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.

The Freelance Contracts Dataset is a robust collection of 1.3 million contracts extracted from a leading freelancing platform, offering significant insights into the dynamics of the freelance economy. This dataset is essential for data analysts, researchers, and business strategists looking to explore the gig economy.

Key Features: - Job Details: Each contract includes job ID, title, start, and end dates. - Freelancer Information: Identifies freelancers through a unique ID. - Financial Data: Includes total hours worked, total amount paid, and hourly rates.

Potential Applications:

• Analyze trends in freelance job postings across various industries.
• Investigate how project duration relates to earnings and freelancer performance.
• Understand pricing strategies and budget allocations for freelance projects.

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.

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.

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

This report explores how UK gig economy platforms such as Uber and Deliveroo use surveillance technologies—including GPS tracking, automated decision-making, and performance monitoring—and the effects these have on workers’ privacy, autonomy, and livelihoods. Through an analysis grounded in Participatory Design, Design Justice, and Intersectionality, the report critiques how these systems often reinforce power imbalances, algorithmic bias, and worker precarity. It investigates the ethical implications of opaque algorithms and data practices, examining how workers’ rights are compromised in the absence of transparency and regulation. Drawing on academic research, case studies, and policy reports, the report argues for a shift toward more inclusive, ethical, and worker-centered design of platform technologies, and highlights the importance of accountability and co-design in creating fairer futures for gig workers.

ABSTRACT Contemporary career studies based on the protean career theory emphasize individual characteristics such as adaptability and flexibility. However, it is important to consider elements that permeate and influence the individual’s agency and career paths. This study analyzes how the perspectives of time and context, in addition to the individual’s agency, influence the career paths of gig economy workers. The qualitative analysis of data collected from interviews with 57 Brazilian app-based drivers enabled developing an empirically based typology with eight workers’ profiles and different ways of adaptation, including how they experience the activity and how they redirect their professional paths. Although adaptability marks their career trajectory, the time and context limit the area in which they can work and exercise agency. This study joins a broader movement in the career field and international studies that seek a better understanding of the gig economy and its consequences. The emergence of app-based activities resulting from a particular time and technological context brings different ways to adapt and change career plans.

This dataset was collected as part of a study investigating the gender pay gap in the freelancing sector of Bangladesh, with a particular focus on the online platform, Freelancer.com. The dataset consists of self-reported data from 210 randomly selected freelancers, who were among the top search results with good reviews on the platform. The data were collected directly from the profiles of these freelancers, and the link to each profile is included in the dataset. The dataset provides comprehensive information about each freelancer, including their gender, hourly payment rate, number of reviews, number of recommendations, job completion rate, budget adherence rate, on-time delivery rate, repeat hire rate, payment verification status, total work experience, location, membership type, monthly investment on Freelancer.com, type of work, type of education, institution name, degree name, education level, years of education, and preferred freelancer status. The purpose of this dataset is to provide insights into the relationship between these factors and the hourly earnings of freelancers, with a particular emphasis on exploring any disparities between male and female freelancers. The scope of the dataset extends to the digital gig economy in Bangladesh, and its nature is quantitative. This dataset is intended for use in further research aiming to understand the complexities of the gender pay gap in the freelancing sector, and to devise effective strategies to bridge this gap.

Uber And Lyft Drivers Carjackings

Dataset from: "Uber And Lyft Drivers Are Being Carjacked at Alarming Rates"

About this dataset

Original Author: The dataset was first posted here By The Markup

This dataset contains information on carjackings of gig economy workers in the United States. columns include: state, city, date, company, carjacking_happened_via_the_app, driver_deceased, source, additional_sources

How to use the dataset

This dataset can be used to research gig worker carjackings in the United States. The data includes information on the date, location, and circumstances of each carjacking, as well as the name of the gig economy company involved. Researchers can use this dataset to study patterns in carjackings, identify risk factors for drivers, and develop strategies for prevention and response

