Where is the next generation of craftsmen in Switzerland? (And elsewhere?)

According to the latest Nahtstellenbarometers – Education Decisions after Compulsory Schooling, published by Innovation SBFI and State Secretariat for Education, Research and Innovation (SERI), the most desired professions for vocational training in Switzerland are commercial employees, followed by healthcare assistants and information technologists. This means that none of the professions in handicraft are among the top 5. Compared to boys, there are 15% less girls aspiring to follow the vocational training, and not one single girl reports to be interested in choosing an apprenticeship as electrician, as a car mechanic or as a polytechnical technician. [1]

There is currently a deficit of 42,778 handicraftsmen in Switzerland. This figure was published by a job search website that also pointed out that craftsmen jobs form one of the most advertised job category in years: currently, there is a total number of 198,097 vacancies advertised. Companies in construction, and building and dwelling services are struggling to hire skilled workers. Stefan Danev, Managing Director of an electrical and safety engineering company in Winterthur, ZH, says that “finding personnel to fill our vacancies in the long term is not easy under the current market situation”. [2]

 Growing interest in the academic route

What causes this shortage of craftsmen in Switzerland? The increasing popularity of the academic route is mentioned as a contributing factor for this development. Between the ages of 12 and 14, Swiss adolescents have to decide on going to pre-university baccalaureate schools or searching for apprenticeships offered by companies (this is known as the dual-track system).

Having a long tradition, the dual-track system was well received by parents, by adolescents and by society at large, as it has largely contributed to a very low youth unemployment rate and high levels of workers’ competency and of quality in all kinds of skilled trades in Switzerland.

However, a growing number of parents hold the perception that the vocational system has a lower quality and that students who enter vocational schools are less capable or have lower aspirations compared to students with four-year university degrees. This is especially the case among parents with migration background. International companies, too, add pressure by not recognizing graduates from vocational training as equal to graduates with a bachelor’s degree. This, in turn, forces a lot of young people to hesitate about pursuing vocational training.

Recently, there has been an increase in adolescents choosing the baccalaureate track, especially in the French and Italian parts of Switzerland. This poses a direct challenge for some of the professions in skilled trades. SERI noted in a statement that “across Switzerland, the baccalaureate has gained more interest and the desire for a general education is stronger than last year” [3].

Misconceptions in skilled trades

When looking at the media, one could think that the only prosperous professions in the future are computer-science-related, and that most skilled-trade jobs—especially handicraft—will be replaced by automation and robots. But under certain circumstances, machines do not yet have enough dexterity and fine motor skills to compete with human hands. For example, to grind bearings that has accuracy within a 100th of millimeter needs years of practice and only experienced skilled craftsman can do that and sometimes other than experience, it also required intuition which machines will never have.

One tends to have the misconception of skilled trades as providing little chance of climbing up the career ladder, and, hence, of offering a small salary. But the reality is that there are many top executives in big companies, as well as well-known government figures in Switzerland who have worked their way up from apprenticeship. Highly qualified craftsmen are particularly in demand today and their salary is likely to increase in the near future.

Moreover, the job provides the opportunity to become an entrepreneur. Unfortunately, there are many people in the skilled trades business who intend to pass on their very successful and lucrative business but cannot find a willing successor. Apart from the fact that few young people are trained in such business, it is also reported in various surveys that Generation Y/Millennials (born between 1980-1995) are seeking more security and flexibility in work and prefer having a good, stable salary in an international company, instead of taking on the responsibility of entrepreneurship.

Are young people getting too lazy?

In addition to Switzerland, many other countries like Germany, Belgium, Austria and the United States are facing similar shortages of craftsmen. It is an undeniable fact that the nature of many skilled trades jobs is hard, and that there is a general trend among parents who were in those professions to push their children towards more comfortable jobs. For example, in an apprenticeship at a bakery, one has to start the job at 2am, a tough job that many are unwilling to take up nowadays.

One hardly hears about a child saying that his/her dream job is to become a boilermaker, a metal worker or a welder, because few of them have the opportunity to get introduced to such professions at an early age. With no understanding of or even misconceptions about such jobs, more and more young people are likely to be discouraged from starting an apprenticeship in such professions.

Christoph Thomann, president of the Central Board of Vocational Education and Training Switzerland, believes that the skilled trades are outshined by an increased focus on computers and robots among young people [1]. However, digitalization should not only be regarded as a barrier from choosing vocational training. In fact, vocational training should embrace digitalization and promote an image of the digitalized craftsman, a modern and less traditionally masculine image that attracts more young people, even young girls, to the field.

A collaborative effort

As in the other economic sectors, digitalization has also made its way into skilled trades and is having a profound impact by increasing the productivity and reducing business costs. For example, by using data analysis, roofers can now measure a house with a 3D scanner to order the exact number of roof tiles needed.

Building information modeling (BIM) helps to realize the integration of building information, which ranges from the design, construction and operation of the building to the end of the whole life cycle of the project. A lot of different information is integrated into a three-dimensional model information database for the stakeholders. There are of course many more examples, including virtual reality, drones, 3D mapping and 3D printing.

Despite great efforts in raising awareness of the importance of the professions in skilled trades communities, reality is disappointing. Matthias Engel from the Swiss Building Association appeals to the federal government in Bern by saying: “It is important that politicians do not send the wrong signals. It is important that the government not only promotes secondary and university education, but also vocational training”.

Indeed, a greater effort to actively promote vocational training, diminishing the gap between young men and women and encouraging more young women to take apprenticeships in certain professions ought to be made by the government. Furthermore, it needs to be considered to bring in young people from other countries than Switzerland, if necessary.

