Measuring occupational shortages is challenging as no single indicator captures the imbalance between labour demand and supply. The existing literature has generally taken one of two approaches: subjective or objective measures. The OECD Skills for Jobs Indicator takes the latter approach, creating a composite indicator for occupational imbalances and translating it to skill shortages using O*NET job profiles. The composite indicator consists of six labour market signals, including wage growth, growth in hours worked, the unemployment rate, change in underqualification, employment growth and growth in online job postings. This chapter explains the choice of these six labour market signals and presents the data and methodology used to compute the Skills for Jobs Indicator.
Mapping Skill Shortages in OECD Countries
Abstract
Measuring occupational imbalances is challenging, as no single metric fully captures the imbalance between labour demand and supply for occupations or skills. The literature commonly distinguishes between two broad approaches, subjective approaches or objective approaches, outlined in Box 2.1 (OECD, 2017[52]; McGuinness et al., 2025[88]). The Skills for Jobs Indicator builds on the objective approach developed in OECD (2017[52]) and OECD (2022[89]), which provides a harmonised framework for analysing occupational imbalances across OECD Member and partner countries.
Traditionally, imbalances were measured using subjective approaches, such as employer surveys or expert interviews (Filippucci, Laengle and Marcolin, 2025[37]; OECD, 2026[90]). These can provide early signals of labour market tightness, but their results are often inconsistent. For example, Greek employers report among the highest labour shortages in the Manpower survey and yet they are among the lowest in the Employer Business Indicators survey (European Commission, 2015[91]). Such inconsistencies reflect the subjective nature of self‑reported data (Green, Machin and Wilkinson, 1998[92]). Employers may also conflate genuine labour shortages with firm‑specific recruitment difficulties caused by low wages or poor working conditions (Barnow, Trutko and Schede Piatak, 2013[50]; Green et al., 2020[93]). In some cases, employers may strategically overstate labour scarcity to prompt government intervention, thereby securing supply of the skills they require at wage levels below those that would prevail under undistorted market conditions (Downs, 2009[94]; Hogarth, 2016[95]).
Given these shortcomings, objective approaches have gained increasing prominence in national and international assessments of labour shortages and skill needs (Downs, 2009[94]; OECD, 2017[52]; OECD, 2022[89]; Cedefop, 2024[96]; McGuinness et al., 2025[88]; OECD, 2026[90]). These approaches rely on administrative and statistical labour market data rather than employer perceptions and aim to provide a more consistent and comparable basis for identifying occupational and skill imbalances. A key limitation of the objective approach is that changes in these indicators may also reflect factors unrelated to shortages, such as business cycle fluctuations, labour market regulations or external shocks such as COVID‑19 (OECD, 2017[52]; McGuinness et al., 2025[88]). Combining multiple indicators helps mitigate these limitations, but results may still vary depending on the methodology.
The approach draws on six signals of shortages, including price measures (wage growth), quantity measures (employment growth, unemployment rates, growth in online job postings [OJP] and growth in hours worked) and quality measures (the underqualification rate). As none of these indicators provides a direct measure of shortages, they are combined into a composite index to identify occupational imbalances (see Figure 2.1). To better understand how these measures capture labour market imbalances, the following section examines each indicator in turn, highlighting both their strengths and their limitations as proxies for occupational shortages.
Source: Author’s adaption based on OECD (2017[52]), Getting Skills Right: Skills for Jobs Indicators, https://doi.org/10.1787/9789264277878-en.
2.1.1. Wages
The textbook response to labour or skill shortages are wage increases. When labour demand exceeds supply, upward wage pressure should help restore equilibrium. Empirical research supports this claim: employers tend to raise wages when faced with increased competition for workers (Terry and De Zeeuw, 2018[97]; Le Barbanchon, Ronchi and Sauvagnat, 2023[98]; Groiss and Sondermann, 2025[99]; Zwysen, 2026[100]). As employers typically rely first on less costly and more flexible responses, such as reorganising work, increasing hours, or investing in training, wage increases signal more pronounced or persistent shortages (Healy, Mavromaras and Sloane, 2015[101]).
