlem, and whites in the Upper East Side.
Segregation of consumption is much less than residences but still strong.
The following three panels in figure 7 follow columns 3–5 of table 6 byillustrating the degree of consumption segregation for the respective cases
in which spatial, social, or both types of frictions are set to zero when constructing predicted consumption patterns. Panel C is based on our estimates in which we wholly eliminate spatial frictions, effectively making every restaurant in the city instantly available to any resident of the city.
Comparing panel C to panel B, there is a diminution of the degree of consumption segregation. Yet the change seems modest, consistent with acomparison of columns 2 and 3 of table 6.
When we move to panel D of figure 7, we allow spatial frictions to again
be at their estimated level but now set social frictions to zero. Visually, comparing panels B and D in figure 7, we see a large decline in the degree ofconsumption segregation in each of these neighborhoods. It is importantto recognize that this did not need to be true. Residential segregation plus
spatial frictions to consumption could have been enough to maintain very
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1728

journal of political economy

pen not to do so. Social frictions matter a great deal for consumption integration, consistent with column 4 of table 6.
Finally, we consider the elimination of both spatial and social frictions bysetting both sets of coefficients to zero. Column 5 of table 6 tells us that
there is some further modest decline in the degree of consumption segregation relative to the no-social-frictions case. However, it is sufficiently
modest that it does not stand out clearly to the eye in panel E of figure 7
in any of the three neighborhoods. This again suggests that spatial frictions
explain a small share of consumption segregation.
Next, we examine another part of New York City with considerable, butless extreme, segregation. Figure 8 depicts neighborhoods near the portion of the East River separating the Lower East Side of Manhattan from
Brooklyn.37 Panel A shows strong patterns of residential segregation. Manhattan community district 3 includes Chinatown (with predominantly
Asian residents), the East Village (with predominantly white residents inthe northern and western portions), and the Lower East Side (with predominantly Hispanic residents). Across the river, Brooklyn community
district 1 is home to concentrations of white residents in Greenpoint
and Williamsburg and concentrations of Hispanic residents, especiallyin the areas leading out to (mostly Hispanic) Bushwick. Brooklyn community district 2 includes the mostly white Brooklyn Heights as well as Fort
Greene, with a mixture of black, Hispanic, and white residents. Finally,
Bedford-Stuyvesant (Brooklyn community district 3) is a traditionally black
area that now has white residents in the area near Fort Greene and Hispanic residents in areas proximate to Williamsburg and Bushwick.
Panel B of figure 8 shows that estimated consumption is strikingly more
integrated. Chinatown shows considerable inflows of white and Hispanic
consumers, who are proximate residents, but more modest inflows of
black consumers, who are more remote. The East Village and Lower East
Side are also notably more integrated, again with only modest numbersof black consumers. Greenpoint and Williamsburg host predominantly
white consumers who are augmented by Hispanic consumers residing,presumably, in Williamsburg and Bushwick. Brooklyn Heights and Fort
Greene are similarly host to mostly white consumers augmented by blackand Hispanic consumers. Finally, consumption in Bedford-Stuyvesant isnotably more racially integrated than its residences.
Panels C, D, and E of figure 8 reaffirm our finding that consumption segregation is driven more by social frictions than spatial frictions. To the eye,panel C, which sets spatial frictions to zero, is nearly identical to panel B.
For example, the areas of Chinatown and Bedford-Stuyvesant continue
to be dominated by Asian and black consumers, respectively. By contrast,
37
Table A18 reports shares of residents and predicted consumers by race for the fourcommunity districts depicted in these maps.

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F IG . 8.—Residential and consumption segregation in Lower Manhattan and West Brooklyn. These maps depict community district 3 in Manhattan and
1, 2, 3, and 4 in Brooklyn. See the notes to fig. 7.

1730

journal of political economy

panel D, which sets social frictions to zero, noticeably differs from panel B.
The consumption enclaves of Chinatown and Bedford-Stuyvesant do notentirely disappear, but they are much more integrated. Indeed, setting
both frictions to zero, as in panel E, does little to integrate consumption
more visibly than the removal of social frictions alone.
D. Counterfactuals
We next study how changes in transportation infrastructure and in thelevel of social frictions may affect the level of consumption segregation
we observe.
1.

