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The Gig Economy: Research and Policy Implications of Regional, Economic, and Demographic Trends

January 10, 2017  • Ben Gitis , Douglas Holtz-Eakin , Will Rinehart & Future of Work Initiative

This report was produced by the American Action Forum in collaboration with the The Aspen Institute  Future of Work Initiative .

EXECUTIVE SUMMARY

Over the past few years, policymakers have grappled with the policy implications of the so-called “gig economy.” This paper adds to the small but growing literature by examining regional, economic, and demographic trends in the gig economy and its workers.

To do so, we use the General Social Survey (GSS) to construct three measures of gig economy workers:

•   Gig 1: independent contractors, consultants, and freelancers.
•   Gig 2: Gig 1 + temp agency workers and on-call workers.
•   Gig 3: Gig 2 + contract company workers.

Our broadest measure of gig economy workers, gig 3, is the same measure of gig economy workers employed by Katz & Krueger (2016). Using these measures, we find:

• The data show rapid growth in gig economy employment. From 2002 to 2014, while total employment increased 7.5 percent, gig economy workers increased by between 9.4 percent and 15.0 percent, depending on the definition of gig economy workers. Between 2010 and 2014, growth in independent contractors alone accounted for 29.2 percent of all jobs added during that time period.
• There is tremendous geographic heterogeneity in the utilization of gig economy workers, with the lowest concentrations in Northeast and Upper Midwest states, and highest concentrations in Mountain and Pacific states. Moreover, what little information is available about regional trends is heavily influenced by the recovery from the Great Recession.
• Gig economy workers are less attached to the workforce and may turn to gig economy opportunities for additional income after getting laid off. All employed people in the United States in 2014 worked an average 46.7 weeks in 2013. Gig economy workers, on the other hand, worked an average 41.8 to 44 weeks. Meanwhile, 6.7 percent to 12 percent of gig economy workers have been laid off from previous work. That’s substantially higher than the 5.4 percent rate of all employed people in the country.
• Gig economy workers are also more likely to be part-time workers, and the portion of gig economy workers who are part-time has increased since 2002. They are also much more likely to mainly work at home than traditional workers, though this difference has narrowed in the last decade.
• The pattern of shifts in the income distribution differs between the entire workforce and gig economy workers, and the pattern within gig economy workers is sensitive to the definition. The timeframe also matters: looking at the changes between 2006 and 2014 (which contains both the downturn and recovery) produces a different picture than that from 2010 to 2014 (which focuses on the recovery).
• Pulling these results together, one finds a picture of the gig economy workforce that is, compared to traditional workers, more likely to be married, but also more reliant on part-time work and more likely to have been previously laid off, perhaps as a result of the Great Recession.
• The online gig economy appears to contribute to firm growth. This is especially true in the transportation sector, where online labor platforms have become pervasive. On average, in metropolitan areas the total average annual growths of establishments and receipts in the transportation sector were 7.7 percent and 9.4 percent respectively prior to the introduction of a ridesharing service, and 39.3 percent and 20.4 percent, respectively, in the years after the introduction of a ridesharing service.

Our results suggest some fruitful avenues for further research. First, the role of gig economy employment in cyclical recoveries stands out as an important issue for understanding overall employment patters, labor supply among couples, and the economic well-being of gig employees. At the same time, the regional differences unearthed in these data merit further documentation and understanding. What, for example, is the role of state-level policy in generating these trends?

These findings suggest a stance of forbearance by federal policymakers. The nature of employment and economic well-being patterns is sensitive to data definitions – always an indication that drawing bright policy lines is a problematic endeavor. It also suggests that there may be a role for federalizing the gig economy policy response.

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The gig economy, as reflected by nonemployer firms, is significant and growing fast. Overall, there has been a clear surge in nonemployer firms’ — a measure of contractor and freelance individuals — business activity in the last decade, which almost certainly reflects, at least in part, the rise of online platforms.

Platform-based freelancing is not yet substantially displacing payroll employment—but that could change. Despite the uptick in nonemployer contractors, payroll employment in “rides and rooms” industries has not declined during the last five years. Instead, payroll employment has increased in these industries, particularly in the passenger ground transit sectors.

Online gigging in the rides and rooms industries is so far concentrated in large metropolitan areas. Gig economy activity is unevenly distributed in the rides and rooms industries. The spread of nonemployer firms between 2010 and 2014 occurred mostly in the largest metro areas. No less than 81 percent of the four-year net growth in nonemployer firms in the rides sector took place in the 25 largest metros, while 92 percent occurred in the largest 50 metros.

• 17 min read

Nearly a decade after the founding of Uber and Airbnb it’s still hard to get a handle on the size and importance of either those particular platforms or the larger “gig economy.” 1

On the one hand, the rise of Uber, Lyft, and Airbnb has generated so much controversy about online talent platforms, the changing nature of work, and workers’ rights that it has at times been hard to get a clear fix on the sector and its meaning. 2

On the other hand, the sector’s size and growth has been difficult to clarify, because it has been difficult to measure. Government data-gathering, for example, has not been well positioned to capture the gig economy, in part because it is conceptually complex and in part because the government stopped counting “contingent workplace” arrangements after 2005. 3

Which means that no comprehensive database exists on either employment in the gig economy or its geography.

