According to Cognitive Market Research, the global Gig Economy market size will be 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 an...

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.

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.

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 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.

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:

1. gig-table1-monthly-counts-stats.csv -- the monthly number of active users;
2. gig-table2-halfyear-demo-stats.csv -- the half-year number of active users by socio-demographic variables;
3. gig-table3-halfyear-region-stats.csv -- the half-year number of active users by spatial aggregation;
4. 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

2. 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

3. 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/

4. 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

As the workforce ages, how will the work lives of older people evolve? One way to ease into retirement is to move to the gig economy where workers choose hours and intensity of work that fit their needs and capabilities. However, older workers are often reaping the benefits of the latter end of an implicit contract while gig economy workers are paid their marginal product. We show that age/earnings profiles in the traditional labor market are different than for Uber drivers. While the move to the gig economy generates flexibility, older workers are paid less than their younger coworkers.

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.

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 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 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 gig economy is widely regarded to be a source of secondary or temporary income, but little is known about economic activity outside of the gig economy. Using data from a large, online personal finance application, I document the evolution of non-gig income and household balance sheets surrounding the participation decision for gig economy jobs. This simple analysis reveals striking pretrends in income and assets. In addition to providing insight into the reasons why households enter the gig economy, these findings have potentially important implications for the external validity of previous studies focusing on gig economy activity only.

Market Size and Growth: The global gig economy platforms market is anticipated to grow exponentially, from $463.8 million in 2025 to an estimated $1,274.5 million by 2033. This rapid expansion, at a CAGR of 12.5%, is attributed to the increasing prevalence of remote work, the flexibility and accessibility offered by gig platforms, and the growing availability of skilled workers in the gig workforce. North America and Europe currently dominate the market, with significant contributions expected from emerging markets in Asia Pacific and Latin America in the coming years. Market Drivers, Trends, and Restraints: The growth of the gig economy is driven by several factors, including the rise of the digital economy, the growing demand for specialized skills, and the need for flexibility and autonomy among workers. Additionally, government initiatives that promote entrepreneurship and support the gig economy are contributing to market expansion. Emerging trends such as the use of artificial intelligence (AI), the integration of blockchain technology, and the development of hybrid work models are expected to further fuel market growth. However, challenges such as concerns over job security, lack of access to benefits, and regulatory uncertainties could potentially restrain market growth in certain regions.

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.

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.

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.

The Gig Economy Platforms market has emerged as a transformative force in the modern labor landscape, reshaping how work is approached by both professionals and businesses. Characterized by short-term, flexible job engagements facilitated through digital platforms, the gig economy provides solutions to the increasin

This archive contains replication data and code for "The Self-Employment Effects of the EITC in the Gig Economy", published in the National Tax Journal.

The global digital labor platform market is experiencing robust growth, driven by the increasing demand for flexible work arrangements, technological advancements, and the expansion of the gig economy. The market, encompassing platforms like Uber Eats, DoorDash, Upwork, and Fiverr, facilitates the connection between businesses and independent contractors for various services, ranging from food delivery and transportation to freelance work and home services. While precise figures are unavailable, considering the prominent players listed and the rapid adoption of such platforms, we can estimate the 2025 market size at approximately $250 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033. This growth is fueled by factors such as increased smartphone penetration, improved internet connectivity, and a growing preference for on-demand services across various sectors. The market’s segmentation includes various service categories (e.g., transportation, delivery, freelance skills) and geographical regions, with North America and Europe currently holding significant market shares. However, the market faces certain challenges. These include concerns regarding worker classification and benefits, regulatory uncertainty around gig economy employment laws, and the potential for platform monopolies. Furthermore, fluctuations in economic conditions can impact demand for freelance services. Despite these restraints, the long-term outlook for the digital labor platform market remains positive. The ongoing trend of digitalization and the persistent need for flexible and scalable workforce solutions suggest continuous growth in the coming years. Technological innovations, such as AI-powered matching algorithms and improved payment systems, will further enhance market expansion and efficiency. Competition among existing platforms and the emergence of new players will also contribute to market dynamism.