Most conventional jobs involve hierarchy. A boss divvies up work to the office’s full-time employees awaiting direction and a green light. While still true for the majority of American workers, a growing number of people are picking up work online — accepting jobs with companies that assign, schedule, route, and pay for work through websites or mobile apps. This on-demand “gig work” is unraveling the typical job. Yet none of our current workplace statistics or labor laws reckon with the new employment reality turning APIs into shift managers. Our research team spent the past two years conducting one of the largest, most comprehensive studies of its kind to learn about the lives of on-demand gig workers. One of our greatest challenges was that we didn’t have a representative sample of American workers that could validate and enrich our findings. That is…until now.
Katrin Tiidenberg (Aarhus University, Denmark and Tallinn University, Estonia) and SMC Principal Researcher Nancy Baym (Microsoft Research, New England) have recently published an article in Social Media + Society that analyzes how pregnancy is performed on Instagram. According to Tiidenberg and Baym,
‘Pregnancy today is highly visible, intensely surveilled, marketed as a consumer identity, and feverishly stalked in its celebrity manifestations. This propagates narrow visions of what a “normal” pregnancy or “normal” pregnant woman should be like.’
Drawing on Tiidenberg’s work during her Ph.D. internship with the SMC (2014), the article asks:
‘[W]hether they [women] rely on and reproduce pre-existing discourses aimed at morally regulating pregnancy, or reject them and construct their own alternatives.’
You can read their findings here.
SMC member Mary L. Gray (Microsoft Research, New England; Berkman Kein Center for Internet and Society) and colleague Siddharth Suri (Microsoft Research, New York) have published an article for the Harvard Business Review asking, “just how artificial is Artificial Intelligence?”
Whether it is Facebook’s trending topics; Amazon’s delivery of Prime orders via Alexa; or the many instant responses of bots we now receive in response to consumer activity or complaint, tasks advertised as AI-driven involve humans, working at computer screens, paid to respond to queries and requests sent to them through application programming interfaces (APIs) of crowdwork systems. The truth is, AI is as “fully-automated” as the Great and Powerful Oz was in that famous scene from the classic film, where Dorothy and friends realize that the great wizard is simply a man manically pulling levers from behind a curtain.
For Gray and Suri, the mythos of “full-automation” is akin the Great and Powerful Oz, famously depicted as a man “manically pulling levers from behind a curtain” in the classic American film.
This blend of AI and humans, who follow through when the AI falls short, isn’t going away anytime soon. Indeed, the creation of human tasks in the wake of technological advancement has been a part of automation’s history since the invention of the machine lathe.
Full text of the article is available here.
December 7-13th is Computer Science Education Week!
Recently, feminist media scholars have demanded we take seriously seriously the dearth of women and people of color in computing fields. This week presents the opportunity to broadcast professional role models to inspire young minority techies in pursuit of their STEM dreams, both in industry and in academia.
Source: Microsoft Corporate Blogs
Mary L. Gray, senior researcher at the Social Media Collective, was recently featured in Microsoft’s “17 for ’17: Microsoft researchers on what to expect in 2017 and 2027,” which sought to work against this gap by highlighting 17 women from within their global research organization.
Mary offers insights on the digital world we should anticipate over the next decade and where to position ourselves as scholars.
Just a reminder, January 1 is the deadline for applications for the summer 2017 internship program with the Social Media Collective, at Microsoft Research New England. All the information you need, about the internship, the necessary qualifications, and how to apply, can be found here. During their twelve-week stay, SMC interns devise and execute their own research project, distinct from the focus of their dissertation. The expected outcome is a draft of a publishable scholarly paper for an academic journal or conference. Our goal is to help interns advance their own careers.
The Social Media Collective (in New England, we are Nancy Baym, Tarleton Gillespie, and Mary Gray, with current postdocs Dan Greene and Dylan Mulvin) bring together empirical and critical perspectives to understand the political and cultural dynamics that underpin social media technologies. Primary mentors for this year will be Nancy Baym and Tarleton Gillespie, with additional guidance offered by other members of SMC.
