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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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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
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The winners and the losers of the platform economy: who
Lyn Hoang
, Grant Blank
and Anabel Quan-Haase
Department of Sociology, University of Western Ontario, London, ON, Canada;
Oxford Internet Institute,
University of Oxford, Oxford, UK
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.
Received 4 September 2019
Accepted 19 January 2020
Platform economy; digital
labor; work; internet
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 Department of Sociology, University of Western Ontario, Social Science
Centre room 5306, London, ON, Canada N6A 5C2
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
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,
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.
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
(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.
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.
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
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
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.
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).
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
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,
56.1 (52.9,
46.9 (42, 51.8)
1829 years 18.2 (16.5,
30.3 (27.3,
26.2 (23, 29.4) 41.2 (36.3,
3049 years 29.1 (27.4,
44.5 (41.5,
48.1 (44.9,
38.0 (33.1,
5064 years 29.8 (28.1,
18.2 (15.2,
18.5 (15.3,
16.1 (11.2, 21)
65+ years 22.9 (21.2,
7.0 (4, 10) 7.2 (4, 10.4) 4.7 (0.2, 9.6)
Marital status
55.1 (53.4,
56.3 (53.3,
58.8 (55.6, 62) 49.2 (44.3,
23.1 (21.4,
15.6 (12.6,
16.2 (13, 19.4) 17.6 (12.7,
Never married 21.8 (20.1,
28.1 (25.1,
24.9 (21.7,
33.2 (28.3,
White 65.2 (63.5,
65.0 (62, 68) 71.0 (67.8,
45.0 (40.1,
Black 11.7 (10, 13.4) 10.5 (7.5, 13.5) 7.0 (3.8, 10.2) 19.2 (14.3,
Hispanic 15.5 (13.8,
14.5 (11.5,
13.6 (10.4,
21.0 (16.1,
Other 7.6 (5.9, 9.3) 10.0 (7, 13) 8.4 (5.2, 11.6) 14.8 (9.9, 19.7)
27.6 (25.9,
35.2 (32.2,
38.5 (35.3,
24.6 (19.7,
Some college 31.5 (29.8,
35.4 (32.4,
36.9 (33.7,
34.7 (29.8,
High school or
40.9 (39.2,
29.4 (26.4,
24.6 (21.4,
40.7 (35.8,
Income $34,700
Citizenship (US
95.3 (93.6,97) 94.8 (91.8,
99.9 (96.7,
89.3 (84.4,
Northeast 19.6 (17.9,
16.2 (13.2,
17.8 (14.6, 21) 10.8 (5.9, 15.7)
Midwest 21.0 (19.3,
21.4 (18.4,
24.0 (20.8,
12.6 (7.7, 17.5)
South 37.2 (35.5,
37.5 (34.5,
34.3 (31.1,
49.8 (44.9,
West 22.2 (20.5,
24.9 (21.9,
23.9 (20.7,
26.8 (21.9,
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.
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
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.
Table 4. Odds ratios from logistic regressions predicting participation in platform work by type.
Labor-Exchange Platforms Online Selling Platforms
driving Delivery
Other platform
Selling used
Selling homemade
Selling consumer
Selling other
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)
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
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.
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
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
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.
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
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
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
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
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.
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.
Disclosure statement
No potential conict of interest was reported by the author(s).
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.; see
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.
Lyn Hoang
Grant Blank
Anabel Quan-Haase
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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)
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)
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)
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)
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.
... This model structurally benefits platform companies as workers bear the burden of employment. However, Hoang et al. (2020) argue that the gig economy should not be examined as a homogenous industry, and it is delineated by the typology of gig work. Looking at who benefits from the gig economy through a framework of occupational segregation, they find that both optimistic and pessimistic views of gig work exist. ...
... Workers can both be disadvantaged and advantaged through platform work, with some workers becoming more precarious due to limited workplace protections and others accessing further income that would have otherwise been unavailable to them. However, Hoang et al. (2020) utilise the American Trends Panel (ATP) survey from America, which includes questions of citizenship but fails to adjust for undocumented migrants. This further entrenches the marginalisation of precarious migrant workers that are unable to access the benefits that come with residency or citizenship status, furthering their need to interact with precarious labour for income (Könönen, 2019). ...
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Digital platforms are the newest technological wave that is reshaping and reconfiguring the economic and labour landscape. Digital platforms often known as the gig economy are increasingly adopting app‐based models to connect consumers with workers to complete their on‐demand tasks. However, on‐demand platforms continue to rely on the unequal division of labour and the precarious nature of the work to create labour markets that can respond accordingly to the increase in service provision. This review highlights two main themes that have emerged within the on‐demand gig economy in the current literature—mythical autonomy and algorithmic control and misclassification of labour and the complexity of migrant workers in navigating this space. Finally, this review calls for further research into the inside/outside dichotomy of migrant labour within the gig economy and their experiences of labour exploitation through app‐based digital platforms.
