Odds ratios of being digitally excluded by various characteristics in January to March 2020, using data from the Labour Force Survey with geographical coverage of the UK.

In light of the rapid development of digital technology, it is imperative to study the impact of digital technology on the labour force’s entrepreneurial choices with the utmost urgency. This paper first constructs a theoretical mechanism for how digital technology affects individual entrepreneurship. It then empirically examines data from the China General Social Survey (CGSS) to test the theory. The results show that digital technology significantly increases individual entrepreneurial choices. Furthermore, the conclusions of the study are robust even when the estimation method and variable measurement are changed. Finally, the study finds that digital technology has the greatest impact on entrepreneurship among individuals with low education, the second-largest impact on those with medium education, and the third-largest impact on those with high education. Individuals with higher education levels have the second largest impact on the entrepreneurship of individuals with higher education levels, while the smallest impact is observed in this group. Digital technology development has a stronger role in promoting entrepreneurship of individuals with urban household registration than those with rural household registration. In terms of sub-region, digital technology has a larger role in individual entrepreneurship in the eastern and central regions, and has a less significant role in the western region. The findings of this study suggest that there is a need to implement measures to accelerate the pace of digital technology development, enhance the training of entrepreneurial skills and attitudes among highly educated individuals, and direct efforts towards enhancing digital technology development in rural and western China.

Full edition for scientific use. In addition to the core variables of the Microcensus Labour Force Survey (LFS), the LFS also has so-called ad-hoc-modules (AHM) that can vary from year to year. The EU-LFS ad-hoc-module 2022 on ‘Job skills’ focuses on relevant job skills of employed persons: working on digital devices, reading work-related manuals and technical documents, doing complex calculations, hard physical work, finger dexterity, interacting with people from the same enterprise as well as with people from outside the enterprise, advising, training or teaching others, autonomy on tasks, performing repetitive tasks and performing tasks precisely described by strict procedures. The questions of the ad-hoc-module were asked following the questions of the basic programme. The dataset also includes all questions of the main survey of the Microcensus 2022.

As of 2023, artificial intelligence (AI) has shown to improve work performance for both lower-skilled and higher-skilled workers. While the improvement gained from the use of AI was higher for lower-skilled workers with a performance score of 6.06, higher-skilled workers continued to perform better with and without the technology.

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

In light of the rapid development of digital technology, it is imperative to study the impact of digital technology on the labour force’s entrepreneurial choices with the utmost urgency. This paper first constructs a theoretical mechanism for how digital technology affects individual entrepreneurship. It then empirically examines data from the China General Social Survey (CGSS) to test the theory. The results show that digital technology significantly increases individual entrepreneurial choices. Furthermore, the conclusions of the study are robust even when the estimation method and variable measurement are changed. Finally, the study finds that digital technology has the greatest impact on entrepreneurship among individuals with low education, the second-largest impact on those with medium education, and the third-largest impact on those with high education. Individuals with higher education levels have the second largest impact on the entrepreneurship of individuals with higher education levels, while the smallest impact is observed in this group. Digital technology development has a stronger role in promoting entrepreneurship of individuals with urban household registration than those with rural household registration. In terms of sub-region, digital technology has a larger role in individual entrepreneurship in the eastern and central regions, and has a less significant role in the western region. The findings of this study suggest that there is a need to implement measures to accelerate the pace of digital technology development, enhance the training of entrepreneurial skills and attitudes among highly educated individuals, and direct efforts towards enhancing digital technology development in rural and western China.

This dataset pertains to a research project investigating the social, cultural, and economic consequences of COVID19 on independent arts workers, specifically in the theatre sector, across England, Scotland, Wales, and Northern Ireland. The project recognised the unique vulnerability of this workforce in dealing with the impact of COVID19. Their workplaces closed overnight and their sector transformed as theatres moved to digital delivery, and their employment status (freelance) made them ineligible for the UK government’s Coronavirus Job Retention Scheme. The motivation of the project was to understand: the employment experiences of this workforce during the first 18 months of the pandemic; how the pandemic affected their planning for the future; how the pandemic changed their creative practices and skills; what impact government and sectoral policy had on the workforce; and to find strategies for government and industry to support this precarious workforce. This data collection includes survey responses (n=397) to an online survey which ran from 23/11/2020 to 19/03/2021,

This dataset comprises survey responses from 42 undergraduate business students who participated in a semester-long digital business simulation (Marketplace Simulations). The data captures self-reported skill development across four constructs:

Decision-Making (analytical thinking, strategic planning, risk management, adaptive iteration),

Teamwork (communication, conflict resolution, role adaptation, accountability),

Leadership (initiative, motivation, ethical judgment, strategic vision),

Job Market Preparedness (career confidence, professional collaboration, adaptability).

Data Collection:

Survey Tool: Administered via Qualtrics using 16-item Likert scales (1 = Strongly Disagree to 5 = Strongly Agree) for each construct, grouped into subconstructs.

Open-Ended Responses: Qualitative insights on skill application and perceived career readiness.

Files Included:

Survey Items Constructs and Subconstructs: Full survey instrument with item wording and thematic grouping.

Excel Files: Raw response data for each construct:

Decision-Making Skill Development (Supplementary Appendix A)

Teamwork Skill Development (Supplementary Appendix B)

Leadership Skill Development (Supplementary Appendix C)

Job Market Preparedness (Supplementary Appendix D)

Key Variables:

Quantitative: Composite scores and item-level ratings for each skill domain.

Qualitative: Student reflections on simulation impacts (e.g., "The simulation taught me to balance risks and rewards in decision-making").

Percentage of enterprises that arranged training or development activities to employees, by North American Industry Classification System (NAICS) code and enterprise size, based on a one-year observation period. Training and development activities include job specific training, managerial training, training in new technology, training in new business practices, training in international business, digital skill training, data literacy skill training, coaching and mentoring for employees, and other training or development.

This report contains information on 112,200 jobs for people employed (directly and indirectly) by local authority adult social services departments in England as at September 2018. The report will be of interest to central government (for policy development, monitoring and workforce planning), local government (for benchmarking), charities, academics and the general public. The report does not include information on staff employed in the independent sector (private and voluntary) or children's social services departments (published separately by the Department for Education). This report has used data collected by the National Minimum Data Set for Social Care (NMDS-SC) for the past seven years (from 2011). The NMDS-SC is managed by Skills for Care (SfC) on behalf of the Department of Health and Social Care (DHSC) and has been collecting information about social care providers and their staff since early 2006. Please note: On 6th March 2019, the CSV file was updated to correct some discrepancies which included missing ‘Jobs by sickness days’ data. None of the other remaining publication outputs were affected.