Uber has TV ads now. The one I see most often is called “Get Your Side Hustle On”. It opens with a thirty-something black male Uber driver telling us, somewhat wearily, “These days, everyone needs a side hustle.” Then the upbeat horns pick up, he and his passenger start dancing, and he tells us how Uber helps drivers move “from earning, to working, to chilling at the push of a button.” He’s earning in his car, working when he’s teaching middle-school chemistry, and chilling when he’s passed out on the couch in the middle of the day, his daughter reading beside him. The side hustle is what helps you make ends meet. Uber, now valued at around $62.5 billion, helps you get your side hustle on whenever you have spare time to slip between your full-time job, your childcare responsibilities, your social life, and your sleep schedule.
There’s some romance to this story, of course; Americans love a hustler. But all credit to Uber, because this ad seems to be an accurate representation of their business model and the reason why they, founded in 2009 and officially launched in 2011, and the rest of the gig economy have grown so rapidly in the wake of the 2008 financial crisis. New data from Pew show that folks on the fringes of the formal labor market, those without secure jobs or the sort of wealth that provides a cushion in tough times, are seeking out gig economy work to make ends meet.
This a sizable group: 8% of Americans earned money from technology-enabled gig work last year. Pew calls these tools for soliciting drivers, handymen, shoppers, and data-enterers ‘labor platforms’, distinct from the ‘capital platforms’ used to rent your home or sell your bespoke wares. Another 18% of Americans made money from the latter in the last year. It is no coincidence that the growth and success of gig platforms has taken place during a period of stagnant wages and labor market bifurcation (i.e., the jobs generated in the wake of the crisis have been concentrated in high-wage knowledge sectors and low-wage service sectors, with the middle increasingly disappearing). It is precisely because so many Americans have needed to find a little work on the side that these gig platforms are thriving. These days, everyone needs a side hustle.
This is not an altogether new phenomenon. The so-called ‘informal economy’ often grows during recessions. When good jobs are hard to find, people seek out other, less-regulated means to put food on the table: selling food out of a cooler at the bus stop, taking in neighbors’ laundry, offering handyman services to other members of your church, or driving an unlicensed taxi for a few spare bucks. In previous eras, these would have been largely off-the-books, cash-only exchanges, because individual hustlers don’t want to get the health department, the taxicab commission, or the taxman involved. But the genius of Uber, TaskRabbit, and the like is that they formalize these previously informal exchanges by making them accessible to any consumer with a credit card and a smartphone, while simultaneously retaining the informality that frees the company from the obligations employers typically owe employees or regulators. And of course, gig platforms create many new opportunities for this work just by providing extensive logistical support for it, support that justifies their extraction of rent from this newly formalized work conducted on their platforms.
What was the macroeconomic soil in which these business models took root? According to the Economic Policy Institute, while US workers’ productivity has grown by leaps and bounds since 1979, their real wages have barely budged—and low-wage workers’ pay has actually fallen. The exception is the top 5% of earners, whose wages have grown 41% since 1979. So most of our wages haven’t grown in a few decades, while the cost of expensive, essential outlays like housing, healthcare, and college have soared.
More recently, the 2008 financial crash destroyed many Americans’ financial safety nets by wiping out their main sources of wealth—their investment in their homes and their retirement accounts, typically 401Ks—and put serious strain on other savings and investments, if they had them. There has always been a massive wealth gap between white Americans and people of color, which severely restricts the social mobility of the latter, since inherited wealth is a crucial ingredient in affording big things like housing and college and smaller things like unpaid internships This gap widened a great deal in the wake of the housing crash, with black and Latino households losing three and four times more wealth respectively than white households between 2007 and 2010. And while the unemployment rate has finally fallen back to pre-recession levels, the jobs that we have regained since the recession have not been good ones. The National Employment Law Project found that while employment losses during the recession were concentrated in mid-wage and higher-wage industries, the employment gains during the recovery have been concentrated in low-wage industries. We’ve had an uneven recovery, especially for people of color.
How does Pew’s new data on gig economy workers fit into these trends? Well, the data only provide a snapshot. To confirm my speculation that gig platforms capture precarious Americans’ informal work and extend the opportunity for a side hustle to others, we’d need to know more about trends in gig economy work across time and geography (e.g., whether tighter local labor markets discouraged gig work or not), and what sort of other work gig workers are doing. But this snapshot seems to support my suspicions:
- 56% of labor platform users say the money they earn through those platforms is either ‘essential’ or ‘important’ to meeting their basic needs, as opposed to being ‘nice to have’ (42%). They’re more likely to have a household income below $30,000 (57%), be nonwhite (64%), and lack a college degree (52%).
