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The winners and the losers of the platform economy: who participates?

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The platform economy is rapidly transforming the dynamics of the labor market. Optimists argue platform work functions as a social equalizer, opening opportunities for additional earnings for those who need it most. Pessimists suggest that the platform economy widens earning disparities by providing additional income to people who already have good jobs. We contribute to this debate by examining who participates in the platform economy and their motivation for participation, using a US nationally representative sample. Our findings offer support for both perspectives. Those who participated in labor-exchange platforms were more likely to come from disadvantaged backgrounds. By contrast, those who participated in online selling platforms were more likely to come from more affluent backgrounds. When we further examined different types of platform work, we found that different types of platform work were performed by different demographic and social groups. In addition, participation in some platform work, such as rideshare driving and house/laundry cleaning, is motivated out of necessity, while other platform work, such as selling used goods and performing online tasks, is generally used to supplement incomes. Distinct occupations tend to benefit different social groups in different ways and, taken together, disadvantaged groups are less likely to perform types of platform work that would improve their economic position and reduce income disparities. This tends to offer more support for the pessimist’s perspective. We conclude that the platform economy is strongly segregated by occupation and it should be examined as a set of distinct occupations rather than a homogenous industry.
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Information, Communication & Society
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The winners and the losers of the platform
economy: who participates?
Lyn Hoang, Grant Blank & Anabel Quan-Haase
To cite this article: Lyn Hoang, Grant Blank & Anabel Quan-Haase (2020): The winners and the
losers of the platform economy: who participates?, Information, Communication & Society, DOI:
10.1080/1369118X.2020.1720771
To link to this article: https://doi.org/10.1080/1369118X.2020.1720771
Published online: 04 Feb 2020.
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The winners and the losers of the platform economy: who
participates?
Lyn Hoang
a
, Grant Blank
b
and Anabel Quan-Haase
a
a
Department of Sociology, University of Western Ontario, London, ON, Canada;
b
Oxford Internet Institute,
University of Oxford, Oxford, UK
ABSTRACT
The platform economy is rapidly transforming the dynamics of the
labor market. Optimists argue platform work functions as a social
equalizer, opening opportunities for additional earnings for those
who need it most. Pessimists suggest that the platform economy
widens earning disparities by providing additional income to
people who already have good jobs. We contribute to this debate
by examining who participates in the platform economy and their
motivation for participation, using a US nationally representative
sample. Our ndings oer support for both perspectives. Those
who participated in labor-exchange platforms were more likely to
come from disadvantaged backgrounds. By contrast, those who
participated in online selling platforms were more likely to come
from more auent backgrounds. When we further examined
dierent types of platform work, we found that dierent types of
platform work were performed by dierent demographic and
social groups. In addition, participation in some platform work,
such as rideshare driving and house/laundry cleaning, is
motivated out of necessity, while other platform work, such as
selling used goods and performing online tasks, is generally used
to supplement incomes. Distinct occupations tend to benet
dierent social groups in dierent ways and, taken together,
disadvantaged groups are less likely to perform types of platform
work that would improve their economic position and reduce
income disparities. This tends to oer more support for the
pessimists perspective. We conclude that the platform economy
is strongly segregated by occupation and it should be examined
as a set of distinct occupations rather than a homogenous industry.
ARTICLE HISTORY
Received 4 September 2019
Accepted 19 January 2020
KEYWORDS
Platform economy; digital
labor; work; internet
Introduction
Earning income online has become a global phenomenon (Graham, Hjorth, & Lehdon-
virta, 2017; Huws, Spencer, & Joyce, 2016; Lehdonvirta, 2018). However, like other econ-
omic changes the platform economy has winners and losers. Who benets and who loses?
The answer to this question has revolved around two opposing perspectives. On the one
hand, digital optimists argue that the platform economy has increased the bargaining
power of workers by allowing them to transcend their local labor markets and nd
© 2020 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Lyn Hoang lhoang3@uwo.ca Department of Sociology, University of Western Ontario, Social Science
Centre room 5306, London, ON, Canada N6A 5C2
INFORMATION, COMMUNICATION & SOCIETY
https://doi.org/10.1080/1369118X.2020.1720771
work globally (Fabo, Karanovic, & Dukova, 2017). Optimists suggest that the platform
economy reduces unemployment by supplying jobs to individuals who typically face bar-
riers to labor force entry (Graham et al., 2017). Additionally, the exibility aorded by the
platform economy allows individuals to supplement their existing incomes (Drahokoupil
& Jepsen, 2017; Fabo et al., 2017), which can be a particular benet to low-income workers
in precarious jobs.
On the other hand, pessimists argue that platforms disadvantage workers because
employers operate outside current regulatory frameworks, thereby reducing labor protec-
tions and workersrights (Silberman & Harmon, 2018; Stewart & Stanford, 2017). Instead
of providing alternative work that equalizes opportunities, the precarious, low pay of the
platform economy worsens income gaps (Drahokoupil & Jepsen, 2017; Healy, Nicholson,
& Pekarek, 2017; Lehdonvirta, 2018). Digital inequalities often mirror existing social
inequalities so that traditionally disadvantaged groups are also disadvantaged online
(Lutz, 2019). Consequently, the auent and wealthy benet most from the use of plat-
forms to generate prot from their capital (Ravenelle, 2017; Schor, 2017), while disadvan-
taged groups rarely reap equivalent benets (Lutz, 2019).
Which perspective is true? Given that vast numbers of people have decided to work in
the platform economy instead of working in traditional jobs, it is important to determine
who actually benets from platform work. This question is especially pressing given that
inequality has risen drastically in recent decades (Wilkinson & Pickett, 2010). If the plat-
form economy is neutral or even reverses inequality, then governments can promote plat-
form work as a legitimate alternative to traditional work to combat poverty and
unemployment (Graham et al., 2017; Ravenelle, 2017). However, if the platform economy
further benets already advantaged social groups, the rise of platform work could exacer-
bate existing and emerging inequalities.
The present paper sheds light on this debate by analyzing the American Trends Panel
(ATP) 2016 data, a US nationally representative sample. In his analysis of the data,
A. Smith (2016) investigated the importance of demographic variables in the rate of par-
ticipation in various types of platform work. We expand this descriptive investigation by
drawing on occupational segregation to examine dierent types of platform work. We also
examine how income-based needs aect participation in dierent types of platform work.
To address these objectives, we investigate three related research questions:
RQ1: Who participates in platform work?
RQ2: Do those who participate in dierent types of platform work have dierent demo-
graphic characteristics?
RQ3: Why do people participate in platform work? How important is the income earned
from platform work?