... Ancak ne isteyen herkes çevrimiçi işgücü piyasasına katılabilmekte ne de herkes bu piyasada kalıcı olabilmektedir. Başta yerleşik toplumsal eşitsizliklerin bir yansıması olan dijital eşitsizlikler ve geleneksel işgücü piyasasındaki istihdamın niteliği, geleneksel işgücü piyasasında dezavantajlı olan grupları çevrimiçi işgücü piyasasına katılım konusunda da dezavantajlı hale getirmektedir (Hoang, 2020). Platform çalışmasına katılanların yaş, eğitim düzeyi ve cinsiyet bileşimi bu görüşü destekler niteliktedir. ...
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Kadınların emek piyasalarına katılımı, istihdam biçimleri ve koşulları üzerinde küresel düzeyde önemli yapısal kısıtlamalar ve derin eşitsizlikler yaşanmaktadır. İmalat sektöründe ve hizmetler sektöründe uzun zamandır devam eden dış kaynak kullanımı ve taşeronlaştırma eğiliminin bir devamı niteliğinde olan ve cinsiyetsiz olduğu kabul edilen dijital emek platformlarının yükselişi, kadınların işgücü piyasasında karşılaştığı bu eşitsizlikleri azaltacağına ilişkin beklentileri yükseltmiştir. Bu beklentiye sebep olan ise, dijital çalışma platformlarına katılım konusunda çalışanlara daha az engel çıkardığı, uluslararası bir müşteri tabanına erişimi olanaklı kıldığı ve piyasaya dâhil olanlara hızlı bir şekilde gelir elde etme olanağı sağladığına ilişkin düşüncedir. Ancak dijital emek platformlarını toplumsal cinsiyet merceğinden inceleyen araştırmalar geleneksel emek piyasalarında görülen kalıcı ve ısrarlı eşitsizliklerin dijital emek platformlarına da yansıdığını göstermektedir. Bu nedenle çalışmamızda web tabanlı emek platformlarıyla sınırlı olmak üzere bu yansımaların nasıl ve neden gerçekleştiğini incelemeye çalıştık. Bunu yaparken öncelikle dijital emek platformlarının yükselişinin nedenlerine ve bu platformların çalışma ilişkisinde güvencesizlik yaratan özelliklerine odaklandık. Literatür incelemesine ve konuya ilişkin yapılmış araştırmaların sonuçlarına dayanarak makalede, dijital ve teknolojik beceri eşitsizliği nedeniyle kadınların dijital emek platformlarına erkeklere göre daha az katıldığı, mesleki ayrışma ve cinsiyete dayalı ücret farkının çevrimiçi piyasaya çok güçlü bir şekilde yansıdığı, kadınların emek platformlarına katılımının ana nedenlerinden birisinin ücretsiz bakım sorumlulukları nedeniyle evde çalışma olanağını elde etmek olduğu sonucuna ulaştık.
... This is supported by the spread of the platform economy which facilitates individuals of various crafts and industries to work as independent contractors or freelancers and thereby creates more diverse opportunities for self-employment on the labor market (Farrell and Greig 2016). This facilitation leads to a growing number of individuals from all professions and various starting points entering self-employment (Hoang et al. 2020), with that the group of the selfemployed becomes increasingly heterogeneous (Bögenhold 2019). The sources of heterogeneity among the self-employed are manifold and have been addressed from various angles, e.g., via starting points (Carrasco 1999), sectors (Faggio and Silva 2014), motivations (de Vries et al. 2019) or terms of self-employment (Bögenhold 2019). ...
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Although the self-employed represent 16.7% of the Dutch labor force (OECD 2020), their internal heterogeneity in profiles regarding motivations, characteristics and career trajectories remains unclear. Yet, understanding self-employment profiles and their spatial distribution may help understand differences in career progression of the self-employed. This study identifies and describes patterns in long-term career trajectories of the Dutch self-employed, and it explores spatial differences along the urban hierarchy. The study uses a life-course approach and register data of the whole population to find common patterns of careers among a sample of Dutch self-employed ( N = 42,028) and their spatial distribution. We investigated careers through sequence and cluster analysis of individuals’ socio-economic statuses between 2003–2018. The analysis identifies 7 career clusters that collapse into three main career profiles: Mixed self-employment careers that combine self-employment with wage-employment, stable self-employment, and precarious self-employment. The clusters differ importantly in terms of the individual characteristics of the self-employed including age, gender, educational level and income. In terms of spatial distribution, the study shows that self-employment career profiles follow the urban hierarchy. Urban regions give way to all types of self-employment, while rural regions mainly exhibit stable self-employment. Precarious self-employment presents differently in urban and rural areas; in urban labor markets, we find self-employed individuals vulnerable to economic shocks, losing their jobs as a consequence of the financial crisis in 2007/08. In rural regions, formerly inactive workers become self-employed following the crisis.