- Recalling our black male middle-school teacher going from chilling to working at the push of a button, labor platform users for whom those earnings are essential or important are more likely to say they use the platform because it gives them control over their own schedule (45%) and because there are few jobs in their area (25%). Those who say the money is nice to have are more often (62%) motivated by the work being fun, or just something to do.
- 14% of black Americans and 11% of Latinos earned money from online gig work in the past year, compared to 5% of whites. Black Americans are more likely to have done physical gigs like driving or taking in laundry (5%) than white Americans (1%)
- Fewer than half (44%) of technology-enabled gig workers are employed full-time. 32% are unemployed.
- Americans making less than $30,000 per year are more than twice as likely (10%) to do gig work than Americans making more than $75,000 per year (4%).
- Compared to Americans overall, technology-enabled gig workers are less likely to have health insurance (10% lower than the national average), a credit card (15% lower), or a retirement account (13% lower).
Importantly, Pew finds large differences between labor and capital platforms; users of the latter are older, whiter, wealthier, more highly educated, and less reliant on these earnings than gig workers. Who, then, is most likely to be a gig worker who needs that side hustle? A working-class person of color without a college degree who is fitting that hustle in between other life tasks because they’re making less than $30,000 a year, lack a financial safety net, and struggle to afford healthcare. So, that Uber ad wasn’t 100% correct: Some people need a side hustle more than others these days.
The language of the ‘sharing economy’ positions all of us equally in the same community of app users. Indeed, the main advocacy group for the industry, now packaging portable benefits for gig workers, is simply called Peers. But if we read the latest data alongside earlier data on consumers of gig platform labor, it becomes clear that we are not all on the same page. An earlier Pew report found that super-users who purchase services from six or more of these platforms are generally digitally literate, college-educated urbanites making $75,000 or more. The gulf between frequent suppliers of labor to these platforms and frequent purchasers of that labor mirrors the gulf in the labor market that has been growing for decades but which ballooned after the recession: Low-wage service jobs with unpredictable schedules and no benefits on one side, and high-wage knowledge economy jobs concentrated in urban areas on the other.
That so many are desperate to supply their labor for these platforms must surely be a major factor in their growth. They were the right model for the right moment. With good jobs drying up and people looking for extra, flexible, informal work, these digital platforms were ready to welcome them. In precarious times, the side hustle is a growth industry.
Dan is a postdoctoral researcher with the Social Media Collective at Microsoft Research New England. He studies the institutions and technologies that teach us how, where, and why to work in the information economy. You can learn more about him and his research at dmgreene.net.
We shared our survey questions and preliminary findings with the Pew Research Center for Internet, Science and Tech as they designed their survey, “Gig Work, Online Selling and Home Sharing.” Pew wanted to develop a better way to gauge how many people, from a representative sample of the U.S. population, participate in gig work, ridesharing (think apps like Uber and Lyft) and homesharing (via sites like Airbnb and VRBO). It is hard to get a good headcount of those earning an income in the gig economy because the words to describe these jobs change with the launch of a new on-demand service or court case challenging what it means to “work” for a mobile app. Ridesharing and homesharing are more visible in the media. But a variety of jobs are quietly shifting online to become on-demand gig work, too. TaskRabbit and Thumbtack, for example, connect consumers with trade workers available to do the task. Crowdflower and Amazon Mechanical Turk are two of the more popular “crowdsourcing” platforms. They offer companies a way to post tasks online to a pool of people who have signed up to sift through the platform’s online listings of work opportunities. These public crowdsourcing platforms are the tip of the spear. Today, nearly every large tech company developing artificial intelligence uses proprietary services like these. The on-demand labor that AI-fueled jobs create is hard to measure, let alone see. The typical jobs performed on these platforms are white-collar office gigs, like transcribing audio, labeling images, and reviewing social media material flagged as “adult content” or “not safe for work.”
Before Pew’s report, scholars and policymakers had only the Contingent and Alternative Employment Arrangements survey, last run in 2005, to estimate the size and growth rate of this workforce. A lot has changed since then but worker surveys never caught up with the technology trends radically altering the workplace.
The Pew’s findings confirmed everything we learned. It is the perfect complement to our roughly 200 in-person interviews, tens of thousands of survey responses, dozens of behavioral experiments and big data analyses of gig work platforms. It also spotlights how quickly temp and contract work have changed for U.S. workers since the Great Recession.
According to the Pew report, about 5% of the U.S. population, or 1-in-20 people, does some form of online gig work. To put that in perspective, online gig work was a far more common source of income than homesharing (at about 2%) or ridesharing (around 1%).
