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)

This table contains monthly, quarterly and yearly figures on the labour participation and unemployment in the Netherlands. The population of 15 to 75 years old (excluding the institutionalized population) is divided into the employed, the unemployed and the people who are not in in the labour force. The different groups are further broken down by sex and age. Next to the original monthly figures on the labour force you can also find monthly figures that are seasonally adjusted.

Data available from: January 2003

Status of the figures: The figures in this table are final.

Changes as of 19 June 2025: The figures for May 2025 have been added

When will new figures be published? New figures on the most recent month are published monthly, in the third week of the month.

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

This table contains quarterly and yearly figures on labour participation in the Netherlands. The population of 15 to 74 years of age (excluding the institutionalized population) is divided into the employed labour force, the unemployed labour force and those not in the labour force. The employed labour force is subdivided on the basis of the professional status, and the average working hours. A division by sex, age and level of education is available.

Data available from: 2013

Status of the figures: The figures in this table are final.

Changes as of April 30, 2025: The figures for the 1st quarter 2025 have been added.

Changes as of November 14, 2024: The figures for 3rd quarter 2024 are added. Figures have been added on labor participation based on whether or not the state pension age has been reached.

Changes as of August 17, 2022: None, this is a new table. This table has been compiled on the basis of the Labor Force Survey (LFS). Due to changes in the research design and the questionnaire of the LFS, the figures for 2021 are not automatically comparable with the figures up to and including 2020. The key figures in this table have therefore been made consistent with the (non-seasonally adjusted) figures in the table Arbeidsdeelname, kerncijfers seizoengecorrigeerd (see section 4), in which the outcomes for the period 2013-2020 have been recalculated to align with the outcomes from 2021. When further detailing the outcomes according to job and personal characteristics, there may nevertheless be differences from 2020 to 2021 as a result of the new method.

When will new figures be released? New figures will be published in July 2025.

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

China Unemployment Rate: Age 25 to 59 data was reported at 4.100 % in Jun 2023. This stayed constant from the previous number of 4.100 % for May 2023. China Unemployment Rate: Age 25 to 59 data is updated monthly, averaging 4.600 % from Nov 2017 (Median) to Jun 2023, with 67 observations. The data reached an all-time high of 5.600 % in Feb 2020 and a record low of 4.100 % in Jun 2023. China Unemployment Rate: Age 25 to 59 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GB: Surveyed Unemployment. According to NBS news, from Aug 2023, the urban surveyed unemployment rate of the young people by age group will be suspend. 根据国家统计局新闻发布会，自2023年8月份起，全国青年人等分年龄段的城镇调查失业率将暂停发布.

Job Search Engines Market size was valued at USD 19.14 Billion in 2024 and is projected to reach USD 105.4 Billion by 2031, growing at a CAGR of 14.2% during the forecast period 2024-2031.

Rising Employment Opportunities: As economies around the world expand, businesses scale up operations, subsequently creating more job opportunities. This growth in employment facilitates a greater need for efficient job search engines to match job seekers with potential employers. Certain sectors such as technology, healthcare, and renewable energy are growing rapidly, leading to an increase in job vacancies. Specialized job search engines cater to these niches, driving market growth. Populous countries with large, young workforces contribute to the increased number of job seekers utilizing job search engines. Growing Internet Penetration: As internet access becomes more widespread globally, more individuals can use online platforms, including job search engines. This is particularly notable in developing regions where internet adoption is accelerating. Lower costs of internet services and devices have made it more feasible for a broader audience to go online, boosting the user base for job search engines. The availability of high-speed internet makes the use of job search engines more convenient and effective, supporting features such as real-time notifications and the ability to upload and download large files (e.g., resumes and portfolios). Shift to Digital Recruitment: The integration of data analytics and AI in recruitment processes enables job search engines to offer more personalized and streamlined experiences for both job seekers and employers. Improved algorithms and machine learning facilitate better job-candidate matching, increasing the effectiveness and appeal of digital recruitment platforms. Digital platforms reduce the costs associated with traditional recruitment methods (e.g., print advertising and in-person job fairs). Employers benefit from decreased hiring costs, while job search engines profit from increased business. Increased Mobile Device Usage: With the global proliferation of smartphones, a significant portion of job searches is conducted via mobile apps and mobile-optimized websites. Job search engines that offer robust mobile platforms are experiencing higher engagement. Mobile devices provide unparalleled flexibility and convenience, allowing users to search for jobs, set up alerts, and apply for positions from anywhere, at any time. Innovative mobile apps designed by job search engines offer features such as GPS-based job searches, voice and video interviews, and chat support, which enhance the user experience. Technological Advancements: Innovations in AI, machine learning, and big data analytics enhance the functionality of job search engines, providing personalized job recommendations and improving match accuracy.