As a result, debates have flared over the true size and significance of the sector. Some skeptics, by way of aggregate self-employment statistics, conclude that “proof of the revolution…is hard to find.” Others have worked directly with platform company data or leveraged other proprietary information to assess the size and nature of online gigging. Overall, these national analyses have tended to describe a small but rapidly growing realm of platform-enabled freelancing. So far, though, the findings have yet to be extended to city-by-city estimates of growth or comparisons of activity across metro areas.

However, it turns out that for all of the limitations of the available data, additional light can in fact be thrown on the online gig economy. Specifically, insight can be gleaned—if one knows where to look for it—from an obscure Census Bureau dataset on “nonemployer firms,” which tracks the activity of “businesses” that earn at least $1,000 per year in gross revenues (or $1 in construction) but employ no workers. 4

As it happens, the vast majority of these “businesses”—up to 93 percent of them in the rides and rooms industries—turn out to be self-employed, unincorporated sole proprietors. 5  In other words, they are individuals earning income by freelancing or contracting with other businesses such as Uber, Lyft, and Airbnb. All of which means that one can learn a lot by analyzing these “firms’” proliferation and location—especially since the data (derived from tax records at the Internal Revenue Service) are available at fairly detailed levels of industrial activity (NAICS codes) and geography (counties). 6

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And so one of us looked last year at one city (early-adopting San Francisco) and at two leading gig economy segments (rides and rooms) and found a substantial rise in platform “gigs” between 2009 (when uptake began) and 2013 (the latest year data was available).  The platform economy was clearly trackable and substantial—if only in one unique city. 7

Yet now, with another year of data available, it is possible to update and expand the initial analysis.  Therefore, we look here at the two industries most closely associated with the online gig economy—peer-to-peer ride-sharing and peer-to-peer room-sharing—and assess gig activity both nationally and with a particular focus on the 50 largest metropolitan areas. For comparison, we benchmark these trends against trends in nonemployer firms for the entire economy, and against payroll employment in the requisite industries. In addition, we used corporate websites and local news outlets to confirm that Uber, Lyft, and Airbnb were in fact operating in the 50 large metro areas during the years examined. They were.  Uber and Lyft were running in many of the 50 metros’ principal cities by 2012 and in most of them by 2013. Airbnb was operating in all of the metros by 2012. 8

What, then, do we see in these data focused on the rides and rooms industries? Three major findings stand out:

The gig economy, as reflected by nonemployer firms, is significant and growing fast.

Overall, there has been a clear surge in nonemployer firms’ business activity in the last decade, which almost certainly reflects, at least in part, the rise of online platforms.

To begin with, nonemployer firms—though still relatively small in overall economic value historically (accounting for about 3 percent of total business receipts)—are becoming a more important factor in the entire economy, having come to encompass nearly 24 million “businesses” in 2014 up from 15 million in 1997 and 22 million in 2007. (By comparison, total U.S. payroll employment was about 145 million in 2014, up from 129 million in 1997). To be sure, some of the recent growth in nonemployer firms reflects the spread of myriad investment vehicles and other partnerships such as limited partnerships that are increasingly used for disbursing a particular pools of funds. However, self-employed, sole proprietor, unincorporated firms (that is, contractors and freelancers) constitute the lion’s share of this activity (seven of every eight nonemployer firms). In short, gig employment—whether digital platform enabled or not—has been growing.

Turning to the rides and room industries, the nonemployer firms data documents parallel, but differently scaled, increases in activity in the two industry segments associated with passenger ground transit after 2010 (when Uber launched in San Francisco) and in the two industries linked to traveler accommodation after 2008 (the year Airbnb opened). Granted, nonemployer firms in the taxi and limousine industry were a big part of the “rides” sector prior to the arrival of Uber and Lyft. 9  However, after 2010, independent contractor growth in the ground transportation industry suddenly takes off—and then explodes in 2014 (a trend that is likely to have continued in 2015 and 2016).  In that year the nonemployer firm growth rate in ride-sharing was 34 percent, compared with 4 percent for payroll employment in the industry. Between 2010 and 2014, nonemployer firms in ride-sharing grew by 69 percent while payroll employment grew by just 17 percent.

Trends in the “rooms” sector are similar but of a lesser magnitude. Nonemployer firms in the sector play a much more modest role in employment, which makes sense given how many employees it takes to operate a typical hotel. Nonemployer firms, on the other hand, contract out things like cleaning services, and meals and entertainment that will spill over into the local economy. Even so, nonemployer firms have been growing at a faster pace in the rooms business than has payroll employment, particularly after 2008 when Airbnb launched. An especially sharp jump is apparent in 2014—when more than one-half of the period’s growth occurs (specifically, a one-year increase of 9 percent). Moreover, home-sharing sectors are likely especially undercounted in the data, because of tax reporting requirements—which again, are not mandatory until a room-sharing host exceeds $20,000 in receipts or 200 transactions each year. In addition, recent reports suggest that many Airbnb hosts are not individuals, but businesses renting properties on a permanent basis—an activity that likely does not show up in the data.

