Changes to jobs and skills are set to have large-scale effects on companies, government and individuals across the global community. What does the future hold? How can you find the right talent to ensure growth? How can you make informed and socially conscious decisions when faced with major disruptions to jobs and skills?

The analysis that forms the basis of this report is the result of an extensive survey of Chief Human Resources and Chief Executive Officers of leading global employers which aims to give specificity to these discussions. The survey aims to capture executives’ current planning and projections related to jobs and skills in the period leading up to 2022.

Figure A1: Future of Jobs Survey 2018 framework

Survey Design

There are three core concepts that are key to the construction of the Future of Jobs Survey: job roles, tasks and skills. Task are defined as the actions necessary to turn a set of inputs into valuable outputs. As such, tasks can be considered to form the content of jobs. Skills, on the other hand, are defined as the capabilities that are needed to complete a task. In essence, tasks are what needs to be done and skills define the capacity to do them.

The original Future of Jobs Survey employed to produce the first Future of Jobs Report, in 2016, was informed by an extensive literature review on the various dimensions covered by the survey, and by continuous consultation with leading experts from academia, international organizations, business and civil society through the World Economic Forum’s Global Agenda Council on the Future of Jobs and Global Agenda Council on Gender Parity, which served as partners and advisory bodies to the study. This second edition of the survey adjusted that approach on the basis of lessons learned from that first endeavour.

The updated 2018 survey now consists of three interrelated parts. Part I maps the trends that are set to positively and negatively impact business growth, the technologies that are likely to play a part in that expansion, the rationale and barriers related to this technology expansion, employers’ preferred ecosystem for support, and the workforce shifts that will be needed to effect those changes. Part II maps three interlocking pillars of the labour market—occupations, skills and tasks—and provides employers with an opportunity to share the jobs that are set to experience stable, declining and rising demand. Part II also asks employers to estimate the current and future composition of their workforce, and the division of labour between humans, machines and algorithms. Part III gives survey respondents an opportunity to share their current plans for the period up to 2022 as they pertain to closing key skills gaps in their enterprises. In particular, the survey asks employers to rate the likelihood of employing a variety of strategies aimed at ensuring their businesses have the right talent to grow, to give specificity to the scale of their future reskilling needs, and to share a range of detailed information about their current and future reskilling provision.

Representativeness

The survey collection process was conducted via an online questionnaire, with data collection spanning a nine-month period from November 2017 to July 2018. The survey set out to represent the current strategies, projections and estimates of global business, with a focus on large multinational companies and more localized companies which are of significance due to their employee or revenue size. As such there are two areas of the future of jobs that remain out of scope for this report—namely, the future of jobs as it relates to the activities of small and medium-sized enterprises and as it relates to the informal sectors of, in particular, developing economies.

The Future of Jobs Survey was distributed to relevant companies through extensive collaboration between the World Economic Forum and its constituents, amplified by regional survey partners. The survey is also the result of extensive cross-departmental coordination within the World Economic Forum during which the Forum’s Business Engagement Team, Centre for Global Industries and Centre for Regional and Geopolitical Affairs supported the report team’s efforts to sub-select relevant samples. For key partners in the survey distribution process, please refer to the Survey Partners and Acknowledgements sections.

Detailed sample design specifications were shared with survey partners, requesting that the sample of companies targeted for participation in the survey should be drawn from a cross-section of leading companies that make up a country or region’s economy, and should include—although not necessarily be limited to—national and multinational companies that are among the country’s top 100 employers (either by number of employees or by revenue size). In cases where we worked with a regional partner organization we requested additional focus on strong representation from key sectors represented in that geography. To ensure that the survey was representative of the relevant population, the report team conducted additional analysis, confirming the number of responses as well as the size of each respondent’s revenue and employee pool.

The final sub-selection of countries with data of sufficient quality to be featured in the report was based on the overall number of responses from companies with a presence in each country—and within that subset, was based on the number of companies headquartered in the relevant location and the diversity of the sample in relation to the companies’ number of locations. In particular, the aim was to arrive at a sample in which more than two-fifths of the companies were large multinational firms, and a reasonable range of companies maintained a focused local or regional presence. The final sub-selection of industries included was based on the overall number of responses by industry, in addition to a qualitative review of the pool of named companies represented in the survey data.

After relevant criteria were applied, the sample was found to be composed of 12 industry clusters and 20 economies. Industry clusters include Aviation, Travel & Tourism; Chemistry, Advanced Materials & Biotechnology; Consumer; Energy; Financial Services & Investors; Global Health & Healthcare; Information & Communication Technologies; Infrastructure; Mining & Metals; Mobility; Oil & Gas; and Professional Services. Economies include Argentina, Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Mexico, Philippines, Russian Federation, Singapore, South Africa, Republic of Korea, Switzerland, Thailand, United Kingdom, United States and Vietnam—collectively representing about 70% of global GDP. In total, the report’s data set contains 313 unique responses by global companies, collectively representing more than 15 million employees (see Table 1 in Part 1).

Classification Frameworks for Jobs and Skills

Similar to the initial report, this year’s report employed the Occupational Information Network (O*NET) framework for its categories of analysis for jobs, skills and tasks. O*NET was developed by the US Department of Labor in collaboration with its Bureau of Labor Statistics’ Standard Classification of Occupations (SOC) and remains the most extensive and respected classification of its kind. In its unabridged form, the O*NET-SOC taxonomy includes detailed information on 974 individual occupations in the United States, grouped into approximately 20 broader job families, which are regularly revised and updated for new and emerging occupations to keep up with the changing occupational landscape.

For this edition of the report, the Generalized Work Activities segment of the O*NET methodology was used to form the list of tasks used in the survey. In addition, for the classification of skills, the report team employed an abridged version of the “Worker Characteristics” and Worker Requirement classifications; in particular, bundles 1.A., 1.C., 2.A., and 2.B. Additional details about the composition of the skills list used in this report can be found in Table A1.

Table A1: Classification of skills used, based on O*NET content model