Remote Work Trends: The rise of remote work opportunities, especially post-pandemic, has increased the demand for job search engines that specialize in remote and freelance job listings.

Employer Branding: Companies use job search engines to build and promote their employer brand, attracting top talent by showcasing their work culture, benefits, and career opportunities.

Government Initiatives: Supportive government policies and initiatives aimed at reducing unemployment and promoting job creation boost the usage of job search engines.

Gig Economy Growth: The expanding gig economy, characterized by short-term contracts and freelance work, drives the need for specialized job search engines catering to gig workers.

Globalization and Cross-Border Employment: Increasing globalization and the trend of cross-border employment necessitate job search engines that facilitate international job searches and candidate sourcing.

Professional employer organizations (PEOs) have fared with volatility in recent years. The labor market quickly recovered from the pandemic and the unemployment rate has hovered near record low levels, intensifying competition for talent. PEOs have been essential in a market where many companies struggled to staff up, while also helping businesses implement strategies to reduce attrition. At the same time, elevated interest rates in response to inflation have constrained growth among critical clients and tempered corporate profit, curtailing spending on PEO services. Overall, industry revenue is forecast to decline at a CAGR of 2.5% over the past five years to $215.9 billion, including growth of 1.7% in 2025. The importation of technology has already had a major impact on PEOs, with online job boards creating opportunities to match candidates with employers, while the adoption of automation technologies has improved the efficiency of matching candidates with job listings. The artificial intelligence (AI) revolution will streamline routine tasks like payroll processing, benefits administration and recruitment for PEOs, allowing companies to leverage new capabilities to improve strategic decision-making processes for clients. At the same time, the scale of change will encourage more companies to consolidate as larger companies are better able to invest in technology and spread administrative costs across a larger client base. Amid this changing landscape, profit margins will hold steady as growth opportunities will be counterbalanced by steep competitive pressures.PEOs will grow in the coming years, interest rates temper, strengthening corporate finances and better enabling companies to spend on external services. Economic expansion will encourage entrepreneurship, with a rise in the number of businesses creating new need for PEOs. As a result, industry revenue is forecast to increase at a CAGR of 1.6% over the next five years to 2030, although ongoing inflation fears alongside the imposition of tariffs could undermine these trends. PEOs will remain essential to helping companies navigate a changing economic landscape in the coming years, as the expansion of the gig economy reclassifies workers.

Registered Unemployment Rate: Urban: Hebei data was reported at 3.080 % in 2021. This records a decrease from the previous number of 3.460 % for 2020. Registered Unemployment Rate: Urban: Hebei data is updated yearly, averaging 3.380 % from Dec 1980 (Median) to 2021, with 34 observations. The data reached an all-time high of 4.000 % in 2004 and a record low of 0.600 % in 1985. Registered Unemployment Rate: Urban: Hebei data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GB: Registered Unemployment.

China Unemployment Rate: Age 16 to 24 data was reported at 21.300 % in Jun 2023. This records an increase from the previous number of 20.800 % for May 2023. China Unemployment Rate: Age 16 to 24 data is updated monthly, averaging 13.700 % from Jan 2018 (Median) to Jun 2023, with 66 observations. The data reached an all-time high of 21.300 % in Jun 2023 and a record low of 9.600 % in May 2018. China Unemployment Rate: Age 16 to 24 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GB: Surveyed Unemployment. According to NBS news, from Aug 2023, the urban surveyed unemployment rate of the young people by age group will be suspend. 根据国家统计局新闻发布会，自2023年8月份起，全国青年人等分年龄段的城镇调查失业率将暂停发布.