Popular in Sociology

Culture and Society

The City as a Growth Machine: Toward a Political Economy of Place

New York University Harvey Moltoch

Culture and Society

Carceral Entrapments: Views from the Prison/Street Interface in India

Indian Institute of Technology Delhi Mahuya Bandyopadhya

Culture and Society

Pre-problem families: predictive analytics and the future as the present

University of Southampton Rosalind Edwards

Ethnicity and Racism

Were Muslim Barbarians Really Knocking On the Gates of Ontario?

University of Ottawa Natasha Bakht

Culture and Society

Poor People’s Survival Strategies

The University of Texas at Austin Javier Auyero

Ethnicity and Racism

Sociology of Whiteness

Arizona State University Monica McDermott

Ethnicity and Racism

Narratives of Harm and the case for detention abolition

Queen's University Sharry Aiken

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

View more insights in Social Sciences - Sociology - Culture and Society

View more figures from University of Western Ontario

View the page of Lyn Hoang

Read the Study

· Tools Cite Embed

The platform economy is rapidly transforming the dynamics of the labor market. Lyn Hoang discusses who participates in the platform economy and their motivation for participation, using a US nationally representative sample.

Image courtesy of interviewee. August 14, 2024

Log-in or Sign-up to Faculti
Currently viewing this subject insight as a guest. You have insight(s) remaining for this month.

Workspace

Faculti AI is thinking...

< >

Summary
Related Studies
Faculti AI
Key Points
Key Quotes
Translate
Transcript

Export as PDF

This video is a transcript of a discussion about a study on the platform economy, specifically focusing on its workers. The conversation features a person named Lynn who explains the research's context, aims, methodology, and findings.

Key points from the discussion include:

1. **Introduction to the Platform Economy**: The platform economy includes various digital services such as food delivery, ride-sharing, and online marketplaces (e.g., Etsy, eBay). The research aims to understand the experiences of workers within this economy, identifying "winners" and "losers."

2. **Research Context**: The study was motivated by the increasing reliance on digital platforms for work, particularly heightened during the COVID-19 pandemic. It recognizes that not all platform work offers the same benefits and that demographic factors influence the likelihood of individuals engaging in different types of platform work.

3. **Literature Review**: Existing literature often treats platform work as homogeneous or focuses on specific forms without comparison. There are prevailing optimistic views about platform work empowering disadvantaged groups and pessimistic views seeing it as exploitative, lowering income to a "race to the bottom."

4. **Methodology**: The study used data from the American Trends Panel from the Pew Research Center and employed logistic regression to analyze patterns of employment within the platform economy, distinguishing between labor exchange work and online selling work.

5. **Findings**:
- Males under 50, better educated, and with higher incomes were most likely to engage in platform work.
- Labor exchange platform workers often came from disadvantaged backgrounds, while online sellers were typically from affluent backgrounds.
- Gender roles were evident: men were more likely to drive for ridesharing, while women were involved in home services. Age and race also influenced participation rates.
- Income needs played a crucial role—those who relied on platform work tended to engage in labor exchange types, while those with less reliance pursued online selling.

6. **Conclusions**: The research found support for both optimistic and pessimistic perspectives of platform work. While it can provide opportunities for some, it does not significantly reduce income inequality. The study identified a need to recognize differences in types of platform work and highlighted "occupational segregation," where different demographic groups gravitate towards various types of jobs based on their social backgrounds.

7. **Limitations**: The study's findings are based on U.S. data, limiting generalizability to other contexts, and did not encompass all forms of platform work, such as content creation.

Overall, the discussion highlights the importance of examining the platform economy from the perspective of the workers and suggests further investigation is needed to understand its broader impacts on income inequality and employment dynamics.

Language:

Export as PDF

Export as PDF
Here are five key points derived from the video about the platform economy:

1. **Consumer Participation vs. Worker Perspective**: The video highlights that while many individuals participate in the platform economy as consumers (e.g., ordering food, ride-sharing), it is essential to explore the experiences of workers who perform the labor. Understanding this perspective helps in assessing whether there are clear winners or losers within the platform economy.

