As labour markets evolve, skills signalling has become increasingly popular. This chapter examines skills signalling trends across OECD countries, focusing on who signals skills, which skills are most frequently signalled, and how patterns vary by industry, demographic group, and country. Findings suggest that direct skills signalling is on the rise, particularly for digital, business, and sector-specific skills. Young professionals signal more digital and transversal skills, while mid-career workers are more likely to highlight industry expertise. Women are more likely to signal digital skills, whereas men are more likely to signal possessing green skills. Finally, findings indicate that signalling skills can reduce employment gaps, reinforcing the importance of skills-first practices in an evolving labour market.
Empowering the Workforce in the Context of a Skills-First Approach
Abstract
The way in which skills are signalled in the labour market is becoming just as important as the set of skills individuals possess and companies demand. In a rapidly evolving labour market, individuals must continuously demonstrate what they can do to secure employment, advance in their careers, and adapt to new challenges. While formal education remains a crucial pathway to acquiring knowledge and foundational skills, employers increasingly seek evidence of practical, job-related skills often acquired through alternative pathways. As presented in Chapter 1, this shift is driven by changes in work and the rise of new learning pathways that allow individuals to acquire skills outside formal degree programmes.
2.1.1. Degrees alone no longer fully reflect the skills individuals possess
Until recently, formal educational qualifications served as the primary, if not unique, indicator of an individual's skills and potential in the labour market. However, as industries evolve and the nature of work changes, how degrees are interpreted and used as signals of skills is being re-evaluated. While still a strong and relevant signal across many roles, traditional higher education credentials may not be sufficient to fully convey an individual’s skills.
Many graduates need to complement their formal education with additional credentials, work experience, and practical demonstrations of their abilities to remain competitive. Similarly, individuals who have acquired valuable expertise through work experience, online learning, or non-formal training may not have traditional degrees but possess skills that are just as relevant and in demand.
2.1.2. Bridging the gap between degrees and skills
A growing body of evidence suggests that the relationship between formal education and actual skill levels is more complex than previously assumed. While degrees provide a structured foundation for learning, they do not always equate to workplace readiness. Research indicates that rising wages and employment outcomes are often more closely linked to specific skill sets than to degree attainment alone (Busso, Muñoz and Montaño, 2020[1]). Moreover, studies have shown that mid-career professionals frequently develop skills outside of their original field of study, highlighting the need for mechanisms that allow them to signal their skills to employers effectively (Heisig, Gesthuizen and Solga, 2019[2]).
Furthermore, higher educational qualifications do not always mean that individuals will possess higher levels of basic skills. Figure 2.1 illustrates that literacy and numeracy proficiency levels vary significantly across OECD countries, even among individuals with similar educational backgrounds. For example, tertiary graduates in some countries demonstrate skills comparable to upper-secondary graduates in high-performing education systems such as Finland and Japan. This suggests that while degrees remain an important credential, they do not always provide the same level of skill development, nor a clear or uniform measure of an individual’s skills. These differences reflect differences in the quality of initial education systems and the importance of learning opportunities over the life course in supporting skills development in working-age populations.
Average literacy proficiency score, 25–65-year-olds
Note: Caution is required in interpreting results due to the high share of respondents with unusual response patterns. See the Note for Poland in the Survey of Adult Skills – Reader's Companion: 2023 (OECD, 2024[3]).
Source: OECD (2024[4]), Do Adults Have the Skills They Need to Thrive in a Changing World? Survey of Adult Skills 2023, https://doi.org/10.1787/b263dc5d-en.
2.1.3. Degrees still matter – but skills must be visible
Degrees remain valuable, providing foundational knowledge, critical thinking skills, and discipline-specific expertise. Employers are not rejecting degrees but reframing their value within a broader skills-based approach (see Chapter 3). The shift towards skills-based hiring does not diminish the importance of formal education; instead, it emphasises the need for graduates to translate their academic learning into demonstrable skills. Increasingly, hiring processes assess candidates based on their ability to perform specific tasks, rather than solely relying on degree classifications. Digital job platforms, competency-based hiring assessments, and industry-recognised certifications are reinforcing this trend (Baird, Ko and Gahlawat, 2024[5]).
Furthermore, lifelong learning and alternative credentials play a greater role in workforce development (see Chapter 1). Many professionals complement their degrees with micro-credentials, online courses, and industry certifications to keep up with changing job demands. This does not mean that degrees are losing relevance – rather, how individuals present and signal their qualifications needs to align more closely with the skills employers seek. As the labour market continues to evolve, it is crucial to develop better mechanisms for skills signalling that allow individuals – regardless of whether they hold a degree – to effectively communicate their skills. Degrees will remain an important asset, but their currency in the labour market will be strongest when paired with clear, validated demonstrations of skills that reflect the needs of today’s workforce.
A unique feature of this report is its ability to leverage LinkedIn data to provide an in-depth analysis of skills signalling behaviours. It also combines other key sources, such as the Labour Force Survey and the OECD Programme for the International Assessment of Adult Competencies (PIAAC), to provide a comprehensive perspective on skills-based hiring trends (see Box 2.1 for an overview of the dataset used in this analysis).
Understanding how individuals signal their skills in the labour market requires access to rich, real-time data on job-seeking behaviour, employer preferences, and workforce skills. This report provides a unique opportunity to explore skills signalling trends by integrating insights from multiple complementary data sources.
A key contribution of this analysis is the use of LinkedIn data, which offers granular, real-time insights into how individuals present their skills in digital labour markets. The dataset includes millions of anonymised LinkedIn profiles, capturing self-reported skills, peer-endorsed skills, and employment histories across various industries and occupations. This enables an examination of how skills are signalled beyond formal qualifications and how different groups – including those without traditional degrees – highlight their skills in online job search platforms.
This analysis integrates LinkedIn data with other major labour market surveys, including the OECD Programme for the International Assessment of Adult Competencies (PIAAC) and Labour Force Surveys to ensure a comprehensive and balanced assessment.
By combining these diverse data sources, this report offers an unprecedented view of skills signalling trends, enabling policymakers, educators, and employers to better understand how individuals navigate labour markets and communicate their skills in an increasingly skills-driven economy.
Source: OECD (2024[6]), PIAAC data and methodology, www.oecd.org/en/about/programmes/piaac/piaac-data.html; Eurostat (2024[7]), EU labour force survey, https://ec.europa.eu/eurostat/web/microdata/european-union-labour-force-survey; LinkedIn (2024[8]), Data for impact, a partnership for economic opportunity, https://economicgraph.linkedin.com/data-for-impact.
Terms such as skills-based hiring, alternative credentials, micro-credentials, and competency-based assessments are frequently used in this analysis to describe emerging labour market trends. Additionally, distinctions between formal, non-formal, and informal learning and between transversal, technical, and job-specific skills are crucial for understanding how individuals navigate and communicate their skills. Box 2.2 provides an overview of some of the key terms relevant to the analysis and data, including digital and green skills and skills penetration, ensuring a consistent interpretation of the concepts underpinning this discussion. Table 1.1 in Chapter 1 also includes definitions of formal, non-formal and informal learning.
Type of education and learning
The OECD classifies learning into three broad categories: formal, non-formal, and informal learning, based on the level of structure, intent, and recognition of learning outcomes. These definitions align with international frameworks, such as the International Standard Classification of Education (ISCED).
Formal learning refers to structured learning in an institutional setting, is intentional from the learner’s perspective, and leads to a recognised qualification or certificate. This includes education and training provided in schools, universities, and vocational training institutions.
Non-formal learning is structured and intentional but occurs outside the formal education system. It does not necessarily lead to a formal qualification but can result in certifications, badges, or other recognitions of learning. Examples include workplace training, online courses, and adult education programmes.
Informal learning occurs daily, is often unintentional, and lacks structured instruction or assessment. It includes learning from work experience, social interactions, or self-directed activities such as reading or experimenting with new technologies.
Digital skills
To this report, digital skills follow LinkedIn’s classification, referring to the ability to use digital devices, applications, and networks to manage information, create content, communicate, and solve problems. Unless otherwise stated, digital skills include disruptive tech skills (see definition below).
Green skills
Green skills are skills that enable the environmental sustainability of economic activities. This encompasses a broad range of competencies, including engineering and technical skills essential in green sectors and cross-functional skills that promote sustainable practices across various industries. This report follows LinkedIn’s classification, which is based on the O*NET framework, covering key activities such as pollution prevention, renewable energy generation, and sustainable procurement.
Skills penetration
Skills penetration measures how frequently specific skills appear within different occupations and industries. It indicates the prevalence and demand for skills in job roles. For instance, if a large proportion of data scientists list artificial intelligence (AI) as a top skill, AI would have high skill penetration in that field.