Research Ideas

• Finding trends in gig worker carjackings across the United States
• Determining which cities are most at risk for gig worker carjackings
• Analyzing which companies' gig workers are most at risk for carjackings

Acknowledgements

We would like to thank the researchers at the University of Michigan for their work in compiling this dataset

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: carjackings.csv | Column name | Description | |:------------------------------------|:---------------------------------------------------------------------------------------| | state | The state in which the carjacking occurred. (String) | | city | The city in which the carjacking occurred. (String) | | date | The date on which the carjacking occurred. (Date) | | company | The company for which the driver was working at the time of the carjacking. (String) | | carjacking_happened_via_the_app | Whether or not the carjacking occurred through the use of the company's app. (Boolean) | | driver_deceased | Whether or not the driver was killed as a result of the carjacking. (Boolean) | | source | The source of the information on the carjacking. (String) | | additional_sources | Any additional sources of information on the carjacking. (String) |

This survey intends to fill a gap by carefully documenting the characteristics of the workers engaged in alternative work arrangements and the gig economy in the United Kingdom. We collected the data via an online survey of 20,000 respondents representative of the UK working-age population (18-65 years old). Alternative work arrangements, such as independent contractors, zero hours contract workers, temporary help agency workers and contract company workers, are a growing and increasingly important feature of the labour markets of many developed economies. However, little is known about the nature and variety of these new types of jobs in terms of employment conditions (e.g. pay, hours worked, patterns of work), workers’ characteristics including workers’ preferences for different working arrangements, and employment rights.1) Developing Skills: Strengths and Weaknesses in the System, and What can be Done. The new Industrial Strategy must consider how the education system can create the general and specific skills needed by businesses today and in the future. CEP will synthesise evidence on strengths and weaknesses in the current education system at all levels (schools, colleges and universities), and highlight where improvements can be made. This might be within the current system, or through the design of new mechanisms to incentivise individuals and firms to invest in training. In conjunction with researchers from the Centre for Vocational Education Research (CVER), we can produce a synthesis of findings on technical and vocational education. We will also be able to study in more depth firm-level relationships with higher education institutions, with the aim of better understanding how they impact on local economies. 2) Driving Growth across the Country: Mapping the Data on Firms and Labour Markets. The starting point for developing appropriate regionally focused growth policies is to understand the status quo, to carefully document how this has changed over time, and to better understand the factors underlying and driving geographic differences. The CEP is developing better data to describe the geographic spread of industry, and associated variation in labour market patterns like problems of real wage stagnation and the rise of new types of work arrangements, including the gig economy. As well as analysis of the digital economy, and the opportunities and threats it poses for the labour market and implications for the future. We will seek to release reports describing the data, map the relevant metrics (including productivity, investment, employment and pay) so that policymakers and stakeholders could benchmark their own regions/sectors. 3) Supporting Businesses to Start and Grow: Drivers of Innovation and Diffusion. The Industrial Strategy should be focused on addressing market failures which cause barriers to investment in innovation, technologies or organisational practices that drive productivity growth. One strand of work using patents will analyse the innovation spill overs between technologies and places, and the types of policy that stimulate business RD and innovation. The spill over analysis would also be linked with new measures of regional Total Factor Productivity (TFP) to understand the local economic impact of innovation. This work will help guide policymakers to where the payoffs of RD investment might be greatest for the UK. In this context, we would consider payoffs both in terms of economic growth but also in terms of achieving non-economic objectives such as lower greenhouse gas emissions and more secure energy supply. We also plan to produce a report together with the LSE's Grantham Institute on clean growth. Another strand of work will consider the role of financial constraints on firm growth, and the extent to which these have contributed to recent poor productivity performance in the UK. And finally, we will explore the relationships between management practices and investment efficiency. 4) Encouraging Trade and Inward Investment: Brexit and Industrial Strategy. Many UK firms (particularly SMEs) face longstanding barriers to exporting, and Brexit will create new challenges in this area. At the same time, while UK Foreign Direct Investment (FDI) performance has been stronger to date, there are concerns that Brexit will induce multinationals to relocate in order to maintain access to the single market. CEP will seek to understand the likely trade and FDI impacts of Brexit, and the implications for labour markets and consumers. This will include a deeper analysis of the sectoral and regional dimensions, and should be useful to inform the scope and form of industrial policy response. We collected the data via an online survey that was administered by a company specialized in the deployment of surveys to large panels of respondents online. The sample of respondents was obtained by imposing sampling quotas on the population of respondents included in the online panels. Sampling quotas have been imposed on gender, age categories, employment status and region of residence. The value of the quotas has been derived by the authors using the UK Labour Force Survey. From comparisons of the characteristics of the sample of respondents with statistics from the Labour Force Survey, we believe the sample represents the population of interest reasonably well. A total of 20,000 individuals took the survey. Of these, 16,994 remained in the sample after screening out those whose only work in the previous week was filling out surveys, those who did not work at all, resided outside the UK, were outside the age range 18-65, or provided nonsensical responses to open questions. The survey was conducted between February 5 and March 2, 2018. This is the month after tax returns are due in the UK – a timing aimed at guaranteeing a good recollection of income and other tax-related measures by the respondents.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Gig Harbor: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 228(4.88%) households where the householder is under 25 years old, 1,284(27.48%) households with a householder aged between 25 and 44 years, 1,343(28.75%) households with a householder aged between 45 and 64 years, and 1,817(38.89%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the city of Gig Harbor, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gig Harbor median household income by age. You can refer the same here