Examining platform work in the US context is important for several reasons. First, the US
is leading the growth of the global platform economy (Consultancy, 2018), and many well-
known platform companies originated in the US. In fact, American-based companies gen-
erate three-quarters of the total revenue of the global platform economy (Evans & Gawer,
2016). Also important is that the US regulatory context is distinct from other parts of the
world: neoliberal employment laws allow companies to operate more freely in comparison
to companies located in Europe and Asia, providing a more favorable regulatory
2L. HOANG ET AL.
framework for growth (Consultancy, 2018; Dittrich, 2018). In addition, van Doorn (2017)
stresses the need for US studies because with its distinct socio-economic climate and racial
history, many of the issues and insights will also increasingly pertain to other Wes-
tern countries(p. 900). Thus, since the US is at the forefront of platform work it can serve
as an important case study providing insight into how the platform economy intersects
with existing and emerging inequalities.
Literature review
Dening the platform economy
We follow current practice by using the term platform economy. It has several advantages
over other terms: the term is relatively neutral (Kenney & Zysman, 2016), and it functions
as an umbrella concept, encompassing a wide range of platforms (Lehdonvirta, Kässi,
Hjorth, Barnard, & Graham, 2019; Schwellnus, Geva, Pak, & Veidel, 2019). The European
Commission (2016) also favors the broader term platform economy and denes it as
including all business models and capital earning activities facilitated by collaborative
websites, mobile apps, and social networking sites that create an open marketplace for
goods or services produced and/or provided by individuals.
This broad denition of the platform economy includes: (1) one-sided business-to-con-
sumer platforms, such as Amazon and Netix, (2) multi-sided platforms that match
workers to consumers and/or businesses on a per-service or gig basis, such as Uber and
Amazon Mechanical Turk, and (3) multi-sided peer-to-peer platforms, such as Airbnb
and eBay (Schwellnus et al., 2019). To stress the common element of exploitation under-
lying platform work, scholars such as Fuchs (2014) have treated it as a homogenous indus-
try rather than examining specic occupations. This uniform treatment of platform work
overlooks dierences in compensation, working hours, and the material resources and
skills required to complete work. It also disregards the physical, ideological, and social
impacts that dierent types of platform work have on the lives and bodies of workers
(Fuchs, 2014). To take into account these dierential impacts, we move away from treating
platform work as a homogenous category, deconstructing platform work into several
diverse earning opportunities with distinct outcomes (Newlands, Lutz, & Fieseler, 2018;
Schor, 2017).
Perspectives on platform work
Optimists: platform work as a potential equalizer
Optimists point to a long list of advantages of platform work over traditional work and
stress how its low entry requirements remove traditional barriers to labor market entry
thereby functioning as a social equalizer (Drahokoupil & Jepsen, 2017; Kenney & Zysman,
2016). For example, the platform economy has simplied the process of nding and per-
forming work so that jobseekers can begin earning immediately, bypassing long hiring or
unpaid training periods (Peticca-Harris, deGama, & Ravishankar, 2018). This benets
individuals looking to smooth the transition between traditional jobs and also those seek-
ing to earn money while pursuing non-earning endeavors such as schooling, childcare,
and caring for aging parents (Hall & Krueger, 2018; Peticca-Harris et al., 2018). Platform
companies also make earning a living easier by fostering delocalized work, remote work,
INFORMATION, COMMUNICATION & SOCIETY 3
and exible work hours (Drahokoupil & Jepsen, 2017; Fabo et al., 2017). Unemployed
individuals may transcend local job shortages by working for transnational corporations,
often while remaining in their local communities. It empowers underemployed workers,
who can engage in skill arbitrage, meaning they can sell their labor capabilities to global
companies for more than they would earn in their local markets (Graham et al., 2017).
Another equalizing feature is the zero-hour contracts oered by many platform compa-
nies, which enable individuals to work as much (or as little) as they self-determine without
being penalized for stopping or restarting (Peticca-Harris et al., 2018). Control over work-
ing hours makes it easier for individuals to accommodate other social roles and responsi-
bilities (i.e., caregiver, parent, student), which may preclude them from working
traditional jobs (Peticca-Harris et al., 2018; van Doorn, 2017).
Platform work can also serve as a social equalizer by reducing economic exclusion on
the basis of age, religion, race, class, gender, and disability (Graham et al., 2017). For
example, entry barriers into traditional taxi and chaueur industries make it more dicult
for young people and women to obtain such work, which is why there are higher percen-
tages of these social groups working for Uber (Hall & Krueger, 2018).
Pessimists: platform work as exploitation
Pessimists suggest that the platform economy promotes neoliberal economic trends and
policies which treat workers as a commodity and undermine market regulations (Schor,
2017; Silberman & Harmon, 2018). In the platform economy, individuals no longer
work for a single employer, instead they perform tasks for multiple companies as self-
employed contractors (Stewart & Stanford, 2017). This deconstruction of the employer-
worker relationship shifts the risk of prot or loss onto the individual (Kuhn, 2016),
and erodes the responsibilities and obligations expected from employers (Drahokoupil
& Jepsen, 2017; Stewart & Stanford, 2017). Further to adding economic risks, it excludes
individuals from an array of rights and benets including minimum wage, paid sick/
vacation leave, parental leave, overtime pay, health insurance, pensions, and compensation
from work-related illness/injury, and a right to safe working conditions (Silberman & Har-
mon, 2018). In particular, when these fundamental rights are disregarded, these individ-
uals are left alone to cope with often dicult situations.
Digital platforms atomize once whole work activities into microtasks distributed among
a number of competing individuals in an aggressive global market (Drahokoupil & Jepsen,
2017; Healy et al., 2017; Lehdonvirta, 2018). As a consequence, the platform economy does
not create rewarding jobs, but rather disenfranchises workers, who wait for the next poorly
paid gig to materialize online (Healy et al., 2017; Stewart & Stanford, 2017). Delocalization
of workoften viewed as an advantage of platform workaccelerates the race to the bottom
by further cheapening labor. Due to delocalization, employers are no longer limited to
their local market, nor do they have to physically move their operations to lower-cost
countries. Employers can practice labor arbitrage, buying labor digitally for less (Graham
et al., 2017), thereby forcing individuals to undercut one another and work for less com-
pensation to secure work (Fabo et al., 2017; Graham et al., 2017).
In sum, optimists argue that platform work can reduce inequality or even reverse it by
providing disadvantaged social groups with alternative and exible work to help ll
income or employment gaps. By contrast, pessimists contend that platform work exploits
individuals and reduces their labor power which further exacerbates existing inequalities.