Source: OECD (2005[9]), Promoting Adult Learning, https://doi.org/10.1787/9789264010932-en; LinkedIn (2022[10]), Global green skills report 2022, https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/global-green-skills-report/global-green-skills-report-pdf/li-green-economy-report-2022.pdf; Zhu, Fritzler and Orlowski (2018[11]), Data Insights: Jobs, skills and migration trends methodology and validation the results, https://documents.worldbank.org/pt/publication/documents-reports/documentdetail/827991542143093021/world-bank-group-linkedin-data-insights-jobs-skills-and-migration-trends-methodology-and-validation-results.
Skills signalling plays a crucial role in how individuals navigate the labour market, shaping their employment opportunities and career trajectories. Across OECD countries, individuals increasingly highlight their skills through various mechanisms, from digital platforms to employer-endorsed credentials and certifications. However, the way skills are signalled – and which skills are emphasised – varies significantly depending on industry needs, technological advancements, demographic factors, and national labour market structures.
This section examines how skills signalling patterns evolve across OECD economies. It focuses on who signals their skills, which skills are most signalled, and how these trends differ across industries and demographic groups.
2.2.1. Individuals are increasingly signalling their skills, especially those linked to their specific industry
In recent years, direct skills signalling has gained significant momentum, as individuals actively showcase their skills through various platforms and tools. Digital platforms have become particularly instrumental in this process, allowing individuals to signal their skills through self-reported listings, peer endorsements, and validated online assessments or certifications.
Two indicators can be used to characterise trends in signalling skills: whether individuals are signalling skills and the extent to which skills are being signalled. The first is measured by the probability of adding skills (i.e. extensive margin), while the second is captured by the average number of skills signalled (i.e. intensive margin). Both indicators can be adjusted by restricting the analysis to individuals who have already signalled at least one skill in their profile. This conditional probability of adding skills and the conditional average number of skills added ensure that the analysis focuses on active users – those who frequently update their profiles and actively engage in skills signalling.
The increasing probability of adding skills and the growth in the average number of skills signalled – both conditional and unconditional – highlight the rising importance of skills signalling across OECD countries (see Figure 2.2). In 2023, the conditional probability of adding skills (3.2%) was twice that of 2018 (1.5%), demonstrating a growing emphasis on showcasing skills. Similarly, the average number of skills signalled increased during the same period. This trend accelerated significantly post-2021, likely reflecting labour market adjustments in response to evolving workforce demands following the pandemic. As sectors rapidly digitalised and remote work became widespread, many workers were compelled to reskill, particularly in areas such as digital services, online collaboration, and platform-based work (Chepeliuk, 2021[12]). Simultaneously, employers turned increasingly to digital recruitment tools, including platforms like LinkedIn, to identify candidates with relevant and adaptable skill sets (Skibska and Konovalova, 2024[13]). In this context, skills signalling emerged as a critical strategy for individuals to remain visible, competitive, and aligned with evolving job market needs, especially in industries undergoing transformation or recovery
On average, OECD countries
Note: The probability of signalling skills and the average number of signalled skills can be measured conditionally and unconditionally. The unconditional indicators reflect trends across all individuals, while the conditional indicators are restricted to those who have already signalled at least one skill. This adjustment ensures the analysis focuses on active users, capturing those who frequently update their profiles and actively engage in skills signalling.
Source: LinkedIn, 2024.
Skills signalling is expanding across OECD countries, though its pace and intensity vary significantly (see Figure 2.3). Chile, Costa Rica, Colombia and Mexico have experienced the largest growth differentials in skills signalling between 2018 and 2023, with individuals increasingly adding skills to their profiles and presenting more comprehensive skill sets. In contrast, Japan and Korea exhibit lower probabilities of adding skills and narrower skill profiles, suggesting that skills signalling is less prevalent in these labour markets.
This trend indicates that skills signalling is more prominent in labour markets with higher levels of informality. In Latin America, approximately 50% of the working-age population was informally employed in 2022 (ILO, 2022[14]). In such contexts, formal qualifications alone are often insufficient for career advancement since workers frequently rely on practical experience and informal learning for skill development (Cano-Urbina, 2015[15]), leading to a reliance on skills signalling as a key strategy for improving job prospects. Conversely, in more formal labour markets, where hiring practices prioritise formal qualifications and seniority over demonstrable skills, skills signalling tends to be less prevalent. In countries like Japan and Korea, where rigid employment structures and long-term career pathways are more common, credential-based hiring remains the dominant practice, actively reducing individuals’ need to signal their skills in digital labour markets.
Source: LinkedIn, 2024.
Countries with a higher likelihood of individuals adding skills to their profiles also tend to have a greater average number of skills signalled. This positive correlation shows that in countries where individuals are more proactive in updating their skill sets, they also present a broader array of skills. Several factors may lead to this. Countries with higher digital job platform usage rates tend to see greater participation in skills signalling. Similarly, in countries with stronger lifelong learning cultures, individuals are more likely to acquire new skills and update their profiles regularly. For instance, New Zealand has one of the highest participation rates in adult learning among OECD countries (48% of the adult population participates in learning opportunities) (OECD, 2023[16]). Nordic countries have embedded lifelong learning into national strategies, fostering environments where individuals take greater ownership of their skill development through regular, structured learning opportunities (OECD, 2020[17]; OECD, 2023[18]).
Not all skills are signalled equally. An analysis of the five major skill groups – tech, disruptive, industry, business, and transversal skills (see Box 2.3 for definitions) – reveals distinct trends in how individuals prioritise skills signalling. Across OECD countries, industry and business skills consistently dominate, reflecting their relevance across sectors and strong employer demand (see Figure 2.4). In 2023, the probability of signalling specialised industry skills reached 2.3% – more than double the rate in 2018. Business skills followed a similar trajectory, increasing at a similar rate of 1.7% in 2023. This trend underscores the growing demand for sector-specific expertise. Projections indicate that between 2019 and 2030, the demand for skills related to interacting with computers, problem solving and analysing data is expected to rise (OECD, 2023[18]), particularly in areas such as supply chain management, financial analysis, and regulatory compliance (Morgan McKinley, 2023[19]).
While tech skills have also grown, particularly post-2021, their increase has been more gradual than that of specialised industry skills. Tech skills are becoming more integrated across various sectors rather than confined to technology-driven industries. These skills, which include using digital devices, communication applications, and information management tools, are increasingly fundamental for workplace productivity, collaboration, and problem-solving, making them valuable across diverse occupations. However, as these skills become more widely assumed to be possessed by individuals, they may be perceived as basic skills; employers will expect individuals to possess them irrespective of their educational or professional background. While this will reduce individuals’ need to signal these skills actively, it puts increased pressure on them to acquire them to remain competitive in the labour market.
Note: OECD average. See Box 2.3 for definitions.
Source: LinkedIn, 2024.
Understanding skills signalling requires distinguishing between different types of skills that individuals highlight in the labour market. The following five skill groups capture the breadth and specificity of skills signalled across industries:
Transversal Skills: Non-cognitive abilities and personality traits valued by employers but not measured by traditional achievement tests. These include skills such as communication, adaptability, teamwork, and leadership, which are essential for workplace success but cannot be predicted by IQ or standardised assessments.
Business Skills: Knowledge and skills necessary to start, manage, or operate an enterprise. Examples include business management, project management, and entrepreneurship, which are critical for organisational leadership, strategic planning, and operational efficiency.
Tech Skills: A range of abilities that enable individuals to manage information using digital devices, communication applications, and networks. These skills facilitate creating and sharing digital content, collaboration, and problem-solving in technology-driven environments.
Disruptive Skills: Skills related to developing and applying emerging technologies expected to reshape labour markets. Examples include robotics, genetic engineering, and artificial intelligence, key drivers of innovation and industrial transformation.
Specialised Industry Skills: Domain-specific or industry-focused skills that do not fall into the other categories. These skills are less transferable across jobs but are crucial within sectors, such as aviation maintenance, pharmaceutical research, or precision manufacturing.
Source: LinkedIn (2022[20]), LinkedIn data primer: Indicators, dimensions, and coverage of LinkedIn data available through the Development Data Partnership.
The distribution of the types of skills signalled is relatively consistent across OECD countries, but certain patterns stand out (see Figure 2.5). Israel, Korea and Poland stand out with higher shares of disruptive and tech skills, reflecting their strong focus on innovation-driven sectors and early adoption of emerging technologies. Israel boasts a thriving startup ecosystem and substantial research and development (R&D) investment, particularly in life sciences, cybersecurity, and defence technology (Deloitte, 2024[21]). Korea, the second-largest R&D spender among OECD economies, is leading in semiconductors, ICT infrastructure, and 6G development, with hubs like Pangyo Techno Valley fostering a dynamic tech ecosystem (OECD, 2023[22]). Poland, meanwhile, has rapidly expanded its high-tech sectors, with government-backed initiatives supporting entrepreneurship, digitalisation, and foreign trade, boosting its position as a rising tech hub (Thunes, 2024[23]). This alignment in signalling disruptive and tech skills reflects these countries’ strategic prioritisation of innovation and digital transformation.
