In 2023, the projected gross volume of the gig economy is expected to reach ***** billion U.S. dollars. The gig economy is commonly defined as digital platforms that allow freelancers to connect with potential clients for short-term jobs, contracted work, or asset-sharing.

Introduction

Gig Economy Workforce Statistics: There is hardly any time when gig economy will serve as a powerful thrust in the global labour market as these days. In 2024, average more than 36% workers in the United States are working as freelancers or work on contracts, or use platforms to get a job. Flexible working fills industries such as transportation, IT, creative services, and education; it pours more than $1.2 trillion into the economy in USA.

The global gig economy is estimated to cross over $455 billion in 2025 through digital platforms in response to changing work preferences. Most such workers in the world are from millennials and Gen Z, who prefer seeking freedom, some extra bucks, and work formats that can offer more options to work, offering the possibility of doing so from home.

As employment models change, the stats on gig workers will open up ways for businesses, policymakers, and economists to know better how to engage within this segment and develop the policy around it.

According to Cognitive Market Research, the global Gig Economy market size was USD 561245.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 17.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 224498.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 168373.56 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.7% from 2024 to 2031.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 129086.40 million in 2024 and will grow at a compound annual growth rate (CAGR) of 19.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 28062.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 11224.90 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.9% from 2024 to 2031.
The transportation-based services category is the fastest growing segment of the Gig Economy industry

Market Dynamics of Gig Economy Market

Key Drivers for Gig Economy Market

Changing work approach driving the gig economy

The shift in work approach, particularly among younger generations, is a key driver of the gig economy. Millennials and Gen Z are prioritizing work that aligns with their passions and interests, seeking flexibility and autonomy over traditional career paths. The shift is majorly driven by the desire for work-life balance, alternate income sources and ability to work remotely, from anywhere. This shift has been on the rise particularly since the global pandemic that had pushed people to work from their homes and across various digital platforms. Businesses are embracing the flexible work arrangements to reduce costs and access specialized skills.

For instance,

Global research from the World Employment Confederation (WEC) finds that 83% of senior executives say that, since the pandemic, workers place as much value on flexibility in terms of when and where they work as on compensation.
A 2022 LinkedIn survey found that Gen Z workers were the cohort most likely to have left a role because of a perceived lack of flexibility (72% fell into this category, compared with 69% of Millennials, 53% of Gen X and 59% of Baby Boomers).
53% of Gen Z workers who freelance are moving away from traditional 9-to-5 jobs in favor of full-time freelancing.

(Source:https://insights.wecglobal.org/the-work-we-want/home/workplace-policy-younger-generations#:~:text=For%20example%2C%20new%20global%20research,the%20priorities%20of%20younger%20people. )

(Source: https://www.upwork.com/resources/gig-economy-statistics )

The digitalization of work is fueling demand for more gigs

Driven by technological advances and the increasing digitalization of skills and processes, the gig economy has expanded rapidly, by making work accessible to more people around the globe. The rise of online marketplaces like Upwork, Uber and Fiverr have made it easier for freelancers to find work and for companies to access a more flexible workforce. Improved technology and digital infrastructure have further made it easier and cheaper to connect with gig workers. The rise of e-commerce platforms and on-demand services such as ride-sharing, food delivery rely majorly on gig workers, contributing significantly to the growth of gig economy. Digital tools like instant messaging and video conferencing along with collaborative platforms like slack, MS Teams make it easy for employees to communicate from anywhere at any time.

With Artificial intelligence (AI) becoming one of the fastest-growing sectors and skill sets for independent professionals, AI has contributed to the growth of gig economy. AI is significantly impacting the gig economy by automating tasks, improving matching of workers and jobs. AI powered platforms also help streamline the recruitment process for businesses, by matching candidates with suitable projects based on skills, experience and availability.

For instance,

95% of respondents said generative AI makes them more competitive and 66...

