One of the unexpected consequences of the COVID-19 pandemic has been its impact on labor markets. As they shut down, reopened, quarantined, and vaccinated, governments around the world found that the pandemic’s impact on the labor market was outstripping the ability to track it using traditional tools. And governments can’t respond to problems they can’t see.
Governments around the Asia-Pacific region have been turning to real-time labor market data to keep up with this rapid change. In a new report for Asia Pacific Economic Cooperation (APEC), Emsi Burning Glass has examined the use of real-time job postings data around the Pacific Rim by policymakers. The report finds that big data can augment traditional sources of data and are especially useful in times of economic shock – something that the global economy continues to experience as it faces labor market shortages, supply chain crises, and additional pandemic waves.
The report looks at traditional sources of labor market information across all APEC economies, such as labor market surveys that are conducted annually, and compares the data available to a range of real-time sources. These include:
Online job ads and employee work history data, such as online CVs, which shows employer demand for skills and jobs and workers’ supply of those skills
Human capital management data sourced from payroll and HR systems
Online learning platform data demonstrating skills that workers are learning
Online gig economy data that shows where gig workers work, who takes jobs, and the types of jobs workers will accept for pay rates
Knowledge sharing and communication platform data, such as information sourced from Stack Overflow or Slack.
While big data doesn’t boast as long a time horizon as traditional sources that have often been collected for five or more decades, real-time data gives policymakers access to data that is more granular and can capture emerging skills, such as changes in technology, much more quickly.
Policymakers across many APEC economies have already taken advantage of this to help bolster their understanding of the labor market and create timely, targeted policy around a number of issues. Some key examples covered in the report include:
The National Skills Commission of Australia uses big labor market data in their Jobs and Education Data Infrastructure (JEDI) tool. JEDI provides real-time data on the Australian labor market, helping policymakers to navigate the changing economy by providing data on the labor market, workforce changes, and current and emerging skills needs.
In New Zealand, Tokona Te Raki is using big labor market data to drive longer-term systemic change to boost Māori success and tackle inequality. They aim to produce an understanding of current labor market data to support better employment outcomes for Māori and inform the business case for further investment in tools to enable future indigenous workforce development.
Singapore Smart Nation and Digital Government Office (SNDGO) worked with LinkedIn Economic Graph as part of Singapore’s AI Strategy. The goal of the collaboration was to explore where the necessary AI talent works and what skills they have across regions. Big labor market data enabled understanding of emerging skills and trends that is otherwise not possible with traditional data. In addition, Infocomm Media Development Authority in Singapore has been using Emsi Burning Glass data since 2017 for early-stage analysis to help glean insights of local talent demand to facilitate making policy decisions to help career counselors provide guidance around Media and ICT roles.
The World Bank and the government of Malaysia released a report on Malaysia’s skill shortages and critical occupations. They use postings data to learn the skill and experience requirements of high-demand occupations and help create their list of critical occupations. This list is then used to align workforce development policies with employer demands.
Indonesia also created a critical occupation list to highlight shortages and potentially strategic investment areas. Additionally, they have an Online Skills and Vacancy Outlook initiative which collects online job postings by occupation. These initiatives work to analyze skill imbalances and help policymakers to make investments in training programs and adjust incentives.
For governments and policymakers looking to use big data to expand their labor market understanding, the report recommends three immediate steps:
Understand and assess the full scale of costs. In any of the partnership methods described above, there are up-front costs as well as dynamic costs. It is important to consider the costs of initial creation and set-up, people and analytical resources, and maintenance of taxonomical and data updates.
Begin with a small-scale pilot project. This pilot project should aim to solve a very specific problem, like determining top skills required for each occupation in the economy. Initially, the government may face some hesitation from stakeholders exclusively used to traditional data. It is important that the first project starts small to gain trust in the data.
Once trust is gained, start a larger project. After the initial data scoping to ensure big labor market data can be helpful and used to solve a problem, the government can consider larger scale projects. Further projects can include work like the examples described earlier, including identifying labor shortages, emerging skills, or growing occupations.
For more details, download the complete Big Data for the Labor Market report.