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 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

this graph was create in Power Bi:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F13cc72b2c805991d7af10ea6aa396cd0%2FSem%20ttulo_page-0001.jpg?generation=1710364280999487&alt=media" alt="">

Understanding Global Work Patterns: A Deep Dive into Working Hours Across Countries and Over Time

Introduction

Work, an integral part of human life, has undergone significant transformations over the past century and a half. The amount of time individuals dedicate to work has shifted, reflecting changes in societal norms, economic structures, and technological advancements. This exploration delves into the intricate dynamics of working hours worldwide, shedding light on disparities across countries and within societies. By examining historical trends and contemporary data, we gain insights into the evolving nature of work and its profound impact on individuals' lives.

Historical Context

The Industrial Revolution marked a pivotal moment in human history, fundamentally altering the nature of work. With the mechanization of industries, the concept of the traditional workday emerged, characterized by long hours and minimal breaks. Throughout the 19th and early 20th centuries, workers endured grueling schedules, often exceeding 12 hours per day, six days a week. This relentless pursuit of productivity came at the expense of worker well-being and family life, prompting calls for labor reforms.

Labor Movements and Reform

The rise of labor movements in the late 19th and early 20th centuries sparked a wave of social change, advocating for shorter workdays and improved working conditions. The landmark achievements, such as the eight-hour workday and weekends off, marked significant milestones in the fight for workers' rights. Countries worldwide implemented labor laws to regulate working hours, aiming to strike a balance between economic productivity and human welfare. These reforms laid the foundation for the modern workweek and paved the way for further advancements in labor standards.

Contemporary Work Patterns

In the 21st century, the landscape of work continues to evolve, shaped by globalization, technological innovation, and shifting societal values. While many industrialized nations have embraced shorter workweeks and increased leisure time, disparities persist on a global scale. Developed countries typically exhibit lower average working hours, accompanied by robust social welfare systems and flexible labor policies. In contrast, developing economies often grapple with longer work hours, driven by economic necessity and informal employment practices.

Regional Disparities

Regional variations in working hours highlight the complex interplay of cultural, economic, and political factors. In Europe, countries like France and Germany have embraced a culture of work-life balance, with statutory limits on working hours and generous vacation entitlements. Scandinavian nations, renowned for their progressive social policies, prioritize employee well-being through initiatives such as flexible work arrangements and parental leave. In contrast, regions like Asia and the Middle East experience longer work hours, influenced by cultural norms emphasizing diligence and dedication.

Gender Dynamics

Gender disparities in working hours remain a persistent challenge, reflecting entrenched inequalities in the workplace. Women often shoulder disproportionate caregiving responsibilities, leading to reduced participation in the labor force and truncated career trajectories. The gender pay gap further exacerbates these disparities, perpetuating a cycle of economic disadvantage for women. Addressing gender inequities in working hours requires multifaceted interventions, including affordable childcare, parental leave policies, and workplace diversity initiatives.

The Gig Economy and Flexible Work The rise of the gig economy and remote work arrangements has reshaped traditional notions of employment and working hours. Freelancers and independent contractors enjoy greater flexibility in scheduling, blurring the boundaries between work and personal life. Digital platforms have facilitated the emergence of remote work opportunities, enabling individuals to customize their work hours and locations. However, concerns persist regarding job security, benefits coverage, and the erosion of traditional labor protections in the gig economy.

Impact on Well-being

The relationship between working hours and well-being is complex, influenced by factors such as job satisfaction, socioeconomic status, and work-life balance. While longer work hours may boost productivity in the short term, they can lead to burnout, stress, and diminished quality of life over time. Conversely, shorter workweeks and increased leisure time have been linked to improved mental health, greater h...

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 emergence of the so-called “gig economy” has reshaped the labor market and, potentially, the politics of the safety net. Much of the American welfare state is based on a traditional model of employment, excluding most gig workers from benefits like subsidized employer-provided health insurance and unemployment insurance. Despite these trends, there is little research how these changes might affect politics. Are gig workers likely to become a relevant constituency on social welfare and other issues? To address this, we conducted a unique online survey examining policy attitudes and political behaviors among gig workers compared to traditional workers. Our findings indicate that people who view gig work as their “main job” tend to lack access to traditional social insurance and employer-provided benefits, as expected, and rely more on means-tested assistance programs (e.g., food stamps). Consequently, gig workers exhibit higher support than traditional workers for expanding social welfare programs, and are more engaged on issues that affect gig workers. In terms of participation, gig workers are less likely to vote but more likely to engage in non-voting political activities like protest than traditional workers. This study contributes to the understanding of social welfare politics in the new era of the labor market and highlights a growing constituency for expanding the safety net.

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) |

For the financial year 2021, around ** percent of gig work is projected to be in high skilled jobs, ** percent in medium skilled and ** percent in low skilled jobs. The trends reflect a gradual increase in high and low skilled jobs till 2030.

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.

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.

Looking for a clean Upwork jobs dataset? The Upwork Freelance Jobs Dataset offers 300 verified job listings from the freelancing platform Upwork. It includes rich metadata such as job titles, posting dates, descriptions, required skills, client location, employment type, project duration, pricing (fixed or hourly), and more — perfect for freelance market analysis, job-market research, data science projects, or trend analysis across remote job postings.

