Literature on Inequality and Discrimination in the Gig Economy

Inequality and discrimination in the labor market is a persistent and sometimes devastating problem for job seekers. Increasingly, labor is moving to online platforms, but labor inequality and discrimination research often overlooks work that happens on such platforms. Do research findings from traditional labor contexts generalize to the online realm? We have reason to think perhaps not, since entering the online labor market requires specific technical infrastructure and skills (as we showed in this paper). Besides, hiring processes for online platforms look significantly different: these systems use computational structures to organize labor at a scale that exceeds any hiring operation in the traditional labor market.

To understand what research on patterns of inequality and discrimination in the gig economy is out there and to identify remaining puzzles, I (Floor) systematically gathered, analyzed, and synthesized studies on this topic. The result is a paper recently published in New Media & Society.

I took a systematic approach in order to capture all the different strands of inquiry across various academic fields. These different strands might use different methods and even different language but, crucially, still describe similar phenomena. For this review, Siying Luo (research assistant on this project) and I gathered literature from five academic databases covering multiple disciplines. By sifting through many journal articles and conference proceedings, we identified 39 studies of participation and success in the online labor market.

Most research focuses on individual-level resources and biases as a source of unequal participation, rather than the role of the platform.

Three approaches

I found three approaches to the study of inequality and discrimination in the gig economy. All address distinct research questions drawing on different methods and framing (see the table below for an overview).

Approach 1 asks who does and who does not engage in online labor. This strand of research takes into account the voices of both those who have pursued such labor and those who have not. Five studies take this approach, of which three draw on national survey data and two others examine participation among a specific population (such as older adults).

Approach 2 asks who online contractors are. Some of this research describes the sociodemographic composition of contractors by surveying them or by analyzing digital trace data. Other studies focus on labor outcomes, identifying who among those that pursue online labor actually land jobs and generate an income. You might imagine a study asking whether male contractors make more money on an online platform than female contractors do.

Approach 3 asks what social biases exist in the hiring process, both on the side of individual users making hiring decisions and the algorithms powering the online labor platforms. Studies taking this approach tend to rely on experiments that test the impact of some manipulation in the contractor’s sociodemographic background on an outcome, such as whether they get featured by the platform or whether they get hired.

This is a table that gives an overview of the three approaches identified in the scoping review. For every approach, it lists the central research question, the method, and the number of papers.

Extended pipeline of online participation inequalities

In addition to identifying these three approaches, I map the outcomes variables of all studies across an extended version of the so-called pipeline of participation inequalities (as coined and tested in this paper). This model breaks down the steps one needs to take before being able to contribute online, presenting them in the form of a pipeline. Studying online participation as stages of the pipeline allows for the identification of barriers since it reveals the spots where people face obstacles and drop out before fully participating. Mapping the literature on inequality and discrimination in the gig economy across stages of a pipeline proved helpful in understanding and visualizing what parts of the process of becoming an online contractor have been studied and what parts require more attention.

I extended the pipeline of participation inequalities to fit the process of participating in the gig economy. This form of online participation does not only require having the appropriate access and skills to participate, but also requires garnering attention and getting hired. The extended pipeline model has eleven stages: from having heard of a platform to receiving payment as well as reviews and ratings for having performed a job. The figure below shows a visualization of the pipeline with the number of studies that study an outcome variable associated with each stage.

This image is a drawing of a pipeline made up of various pieces. Inside each piece, it indicates the corresponding stage of the process of becoming an online contractor. It also has numbers of how many studies examined each pipeline stage. At the end of the pipeline, there is two water droplets that represent labor outcomes (payments and reviews/ratings).
The extended pipeline of participation inequalities, specific to the process of becoming an online contractor, with the number of studies that examined each stage

When mapping the studies across the pipeline, we find that two stages have been studied much more than others. Prior literature primarily examines whether individuals who pursue work online are getting hired and receiving a payment. In contrast, the literature in this scoping review hardly examined earlier stages of the pipeline.

So, what should we take away?

After systematically gathering and analyzing the literature on inequality and discrimination in the online labor market, I want to highlight three takeaways.

One: Most of the research focuses on individual-level resources and biases as a source of unequal participation. This scoping review points to a need for future research to examine the specific role of the platform in facilitating inequality and discrimination.

Two: The literature thus far has primarily focused on behaviors at the end of the pipeline of participation inequalities (i.e., having been hired and received payment). Studying earlier stages is important as it might explain patterns of success in later stages. In addition, such studies are also worthwhile inquiries in their own right. Insights into who meets these conditions of participation and desired labor outcomes are valuable, for example, in designing policy interventions.

Three: Hardly any research looks at participation across multiple stages of the pipeline. Considering multiple stages in one study is important to identify the moments that individuals face obstacles and how sociodemographic factors relate to making it from one stage to the next.

For more details, please find the full paper here.

Floor Fiers is PhD candidate at Northwestern University in the Media, Technology, and Society program. They received support and advice from other members of the collective. Most notably, Siying Luo contributed greatly to this project as a research assistant.

Do generous attitudes underlie contributions to user-generated content?

User-generated content on the Internet provides the basis for some of the most popular websites, such as Wikipedia, crowdsourced question-and-answer sites like Stack Overflow, video-sharing sites like YouTube, and social media platforms like Reddit. Much (or in some cases all) of the content on these sites is created by unpaid volunteers, who invest substantial time and effort to produce high quality information resources. So are these volunteers and content contributors more generous in general than people who don’t contribute their time, knowledge, or information online?

We (Floor Fiers, Aaron Shaw, and Eszter Hargittai) consider this question in a recent paper published in The Journal of Quantitative Description: Digital Media (JQD:DM). The publication of this particularly is exciting because it pursues a new angle on these questions, and also because it’s part of the inaugural issue of JQD:DM, a new open-access venue for research that seeks to advance descriptive (as opposed to analytic or causal) knowledge about digital media.

The study uses data from a national survey of U.S. adult internet users that includes questions about many kinds of online contribution activities, various demographic and background attributes, as well as a dictator game to measure generosity. In the dictator game, each participant has an opportunity to make an anonymous donation of some unanticipated funds to another participant in the study. Prior experimental research across the social sciences has used dictator games, but no studies we know of had compared dictator game donations with online content contributions.

Sharing content. GotCredit via flickr.

Overall, we find that people who contribute some kind of content online exhibit more generosity in the dictator game. More specifically, we find that people producing any type of user-generated content tend to donate more in the dictator game than those who do not produce any such content. We also disaggregate the analysis by type of content contribution and find that donating in the dictator game only correlates with content contribution for those who write reviews, upload public videos, pose or answer questions, and contribute to encyclopedic knowledge collections.

So, generous attitudes and behaviors may help explain contributions to some types of user-generated content, but not others. This implies that user-generated content is not a homogeneous activity, since variations exist between different types of content contribution.

The (open access!) paper has many more details, so we hope you’ll download, read, and cite it. Please feel free to leave a comment below too.

Paper Citation: Fiers, Floor, Aaron Shaw, and Eszter Hargittai. 2021. “Generous Attitudes and Online Participation”. Journal of Quantitative Description: Digital Media 1 (April).