How important is earning money from gig work to those who do it? Are we talking about college students earning beer money or people trying to put food on the table? According to Pew:
· Only 8% of those surveyed said the money they earned from selling goods online is “essential for meeting my basic needs.”
· Eighteen percent said the same of money earned from homesharing.
· But roughly one quarter of those doing gig work reported that the money they earned was essential for meeting their basic needs.
· Another one quarter said the money was important.
According to the Pew analysis, “workers who describe the income they earn from these platforms as ‘essential’ or ‘important’ are more likely to come from low-income households, more likely to be non-white and more likely to have not attended college.”
The reliance on gig work income reported in the Pew survey is echoed in our own survey of over 2,000 gig workers, collected across 4 different platforms. Over half of our study’s respondents reported that they had a minimum amount of money that they needed to make that month from gig work.
Part of gig work’s appeal is a chance to manage one’s own workflows. Of the people who said doing gig work was “essential” or “important” in the Pew survey:
· Just under half reported that they do this work because they have a “need to control their own schedule.”
· Another quarter said there was a “lack of other jobs where they live.”
In fact, according to one of our study’s experiments, gig workers were willing to take somewhere between a $0.40/hour and $0.80/hour pay cut to work on tasks that allowed them some degree of control over when they complete the task. And almost every one of our interview participants described balancing care for a loved one or completing a school program as the kind of constraint that pushed them to seek online work. Clearly, people do this kind of work because they need the job, they need to control their schedules and/or they don’t have a lot of employment options in their hometowns.
Pay attention to online gig work because it is dramatically reshaping our society. Labor economists Lawrence Katz and Al Krueger estimate that conventional temp and alternative contract-driven work rose from 10 to 16%, accounting for all net employment growth in the US economy in the past decade. Assuming Pew’s trends continue at the current rate, by the year 2027, nearly 1 in 3 American adults will transition to online platforms to support themselves with on-demand gig work. This is only bad news if we do nothing to change the outdated laws and structures in place to support working people. Ignoring corporate and consumer dependency on an on-demand gig workforce is not a sustainable strategy.
Pew’s study is robust proof that the world of work — what we spend most of our time doing — is quickly moving online. Platform start-ups are cropping up every day to connect people directly with employers for short-term gig work. There is no evidence that this trend will reverse and every indication that the move to on-demand gig work is well underway. The future of work will look more like the apps and web-based platforms that we have been studying the past two years than the “traditional” employment around (some of us) today. These workers may be difficult to see but they are vital to the future of our economy. Our country cannot afford to leave them behind.
Siddharth Suri (@ssuri) is a Senior Researcher at Microsoft Research, New York City. Mary L. Gray (@maryLgray) is a Senior Researcher at Microsoft Research, Associate Professor at Indiana University and Fellow at the Harvard University Berkman Klein Center for Internet & Society. They are writing a book about workers’ experiences of the on-demand economy. You can read more about their research at inthecrowd.org.
This post was spurred by an email from Tarleton Gillespie and Hector Postigo to contribute to a collected series that appeared on Culture Digitally today. The focus of that series is for scholars to “think hard about our own work and research agendas, and how they should shift to face new political realities.”
I didn’t have a chance to contribute to this collection yesterday because I was trying to figure out how to prepare a lecture on the networked press to a class of undergraduates I’d never met before. Months ago, I agreed to give a guest lecture in Henry Jenkins’s Communication & Technology class and it never occurred to me that November 9th would be such an ominous day.
I hadn’t slept the previous night as I tried to quell a dizzying headache and nausea, thinking about what I could possibly say to students about how a field I’m supposedly an expert in got everything so wrong. I spent the morning avoiding news coverage, deeply angry at an institution I’m supposed to be invested in. Instead, I played with different lecture openings, looked at old slides, arranged and rearranged arguments I’ve made countless times before. I thought that I’d just rely on a PowerPoint deck I knew well, get through the 90 minutes, and return to my fog.
Time eventually ran out, I bundled up my preparation as it was, and made my way to campus.
I walked into the room still unclear about what I’d say. I didn’t know these students (I was the guest lecturer) and, after seeing several “Make America Great Again” t-shirts on campus the previous day, I wasn’t sure what they’d be feeling. I mentally prepared myself for everything from tears (theirs and mine) to being drawn into an encounter with celebrating Trump supporters.