However, institutional factors can weaken the link between wages and labour market conditions. In some sectors, particularly the public sector, wage‑setting rules limit the scope to offer competitive wages for high-demand occupations or senior positions (Katz and Krueger, 1993[102]; International Bank for Reconstruction and Development / The World Bank, 2021[103]; Marcinkowski, Butnaru and Rabrenović, 2024[104]). Collective bargaining can also compress wage responsiveness, as wages adjust following re‑negotiation of collective agreements rather than current labour shortages (Kölling, 2022[105]; Adamopoulou, Díez-Catalán and Villanueva, 2025[106]). Adjustments to the minimum wage may also lead to wage growth that is unrelated to skill shortages, particularly in lower‑skilled occupations.
Compositional effects may further cloud the interpretation of wage developments. Changes in the workforce structure – such as the retirement of senior, higher-paid workers, or the entry of younger cohorts – can affect average wage growth independently of underlying wage pressure (OECD, 2017[52]).
2.1.2. Employment
Employment growth within an occupation indicates rising demand for workers with a given skill set and when demand persistently exceeds supply, shortages may emerge. Yet, employment growth is an imperfect indicator of labour shortages, as rising employment may also indicate that firms can fill vacancies without major difficulties. For example, during periods of economic recovery, rapid employment growth often reflects the re‑absorption of previously unemployed or underemployed workers rather than labour scarcity. Moreover, when employers perceive shortages to be temporary, they may opt for other measures instead of expanding employment. Such adjustments may include increasing hours worked, reallocating tasks within firms or raising wages to retain existing staff (Mathä et al., 2021[107]). Finally, employment growth is also shaped by cross-country differences in employment protection legislation, which can slow firms from adjusting employment when facing shortages (Martin and Scarpetta, 2012[108]). In countries with strict hiring and firing rules, employment changes may be a less reliable signal of labour imbalances.
2.1.3. Unemployment rate
A low unemployment rate for a specific occupation may indicate a shortage, as few qualified candidates are available. In contrast, a high unemployment rate may indicate a surplus. A key limitation is that occupation-specific unemployment measures implicitly assume that jobseekers are tied to their previous occupation. In practice, however, workers may switch occupations in response to changes in wages, working conditions or preferences (Causa et al., 2022[8]; Liu-Lastres, Huang and Bao, 2023[109]). In addition, such measures do not capture recent graduates who are available to fill vacancies but lack labour market experience. Moreover, even when the unemployment rate for an occupation is low overall, available workers may be located in regions different from where the jobs are. Finally, a low unemployment rate may mask a surplus if workers are employed but working fewer hours than they would like (Hashimoto, Ono and Schlegl, 2023[110]).
2.1.4. Hours worked
Increases in hours worked can serve as an early signal that firms are struggling to fill vacancies, as work intensity can typically be adjusted more rapidly than employment levels (Hamermesh, 1986[111]; Hart, 2023[112]). Firms tend to increase working hours for current employees when they face short-term demand fluctuations or slow recruitment processes (Healy, Mavromaras and Sloane, 2015[101]). However, relying on higher work intensity is generally not a viable long-term response to labour shortages, as overtime work can increase turnover, potentially worsening staffing shortages faced by firms (Tan et al., 2020[113]; Bae, 2024[114]). Furthermore, some occupations do not allow deviation from prescribed schedules, limiting adjustments through working hours (Hart, 2023[112]).
2.1.5. Vacancies
Hiring often begins with the posting of a vacancy to attract job seekers (Davis, Faberman and Haltiwanger, 2013[115]). Job vacancies are therefore widely used as a proxy for labour demand (Downs, 2009[94]; Causa et al., 2022[8]; Domash and Summers, 2022[116]; Barnichon and Shapiro, 2024[117]; Causa et al., 2025[9]). High vacancies (labour demand) relative to unemployment (labour supply) may indicate that employers struggle to find suitable workers.