What can AI and big data do for contractors, headhunting firms specializing in skilled trades jobs and government agencies planning to promote vocational training?  How can you use digital platforms to hire the people with the skills you need? Write now to sales@janzz.technology and let JANZZ.technology help you with our intelligent data.

 

[1] gfs.bern. 2019. Nahtstellenbarometer 2019. URL: https://cockpit.gfsbern.ch/de/cockpit/nahtstellenbarometer-2019/ [2019.07.25]

[2] Ulrich Rotzinger and Julia Fritsche. 2019. In der Schweiz fehlen 42’778 Handwerker. URL: https://www.blick.ch/news/wirtschaft/in-der-schweiz-fehlen-42778-handwerker-schreiner-sanitaere-und-elektroinstallateure-verzweifelt-gesucht-id15399544.html?fbclid=IwAR0TU2tUpeSmljN23gLwK5S09DOpvURnFdNqBNoR6nRSfLo_Z3ChojKrVYE [2019.07.25]

[3] Isobel Leybold-Johnson. 2019. What careers did Switzerland’s students choose this year? URL: https://www.swissinfo.ch/eng/continuing-education_what-careers-did-switzerland-s-students-choose-this-year-/45035674 [2019.07.25]

Middle-skilled workers to be hit hardest by digitization

While low-skilled workers are going to suffer the most from the consequences of digital transformation long-term (with some exceptions), opportunities for middle-skilled jobs are shrinking the most, according to recent observations in OECD countries.

We used to talk about digitalization and automatization only as processes that will change our working environment in the future, for example through the replacement of humans by robots. Meanwhile, the situation has changed: many of us already feel the effects of digitization and automation. These effects are likely to be amplified even further in future.

Due to the energy revolution that digitalization induced, many companies in the energy business, blindsided by the speed of this revolution, are faced with overcapacity. In Switzerland, General Electric (GE) has just announced a major workforce reduction, which causes 450 employees in Baden and Birr to lose their job. In order to compete with international online providers Migros, one of Switzerland’s biggest retail companies, too, undergoes such transformation. In June, the Genossenschaft Migros Ostschweiz released the dismissal of 90 employees in Gossau. The Organization for Economic Cooperation and Development (OECD) predicts difficult times for Swiss employees: 700,000 jobs are associated with a “high risk of automation.” [1] Moreover, this is only a small, partial reflection of the whole, global labor market.

It is difficult to account for the full impact of digitalization since it bears both positive and negative effects for the job market. Statistical evidence however indicates that digitalization affects the distribution of work, income and wages. [2] With skills like problem-solving, creative thinking and complex communication that are complementary to digitization, high-skilled workers tend to benefit the most from digitalization. As a result, we can observe an increase in high-skilled jobs in most OECD countries. Likewise, the share of low-skilled jobs grew while the share of middle-skilled jobs decreased. [3]

Why are middle-skilled workers at greatest risk to be disadvantaged under digitalization? Martin Wörter, Professor of Innovation Economics at ETH Zurich explains that “repetitive activities in the office or in industrial production can be replaced more easily by computers or robots.” Federal employment statistics for Switzerland back up this statement. Within 20 years the number of office workers decreased by 150,000 while the number of craftsmen fell by 90,000. Conversely, the number of academic professions grew by 470,000. [1]

However, it is short-sighted for companies to simply lay off unqualified workers and to replace them with employees who fit the demanded skills profile. Since skill requirements are changing faster than ever, even if companies could replace their unqualified workers today, what about tomorrow? The only way to solve this problem is to enable reskilling of the existing workforce. Further training could largely reduce redundancies and benefit the company at large. As Bruno Staffelbach, Professor of Human Resource Management and President of the University of Lucerne, says: “Company-specific know-how will become even more important in the future. However, employees can only acquire these skills on the job in their company.” [1]

Many companies have realized this and adopted effective skills development programs.  However, as we have written before on this site, workforce reskilling requires an ecosystem approach that involves individuals, companies, industries, as well as governments. Based on calculations by the World Economic Forum, in the US 45% of workers at risk could be collectively reskilled through businesses working together. If combined with governmental efforts, this number could increase to even 77%. [4]

For almost a decade, JANZZ.technology has been watching and collaborating in many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in the job market. Our latest product, Labor Market Dashboard uses real time data in order to establish important labor market indexes such as the most required skills, the most searched for positions or the female/male ratio. Should we have caught your interest and should you wish to learn more about JANZZ.technology’s offers, please write now to sales@janzz.technology

 

 

[1] Albert Steck. 2019.Digitalisierung gefährdet Jobs von Mittelqualifizierten am stärksten. URL: https://nzzas.nzz.ch/wirtschaft/digitalisierung-gefaehrdet-jobs-von-mittelqualifizierten-am-staerksten-ld.1492570#swglogin [2019.07.02]

[2] OECD. 2015. OECD skills outlook 2015: youth, skills and employability, OECD Publishing, Paris, URL: https://doi.org/10.1787/9789264234178-en [2019.07.02]

[3] OECD. 2019. OECD skills outlook 2019: thriving in a digital world, OECD Publishing, Paris, URL: https://doi.org/10.1787/df80bc12-en [2019.07.02]

[4] Borge Brende. 2019. We need a reskilling revolution. Here’s how to make it happen. URL: https://www.weforum.org/agenda/2019/04/skills-jobs-investing-in-people-inclusive-growth/ [2019.07.02]

Occupational classification systems in the digital age

People have long been monitoring the economic activities of our society. It is said that during the Chinese Tang Dynasty (618-907) there were 36 different job types. Fittingly, the period marks the origin of the famous Chinese saying that ‘every trade has its master’ (san shi liu hang, hang hang chu zhuang yuan).