4L. HOANG ET AL.
To our knowledge, the current literature has not reconciled whether or not the platform
economy benets some social groups more than others. One way to assess these opposing
views is to deconstruct platform work and use occupational segregation theory to look at
dierent types of work.
Theoretical framework
Platform work has been conceptualized as a homogenous industry to stress common
elements of exploitation that underlie all platform work (Fuchs, 2014). Yet, platform
work contains very dierent ways in which people earn money. While platform work
can be categorized in dierent ways, a useful distinction separates online selling from
labor-exchange platform work (Baber, 2019; Smith, 2016). In labor-exchange platform
work, people can quickly exchange their labor, time, and skills for income. By contrast,
in online selling platform work people use their personal assets or goods to earn
money. Distinguishing between these two types of platform work demonstrates that
each is associated with a dierent set of earnings, rewards, and social status. For example,
earning US$300/night renting a luxury condo in New York City does not provide the same
rewards as driving for a ridesharing company at 2 am in New York City for US$40. This
motivates our investigation into who participates in what type of platform work.
To examine dierential participation in types of platform work, we employ occu-
pational segregation as our theoretical framework. Occupational segregation describes
the distribution of workers across dierent occupations based on demographic character-
istics (Tilcsik, Anteby, & Knight, 2015), and is a key mechanism underlying inequalities
within the workplace including income gaps, job authority, and allocation of promotions
(Reskin, 1993). The literature suggests that two mechanisms underlie occupational segre-
gation: sorting and self-selection.
Sorting occurs when social groups sort into particular types of work on the basis of
demographic characteristics to match preexisting notions of the appropriateworker
(Demiralp, 2011). Occupational sorting is well-established in traditional industries
(Charles & Grusky, 2004), and some evidence suggests that it is also in platform work.
For example, Hall and Krueger (2018) found that more women work as Uber drivers
than as taxi drivers, yet their representation within this industry is still considerably
lower than the overall share of women in the workforce. This suggests that fewer
women sort into this particular type of platform work since it is still regarded as mens
work. We propose that cultural and social norms inuence who performs what type of
platform work.
The second mechanism, self-selection,stresses the role of individual agency in choos-
ing occupational roles. Individuals self-select into occupations, or rather they choose jobs
that better match their behavioral disposition or their preferences for the jobs character-
istics including wages, social benets, geographic location, and perceptions about the
work environment (Antecol, Jong, & Steinberger, 2008; Tilcsik et al., 2015). Research
by Ayers, Banaji, and Jolls (2015) demonstrates that individuals may self-select into or
out of certain platform work depending on their personal preferences or experiences
with discrimination. For instance, they found evidence that prospective buyersracial
prejudice negatively impacted African American sellers on eBay marketplace who con-
sistently received lower bids, or fewer oers for their goods in comparison to white sellers
INFORMATION, COMMUNICATION & SOCIETY 5
(Ayers et al., 2015). These researchers concluded that buyers rely on their racialized per-
ceptions about sellers in order to minimize their risk when purchasing items that cannot
be inspected beforehand (Ayers et al., 2015). This suggests that past experiences of dis-
crimination can cause marginalized groups to go to great lengths to avoid disclosing or
signaling race (Ayers et al., 2015), or these individuals may later self-select out of this
work entirely, as the earnings for performing this work may not be suciently rewarding.
Drawing on the theoretical framework of occupational segregation, we do not see all
jobs in the platform economy as equally accessible to all social groups. Even though plat-
form work provides alternatives to traditional employment, we argue that social processes
such as occupational segregation with mechanisms of sorting and self-selection preclude
this type of work from being an equalizer.
Methods
We draw from a 2016 Pew Research Center dataset, the American Trends Panel (ATP)
Wave 19. The ATP Wave 19 allows us to eectively address the research questions because
it is a nationally representative survey of US adults that provides extensive information
regarding online earning activities. The nal weighted sample is broadly representative
of the US population (Table 1) and has been weighted in three stages to (1) adjust for
dierential probabilities of selection into the survey, (2) account for dierential propensity
to join the panel and remain active, and (3) align the sample to population benchmarks
(e.g., gender, age, education, race, and daily internet usage). The survey was available in
English and Spanish. To answer RQ1 and RQ2, the ATP Wave 19 contains key variables
measuring a respondents background. Most importantly, to answer RQ3, the ATP Wave
19 contains variables measuring how the income earned through platform work contrib-
utes to an individuals income needs.
Table 1. Weighted sample characteristics, (N= 4579).
Percent Mean SD
Sex Male 49%
Age 1829 years 12%
3049 years 28%
5064 years 32%
65+ years 28%
Marital status Married/cohabiting 63%
Divorced/separated/widowed 21%
Never married 16%
Race White 78%
Black 8%
Hispanic 8%
Other races 6%
Education College graduate+ 50%
Some college 32%
High school or less 18%
Income $35,400 $24,800
Citizenship US citizen 98%
Region Northeast 18%
Midwest 22%
South 36%
West 24%
Data source: 2016 American Trends Panel Wave 19.
6L. HOANG ET AL.
Sample
The response rate for the ATP Wave 19 is acceptable: 82% and 74% for the online and
paper-based versions, respectively. The combined sample comprises N= 4579, of which
49% were males (see Table 1). The majority of respondents were white (78%), followed
by black (9%), Hispanic (8%), and people who reported otherraces (6%). The mean
income for the sample was US$35,400. Twenty-four percent of respondents reported hav-
ing participated in platform work, making it an important dataset on the topic. While our
sample is representative of the US population in terms of sex and region, respondents were
generally older and more educated than the average US citizen (Smith, 2019).
Measures: dependent variables
Platform economy as industry. We computed three variables to measure participation in
the platform economy in dierent categories of platform work. The ATP Wave 19
included questions about respondentsparticipation in multi-sided platform work includ-
ing labor-exchange platform work and online selling platform work (Smith, 2016). The
ATP Wave 19 did not include questions on one-sided business-to-consumer platforms
because this type of platform work--like working for Netix--is limited to a few specialized
workers (e.g., lm directors); thus, reaching this population via an online or telephone sur-
vey would be dicult. All three dependent variables were dichotomized; non-participation
was coded as 0and participation was coded as 1.
Our rst dependent variable participation in platform workis based on the notion of
platform work as a homogenous industry (n= 1088) and measures overall participation in
the platform economy in the past year. It includes participation through labor-exchange
platforms and/or online selling platforms.
