Skill requirements are constantly changing. Explore what skills workers were most likely to add for each industry in the last year below.
The Industry Skills Needs metric captures which skills are most likely to be added to a member's profile in one industry compared to other industries. It's calculated using an adapted version of a text mining technique called Term Frequency - Inverse Document Frequency (TF-IDF). This method gives more weight to a skill for an industry if more members in the industry list the skill on their profiles and the skill is more unique to the industry. The skills included are those added while a member holds a particular occupation (the skill flow approach). While the skill flow approach creates a trade-off whereby long-held basic skills, such as Microsoft Office being given a lesser weight, the approach is shown to be stronger at identifying the latest emerging skills in a specific industry than including all historical skills that are added during prior occupations. On balance, since the objective of this metric is to detect the latest skills needs, a skill flow approach is adopted.
The digital economy requires a balance in skills that are fundamental and transferrable across occupations and industries, and those that are specialized. Explore how skills are spreading across industries globally below.
The Skill Penetration metric looks at how many skills from each of LinkedIn's
skill groups* appear among the top 30 skills for each occupation in an
industry. For example, if 3 of 30 skills for Data Scientists in the Information
Services industry fall into the Artificial Intelligence skill group, Artificial
Intelligence has a 10% penetration for Data Scientists in Information Services.
These penetration rates are averaged across occupations to derive the industry
averages reported above. It is likely this metric is best at capturing skill
penetration across tradable and knowledge-intensive sectors. For example, it may
under-estimate the adoption of AI in Manufacturing, since LinkedIn members are less
likely to be in this sector compared to others.
* The 50,000 skills in LinkedIn's taxonomy are categorized into 249 skill groups
The traditional development model may not always be relevant, and countries may be able to benefit from the digital economy sooner than they think. Explore what's emerging in your country below.
The Growth from Industry Transitions metric looks at how LinkedIn members are moving across industries (based on net transition: in minus out). For example, if a net of 50 members joined a Biotech industry with 1,000 members in a particular country after leaving jobs in other industries, that Biotech industry would have grown by 5% due to transitions in that year. We calculate these rates across all industries on an annual basis, and report an average of the last three years. The metric above is built entirely on a sample of LinkedIn members that have a company registered on LinkedIn on their profile. Since white-collar workers in knowledge-intensive services sectors are more likely to be on LinkedIn, the growth rate above may not accurately represent sectors like manufacturing and mining that tend to have more blue-collar workers.
Skills training isn't enough if developing countries can't retain their talent and leverage diaspora networks. Explore who your country competes against, and what skills and industries are at play below.
|Loss of 0-10||Loss of 10-20||Loss of 20+||Gain of 0-10||Gain of 10-20||Gain of 20+|
* Countries Gaining From / Losing To - The net gain or loss of members from another country divided by the average LinkedIn membership of the target (or selected) country during the time period, multiplied by 10,000.
† Industries Gaining / Losing - The net gain or loss of members from another country working in a given industry divided by the number of LinkedIn members working in that industry in the target (or selected) country, multiplied by 10,000.
‡ Skills Being Gained / Lost - The net gain or loss of members from another country with a given skill divided by the number of LinkedIn members with that skill in the target (or selected) country, multiplied by 10,000.
All the metrics above are based on net migration (arrivals minus departures). These net migration figures are normalized by LinkedIn member count in a given country to enable fairer comparisons across samples. For example, considering Canada as the country of interest, net flows of migrants to/from the US in absolute terms are normalized by total LinkedIn membership count in Canada. Similar method is used for calculating the skill and industry gains/loss associated with bilateral migrtion flows. We calculate all on an annual basis, and report an average of the last three years. To protect member privacy, LinkedIn only shows aggregated data with at least 50 observations. Blanks above are generally due to bilateral flows not crossing this threshold, or countries only having net gains or losses on LinkedIn. Since LinkedIn data has better coverage of white-collar workers in knowledge-intensive services sectors, these migration flows are unlikely representative of the entire migration landscape of a country, but can shed light on some of the most dynamic and innovative sectors.