Key Features

• Comprehensive Job Metadata: Each entry contains url, title, posted_at, description, employment_type, skills, client_location, hrs_per_week, project_duration, fixed_price, experience, job_type, project_type, total_jobs_posted_by_client, total_spent_by_client, uniq_id, and scraped_at.
  Crawl Feeds
• CSV Format: Ready to Use: Data is delivered in a straightforward CSV file, no need for scraping or cleaning.
• Verified Data Snapshot: 300 records captured as of the latest crawl (11 Jan 2021) — good for baseline analytics or historic analysis of Upwork job trends.
• Instant Access & Free: Price is USD $0.0 for this dataset — accessible to analysts, researchers or freelancers looking to explore or use job-market data without cost.

Use Cases

• Freelance Market Research: Analyze remote job demand, skill requirements, and pricing trends across Upwork listings.
• Data Science & Trend Analysis: Build models or dashboards to track shifts in job type, popular skills, client geographic distribution, and contract types over time.
• Client Prospecting & Lead Generation: Identify clients, frequent posters or high-spend clients based on total_jobs_posted_by_client and total_spent_by_client.
• Academic or Economic Research: Study gig-economy dynamics, remote work trends, skill demand distribution, or labour-market shifts globally.

Who Can Use It

• Researchers & Analysts exploring remote job markets, gig economy trends, or skill-demand dynamics.
• Freelancers & Agencies looking to study job posting patterns, niches in demand, or potential clients.
• Data Scientists / ML Engineers building datasets for job-market modelling, demand forecasting, or skill-trend analysis.
• Students & Educators needing real-world job-listing datasets for academic projects, economic studies, or teaching data analysis.

Why Use This Dataset Instead of Manual Scraping?

• Time-saving: Avoid writing scrapers, managing IPs or dealing with anti-scraping blocks.
• Verified & Clean Data: Dataset entries are pre-scraped and formatted, reducing manual data cleaning efforts.
• Free & Instant Access: No cost barrier for experimentation or analysis.
• Snapshot for Analysis: Provides a stable dataset snapshot that can be compared with newer datasets to observe market evolution.

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.

This dataset contains synthetic data representing salary patterns of delivery personnel (delivery boys) working across multiple cities and online delivery platforms in 2024. It is designed to help analyze and predict how different work-related and contextual factors influence monthly earnings in the gig economy. the data includes 1,000 records, each describing a delivery worker’s daily work hours, working days per month, delivery volume, and whether they work during peak demand periods. Salaries vary according to city, workload, and platform, simulating real-world variations observed across major Indian and international locations.

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.

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 graph was created in PowerBi,Tableau and Loocker Studio :

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ntroduction

The National Sample Survey (NSS) Multi Indicator Survey (MIS) 78th Round (2020-21) is a comprehensive dataset that provides key socio-economic insights about Kolkata and other regions of India. Conducted by the National Statistical Office (NSO), Ministry of Statistics and Programme Implementation (MoSPI), Government of India, this survey aimed to gather extensive data on multiple indicators, including education, health, employment, migration, consumption patterns, and digital access.

This document provides a detailed explanation of the Kolkata-specific findings of the NSS 78th Round, offering insights into various socio-economic dimensions of the city's population.

Objectives of the NSS 78th Round

The primary objectives of the 78th Round Multi Indicator Survey were:

To assess the education levels and literacy rates in Kolkata.

To understand household health conditions and access to healthcare facilities.

To analyze employment and labor force participation in urban settings.

To examine migration trends within and outside Kolkata.

To evaluate consumption patterns and expenditure levels.

To study digital access and usage among households.

Key Findings for Kolkata

1. Education and Literacy

The survey revealed that Kolkata maintains a high literacy rate, with a considerable percentage of its population having completed secondary and higher education.

A growing number of children are enrolled in private schools, though government schools still play a significant role.

Female literacy has shown an increasing trend, but disparities still exist in lower-income communities.

1. Health and Healthcare Access

Kolkata has a high hospital density, with most households reporting access to primary healthcare centers and hospitals.

The survey recorded a moderate prevalence of chronic diseases, including diabetes and hypertension, particularly among the elderly.

Public healthcare facilities are widely used, but there is significant reliance on private hospitals, especially for specialized treatments.

1. Employment and Labor Market

The workforce participation rate in Kolkata remains steady, with a majority engaged in the service sector, trade, and informal employment.

There has been a decline in manufacturing jobs, partly due to automation and industry shifts.

The gig economy and self-employment have seen a rise, reflecting national trends.

1. Migration Trends

Kolkata experiences both in-migration and out-migration, with many individuals moving to the city for employment and education.

The survey indicated that a large percentage of migrants come from rural West Bengal, Bihar, and Jharkhand.

Out-migration has been observed primarily among skilled professionals seeking opportunities in other metropolitan cities or abroad.

1. Consumption Patterns and Expenditure

The average household consumption expenditure in Kolkata is higher than the national average, reflecting its status as a major urban center.

Food consumption patterns indicate a preference for cereals, fish, and dairy products, with an increase in processed food consumption.

Housing and transportation form a significant portion of monthly expenses for urban residents.

1. Digital Access and Usage

The survey highlighted a strong penetration of digital connectivity, with most households having access to smartphones and the internet.

Digital literacy is improving, with increased use of online banking, e-commerce, and educational platforms.

However, a digital divide persists among lower-income groups and elderly populations.

Policy Implications

Based on the survey findings, the following policy recommendations are suggested:

Enhancing educational infrastructure to bridge the literacy gap in underprivileged areas.

Strengthening public healthcare systems to reduce dependence on private hospitals.

Promoting employment generation programs and support for informal workers.

Affordable housing initiatives to address rising living costs in Kolkata.

Expanding digital literacy programs to bridge the digital divide.

Conclusion

The Kolkata-specific insights from the NSS 78th Round (2020-21) offer valuable data for policymakers, researchers, and urban planners. These findings provide a comprehensive picture of the city's socio-economic...