Countries like Poland and the Slovak Republic, with dynamic and specialised industries, exhibit a balanced distribution of individuals signalling specialised industry and business skills. This equilibrium reflects the complementarity between sector-specific expertise and transferable skills, allowing workers to remain competitive and adapt to evolving sectors (Grundke et al., 2017[24]). For example, Poland, recognised for its strong tech talent, is experiencing increasing skills shortages in project management and ICT operations, highlighting a growing demand for specialised expertise (PARP, 2023[25]).
Note: Countries are ordered based on the sum of the share of disruptive and tech skills. See Box 2.3 for definitions.
Source: LinkedIn, 2024.
2.2.2. Transversal and disruptive skills are on the rise, becoming more prominent in skills signalling profiles
The increasing signalling of emerging technology (disruptive) and transversal skills underscores their growing importance for career success in a rapidly evolving labour market. Figure 2.6 highlights two key trends: first, transversal skills have experienced the highest growth in the probability of being signalled across OECD countries between 2018 and 2023; and second, individuals are signalling a greater number of skills overall, particularly disruptive and transversal skills.
Transversal skills have become the fastest-growing skill category in skills signalling, with individuals in 2023 more than 2.5 times more likely to signal them compared to 2018. According to LinkedIn data, language proficiency reliability, co‑operation, conversation management, and continuous improvement frequently appear among the top ten most signalled skills in OECD countries (Marconi, Vergolini and Borgonovi, 2023[26]). As automation and AI reshape job roles, workers are increasingly highlighting these skills to differentiate themselves and remain competitive (Lyu and Liu, 2021[27]).
Some less frequently signalled skills are gaining prominence and moving up in the rankings of most signalled skills, reflecting the growing need to demonstrate resilience, leadership, and creative thinking. These skills are increasingly valued for career transitions, remote work, and cross-functional collaboration, making them critical for long-term employability (Succi and Canovi, 2019[28]). The rise of professional networking platforms and digital job boards has further facilitated the visibility of these skills (Ramos-Monge, Fox and Garcia-Piquer, 2023[29]), even though validating or certifying them remains a challenge (Costantino and Rodzinka, 2022[30]).
Note: In Panel A, the values represent how many times higher the probability of adding skills in 2023 is compared to 2018. In Panel B, the values indicate the percentage change of the average number of skills signalled between 2018 and 2023. See Box 2.3 for definitions.
Source: LinkedIn, 2024.
Disruptive skills are also becoming increasingly prominent in skills signalling, particularly at the intensive margin, as individuals not only signal them more frequently but also list a greater number of these skills. Between 2018 and 2023, the average number of disruptive skills signalled across OECD countries grew by 54% (see Figure 2.6), this is equivalent to adding almost one full skill more on average per individual during this period. These skills are becoming essential across a broad range of industries, reflecting the growing need for workers who can integrate and leverage emerging technologies (WEF, 2025[31]).
Changes in the average number of disruptive skills signalled by individuals vary significantly across sectors, reflecting differences in the demand for emerging technologies and the pace of digital transformation. Looking at this growth rate, sectors such as real estate and equipment rental services (95%), financial services (82%), and construction (75%) have experienced the highest percentage increases in disruptive skills signalling between 2018 and 2023 (see Figure 2.7). This trend highlights the growing adoption of emerging technologies in industries that traditionally relied on manual processes and conventional business models but are now undergoing rapid digitalisation and innovation-driven shifts.
In the real estate and equipment rental sector, the rise in disruptive skills signalling is likely driven by the increasing adoption of AI-powered property analytics, blockchain for smart contracts, and digital platforms for asset management. For instance, Yardi Property Management, a real estate management software designed to help property managers, landlords, and real estate professionals streamline operations, ranks among the top-signalled skills on LinkedIn in this sector. As these technologies transform property valuation, leasing, and investment decisions, professionals are increasingly signalling expertise in data-driven decision-making, automation tools, and digital transactions to enhance their competitiveness (ONET, 2025[32]).
The financial sector has seen a sharp increase in disruptive skills signalling due to the widespread adoption of fintech innovations, AI-driven risk assessment, and blockchain-based financial transactions. The integration of algorithmic trading, digital banking, and cybersecurity protocols has made it imperative for professionals to demonstrate proficiency in AI, blockchain applications, and regulatory technology (RegTech) to align with the industry’s technological evolution.
Similarly, the construction sector increasingly leverages AI, automation, and digital twin technology to enhance project management, efficiency, and sustainability. The growing use of predictive analytics, 3D printing, and robotic construction has led individuals to signal expertise in emerging construction technologies, automation systems, and digital project modelling.
OECD average
Note: See Box 2.3 for definitions.
Source: LinkedIn, 2024.
Disruptive skills reflect technological advancements and emerging industry demands, while transversal skills are essential for navigating these transformations. Comparing their growth rates helps assess whether workers are keeping pace with technological shifts and balancing technical expertise with interpersonal and problem-solving skills, both of which are crucial for operating effectively in evolving work environments. As automation and AI take over routine tasks, jobs increasingly require a strong mix of disruptive skills – to work with new technologies – and transversal skills, which are critical for collaboration, creativity, and decision-making tasks that machines cannot fully replicate.
Figure 2.8 illustrates the percentage change in the average number of disruptive and transversal skills signalled across OECD countries between 2018 and 2023. The figure reveals significant cross-country variation in how individuals signal these two skill categories. While most OECD countries have experienced growth in both skill types, the relative increases vary. Countries such as Austria and Estonia are positioned toward the upper-right quadrant but do not stand out significantly, suggesting that individuals in these countries actively signalling disruptive and transversal skills.
At the opposite end of the spectrum, countries like Japan and Korea exhibit lower growth in both transversal and disruptive skills signalling. Cultural and economic factors may contribute to these trends, such as a strong reliance on traditional qualifications and structured career progression models. For instance, Japanese companies continue to emphasise traditional hiring methods, prioritising firm-specific qualification requirements over skills-based assessments (Rear, 2020[33]). Similarly, in Korea, the education system has been characterised by a strong emphasis on academic achievement and university degrees, with vocational education and training (VET) only recently benefiting from efforts to shift societal preferences away from traditional academic pathways (Byun and Park, 2017[34]).
Note: Growth rate is measured as a percentage change of the average number of skills, disruptive or transversal, signalled between 2018 and 2023.
Source: LinkedIn, 2024.
Colombia and Mexico have experienced some of the highest increases in disruptive skills signalling, which may reflect individuals’ need to highlight skills that are critical for technological transformation and the growing adoption of digital and green technologies – many of which may have been acquired through non-formal or informal learning pathways. In Mexico, in particular, participation in informal learning is comparatively high, exceeding the Latin American average (OECD, 2021[35]). Additionally, in Colombia, the energy sector has been a major driver of digital transformation, with companies integrating Industry 4.0 technologies to enhance operational efficiency and sustainability, reshape workforce demands and increase the need for digital expertise (Giraldo et al., 2021[36]). Similarly, in Mexico, leading corporations are adopting digital transformation strategies not only to improve efficiency but also to address sustainability challenges, driving demand for skills in digital innovation (Diaz and Montalvo, 2022[37]).
Germany, Israel and the Netherlands are notable for their significant growth in transversal skills signalling, while concurrently experiencing moderate increases in disruptive skills signalling. This pattern indicates robust employer demand for leadership, communication, and adaptability, especially within industries where automation and AI are enhancing, rather than replacing, human decision-making. Studies reveal that as digitalisation advances, the necessity for distinctly human skills, such as negotiation, problem-solving, and strategic thinking, is rising across sectors (Börner et al., 2018[38]).
2.2.3. The prevalence of skills signalling varies across different sociodemographic groups
Not all individuals showcase their skills in the same way, with the same frequency, or across the same skill categories. Sociodemographic factors – such as age, gender, educational attainment, and employment status – play a crucial role in how and why individuals highlight their skills in professional contexts, particularly on social networking platforms (see Figure 2.9 and Figure 2.10). These factors shape access to skill development opportunities, career expectations, and strategic decisions regarding skills signalling, influencing how individuals position themselves in the labour market.
Average, OECD countries
Note: “Not working” individuals refer to active LinkedIn users who are currently outside the labour market or searching for jobs. The age group categories align with generational classifications used in LinkedIn data: Generation Z (born between 1997-2012), Millennials (born between 1981-1996), Generation X (born between 1965-1980), and Baby Boomers (born between 1946-1964).