The second dependent variable participation in online selling platformsincludes only
people who have sold something online in the past year (n= 914). The third dependent
variable participation in labor-exchange platformsexamines people who have partici-
pated in labor-exchange platform work (n= 401). The three variables allow us to disam-
biguate overall eects from those specic to labor-exchange platform work. It is important
to note that participation in online selling platformsand participation in labor-exchange
platformsare not mutually exclusive; there were 227 respondents who reported partici-
pating in both.
Platform economy as types of work. We computed ve additional variables that decon-
struct types of labor-exchange platform work into rideshare driving,delivery,online
tasks(e.g., coding, data entry, and taking surveys), house/laundry cleaning,orother
platform work(e.g., babysitting, mystery shopping, and legal services). We also computed
four variables to deconstruct types of online selling into selling used goods,selling home-
made goods,selling consumer brands, and selling other goods(e.g., insurance, sports
tickets, and artwork). All variables were dichotomized: 0= did not engage in this type
of platform work in the past year and 1= did engage.
Measures: independent variables
Demographic variables. Key demographic variables include sex, age, marital status, race,
education, income, citizenship status, and region. Age was coded as a four-category
INFORMATION, COMMUNICATION & SOCIETY 7
variable: 1829-year-olds,3049-year-olds,5064-year-olds, and 65+. Marital status
was recoded as: married/cohabiting,divorced/separated/widowed, and never married.
Race was coded as white,black,Hispanic, and other races. Education was coded as
high school degree or less,some college, and college graduate or higher. Self-reported
earnings were also included as a continuous measure of income. A dummy variable was
included to measure citizenship status and distinguished US citizenfrom non-US citi-
zen. This variable is relevant because the platform economy can be particularly benecial
to individuals who do not have legal visas or work permits required to work in a country
(Graham et al., 2017). Region was a control variable and coded as four dummies: North-
east,Midwest,South, and West.
Income-based needs. A key independent variable in our analysis was self-reported income-
needs. Respondents were asked how the income they earned contributed to satisfying their
basic needs. We recoded this variable into two categories to distinguish clearly the impor-
tance of income: 0for cases where the income was either essential or important to meet-
ing ones basic needs, and 1for cases where the respondent reported they can live
comfortably without the additional income. For our analyses, this variable provided
insights into the extent to which earnings from platform work dierentially benets
individuals.
Data analysis
We present rst the weighted sample distribution for participation in the platform econ-
omy (Table 2), comparing those who did participate in platform work overall (labor-
exchange and/or online selling) to those who did not participate in any form of platform
work. We also examine participation in labor-exchange and online selling platform work.
Then, we report the logistic regression analyses (Table 3) to demonstrate which demo-
graphic factors are associated with participation in online selling and labor-exchange plat-
form work. Finally, we present the results from logistic regressions (Table 4) on each
subtype of online selling and labor-exchange platform work to show how respondents per-
ceive the income they earn from their participation.
Results
RQ1: who participates in platform work?
Table 2 addresses RQ1: who participates in platform work? When looking at those who
did not participate in platform work in the past year, 55% of this group were female com-
pared to 46% of those who did participate in platform work. Fifty-three percent were over
the age of 50, whereas those who engaged in platform work were younger: only 25% were
over 50 years old. Those who participated in platform work were about as likely to be mar-
ried or cohabiting, but they were six percentage points more likely to be never married
(22% vs 28%). Racial characteristics were within two percentage points. Those who
engaged in platform work were six percentage points more likely to be college graduates
(28% vs. 35%), and 12 percentage points less likely to have no more than a high school
degree. Individuals who participated in platform work also reported about US$3,000
more in average income compared to those who did not participate ($34,700 vs. $37,800).
8L. HOANG ET AL.
In sum, we found dierences in sex, age, education, and income between those who did
not participate in any platform work and those who had. More specically, those who par-
ticipated in platform work were more likely to be male, younger (under the age of 50 years
old), better educated, and have greater average incomes than those who did not participate
in platform work in the past year.
Table 2. Weighted sample characteristics for participation in platform economy, (N= 4579).
Did not participate in
any platform work
Participated in platform
work
Participated in online
selling platforms
Participated in labor-
exchange platforms
%|M(SD) CI (±1.7%) % | M(SD) CI (±3.0%) % | M(SD) CI (±3.2%) % | M(SD) CI (±4.9%)
Sex (Male) 45.3 (43.6, 47) 54.1 (51.1,
57.1)
56.1 (52.9,
59.3)
46.9 (42, 51.8)
Age
1829 years 18.2 (16.5,
19.9)
30.3 (27.3,
33.3)
26.2 (23, 29.4) 41.2 (36.3,
46.1)
3049 years 29.1 (27.4,
30.8)
44.5 (41.5,
47.5)
48.1 (44.9,
51.3)
38.0 (33.1,
42.9)
5064 years 29.8 (28.1,
31.5)
18.2 (15.2,
21.2)
18.5 (15.3,
21.7)
16.1 (11.2, 21)
65+ years 22.9 (21.2,
24.6)
7.0 (4, 10) 7.2 (4, 10.4) 4.7 (0.2, 9.6)
Marital status
Married/
cohabiting
55.1 (53.4,
56.8)
56.3 (53.3,
59.3)
58.8 (55.6, 62) 49.2 (44.3,
54.1)
Divorced/
separated/
widowed
23.1 (21.4,
24.8)
15.6 (12.6,
18.6)
16.2 (13, 19.4) 17.6 (12.7,
22.5)
Never married 21.8 (20.1,
23.5)
28.1 (25.1,
31.1)
24.9 (21.7,
28.1)
33.2 (28.3,
38.1)
Race
White 65.2 (63.5,
66.9)
65.0 (62, 68) 71.0 (67.8,
74.2)
45.0 (40.1,
49.9)
Black 11.7 (10, 13.4) 10.5 (7.5, 13.5) 7.0 (3.8, 10.2) 19.2 (14.3,
24.1)
Hispanic 15.5 (13.8,
17.2)
14.5 (11.5,
17.5)
13.6 (10.4,
16.8)
21.0 (16.1,
25.9)
Other 7.6 (5.9, 9.3) 10.0 (7, 13) 8.4 (5.2, 11.6) 14.8 (9.9, 19.7)
Education
College
graduate+
27.6 (25.9,
29.3)
35.2 (32.2,
38.2)
38.5 (35.3,
41.7)
24.6 (19.7,
29.5)
Some college 31.5 (29.8,
33.2)
35.4 (32.4,
38.4)
36.9 (33.7,
40.1)
34.7 (29.8,
39.6)
High school or
less
40.9 (39.2,
42.6)
29.4 (26.4,
32.4)
24.6 (21.4,
27.8)
40.7 (35.8,
45.6)
Income $34,700
(25,000)
(34,110,
35,289)
$37,800
(25,000)
(36,666,
38,934)
$40,400
(24,000)
(39,107,
41,693)
$29,400
(24,000)
(27,959,
30,841)
Citizenship (US
citizen)
95.3 (93.6,97) 94.8 (91.8,
97.8)
99.9 (96.7,
103.1)
89.3 (84.4,
94.2)
Region
Northeast 19.6 (17.9,
21.3)
16.2 (13.2,
19.2)
17.8 (14.6, 21) 10.8 (5.9, 15.7)
Midwest 21.0 (19.3,
22.7)
21.4 (18.4,
24.4)
24.0 (20.8,
27.2)
12.6 (7.7, 17.5)
South 37.2 (35.5,
38.9)
37.5 (34.5,
40.5)
34.3 (31.1,
37.5)
49.8 (44.9,
54.7)
West 22.2 (20.5,
23.9)
24.9 (21.9,
27.9)
23.9 (20.7,
27.1)
26.8 (21.9,
31.7)
n3491 1088 914 401
Data source: 2016 American Trends Panel Wave 19.