Today, jobs are changing at such a speed that it is almost impossible to give an exact number of the occupations that affect our daily life. Compiling statistical records of occupations is also becoming complicated since jobs are changing, disappearing and emerging. There used to be only ‘the’ manager, but now there is a PI manager, an IT manager, a project manager, an intergenerational engagement manager, you name it.

Thus, other than listing simply all occupations for statistical purposes, job descriptions, skill and experience requirements, education levels and more aspects are integrated, too, in occupation-related databases. That way, we can not only better understand the jobs of today but also develop more sophisticated systems that are able to perform more complex services with occupation data. For example, this enables performing the tasks of career planning, job searching, identifying trends or guiding policy design.

US-based classification systems

The United States Department of Commerce released the Standard Occupational Classification (SOC) in 1977. Back then, many programs by the US government began collecting statistics which is why the federal government needed a unified occupational classification system. SOC entails a short description and illustrative examples for each job. It is classified based on the type of work performed, but rarely on the level of skills and education needed for a specific position [1]. The latest version of SOC was published in 2018.

 The online database O*Net is an expansion of SOC and was created during the mid-1990s by the US Department of Labor’s Employment and Training Administration. O*Net can be freely accessed and downloaded by job seekers, students, businesses researchers and workforce development professionals alike. Compared to SOC, it is a much more sophisticated system with more detailed information such as tasks, technology skills, knowledge, abilities, education level and work style.

 Europe-based classification systems

The international Standard Classification of Occupations (ISCO) is maintained and managed by the International Labour Office (ILO). ISCO is the main international classification of occupation-related data and used for international exchange, reporting and comparison. It also serves countries and regions that want to either further develop their own occupational classifications or directly adapt one from ISCO-08. Examples include Ö-ISCO in Austria, Styrk-08 in Norway, COCR-2011 in Costa Rica, NOC 2016 in Canada and most national occupational classifications in Asia.

In July 2017, the European Union launched the first version of a European multilingual classification of skills, competencies, qualifications and occupations (ESCO) that is also based on ISCO-08. ESCO aims to create a common understanding of occupations, skills, knowledge and qualifications across the EU’s official 24 languages that enables employers, employees and educational institutions to better understand needs and requirements. Under freedom of movement ESCO could aid in making up for skill gaps and unemployment in the different member states, as the President of the European Commission Jean-Claude Juncker states [2].

Industry classifications

Industry classifications or industry taxonomies group companies by industry and in terms of production processes, products or job positions. They serve national and international statistical agencies for the analysis, comparison and summarization of economic conditions. Well-known industry taxonomies include NAICS, ISIC, GICS, NAF 2015 and MUPCS.

Furthermore, a shift from occupational classifications towards skills classification has been observed. This shift is linked to an attempt of improving classifications’ ability to aid in career guidance and the conduction of upskilling and reskilling. The United Kingdom and the innovation foundation Nesta have built the UK’s first data-driven skills taxonomy. It allows for measuring the country’s supply and demand of skills and for preventing skill shortages. The social media platform LinkedIn has also built a skills taxonomy for its users.

Chinese classification systems

China started to create its occupational classification in 1995. After four years, the country released its first version. Currently in use is a version from 2015 that aims to keep pace with the fast-changing employment sector. The Chinese classification has 4 digits with 1838 professions in total.

Compared to O*Net, which was created during about the same time period, there is still much room for improvement in the Chinese occupational classification. Specifically, it could be improved with regard to accessibility, continuous data updating and the provision of guidance for students and job seekers [3]. However, the problem of lacking in updated data is not unique to the Chinese occupational classification. This issue is shared with many other classifications, including O*Net.

A new concept for occupational classification systems

The creation of a traditional expert consultation taxonomy is time-consuming, costly and, most importantly, will lack the ability to continuously adapt to the world’s fast-changing working environment.  Therefore, a new solution is needed. One that can inform the labor market constantly and make job seekers, students, education providers, employers and policy-makers alert for change and empowered to react.

With digitalization, a data-based information collection methodology can revolutionize the way classification systems are created. At JANZZ.technology, we have mapped all international occupational classification systems and others in our ontology. (If you would like to learn about the difference between taxonomy and ontology, please check https://janzz.technology/ontology-and-taxonomy-stop-comparing-things-that-are-incomparable/).

This mapping allows us to analyze complex sets of occupational data and to annotate it with intelligent and standardized meta-data, which makes the data comparable in further processes like benchmarking, matching or statistical analyses. Our JANZZclassifier! is a product for everyone who has large volumes of (unstandardized) occupation-related data such as job titles, hard and soft skills and, particularly, training/qualifications. It enables you to simply run your data through our API and will return more meaningful data and, if desired, one of the standard classifications.

Above all, we are using real-time data, both from our users, our partners and the labor market in order to constantly update our database. It is the new way to develop classification systems in the digital age. Please write now to sales@janzz.technology if you wish to learn how our ontology may assist you.

[1] Jeffrey H. Greenhaus and Gerard A. Encyclopedia of career development.

[2] ESCO (2015). ESCO strategic framework. Vision, mission, position, added value and guiding principles. Brüssel.

[3] LI Wen – Dong and SHI Kan. 2006. A brief introduction to the development of the U.S. national standard occupational classification system and its implications to China. URL: https://www.docin.com/p-1479318301.html [2019.06.24]

Ambitious working women may find it hard in Switzerland

Despite being ranked among the world’s most livable places in terms of living standard, education, and healthcare, Switzerland’s cities might still not be the most ideal residence for aspirational working women, especially not for ambitious working moms. According to the 2018 Gender Gap Report, Switzerland ranked 20th. It specifically showed a significant gap between women and men in the areas of economy and political participation.