Source: LinkedIn, 2024.
Gender differences reflect industry representation and self-promotion trends
While men and women have an equal probability of adding skills to their profiles, differences emerge in the number of skills signalled. In 2023, both men and women had a 3% probability of adding skills, a rate that has doubled since 2018 (1.5%), indicating that skills signalling has increased at the same pace for both genders (see Figure 2.9, Panel A). However, in terms of intensity, men signalled slightly more skills (13) on average than women (12) in 2023 (see Figure 2.9, Panel B).
Note: "Not working" individuals refer to active LinkedIn users who are currently outside the labour market or searching for jobs. The age group categories align with generational classifications used in LinkedIn data: Generation Z (born between 1997-2012), Millennials (born between 1981-1996), Generation X (born between 1965-1980), and Baby Boomers (born between 1946-1964).
Source: LinkedIn, 2024.
Beyond differences in the number of skills signalled, men and women also prioritise different types of skills. Although the differences are not pronounced for most skill categories, Figure 2.10, Panel A shows that women are more likely to signal transversal skills than men, while men are more likely to signal tech skills compared to women.
The gender differences in skills signalling likely reflect differences in industry representation, occupations, and career advancement expectations. Men are more likely to work in technical fields such as engineering, IT, and finance, where signalling a broad range of specialised skills is common, while women tend to be concentrated in sectors where fewer technical skills are explicitly documented in professional profiles (Keller, 2019[39]). This occupational segregation extends beyond job titles to the tasks performed, often favouring men in roles that demand technical skill signalling (Keller, 2019[39]). Additionally, women are often more conservative in self-promotion, potentially leading to underreporting of skills despite possessing similar skills (DeJesus, Umscheid and Gelman, 2021[40]). Evidence shows that female researchers use fewer positive, self-promotional words than men, which affects their visibility and perceived expertise – patterns that likely extend to the labour market, where women’s skills and contributions may be under-recognised, reinforcing gender disparities in professional advancement (DeJesus, Umscheid and Gelman, 2021[40]). See Chapter 3, Box 3.2 on existing forms of hiring bias and how they may also affect the skills signalling behaviour of different groups.
Notably, there are no significant gender differences in signalling business skills, suggesting that both men and women equally highlight managerial, strategic, and entrepreneurial skills. This may reflect the increasing presence of women in leadership and business roles, as well as a shared recognition of business skills as essential for career progression across industries.
Younger individuals are more active, and older professionals are more selective in skills signalling
Younger individuals, particularly early-career professionals, are the most proactive in skills signalling, leveraging it to establish their professional identity and compete in the job market. In 2023, young adults had the highest probability of adding skills (5%), surpassing all other age groups and experiencing the largest growth in skills signalling over the study period (see Figure 2.9, Panel A). This trend may reflect their greater familiarity with digital labour platforms and AI-powered HRM tools, which rely on algorithm-driven profile scanning and skills-based hiring systems (Martindale and Lehdonvirta, 2023[41]). Younger workers, particularly those under 35, are also more adept at online professional branding, ensuring their digital presence aligns with automated hiring processes (Blyth et al., 2022[42]). Additionally, their higher signalling rate compensates for limited work experience and academic credentials, making them more competitive in a labour market that increasingly values skills-based assessments over traditional qualifications.
Older professionals signal fewer skills but tend to highlight a broader range of them. Mid-career (30‑49) and mature adults (50‑59) signal more skills on average than younger cohorts, reflecting greater accumulated experience and specialised expertise. In 2023, mid-career and mature adults signalled three and two more skills, respectively, than young adults, who averaged 11 skills. This may suggest that mid-career professionals engage in skills signalling more selectively, emphasising leadership, industry-specific knowledge, and strategic skills as they progress in their careers.
Seniors (60+) are the least likely to engage in skills signalling, with just over 1% doing so in 2023, and when they do, they signal fewer than 10 skills on average. They have also shown the slowest growth in skills signalling between 2018 and 2023, indicating lower engagement with skills-based hiring trends. This may be due to limited familiarity with digital tools, less exposure to skills-first hiring practices, and fewer incentives to update profiles, particularly for those nearing retirement (Vigtel, 2018[43]). Seniors often rely more on professional networks and extensive work experience, reducing the perceived need to actively showcase skills on digital platforms (Tunney and Oude Mulders, 2021[44]). See Chapter 3 for an in-depth look at the considerations and features of programmes that will be relevant for different groups, based on differences in their behaviour and the types of skills signalling which they engage in.
The type of skills signalled varies with career stage, industry experience, and technological adaptation (see Figure 2.10). Young adults signal more disruptive and transversal skills than any other group, reflecting greater engagement with emerging technologies and an emphasis on adaptability, communication, and teamwork. Their higher signalling of disruptive skills may stem from exposure to digital transformation through education and entry-level roles in innovation-driven sectors, while transversal skills are likely highlighted to compensate for limited work experience. In contrast, mid-career and mature adults signal the highest number of industry-specific and business skills, reinforcing greater specialisation and leadership responsibilities accumulated over time. This pattern aligns with growing managerial roles and technical expertise needed at later career stages. Seniors signal the fewest skills across all categories, reinforcing their lower engagement in skills signalling overall. Higher education positively correlates with the number of skills signalled, while lower education levels correlate with more frequent skills signalling.
Educational attainment strongly correlates with skills signalling, particularly in terms of intensity, as individuals with higher education levels tend to signal a greater number of skills (see Figure 2.9 Panel B). In 2023, individuals with a master’s degree or higher signalled an average of over 14 skills, compared to around 11 skills for those with less than upper secondary education across OECD countries. This trend reflects greater exposure to diverse learning opportunities, specialised training, and professional environments that demand a broader skill set. Higher education often involves multidisciplinary coursework, research, and industry engagement, equipping individuals with a wider range of skills to showcase in professional settings.
However, individuals with upper secondary education or less are more likely to add skills than those with higher education, suggesting a greater need to actively demonstrate their skills in labour markets where formal qualifications play a lesser role in hiring decisions (see Figure 2.9 Panel A). Without advanced degrees, these individuals may rely more on skills signalling to enhance their employability, often showcasing industry-specific, technical, or practical skills gained through work experience or alternative learning pathways.
The type of skills signalled also varies significantly by education level. Figure 2.10, Panel E shows that disruptive, specialised industry and business skills follow a clear pattern: individuals with higher education levels, particularly those with a master’s degree or higher, signal the highest number of these skills, highlighting their specialised expertise and managerial skills. This aligns with the fact that higher education often equips individuals with technical knowledge, strategic decision-making and leadership skills, making them more relevant for career advancement (Shields and Sandoval Hernandez, 2020[45]).
In contrast, transversal skills are signalled at lower levels across all education categories, but individuals with upper secondary education or less are slightly more likely to highlight them. This suggests that those with fewer formal credentials highlight interpersonal and adaptable skills to strengthen employability, especially in sectors where technical degrees are less critical for hiring (Succi and Canovi, 2019[28]). Similarly, transversal skills play a vital role in non-degree-dependent jobs, where adaptability, teamwork, and communication often compensate for the lack of formal qualifications (Frunzaru and Corbu, 2020[46]).
Skills signalling peaks in mid-career, as early-career professionals update frequently, and late-career professionals are more selective
Work experience follows a similar pattern to age in shaping skills signalling, influencing both frequency and intensity. The likelihood of adding skills to a profile declines with years of experience – individuals with no work experience (8%) are eight times more likely to add skills than those with 30 or more years of experience (1%). This suggests that early-career professionals are more proactive in updating their profiles to establish credibility, stand out in a competitive job market, and compensate for limited work history. In contrast, late-career professionals rely more on accumulated experience, professional networks, and reputation, reducing the need for frequent skills updates.
However, the number of skills signalled follows an inverted U-shape pattern. Individuals with no experience signal an average of 8 skills, while those with over 30 years of experience signal slightly more (10 skills). The highest number of skills signalled occurs among mid-career professionals, with those having 10 to 20 years of experience signalling 14 skills, followed by those with 20 to 30 years (13 skills) and 0 to 10 years (12 skills). This suggests that skills signalling peaks in mid-career stages, when individuals have accumulated diverse expertise and actively highlight their skills for career progression. Early-career professionals may have fewer skills to showcase, while late-career professionals may focus on signalling only their most relevant and strategic skills.
The type of skills signalled also varies with experience, reflecting career progression and shifting skill priorities. More experienced individuals signal more business skills, emphasising managerial expertise, strategic decision-making, and leadership skills accumulated over time. In contrast, those with less experience tend to signal more specialised industry, disruptive, and tech skills, likely as a strategy to compensate for limited professional experience and enhance employability. Notably, individuals with no work experience do not stand out in any specific skill category, suggesting they may signal a broad mix of skills rather than specialising in a particular area to increase their attractiveness to employers.