According to a survey in March 2020, ** percent of worldwide workers in the gig economy have lost their job due to the coronavirus (COVID-19) pandemic. On top of this, another ** percent had their hours decreased.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

According to Cognitive Market Research, the global Gig Economy Platforms Market size was USD 24512.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 20.80% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 9805.00 million in 2024 and will grow at a compound annual growth rate (CAGR) of 19.0% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 7353.75 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 5637.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 22.8% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1225.63 million in 2024 and will grow at a compound annual growth rate (CAGR) of 20.2% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 490.25 million in 2024 and will grow at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2031.
The freelancer category is the fastest growing segment of the Gig Economy Platforms industry

Market Dynamics of Gig Economy Platforms Market

Key Drivers for Gig Economy Platforms Market

Adapting Employment Preferences and Workforce Dynamics to Fuel Market Growth

The market for gig economy platforms has grown significantly due in large part to the shifting dynamics of the global workforce. Employees are increasingly looking for work-life balance, flexibility, and autonomy—things that traditional employment models could not always offer. An alluring substitute is the gig economy, which gives people the freedom to select their own clients, projects, and working hours. The independence and business prospects that come with gig employment are especially valued by the younger generation. Additionally, the gig economy gives those with specific knowledge and abilities a way to make money off of their abilities and grow their professional networks. Businesses' need for flexible and affordable labor solutions that allow them to grow operations effectively and access specialized skill sets when needed is another factor driving the need for gig employment.

Digital connectivity and technological advancements will propel market expansion

The market for gig economy platforms has been significantly influenced by technological developments, especially in the areas of internet and mobile technologies. High-speed internet connections and cellphones have made it easier for gig workers and employers to connect seamlessly, enabling real-time communication, job matching, and payment processing. Businesses may now more easily hire independent contractors and freelancers from around the globe thanks to the increased digital connectivity that has also made remote work and collaboration possible. With the introduction of blockchain, 5G networks, and artificial intelligence, gig economy platforms are well-positioned to expand their capabilities and offer more specialized and effective services to satisfy the demands of employers and employees.

Restraint Factor for the Gig Economy Platforms Market

Legal and Regulatory Uncertainties to Restrain Market Growth

The designation of gig workers as independent contractors or employees is a topic of continuous discussion and legal scrutiny as the gig economy upends conventional employment patterns. The rights, benefits, and protections of employees as well as the obligations and liabilities of platform firms are all significantly impacted by this classification. It is a difficult task that calls for cooperation between platform businesses, legislators, and labor organizations to strike a balance between the gig economy's demand for flexibility and innovation and providing sufficient protection for workers. Gig workers frequently deal with unstable income, a lack of job security and benefits, and the possibility of exploitation. Businesses that depend on rating and review systems may find it difficult to maintain quality control.

Key Trends for

Gig Economy Platforms

Emergence of Niche and Industry-Specific Platforms: Beyond general freelancing, there is a rise of platforms specifically designed for sectors such as healthcare, legal services, education, and logistics. These specialized gi...

This statistic depicts the income distribution of gig economy workers' annual income in the United States in 2018. During the survey, ** percent of respondents reported making 100,000 or more U.S. dollars annually as a gig economy worker.

We've looked at the performance of the UK gig economy, including how it has been affected by COVID-19, the Supreme Court ruling against Uber and the availability of workers.

The dataset consists year and occupation wise numbers of gig workers in India. Occupations include House Keeping and Restaurant Workers, Motor Vehicle Drivers, Finance Agents, Brokers Etc., Business Professionals, Computer Professionals, Secretaries and Clerks, Shop and Market Sales Persons and Others.

The dataset contains the estimated gig workforce in India and their share of the total workforce as per NITI Aayog's Study Report - India's Booming Gig and Platform Economy.

This statistic shows the number of freelancers in the United States from 2017 to 2028. It is projected that in 2027, **** million people will be freelancing in the United States and will make up **** percent of the total U.S. workforce.