However, job vacancies capture only a subset of overall hiring demand, as many positions are filled through informal channels such as employee referrals or internal recruitment (Ioannides and Loury, 2004[118]; Davis, Faberman and Haltiwanger, 2013[115]). These channels are often preferred by firms because they reduce search costs and increase the likelihood of successful and durable matches (van Ours, 1989[119]; Burks et al., 2015[120]; Gürtzgen and Pohlan, 2024[121]). Vacancy-based measures should therefore be interpreted as partial indicators of labour demand.
Beyond these conceptual limitations, commonly used vacancy-based indicators, such as vacancy rates, do not capture how long positions remain unfilled. If vacancies are filled quickly, high vacancy rates may simply reflect normal labour market dynamics rather than genuine shortages. Second, vacancy levels vary substantially across occupations (Barnow, Trutko and Piatak, 2013[122]). Occupations with low pay and limited career prospects, such as those in the hospitality sector, tend to have higher turnover rates and therefore more vacancies (Downs, 2009[94]; McGinley et al., 2014[123]; Dogru et al., 2023[124]). In contrast, there may be fewer civil service openings, as public-sector workers tend to stay in their job for long periods (Fontaine et al., 2020[125]). Finally, hiring difficulties may reflect poor working conditions, low pay, or ineffective human resource practices rather than genuine labour shortages (OECD, 2017[52]).
Using vacancy data as an indicator of labour demand also requires careful methodological consideration. Official vacancy statistics are typically produced either by national statistical offices, based on representative employer surveys of positions firms intend to fill in the near future, or by public employment services, based on vacancies reported by employers for job-matching purposes (Hershbein and Kahn, 2018[126]). However, vacancy data from public employment services may not be fully representative of overall labour demand, as reporting is typically voluntary and recruitment often takes place through alternative channels. This can lead to systematic biases in coverage, with high-skilled occupations typically underrepresented (Zilian, Zilian and Jäger, 2021[127]). Cross-country comparisons are further complicated by differences in vacancy definitions and measurement practices (Napierala, Kvetan and Branka, 2022[128]).
OJP are a complement to official job vacancy statistics, providing timely and detailed information on labour demand (Sostero and Fernàndez-Macías, 2021[129]; Fabo and Kurekova, 2022[130]). Conceptually, OJP capture only a subset of all vacancies – namely, those that are publicly advertised on online platforms. Moreover, the decision to advertise a vacancy online is not random, which introduces additional selection bias in OJP data. OJP tend to overrepresent high-skilled jobs and underrepresent lower-skilled jobs, public sector jobs and jobs in small businesses (Hershbein and Kahn, 2018[126]; Cammeraat and Squicciarini, 2021[131]; Sostero and Fernàndez-Macías, 2021[129]; Tsvetkova et al., 2024[132]). However, this may be less of a problem when there are labour shortages, as firms are then forced to broaden their search through formal vacancy postings (Moreno Galbis, Wolff and Herault, 2020[133]; Fabo and Kurekova, 2022[130]).
Another concern is that the declining cost of online recruitment enables firms to post more ads or keep them open longer, increasing observed vacancy counts independently of labour demand (Causa et al., 2025[9]). Comparisons between OJP and official vacancy statistics reveal substantial discrepancies across countries, with some datasets reporting significantly more vacancies than the official statistics (Tsvetkova et al., 2024[132]). This discrepancy may partly reflect differences in coverage, definitions and data collection methods. However, it may also arise from postings that employers have no immediate intention of filling, such as to build a candidate pipeline, signal growth to investors, or manage employee expectations (Weber Handwerker and Pepper, 2025[134]).
2.1.6. Underqualification
When labour demand and supply are misaligned, the quality of matches deteriorates (OECD, 2017[52]; Brunello and Wruuck, 2021[33]; Filippucci, Laengle and Marcolin, 2025[37]). During economic expansions, when the labour market tightens, employers may struggle to find candidates with the desired skills and adjust by hiring workers whose skills do not fully match job requirements (Veneri, 1999[49]). Conversely, during recessions, when jobs become scarce, skilled workers are more willing to accept positions requiring lower levels of education and fewer skills (Liu, Salvanes and Sørensen, 2016[135]; Blázquez Cuesta, Pérez Navarro and Sánchez-Mangas, 2025[136]). As a result, expansions tend to increase underqualification and underskilling, while downturns raise the incidence of overqualification and overskilling. Beyond cyclical dynamics, structural trends – including digitalisation, decarbonisation and population ageing – increase demand for specialised skills that may be scarce in the labour force, thereby heightening the risk of mismatch (Filippucci, Laengle and Marcolin, 2025[37]). Therefore, a rise in the underqualification rate may signal labour shortages and is used as a quality-based shortage indicator. Overqualification, by contrast, is more likely to reflect skill underutilisation.