In Switzerland, women make up only 33.9 percent of senior positions, which include managers, senior officials and legislators. This share has barely changed over the past ten years. The report furthermore shows an inequality in income between women and men, with an estimated average income of $53,362 for women and one of $76,283 for men. [1] One might link the wide income gap to the disproportional representation of women in higher-ranking or higher-paid positions, occupations and industries, but the reasons are more complex.

Gender inequality, a global issue

Gender inequality in the workplace is a global issue that does not correlate directly with a country’s level of development. Thus, Switzerland is an illustrative example among many others. Extreme cases include Japan and South Korea. South Korea, one of the largest economies in Asia, recently made the abysmally low 116th place out of 144 countries in the World Economic Forum’s gender equality ranking [2].

As a part of one of the most advanced, wealthy and democratic nations, Japanese women have always been kept on the fringes of both the economic and political stage. In a worldwide comparison of developed countries, they thus remain in an extraordinarily disadvantaged position. Last year’s news about Tokyo Medical University illustrates this even further: the university altered the entrance examination scores of female candidates. Since such a move will only lead to even fewer female doctors in Japan, this makes the country’s society appear regrettably backward.

 Protest by women

For Swiss women, however, many things have developed positively since the advent of the #MeToo movement and the introduction of women’s quota. In tech and other sectors, personnel consultants observe a trend towards increasing the proportion of women in the executive committees. Simone Stebler from the personnel consultancy Egon Zehnder says that “today, gender diversity is an integral part of the business plan of most companies.” [3]

The Swiss multinational investment bank UBS wants to occupy at least one third of its leadership positions with women. The bank’s gender equality officer says: “We would like to see the proportion of women rising, especially in leadership positions.” Similarly, the pharmaceutical giant Novartis employs nearly as many women as men in its research cadre. Other international companies such as Swiss Re and Axa could also be counted towards such exemplary Swiss employers. [4]

Balancing the competing interests of family and work

However, global experts indicate that for women, the reconciliation of work and family still poses a big challenge. Difficulties in achieving a feasible work-life balance forced many women in Japan and South Korea to leave their jobs. In Switzerland, too, the traditional model of women as caretakers of the domestic realm persists, which is why part-time work is still the norm for Swiss working moms. According to the Federal Statistical Office, 4 out of 10 women work full-time, compared to 1.7 out of 10 men [3].

Valérie Borioli Sandoz, from the union group Travail.Suisse for equality policy, believes that part-time work is the “least bad solution for the reconciliation of work and family. However, it can be a trap, since it is very difficult to escape from it.” [5] Although part-time jobs are a luxury for Swiss moms, they also hold them back from taking certain occupations and higher positions in the workplace.

 One of the main reasons is cultural

Experts call on innovative and open ways to shape our working environment, including more flexible working hours and the co-fulfillment of top management roles. Even with structural changes in companies, the problem of missing female presentation will not be solved easily.

The traditional image of women counts as one of the major reasons why women participate less intensively in official labor markets. Gender and labor market expert Professor Barbara Riedmueller (Freie University in Berlin) points out that women occupying top positions are more accepted in countries where family services are traditionally outsourced. The primary examples are the Scandinavian countries. Gender equality can only be achieved if women no longer have to depend on men. This, however, is a question of changing the culture of a given society. [3]

 Valuing traditionally female occupations

Although there has been great progress for the rights of women in several societies, women are still often found in the working fields of health care, childcare and maintenance, where salaries remain medium to low. By contrast, men dominate high-paid fields such as computer programming, engineering, and technical work.  However, it is reductive to argue that women should only be encouraged to take more top management roles, to break into the male-dominated high-income industries or to demand equal payment in order to shorten the income gap.

 Currently, there are over 6000 professional care vacancies in Switzerland. This number has increased by double the size compared to five years ago.[6] While we need to promote gender diversity in less-female-dominated occupations and industries, we also have to improve women’s salaries, work-life balance, training and further education in traditionally ‘female careers’. This is necessary to reassert the value of such careers and to motivate more young people (male and female) to choose them as a profession. Eventually, this will benefit the health care sector and, as a result, society at large.

This can only be achieved if we all agree to increase the healthcare expenditure in order to heighten the wages in professional care. This way, governments have the financial resources to change the current situation. They need to financially support traditionally under-paid jobs, since caretakers and similar positions’ contribution to society is absolutely crucial. However, the question is whether everybody is really willing to pay for this.

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in job market. If you are interested, please write now to sales@janzz.technology

 

 

[1] Swissinfo. 2018. Swiss gender partiy ranking shows slow progress. URL: https://www.swissinfo.ch/eng/global-gender-gap-report-_swiss-gender-parity-ranking-shows-slow-progress/44624274 [2019.06.13]

[2] Heather Barr. 2018. South Korean women are fed up with inequality. URL : https://www.hrw.org/news/2018/06/14/south-korean-women-are-fed-inequality [2019.06.13]

[3] Franziska Scheven. 2019. Berufstätige Frauen haben heute gute Aufstiegschancen – aber nur, wenn sie Vollzeit arbeiten. URL: https://www.nzz.ch/panorama/frauen-und-karriere-nach-metoo-kinder-hemmen-die-karriere-ld.1455524[2019.06.13]

[4] Franziska Phister. 2019. Mann oder Frau: wer schaff es nach oben? URL: https://nzzas.nzz.ch/wirtschaft/geschlechterkampf-mann-oder-frau-wer-schafft-es-nach-oben-ld.1458619#swglogin#swglogin [2019.06.13]