Note: CI denotes 95% condence intervals based on a simple random sample. M(SD) denotes mean and standard deviation.
INFORMATION, COMMUNICATION & SOCIETY 9
To further examine RQ1, we compare the characteristics of respondents who partici-
pated in online selling (70%) and those who participated in labor-exchange platform
work (30%; Table 2). We found more males participated in online selling (56%) compared
to labor-exchange platform work (47%), this 9% represents a considerable dierence in
occupational segregation. In terms of age, the majority of respondents who participated
in labor-exchange platform work were between 18 and 29-year-olds (41%). By contrast,
nearly 50% of those who engaged in online selling were 3049-year-olds. This dropped
to 38% when we examined participation in labor-exchange for the same age group.
Fifty-nine percent of respondents who were married or cohabiting reported engaging in
online selling compared to forty-nine percent who reported participating in labor-
exchange. Those who engaged in labor-exchange were also eight percentage points
more likely to be never married compared to those who engaged in online selling (25%
vs 33%). Mean incomes were considerably higher for those who participated in online sell-
ing (US$40,400) in comparison to those who participated in labor-exchange platform
work (US$29,400). College graduates also show greater participation in online selling
than in labor-exchange platform work, but this trend is reversed for those with high school
or less. No dierences in participation were found between individuals with some college.
We found a few regional dierences for participation in online selling versus labor-
exchange platform work.
In sum, we found dierences across all demographic variables between participating in
online selling and labor-exchange platform work. In particular, those who engage in online
Table 3. Odds ratios from logistic regressions predicting participation in labor-exchange platforms and
online selling platforms.
Labor-Exchange Platforms Online Selling Platforms
Sex (Male)
Female 1.11 0.68**
Age (1829 years)
3049 years 0.58* 0.99
5064 years 0.21*** 0.39***
65+ years 0.09*** 0.18***
Marital Status (Married/cohabiting)
Divorced/separated/widowed 1.28 1.25
Never married 0.90 0.77
Race (White)
Black 1.76 0.60
Hispanic 1.32 0.82
Other 1.77 0.81
Education (College graduate+)
Some college 1.33 0.90
High school or less 1.22 0.54**
Income 0.92 1.04
Region (Northeast)
Midwest 1.01 1.28
South 2.41*** 1.15
West 1.53 1.25
Citizenship (US citizen)
non-US citizen 1.51 0.42*
Constant 0.10*** 0.46*
N4414 4424
McFaddensR
2
0.115 0.081
Data source: 2016 American Trends Panel Wave 19.
Notes: Reference categories in parentheses. *p< 0.05. **p< 0.01. ***p< 0.001.
10 L. HOANG ET AL.
Table 4. Odds ratios from logistic regressions predicting participation in platform work by type.
Labor-Exchange Platforms Online Selling Platforms
Rideshare
driving Delivery
Online
tasks
House/laundry
cleaning
Other platform
work
Selling used
goods
Selling homemade
goods
Selling consumer
brands
Selling other
goods
Sex (Male)
Female 0.19** 0.58 0.79 3.80* 1.18 1.09 0.71 0.86 0.72
Age (1829 year olds)
3049 year olds 1.26 0.48 0.17** 0.36 3.64* 0.64 1.62 0.94 1.74
5064 year olds 0.38 0.64 0.27* 0.67 2.25 0.62 1.08 0.60 3.15*
65 or older 0.34 0.98 0.25 0.29 6.56* 0.71 0.95 0.62 2.39
Marital Status (Married/cohabiting)
Divorced/separated/
widowed
1.72 0.64 0.55 2.05 1.10 0.74 1.14 0.47 1.38
Never married 1.06 2.05 0.61 1.72 0.85 0.58 1.74 1.54 1.32
Race (white)
Black 6.16* 3.74 1.90 1.70 0.23* 0.63 1.56 6.73*** 0.32
Hispanic 1.32 0.97 1.44 0.10* 0.61 0.69 0.26 1.73 1.68
Other 18.8*** 1.61 2.02 0.28 0.23 0.70 1.78 1.29 2.22
Education (College graduate+)
Some college 0.67 1.97 2.27 1.87 0.32* 0.73 0.87 2.04 1.05
High school or less 2.50 1.97 1.69 3.29 0.17** 0.54 0.64 2.63 1.49
Income 1.47** 1.07 0.99 1.09 0.69** 1.02 0.89 0.97 0.98
Region (northeast)
Midwest 0.32 2.21 2.66 1.09 0.63 2.15 1.15 0.44 0.64
South 0.52 0.35 1.08 1.12 1.28 1.12 2.04 0.39 1.05
West 1.83 1.22 2.57 0.41 0.88 1.62 2.95* 0.54 0.61
Citizenship Status (US citizen)
non-US citizen 2.85 0.64 0.69 1.78 0.18 6.10 0.62 2.23 1.26
Income-based needs (Essential/important)
Could live without it 0.19** 0.51 3.66** 0.24* 1.04 2.68*** 0.37** 0.56 0.29***
Constant 0.06* 0.14 1.48 0.09 2.44 2.08 0.20 0.25 0.28
n272 272 272 272 272 890 890 890 890
McFaddensR
2
0.31 0.14 0.18 0.24 0.18 0.09 0.11 0.14 0.12
Data source: 2016 American Trends Panel Wave 19. Note: Reference categories in parentheses. *p< 0.05. **p< 0.01. ***p< 0.001.