The gig economy, encompassing freelance work and on-demand services, is experiencing robust growth, driven by technological advancements, evolving work preferences, and a desire for flexible employment options. The market, estimated at $300 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1 trillion by 2033. This expansion is fueled by several key factors: the increasing adoption of digital platforms connecting businesses with independent contractors; a growing preference among workers for flexible schedules and autonomy; and the scalability these platforms offer businesses needing temporary or project-based assistance. Furthermore, the gig economy's diverse sectors, including transportation (DoorDash, Favor Delivery, Turo), home services (TaskRabbit, BellHops), professional services (Guru.com, Upwork, Fiverr), and pet care (Rover), contribute to its overall market strength.
However, challenges remain. Regulatory uncertainties surrounding worker classification and employment benefits pose significant hurdles. Competition among gig platforms is fierce, requiring constant innovation and adaptation to maintain market share. Fluctuations in the broader economy can also impact demand for gig services. Despite these restraints, the overall trajectory suggests a continued expansion of the gig economy, driven by ongoing technological advancements, evolving workforce demographics, and the increasing reliance of businesses on flexible talent pools. The major players, including TaskRabbit, Upwork, and Fiverr, are well-positioned to capitalize on this growth, provided they navigate the regulatory and competitive landscapes effectively. Successful strategies will likely involve investments in technology, focus on user experience, and proactive engagement with regulatory bodies.

The type of 'gig work,' already familiar to many workers through popular platforms like Uber and Deliveroo, is seen as a potential model for the future of employment. The gig economy blurred the lines between employed and self-employed statuses, with gig workers classified as self-employed, thus missing out on state support available to employed individuals. The project explored how labour market conditions for gig economy workers affected their financial security. It also examined the role of social security provisions in Italy, Sweden, and the UK. The study was initially positioned within broader debates on the increasing precarity of work and the evolving role of the welfare state in European societies. The data collection of this study was conducted between October 2020 and May 2021. 101 platform workers were recruited and invited to take part in in-depth interviews, which lasted from 38 to 119 minutes. Due to Covid-19, all the interviews were conducted remotely, using instruments that maximised participants’ privacy and minimised the risks of data breaches. To guarantee high-quality comparative material, the interviews were conducted by the PI of the project in English in the UK and Sweden, and in Italian and English in Italy, depending on the preference of the participant.

The gig economy has witnessed remarkable growth in India, offering workers flexibility but often lacking in traditional social security benefits. This research aims to explore the multifaceted factors influencing the social security landscape for gig workers in India. The study draws upon a wide range of data sources, including government reports, labor surveys, academic research, and surveys from non-governmental organizations.

This repository contains four datasets about the number of active users of selected mobile apps purchased from Selectivv company (https://selectivv.com/). Details regarding the data may be found below:

How data was collected: Selectivv uses programmatic advertisements systems that collect information on about 24 mln smartphone users in Poland

Apps:

Transportation: Uber, Bolt Driver, FREE NOW, iTaxi,

Delivery: Glover, Takeaway, Bolt Courier, Wolt;

Unit: an active user of a given app. Active = used given app at least 1 minute in a given period (e.g. 1 unit during whole month, half-year).

Period: 2018-2018; monthly and half-year data

Spatial aggregation: country level, city level, functional area level, voivodeship level. Functional area is defined as here https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/

Activity time: measured by activity time of given app (in hours; average and standard deviation)

Datasets:

gig-table1-monthly-counts-stats.csv -- the monthly number of active users;

gig-table2-halfyear-demo-stats.csv -- the half-year number of active users by socio-demographic variables;

gig-table3-halfyear-region-stats.csv -- the half-year number of active users by spatial aggregation;

gig-table4-halfyear-activity-stats.csv -- the half-year activity time by working week, weekend, day (8-18) and night (18-8).