The COVID‑19 pandemic triggered a large economic contraction, resulting in the loss of 114 million jobs globally by the end of 2020 (OECD, 2021[137]). As a result, it introduced disruptions that affected the labour market indicators used in this analysis. These effects were highly heterogeneous across countries, sectors and occupations.
In the United States and Canada, labour‑market adjustment occurred primarily through layoffs, leading to significant reductions in employment and corresponding increases in unemployment (OECD, 2021[137]). By contrast, in many European economies that implemented short‑time work and job‑retention schemes, adjustment took place mainly through reductions in working hours rather than job losses: an estimated 90% of total hours lost were borne by workers who remained employed. Hours fell most sharply in low-paying and contact-intensive sectors, such as hospitality, tourism, arts and leisure. Usual hours worked, which are typically less volatile than actual hours, also decreased in many EU Member States between 2019 and 2022 (Eurofound, 2023[138]).
Although government policies mitigated the impact on employment, some workers still temporarily withdrew from the labour force due to health risks, care responsibilities, or limited job opportunities (OECD, 2021[139]; Braakmann, Eberth and Wildman, 2022[140]). These inactive workers are not captured in the official unemployment statistics.
During the early stages of the COVID‑19 pandemic, the high uncertainty and restrictions on movement led to a collapse in job vacancies across all sectors and occupations (Forsythe et al., 2020[141]; OECD, 2021[137]). Following economic reopening and fiscal support, vacancies surged sharply before returning to pre‑COVID levels by 2025 (Feist, 2024[142]; OECD, 2025[6]). Nevertheless, whether vacancy rates provide a clear signal of labour demand during and immediately after the COVID‑19 pandemic remains debated (Restrepo-Echavarria and Cheremukhin, 2022[143]; Horwich and Mongey, 2023[144]; Causa et al., 2025[9]).
The pandemic also distorted wage signals, making wages less responsive to underlying labour market tightness. In many countries, governments introduced job-retention schemes under which employers received support covering a significant share of the wages of employees working reduced hours (OECD, 2021[137]). As a result, nominal wages generally did not adjust downwards during the pandemic, reflecting the well-documented downward rigidity of wages (Braakmann, Eberth and Wildman, 2022[140]). Consequently, the widespread use of job-retention schemes may have introduced some distortions in wage statistics (OECD, 2024[145]).
Taken together, these factors suggest that labour market indicators observed during the pandemic and the immediate recovery period should be interpreted with caution. The unprecedented nature of the shock, combined with widespread policy interventions, temporarily weakened the link between standard indicators and underlying labour market imbalances. As a result, signals derived from employment, unemployment, vacancies, hours worked and wages may not reliably reflect the presence or absence of occupational shortages in these years.
Building on previous editions of the OECD Skills for Jobs Database, the Skills for Jobs Indicator is computed in two steps: first, a composite indicator for whether each occupation is in shortage or surplus is computed based on six labour market signals (OECD, 2017[52]; OECD, 2022[89]). Second, the occupation shortages are converted to skill shortages using the O*NET skills taxonomy. The methodology is designed to draw on internationally comparable data and use a harmonised method, ensuring consistency in the cross-country measurement of occupational and skill imbalances for 38 countries. It has been modified slightly since the previous edition by including an additional signal, OJP, and adjusting the method used to translate occupation shortages to skill shortages. This section describes the methodology used to construct the Skills for Jobs Indicator.