Non-working individuals are more likely to signal skills, while employed individuals signal a greater variety
Non-working individuals are more likely to signal skills than those who are employed, but working individuals signal a higher number of skills on average. In 2023, non-working individuals had a higher probability of adding skills to their profiles, suggesting that skills signalling plays a crucial role in improving employability and facilitating re-entry into the labour market (see Figure 2.9, Panel A and Panel B). This trend likely reflects a strategic effort by job seekers to showcase their skills and remain visible to potential employers.
However, while non-working individuals signal skills more frequently, employed individuals signal a greater number of skills overall (see Figure 2.10, Panel B). In 2023, working individuals signalled an average of over 12 skills, compared to fewer than 10 for those not working. This pattern holds consistently across all skill types, including transversal, business, and industry-specific skills. This difference underscores how active engagement in the labour market drives skill accumulation and documentation, as employed individuals seek to adapt to evolving job demands, advance their careers, and remain competitive. At the same time, this suggests that unemployed individuals may experience additional obstacles to accessing the tools and information and skills signalling, compounding their disadvantage in the labour market to engaging in opportunities.
2.2.4. The type of skills individuals signal often depends on the context, reflecting the diverse needs of different industries and countries
Skills signalling is not uniform across industries and countries; it is shaped by sectoral demands, labour market structures, and national policy priorities. Industry-specific requirements, technological advancements, and regional workforce strategies influence the skills individuals choose to signal. Understanding these patterns helps to identify how professionals adapt their skill portfolios to remain competitive in diverse economic contexts.
Sector-specific vs. cross-sectoral skills signalling
Some skills are highly specialised and remain confined to specific industries, while others are widely applicable across multiple sectors. Figure 2.11 presents a heatmap illustrating the extent to which certain skills are signalled across different sectors in OECD countries. Lighter shades indicate skills primarily signalled within a limited number of sectors, while darker shades represent skills widely recognised across industries.
Specialised industry skills are critical for sector-specific employment and are consistently signalled across countries. For example, “Food and Beverage Operations” is predominantly signalled in the hospitality sector, while “Construction” is mainly signalled within the construction industry. These skills remain tightly linked to their respective sectors, underscoring their limited transferability and the importance of targeted training for industry-aligned career pathways.
In contrast, some skills are broadly signalled across multiple industries within the same country, reflecting their versatility and widespread demand. Skills such as “Liability”, “Independence”, and “Co‑operation” frequently appear across various sectors, suggesting that employers value these skills for different job functions. The darker shades in the heatmap highlight countries where these transversal skills are essential across multiple industries, aligning with trends that prioritise adaptability, communication, and organisational skills in the evolving labour market.
Differences in widely signalled skills across OECD countries
The type of widely signalled skills across industries varies significantly by country, reflecting differences in workplace culture, labour market structures, and economic contexts. While some countries emphasise autonomy and self-reliance, others prioritise collaboration and teamwork, and some place greater importance on trust and accountability.
In Belgium and France, “Independence” is one of the most frequently signalled skills across multiple sectors, indicating that self-reliance and the ability to work autonomously are highly valued. This trend aligns with labour market structures and institutional contexts that encourage individual initiative, particularly for self-employed individuals in knowledge-intensive fields such as consulting and professional services (Focacci and Pichault, 2023[47]; Beuker et al., 2019[48]). Education systems in these countries may also reinforce self-directed learning, further embedding independence as a key professional asset (Aljafari, 2019[49]).
In Denmark, the Netherlands, Norway, and Sweden, “Co‑operation” is the most widely signalled skill across industries, reflecting the Nordic labour markets’ emphasis on teamwork, consensus-building, and egalitarian work environments (Bévort and Einarsdottir, 2021[50]). These countries have flat organisational hierarchies and cross-functional collaboration structures, making co‑operation a key skill for success. This pattern may suggest that individuals in these labour markets highlight teamwork, collective problem-solving, and shared decision-making as critical workplace attributes.
In Chile, Colombia, Costa Rica, and Mexico, “Liability” is the most frequently signalled skill across sectors, pointing to the high value placed on reliability, trust, and accountability in these economies. Given the prevalence of informal employment and contractual instability in many Latin American countries, signalling reliability and responsibility is particularly important for securing stable job opportunities. This trend may also reflect employer preferences in rapidly changing markets, where adaptability and accountability are essential for managing uncertainty and business transformations.
Note: The heatmap illustrates the distribution of the 20 most signalled skills across OECD countries in 2023, based on the number of sectors in which each skill appears. Lighter shades indicate skills that are primarily signalled in a few sectors, highlighting their sector-specific nature, while darker shades represent skills that are signalled across multiple industries, reflecting their broader applicability. The intensity of the shading provides insights into how skills are concentrated or dispersed across OECD countries.
Source: LinkedIn, 2024.
Growing divergence in skills signalling practices across sectors over time
Figure 2.12 highlights sectoral variations in skills signalling trend, both in individuals’ probability and intensity of signalling, showing a shift from a more compressed distribution in 2018 (yellow dots) to a more dispersed scatter in 2023 (red dots). This increasing divergence suggests that skills signalling practices are evolving differently across industries.
OECD average
Source: LinkedIn, 2024.
Sectors experiencing rapid transformation, such as “technology, information & media”, and “education”, have shown the most significant increases in both the probability of skills signalling and the number of skills signalled. These fields are at the forefront of digitalisation and structural change, requiring professionals to update and document their evolving skills continuously. In the education sector, the rise in skills signalling likely reflects the integration of digital tools, pedagogical innovations, and shifting qualification standards. In the United States, for instance, 25% of the teaching workforce, including 60% of principals, use AI tools in the classroom and for their work (Kaufman et al., 2025[51]). Estimates suggest that between 2020 and 2030, the global investment in education technologies, including apprenticeship management systems, simulators, and virtual reality (VR) and augmented reality (AR) will grow on average by 16.3% each year, multiplying by 2.5 in a 10-year period (OECD, 2023[52]). This evolving skills landscape underscores the importance of continuous professional development and robust skills signalling mechanisms to ensure that educators remain equipped for the future of teaching and learning.
Traditional industries such as farming, construction, and extractive sectors (oil, gas & mining) have exhibited lower levels of skills signalling probability and intensity. While these industries have seen some growth in skills signalling, the rate of change remains modest, suggesting that formal credentials, practical experience, and sector-specific certifications continue to play a dominant role in hiring and career progression.
The shift from a more compressed distribution in 2018 to a more dispersed distribution in 2023, as seen in Figure 2.12 highlights the increasing divergence in skills signalling practices across sectors. In 2018, industries exhibited relatively similar levels of skills signalling probability and intensity, suggesting a more homogeneous approach. By 2023, however, the widening spread of data points indicates that some sectors – such as technology, education, and professional services – have significantly accelerated their skills-signalling practices, while others – such as agriculture, construction, and extractive industries – have maintained lower engagement in updating skill profiles. This divergence likely reflects sector-specific transformations, where industries undergoing rapid digitalisation and structural change require workers to actively signal new skills to remain competitive.
Several factors explain why skills signalling is evolving differently across sectors:
Digital transformation: Technology-driven industries such as IT, finance, and professional services require constant skill updates, increasing the need for workers to signal expertise in AI, cloud computing, and data analytics. In contrast, in sectors with slower digital adoption, such as agriculture and construction, workers have less urgency to update their skill profiles.
Upskilling opportunities: Knowledge-intensive fields like education and professional services encourage continuous training and certification, leading to greater skills signalling. Meanwhile, industries that rely on on-the-job learning, such as hospitality and manufacturing, provide fewer incentives for workers to document new skills.
Job specialisation: Highly specialised roles, such as cybersecurity and software development, demand frequent skill updates, making skills signalling essential. In contrast, generalist roles in public administration and retail require less frequent updates.
Hiring practices: Sectors adopting skills-based hiring, such as tech and logistics, incentivise professionals to actively signal their expertise. Meanwhile, industries still relying on credential-based hiring, such as public administration and traditional manufacturing, place less emphasis on skills signalling, as career progression remains tied to formal qualifications. See Chapter 3 for an in-depth discussion on the potential consequences of unequal uptake of skills signalling practices for individuals and employers alike.
Signalling digital and green skills has grown steadily, with shifts highlighting their evolving importance in the dual transitions. Signalling the right set of skills can gain relevance, particularly in sectors that are in continuous transformation, such as those connected to the green and digital transition. This is even more key when the level of skills shortages is more accentuated in some sectors than others. It is expected that the skills gap in the green economy will rise to 7 million by 2030, especially in solar, wind, and biofuel technologies, which are pillars of the green transition (BCG, 2023[53]). Similarly, digital occupations,1 which represent 11% of online job postings on average in selected OECD countries, have increased 2 to 4 times in the last decade, and for some specific roles and countries, 6 times, across roles such as ICT managers in Canada and Singapore (OECD, 2022[54]).
