Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.

Self-constructed digital economy index data analyzed in the Humanities and social sciences communications article, "The widening gender wage gap in the gig economy in China: The impact of digitalization"

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(FVRR) Fiverr: Gig Economy Growth Fuels Future

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Upwork (UPWK) Gigs & Gig Economy Growth: What's Next for the Freelance Platform?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

As of 2022, Tallinn, Tbilisi, Sao Paulo, and Buenos Aires were ranked as the cities most friendly to the sharing economy based on the 2022 Sharing Economy Index. Warsaw, Kyiv, and Mexico City followed behind with scores reaching ***. The sharing economy index takes into consideration the following factors: ride-hailing services, flat-sharing services, availability of e-scooters, carsharing apps, gym sharing, and ultrafast delivery apps.

This file contains the complete dataset collected by the four surveys described in the companion paper, in Microsoft Excel (XLSX) format.

The workbook contains an index sheet with full details of each included worksheet, followed by a data keys sheet explaining any abbreviations, annotations, and labels used throughout the datafile.

The file has been verified to open in Microsoft Excel (https://products.office.com/excel) and Libre Office (https://www.libreoffice.org)

Global Sharing Economy market size is expected to reach $611.03 billion by 2029 at 25.7%, segmented as by shared transportation, ride-hailing services (uber, lyft), carpooling and car sharing (zipcar, blablacar), bike and scooter sharing (lime

Abstract The term sharing economy is used in specialized literature to identify how the Internet, smartphones, and applications are changing the global economic dynamic. This article presents documentary research focused on private sharing applications that have emerged in recent decades, intending to contribute to the improvement of local public management. Descriptive data analysis and regression were used to characterize the local governments’ adherence to new technologies and to identify how these new technologies affect the fiscal performance of municipalities measured by the FIRJAN Fiscal Management Index. The results obtained show that shared economy Apps can contribute in different ways, with emphasis on greater cooperation and coordination within and between local governments, reduction in the underutilization of assets, greater access and improvement in the quality of public services, and greater interaction and citizen participation in public decisions. The estimated regression shows that the use of new communication technologies contributes to improving the municipalities’ fiscal performance. However, these technologies are little used and should be encouraged in local public administrations.

Secondary data are not available from ABS Agricultural and Population censuses for economic indicators and measures at a scale matching the NSW water sharing plan (WSP) regions. NSW DPE – Water purchased customised data for all WSP regions from 2006, 2011, 2016 and 2021 ABS censuses.

The dataset contains following anonymised census data for each of the WSP regions:

• Agricultural commodity data for 2006, 2011, 2016, and 2021
• Water use data for 2011, 2016 and 2021
• Gross value of irrigated agricultural production data for 2016 and 2021
• Employment by industry and occupation for 2006, 2011, 2016 and 2021
• Population by age distribution for 2006, 2011, 2016 and 2021
• Aboriginal population, families, dwellings for 2006, 2011, 2016 and 2021
• Average weekly earnings, full time, part time for 2006, 2011, 2016 and 2021
• Interstate and regional migration (where possible) for 2006, 2011, 2016 and 2021
• Socio-Economic Indexes for Areas (SEIFA) including its component scores (subject to high standard errors depending on the size of the custom region) for 2006, 2011 and 2016

Note: File Notes on ABS data by NSW water sharing plan regions.docx provides a comprehensive overview of the data's limitations that must be taken into consideration when using it..

Water acquisition programs support instream flows and other environmental water needs in many areas of the western United States. They have evolved over several decades, expanding from relatively simple two-party transactions involving modest water volumes to complex, multi-sector deals that move substantial volumes of water back to nature. Such programs now represent an important water management tool and impetus for collaboration among stakeholders, yet most evaluations of these programs' outcomes and effectiveness have focused exclusively on environmental metrics, without adequate attention to impacts on other water users or local economies. Given the importance of accomodating environmental water needs more fully in water resource plans, and the need to expend program resources carefully, a systematic, multi-objective evaluation framework is needed. This paper fills that need by articulating a suite of relevant environmental and socio-economic indicators for evaluating environmental transaction programs and portfolios. We have applied these indicators to environmental water transaction programs located in the western United States of Oregon, Montana and Nevada. The application to local programs illustrates both the challenges and the value of applying a systematic evaluation framework. More importantly, it quanitifies tradeoffs between sectors and helps identify opportunities for creative water transactions that benefit multiple sectors and the environment. The indicators are useful both in evaluating transactions already completed and in strategic planning and prioritizing for future transactions. A detailed guidebook to assist parties in applying this evaluation framework is available online.

The sustainable development of the sports industry has garnered extensive attention worldwide. In this study, after a rigorous explanation of the connotation of the sports industry development resilience coefficient (SIDRC), the Topsis model and exploratory spatial data analysis were comprehensively employed to evaluate and visualize the SIDRC of 285 cities in China. Additionally, a spatial econometric model was constructed to explore the influencing factors of SIDRC. The major conclusions drawn from this study are as follow: (1) While the SIDRC has improved significantly over the study period, it still remains overall at a low level of resilience with a widening gap between cities. (2) A strong spatial imbalance exists in the distribution of SIDRC, with coastal regions demonstrating greater resilience compared to the central and western regions, and provincial capital cities faring better than other cities. (3) Policy support index, economic development level, structural diversity of the sports industry, and social participation play crucial roles in promoting SIDRC. Finally, social participation has a positive impact on SIDRC in neighboring cities by facilitating resource sharing, market expansion, and extending the industrial chain. The paper concludes by offering recommendations such as increasing the construction of sports markets and public participation, which can optimize the layout of the sports industry and enhance industrial development resilience.