2.2.1. Step 1: Computing occupational imbalances
The occupational imbalance indicator is a composite indicator that ranks occupations according to the extent to which they are in shortage or surplus within each country. It is based on six signals of labour market pressure: hourly wage growth, growth in hours worked, employment growth, the unemployment rate, the annual change in the underqualification rate and growth in the number of OJP. Since the unemployment rate and labour shortages are negatively related, i.e. low unemployment rate is associated with shortages, the inverse unemployment rate is used in the indicator (definitions of the variables underlying each signal are provided in Box 2.3) Using the inverse unemployment rate allows all labour market signals to enter positively into the Skills for Jobs Indicator. The selection of these signals is grounded in the theoretical and empirical evidence presented in Section 2.1.
The inclusion of OJP growth is a recent refinement of the indicator. From a theoretical perspective, OJP are a good proxy for labour demand and a sharp increase in the number of OJP may signal an increase in labour market pressures. Their use has previously been constrained by limited availability of cross-country comparable data and sufficiently long time series. However, recent advances in data collection and processing have improved the coverage and reliability of OJP data, enabling OJP to be included in the indicator.
Main data sources
To compute the indicator, data on each signal is needed. For all the signals, except for the number of OJP, national labour force surveys compiled by official statistical agencies are predominantly used. These surveys are designed to produce official national statistics on employment and unemployment, ensuring representativeness of the labour force, and often adhere to the same definitions of key indicators, enabling cross-country comparability. Where such data are not available, comparable information is obtained from alternative representative national surveys. Annex Table A.1 provides an overview of the data sources for each country.
Lightcast data is used to compute the number of OJP by occupation. Lightcast data is gathered and formatted by the company Lightcast. They use web scraping algorithms to gather information on OJP, including the occupation, industry and skill requirements, in 165 countries. The coverage of Lightcast data is continuously under development and in recent years they have added numerous new countries, such as Korea. However, this implies that for some countries, the time period is too short to compute the OJP sub-indicator. In such cases, the OJP sub-indicator is excluded (Annex Table A.1 provides an overview of whether the OJP sub-indicator is excluded for each country). For Korea, a national data source was available and used instead.1 For the majority of countries, Lightcast data is available from 2018, which implies that the vacancy sub-indicator is only included for the most recent years for these countries. This implies that the indicators before and after the inclusion of the vacancy component are not directly comparable.
The six signals of labour market pressure are defined as follows:
Hourly wage is imputed from individuals’ gross income from their main job, including all monetary compensation, such as bonuses, overtime pay, etc., divided by the hours usually worked. The median is calculated for each occupation.
Hours worked are based on the hours usually worked by individuals in their primary job. The mean is calculated for each occupation.
Number of employed is the number of people employed in the occupation as their primary job. It follows the ILO’s definition of employed, which includes all people within the working age who performed work in the reference period or people who were employed in the reference period but did not work, e.g. because they were on sick leave or maternity leave.
Inverse unemployment rate of each occupation is calculated based on the following formula:
The occupation for the unemployed is based on unemployed people’s previous occupation.
Underqualification rate is the share of employed people with their highest attained education level below the modal education level of all employed individuals in the same occupation. The modal education level is calculated at a more granular occupation level to account for differences between occupations within broader occupation groups. The education level is based on the International Standard Classification of Education (ISCED), and the ISCED categories were combined into five categories: primary education or less, lower secondary education, upper-secondary education, post-secondary non-tertiary education and tertiary education.
Number of OJP is the total number of OJP for each occupation.
As with the OJP data, data is not available for all sub-indicators in all countries. This is due to various reasons, e.g. data confidentiality constraints or the absence of sufficiently reliable data sources. In such cases, the affected sub-indicators are excluded from the calculation. However, inclusion in the OECD Skills for Jobs Database requires that the wage, employment and usual hours worked sub-indicator can be computed at a minimum. Annex Table A.1 provides an overview of the sub-indicators included in the indicator for each country.