This section describes the main trends of signalling digital and green skills, looking closely at those on AI engineering. It also analyses the prevalence of these skills in the last decade and across countries, looking closely at gender and age disparities.
2.3.1. Changes in the frequency, intensity and prevalence of green and digital skills signalling
The signalling of digital and green skills has been steadily increasing, both in terms of extensive and intensive margins. On average, the probability of signalling digital and green skills in 2024 was 1.5 and 2 times higher, respectively, than in 2018. Similarly, the average number of these skills signalled per individual has slightly increased, rising by 0.5 skills between 2018 and 2023 – a smaller gain compared to the overall increase in the average number of skills signalled. This upward trend has been particularly pronounced since the COVID-19 pandemic, which accelerated the adoption of digital technologies and placed sustainability at the forefront of global policy agendas. The steepest growth occurred from 2022 onward, reflecting the increasing demand for professionals equipped with skills in digital transformation, renewable energy, and environmental sustainability. Governments and industries have intensified efforts to upskill workers in these areas, further reinforcing the visibility and relevance of these skills in professional profiles.
While the signalling of AI engineering skills has also grown significantly, it remains relatively low compared to digital and green skills. In 2023, the probability of signalling AI engineering skills was 0.06% on average, making it 20 times less likely to be signalled than digital skills (see Figure 2.13. This disparity suggests that while AI is a rapidly evolving field, its specialised nature limits its widespread adoption across industries. AI-related skills are more concentrated in specific sectors, such as software development, data science, and high-tech manufacturing, where expertise in machine learning, neural networks, and algorithm optimisation is critical.
OECD average
Note: The definition of green and digital skills is included in Box 2.2.
Source: LinkedIn, 2024.
The signalling of digital skills has evolved in response to sectoral transformations and changing skill demands across industries. As digital literacy becomes more widespread, certain digital skills are no longer signalled as frequently. This is partly because they are now assumed to be basic skills that individuals are expected to possess, and partly because signalling them provides little differentiation or added value in a competitive job market.
Advanced digital skills – particularly those related to data analysis, data mining, programming languages, and statistical software – have gained prominence in recent years (see Figure 2.14, Panel A). Since 2016, skills such as data analysis, Python (programming language), and SQL have been increasingly signalled, with their growth accelerating significantly after the COVID-19 pandemic. These skills now constitute the most frequently signalled digital skill group on average across OECD countries. Their rising importance reflects the growing demand for data-driven decision-making and the integration of automation and artificial intelligence across industries.
Basic digital skills, such as proficiency in Microsoft Word and PowerPoint, have declined in signalling frequency. This trend suggests that these tools are now widely used and no longer serve as differentiating factors in the labour market. As digital tools become standardised across workplaces, employers are placing greater value on more specialised and technical digital skills that offer a competitive advantage.
Another notable shift in digital skills signalling is the transition from general concepts to specific tools and applications. For example, signalling general social media proficiency has declined, while expertise in specialised tools such as Adobe Photoshop has seen slight growth. This change reflects the increasing demand for applied digital skills that are directly relevant to specific job functions, particularly in fields such as marketing, design, and content creation.
OECD average
Note: Heatmap showing the intensity with which a skill is signalled across sectors and countries. The darker the colour (dark blue or dark green), the more widely the skill is signalled across multiple sectors and countries. All listed skills (digital and green) are already among the most frequently signalled on the platform. The definition of green and digital skills is included in Box 2.2.
Source: LinkedIn, 2024.
While green skills remain essential in labour markets transitioning towards sustainable and low-carbon economies, the specific skills that individuals choose to signal have evolved (See Figure 2.14, Panel B).
A key trend is the growing emphasis on technical and operational green skills. Skills related to maintenance and repair and process optimisation in manufacturing have gained prominence, reflecting the increasing focus on resource efficiency, circular economy practices, and sustainable industrial operations. As industries strive to reduce waste, lower emissions, and enhance energy efficiency, professionals are more likely to signal hands-on, technical skills that are directly applicable to greener production processes.
Broad environmental awareness and theoretical sustainability knowledge are becoming less frequently signalled. Skills such as environmental awareness and environmental science, which were once widely recognised, have declined in relevance among signalled green skills. This shift may indicate that general sustainability knowledge is now widely assumed, reducing the need for individuals to signal it explicitly. Additionally, as businesses move beyond awareness-building towards actionable sustainability strategies, employers may prioritise practical, applied green skills over general environmental knowledge.
Sustainability and renewable energy skills continue to be signalled but have become slightly less prominent than in 2018. This suggests that while these skills remain valuable, they may no longer provide the same level of competitive differentiation as before. As renewable energy technologies mature and sustainability becomes a core business practice across industries, individuals may be focusing on more specialised green skills that align with emerging regulatory frameworks, technological innovations, and sector-specific sustainability initiatives.
Evolving patterns in digital and green skills signalling reflect similar shifting workforce demands across countries
Figure 2.16 and Figure 2.17 are heatmaps that illustrate changes in the ranking of the most signalled green and digital skills across OECD countries between 2018 and 2023 (see Box 2.2 for definitions of green and digital skills). Green cells indicate that a skill has moved up in the ranking, meaning it has become more frequently signalled in 2023 compared to 2018, while red cells indicate a decline in ranking, reflecting reduced signalling over the same period. Grey cells represent skills that have remained unchanged. The skills in each figure are ordered based on their cumulative ranking changes across OECD countries, providing insights into how labour market trends are reshaping the relative importance of different green and digital skills.
The heatmap in Figure 2.16 reveals a clear shift in green skills signalling across OECD countries between 2018 and 2023. Technical and industry-specific skills, such as “Maintenance and Repair” and “Process Optimisation (Manufacturing)”, have gained prominence, with widespread increases in ranking. This reflects a growing demand for operational efficiency, resource optimisation, and sustainable industrial practices.
By contrast, general environmental knowledge skills, including “Environmental Awareness”, “Environmental Science”, and “Energy Efficiency”, have declined in ranking across multiple countries. This suggests that broad sustainability concepts are now embedded in workplace expectations and offer less differentiation in the labour market.
Skills like “Sustainability” and “Renewable Energy” show mixed trends across countries, indicating that their relevance is shaped by national policies, industrial priorities, and the pace of green transition efforts. Similarly, corporate sustainability-related skills, such as “Environmental, Social, and Governance (ESG)” and “Socially Responsible Investing”, exhibit varying trends, reflecting diverse regulatory approaches and corporate commitments to sustainability.
Note: Green cells indicate skills that have risen in rank, reflecting increased signalling in 2023 compared to 2018. Red cells indicate a decline in signalling, while grey cells represent no change. Skills are ordered by their cumulative ranking shifts across countries (from those with the greatest increases to those with the most widespread declines). Countries are listed in alphabetical order. The definition of green and digital skills is included in Box 2.2.
Source: LinkedIn, 2024.
The signalling of digital skills across OECD countries is shifting towards data management, analysis, and programming. As industries become increasingly data-driven, skills such as data analysis and Python programming have gained prominence, consistently ranking among the most frequently signalled digital skills.
A key trend is the rise of SQL, a programming language essential for database management and querying, which has experienced a significant increase in signalling. In countries such as Denmark, Finland, Iceland, and Luxembourg, SQL now ranks among the top five digital skills with the greatest positive ranking shifts. This reflects the growing demand for data management expertise, particularly in sectors such as finance, healthcare, and IT, where businesses rely heavily on structured data processing and analytics.
Despite the growing emphasis on advanced digital skills, fundamental tools like Microsoft Office and Excel remain among the most frequently signalled digital skills across OECD countries. Between 2015 and 2023, their ranking has remained stable, underscoring their continued importance in professional environments.
Note: Green cells indicate skills that have risen in rank, reflecting increased signalling in 2023 compared to 2018. Red cells indicate a decline in signalling, while grey cells represent no change. Skills are ordered by their cumulative ranking shifts across countries (from those with the greatest increases to those with the most widespread declines). Countries are listed in alphabetical order. The definition of green and digital skills is included in Box 2.2.
Source: LinkedIn, 2024.
Excel retains strong relevance in data analysis - it remains widely used for financial modelling, statistical processing, and business intelligence applications. Its advanced capabilities, such as Power Query, dynamic arrays, and AI-driven automation, have sustained its demand. Similarly, Microsoft Office has evolved, incorporating cloud-based collaboration, enhanced security features, and AI-powered tools, reinforcing proficiency in it as an essential workplace skill.