In short, a surge of nonemployer firm activity—explosive in ground transportation and noticeable in accommodations—seems to directly coincide with the large-scale expansion of the gig economy and uptake of online platform services. Whereas payroll employment in the ride-sharing industry grew by 17 percent between 2010 and 2014, nonemployer firms (including gigging freelancers) increased by 69 percent. Likewise, although payroll employment in the home-sharing industry grew by 7 percent, nonemployer firms grew by 17 percent.

Of course, these numbers should be interpreted with caution. While the expansion of nonemployer firms in the two sectors coincides with widening adoption of these online platforms, we cannot say for sure that one caused the other, nor can we say that growth is entirely attributable to ride- and room-sharing apps. However, it is reasonable to conclude that, at a minimum, the emergence of these online platforms is contributing to the growth of the freelance and contractor workforce in a material way.

Platform-based freelancing is not yet substantially displacing payroll employment—but that could change.

Despite the uptick in nonemployer contractors, payroll employment in “rides and rooms” industries has not declined during the last five years. Instead, payroll employment has increased in these industries, particularly in the passenger ground transit sectors.

Again, caution is due when interpreting the data as the underlying dynamics are complex. Nonetheless, the rides and rooms sectors saw 17 percent and 7 percent growth, respectively, in payroll employment between 2010 and 2014. This rise contradicts the widely held belief that platform-based freelancing has displaced wide swaths of existing businesses. Uber and Airbnb, at least between 2010 and 2014, were not in most cases driving traditional incumbent firms to lay off payroll workers or go out of business. Instead, these data lend credence to the contention that Uber and Airbnb are meeting unmet consumer demand or stimulating new demand through innovative service offerings.

Yet it is still early. Nonemployer firm growth is clearly rising, and accelerating in the ride-sharing sector. These trends raise the possibility that the online marketplaces could cannibalize competing payroll businesses in some industries, particularly given the rapid deployment of new technologies. For example, Lyft recently claimed that most of its cars will be self-driving in five years. While that forecast sounds overly-ambitious to us, its actualization would spell trouble for human drivers, both those on payrolls and freelancers alike. More detailed tracking of the industry will be necessary to ascertain how these dynamics play out.

Online gigging in the rides and rooms industries is so far concentrated in large metropolitan areas.

Turning to the geography of nonemployer firm growth, it appears that gig economy activity is unevenly distributed in the rides and rooms industries. To begin with, the spread of nonemployer firms between 2010 and 2014 occurred mostly in the largest metro areas. No less than 81 percent of the four-year net growth in nonemployer firms in the rides sector took place in the 25 largest metros, while 92 percent occurred in the largest 50 metros. For rooms, those figures were, respectively, 56 percent and 70 percent—just slightly more than it was across all industries. (It makes sense that rides would be more concentrated in urban areas than rooms, given that a larger share of the nation’s travel and stays in overnight accommodations lies outside of urban areas.)

A closer look at the distribution of nonemployer firm growth reveals a variegated map of often rapid growth.  Focusing now on just the years 2012–2014 (when Uber, Lyft, and Airbnb were really catching on), growth is explosive for rides across most larger US metro areas, with all 50 large metros gaining activity. For rooms the growth is more muted but also noticeable.

Change in nonemployer firms and payroll in select passenger ground transportation industries (2012-2014)

50 largest metropolitan areas; click on chart legend to resort metro areas

Source: Brookings analysis of Census Bureau and Moody’s data. Note: Payroll employment has been suppressed for Tampa and Milwaukee.

Setting the pace of growth in the ride-sharing industry were seven tech-oriented, mostly Western, metro areas. These seven saw nonemployer-firm activity in the ride industry double during the two years, with San Jose and San Francisco leading the way, followed by Los Angeles , Austin , San Diego , and Nashville . More broadly, more than half (27) of the large metro areas saw 50 percent or better growth in nonemployer firms in ground transportation industries, with two-year growth rates ranging of 94 percent in Boston , 85 percent in Pittsburgh , and 82 percent in Seattle to 63 percent in DC , 61 percent in Indianapolis , and 52 percent in Providence . Lending credence to the suggestion that nonemployer-firm growth reflects gig economy growth is the fact that five of the slowest growing ten metros for nonemployer firms on the rides side are five of the six metros in which either Uber or Lyft had yet to launch during the study years.

By comparison, payroll employment growth in the industry was much more subdued, or negative, in 17 metro areas. 10  In San Jose and Sacramento , where nonemployer firms in ground transportation grew by 145 and 92 percent, respectively, payroll employment declined by 31 percent and 22 percent, respectively.  This evidence suggests that while peer-to-peer rides do not appear to be cannibalizing taxi drivers on payroll in a widespread fashion, the potential may be there in some markets. With that said, the present data do not allow formal analysis of whether displacement is occurring, so the coincidence of nonemployer firm growth and payroll decline should be interpreted with caution, as many other factors could be at play. After all, the shift to contractors in the rides industry began well before the arrival of Uber and Lyft. Meanwhile, the dynamics of individual markets vary widely and lie beyond the scope of this analysis.

