Cutting through the BS

Adaptability and flexibility, digital skills, creativity and innovation, emotional intelligence… Since the pandemic went global, everyone has been talking about the top post-COVID skills employees will need. Going through numerous posts from Forbes over Randstad to EURES, it seems that the key point they have in common is that they are untransparent if not completely unfounded. Despite all the noise they generate, none of these posts give any insight into what data their claims are based on – or whether they have any data at all. Here at JANZZ, we have been analyzing over 500,000 job postings from the last few months for a project in Australia and New Zealand. In this data, just as in any of our other data from similar projects in completely different markets and regions of the world, there is no indication of increased demand for creativity and innovation or for digital skills – which, by the way, should not include the ability to participate in a video call, just as Excel usage does not turn an economist into a STEM profession. The skills that were most in demand across all professions from waiters to senior policy officers were in fact ambition, self-motivation, and ability to work under pressure, independently and in fast changing environments.

But it is not just about skills analysis. When it comes to… well, anything related to jobs and skills, there is an unbelievable amount of BS out there. Here are just a few examples.

Future jobs. According to the WEF’s Future of Jobs Survey 2020, among the top 20 job roles in increasing and decreasing demand across industries, Mechanics and Machinery Repairers are listed as both increasing (#18) and decreasing (#9). The same is true for Business services and administration managers (up #12, down #6). This apparent contradiction is simply stated with no explanation in the text. And yet, this information is just reproduced blindly in numerous blogs and posts. [1]

LinkedIn skills reports. The same is true for all the buzz generated by LinkedIn reports on in-demand skills. Countless articles and posts just reproduce these lists, all completely disregarding the fact that they are based on the data captured in LinkedIn profiles [2], which is strongly biased. For instance, blue-collar professions and industries are massively underrepresented in their data. By contrast, according to the ManpowerGroup Talent Shortage surveys, for 7 consecutive years, skilled trades have been hardest to fill, globally and nationally in almost all countries, along with drivers (especially truck/heavy goods, delivery/courier and construction drivers), manufacturers (production & machine operators), construction laborers and healthcare professionals on this year’s list. Shouldn’t the skills associated with these professions be in higher demand than blockchain or cloud computing?

Skills demand. A Canadian institute created a report based on data and skills taxonomies from a large labor market analytics provider. They introduce the report with the statement “Telling Canadians they need digital skills is not enough; we must be specific.” The report then goes on to identify the top 10 digital skills by number of job postings. Among the top 10 skills are Microsoft Excel and Spreadsheets. There is nothing specific about these “skills”. First off, the term “Microsoft Excel” says absolutely nothing about the skills that are actually needed. Is the candidate expected to just be able to open the application and enter data? Or should they be capable of creating formulas? How complex are the formulas supposed to be? What about charts? Also, what exactly is the difference between the two skills Microsoft Excel and Spreadsheets?

Upskilling. Within a business, upskilling can be very useful. An individual company is fairly fixed in its position and should have a clear strategy which will also largely determine the skill needs of the company and thus, the upskilling strategies. However, developing sustainable upskilling strategies as part of an active labor market policy (ALMP) is a very different challenge. Contrary to what many posts and tech providers say, just upskilling all the unemployed will not lower unemployment numbers sustainably and does not necessarily meet market demands. For instance, in a country where a lot of low-skilled work is on offer, upskilling a jobseeker who is already overqualified will not be of any use. Or if the training offers are of poor quality or not aligned to market needs. This is the reality in many countries.

Job matching. A Dutch tech provider for Employment Services claims that its software solutions can help PES “reduce unemployment figures”. As if that could happen by just using the right job matching tool. For instance, this article (in Italian) in Italy’s renowned newspaper Corriere della Sera illustrates just a few of the issues that need to be resolved before, or at least while, implementing a software solutions for the PES: there are currently 730K job vacancies in Italy, compared with 2.5M active jobseekers plus 13.5M inactive and discouraged. The skills of jobseekers in Italy are not aligned with labor market demand. Training, particularly for the unemployed, is inadequate, of poor quality and disconnected from market needs. PES have insufficient and not adequately trained staff. Italy invests extremely little in ALMPs. They have made plans to increase this budget but have no strategy on how to spend the additional funds. A change of direction would require a vision that does not expire after the next elections, which is an extremely high ask given Italy’s political landscape and history. And yet, the Dutch tech provider still argues that their job portal solutions will make the crucial difference.

Most of what is out there is basically gut feelings and creative marketing. So how about cutting through the BS and finding our way back to an honest, fact-based discussion? Well, to do that, we need to find out what the facts are. But for that, we first need to agree on the basics (for instance, define what constitutes a skill) and then generate reliable data based on these definitions. More about this in the next post…


[1] If you are interested in learning more about the issues surrounding predictive analyses based on occupational data (e.g. skills anticipation), read our CEOs talk featured in a recent ILO report.

[2] According to LinkedIn: The most in-demand skills were determined by looking at skills that are in high demand relative to their supply. Demand is measured by identifying the skills listed on the LinkedIn profiles of people who are getting hired at the highest rates.