Basic digital skills such as Microsoft PowerPoint and Word have declined in ranking across all OECD countries. Their decreasing relevance suggests that proficiency in these tools is now an expectation among employers, rather than a competitive advantage in the labour market.
This shift indicates that employers may be prioritising more specialised digital skills, particularly in data analysis, automation, and coding. As workplaces integrate AI-driven tools and business intelligence platforms, individuals who signal technical digital skills are more likely to stand out, while generic office software proficiency no longer differentiates candidates.
Gender and age shape skills signalling patterns for digital and green skills across OECD countries
The likelihood of signalling digital and green skills varies by gender and age group across OECD countries, which has implications on who can benefit and adapt better to this dual transition.
Data reveal that women are slightly more likely than men to signal digital skills, a difference that is particularly pronounced in countries such as Italy, Austria, and Sweden (see Figure 2.17, Panel A). This pattern may reflect women’s efforts to compensate for their underrepresentation in STEM (science, technology, engineering, and mathematics) and, particularly, ICT occupations (OECD, 2024[55]). Given structural imbalances, women may feel a greater need to signal digital skills to strengthen their professional credibility, enhance employability, and counteract potential biases in male-dominated fields (Son Hing et al., 2023[56]). To improve their competitiveness in the digital economy, women are also increasingly engaging in targeted digital skills training programmes (OECD, 2024[57]), further contributing to higher signalling rates.
Finland presents an exception, where men are more likely than women to signal digital skills. This may be attributed to Finland’s strong ICT sector, where men dominate technical and software-related occupations, comprising 78% of the ICT workforce (WIT, 2021[58]). Finnish boys tend to outperform girls in technical-oriented ICT tasks, whereas girls excel in schoolwork-related and social interaction ICT tasks (Kaarakainen, Kivinen and Kaarakainen, 2017[59]). The Finnish labour market’s focus on engineering, software development, and data-driven sectors likely influences this gendered pattern in digital skills visibility, as these fields require more frequent skills signalling.
Note: Probability has been multiplied by 100. The definition of green and digital skills is included in Box 2.2.
Source: LinkedIn, 2024.
Men are more likely to signal green skills than women across all OECD countries (see Figure 2.17, Panel B). Men have almost double the chance of women in signalling their green skills. This disparity largely reflects male employment dominance in greenhouse gas (GHG)-intensive sectors, which employ individuals with highly transferable skills applicable to green industries (OECD, 2024[60]). Additionally, men are more concentrated in traditionally green-driven sectors, occupying 72% of jobs in the green sector within OECD countries (OECD, 2024[61]).
The widest gender gaps in green skills signalling are observed in Chile, Portugal and Spain, where men’s higher representation in renewable energy and industrial sectors likely accounts for their greater engagement in skills signalling (Alexander, 2024[62]). Conversely, Finland, Japan and Luxembourg report the most balanced gender distribution, suggesting that women in these countries participate more actively in green-related industries or education programmes. This trend aligns with national gender equality policies that have promoted women’s participation in STEM fields, recognising their role in driving environmental innovation and sustainability efforts (OECD, 2024[63]; Xu, 2023[64]). Finland and Japan have implemented strategies to close the gender gap in STEM, a factor that may have indirectly contributed to greater gender parity in green skills signalling.
There are also differences with respect to signalling green and digital skills by age. Regarding digital skills, Figure 2.18, Panel A, shows that young adults consistently exhibit the highest probability of adding digital skills to their profiles, with rates exceeding 0.20 in countries such as Poland, Korea, and Hungary. Digital literacy is increasingly embedded in academic curricula, and younger workers are more likely to engage with online platforms, programming, and data analysis tools to enhance their employability (OECD/ILO/European Union, 2023[65]).
Note: Probability has been multiplied by 100. The definition of green and digital skills is included in Box 2.2.
Source: LinkedIn, 2024.
In contrast, mid-career and mature professionals signal digital skills at lower yet relatively stable rates across countries. Their probability of adding digital skills typically ranges between 0.10 to 0.15, suggesting continued but less frequent engagement with digital upskilling. This could be attributed to industry experience and reliance on accumulated expertise over ongoing technical skills updates. Seniors, however, exhibit the lowest probability of signalling digital skills across all OECD countries, with most rates below 0.10. Their lower engagement likely stems from reduced exposure to emerging digital trends (König and Seifert, 2022[66]), fewer incentives to reskill at later career stages (Yamashita et al., 2024[67]), and lower familiarity with digital job-search platforms. However, in some countries – such as Chile, Italy, and Portugal – seniors demonstrate slightly higher engagement, potentially reflecting targeted digital literacy initiatives or the increasing necessity of digital tools across professional fields.
The likelihood of signalling green skills also varies significantly across age groups, though the patterns differ from those of digital skills. Figure 2.18, Panel B, indicates that mature adults tend to signal green skills at the highest rates across most OECD countries, particularly in Ireland, Lithuania, Poland and the Slovak Republic. Professionals in their 40s and 50s are actively engaging with sustainability-related skills, likely in response to industry-wide shifts towards green transitions. Many mature professionals hold decision-making roles in industries such as energy, manufacturing, and finance, where sustainability considerations have become increasingly important.
Young adults also signal green skills at relatively high rates, highlighting growing awareness of environmental concerns among newer workforce entrants (OECD, 2023[18]). Young individuals have better attitudes and disposition towards environmentally sustainable behaviour, potentially correlated with a disposition to develop and signal related set of skills (OECD, 2023[18]). However, their focus may still be on developing foundational skills and securing employment, making green skills a secondary priority compared to digital skills. As with digital skills, seniors exhibit the lowest probability of signalling green skills, though exceptions exist in countries such as Chile, Costa Rica and Mexico, where seniors (50 or over) are marginally more worried about potential climate threats and may consider engaging with relevant training to develop environmental sustainability skills (OECD, 2023[18]).
The increasing reliance on digital job platforms in the U.S. labour market has transformed how individuals signal their skills. In a labour market characterised by high mobility, sectoral shifts due to automation, and a growing emphasis on skills-based hiring, understanding the impact of skills signalling on employment outcomes is essential.
Skills signalling is gaining relevance in the United States due to the highly decentralised and dynamic nature of its labour market, characterised by shorter job tenures, more frequent career changes, and less standardised hiring practices (Campello, Gao and Xu, 2024[68]). Employers in the United States are at the forefront of adopting skills-based hiring, particularly in rapidly evolving sectors such as technology, healthcare, and logistics. The widespread reliance on platforms like LinkedIn for recruitment and professional networking, coupled with a large, diverse, and highly mobile workforce, makes clear and targeted skills signalling essential for job seekers navigating a fast-paced and flexible employment landscape (Heller and Kessler, 2024[69]).
One conceptualisation of a skills-first hiring approach creates a talent pool (the group of eligible candidates for a job position) composed of workers who have at least half of the top skills of the target job, compared to traditional talent pools comprised of workers who have previously worked in the given occupation). Globally, a skills-based approach could expand talent pools by more than six times, offering a substantial increase in potential candidates across industries and geographies (Lara, 2025[70]).
The United States exhibits the highest increase in the skills-first talent pool among selected OECD countries, with a ratio exceeding 16, significantly outpacing other countries (see Figure 2.19). This rapid expansion of the skills-first talent pool suggests that individuals who effectively communicate their skills, whether through self-reported skills or endorsements, may have a competitive advantage in securing employment. Understanding how skills signalling interacts with hiring practices in this evolving landscape can offer valuable insights into the effectiveness of skills-based hiring, particularly in labour markets where traditional qualifications are becoming less central to recruitment decisions. Moreover, as other OECD countries gradually transition toward a skills-first approach, the experience of employers in the United States offers lessons on the role of skills signalling in improving labour market efficiency and workforce mobility.
Ratio of the skills-first pool and the previous job pool
Note: The “skills-based talent pool” defines the pool by identifying candidates who, in the last five years, worked in roles: 1) with at least 50% overlap in key skills with the target job, and 2) that have a minimum number of transitions into the target role. The skills-based talent pool increase is measured by the ratio of the total number of job candidates in the skills-based talent pool compared to the number identified via prior job title searches. The ranking presented in this figure differs from the one shown in Figure 2.3 because they display different types of information. Figure 2.3 shows the probability of adding skills and the average number of skills added per individual by country, while Figure 2.19 presents the increase in the talent pool based on individuals signalling skills directly related to a given occupation – compared to traditional talent pools, which are composed of individuals who have previously worked in that occupation.
Source: Lara (2025[70]), Skills-based hiring: Increasing access to opportunity, https://economicgraph.linkedin.com/content/dam/me/e/conomicgraph/en-us/PDF/skills-based-hiring-march-2025.pdf.