2. **Diverse Nature of Platform Work**: Not all platform work is identical, and differentiating between the various types is crucial. For example, renting an Airbnb for $400 a night is notably different from driving for Uber for $40 a night. This distinction impacts the benefits and consequences experienced by workers.

3. **Demographic Variability in Participation**: The study found that demographic characteristics predict who participates in platform work. Males under 50, better-educated individuals, and those with higher incomes were more likely to engage in platform work, while labor exchange type participants often come from disadvantaged backgrounds, revealing stark inequalities in who benefits from these platforms.

4. **Impact of Income Needs on Type of Work**: The research indicated that individuals who depend on platform work for their livelihood tend to engage in labor exchange types of work (e.g., ridesharing and house cleaning), whereas those who do not rely on this income are more likely to engage in online selling types of work (e.g., selling homemade items). This reflects how economic necessity drives individuals into less favorable working conditions.

5. **Optimist vs. Pessimist Debate on Platform Work**: The findings support both the optimistic perspective—suggesting that platform work may provide opportunities for disadvantaged individuals—and the pessimistic viewpoint, which argues it primarily benefits affluent individuals and may exacerbate existing income gaps. Thus, while platform work can offer some benefits, it does not significantly contribute to reducing income disparities, indicating the need for deeper consideration regarding its overall societal impact.

Export as PDF
Here are some key quotes from the video:

1. **Introduction to the Platform Economy:**
- "Most of us have probably ordered food from a delivery company... So we've all participated largely as consumers of the platform economy, but on the other end of these exchanges are the individuals who are actually fulfilling the work."

2. **Purpose of the Study:**
- "We wanted to explore the platform economy from the perspective of the workers to see if there were winners or losers within this type of industry."

3. **Impact of Digital Technologies:**
- "As digital technologies have come to take over a lot of our daily life, more people are turning to digital platforms in search of work or income opportunities, especially with COVID-19."

4. **Diversity of Platform Work:**
- "Not all platform work is the same and we shouldn't treat it as the same... There are important benefits and important consequences to which type of platform work you engage in."

5. **Optimistic vs. Pessimistic Perspectives:**
- "The current literature typically either looked at platform work as this homogenized industry... Or you would have studies that focus on individual types without making comparisons among the various different ones."

6. **Demographic Analysis:**
- "Males under the age of 50 years old... individuals who are better educated or already have greater average incomes, were the most likely to engage in platform work."

7. **Differences in Participation:**
- "We found really notable differences... Those performing labor exchange types of platform work are typically coming from disadvantaged social backgrounds."

8. **Income Needs and Participation:**
- "Respondents who indicated not needing their income from platform work... were more likely to engage in online selling types of platform work."

9. **Conclusions on Income Gaps:**
- "We found evidence of both the optimist and pessimist perspectives... However, we also found support for the pessimist perspective."

10. **Occupational Segregation:**
- "In order to explain why some individuals are more likely to pursue certain types of platform work over others, we use the theoretical perspective called occupational segregation."

11. **Limitations of the Study:**
- "We only looked at US data... because platform work transcends borders."

These quotes capture the core ideas of the analysis and findings regarding platform work, including the demographics involved, the dual perspectives on platform economies, and the potential implications for income and job type segregation.

Export as PDF
Here are some articles that discuss similar themes as the transcript you on the platform economy and its workers:

1. **“Gig Economy: A New Understanding of How On-Demand Work is Reshaping Employment”** - This article explores the rise of gig work and its implications for workers' rights, job security, and income disparity, echoing the themes of winners and losers within the platform economy.

2. **“The Impact of the COVID-19 Pandemic on Gig Workers: Evaluating Job Security and Benefits”** - This research focuses on how the pandemic has affected platform workers, analyzing shifts in demographics, job types, and economic stability, similar to the discussion of research convideo in your video.