This dataset is part of the project Commuting Constraints and Labor Productivity: A Field Experiment on Women’s Mobility in India, which investigates how transportation access shapes women’s work productivity and career progression in gig work. The study is designed as a randomized controlled trial (RCT). Female gig workers were randomly assigned to different commuting support conditions to measure the causal impact of mobility constraints and safety considerations on labor supply, earnings, and job choices. Data collection consists of: Baseline survey: Demographics, household composition, transportation patterns, financial status, motivations for joining gig work. Follow-up surveys (two rounds): Daily job activity, commuting modes, safety perceptions, time use, well-being, and household updates. Administrative platform data: Earnings, job assignments, customer ratings, cancellations, and platform logs (not publicly available due to NDA). The dataset allows for longitudinal and experimental analysis of how commuting constraints and safety considerations affect women’s participation and productivity in the gig economy. Access and Embargo The dataset is under embargo for three years to allow the research team to complete analysis and publication. A de-identified version will be made available upon release, in line with ethical and confidentiality standards.

The book price refers to the maximum price aggregated over all formats. Investment is an ordinal metric where 0 indicates ebook, 1 indicates mass market paperback, 2 indicates trade paperback, and 3 indicates hardcover. The rest of the variables are binary.

What is UBER?

Uber Technologies, Inc., commonly known as Uber, is an American technology company. Its services include ride-hailing, food delivery (Uber Eats and Postmates), package delivery, couriers, freight transportation, and, through a partnership with Lime, electric bicycle and motorized scooter rental. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy. Uber is estimated to have over 93 million monthly active users worldwide. In the United States, Uber has a 71% market share for ride-sharing and a 22% market share for food delivery. Uber has been so prominent in the sharing economy that changes in various industries as a result of Uber have been referred to as uberisation, and many startups have described their offerings as "Uber for X".

Information about this dataset

This dataset provides historical data of Uber Technologies, Inc. (UBER). The data is available at a daily level. Currency is USD.

The 2022 CIUS aims to measure the impact of digital technologies on the lives of Canadians. Information gathered will help to better understand how individuals use the Internet, including intensity of use, demand for online activities and online interactions. The CIUS examines, use of online government services, use of social networking websites or apps, smartphone use, digital skills, e-commerce, online work, and security, privacy and trust as it relates to the Internet. The 2022 iteration has been updated to collect data on information sharing online, harmful content online, digital credentials, cryptocurrencies, Artificial Intelligence and working in the Gig Economy. The survey is built off the previous iterations of the CIUS conducted in 2018 and 2020.