The probability and number of skills signalled (see Figure 2.3) and the expansion of the skills-first talent pool (see Figure 2.19) – capture distinct yet complementary dimensions of skills signalling, which explains the differences in country rankings. Figure 2.3 reflects individuals’ behaviour, assessing how frequently LinkedIn users in each country update their profiles by adding skills and how many skills they typically list. This metric is shaped by factors such as digital engagement, user familiarity with skills-based hiring, and platform usage culture. By contrast, Figure 2.19 captures a more structural, labour market perspective, estimating how much a country’s talent pool grows when hiring decisions are based on skill similarity rather than traditional job experience. This measure reflects how responsive a labour market is to skills-based hiring practices, independently of how often individuals signal their skills.
This section extends from the study Skill Signals in a “Digital Job Search Market and Duration in Employment Gaps” by Baird, Ko, and Gahlawat (2024[5]), which examines how skills listed on LinkedIn profiles – whether self-added or endorsed – affect the duration of employment gaps in the United States. The findings highlight how digital skills signalling can improve hiring efficiency, reduce skills mismatches, and facilitate career transitions.
Box 2.4 provides a summary of the methodology used in this research and clarifies key definitions relevant to this section. More detailed calculations and data processing information can be found in the study’s reference annex.
Baird, Ko and Gahlawat (2024[5]) analyse the relationship between the listing of skills on professional online profiles and the duration of employment gaps. The research relies on data drawn from LinkedIn profiles of users in the United States who experienced at least one employment gap between January 2015 and December 2022.
Data and sample
The dataset includes over 13 million employment gaps, representing both unique individuals and multiple employment gaps per person. An employment gap is defined as a period in a user’s employment history where no job is listed, provided the gap is at least one month long. The data are restricted to profiles that had an employment status recorded up to December 2022 to allow for updates in job history before data extraction in early 2024.
Key covariates and controls
The study controls for key factors influencing both skill listing and employment gap duration. Work experience (total prior employment months) and educational attainment capture career differences. Profile activity – LinkedIn login frequency and recency – adjusts for job search intensity. Labour market conditions are accounted for using the LinkedIn Hiring Rate (LHR), reflecting local hiring trends. Industry-fixed effects control for sector-specific influences. To mitigate selection bias, inverse probability weights are generated using Bayesian Additive Regression Trees (BART). These controls enhance robustness, though results remain correlational. Further, the study generates inverse probability weights using Bayesian Additive Regression Trees (BART) to control for selection bias in skills listing.
Interpretation and limitations
While the methodology allows for robust analysis of correlations between skill signalling and employment gap durations, the results remain correlational rather than causal. Selection on unobservable characteristics, such as motivation or offline networking, may influence both skill listing and job outcomes. Additionally, the findings are specific to LinkedIn users and may not fully represent the broader labour market
The study employs an inverse propensity weighted Cox Proportional Hazards Model with time-varying covariates to examine the likelihood of ending an employment gap. This survival analysis method accounts for differences in timing between skill additions and employment transitions. The model is implemented using the coxph function from the R survival package.
Source: Baird, Ko and Gahlawat (2024[5]) Skill Signals in a Digital Job Search Market and Duration in Employment Gaps, https://doi.org/10.1007/s12122-024-09363-y.
2.4.1. Individuals who signal skills are more likely to have shorter employment gaps
Individuals who actively signal their skills experience shorter employment gaps, reinforcing the value of digital labour markets in improving job search efficiency (see Figure 2.20). Each additional skill listed on a LinkedIn profile is associated with a 0.07-month reduction in median employment gap duration, while adding ten skills reduces the gap by approximately 0.7 months. Endorsed skills also contribute to employment gap reduction, though their effect is slightly weaker at 0.4 months per ten skills listed (Baird, Ko and Gahlawat, 2024[5]).
The impact of skills signalling varies by educational background. The largest returns in reducing employment gaps are observed among individuals with no formal education listed on their profiles. Among those with listed educational qualifications, individuals with higher attainment levels see larger returns than those with lower education levels, though all effects are statistically significant. This suggests that skills signalling through alternative ways, such as digital platforms, can be particularly beneficial for those without formal credentials, helping bridge gaps in traditional qualification-based hiring.
Association of adding an extra skill (self-added or endorsed) with the likelihood that the employment spell period will end each month
Note: The figure reports the hazard ratio from the survival model for self-added skills. These are estimated using a Cox Proportional Hazards model with time-varying covariates adjusted and weighted. All values are statistically significant at 1%. The adjusted and weighted model controls for months of work experience, standardised z‑score of time since last login on the LinkedIn platform, indicators for LinkedIn platform activity, LinkedIn hiring rate, the log of the number of connections they had, years since they first registered their LinkedIn account, and fixed effects for year and the prior industry they worked in. It also uses continuous inverse probability weights using BART, Winsorised at the 99.5th percentile, and is based on all the outcome model covariates as well as the average endorsed skills counts for the members’ connected.
Source: Baird, Ko and Gahlawat (2024[5]) Skill Signals in a Digital Job Search Market and Duration in Employment Gaps, https://doi.org/10.1007/s12122-024-09363-y.
The number of skills listed plays a critical role in reducing employment gaps. The first skill added to a profile has the strongest impact on reducing employment gaps, with subsequent skills yielding diminishing returns. This effect remains consistent across education groups, underlining the importance of ensuring that at least one relevant skill is visible in digital profiles. For endorsed skills, the first endorsement has a stronger signalling effect than additional endorsements, likely because it serves as an initial validation of competency, while further endorsements provide less new information to employers.
2.4.2. The source and timing of skills signalling influence gaps in employment
Not all skills have the same effect on reducing employment gaps. Baird et al. (2024[5]) find that disruptive technology skills and transversal skills have the strongest association with faster reemployment, whereas industry-specific and business skills yield smaller, though still positive, returns (see Figure 2.21). The coefficient for disruptive tech skills is more than twice as large as that for business or industry skills, suggesting that employers prioritise emerging technical skills and adaptability in job candidates.
Association of adding an extra skill (self-added) with the likelihood that the employment spell period will end each month, by skill group
Note: The figure reports the hazard ratio from the survival model for self-added skills. These are estimated using a Cox Proportional Hazards model with time-varying covariates adjusted and weighted. The adjusted and weighted model controls for months of work experience, standardised z-score of time since last login on the LinkedIn platform, indicators for LinkedIn platform activity, LinkedIn hiring rate, the log of the number of connections they had, years since they first registered their LinkedIn account, and fixed effects for year and the prior industry they worked in. It also uses continuous inverse probability weights using BART, Winsorised at the 99.5th percentile, and is based on all the outcome model covariates as well as the average endorsed skills counts for the members connected.
Source: Baird, Ko and Gahlawat (2024[5]) Skill Signals in a Digital Job Search Market and Duration in Employment Gaps, https://doi.org/10.1007/s12122-024-09363-y.
The timing of skills signalling also plays a significant role. Skills added during an unemployment period have a substantially stronger effect on reemployment than those listed before the gap began. The impact of skills added while unemployed is nearly four times larger for self-added skills compared to pre-existing skills. This trend may reflect strategic job-seeking behaviour, where individuals update their skill profiles in response to emerging job market demands. Additionally, adding skills during an employment gap may serve as a proxy for job search effort, increasing visibility to recruiters and improving employment prospects.
Work experience also influences how skills signalling affects reemployment. More experienced workers tend to have longer unemployment durations, yet they see larger returns from listing skills, particularly those that are harder for employers to assess through traditional hiring methods. A standard deviation increase in years of experience is associated with a 20% lower probability of exiting unemployment each month, consistent with prior research showing that older workers face longer job search periods due to having more specialised skills, working in selective job markets, and experiencing longer hiring processes.
The effect of skills signalling varies by industry, reflecting differences in hiring practices, skills demand, and recruitment strategies. The highest returns for self-added skills are observed in utilities, manufacturing, and financial services, where skills-based hiring is more prevalent. By contrast, the lowest returns are found in hospitality, administrative support, and entertainment industries, where hiring often prioritises practical experience over digital credentials.
For endorsed skills, the strongest impact is seen in oil, gas, and mining; wholesale trade; and farming, ranching, and forestry. This suggests that in industries where hands-on expertise and technical knowledge are crucial, endorsements may serve as an additional validation mechanism, reinforcing an individual’s credibility.
While there is some variation in returns across industries, the differences are less pronounced than those observed across education groups. This suggests that education level plays a more significant role in shaping the effectiveness of skills signalling than industry type. However, the overall correlation between self-added and endorsed skills returns remains positive across sectors, indicating that in most industries, skills signalling provides tangible benefits for job seekers.
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← 1. Digital occupations include computer and data analysts / administrators, Software developers, programmers and engineers, ICT technicians and data entry clerks, and ICT and HR managers / marketing specialists.