Data on the rooms sector, meanwhile, reveals a more subdued story of change. Still, nonemployer firm growth was widespread, with five metro areas ( Austin , San Francisco , Portland , New Orleans, and San Jose ) registering 37 percent or better two-year firm growth, and 13 metros seeing gains of 20 percent or more. Payroll employment, meanwhile, has held steadier than in the rides industry.

Change in nonemployer firms and payroll in select traveler accommodation industries (2012-2014)

Source: Brookings analysis of Census Bureau and Moody’s data.

Overall, the nonemployer firm data consulted here add to what is known about the development and implications of the online-enabled gig economy.

To be sure, the information here remains imperfect.

For one thing, the nonemployer firms data only allow analysis of potentially gig-driven changes in two industries, given that it remains a proxy measure for platform-based gigging. For another thing, the present analysis remains confined to just two precincts of the much larger gig economy, given that such activity is difficult to capture in more diffuse industry areas, such as crafts, errand-running, and task-fulfillment. For now, the conceptual blurriness of such activities precludes analysis of freelancing on platforms like Etsy, Taskrabbit, or Thumbtack.

Finally, the nonemployer firm trends presented here reflect lagged data, and do not include activity in 2015 and 2016. As such these data clearly underestimate the true amount of activity occurring on these platforms. Given this fact, it seems fair to say the trends noted here represent a lower-bound estimate of online gig growth in these two industries.

And yet, despite these flaws, the nonemployer firm data suggests that the online platform economy is showing up in the official statistics; that it is mostly an urban phenomenon, at least in rides and rooms, where it is having a sizable impact locally; and that its onset in early-adopter cities like San Francisco and San Jose is now extending to other large cities all across the nation. Granted, future analysis will need look more closely at the effects of the gig economy on payroll employment and wages over time.  Whether platform-based gigging serves unmet consumer demand or cannibalizes it from payroll enterprises will be a critical question in the next few years. Likewise, additional work must tackle the size and growth of the gig economy in broader and harder-to-measure industries.

With that said, this analysis does provide a start at a solid cross-sectional picture.

In any event, it is good to know—in the absence of systematic, topic-specific government counts—that the nonemployer firm data can provide a plausible estimate of the growth and geography of some zones of the hard-to-measure gig economy. Though imperfect, these data, we believe, point in the direction that common sense suggests: The platform economy for rides and rooms is now sizable and growing rapidly in many larger metro areas.

Appendix: 50-metro data table providing national and 50-metro absolute figures and % change for nonemployer firms and payroll employment for rides and rooms

• Online ride platforms like Uber and Lyft, as well as Airbnb and other short-term room-rental and accommodations firms, are currently the best-known enablers of what has frequently been called the “gig economy.” We define the “gig economy” as the matching of freelance workers or service providers to customers on a digital platform or marketplace.
• See, among proponents, Arun Sundararajan, <em>The Sharing Economy: The End of Employment and the the Rise of Crowd-Based Capitalism</em> (Cambridge: MIT Press, 2016).  For a critical account of the “gig economy” see Steven Hill, <em>Raw Deal: How the “Uber Economy” and Runaway Capitalism Are Screwing American Workers </em> (New York: St. Martin’s Press, 2015).
• For a good review of non-standard and “contingent” work arrangements and the many data gaps that complicate analysis of them see Annette Bernhardt, “Labor Standards and the Reorganization of Work: Gaps in Data and Research” (Berkeley: Institute for Research on Labor and Employment, 2014).  The Current Population Survey (CPS) Contingent Work Supplement (CWS) attempted to measure contacted work in its worker surveys but was not viewed as highly reliable and was discontinued in 2005.  Earlier this year Labor Secretary Thomas Perez announced that the Bureau of Labor Statistics, in conjunction with the Census Bureau will revive the CPS supplement in the 2017 survey.
• Authors’ analysis of United States Census Bureau, “Nonemployer Statistics.” See <a href=”http://www.census.gov/econ/nonemployer/”>www.census.gov/econ/nonemployer/</a>
• Our analysis of the nonemployer statistics across the entire economy suggests that some 86 percent of nonemployer “firms” are actually self-employed, unincorporated sole-proprietors—in other words, individuals earning income as independent contractors or freelancers. In the rides and rooms industries some 93 percent of the “firms” are freelancers or contractors.  These are exactly the types of workers who seek part-time work in the gig economy.
• For the “rides” sector, the following NAICS codes were used (4853 Taxi and Limousine Service; 4859 Other Transit and Ground Passenger Transportation). For the “rooms” sector, the following NAICS codes were used (7211 Traveler Accommodation; 7213 Rooming and Boarding Houses).
• The San Francisco analysis focused on only the city and central county, rather than the complete metropolitan area.
•  Only in six cases across the three platforms in the 50 metros did the official launch date of the platforms post-date the 2012-2014 period under examination, raising questions about whether gigs in the rides industry drove the growth effects under analysis.   Uber did not launch officially until 2015 in Las Vegas, Portland, and St. Louis and has still not launched in Rochester.  Lyft did not launch until 2015 in Las Vegas and Philadelphia and 2016 in New Orleans and Richmond.  By contrast, Airbnb was operating in all metro areas before 2012.
• In fact, nonemployer firms in the rides industry were nearly as many in number as were payroll employees from the late-1990s (as far back as our data goes) and steadily diverged throughout the 2000s
• Payroll data for Milwaukee and Tampa have been suppressed in our source data.