Platform labor and gig work have become key sites for understanding a nascent "future of work" hallmarked by informalization and digitization. A growing body of research emphasizes how experiences of platform work are mediated not only by algorithms and user interfaces, but also by gender, race, local cultures as well as labor hierarchies. Drawing from ongoing ethnographic research on the digital transformation of healthcare, we show how therapists' experiences of platform labor are centrally shaped by the historical and ongoing feminization of mental health work. Platforms reinscribe feminized labor conditions that are pervasive in the healthcare industry, and yet platform labor appears as 'useful' to some therapists as they navigate a set of precarious career choices fundamentally structured by feminization. We use the analytic of the stopgap to describe platforms' two-fold reproduction of the status quo: first by offering an approximation of freedom to individual workers, helping to forestall a crisis of unsustainable work conditions; and second by reinscribing the same logics of exploitation in order to make labor scalable. This stopgap analytic reorients the focus away from the impact of the platforms technologies as such, towards the conditions that make stopgap solutions necessary for survival. It also points towards the importance of intervening in the conditions of exclusion and exploitation that help to create a market for platform stopgaps.
Purpose This paper aims to explore the prevalence of undeclared activities conducted on digital labour platforms, and then to discuss what policies are likely to be more effective in order to prevent the growth of the informal activities on these platforms. Design/methodology/approach To depict the profile of the digital worker conducting undeclared activities, the sectors where undeclared activities are more prevalent and the effectiveness of deterrent policies, data are reported from 2019 Special Eurobarometer survey covering the European Union member states and the UK. Findings The finding is that 13% of undeclared activities are conducted on digital labour platforms. This practice is more common amongst men, those married or remarried, those living in small/middle towns, in sectors such as repairs/renovations, selling goods/services, assistance for dependant persons, gardening and help moving house. The higher the perceived sanction, the lower the likelihood of undertaking undeclared activities on digital labour platforms. Intriguing, a higher risk of detection is associated with a higher likelihood to use digital labour platform for undeclared activities. Practical implications The attitudes toward risk can be interpreted closer to the gaming context, and not to the working environment, looking at platform workers as being involved in a state versus individual game. Policy makers should consider improving the correspondence of laws and regulations between countries and offering operational assistance for suppliers and consumers. Originality/value This is the first paper to explore the prevalence of undeclared activities conducted on digital labour platforms and to outline the policy measures required to reduce this practice.
A large influx of refugees in several European countries has created challenges at all levels of society, starting with the actors in charge of their integration. During this crisis, social media platforms seem to have played a major role in the refugees’ journey and inclusion in their host countries. Based on in-depth interviews with 28 Syrian refugees who settled in Belgium after 2015, this chapter looks at the ways they use social media in the hope of integrating in the host country and overcoming cultural challenges. Social media use has helped them alleviate their social isolation, access information in their native language, and find out about the rights and responsibilities of citizenship in their new country.
Digital inclusion is a complex matter as the problem of digital inequalities arises from both digital and social exclusion. Digital exclusion manifests differently according to an individual’s personal and social conditions. These personal and social conditions are usually the preconditions of getting people online, but they are also conditions that shape how people use the online tools once they are online. This is why the digital gap, while may be narrowing, is also deepening among certain groups. There is a need for a new understanding of this persistent issue of digital exclusion that connects preconditions and outcomes of digital engagement in order to account for the complexity of digital exclusion. Only then can we develop tailored policies and programmes that address the unique disadvantages of each group. This chapter examines the research on the interconnections between social and digital inclusion, particularly among vulnerable groups of the population. It identifies emerging groups of digital exclusion and summarises the elements of effective digital inclusion programmes.KeywordsAccessInterventionLapsed usersNon-usersSocial exclusion
Founded in 2005, Renren was a popular and leading Chinese social media network, especially among college students. However, after reaching its heyday in 2011, user growth dwindled and advertisers fled. By 2018, Renren morphed from a social network to a secondhand car sale business. This paper reconstructs the history of Renren and documents its failed transformation from a social network to a platform. Grounded in platform studies and a political economy theoretical framework, this paper traces Renren’s platform evolution from two perspectives: first, from an end user’s point of view, it examines how Renren’s user interface registered and mirrored shifting corporate strategies and platformisation processes writ large; second, given Renren’s status as a privately-owned, publicly-traded and for-profit business entity, the paper examines how Renren pursued different strategies in search of a viable business model and later on in managing shareholder value and profitability. Ultimately, this paper presents the rise and fall of Renren first and foremost as a platform historiography project. It then discusses Renren’s demise by looking retrospectively at changing interface design, business strategies, and financialisation against the broader dynamics and shifting sociocultural uses of the commercial Chinese internet.