Transportation Policy and Technology

Table 6 shows that the complete elimination of spatial frictions would
only modestly reduce consumption dissimilarity indices. This bounds
the conceivable impact of driverless cars or other “frictionless” technologies on consumption segregation. How might more immediately feasible transportation projects affect consumption segregation? We consider
two counterfactuals relevant to NYC policy makers. First, we forecast theeffect of the new Second Avenue subway on consumption segregation.
Second, we look at the effects of a general slowdown in NYC transit.
The Second Avenue subway is an ongoing multi–billion dollar expansion of the NYC subway system. When completed, the line will stretch from125th Street in East Harlem all the way down the East Side to Hanover
Square in the Financial District. We compute counterfactual travel times
for this transportation infrastructure improvement and forecast the effecton consumption segregation.38 The results are reported in table 7. The
Second Avenue subway line has almost no effect on consumption segregation. This is the joint effect of the fact that spatial frictions play a relatively
small role in determining consumption segregation and that the Second
Avenue line is inferred to have relatively small effects on travel times forthe majority of the city’s residents.
Second, we study consumption segregation when automobiles andpublic transit are 20 percent slower. In 2014, NYC lowered the speed limit
within the city from 30 miles per hour to 25 miles per hour (Bankoff
2014). Its subway speeds are also down about 20 percent in the last few
years (Rosenthal, Fitzsimmons, and LaForgia 2017). The effects on consumption segregation are all small in magnitude. Our conclusion is that38
Appendix E.2 details how we construct the counterfactual transit times. Our computation captures only the value of new subway connections in the network graph and does
not assign any value to benefits such as alleviating overcrowding, which is a major motivation for the Second Avenue project.

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how segregated is urban consumption?

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TABLE 7
Counterfactual Consumption Dissimilarity
Estimated(1)

2nd Ave.
(2)

.315
.352
.142
.190
.205

.315
.352
.142
.190
.206

Slowdown(3)

Social Change(4)

A. Dissimilarity Index
Asian
Black
Hispanic
White
White or Hispanic

.318
.354
.145
.194
.208

.300
.330
.133
.170
.189

B. Pairwise Dissimilarity
Asian-black
Asian-Hispanic
Asian-white
Black-Hispanic
Black-white
Hispanic-white

.495
.288
.278
.328
.354
.159

.494
.288
.278
.328
.354
.159

.498
.291
.281
.330
.357
.164

.469
.274
.264
.309
.328
.144

Note.—This table reports venue-level dissimilarity indices based on the coefficient estimates in cols. 4–6 of table 2. Col. 1 uses the estimated coefficients. Col. 2 introduces thedecrease in public transit times due to the Second Avenue subway. Col. 3 increases all travel
times by 20 percent. Col. 4 reduces the (absolute value of the) coefficients on all social friction covariates by 20 percent.

even quite substantial interventions in the transport sphere are unlikely tohave a major impact on the integration of consumption.
2.

Social Frictions

Next we examine the effects of a decline in demographic-linked social
frictions on consumption segregation. At first glance, this may appear
to be an odd policy exercise, since social frictions likely reflect a variety
of factors, such as tastes and social networks, that are not immediately
under policy makers’ control. Yet there are government policy initiatives
at the federal, state, and municipal levels that aim to encourage understanding and prevent tensions between different demographic groups.39
The counterfactual that we examine is the reduction of the magnitude
of social frictions by 20 percent.40 The results are reported in column 4 of
39
At the federal level, the Community Relations Service within the Department of Justice (https://www.justice.gov/crs) “is the Department’s ‘Peacemaker’ for community conflicts and tensions arising from differences of race, color, national origin, gender, gender
identity, sexual orientation, religion and disability.” It “facilitates the development of viable, mutual understandings and solutions to the community’s challenges.” In New York
City, the Commission on Human Rights (https://www1.nyc.gov/site/cchr/community/community.page) has a dual mandate, to educate the public about legal protections
for a variety of groups and “encouraging understanding and respect among New York
City’s many communities.”40
Our choice of this magnitude is admittedly arbitrary. Reducing social frictions by 20 percent might be an implausibly large change. On one side, 42 percent of Americans wereThis content downloaded from 128.135.098.037 on July 30, 2019 08:19:12 AM
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1732

journal of political economy

F IG . 9.—Harlem gentrification scenario. We compute the change in black residents’ expected utility in the striped tract if the surrounding light gray tracts were to exhibit thecharacteristics of the dark gray tracts. Table 8 reports the changes in these characteristics.