INFORMATION, COMMUNICATION & SOCIETY 11
selling tend to come from more advantageous backgrounds: they are more likely to be
male, white, middle-aged, college-educated, and have greater average incomes than
those who perform labor-exchange platform work. By contrast, those who participate in
labor-exchange are more likely to come from more disadvantaged backgrounds: female,
under 30 years old, non-white (black, Hispanic, and other races), and have no more
than a high school education. This calls for more nuanced examinations of the dierent
types of platform work, as it cannot be viewed as a homogenous industry with equal par-
ticipation across social groups.
RQ2: do those who participate in dierent types of platform work have dierent
demographic characteristics?
RQ2 is addressed in Table 3, which presents the logistic regressions predicting partici-
pation in labor-exchange platform work and online selling platform work. We comment
primarily on statistically signicant coecients. When looking at participation in labor-
exchange platforms, 1829-year-olds have greater odds of participating compared to
older age groups. Those aged 3049 are 42% less likely to participate in labor-exchange
platforms compared to the youngest age group. More signicantly, we found that those
aged 5064 and those aged 65 and older had much lower odds of participating, they
are 79% and 91% less likely to participate, respectively. We also found that in terms of
region, those living in the South are more than twice as likely to participate in labor-
exchange platforms compared to those in the Northeast.
In terms of participating in online selling platforms, we found that females were 32%
less likely to engage in online selling than males. Age also predicts participation in online
selling, with those aged 5064, and those aged 65 and older having signicantly lower odds
of participating than 1829-year-olds. Education also plays a role in terms of online sell-
ing. Respondents with no more than a high school education have half the odds of enga-
ging in online selling than college graduates. Interestingly, citizenship status was
signicant with non-US citizens having less than half the odds of participating than US
citizens.
These ndings reveal that there are dierences in demographic characteristics of those
who participate in labor-exchange and online selling platform work. To further explore
whether social groups have dierent chances to participate in dierent types of platform
work, and to address whether occupational segregation is occurring, we examine subtypes
of platform work and RQ3 next.
RQ3: why do people participate in platform work? How important is the income
earned from platform work?
RQ3 is addressed in Table 4.Werst present the logistic regressions for participation in
the ve types of labor-exchange platforms and the four types of online selling platforms.
Then we discuss the importance of the income earned for participation in each type of
platform work. Again, we comment primarily on statistically signicant coecients. Ride-
share drivers are 81% more likely to be male than female. Race is the biggest predictor of
driving for rideshare: black respondents were 6.1 times more likely, and individuals from
other races were over 18.8 times more likely to drive than white respondents. It is
12 L. HOANG ET AL.
important to note that due to the small number of respondents who reported being from
other races and driving for rideshare, this odds ratio of 18.8 is unstable. Income is posi-
tively associated: each increment of income increases the likelihood of being a rideshare
driver by 47%.
Looking at online tasks in Table 4, respondents are generally younger. In particular, 18
29-year-olds are signicantly more likely to perform this platform work than older indi-
viduals. By contrast, those who perform other platform work are older, and seniors are six
times more likely to perform this work than 1829-year-olds. These individuals are also
highly educated; college graduates have the greatest odds of performing other platform
work compared to those with less education.
Females are over three times more likely to engage in house/laundry cleaning than
males. In terms of race, Hispanics have lower odds for engaging in this work compared
to white respondents.
For selling used goods and homemade goods, there are no statistical dierences by sex,
age, marital status, race, education, and income. However, respondents living in the West
are twice as likely to sell homemade goods compared to those living in the Northeast.
Interestingly, the only strong predictor for selling consumer brands is race: black respon-
dents have over six times the odds of engaging in this activity than white respondents. A
noteworthy nding is that unlike the other types of selling, sellers of other goods are older.
Respondents aged 5064 have more than three times the odds of participating in this plat-
form work than the youngest age group.
These ndings demonstrate that the chances of participating in the various types of
platform work do dier according to demographic characteristics. To provide context
on why this may occur, we examine income needs. We found signicant dierences across
all types of platform work. Respondents who indicate that they could live without the
income earned from platform work have 3.7- and 2.7-times greater odds of performing
online tasks and selling used goods, respectively. This suggests that respondents who per-
ceive their online earnings as neither essential nor important to meeting their basic needs
are less likely to engage in any type of platform work and, if they did engage, they are more
likely to perform online tasks and sell used goods. By contrast, respondents who report
that they could live without the income from platform work are 81%, 76%, 63%, and
71% less likely to participate in ridesharing, house/laundry cleaning, selling homemade
goods, and selling other goods, respectively. In other words, those who consider their
income from platform work as essential or important to meeting their income needs
are more likely to participate in rideshare driving, house/laundry cleaning, selling home-
made goods, and selling other goods. Income-based needs seem to have no eect on deliv-
ery, selling consumer brands, and the category of otherplatform work.
Discussion
Inequality is a pressing social problem; income inequality in OECD countries is increasing,
and the size of the gap between the top and bottom 10% is unprecedented (Wilkinson &
Pickett, 2010). The platform economy has emerged as a potential equalizer, opening
opportunities for new and additional earnings for low-income individuals. Despite
much speculation, there is little empirical data showing who wins and who loses in the
platform economy. This paper provides evidence that the platform economy in the US
INFORMATION, COMMUNICATION & SOCIETY 13
does not provide identical benets for all social groups. We nd that the platform econ-
omy extends traditional work dynamics using many of the same social mechanisms to
recreate patterns of advantage or disadvantage. In other words, socioeconomic and demo-
graphic characteristics have a major impact on who performs what type of platform work.
Who benets from work in the platform economy?
When we examine overall participation in the platform economy, it appears that those
who participated in platform work come from more advantageous social backgrounds
compared to those who have not participated in platform work in the past year. In par-
ticular, males, those in their middle ages, and those with a college degree or at least
some college education participated in platform work at greater rates. However, these
initial ndings do not take into account the dierential reward systems associated with
distinct types of platform work. An individual selling a used item that they no longer
have any use for is not the same as an individual who cleans homes in order to meet
their income needs. Indeed, the platform economy is diverse not only in the types of activi-
ties performed, but in how they are compensated (Schor, 2017). Thus, when we examine
labor-exchange and online selling platform work separately, we found clear dierences in
the individuals who participated. Those who participated in labor-exchange platforms
appeared to come from more disadvantaged social backgrounds: the majority of them
are non-white, many of them were under the age of 30, most of them have no more
than a high school education, and they also have average incomes lower than US
$30,000. This supports the digital optimistsargument that the platform economy benets
disadvantaged groups by providing them with additional earning opportunities and
employment. However, when we examine the practice of selling something online as a dis-
tinct type of platform work, the platform economy becomes less favorable to disadvan-
taged social groups and instead benets middle-aged individuals, those who are college-
educated, and those who are white. These individuals had an average income greater
than US$40,000. This supports the pessimistsargument that platform work widens earn-
ing disparities by providing additional income to people who already have good jobs.