3. **“Gender and the Gig Economy: Understanding the Different Experiences of Men and Women in Digital Platforms”** - This article investigates occupational segregation and gender roles in gig work, aligning with the findings on gender participation in platform work mentioned in your transcript.

4. **“Platform Work in the U.S.: Analyzing Employee Demographics and Economic Outcomes”** - This study uses data analysis to detail who participates in the platform economy, much like the methodology discussed in your video, and emphasizes demographic influences on platform work engagement.

5. **“Exploring the Dual Nature of the Platform Economy: Empowerment vs. Exploitation”** - This article reviews the contrasting views on the platform economy—celebrating its potential for empowerment while critiquing the potential for exploitation, reflecting the dual perspectives noted in your discussion.

6. **“Digital Labor: The New Class Divisions in an Online Economy”** - This piece delves into the inequalities present within different forms of platform work, highlighting how socio-economic status affects participation, echoing the need for recognition of different platform work types from your findings.

7. **“Understanding the Challenges of Work in the Gig Economy: A Sociological Perspective”** - This scholarly article examines the social implications and lived experiences of gig workers, offering insights into job dynamics and the broader socioeconomic effects of platform work.

These articles would provide a broader perspective on the issues surrounding the platform economy and could serve as useful resources for further understanding the complexities and implications discussed in the mentioned study.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.155 --> 00:02.445
Most of us have probably ordered food from

00:03.005 --> 00:06.325
a delivery company or, used a Uber company

00:06.425 --> 00:07.645
for ride sharing services

00:07.905 --> 00:10.845
or even ordered something off of Etsy and eBay.

00:10.905 --> 00:13.405
So we've all participated largely as consumers

00:13.405 --> 00:15.605
of the platform economy, but on the other end

00:15.605 --> 00:17.805
of these exchange are the individuals

00:17.805 --> 00:20.965
who are actually fulfilling the work, engaging in the labor,

00:21.265 --> 00:24.565
or are, using their digital skills

00:24.705 --> 00:25.845
to make money off of it.

00:26.305 --> 00:29.445
And so we wanted to explore the platform economy from the

00:29.445 --> 00:31.925
perspective of the workers to see if there were winners

00:31.925 --> 00:34.285
or losers, within this type of industry.

00:34.985 --> 00:37.645
Um, and we found that this project was really important

00:37.765 --> 00:41.445
because as digital technologies have come to take over a lot

00:41.445 --> 00:43.845
of our daily life, more people are turning

00:43.985 --> 00:46.165
to digital platforms in search of work

00:46.185 --> 00:49.085
or income opportunities, especially with COVID-19,

00:49.135 --> 00:51.885
we're seeing the, like the dark rise in people,

00:52.225 --> 00:53.445
pursuing platform work.

00:54.145 --> 00:57.885
And what motivated us was this idea that well,

00:58.465 --> 01:00.165
not all platform work is the same

01:00.225 --> 01:01.845
and we shouldn't treat it as the same.

01:02.515 --> 01:05.565
Renting a Airbnb in New York City

01:05.565 --> 01:09.885
for $400 a night is not the same as driving an Uber,

01:09.885 --> 01:10.885
for $40 a night.

01:11.345 --> 01:14.285
And so there are important benefits.

01:14.285 --> 01:17.085
They're important consequences to which type

01:17.085 --> 01:18.405
of platform work you engage in.

01:18.785 --> 01:21.565
And so we wanted to assess who is more likely

01:21.745 --> 01:23.965
to engage in each of the different types of platform

01:23.965 --> 01:25.405
or some of the types of platform work.

01:25.405 --> 01:26.805
There are. We couldn't examine all of them.

01:26.985 --> 01:28.925
And we were writing this paper in 2019.

01:29.265 --> 01:31.525
Um, it was literature was just starting to grow.