Detailed description:

1. gig-table1-monthly-counts-stats.csv

Structure:

month - YYYY-MM-DD -- we set all dates to 15th of given month but actually the data is about the whole month (active users in whole period); 2018-01-15 to 2021-12-15

app -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

category -- Transportation, Deliver

1. gig-table2-halfyear-demo-stats.csv

Structure:

gender -- men, women

age -- 18-30, 31-50, 51-64

country -- Poland, Ukraine, Other

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

students -- the share of students within a given row

parents_of_children_0_4_years -- the share of parents of 0-4 years children in a given row

parents_of_children_5_10_years -- the share of parents of 5-10 years children in a given row

women_planning_a_baby -- the share of women planing a baby in a given row

standard -- the share of standard smartphones in a given row

premium_i_phone -- the share of iPhone smartphones in a given row

other_premium -- the share of other premium smartphones in a given row

category -- Transportation, Delivery

1. gig-table3-halfyear-region-stats.csv

Structure:

group -- Voivodeship, Functional Area, Cities

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

region_name:

Cities -- Białystok, Bydgoszcz, Gdańsk, Gdynia, Gorzów Wielkopolski, Katowice, Kielce, Kraków, Łódź, Lublin, Olsztyn, Opole, Poznań, Rzeszów, Sopot, Szczecin, Toruń, Warszawa, Wrocław, Zielona Góra

Functional Area -- Functional area - Białystok, Functional area - Bydgoszcz, Functional area - Gorzów Wielkopolski, Functional area - GZM, Functional area - GZM2, Functional area - Kielce, Functional area - Kraków, Functional area - Łódź, Functional area - Lublin, Functional area - Olsztyn, Functional area - Opole, Functional area - Poznań, Functional area - Rzeszów, Functional area - Szczecin, Functional area - Toruń, Functional area - Trójmiasto, Functional area - Warszawa, Functional area - Wrocław, Functional area - Zielona Góra

Voivodeship -- dolnośląskie, kujawsko-pomorskie, łódzkie, lubelskie, lubuskie, małopolskie, mazowieckie, opolskie, podkarpackie, podlaskie, pomorskie, śląskie, świętokrzyskie, warmińsko-mazurskie, wielkopolskie, zachodniopomorskie

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

category -- Transportation, Delivery

Please note that:

the number of active users in a given functional area = number of active users in a city and a functional area of this city

the number of active users in voivodeship = number of active users in a city, its functional area and the rest of the voivodeship where this city and functional area is located

More details here: https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/

1. gig-table4-halfyear-activity-stats.csv

Structure:

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

day -- Mondays-Thursdays, Fridays-Sundays

hour -- day (8-18), night (18-8)

activity_time -- in hours

statistic -- Average, Std.Dev. (standard deviation)

category -- Transportation, Delivery

In 2021, only one percent of gig economy workers in the United States reported being very dissatisfied with independent work. In contrast, ** percent of people working in the gig economy reported being very satisfied with their job.

The dataset is based on mobile big data obtained from advertisement systems on smartphones. It provides an upper bound of the number of drivers and couriers at very low levels of spatial aggregation.

This statistic shows the industries where gig economy workers are currently employed in the United States in 2018. During the survey, 14 percent of respondents reported working in government or the public sector.

The Gig Rights Project survey was undertaken to gain a deeper understanding UK platform worker support for labour rights, policy interventions and collective representation. Therefore, the aim of the survey was to gather data on the policy and representation preferences of these workers as well as other key factors which might influence these preferences. Respondents were asked to select the three labour rights and policies that would most benefit their working life if applied to their platform work. Respondents had a choice of 13 labour rights and 13 other policies to make their selection from. These labour rights and policies were generated from reviewing publicly available policy recommendations and discussions with our external advisory partners (Advisory, Conciliation and Arbitration Service (Acas), Charted Institute for Personnel and Development (CIPD), International Labour Organization (ILO), Royal Society for the Encouragement of Arts, Manufactures and Commerce (RSA) and Trade Union Congress (TUC). We additionally adopted questions from the Understanding Society, COLLEEM, iLabour, Skills and Employment, British Social Attitudes surveys in order to collect data on respondents’ platform work, working conditions, representation preferences, and political values.

The survey was piloted with five current or former platform workers: two current remote platform workers, a former delivery local platform worker, a former ridehail local platform worker and a current handyperson local platform worker. Where possible, to ease comparison with existing quantitative research, we based our questions or survey items on established social surveys. Improvements were made to the wording of the questions based on the feedback provided during the piloting. The research received ethical approval from the Bristol University School of Management Research Ethics Committee.