[5] Jessica Davis Plüss. 2019. Mothers face double-edged sword in Swiss workplace culture. URL: https://www.swissinfo.ch/eng/international-women-s-day_mothers-face-double-edged-sword-in-swiss-workplace-culture/44801716 [2019.06.13]

[6] Albert Steck. 2019. Offene Stellen auf Höchststand. http://jobs.nzz.ch/news/6/arbeitswelt/artikel/421/offene-stellen-auf-hochststand [2019.06.13]

What you should know when choosing your AI recruitment software

In a report, Deloitte presents the evolvement of HR technology in four stages. The first stage describes the period during the 1970s and 1980s, when the main attention of software vendors was on systems that help HR managers make records. During the second stage between the 1990s and early 2000s, capabilities to support recruiting, training and performance controlling were developed. Around 2010, at the third stage, vendors started to offer cloud services and more user-friendly systems to engage with employees’ self-services.

The Deloitte report claims that today we are in the fourth stage of HR technology. In order to react to work environment microtrends, vendors have to design tools that are targeted for teams, individuals and networks and also enhance people’s productivity. At JANZZ.technology, we too think that an HR software should assist HR managers in being more productive and in focusing more on value-added tasks. For example, it should reduce the effort needed for mundane tasks such as screening thousands of candidates and should enable to focus more on value-added tasks such as interviewing candidates. We further believe that this is going to be achieved by means of Artificial Intelligence (AI).

AI for recruiting

Talent acquisition is undeniably one of the most important parts of corporation management. This makes the recruitment software market the most competitive and interesting market to observe. Governmental organizations like Public Employment Services (PES) are also actively seeking solutions to deal with this matter. According to Crunchbase, solely in 2018 recruiting software startups received over $ 600 million of VC finance. In the latest Market Map by HR Tech China, recruiting software vendors constitute the largest part of all HR technology vendors.

The offers made by vendors include the testing and assessment of candidates, background checks, video interviewing and many recruitment platforms. Since early 2017, AI has emerged in the recruiting process. The ensuing rise of recruiting software’s so-called AI capabilities could make any HR manager feel overwhelmed.

‘AI for recruiting’ is an emerging technology used in HR recruitment processes. It employs AI technology and mainly aims to reduce repetitive, time-consuming and banal tasks, which helps recruiters and hiring managers focus on value-added activities. 52% of talent acquisition leaders state that the hardest part of recruitment is the screening of candidates from a large applicant pool. [1] For instance, an AI recruitment software can screen thousands of applications and recommend the top 5 candidates in a blink. Thus, HR managers using AI for recruiting will have more resources to assess the top picks in-depth, which in turn heightens their chance to really find the best suitable candidate.

By contrast, the larger public uses the word AI vaguely and often even inaccurately. Many companies use ‘AI’ to describe their products in order to make them appear as ‘upgraded to the next level’. In most cases, this sort of product advertising promises too much. It is therefore extremely important to be able to evaluate both such products and their vendors. The process is similar to that of hiring: only if you have various ways to assess options, you can find the best one possible.

Evaluating AI for recruiting technology

How should HR avoid over-promised software when choosing from all the products? We have come up with three principles to go by when choosing AI recruitment software:

Principle I: Be aware of the bias within AI recruiting software

Last year, the story about the Amazon AI recruiting tool secretly being biased against women was a wake-up call for all of us: machine learning can be just as biased as human beings. Therefore, it is extremely important to focus on algorithmic fairness and transparency.

You must be aware of how the software processes personal information data such as date of birth, gender and nationality. Which factors does the software take into consideration when matching? And does the software ignore irrelevant factors?

Apart from algorithms you have to make sure that the software has representative training data. In the case of the Amazon AI recruiting tool, the tool turned against female applicants because, for over ten years, the company trained its computer models with resumes summited by male applicants. Hence, make sure you ask your software vendor how they deal with their data source.

Principle II: Be sure to test before you purchase

Before buying a car, you would certainly test drive it first. This rule applies to buying recruiting software as well. Since a recruitment software system is an expensive and long-term investment for your business, it is wise to complete a POC (proof of concept).  In this trial run you’ll find out if the software can really solve your prioritized problems, perform the promised functions and handle your data within the required scale and scope.

JANZZ.technology conducted a POC with an intergovernmental organization to find out if our solution can truly help them save time when finding suitable candidates. Our AI software competed against their HR team in matching candidates worldwide to their open junior intern positions. After screening over thousands of applications, they were pleasantly surprised by our results.

Most good software vendors offer free trials. It is important to prepare your test data well in order to get the most out of your practice and maximally optimize this process. One of the often-ignored aspects when choosing a recruitment software is the maintenance and support needed after the purchase. Only with constant updating, the software is able to develop in parallel with the fast-changing markets, customer requirements and ongoing digitalization. Don’t simply trust marketing lines such as “50 other leading companies from your industry are use our software” or “top 100 out of the top 500 companies are also using our software”.

Principle III: Taking the “soft skills” of an AI recruiting software into consideration

Just like assessing a job candidate by evaluating his/her skills and soft skills, an AI recruiting software has its skills and soft skills as well. We determine the hard skills of a software as functionality, accuracy, data security, speed, languages capability and similar highly valuated competences.

However, many might overlook the soft skills of an AI recruiting software. Soft skills of an AI recruiting software are the ability to deeply understand the language, education, working and social system etc. of your practice region, and the ability to localize certain regions or countries.