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Gig economy in the U.S. - statistics & facts

The gig economy is a section of the economy which consists of independent contractors and freelancers who perform temporary, flexible jobs. Gig economy workers have many different reasons for starting work in the gig economy and tend to prefer the flexible working hours and extra income that the gig economy allows them to have. The gig economy does not only consist of people who exclusively work gig jobs, as the majority of gig economy participants have a full-time position in addition to their gig work. According to a BLS survey on the contingent worker population , the term “contingent workers” includes freelancers, independent contractors, consultants, and other non-permanent workers who are hired on a per-project basis. Income among gig economy workers can vary greatly, with 55 percent reporting they make under 50,000 USD annually . However, financial motivations also vary among contingent workers, and gig work is not always a primary source of income. The share of high-earning independent workers, those making 100,000 USD and above, has also increased since 2011. One issue experienced by some gig economy workers is “wage theft,” or not being paid for completed work. 51 percent of freelancers have experienced wage theft at least once , and 44 percent of respondents believe this is because freelancers are not taken seriously . 56 percent of gig economy workers in the U.S. report having two or more jobs or projects , and 58 percent report working 30 hours or less per week. Work-life balance and flexible working times are both very important for people working in the gig economy in the United States. Participating in the gig economy also allows for the development of different skill sets across many different fields . Job satisfaction among gig economy workers is high, with 80 percent reporting in September 2018 that they are either very satisfied or somewhat satisfied with their current job. Female gig economy workers also report finding their gig work fulfilling and found the fact that they enjoy the work to be a surprising aspect of participating in the gig economy. The share of full-time independent workers who would prefer traditional employment has fallen in recent years, from 34 percent in 2012 to 22 percent in 2018. Gig economy workers are not traditional salaried employees, and many do not have access to the same employer-provided benefits that traditional employees do. This can make saving for retirement difficult for gig workers, and according to a May 2018 survey, 27 percent of gig workers whose gig work was their main job had no retirement savings . Baby Boomers are also participants in the gig economy, with 46 percent reporting they took up gig work in order to make ends meet . The number of gig economy workers in the United States is expected to increase over the next 10 years, and a majority of the American workforce is expected to be freelance by 2027. This text provides general information. Statista assumes no liability for the information given being complete or correct. Due to varying update cycles, statistics can display more up-to-date data than referenced in the text. Show more Published by Statista Research Department , Feb 22, 2024

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• Premium Statistic Access to employer-sponsored retirement among full-time and gig workers U.S. 2018
• Premium Statistic Retirement savings among gig economy workers U.S. 2018
• Basic Statistic Health insurance attainment methods of freelancers U.S. 2019

Access to employer benefits among full-time employees and gig workers in U.S. 2018

Access to employer-based benefits among full-time employees and gig workers in the U.S. in 2018

Access to employer-based benefits in the gig economy in the U.S. by generation 2018

Access to employer-based benefits among gig economy workers in the U.S. in 2018, by generation*

Access to employer-sponsored retirement among full-time and gig workers U.S. 2018

Access to employer-sponsored retirement plans among full-time employees and gig economy workers in the U.S. in 2018

Retirement savings among gig economy workers U.S. 2018

Accumulated retirement savings among gig economy workers in the U.S. in 2018, by type

Health insurance attainment methods of freelancers U.S. 2019

Distribution of health insurance attainment methods by freelancers in the United States in 2019

Motivations and job satisfaction

• Basic Statistic U.S. top reasons for independent work 2022
• Premium Statistic Reasons for choosing independent work in the U.S. by gender 2020
• Premium Statistic Satisfaction of gig economy workers with independent work U.S. 2021
• Premium Statistic Share of full-time independent workers who prefer a traditional job U.S. 2012-2020
• Premium Statistic Salary requirements of freelancers to return to a traditional job U.S. 2019
• Premium Statistic U.S. leading concerns among freelancers 2020
• Premium Statistic U.S. gig worker perception on the fairness of platforms 2021
• Premium Statistic U.S. gig workers experiencing unwanted sexual advances 2021, by gender

U.S. top reasons for independent work 2022

Leading reasons for doing contract, freelance, or temporary work in the United States in 2022

Reasons for choosing independent work in the U.S. by gender 2020

Reasons for choosing independent work in the United States in 2020, by gender

Satisfaction of gig economy workers with independent work U.S. 2021

Satisfaction of gig economy workers with independent work in the United States in 2021

Share of full-time independent workers who prefer a traditional job U.S. 2012-2020

Share of full-time independent workers who would prefer a traditional job in the U.S. from 2012 to 2020

Salary requirements of freelancers to return to a traditional job U.S. 2019

Salary increase required for freelancers to return to a traditional job in the United States in 2019

U.S. leading concerns among freelancers 2020

Leading concerns of freelance workers in the United States in 2020

U.S. gig worker perception on the fairness of platforms 2021

Perception of gig workers on the fairness of pay, assignments, and benefits of gig platforms in the United States in 2021

U.S. gig workers experiencing unwanted sexual advances 2021, by gender

Share of gig workers who have experienced unwanted sexual advances while working in the United States in 2021, by gender

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December 2023

A better future for self-employment.