table 7. The direct effect of reducing social frictions on consumption segregation is modest relative to the overall level of consumption segregation.
The residual consumption segregation reflects taste differences that may
well attenuate over time with reductions in other dimensions of segregation but are unlikely to disappear over any short or even medium horizon.
VI. Gentrification
Public concerns about gentrification are primarily about displacement of
one group by another. Nominally this is an issue of social class, but in practice it also has a racial component: neighborhoods shifting composition
from less affluent minorities toward more affluent whites. Since tastes for
consumption venues differ by class and race, there is a second concernthat the commercial mix may also shift away from the set of venues wellworried “a great deal” about race relations in 2017, a peak since Gallup began asking thequestion in 2001. On the other, race relations improved considerably over recent decades
in a number of dimensions. To mention a single measure, approval of black-white marriage rose from 4 percent in 1958 to 87 percent in 2011. The realm of conceivable changes
in social frictions is quite broad, and we are not aware of evidence disciplining the conceivable magnitude of policy-induced changes in social frictions.

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how segregated is urban consumption?

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TABLE 8Harlem Gentriﬁcation Scenario
Change in

Mean

Standard
Deviation

Asian residential shareBlack residential share
Hispanic residential share
White residential share
Robberies per resident
SSI
Yelp rating
Price ($ to $$$$)Median household income (thousands)
EDD
Number of restaurants

.076
2.565
2.112
.611
2.005
21.177.137.667
76.543
.541

.057
.095
.083
.173
.003
.273
1.037
.988
58.998.121
102

Note.—The table reports the changes in characteristics for the gentrification scenario
depicted in fig. 9.

suited to poor and minorities to those favored by affluent whites. This
amounts to a concern about the potential for gentrification to erode thebenefits of living in a neighborhood for those who stay behind. In New
York City, the presence of rent-stabilized apartments and New York City
Housing Authority buildings, with low turnover rates, makes this question
salient.
We employ our estimates to quantify the welfare impact of gentrification for incumbent residents by examining the consequences of changes
in resident and restaurant composition. The gentrification scenario westudy is depicted in figure 9. We select one low-income, majority-black
census tract in Harlem (the striped polygon) and compute the change
in black residents’ expected utility if the surrounding census tracts inCentral Harlem (in light gray) were to exhibit the residential and restaurant characteristics of high-income, majority-white census tracts of theUpper East Side (in dark gray). In doing so, we hold the number of restaurants fixed while allowing their characteristics to change to those typical of the Upper East Side. The changes in restaurant and residential
characteristics are summarized in table 8.
As a result of this gentrification, black residents of the unchanged Harlem census tract experience a decrease in the expected utility of patronizing
restaurants. Stated in terms of spatial frictions, this welfare loss is equivalent to each gentrifying restaurant in Central Harlem becoming nearlyfour times as far away from the residents’ homes by both car and public
transit. This welfare loss can be decomposed using a simple approximation of the utility change that we derive in appendix E.3:!

0
Ui 2 Ui ≈ o Pij  exp g2g DXij2 1 b1g DZj1 1 b2g DZij2 2 1 ,j∈J GThis content downloaded from 128.135.098.037 on July 30, 2019 08:19:12 AM
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journal of political economy

TABLE 9Welfare Losses Due to Gentriﬁcation of Surrounding Harlem Neighborhoods

Transit Time Increase
Equal to Welfare Loss

Initial
Visit
Share

291%

.101

Change in Value of Characteristics (gDXi , DZi )SocialFrictions

Restaurant
TraitsOther
Traits

21.76

2.069

.692

Note.—Welfare loss is expressed as the percentage increase in transit times from homethat would be equivalent to the welfare loss associated with the covariate changes due to gentrification. See app. E.3 for details. Initial visit share is oj∈J Pij . Social frictions are EDD, SSI,
EDD  SSI, and racial and ethnic population shares of kj. Restaurant traits are price, rating,cuisine category, and price and rating interacted with median household income. Other
traits are destination income, differences in incomes, and robberies per resident.
G