We promised at the beginning of this paper to reconcile the debate between digital opti-
mists and pessimists for whether platform work benets disadvantaged social groups by
providing them with earning opportunities, or whether it benets auent individuals
by providing them with opportunities to supplement their incomes. Our initial ndings
oer support for both perspectives because we explored work in the platform economy
through distinct occupations rather than as a homogenous industry. By doing so, we
are able to see that disadvantaged social groups and auent social groups do not partici-
pate in the same types of platform work. These nuanced ndings are important because
they indicate that while the platform economy may benet many social groups, it does
not benet them all equally since they do not engage in the same types of work.
Occupational segregation in platform work
To reiterate, we found that demographic variables are associated with participation in dier-
ent types of platform work. For instance, 1829-year-olds are more likely to engage in labor-
exchange platform work than individuals over the age of 30. By contrast, 1829-year-olds are
14 L. HOANG ET AL.
more likely to engage in online selling than individuals who are over the age of 50. As well
males, college graduates, and US citizens are more likely to engage in online selling compared
to females, those with no more than a high school education, and non-US citizens. These
ndings strongly indicate that the platform economy is occupationally segregated.
Table 4, which examines the subtypes of labor-exchange platforms and online selling
platforms, further provide evidence that dierent social groups participate in dierent
types of platform work. For instance, we found that men are more likely to work in ride-
share driving, whereas women are more likely to perform house/laundry cleaning. Simi-
larly, race predicted participation in rideshare driving, house/laundry cleaning, and the
selling of consumer brands. In particular, black respondents have higher odds of perform-
ing these types of work than white respondents. By contrast, black respondents are less
likely to perform other platform work compared to white respondents. We also found
that age predicted participation in performing online tasks, other platform work, and sell-
ing other goods. Consequently, disadvantaged individuals and auent individuals do not
have the same likelihood of participating in each type of platform work.
The ndings from Table 4 also reveal dierences in how individuals regard their earn-
ings. Individuals selling used goods or performing online tasks are more likely to use their
earnings to supplement their incomes. In comparison, individuals who engage in rideshare
driving, house/laundry cleaning, selling homemade goods, and selling consumer brands
are more dependent on their earnings to meet their basic needs. These dierences high-
light that many individuals are turning to the platform economy out of necessity. How-
ever, others are using platform work to supplement their incomes, which potentially
increases income inequality. Future research should continue to examine the motivations
that drive individuals to enter dierent parts of the platform economy.
Our ndings also reveal that entry barriers to platform work are not always low. For
instance, individuals with above average incomes are more likely to participate in ride-
share driving than those with below average incomes. A partial explanation of these
ndings is Ravenelles(2017) argument that success and higher earnings in the platform
economy require external material resources and skills. Working for ridesharing compa-
nies requires access to a personal vehicle and insurance, and these are barriers for individ-
uals with low incomes.
Taken together, these ndings provide evidence against the common-goods rhetoric
put forth by digital optimists. Based on these ndings, we suggest that the platform econ-
omy may not help disadvantaged groups reduce the income gap by providing them access
to earning opportunities because they are less likely to perform the kinds of platform work
most likely to improve their economic position. In addition, by working in the platform
economy instead of traditional occupations they have fewer protections and workers
rights, which may actually worsen their situations. By contrast, auent social groups
use their preexisting advantages to scoop up the better compensated work, which contrib-
utes to worsening income inequality.
The theory of occupational segregation helps explain how these patterns persist. The
process of matching individuals to occupations relies in part on traditional understandings
of who should perform certain types of work. Thus, mechanisms of sorting and self-selec-
tion encourage individuals to pursue certain jobs based on societal expectations of the
work as men or womens work, white or non-white, and manual or non-manual. Our
results support occupational segregation explanations of platform work: we found work
INFORMATION, COMMUNICATION & SOCIETY 15
that involves house/laundry cleaning remains womens work, while rideshare driving
remains mens work and minority work.
Limitations and future research
We found evidence of occupational segregation with social groups participating dierently
in certain types of platform work. The strong eect of age on participation in certain types
of platform work has implications for future research on dierences in digital media adop-
tion, use and mastery across age groups (Blank & Groselj, 2014; Haight, Quan-Haase, &
Corbett, 2014). For example, older adults (65+) could benet from obtaining supplemental
income for retirement. Future research should also address why younger and older adults
participate in the platform economy dierently. Research across the globe has also demon-
strated that younger adults are more likely to be online, to engage in more types of activi-
ties, and to have greater skills (Hargittai & Dobransky, 2017; Hunsaker & Hargittai, 2018;
Quan-Haase, Williams, Kicevski, Elueze, & Wellman, 2018). Examining the relationship
between participation in the platform economy, age, and digital skills could create inter-
ventions to support adults of all ages benet from exible platform work. This seems par-
ticularly important given recent changes in labor force participation, with individuals
continuing to work past the traditional age of 65 for retirement. Moreover, the platform
economy could benet older adults in other ways. For example, older adults can rent out
parts of their homes, thereby not only earning additional income to combat poverty, but
also gaining companionship (BBC News Online, 2017).
Ourstudyhasseverallimitationsthatcreateopportunities for follow-up research. First,
while representative of the US population, our dataset is not representative of the overall glo-
bal platform economy population. Future research can conduct comparative work by using
cross-country surveys to investigate how occupational segregation in the platform economy
takes place in dierent nations and regions. Second, the dataset omits other ways in which
people participate in the platform economy such as content creation. Future research can
addresstheseotherwaysofearningincomeanddeterminewhethertheseearningopportu-
nities are equally accessible across social groups. Third, the dataset included only demo-
graphic predictor variables. Future research could examine other mechanisms that
contribute to occupational segregation in platform work. Fourth, these cross-sectional data
cannot demonstrate any temporal increase or decrease in income inequality. Future research
employing longitudinal data could examine how platform work impacts social mobility.
Finally, our research does not examine other forms of social inequality such as the dierences
in the experiences of women, men, and LGBTQ+ individuals who perform the same type of
platform work. For example, do women and LGBTQ+ individuals experience prejudice or
fears of sexual abuse or violence when they perform mensworksuch as driving for
Uber? Future research could address these other types of inequalities as they too have impli-
cations for understanding how work in the platform economy becomes segregated.