For instance, a country like Spain has more than one language: Castilian(Spanish), Catalan, Valenciana, Galician and Basque. A good AI recruiting software should be able to understand the different languages and know the common terms that are used in the four different languages to refer to exactly the same thing.

Conducting job matching across Europe is not an easy task, for instance because each of the 44 countries has its own education system (even with the bologna framework). It is an enormous amount of work to compare the different education levels and match people to jobs. Does your vendor have the right knowledge to solve such problems within your practice region?

Besides language and education, there are many more and equally important categories that need to be taken seriously.  As an international corporation operating in different countries, you want to have a software which understands all your markets.

Limitations of AI 

You may have heard of all the promised benefits of using an AI recruiting software. Before the AI can do its magic, we are here to give you a word of caution. Don’t expect the AI software to make the hiring decision on its own. Many of the previous use cases proved to us that the technology just is not ready yet. “How to make sure the algorithm is really interpretable and explainable – that is quite far off.” [2]

If you are expecting AI and the algorithm to do their job well, it is equally important for you to check your company’s or organization’s readiness for AI, in order to maximize its power. We all know that identifying patterns and making predictions requires a large quantity of data. With the widespread open-source algorithms, the real game changer is the data used to train the algorithms. Any business or organization should have a clear plan on how to generate the quantitative and qualitative data which will help your AI recruiting software get more accurate results, this is economically profitable for your business as well.

JANZZ.technology supplies AI solutions for your recruiting system and helps you find the right skills and talents. The ontology JANZZon! and the smart matching engine JANZZsme! make complex problems such as job and skills matching computable and completely change the way we handle skills and talent searching. The applications of JANZZ.technology are structured semantically, meaning occupations, specialization, function, skills and qualifications etc. are interlinked logically. The JANZZ.technology applications can deliver meaningful results for complex searches in real time and across multiple languages. Our applications are constantly fed with new data generated from our users, therefore they become more accurate over time. Let the tools of JANZZ.technology assist you in finding your best fit candidates.  For a demo, please write now to sales@janzz.technology

[1] ideal. 2019. AI for recruiting: A definitive guide for HR professionals. URL: https://ideal.com/ai-recruiting/ [2019.05.28]

[2] Jeffrey Dastin. 2018. Amazon scraps secret AI recruiting tool that showed bias against women. URL: https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G [2019.05.28]

How India leverages its demographic dividend

While most of the developed countries in the world are fighting against their aging populations by increasing the retirement age and welcoming migrants, other countries are worrying about how to fit large numbers of young people into the workplace. Deloitte’s Voice of Asia series reported that many countries in Asia have witnessed steady growth in their working age population, with more and more young men and women entering the labor markets each year. India is the top one country on the list.

According to the numbers from the World Economic Forum, half of India’s population is below the age of 25 and a quarter is below the age of 14. Bearing in mind that it is the second most populated country in the world, with 1.3 billion citizens, India makes up for a fifth of the world’s youth. As most economists predicted, India will benefit from the demographic dividend and will have a fast-growing economy.

To realize the demographic advantage, India will need to majorly accelerate job creation and investment in human capital to keep up with the growing working-age population. For currently, there are 17 million yearly entrants into the job market and only 5.5 million created jobs. [1] In a survey conducted by the OECD, over 30% of India’s youth aged from 15 to 29 are neither employed nor in education or training (NEETs). [2]

Isabelle Joumard, senior economist and head of the India desk at the OECD explained: “NEETs include all youth left outside paid employment and formal education and training systems. They are NEET because there are not enough quality jobs being created in the system and because they have little incentives or face too high constraints to be in the education and training systems.” [2]

Looking from a global point of view, youth unemployment rates remain above 20% in some European economies, the Middle East and North Africa have had youth unemployment rates close to 30% and things have continued to worsen over recent years. Nevertheless, the young people who have found work often have to content themselves with jobs that fail to meet their expectations [3] and 16.7% of working youth in emerging and developing economies live in extreme poverty [4].

Why are employment opportunities especially scarce in the least developed countries? Insufficient policy development, poor infrastructure and limited financing channels are among the many reasons for job shortages. According to the findings of e4e (an education for employment initiative driven by the International Finance Corporation and the Islamic Development Bank), the mismatch between education and the labor market demand is a major obstacle to job creation. [5]

As pointed out by several reports, developing an employability-driven skill ecosystem is key for leveraging India’s demographic potential. Rajasthan, the seventh most populated state in India, has a youth population aged below 25 that makes up nearly 55% of the state’s entire population. From 2012 to 2018, its unemployment rate increased from 4.5% to 7.7%. The problem of unemployment in Rajasthan is compounded with issues, such as the lack of quality trainers and the non-alignment of education and skilling. [6]

To tackle these problems, the state has continuously intensified the establishment of education infrastructure. In 2004 it became the first state in the country to implement a skill mission aiming to reduce the gap between demand and supply of skilled workforce and hence increase the employment rate. To further improve the quality of skilling, Rajasthan has founded skill universities, a pioneer project in the country. [6]

In Himachal Pradesh, located in northern India, a large share of employment is in agriculture. More than two thirds of its manpower are self-employed, and the amount of salaried jobs remains very low. In 2018, the Asian Development Bank (ADB) signed a loan with the Indian government to leverage the technical and vocational education and training (TVET) institutions and to scale up skilling ecosystems in Himachal Pradesh.  The plans for the project include transforming 11 employment exchanges into model career centers, modernizing training equipment, employing a training information system and creating better access to quality market-relevant TVET for the state’s youth, in order to prepare them for the changing needs of the labor market. [7]