where J G is set of restaurants that change as a result of gentrification, Pij
is the probability that an incumbent resident would visit restaurant jprior to gentrification, and (DXij2 , DZj1 , DZij2 ) are the average changes inrestaurant and residential characteristics due to gentrification. Table 9 reports the results. The 102 restaurants in the gentrifying area account for
one-tenth of predicted visits by incumbent residents prior to gentrification, so changes in the characteristics of these restaurants and residents
could have large welfare effects.
Our decomposition of the welfare loss differs from the emphases of
popular discussions of gentrification. The changes in restaurants’ prices
and cuisines have very small effects on welfare. The rise in neighborhood
income levels makes consuming in these tracts more appealing to incumbents. Instead the source of welfare losses for incumbent black consumers is primarily due to the social frictions that arise with the shift ofthe surrounding tracts from mostly black to mostly white residents.
This exercise illustrates potential welfare costs of gentrification to incumbent residents beyond increases in housing rents. In this Harlem
example, as well as a Brooklyn example reported in appendix E.3, theconsumption value of the location for incumbent residents falls by ameaningful amount. This decline is not due to changes in restaurants’characteristics but increased social frictions associated with changes insurrounding neighborhoods’ racial demographics.
VII.

Conclusions

We use a novel data source to describe restaurant consumption in NYC
and exploit properties of the conditional-logit discrete-choice model toidentify how consumers value venues’ and locations’ characteristics. Ourdata set allows us to characterize how consumption in the city depends
on travel times, demographic differences, crime rates, restaurant charac-

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how segregated is urban consumption?

1735

teristics, and reviewer characteristics. We then use our estimates to compute measures of consumption segregation for NYC residents. While
NYC is distinctive in terms of its population density and diversity of restaurants, we believe that our paper’s results for the largest city in the
United States both are interesting in their own right and establish a basis
for studying consumption segregation in other settings.
Both spatial and social frictions influence consumption choices. Consistent with theories of spatial competition, spatial frictions play a largerole in determining the spatial distribution of consumption within thecity. Our estimates show that measures of travel time, from both home
and work by both public transit and car, are relevant for predicting the restaurants patronized by NYC Yelp reviewers. Across origin-mode pairs, halving the minutes of travel time to a venue would imply that the reviewer
would be two to nearly four times more likely to visit the venue from thatorigin by that mode.
Social frictions are suggested by the finding that consumers are lesslikely to visit restaurants in neighborhoods with different residential demographics. A venue in a location one standard deviation more demographically distant from a user’s home location is 25–50 percent less likely
to be visited. Reviewers are also more likely to visit restaurants in neighborhoods where a larger fraction of the residents share the user’s race. Thesesocial frictions are asymmetric, in the sense that the negative effects oftract-level demographic differences are larger for black consumers, yetblack consumers experience larger tract-level demographic differences
during their average restaurant visit.
While our estimation approach exploits data on the decision to eat at
restaurants across NYC, the consequences of spatial and social frictions
are likely to apply to a much broader scope of life in the city. These would
include both the broader scope of consumption of nontradable services,from bars to retailers, and the vast array of nonmarket activities that cause
residents to traverse the city. For example, in our gentrification exercises,
the reduction in the value of restaurant consumption for incumbent residents is much more attributable to increases in social frictions than changes
in restaurant characteristics. These demographic changes presumably haveconsequences for many nonrestaurant dimensions of urban life.
We use our estimates to characterize predicted consumption segregation
for the city’s population. While spatial frictions, social frictions, and demographic differences in tastes cause dissimilarity in consumption choices
across racial and ethnic groups, dissimilarity indices for consumption are
considerably lower than the dissimilarity indices for residential locations.
Life in NYC is less segregated than one might infer from looking at residential segregation alone. Our analysis of these patterns reveals that social
frictions contribute more to consumption segregation than spatial frictions. A consequence of this finding is that improved transportation link-

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1736

journal of political economy

ages within the city would only modestly integrate consumption further,
given existing residential patterns. Eliminating social frictions would resultin substantially more integrated consumption.
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