Data breaks
While national surveys, such as labour force surveys, are designed to ensure comparability between waves, methodological changes are sometimes introduced. For instance, during the COVID‑19 pandemic, some surveys shifted from in-person to telephone‑based data collection. Such changes can result in significant data breaks, complicating the interpretation of trends as observed variations in sub-indicators may reflect both changes in labour market conditions and survey methodology. To mitigate the influence of such discontinuities, each sub-indicator underwent visual inspection for inconsistencies at the occupation-level and breaks were adjusted backwards where necessary. The post-break time series were backward-adjusted using the country-level growth rate in the break year and the growth rates of the pre‑break series for earlier years. Annex A provides an overview of the corrected breaks.
Occupation codes
The occupational imbalance indicator is computed at the two‑digit ISCO‑08 level. ISCO is a classification of occupations managed by the ILO and designed to facilitate international communication on occupations. It classifies occupations from the one‑digit level to the four‑digit level. For the Skills for Jobs Indicator, there is a trade‑off between using a very-detailed ISCO level and ensuring that the sample size underlying each occupation is sufficient to produce robust statistics. To produce detailed information while preserving a sufficient sample size, the two‑digit ISCO level was chosen.2 If sample sizes at the two‑digit level are still insufficient, occupations are merged with closely related occupations (see Annex Table A.1 for an overview of merged occupations by country). For comparability in the report, a common set of merged occupation groups is used for all countries, while the dashboard displays individual occupation indicators whenever these are available.3
Not all countries use the ISCO classification for official statistics but instead rely on national classification systems. For these countries, crosswalks are used to map national occupation codes to ISCO. These mappings are rarely one‑to‑one, as a single national occupation may correspond to multiple ISCO occupations and vice versa. When one occupation is matched with multiple ISCO occupations, employment counts are apportioned evenly between the corresponding ISCO categories. Employment is then used as a weighting factor in the calculation of the sub-indicators. Lightcast also uses its own occupational taxonomy; however, for most countries, the data are also available using ISCO classification. Where this is not the case, crosswalks are used to convert occupation codes to the ISCO classification.
Computing the composite indicator
Each sub-indicator of the composite indicator is constructed in two steps. First, the underlying trend in each labour market signal is estimated at the occupational level to account for cyclical fluctuations. Second, the trends in the individual signals are compared with economy-wide developments to assess whether labour demand is growing or declining relative to the rest of the economy.
Accounting for cyclical fluctuations is important because the sub-indicators used to capture occupational imbalances respond not only to changes in labour demand and supply, but also to broader macroeconomic dynamics associated with economic expansions and contractions. As a result, changes in the signals may reflect cyclical effects rather than imbalances between the supply of and demand for labour. To alleviate this issue, the underlying trend is separated from cyclical fluctuations by applying the Hodrick-Prescott (HP) filter to each signal. Growth rates for wages, hours worked, number of employed and OJP are based on the trend component extracted using the HP filter. The same is the case for the annual change in the underqualification rate.
The aggregation of the sub-indicators into a composite indicator requires that they are expressed on a common scale. To this end, each sub-indicator is standardised within country and year by subtracting the country-year-mean and dividing by the standard deviation. This implies that the performance of each occupation is evaluated relative to all other occupations within the same country and year.
Given that no single sub-indicator perfectly signals occupational imbalances, they are combined into a composite indicator. The composite indicator is constructed as a weighted average of the sub-indicators, with all sub-indicators entering positively. Equal weights are assigned to all sub-indicators, except for employment and OJP growth, which are assigned half-weights. Employment growth is downweighted because it is an ambiguous indicator, while OJP growth is downweighted because of limitations of OJPs in measuring labour demand (see Section 2.1).
The resulting indicator provides a relative measure of occupational imbalances within each country. Positive values indicate labour shortages, meaning that workers are harder to find than for other occupations, while negative values indicate labour surpluses, meaning that workers are easier to find than for other occupations. Consequently, cross-country differences reflect variation in the relative position of occupations within national labour markets rather than absolute levels of shortages. Occupations are grouped into five categories ranging from “large surplus” to “large shortage” based on the occupational imbalance indicator.