This report considers and challenges two common myths about self-employment.

October 2023

Submission to the senate education and employment legislation committee inquiry into the fair work legislation amendment (closing loopholes) bill 2023.

Experts from the Centre for Future Work recently made a submission to the Senate committee studying the “Closing Loopholes” bill, which would make several reforms to the Fair Work Act.

August 2023

The tip of the iceberg: measuring unemployment in australia.

The Reserve Bank of Australia’s decisions on interest rate increases rely on the concept of balancing inflation and unemployment.

Unacceptable Risks

The gigification of care is creating insecure work, undermining gender inequality and damaging workforce sustainability.

September 2022

Submission to the senate select committee on work and care.

Current work and care arrangements in Australia contribute to economic and social disadvantage for carers, the vast majority of whom are women. Patterns of labour force participation and employment provide clear indicators of the inequities inherent in Australia’s current care and work arrangements. These patterns show we do not have equitably shared care arrangements, nor equitable employment opportunities and outcomes for women. Australia requires much stronger support systems, more effective work and care policies and more secure and fairly-paid jobs to address these problems.

Submission to the Productivity Commission Study on Aged Care Employment

In 2021 the Royal Commission into Aged Care Quality and Safety recommended that gig work, independent contracting and other ‘indirect’ employment arrangements be restricted in the publicly-funded aged care sector.

August 2020

Technology, standards and democracy.

Workers in most industries and occupations worry about the effects of accelerating technological change on their employment security and prospects. New digital technologies are being applied to an increasingly diverse and complex array of tasks and jobs – including artificial intelligence and machine learning technologies which can exercise judgment and decision-making powers. Some studies suggest that as many as half of all jobs may be highly vulnerable to automation and computerisation in coming decades. The NSW Legislative Council has established a Select Committee to examine the impact of technological and other change on the future of work in NSW. The Centre for Future Work has lodged a submission.

February 2019

Turning ‘gigs’ into decent jobs.

What’s a ‘gig’ job, anyway? There’s lots of media hype about how people won’t have jobs in the future (they’re so old-fashioned). Instead they’ll work a never-ending series of gigs. Will they love the supposed ‘freedom’ and ‘flexibility’? Or will they yearn for the good old days when a job provided regular hours … and a regular paycheque?

Subsidising Billionaires: Simulating the Net Incomes of UberX Drivers in Australia

Uber’s rapid growth in point-to-point transportation services has become the most potent symbol of the growth of the so-called “gig economy”: where people perform work on an irregular, on-demand basis, paid by the task, and without the stability or security of traditional paid employment. The expansion of this model has raised concerns regarding the erosion of labour standards and entitlements (including minimum wages, paid leave, and superannuation). This report simulates the net hourly incomes received by UberX drivers in six Australian cities, and finds that they almost certainly earn much less than would be required under relevant minimum wage standards.

August 2017

New research symposium on work in the “gig economy”.

The informal work practices of the so-called “gig” economy are widening existing cracks in Australia’s system of labour regulations, and should be repaired through active measures to strengthen labour standards in digital businesses. That is the conclusion of newly-published research from a special symposium on “ Work in the Gig Economy ,” organised by the Centre for Future Work.

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The biggest perk of gig work might also be its downfall

• Many gig drivers lured by the flexibility of gig work say the flexibility of their job is often limited.
• Drivers for Uber and Lyft who rely on the income often find they must drive during high-demand periods.
• Some drivers say the best rides in their area are at 5 a.m. or 9 p.m.

Many drivers for Uber , Lyft, and other rideshare and delivery apps say they value the flexibility of gig work over any other component. But that flexibility can be limited — drivers often need to work early mornings or late nights to get more profitable rides.

Some gig drivers have recently told Business Insider that because they rely on driving for most or all of their income, deciding when to drive is less of a choice. For instance, Starla, 27, is a single parent who sometimes has to drive 16 hours a day for Uber Eats to provide for her eight-year-old in Jacksonville, Florida. She sometimes drives overnight shifts or late nights on weekends to make ends meet.

For some drivers, the best rides in their area are at 5 a.m. or 9 p.m., while other drivers say midday is most optimal. For drivers with disabilities , single parents, veterans , and others, making enough money to pay bills requires sacrificing family time or other life events.

"The companies' main selling point is their flexibility, turn your app on whenever you want and make some money," Sergio Avedian, Senior Contributor at The Rideshare Guy , told BI. "The caveat in that is making some money."

Many part-time drivers have previously told BI they value gig driving for the ease with which they could supplement their core income. Some drivers only put in 10 to 20 hours a week at their leisure, whether it's after work or on weekends. Some can pull in a few hundred dollars a week to supplement their main income.