I began class. I asked them to put away their laptops and had the lingerers in the back come up to fill out the front row seats.
I started honestly: I told them that I didn’t really know how to begin, that I’d never led a class like this on a day like this, and that I wasn’t even sure this was where I wanted to be right now. Their smiles, nods, and knowing glances at each other put me at ease.
I asked them why they studied Communication. I told them why I did. I told them that our jobs as Communication scholars was to figure out why people act together, build meaning, and share consequences. And I told them that there was never a more important time for us to be world-class at what we did. I told them that we can’t make the mistake fish make: they don’t know what water is because they’re always swimming in it. We can’t not think critically about media because it’s all around us and we think we have little power to shape it.
I asked them to take out a piece of paper and free-form write for 7 minutes on two questions: what do you want from online news? And what do we need from online news? They wrote and I stared out the window.
When the time was up we talked about similarities among their individual desires, where they thought those desires came from, how hard it was to define a “we”, and whose responsibility it was to differentiate a want from a need. All of their comments were peppered with stories from the previous night: how confused they were about what was happening, how unexpected everything was, how disconnected they’d felt from pro-Trump parts of the country. It was exhausting, they said, to have to individually create media worlds that challenged what their instincts made them want. They didn’t know what a public might need from the news. They assumed that the news media knew. And they talked about “objectivity” and “balance” and “neutrality” in all the ways students usually do when they first start thinking about journalism.
I then went old-school. We talked about James Carey’s transmission versus ritual models of communication. I channeled my advisor Ted Glasser to argue that the press exists in traditions not nature (news is never found, it is always made). We looked at the history of the AP Style Guide to see the contingency of language: e.g., how it took the New York Times a long time to call women anything other than “Miss” or “Mrs.” (“why were woman ever defined by their marital status?” one female student said) and why the Times called gay men “longtime companions” instead of lovers or partners in AIDS plague obituaries. We talked about the difference between writing “illegal” versus “undocumented” immigrant. We talked about why the AP captioned an image a young person of colour wading through chest-high water carrying food as a “looter” versus why the Agence-France Presse said two white people in a similar photo were “finding” bread. We talked about gender, and orientation, and race and I claimed victory when one student said “it seems like objectivity is just a construct.” Yes, yes, yes.
We then looked at Pew stats on social media and the news. We looked at data, asked questions about where it came from and what it meant, and tried to write headlines for stories that might be written about the findings. One student said she was “angry” at her Facebook algorithm for keeping from her news about the rest of the country; she’d assumed that her feed of Hillary supporters was similar to everyone else’s. She didn’t understand why a friend in Georgia texted her to say she was nervous about Trump winning because Hillary’s impending win was “all over social media”.
One student asked when news organizations began endorsing candidates and whether such endorsements mean anything anymore. We looked at Pew’s stats about how Republicans, Democrats, and Independents use social media differently, questioned their significance, and started asking questions about the people who weren’t showing up in social media statistics. For me, the best conversation came just as class was about to end. I asked “if many people are getting their news from social media, why don’t platforms endorse candidates?” and one student replied “because they think they’re being neutral and objective, just like the press thought it was.”
I know there are ongoing debates about the empirical bases for filter bubbles and how it’s incredibly hard—and dangerous—to track media circulation using media effects methodologies. That wasn’t the point of our discussion. It was to be fish who noticed and thought about the water. It was to ask new and uncomfortable questions about platforms and news. It was to demand different kinds of data. It was to challenge the wisdom and sincerity of tech leaders who say they’re running technology companies, not media companies. It was to refuse to accept that media systems are only the responsibility of individuals tasked with figuring out the differences between what individuals want and publics need. It was to avoid repeating the mistakes of the past: eras not when “we” thought it was okay to run sexist, homophobic, racist media systems but when those with the power to make such systems thought such failures were acceptable artifacts of chasing the myth of objectivity.
I’ll be honest that teaching hasn’t always been the favourite part of my job. I do it, I’m not terrible at it, I sometimes admire and learn from some of my students, and every now and then I get a high from a class well taught or a student who “gets it” and helps us both see the world differently. But yesterday’s class reminded me of what it sometimes felt like to go to church. Teaching felt like a form of communion: a way not only to transmit information but critically, reflectively, constructively figure out what it means to live together. This is the spirit of the media systems we need now more than ever, that I’m re-energized to help build through teaching and scholarship.