The survey was administered to a strategically targeted ‘river’ sample which included good representation across conceptually important categories, such as remote or local platform work, migrant or UK-born, male or female, younger or older and more or less educated. By doing so, it is possible to highlight where preferences for rights and policies seem unlikely to be influenced by such characteristics due to the absence of substantial differences between groups. Conversely, this approach allows to identify outcomes that are more likely to be sensitive to the actual makeup of the platform worker population. To generate our targeted sample, we advertised our survey directly to UK-based workers active on Facebook and Instagram using the advertising portal. (Facebook Ads Center) which allows the placement of advertisements on both social media platforms. The advantage of this approach is that Facebook and Instagram use is so widespread that self-selection into the sampling frame is not a concern. Recent estimates indicate that approximately 71 per cent of adults in the UK are active on Facebook and are not especially stratified by demographic characteristics.

Using the platform advertising features, we directly targeted our survey at users who, for example, listed their interests as ‘Ubereats’, ‘delivery (commerce)’, ‘Uber (company)’, ‘Drive with Uber’, ‘Taxi Driver’, ‘Hybrid electric vehicle’, ‘TaskRabbit’, ‘Care.com’ or ‘Airtasker’; their employer as ‘Deliveroo’, or their job title as ‘delivery’ ‘Taxi Cab Driver’ or ‘Car Driver’. Users matching these interests, employer or job titles, were targeted with bespoke adverts designed for delivery, drivers and domestic platform workers on Facebook/Instagram. We recruited 257 local platform workers in this manner. Those who completed the survey were offered the chance to win an iPad.

Previous quantitative research has demonstrated the potential for using platform-based adverts to effectively sample remote platform workers. We therefore followed this proven approach and recruited 253 remote platform workers from Upwork - a leading remote work platform. To do this we listed our survey as a job on the platform and in line with quotas for task and gender derived from the International Labour Organization’s (ILO) Online Labour Index. Those who completed the survey were compensated with a £10 payment. Between March and June 2022, 510 UK gig economy workers active on Facebook, Instagram or Upwork were surveyed.

Key findings are strong support for labour rights, trade unions and co-determination. Low pay, insecurity, risk and lack of organizational voice provides a rationale for these preferences. Moreover, platform workers’ preferences are seemingly influenced by wider inequalities, with significant differences according to gender and country of birth. Additionally, remote platform work entails significantly better pay, more flexibility, greater influence over how to do their job, greater sense of doing useful work, better health and safety, less pain, and less work-related insecurity. In contrast, local platform work entails greater organisational influence and less physical isolation.

The data and programs replicate tables and figures from "Measuring the gig economy in Canada using administrative data", by Jeon, Liu and Ostrovsky. Please see the ReadMe file for additional details.

ABSTRACT Purpose: This study aims to investigate how app drivers are giving meaning to their work, taking as a theoretical assumption the model proposed by Rosso, Dekas, & Wrzesniewski (2010). Originality/value: Internationally, the volume of empirical research involving digital labor markets is considered to be low. Nationally, research in the context of Sharing Economy rarely focuses on the labor perspective. Despite being a growing phenomenon, no studies were found on the production of meanings and meaningfulness of work by app drivers. Design/methodology/approach: This qualitative and exploratory research was carried out with 37 app drivers between May and September 2017, in Porto Alegre (RS, Brazil). Randomly selected, respondents were called to a work route by the transport application. The interviews’ content was categorized and analyzed according to the framework of Rosso et al. (2010). Findings: Elements that refer to all the model quadrants were found: “self-connection”, “individuation”, “contribution”, and “unification”. The predominant meaning, however, is desire, seeking and valuing by the agency, in the mechanisms of self-efficacy and self-management, especially in the financial, autonomy and flexibility perspectives. This research contributes to the intersection of the study of the labor world transformations and the construction of meanings and meaningfulness, using a framework little used in Brazilian research. It also collaborates to broaden the understanding of digital labor markets, especially their impact on workers.