"I think the part-timers still do appreciate the flexibility aspect," Avedian said. "If you're a soccer mom, you drop your kids off at school, you can do rideshare for a couple hours before you pick them up. But are you going to be profitable, or are you just wearing your car out or basically falling into the trap of flexibility?"

Lyft announced in February it would guarantee weekly earnings for drivers at 70% or more of what riders paid after taking into account external fees such as local taxes. Lyft noted that the typical US driver earned about $23 per engaged hour — or hours spent picking up or dropping off customers — after expenses. Lyft said about 15% of drivers earn less than 70% or more of their weekly rider payments.

Uber hasn't announced specific policies like this, though the typical US driver earned about $33 per engaged hour before driving expenses, per an announcement in November.

Flexibility can be an illusion

But for the minority of drivers who drive full-time, many of whom cannot work a 9-to-5 job, flexibility may be an illusion, said Lindsey Cameron, an assistant professor of management at the Wharton School who studies algorithms and the gig economy.

"Drivers talk about how they're having to work more hours to earn the same amount of money, not actually having that schedule flexibility," Cameron told BI. "In actuality, for those economically dependent on this work, like to make your child support payments, you've got to go when there's high-demand times. Schedule flexibility at times can be a myth when you're economically dependent on that work."

Related stories

Schedule flexibility allows people with disabilities , for instance, to drive around chemotherapy appointments or school drop-offs. However, Cameron added that drivers who are more financially dependent on these apps are more likely to accept lower fare amounts as they need the money.

Many drivers are lured into gig work by promises of flexibility, though Avedian said growing competition has made it so that more profitable rides are harder to secure.

"Flexibility and freedom was a massive selling point because the demand was outstripping supply, but for the last three, four years, that's kind of out of the window," Avedian said. "The reason for that is every single market that I know of is extremely oversaturated."

Working on their own terms

Still, many drivers would rather work on their own terms than have a set work schedule. Laura Katsnelson, a doctoral candidate in the Strategy Unit at Harvard Business School, told BI that gig drivers "derive substantial value from being able to change which hours they work from week to week," though demand conditions likely impact when drivers choose to work.

In her paper " Being the Boss: Gig Workers' Value of Flexible Work ," she and her colleague Felix Oberholzer-Gee found that when the average DoorDash driver is forced to work exclusively in their most frequently driven blocks of time, hourly earnings are cut by over 11%. Given this arrangement, over two-thirds would be projected to drop out.

"We assume that if they chose a particular time to work on the platform, it's because it made the most sense for them given their set of circumstances at the time," Katsnelson said. "However, it may very well be that certain drivers would prefer a world in which they could work a 9-5 job with employee benefits that pays 'enough' for them to get by. On the other hand, there are likely other subsets of drivers who are well-served by the current DoorDash model because they use it to flexibly supplement their income."

Chasing more lucrative rides

Katie Wells, a geographer at Georgetown University and author of "Disrupting DC: The Rise of Uber and the Fall of the City," said that in her research, many drivers said they would arrange their schedules around the hopes of encountering incentives offered by platforms like bonuses, sometimes working split shifts. This helps them maximize their earnings, but many feel like it's their only option.

Some drivers told her they would work 16-hour overnight shifts on weekends to find higher-paying, better-tipped rides. She said one driver worked from 7 a.m. to 10 a.m., then from 4 p.m. to 10 p.m., because he could only make a profit during these periods. He was "free" to choose his schedule, Wells said, but driving whenever he pleased would mean fewer profitable rides.

"The flexibility of the gig economy is a mirage, and quite a costly one," Wells said. "Uber and its peers appeal to workers with oodles of promises that workers can hold the power to control their work hours. However, the reality for too many workers in too many cities for more than a decade is far from that ideal. Many gig workers end up working much longer hours than they hope, trying to eke out decent earnings."

For Avedian, who also drives part-time, driving during peak hours often doesn't equate to higher earnings. Avedian estimated his utilization rate is 50%, meaning for every hour online, he only gives rides for half an hour. He estimates that for a Saturday driving from 10 a.m. to 6 p.m. in Los Angeles, his gross earnings are $21 an hour before expenses.

"If you work between 10 a.m. and 2 p.m., there's no flexibility because everybody's already at school, at work. There are no trips out there, and you're just going to sit there," Avedian said. "In order for you to maximize your earnings, you have to work when and where there is demand. What kind of flexibility is that if I turn my app on at 2 a.m.? Do you think I'm going to make some money? I don't think so."

Are you a ride-hailing driver who's struggling to pay bills or has had recent success? If you'd like to share your story, reach out to  [email protected] .

Watch: Economists Figured Out The Real Reason It's Nearly Impossible To Get A Taxi In The Rain

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How to Thrive in the 2024 Gig Economy: Insider Tips

G ig work is more popular than ever -- according to Statista, there are 57.3 million freelancers in the U.S. As I write this, it's been just about a year since I jumped into the gig economy on a full-time basis, as a freelance writer and editor.