This article examines imaginaries of platform entrepreneurship in film industries in Ghana. To understand how these imaginaries are spatially shaped and locally defined, we carried out in-depth qualitative research with fifty filmmakers in four regions of Ghana. Digital and platform technologies have long been optimistically celebrated as a way for marginalized creative entrepreneurs, particularly in Africa, to break into global markets and reach unprecedented levels of business success. However, far from being universally adopted by African creative entrepreneurs, these global techno-optimistic imaginaries are continually reworked, contested and subverted in practice. In this article, we show how Ghanaian filmmakers mobilized, deployed and resisted imaginaries of platform entrepreneurship in their efforts to make sense of their situated entrepreneurial practices and to imagine the future of their creative businesses. We found that rather than naïvely adhering to techno-optimist imaginaries, through their practices, Ghanaian filmmaking entrepreneurs challenged the power geometry of the current platform ecosystem dominated by major Silicon Valley players. We contribute empirically rich data on how filmmaking entrepreneurs use and imagine platform technologies, as is necessary when African digital entrepreneurs are surrounded by hype but inadequate data. We also contribute to the literature about how individual platforms and platform types have unique affordances and how these affordances are shaped by the location and socio-economic position of the entrepreneur.
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In this literature review, I summarize key concepts and findings from the rich academic literature on digital inequalities. I propose that digital inequalities research should look more into labor- and big data-related questions such as inequalities in online labor markets and the negative effects of algorithmic decision-making for vulnerable population groups. The article engages with the sociological literature on digital inequalities and explains the general approach to digital inequalities, based on the distinction of first-, second-, and third-level digital divides. First, inequalities in access to digital technologies are discussed. This discussion is extended to emerging technologies, including the Internet-of-things and AI-powered systems such as smart speakers. Second, inequalities in digital skills and technology use are reviewed and connected to the discourse on new forms of work such as the sharing economy or gig economy. Third and finally, the discourse on the outcomes, in the form of benefits or harms, from digital technology use is taken up. Here, I propose to integrate the digital inequalities literature more strongly with critical algorithm studies and recent discussions about datafication, digital footprints, and information privacy.
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Conditions in the sharing economy are often favourably designed for consumers and platforms but entail new challenges for the labour side, such as substandard social-security and rigid forms of algorithmic management. Since comparatively little is known about how providers in the sharing economy make their voices heard collectively, we investigate their opinions and behaviours regarding collective action and perceived solidarities. Using cluster analysis on representative data from across twelve European countries, we determine five distinct types of labour-activists, ranging from those opposed to any forms of collective action to those enthusiastic to organise and correct perceived wrongs. We conclude by conjecturing that the still-ongoing influx of new providers, the difficulty of organising in purely virtual settings, combined with the narrative of voluntariness of participation and hedonic gratifications might be responsible for the inaction of large parts of the provider base in collectivist activities.
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Although research has demonstrated a grey divide where older adults in comparison with younger adults are less involved and skilled with digital media, it has overlooked differences in older adults’ digital skills and media use by treating them as a homogenous group. Based on 41 in-depth interviews with older adults (aged 65+ years) in East York, Toronto, we developed a typology that moves beyond seeing older adults as Non-Users to include Reluctants, Apprehensives, Basic Users, Go-Getters, and Savvy Users. We find a nonlinear association between older adults’ skill levels and online engagement, as many East York older adults are not letting their skill levels dictate their online involvement. They engage in a wide range of online activities despite having limited skills, and some are eager to learn as they go. Older adults often compared their digital media use with their peers and to more tech-adept younger generations, and these comparisons influenced their attitudes toward digital media. Their narratives of mastery included both a positive sense that they can stay connected and learn new skills and a negative sense that digital media might overwhelm them or waste their time. We draw conclusions for public policy based on our findings on how digital media intersect with the lives of East York older adults.