01:31.575 --> 01:33.725
There was a lot of interest, and,

01:34.105 --> 01:36.245
but they were still opposing perspectives.

01:36.865 --> 01:40.365
So the current literature typically either looked at

01:40.685 --> 01:42.885
platform work as this homogenized industry,

01:42.885 --> 01:45.045
without necessarily separating the different types.

01:45.705 --> 01:46.885
Or you would have studies

01:46.955 --> 01:50.525
that focus on individual types without making comparisons

01:50.655 --> 01:51.845
among the various different ones.

01:51.845 --> 01:54.525
So a study would look exclusively at driving for Uber

01:54.865 --> 01:56.445
or exclusively at food delivery.

01:57.225 --> 01:59.645
And then in terms of other literature

01:59.645 --> 02:01.845
that explore platform work, it tended

02:01.845 --> 02:02.885
to fall into two camps.

02:03.225 --> 02:06.565
You were either the optimist camp that saw platform work

02:06.665 --> 02:09.445
as empowering as this great opportunity

02:09.945 --> 02:12.485
for people from the more disadvantaged social groups

02:12.545 --> 02:14.165
to find work, transcend poverty,

02:14.765 --> 02:16.565
transcend income gaps or unemployment.

02:17.225 --> 02:21.525
And they found that it gave them the autonomy to, go

02:21.525 --> 02:22.965
beyond local job shortages

02:22.985 --> 02:25.045
and work for globalized companies.

02:25.345 --> 02:28.885
So they pointed to all of the benefits of platform work

02:28.945 --> 02:30.445
for the most disadvantaged.

02:30.985 --> 02:33.845
On the other hand, you have the pessimist category

02:34.105 --> 02:37.885
who pointed out that while there are some benefits

02:37.905 --> 02:40.325
of platform work, it's largely exploitative

02:40.665 --> 02:44.925
and the work isn't actually, transcending these,

02:44.995 --> 02:47.085
flexibility issues or remote issues.

02:47.085 --> 02:50.405
What they're doing is they're taking really important jobs,

02:50.435 --> 02:52.165
cutting them down in the microtask,

02:52.305 --> 02:54.805
and then making a large global pool

02:54.805 --> 02:56.445
of job seekers fight over them.

02:56.825 --> 02:58.805
Um, so essentially worsening that race

02:58.825 --> 03:01.925
To the bottom or that crowds in the barrel mentality.

03:03.165 --> 03:05.425
And so we really wanted to assess that debate

03:05.485 --> 03:08.185
and see, well, which perspective is true or could be true?

03:08.645 --> 03:12.185
Um, because if we notice that platform work has a neutral

03:12.805 --> 03:14.945
impact or even a beneficial impact,

03:15.175 --> 03:18.305
then we should be promoting it as this alternative source

03:18.605 --> 03:20.825
for finding job or for making money.

03:21.285 --> 03:23.745
But if it's actually worsening income gaps, then we have

03:23.745 --> 03:25.825
to take important notice of that,

03:26.005 --> 03:28.625
and make people aware of some of the challenges

03:28.895 --> 03:31.345
that platform work could offer if they pursue this line

03:31.345 --> 03:33.465
of work, especially over traditional forms

03:33.485 --> 03:35.105
of work if they're making that decision.

03:35.245 --> 03:36.345
In terms of our dataset,

03:36.365 --> 03:39.745
we used the American Trends Panel Wave 19 launched

03:39.765 --> 03:42.145
by Pew Research Center in 2016.

03:42.725 --> 03:44.745
And the reason we selected this dataset was

03:44.745 --> 03:47.185
because it was, nationally representative

03:47.285 --> 03:50.065
of the US population so we could generalize the findings

03:50.445 --> 03:52.585
and also because it was one of the first surveys

03:52.585 --> 03:55.225
that actually asked people about platform work,

03:55.285 --> 03:56.865
beyond just the homogenized industry,

03:56.885 --> 03:59.305
but ask which types they're engaging in,

03:59.445 --> 04:00.545
and a little bit of why

04:00.545 --> 04:01.905
they're engaging in these types of work.

