This data package includes the underlying data files to replicate the calculations and charts presented in The online gig economy’s impact is not as big as many thought, PIIE Policy Brief 22-9.

If you use the data, please cite as: Branstetter, Lee (2022). The online gig economy’s impact is not as big as many thought, PIIE Policy Brief 22-9. Peterson Institute for International Economics.

IntroductionWith the rapid rise of the gig economy globally, its characteristics of promoting employment and facilitating autonomy have supported its rapid growth and development in China. While the flexibility of gig work offers more employment options and income sources for workers, it also caused many problems and uncertainties. Workplace well-being is an important psychological factor that indicates the psychological state of workers and significantly predicts their behavior at work. However, previous studies on the gig economy rarely analyze gig workers’ workplace well-being, which is of great significance to improving their individual emotions, promoting their physical and mental health, and maintaining the sustainable development of the gig economy and society in general.MethodsThis study draws on the cognitive-affective processing system framework to construct a moderated dual-mediator model to explore the dual influence mechanism of job autonomy on gig workers’ workplace well-being. Based on the data of 442 digital gig workers who were mainly engaged in manual labor.ResultsThe survey results show that job autonomy positively affects employees’ workplace well-being, and work alienation and positive emotion mediate this relationship. Perceived algorithmic control can moderate not only the influence of job autonomy on work alienation and positive emotion but also the indirect impact of job autonomy on workplace well-being through work alienation and positive emotion.DiscussionThe finding of this research contributes to expand the comprehension of the relationship between gig-worker job autonomy and workplace wellbeing and this relationship’s underlying mechanism, holding significant implications for management practice.

This dataset is about book subjects and is filtered where the books is Automatic enrolment in the gig economy : modelling for Zurich. It has 4 columns: book subject, authors, books, and publication dates. The data is ordered by earliest publication date (descending).

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

This dataset is about book subjects and is filtered where the books is A master's perspective : understanding Brexit neomercantilism, rent-seeking, the gig economy, and the international trade of an emerging West African economy, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

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.

This dataset supports the research on gender dynamics within the digital gig economy, with a specific focus on the challenges faced by female freelancers in Bangladesh. Stemming from qualitative methods, the data encompasses transcriptions of 14 In-depth Interviews (IDIs) and 1 Key Informant Interview (KII). These interviews shed light on multifaceted adversities that women face within the digital workspace, from societal expectations and infrastructural limitations to experiences of harassment and fraud. The narratives provide a comprehensive understanding of pronounced gender biases, both overt and covert, that permeate the digital freelancing space in Bangladesh. Data Composition: 14 Nonverbatim Transcriptions of In-depth Interviews (English translated format) 1 Nonverbatim Transcription of a Key Informant Interview (English translated format) Privacy Note: The data has undergone stringent privacy measures. Original names, work account details, and locations have been removed to uphold the confidentiality and anonymity of the participants.

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.

People in work on zero hours contracts in their main job, including by age, sex and region, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.