The occupational imbalance indicator is shaped by the underlying data and methodology. Consequently, its results may differ from country-specific analyses and other indicators that rely on alternative approaches (Skills England, n.d.[146]; McGuinness et al., 2025[88]). This is generally the case for objective indicators, as results depend on the methodological choices made. In the context of occupational imbalances, differences may arise from the selection of labour market signals and the aggregation methodology. Furthermore, the Skills for Jobs Indicator captures relative imbalances by assessing the performance of occupations compared with others within the same country and year, while other indicators may measure absolute imbalances by comparing labour supply and demand directly.
2.2.2. Step 2: Translating occupation shortages into skill shortages
To provide an indicator of skill shortages, occupation-level shortages are translated to skill shortages using occupation profiles produced by O*NET. O*NET provides a comprehensive mapping between all occupations and knowledge, abilities and skills for the United States and is widely regarded as one of the most comprehensive and methodologically sound sources on skill use in employment (Beblavy et al., 2016[147]). Importantly, each occupation-skill pair is assigned a level of the skill required for the occupation and an importance score indicating the significance of the skill for the occupation (see Box 2.4 for more detail), which enables the computation of weights for each pair. Further, the occupation profiles are regularly updated to capture emerging skills and evolving job requirements.
The O*NET database provides a detailed and standardised overview of occupation profiles, including detailed information on worker attributes and job characteristics. Developed by the United States Bureau of Labor Statistics, O*NET is designed to capture occupation profiles in the United States’ labour market. Portions of every profile are updated annually using multi-method data collection, including worker surveys and occupational experts. Its breadth, level of detail and relevance have made it a used resource for labour market analysis beyond the United States.
For the Skills for Jobs Indicator a subset of the dimensions included in the occupation profiles are used. These include:
1. Knowledge, referring to information and understanding used across different areas of work (e.g. engineering and technology, mathematics or management);
2. Skills, capturing developed capacities that facilitate learning and task performance, including both essential skills (e.g. active listening or writing) and transversal skills (e.g. negotiation or programming);
3. Abilities, capturing natural talents and capabilities that affect how well someone can perform their work (e.g. originality or manual dexterity).
Each knowledge, skill and ability dimension is assessed in O*NET using two metrics: importance and level. Importance reflects how essential a given dimension is for job performance (measured on a scale from 1 “not important” to 5 “extremely important”) and level captures the degree of proficiency required (measured on a scale from 0 to 6).
Source: www.onetcenter.org/.
While O*NET is designed to reflect occupation profiles in the United States, it has been extensively used for analysis of other countries than the United States (Goos and Manning, 2007[65]; Aedo and Walker, 2012[148]). Nevertheless, important caveats apply as differences may exist between occupation profiles in the United States and those in other countries included in the OECD Skills for Jobs Database. In particular, O*NET occupation profiles may be less relevant for low-income countries, where technological adoption, institutional frameworks and job design can differ substantially from those in the United States. Potential measurement biases should also be acknowledged. For instance, the composition of the O*NET survey respondents may be skewed towards more highly educated individuals, possibly resulting in upward-biased estimates of skill requirements (Handel, 2016[149]), although this cannot be directly verified due to limited information on respondents.
The skill shortage indicator builds on the occupational imbalance indicator multiplied by the normalised level and importance of the skill for each occupation. This assigns greater weights to skills that are both highly important and required at a high level. To avoid occupation shortages and surpluses cancelling each other out for the skill indicator and acknowledge that skills may not be readily transferable between occupations, it only focusses on occupations in shortages. This approach implies that only occupations in shortage feed into the measure of skill shortages. When aggregating the results to the skill level, occupation shortages are weighted by their employment shares, ensuring consistency with the relative size of occupations in the labour market.
← 1. Since Lightcast data is only available from 2025 for Korea, information on job openings from the Occupational Labor Force Survey at Establishments is used instead. It covers all industries, except agriculture, forestry and fishing, and asks employers about the number of vacancies for which the establishment is actively recruiting.
← 2. ISCO category Subsistence Farmers, Fishers, Hunters and Gatherers (63) is excluded from the indicator, as subsistence producers are not classified as employed under the ILO definition, which limits employment to work performed for pay or profit.
← 3. The OECD Skills for Jobs Dashboard is available here: www.oecd.org/en/data/dashboards/skills-for-jobs.html.