Acknowledgements
Pew Research Center bears no responsibility for the analyses or interpretations of the data pre-
sented here. The opinions expressed herein, including any implications for policy, are those of
the authors and not of Pew Research Center.
16 L. HOANG ET AL.
Disclosure statement
No potential conict of interest was reported by the author(s).
Funding
This work was supported by The Research Council of Norway: [grant number 275347]; Social
Sciences and Humanities Research Council of Canada: [grant number R3603A20].
Notes on contributors
Lyn Hoang (MA, University of Western Ontario) is currently completing her Ph.D at the University
of Western Ontario. Her scholarly interests are digital sociology, new ways of working in the plat-
form economy, and social inequality.
Grant Blank (Ph.D University of Chicago) is the Survey Research Fellow at the Oxford Internet
Institute and Senior Research Fellow of Harris Manchester College, both part of University of
Oxford, United Kingdom. He is a sociologist specializing in the social and cultural impact of the
Internet, the digital divide, statistical and qualitative methods, and cultural sociology. He is cur-
rently working on analyses of the 2019 wave of the Oxford Internet Survey (OxIS). In 2015 he
was awarded the Lifetime Achievement Award from the Communication, Information Technology
and Media Sociology section of the American Sociological Association. He can be reached at grant.
blank@gmail.com; see http://www.oii.ox.ac.uk/people/blank/.
Anabel Quan-Haase is Professor of Information and Media Studies and Sociology at Western Uni-
versity and director of the SocioDigital Media Lab. Her work focuses on social change, social media,
and social networks. She engages in interdisciplinarity, knowledge transfer, and public outreach.
She is the coeditor of the Handbook of Social Media Research Methods with Luke Sloan (Sage,
2017), coauthor of Real-Life Sociology with Lorne Tepperman (Oxford University Press, 2018)
and the author of Technology and Society (3rd ed., Oxford University Press, 2020). Through her
policy work she has cooperated with the Benton Foundation, Partnership for Progress on the Digi-
tal Divide, Federal Communications Commission (FCC), and Canadas Digital Policy Forum. Dr.
Quan-Haase is chair of CITAMS for 20192020 and the past president of the Canadian Association
for Information Science.
ORCID
Lyn Hoang http://orcid.org/0000-0002-2854-9447
Grant Blank http://orcid.org/0000-0002-6821-0958
Anabel Quan-Haase http://orcid.org/0000-0002-2560-6709
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INFORMATION, COMMUNICATION & SOCIETY 19
Appendix. Weighted sample characteristics for participation in platform work by type of work
Labor-exchange platforms Online selling platforms
Online tasks Selling used goods Selling consumer brands Selling other goods
%|M(SD) CI (± 6.8%) % | M(SD) CI (± 3.9%) % | M(SD) CI (± 9.4%) % | M(SD) CI (± 8.3%)
Sex (Male) 44.7 (37.9, 51.5) 56.4 (52.5, 60.3) 60.2 (50.8, 69.6) 59.4 (51.1, 67.7)
Age
1829 years 54.6 (47.8, 61.4) 27.8 (23.9, 31.7) 36.0 (26.6, 45.4) 16.8 (8.5, 25.1)
3049 years 30.7 (23.9, 37.5) 47.8 (43.9, 51.7) 49.2 (39.8, 58.6) 44.0 (35.7, 52.3)
5064 years 11.4 (4.6, 18.2) 16.9 (13, 20.8) 11.9 (2.5, 21.3) 32.2 (23.9, 40.5)
65+ years 3.3 (3.5, 10.1) 7.5 (3.6, 11.4) 2.9 (6.5, 12.3) 7.0 (1.3, 15.3)
Marital status
Married/cohabiting 49.2 (42.4, 56) 61.6 (57.7, 65.5) 48.8 (39.4, 58.2) 51.8 (43.5, 60.1)
Divorced/separated/widowed 14.5 (7.7, 21.3) 14.2 (10.3, 18.1) 12.0 (2.6, 21.4) 24.8 (16.5, 33.1)
Never married 36.3 (29.5, 43.1) 24.2 (20.3, 28.1) 39.2 (29.8, 48.6) 23.4 (15.1, 31.7)
Race
White 44.4 (37.6, 51.2) 75.0 (71.1, 78.9) 49.5 (40.1, 58.9) 62.4 (54.1, 70.7)
Black 16.9 (10.1, 23.7) 5.1 (1.2, 9) 25.1 (15.7, 34.5) 4.4 (3.9, 12.7)
Hispanic 24.4 (17.6, 31.2) 12.4 (8.5, 16.3) 16.4 (7, 25.8) 17.9 (9.6, 26.2)
Other 14.3 (7.5, 21.1) 7.5 (3.6, 11.4) 9.0 (0.4, 18.4) 15.3 (7, 23.6)
Education
College graduate+ 16.9 (10.1, 23.7) 42.1 (38.2, 46) 20.9 (11.5, 30.3) 32.0 (23.7, 40.3)
Some college 44.6 (37.8, 51.4) 36.3 (32.4, 40.2) 49.5 (40.1, 58.9) 33.3 (25, 41.6)
High school or less 38.5 (31.7, 45.3) 21.6 (17.7, 25.5) 29.6 (20.2, 39) 34.7 (26.4, 43)
Income $26,600 (23,000) (24,791, 28,409) $42,300 (24,000) (40,650, 43,950) $33,500 (25,000) (30,351, 36,649) $34,900 (26,000) (32,003, 37,797)
US citizen 91.3 (84.5, 98.1) 97.1 (93.2, 101) 96.7 (87.3, 106.1) 97.4 (89.1, 105.7)
Region
Northeast 10.8 (4, 17.6) 17.5 (13.6, 21.4) 24.9 (15.5, 34.3) 18.4 (10.1, 26.7)
Midwest 13.2 (6.4, 20) 26.2 (22.3, 30.1) 18.4 (9, 27.8) 21.1 (12.8, 29.4)
South 49.1 (42.3, 55.9) 31.1 (27.2, 35) 32.9 (23.5, 42.3) 42.5 (34.2, 50.8)
West 26.9 (20.1, 33.7) 25.2 (21.3, 29.1) 23.8 (14.4, 33.2) 18.0 (9.7, 26.3)
n208 621 108 141
Data source: 2016 American Trends Panel Wave 19.
Notes: Categories with fewer than 100 respondents were removed due to large sampling errors.
CI denotes 95% condence intervals based on a simple random sample. M(SD) denote mean and standard deviation.
20 L. HOANG ET AL.
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