04:02.205 --> 04:04.425
So this is what made this particular data set

04:04.425 --> 04:05.585
really important to use.

04:06.125 --> 04:07.865
Um, and then in terms of what we did,

04:07.885 --> 04:10.505
we performed logistic regression to see likelihood

04:10.725 --> 04:12.665
of engaging in platform work.

04:13.045 --> 04:15.105
Um, either general, are you more likely

04:15.105 --> 04:16.425
to engage in platform work versus

04:16.525 --> 04:17.665
not engage in platform work?

04:17.685 --> 04:20.145
And then more specific for each of the various types

04:20.145 --> 04:22.625
of platform work, we subdivided platform work

04:22.625 --> 04:23.745
into two categories.

04:23.845 --> 04:25.865
So we either had labor exchange types

04:25.865 --> 04:27.465
of platform work, so those are the obvious.

04:27.485 --> 04:28.985
You work for company, you perform labor.

04:29.565 --> 04:32.345
Um, and then we had the online selling types

04:32.345 --> 04:35.185
of platform work, which is you either are selling a used

04:35.185 --> 04:37.545
item, a homemade item,

04:38.285 --> 04:40.345
or even participating like branded,

04:40.345 --> 04:41.745
multi-level marketing selling.

04:41.965 --> 04:44.145
We had three primary research questions

04:44.145 --> 04:46.665
that we were investigating in our study.

04:47.085 --> 04:48.945
The first question was looking at

04:49.285 --> 04:52.345
who participates in the platform economy versus who doesn't.

04:52.805 --> 04:55.065
And then with our second research question, we wanted

04:55.065 --> 04:57.905
to take that further and see if certain demographic

04:57.905 --> 05:00.785
characteristics could actually predict participation in each

05:00.785 --> 05:02.785
of the various types of platform work we had.

05:03.085 --> 05:04.825
And then with our third research question, we wanted

05:04.825 --> 05:07.585
to look at why people participate in the platform economy,

05:08.125 --> 05:11.785
and whether income needs, so using platform work

05:11.785 --> 05:13.465
to fulfill your basic needs

05:13.485 --> 05:15.385
or meet your basic needs, play the role in that.

05:16.045 --> 05:18.385
So with our first research question,

05:18.535 --> 05:20.865
when we look at platform economy as a whole

05:20.865 --> 05:22.345
or platform work as a whole, we found

05:22.345 --> 05:26.425
that males under the age of 50 years old, individuals

05:26.425 --> 05:27.505
who are better educated

05:27.765 --> 05:30.105
or already have greater average incomes,

05:30.315 --> 05:32.785
we're the most likely to engage in platform work.

05:34.065 --> 05:35.325
So this is a great finding,

05:35.435 --> 05:38.365
however, when we actually look into our second research

05:38.565 --> 05:41.645
question and looked at those more differences according

05:41.705 --> 05:45.045
to what type of platform work you are engaging in, is it

05:45.055 --> 05:46.805
labor exchange type of platform work

05:46.805 --> 05:48.205
or online selling platform work?

05:48.545 --> 05:50.565
We found really notable differences.

05:51.065 --> 05:53.405
So when you compare the individuals

05:53.405 --> 05:56.245
who are performing labor exchange types of platform work,

05:56.395 --> 05:58.245
they're typically the ones coming from

05:58.245 --> 05:59.765
disadvantaged social backgrounds.

05:59.915 --> 06:03.085
They're more likely to be women, under the age of 30,

06:03.715 --> 06:06.605
non-white, no more than a high school education,

06:06.695 --> 06:07.805
lower average income.

06:08.145 --> 06:09.685
And then when you're looking at the individuals

06:09.685 --> 06:12.165
who are more likely to engage in those online selling types

06:12.165 --> 06:13.525
of platform work, they're more likely

06:13.525 --> 06:15.285
to come from those affluent backgrounds.

06:15.595 --> 06:19.325
They're the, above average incomes, better educated men.

06:19.705 --> 06:22.645
Um, and so those are really interesting findings that,

06:23.845 --> 06:25.645
reaffirmed our position

06:25.675 --> 06:27.565
that you can't treat the platform economy

06:27.585 --> 06:29.045
as this homogenous industry.

06:30.075 --> 06:32.575
Um, and then we also use logistic regression

06:32.575 --> 06:35.455
to further examine demographic characteristics,

06:35.455 --> 06:38.295
predicting participation in each of the five different types

06:38.295 --> 06:39.695
of labor exchange platform work,

06:39.955 --> 06:41.655
and also the, different types

06:41.655 --> 06:43.095
of online selling platform work.

06:43.295 --> 06:44.495
I won't go through all the findings,

06:44.495 --> 06:47.175
but some of the most, important ones we saw was that,

06:47.835 --> 06:49.655
men were more likely than women

06:49.675 --> 06:51.215
to participate in ride share driving.

06:51.545 --> 06:53.495
Women were more likely than men,

06:53.495 --> 06:55.135
to participate in house cleaning

06:55.355 --> 06:58.175
or laundry cleaning, types of platform work

06:58.875 --> 07:00.295
age also influenced whether

07:00.295 --> 07:02.735
or not you were going to be a ride share driver.

07:02.875 --> 07:04.575
So, individuals who were 18

07:04.575 --> 07:06.055
to 29 years old were more likely

07:06.075 --> 07:08.575
to be ride share drivers compared to older adults.

07:09.085 --> 07:10.775
Race was a really big predictor

07:10.775 --> 07:12.135
for ride share driving as well.

07:12.595 --> 07:16.775
So non-white respondents had a higher likelihood of,

07:16.775 --> 07:18.255
reporting, engaging in ride share

07:18.255 --> 07:19.855
driving compared to online selling.

07:20.595 --> 07:21.895
So those are really notable.

07:22.315 --> 07:25.375
Um, and those were also significant findings as well.

07:26.435 --> 07:29.085
When we looked at our third question about income needs

07:29.085 --> 07:33.165
and how they play a role, we also found that the respondents

07:33.165 --> 07:36.485
who indicated not needing their income from platform work

07:36.585 --> 07:37.805
so they could live without it

07:38.225 --> 07:41.125
or it wasn't important, to meeting their needs,

07:41.475 --> 07:42.565
they were the more likely

07:42.585 --> 07:44.605
to engage in those online selling types of platform work.

07:44.905 --> 07:47.725
By contrast, the individuals who rely on their earnings

07:47.745 --> 07:50.685
to meet their daily needs to meet their living expenses,

07:50.875 --> 07:52.765
they were engaging in the labor

07:53.045 --> 07:54.125
exchange types of platform work.

07:54.265 --> 07:57.525
In terms of conclusion, our primary,

07:58.085 --> 08:00.405
argument is when you're looking at that optimist

08:00.425 --> 08:03.605
or pessimist perspective, which does our finding support,

08:04.185 --> 08:06.085
and we honestly found evidence of both.

08:06.265 --> 08:08.165
So when you look at the optimist perspective,

08:08.305 --> 08:11.645
we found evidence that those individuals from those socially

08:11.645 --> 08:13.765
disadvantaged backgrounds are pursuing labor

08:14.045 --> 08:15.165
exchange types of platform work.

08:15.465 --> 08:17.365
Um, so it could be really important

08:17.365 --> 08:20.565
for helping reduce poverty or lowering, income gaps.

08:20.875 --> 08:22.925
However, we also found support

08:22.925 --> 08:24.125
for the pessimist perspective,

08:24.215 --> 08:27.165
which is when you look at the types of work

08:27.165 --> 08:28.845
that these individuals are engaging in,

08:28.985 --> 08:31.685
the affluent individuals are the ones engaging in

08:31.785 --> 08:32.845
online selling types.

08:33.225 --> 08:34.285
So when you're looking at

08:34.435 --> 08:37.045
what will platform work reduce income gaps,

08:37.585 --> 08:39.565
our perspective is generally not really,

08:40.055 --> 08:43.645
while the individuals from disadvantaged,

08:43.645 --> 08:45.325
backgrounds are pursuing platform work,

08:45.555 --> 08:47.365
they're not doing the types of work

08:47.365 --> 08:50.205
that could reduce the income gap significantly.

08:50.625 --> 08:53.285
And on top of that, you still have affluent individuals

08:53.285 --> 08:56.965
who are participating and growing their income supplementing

08:56.965 --> 08:58.045
their already large income.

08:58.185 --> 08:59.605
So that could actually worsen the

08:59.605 --> 09:00.685
income gap that we're seeing.

09:01.465 --> 09:04.765
Um, and then another key conclusion that we make is in order

09:04.765 --> 09:07.245
to explain why some individuals are more likely

09:07.345 --> 09:09.645
to pursue certain types of platform work over others,

09:10.105 --> 09:12.325
we use the theoretical perspective called

09:12.445 --> 09:14.005
occupational segregation.

09:14.545 --> 09:17.845
And this theory looks at how different fields,

09:17.845 --> 09:20.885
different jobs might be segregated along the lines of race

09:21.105 --> 09:24.245
or gender or age and various other characteristics.

09:24.705 --> 09:27.725
Um, and two primary mechanisms that segregate jobs

09:27.825 --> 09:30.485
or industries is sorting and self-selection.

09:30.825 --> 09:32.165
So sorting is the idea

09:32.165 --> 09:34.725
that we sort into jobs based on our preexisting

09:34.725 --> 09:37.885
understandings of who performs this type of work.

09:38.265 --> 09:39.845
Um, and then self-selection is

09:39.845 --> 09:42.725
where we may have more individual agency to pick jobs

09:43.195 --> 09:46.965
that fit our, interest or requirements.

09:47.795 --> 09:51.255
So when we look at our finding that, for instance,

09:51.305 --> 09:52.335
women are more likely

09:52.475 --> 09:55.415
to do the house cleaning platform work, men are more likely

09:55.435 --> 09:58.215
to do ride sharing platform work that,

09:58.265 --> 10:00.735
helps support this idea of occupational segregation.

10:01.225 --> 10:03.735
Women are still doing the traditional women work.

10:04.035 --> 10:05.815
Men are still doing that traditional men work.

10:06.195 --> 10:09.375
Um, and so we are seeing platform work being segregated

10:09.385 --> 10:12.575
along those race, age, and sex lines as well.

10:12.595 --> 10:13.775
In terms of our limitations,

10:13.885 --> 10:16.135
obviously we only looked at US data,

10:16.515 --> 10:18.455
and so our results are only representative

10:18.515 --> 10:20.655
or generalizer to the US population.

10:20.885 --> 10:22.175
This is a little bit of a limitation

10:22.175 --> 10:24.335
because again, platform work transcends borders.

10:24.485 --> 10:26.695
It's globalized. You can have individuals

10:26.695 --> 10:29.255
who performing work in multiple different countries.

10:29.435 --> 10:30.975
Um, and so we can't speak to

10:31.115 --> 10:33.415
how these findings might play out in other

10:33.415 --> 10:34.615
population contexts.

10:34.875 --> 10:36.735
And of course, we didn't look at every single

10:37.095 --> 10:38.375
possible platform work that exists.

10:38.475 --> 10:40.615
We missed out on, for instance, content creation,

10:40.615 --> 10:42.255
which is a big part of the platform economy.

10:42.755 --> 10:45.535
Um, so those are some of the limitations that we faced.

Faculti AI can make mistakes