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The relations between technology, work organization, worker power, workers’ rights, and workers’ experience of work have long been central concerns of CSCW. European CSCW research, especially, has a tradition of close collaboration with workers and trade unionists in which researchers aim to develop technologies and work processes that increase workplace democracy. This paper contributes a practitioner perspective on this theme in a new context: the (sometimes global) labor markets enabled by digital labor platforms. Specifically, the paper describes a method for rating working conditions on digital labor platforms (e.g., Amazon Mechanical Turk, Uber) developed within a trade union setting. Preliminary results have been made public on a website that is referred to by workers, platform operators, journalists, researchers, and policy makers. This paper describes this technical project in the context of broader cross-sectoral efforts to safeguard worker rights and build worker power in digital labor platforms. Not a traditional research paper, this article instead takes the form of a case study documenting the process of incorporating a human-centered computing perspective into contemporary trade union activities and communicating a practitioner’s perspective on how CSCW research and computational artifacts can come to matter outside of the academy. The paper shows how practical applications can benefit from the work of CSCW researchers, while illustrating some practical constraints of the trade union context. The paper also offers some practical contributions for researchers studying digital platform workers’ experiences and rights: the artifacts and processes developed in the course of the work.
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In this inductive, qualitative study, we observe how Uber, a company often hailed as being the poster-child of the sharing economy facilitated through a digital platform may also at times represent and reinforce postcapitalist hyper-exploitation. Drawing on the motivations and lived experiences of 31 Uber drivers in Toronto, Canada, we provide insights into three groups of Uber drivers: (1) those that are driving part-time to earn extra money in conjunction with studying or doing other jobs, (2) those that are unemployed and for whom driving for Uber is the only source of income, and (3) professional drivers, who are trying to keep pace with the durable digital landscape and competitive marketplace. We emphasize the ways in which each driver group simultaneously acknowledges and rejects their own precarious employment by distancing techniques such as minimizing the risks and accentuating the advantages of the driver role. We relate these findings to a broader discussion about how driving for Uber fuels the traditional capitalist narrative that working hard and having a dream will lead to advancement, security and success. We conclude by discussing other alternative economies within the sharing economy.
Global online platforms match firms with service providers around the world, in services ranging from software development to copywriting and graphic design. Unlike in traditional offshore outsourcing, service providers are predominantly one-person microproviders located in emerging-economy countries not necessarily associated with offshoring and often disadvantaged by negative country images. How do these microproviders survive and thrive? We theorize global platforms through transaction cost economics (TCE), arguing that they are a new technology-enabled offshoring institution that emerges in response to cross-border information asymmetries that hitherto prevented microproviders from participating in offshoring markets. To explain how platforms achieve this, we adapt signaling theory to a TCE-based model and test our hypotheses by analyzing 6 months of transaction records from a leading platform. To help interpret the results and generalize them beyond a single platform, we introduce supplementary data from 107 face-to-face interviews with microproviders in Southeast Asia and Sub-Saharan Africa. Individuals choose microprovidership when it provides a better return on their skills and labor than employment at a local (offshoring) firm. The platform acts as a signaling environment that allows microproviders to inform foreign clients of their quality, with platform-generated signals being the most informative signaling type. Platform signaling disproportionately benefits emerging-economy providers, allowing them to partly overcome the effects of negative country images and thus diminishing the importance of home country institutions. Global platforms in other factor and product markets likely promote cross-border microbusiness through similar mechanisms.
As the world population ages and older adults comprise a growing proportion of current and potential Internet users, understanding the state of Internet use among older adults as well as the ways their use has evolved may clarify how best to support digital media use within this population. This article synthesizes the quantitative literature on Internet use among older adults, including trends in access, skills, and types of use, while exploring social inequalities in relation to each domain. We also review work on the relationship between health and Internet use, particularly relevant for older adults. We close with specific recommendations for future work, including a call for studies better representing the diversity of older adulthood and greater standardization of question design.
Gig economy platforms seem to provide extreme temporal flexibility to workers, giving them full control over how to spend each hour and minute of the day. What constraints do workers face when attempting to exercise this flexibility? We use 30 worker interviews and other data to compare three online piecework platforms with different histories and worker demographics: Mechanical Turk, MobileWorks, and CloudFactory. We find that structural constraints (availability of work and degree of worker dependence on the work) as well as cultural-cognitive constraints (procrastination and presenteeism) limit worker control over scheduling in practice. The severity of these constraints varies significantly between platforms, the formally freest platform presenting the greatest structural and cultural-cognitive constraints. We also find that workers have developed informal practices, tools, and communities to address these constraints. We conclude that focusing on outcomes rather than on worker control is a more fruitful way to assess flexible working arrangements.