Before quitting my W-2 job, I had ramped up my freelance work, and by the time I was ready to turn in my letter of resignation, I knew that I could make a living in the gig economy and I had built myself some runway for financial security. Here are a few tips you can use to do the same and thrive in 2024's gig economy.

Prioritize your savings

You might not have the luxury of saving money before becoming a gig worker if you are currently out of work. But I highly recommend ensuring you have money saved if you intend to do what I did and say sayonara to your regular job.

Unfortunately, many Americans are on the razor's edge when it comes to their savings accounts . The Ascent's research team found that the median savings account balance was just $1,200 as of July 2023. Life is expensive, and low wages and the pressure of unplanned bills can make it difficult to save.

The great thing about becoming part of the gig economy is that you will no longer be limited to a set salary (or number of hours, if you had a full-time hourly job). This could make it easier to build up savings for emergencies, future goals, or investing -- not to mention sleep better at night.

Create routines

As a gig worker, you'll no longer be beholden to an employer's schedule or directives. But if you want to succeed at being your own boss, it's important to make your own routines and determine a schedule that works for you. This will depend on the particular type of work you're interested in. If you want to drive for a ride-hailing service like Lyft or Uber, you'll want to figure out when and where the busiest times for rides are in your city, and prioritize being available to work then.

If your gig work is more self-directed like mine, you can design your own schedule based on your needs. Got young kids? You might want to arrange your work days so you're free when they come home from school. Early riser? Personally, I do some of my day's work before the sun even comes up.

Don't forget to also plan for time off -- remember, just because you can work all the time, doesn't mean you should . Gig workers don't get paid vacation time, but this doesn't mean you can't give yourself that gift. At the end of 2023, I created a small pool of "paid time off" for myself. I haven't had to dip into that money yet, but it's comforting to know that if I have a day where I can't work, I can cover my shortfall from it.

Build new skills

The gig economy is dynamic and always changing. If you're hoping to thrive, you have to be ready to grow and change with it. To that end, it's worthwhile to add to your skillset as you progress. For example, if you're a freelance graphic designer, stay up to date on the latest and greatest software programs, and consider adding copywriting to your skills, to become a more well-rounded marketing professional.

Artificial intelligence is increasingly becoming part of the working world, so if you're in a field that will be particularly impacted by it (such as data analysis and entry, web development, and tax preparation, according to Pew Research), gear up now. Investigate the AI tools available to you and get familiar with them (or at least size up your competition and find ways you can stand out to an employer as a human worker).

Don't neglect taxes

Finally, if you're hoping to thrive in the gig economy, there is a major piece of business you need to attend to: taxes. If you used to be a W-2 employee, you probably didn't think about taxes all that often before -- perhaps just at the end of January, when you'd receive a W-2 in the mail, and then again when you file your tax return.

But gig workers are often small business owners, and taxes are a much bigger part of our world. You might need to pay quarterly estimated taxes four times a year (January, April, June, September) and if you neglect to do this, you can be penalized by the IRS.

The whole tax thing made me nervous when I started freelancing, so I sought out a tax professional to work with, but the best tax software for small businesses can also help you figure out what you owe. And don't forget about deductions! As a gig worker, you'll get to take deductions W-2 employees can't, such as for your home office and any supplies you purchase, like a new computer if you're a writer or graphic designer.

The gig economy has brought a revolution in the who, what, why, where, and when of Americans working. If you intend to jump into it yourself this year, I hope these tips help.

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While service quality is the main driver of Americans’ tipping decisions, about three-in-ten U.S. adults also cite workers’ pay before tips as a major factor they consider.

Americans who have worked for tips themselves are usually more likely to leave one

43% of U.S. adults say they have ever personally worked in a job where they received tips. Roughly six-in-ten (57%) have not had this experience.

Tipping Culture in America: Public Sees a Changed Landscape

72% of U.S. adults say tipping is expected in more places today than it was five years ago. But even as Americans say they’re being asked to tip more often, only about a third say it’s extremely or very easy to know whether (34%) or how much (33%) to tip for various services.

Visions of the Internet in 2035

Asked to "imagine a better world online," experts hope for a ubiquitous – even immersive – digital environment that promotes fact-based knowledge, offers better defense of individuals’ rights, empowers diverse voices and provides tools for technology breakthroughs and collaborations to solve the world’s wicked problems.

Racial and ethnic differences stand out in the U.S. gig workforce

There are racial and ethnic differences in who takes on gig platform jobs and the negative experiences some of these workers say they face.

Q&A: How Pew Research Center studied gig workers in the U.S.

A recent Center survey focused on gig platform work. Here is more information about how we crafted the survey and what we learned from it.

The State of Gig Work in 2021

Some 16% of Americans have ever earned money from an online gig platform. While most gig platform workers say they have had a positive experience with these jobs, some report facing on-the-job troubles like being treated rudely or sexually harassed.

In Changing U.S. Electorate, Race and Education Remain Stark Dividing Lines

The gender gap in party identification remains the widest in a quarter century.

Not all unemployed people get unemployment benefits; in some states, very few do

Despite some broad federal guidelines, claimants still face a hodgepodge of different state rules governing how they can qualify for benefits.

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .