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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. 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

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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.

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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.

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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.

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

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Most of us have probably ordered food from

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a delivery company or, used a Uber company

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for ride sharing services

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or even ordered something off of Etsy and eBay.

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So we've all participated largely as consumers

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of the platform economy, but on the other end

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of these exchange are the individuals

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who are actually fulfilling the work, engaging in the labor,

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or are, using their digital skills

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to make money off of it.

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And so we wanted to explore the platform economy from the

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perspective of the workers to see if there were winners

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or losers, within this type of industry.

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Um, and we found that this project was really important

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because as digital technologies have come to take over a lot

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of our daily life, more people are turning

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to digital platforms in search of work

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or income opportunities, especially with COVID-19,

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we're seeing the, like the dark rise in people,

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pursuing platform work.

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And what motivated us was this idea that well,

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not all platform work is the same

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and we shouldn't treat it as the same.

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Renting a Airbnb in New York City

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for $400 a night is not the same as driving an Uber,

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for $40 a night.

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And so there are important benefits.

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They're important consequences to which type

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of platform work you engage in.

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And so we wanted to assess who is more likely

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to engage in each of the different types of platform

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or some of the types of platform work.

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There are. We couldn't examine all of them.

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And we were writing this paper in 2019.

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Um, it was literature was just starting to grow.

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There was a lot of interest, and,

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but they were still opposing perspectives.

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So the current literature typically either looked at

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platform work as this homogenized industry,

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without necessarily separating the different types.

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Or you would have studies

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that focus on individual types without making comparisons

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among the various different ones.

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So a study would look exclusively at driving for Uber

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or exclusively at food delivery.

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And then in terms of other literature

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that explore platform work, it tended

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to fall into two camps.

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You were either the optimist camp that saw platform work

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as empowering as this great opportunity

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for people from the more disadvantaged social groups

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to find work, transcend poverty,

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transcend income gaps or unemployment.

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And they found that it gave them the autonomy to, go

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beyond local job shortages

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and work for globalized companies.

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So they pointed to all of the benefits of platform work

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for the most disadvantaged.

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On the other hand, you have the pessimist category

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who pointed out that while there are some benefits

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of platform work, it's largely exploitative

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and the work isn't actually, transcending these,

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flexibility issues or remote issues.

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What they're doing is they're taking really important jobs,

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cutting them down in the microtask,

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and then making a large global pool

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of job seekers fight over them.

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Um, so essentially worsening that race

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To the bottom or that crowds in the barrel mentality.

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And so we really wanted to assess that debate

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and see, well, which perspective is true or could be true?

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Um, because if we notice that platform work has a neutral

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impact or even a beneficial impact,

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then we should be promoting it as this alternative source

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for finding job or for making money.

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But if it's actually worsening income gaps, then we have

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to take important notice of that,

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and make people aware of some of the challenges

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that platform work could offer if they pursue this line

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of work, especially over traditional forms

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of work if they're making that decision.

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In terms of our dataset,

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we used the American Trends Panel Wave 19 launched

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by Pew Research Center in 2016.

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And the reason we selected this dataset was

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because it was, nationally representative

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of the US population so we could generalize the findings

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and also because it was one of the first surveys

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that actually asked people about platform work,

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beyond just the homogenized industry,

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but ask which types they're engaging in,

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and a little bit of why

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they're engaging in these types of work.

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So this is what made this particular data set

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really important to use.

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Um, and then in terms of what we did,

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we performed logistic regression to see likelihood

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of engaging in platform work.

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Um, either general, are you more likely

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to engage in platform work versus

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not engage in platform work?

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And then more specific for each of the various types

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of platform work, we subdivided platform work

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into two categories.

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So we either had labor exchange types

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of platform work, so those are the obvious.

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You work for company, you perform labor.

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Um, and then we had the online selling types

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of platform work, which is you either are selling a used

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item, a homemade item,

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or even participating like branded,

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multi-level marketing selling.

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We had three primary research questions

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that we were investigating in our study.

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The first question was looking at

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who participates in the platform economy versus who doesn't.

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And then with our second research question, we wanted

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to take that further and see if certain demographic

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characteristics could actually predict participation in each

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of the various types of platform work we had.

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And then with our third research question, we wanted

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to look at why people participate in the platform economy,

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and whether income needs, so using platform work

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to fulfill your basic needs

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or meet your basic needs, play the role in that.

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So with our first research question,

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when we look at platform economy as a whole

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or platform work as a whole, we found

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that males under the age of 50 years old, individuals

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who are better educated

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or already have greater average incomes,

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we're the most likely to engage in platform work.

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So this is a great finding,

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however, when we actually look into our second research

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question and looked at those more differences according

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to what type of platform work you are engaging in, is it

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labor exchange type of platform work

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or online selling platform work?

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We found really notable differences.

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So when you compare the individuals

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who are performing labor exchange types of platform work,

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they're typically the ones coming from

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disadvantaged social backgrounds.

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They're more likely to be women, under the age of 30,

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non-white, no more than a high school education,

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lower average income.

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And then when you're looking at the individuals

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who are more likely to engage in those online selling types

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of platform work, they're more likely

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to come from those affluent backgrounds.

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They're the, above average incomes, better educated men.

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Um, and so those are really interesting findings that,

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reaffirmed our position

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that you can't treat the platform economy

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as this homogenous industry.

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Um, and then we also use logistic regression

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to further examine demographic characteristics,

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predicting participation in each of the five different types

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of labor exchange platform work,

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and also the, different types

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of online selling platform work.

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I won't go through all the findings,

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but some of the most, important ones we saw was that,

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men were more likely than women

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to participate in ride share driving.

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Women were more likely than men,

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to participate in house cleaning

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or laundry cleaning, types of platform work

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age also influenced whether

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or not you were going to be a ride share driver.

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So, individuals who were 18

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to 29 years old were more likely

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to be ride share drivers compared to older adults.

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Race was a really big predictor

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for ride share driving as well.

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So non-white respondents had a higher likelihood of,

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reporting, engaging in ride share

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driving compared to online selling.

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So those are really notable.

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Um, and those were also significant findings as well.

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When we looked at our third question about income needs

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and how they play a role, we also found that the respondents

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who indicated not needing their income from platform work

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so they could live without it

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or it wasn't important, to meeting their needs,

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they were the more likely

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to engage in those online selling types of platform work.

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By contrast, the individuals who rely on their earnings

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to meet their daily needs to meet their living expenses,

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they were engaging in the labor

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exchange types of platform work.

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In terms of conclusion, our primary,

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argument is when you're looking at that optimist

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or pessimist perspective, which does our finding support,

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and we honestly found evidence of both.

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So when you look at the optimist perspective,

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we found evidence that those individuals from those socially

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disadvantaged backgrounds are pursuing labor

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exchange types of platform work.

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Um, so it could be really important

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for helping reduce poverty or lowering, income gaps.

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However, we also found support

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for the pessimist perspective,

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which is when you look at the types of work

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that these individuals are engaging in,

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the affluent individuals are the ones engaging in

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online selling types.

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So when you're looking at

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what will platform work reduce income gaps,

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our perspective is generally not really,

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while the individuals from disadvantaged,

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backgrounds are pursuing platform work,

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they're not doing the types of work

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that could reduce the income gap significantly.

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And on top of that, you still have affluent individuals

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who are participating and growing their income supplementing

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their already large income.

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So that could actually worsen the

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income gap that we're seeing.

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Um, and then another key conclusion that we make is in order

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to explain why some individuals are more likely

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to pursue certain types of platform work over others,

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we use the theoretical perspective called

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occupational segregation.

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And this theory looks at how different fields,

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different jobs might be segregated along the lines of race

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or gender or age and various other characteristics.

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Um, and two primary mechanisms that segregate jobs

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or industries is sorting and self-selection.

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So sorting is the idea

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that we sort into jobs based on our preexisting

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understandings of who performs this type of work.

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Um, and then self-selection is

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where we may have more individual agency to pick jobs

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that fit our, interest or requirements.

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So when we look at our finding that, for instance,

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women are more likely

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to do the house cleaning platform work, men are more likely

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to do ride sharing platform work that,

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helps support this idea of occupational segregation.

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Women are still doing the traditional women work.

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Men are still doing that traditional men work.

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Um, and so we are seeing platform work being segregated

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along those race, age, and sex lines as well.

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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

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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.

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It's globalized. You can have individuals

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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

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population contexts.

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

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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.

"Exploring the Dual Perspectives of Platform Work: Winners and Losers in the Platform Economy"

00:00:00 - Most of us have probably ordered food from a delivery company or, used a Uber company for ride sharing services or even ordered something off of Etsy and eBay. So we've all participated largely as consumers of the platform economy, but on the other end of these exchange are the individuals who are actually fulfilling the work, engaging in the labor, or are, using their digital skills to make money off of it. And so 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. and we found that this project was really important because as digital technologies have come to take over a lot
00:00:43 - to digital platforms in search of work or income opportunities, especially with COVID-19, we're seeing the, like the dark rise in people, pursuing platform work. And what motivated us was this idea that well, not all platform work is the same and we shouldn't treat it as the same. Renting a Airbnb in New York City for $400 a night is not the same as driving an Uber, for $40 a night. And so there are important benefits. They're important consequences to which type of platform work you engage in. And so we wanted to assess who is more likely
00:01:23 - or some of the types of platform work. There are. We couldn't examine all of them. And we were writing this paper in 2019. it was literature was just starting to grow. There was a lot of interest, and, but they were still opposing perspectives. So the current literature typically either looked at platform work as this homogenized industry, without necessarily separating the different types. Or you would have studies that focus on individual types without making comparisons among the various different ones. So a study would look exclusively at driving for Uber or exclusively at food delivery. And then in terms of other literature that explore platform work, it tended
00:02:03 - You were either the optimist camp that saw platform work as empowering as this great opportunity for people from the more disadvantaged social groups to find work, transcend poverty, transcend income gaps or unemployment. And they found that it gave them the autonomy to, go beyond local job shortages and work for globalized companies. So they pointed to all of the benefits of platform work for the most disadvantaged. On the other hand, you have the pessimist category who pointed out that while there are some benefits of platform work, it's largely exploitative and the work isn't actually, transcending these,
00:02:47 - What they're doing is they're taking really important jobs, cutting them down in the microtask, and then making a large global pool of job seekers fight over them. so essentially worsening that race To the bottom or that crowds in the barrel mentality. And so we really wanted to assess that debate and see, well, which perspective is true or could be true? because if we notice that platform work has a neutral impact or even a beneficial impact, then we should be promoting it as this alternative source for finding job or for making money. But if it's actually worsening income gaps, then we have to take important notice of that,
00:03:28 - that platform work could offer if they pursue this line of work, especially over traditional forms of work if they're making that decision. In terms of our dataset, we used the American Trends Panel Wave 19 launched by Pew Research Center in 2016. And the reason we selected this dataset was because it was, nationally representative of the US population so we could generalize the findings and also because it was one of the first surveys that actually asked people about platform work, beyond just the homogenized industry, but ask which types they're engaging in, and a little bit of why they're engaging in these types of work. So this is what made this particular data set really important to use.

"Analyzing Participation in the Platform Economy: A Logistic Regression Study on Demographic Influences and Income Needs"

00:04:07 - we performed logistic regression to see likelihood of engaging in platform work. either general, are you more likely to engage in platform work versus not engage in platform work? And then more specific for each of the various types of platform work, we subdivided platform work into two categories. So we either had labor exchange types of platform work, so those are the obvious. You work for company, you perform labor. and then we had the online selling types of platform work, which is you either are selling a used item, a homemade item, or even participating like branded, multi-level marketing selling. We had three primary research questions that we were investigating in our study.
00:04:49 - who participates in the platform economy versus who doesn't. And then with our second research question, we wanted to take that further and see if certain demographic characteristics could actually predict participation in each of the various types of platform work we had. And then with our third research question, we wanted to look at why people participate in the platform economy, and whether income needs, so using platform work to fulfill your basic needs or meet your basic needs, play the role in that. So with our first research question, when we look at platform economy as a whole or platform work as a whole, we found that males under the age of 50 years old, individuals who are better educated
00:05:30 - we're the most likely to engage in platform work. So this is a great finding, however, when we actually look into our second research question and looked at those more differences according to what type of platform work you are engaging in, is it labor exchange type of platform work or online selling platform work? We found really notable differences. So when you compare the individuals who are performing labor exchange types of platform work, they're typically the ones coming from disadvantaged social backgrounds. They're more likely to be women, under the age of 30, non-white, no more than a high school education, lower average income.
00:06:09 - who are more likely to engage in those online selling types of platform work, they're more likely to come from those affluent backgrounds. They're the, above average incomes, better educated men. and so those are really interesting findings that, reaffirmed our position that you can't treat the platform economy as this homogenous industry. and then we also use logistic regression to further examine demographic characteristics, predicting participation in each of the five different types of labor exchange platform work, and also the, different types of online selling platform work. I won't go through all the findings, but some of the most, important ones we saw was that, men were more likely than women
00:06:51 - Women were more likely than men, to participate in house cleaning or laundry cleaning, types of platform work age also influenced whether or not you were going to be a ride share driver. So, individuals who were 18 to 29 years old were more likely to be ride share drivers compared to older adults. Race was a really big predictor for ride share driving as well. So non-white respondents had a higher likelihood of, reporting, engaging in ride share driving compared to online selling. So those are really notable. and those were also significant findings as well. When we looked at our third question about income needs and how they play a role, we also found that the respondents
00:07:36 - so they could live without it or it wasn't important, to meeting their needs, they were the more likely to engage in those online selling types of platform work. By contrast, the individuals who rely on their earnings to meet their daily needs to meet their living expenses, they were engaging in the labor exchange types of platform work. In terms of conclusion, our primary, argument is when you're looking at that optimist or pessimist perspective, which does our finding support, and we honestly found evidence of both. So when you look at the optimist perspective, we found evidence that those individuals from those socially disadvantaged backgrounds are pursuing labor

"Examining Income Inequality and Occupational Segregation in the Platform Economy"

00:08:15 - so it could be really important for helping reduce poverty or lowering, income gaps. However, we also found support for the pessimist perspective, which is when you look at the types of work that these individuals are engaging in, the affluent individuals are the ones engaging in online selling types. So when you're looking at what will platform work reduce income gaps, our perspective is generally not really, while the individuals from disadvantaged, backgrounds are pursuing platform work, they're not doing the types of work that could reduce the income gap significantly. And on top of that, you still have affluent individuals who are participating and growing their income supplementing
00:08:58 - So that could actually worsen the income gap that we're seeing. and then another key conclusion that we make is 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. And this theory looks at how different fields, different jobs might be segregated along the lines of race or gender or age and various other characteristics. and two primary mechanisms that segregate jobs or industries is sorting and self-selection. So sorting is the idea that we sort into jobs based on our preexisting understandings of who performs this type of work.
00:09:39 - where we may have more individual agency to pick jobs that fit our, interest or requirements. So when we look at our finding that, for instance, women are more likely to do the house cleaning platform work, men are more likely to do ride sharing platform work that, helps support this idea of occupational segregation. Women are still doing the traditional women work. Men are still doing that traditional men work. and so we are seeing platform work being segregated along those race, age, and sex lines as well. In terms of our limitations, obviously we only looked at US data, and so our results are only representative
00:10:42 - This is a little bit of a limitation because again, platform work transcends borders. It's globalized. You can have individuals who performing work in multiple different countries. and so we can't speak to how these findings might play out in other population contexts. And of course, we didn't look at every single possible platform work that exists. We missed out on, for instance, content creation, which is a big part of the platform economy. so those are some of the limitations that we faced.

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