JANZZ.technology helps governments place workforce into labor markets with AI technology. In collaboration with MTESS and DGE, we have successfully implemented ParaEmpleo — a job matching solution in Paraguay. The collaboration between Paraguay and JANZZ.technology is within the framework of the Support Program for Labor Insertion which was supported by the Inter-American Development Bank (IDB) since 2011.  The representative of IDB in Paraguay, María Florencia Attademo-Hirt, spoke highly of JANZZ’s technology, saying: “Innovative tools like this are what will improve the lives of Paraguayans, beyond the Mercosur and regional context”. [8] The innovative use of technology is the right way to solve today’s labor market problems. If you as a governmental organization are looking for solutions to fight against labor market issues in your country, please write now to sales@janzz.technology

 

 

[1] NASSCOM, FICCI and EY. 2017. Future of jobs in India – A 2022 perspective. URL: https://www.ey.com/Publication/vwLUAssets/ey-future-of-jobs-in-india/%24FILE/ey-future-of-jobs-in-india.pdf [2019.04.30]

[2] OECD. 2017. OECD Economic Surveys India. URL: https://www.oecd.org/eco/surveys/INDIA-2017-OECD-economic-survey-overview.pdf [2019.04.30]

[3] Guy Ryder. 2016. 3 ways we can tackle youth employment. URL: https://www.weforum.org/agenda/2016/01/3-ways-we-can-tackle-youth-employment/ [2019.04.30]

[4] ILO. 2017. Global employment trends for youth 2007. URL: https://www.ilo.org/wcmsp5/groups/public/—dgreports/—dcomm/—publ/documents/publication/wcms_598675.pdf [2019.04.30]

[5] Lars Thunell. 2012. How do we create more jobs for young people? URL: https://www.weforum.org/agenda/2012/01/how-do-we-create-more-jobs-for-the-youth/ [2019.04.30]

[6] Pwc and FICCI. 2019. Fast forward: relevant skills for a buoyant Indian economy. URL: http://ficci.in/spdocument/23062/FICCI-PwC-rajasthan-report.pdf [2019.04.30]

[7] ADB. 2018. ADB, India sign $80 million loan to help boost youth employability in Himachal. URL: https://www.adb.org/news/adb-india-sign-80-million-loan-help-boost-youth-employability-himachal [2019.04.30]

[8] IDB. 2019. Algorithms that get you a job in Paraguay. URL: https://www.iadb.org/en/improvinglives/algorithms-get-you-job-paraguay [2019.04.30]

 

It is time to take action

Eric D. Beinhocker writes in his book The Origin of Wealth: Evolution, Complexity, and the Radical Remarking of Economics that, “over 97 percent of humanity’s wealth was created in just the last 0.01 percent of our history.” It is not until 250 years ago that humankind began to evolve into more prosperous and dynamic societies which offer an extraordinary variety of services. [1] Today our economic wealth is still growing, and the speed of this process has even been accelerated by the digitalization and automation of the 21st century.

A positive consequence of this development is the high standard of living provided by the increased productivity. However, we also need to live with the potentially negative consequences, which are challenging the security of our jobs. With the prospect of self-driving cars, how many drivers will remain employable in the next 5-10 years? When automated factories are combined with AI technology, how can workers keep up with the new skills required to complete their tasks?

In today’s world, quick and drastic changes make the promise of life-long security impossible. Powerful technology and automation pose a threat in almost every corner of business. Leaving us asking ourselves the question: will I still be employable in 10 years? The good news is that our jobs aren’t disappearing, they are changing. Still, in the era of digitalization and automation, our workforce constantly needs to keep reskilling and upskilling in order to stay up to date. It is time to take action.

More effort is required

Corporations are increasingly discussing to foster a learning culture environment. Bersin by Deloitte report that organizations with a strong culture of learning are 92% more likely to innovate, they enjoy 37% greater employee productivity and are 58% more prepared to meet future demand. [2]

Stories such as Starbucks’ partnership with a local university to offer its employees an online college degree program are becoming more frequent. Jeff Bezos describes Amazon’s Career Choice program as follows: “For hourly associates with more than one year of tenure, we pre-pay 95 percent of tuition, fees, and textbooks (up to $12,000) for certificates and associate degrees in high-demand occupations”. [3]

In most of the cases, programs like these are only available in large international corporations with substantial financial capabilities. What about the workers in small and median sized companies who are not provided with these opportunities? Randstad’s Workmonitor survey indicates that more than one-third of American workers have not taken any steps to develop new skills within the past year. While it is sensible to expect employees to maintain up-to-date job skills and to actively seek training opportunities, this responsibility clearly shouldn’t fall solely on the individual’s shoulders. [4]

Barriers stopping companies  

Companies have long been aware of the urgency to increase investment for reskilling and upskilling. In this issue, companies could potentially take the lead instead relying fully on governments. But what are the main barriers stopping them from taking action?

According to a recent McKinsey Global Institute report, rethinking and upgrading the current HR infrastructure is considered a priority by 1/3 of the respondent executives. In the US 42%, in Europe 24%, and in the rest of the world 31% of executives believe reskilling and upskilling is a primary concern. However, many companies lack the knowledge of how jobs are going to change, and they are struggling to figure out how digitalization and/or automation is going to change the future skill set needs. This unpredictability makes it hard to foresee which kind of talent they will be needing in the next five to ten years. Of the private-sector business leaders, only 16% have the confidence that they will be able to meet the future potential skill gaps on a large scale. [5]

Small to medium sized companies, as mentioned before, are lacking the capabilities to provide their employees with the necessary training, mostly due to financial inability. For many of them, there aren’t any extra resources available for planning and managing training programs. Even if some of them do offer such opportunities, they are confronted with a bigger risk, for well-trained employees might choose to leave for better employers. 

As stated in the end of the McKinsey Global Institute report, the willingness of the large companies and senior executives to meet challenges lying ahead is strong despite obstacles. However, neither large corporations nor small to medium sized companies should be left alone in this matter, it is important to discuss the role of governments regarding the issue. 

Closing the skill gap is an ecosystem task

Most job trainings are acquired on the job and some job positions are so unique that one cannot receive the right training from any institutional organization. Therefore, it would be efficient for governments to team up with companies to understand and identify skill gaps and to design and conduct training programs, thus preparing the workforce with position-ready skill sets. 

Countries such as Germany and Switzerland attach great importance to their vocational training and apprenticeship models. Their outcome in procuring adult technical skills has shown great success and should be adjusted and expanded to an even larger scale. [6] There are several reasons why this model is especially significant today:

Firstly, due to the frequent updates of technology, the required skills for the jobs have to change more often than before. The model allows the workforce to learn on the job, which will notably minimize the skill gaps. Secondly, the Generation Z born in 1995 and onward have a more practical approach when it comes to education. They adapt to the economics of education and work, measuring their return on investment when choosing what and where to learn. [7] Therefore, the advantage of vocational education and learning while earning matches the learning attitude of today’s generation. Not to mention the economic benefit of having lower youth unemployment rates.   

Low-skilled workers are often left behind by corporate trainings and are most likely to be affected by automation. To solve this issue, governments can provide subsidies to corporation programs to further educate the lower skilled workers. Governments need to ensure a broad-based reskilling, including bringing together different parties such as community colleges and social organizations to provide resources for the vulnerable and disadvantaged groups, such as people with disabilities, single mothers, and refugees. Small to medium sized companies should also be given some degree of financial support when conducting employment training programs. Financial supports could, for instance, come in the form of income tax deductions or public grants to subsidize trainings. [6]

The government of Singapore has taken the step ahead of many others. Singapore has been offering an outstanding program aiming to promote a culture and overall system of lifelong learning and skills mastery. Since 2016, Singapore has added $ 500 into the SkillsFuture account for every Singaporean aged above 25. This account intends to fund citizen approved courses that develop new and relevant skills for career development. The SkillsFuture Singapore Annual Report 2017-18 states that a significant number of Singaporeans have already benefited from this program since the launch.

Maximizing informal ways of learning

Recently, a 14-year-old American boy has been reported to have achieved nuclear fusion in an old playroom in the house of his parents with the assistance of an online amateur physicist’s forum. In its 2019 State of Software Engineers Report, by Hired, it’s claimed that 1 out of 5 software engineers are self-taught. As stated by Tim Cook, Apple is very proud that half of its US employees who were employed in 2018 do not have a formal four-year undergraduate degree. As the resources of knowledge are more open and easier to access, informal education is becoming even more important.

For adults who are already in the working environment, informal education is the most practical way to receive further education while still making a living.  And a significant amount of adult learning is achieved through practical experience, interacting with customers and co-workers and online courses. Therefore, it is important to establish a system to certify the skills people have earned through such informal ways, not only to encourage a learning culture but also to sustain the motivation of learning.

Ironically, while everybody is talking about reskilling and upskilling and how technology is going to change our jobs, some of the fastest growing job fields, such as nursing, medical assistance, elderly care, plumbing and pipefitting, are the ones less affected by technology and automation and don’t need reskilling. However, those jobs are becoming less and less attractive. Perhaps it’s about time to talk less and act on solving feasible problems.

JANZZ.technology has been actively contributing and creating solutions for matching jobs and skills in the digital age such as the newly developed Realtime Labor Market Dashboard. We help corporations, organizations and governments cope with the challenges during the digital transformation and prepare the workforce to adapt to the new labor markets. At JANZZ.technology, we insist on knowing, not guessing. Our technology provides customers with real facts which help make the right decision. The semantic empowered JANZZ.technology is the right tool to rethink and upgrade the current HR infrastructure. For more information, please write now to sales@janzz.technology

[1] Eric D. Beinhocker. 2006. The origin of wealth evolution, complexity, and the radical remarking of economics. Boston, Masaachusetts: Harvard Business School Press.

[2] Deloitte. Leading in learning. URL: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/HumanCapital/gx-cons-hc-learning-solutions-placemat.pdf [2019.04.01]

 [3] Scott Mautz. 2018. Amazon is paying its employees $ 12,000 to train for a job at another company. And it’s brilliant. URL: https://www.inc.com/scott-mautz/amazon-is-paying-its-employees-12000-to-train-for-a-job-at-another-company-its-brilliant.html [2019.04.01]

[4] David W. Ballard. 2017. Managers aren’t doing enough to train employees for the future. URL: https://hbr.org/2017/11/managers-arent-doing-enough-to-train-employees-for-the-future[2019.04.01]

[5] Pablo Illanes, Susan Lund, Mona Mourshed, Scott Rutherford and Magnus Tyreman. 2018. Retraining and reskilling workers in the age of automation. URL: https://www.mckinsey.com/featured-insights/future-of-work/retraining-and-reskilling-workers-in-the-age-of-automation [2019.04.01]

[6] World Economic Forum. 2017. White paper: Accelerating workfore reskilling for the fourth industrial revolution. URL: http://www3.weforum.org/docs/WEF_EGW_White_Paper_Reskilling.pdf [2019.04.01]

[7] Jason Wingard. 2018. Training generation Z. URL: https://www.forbes.com/sites/jasonwingard/2018/11/21/training-generation-z/#74223a04bde0[2019.04.01]

Are you disappointed with the keyword-based application tracking systems (ATS)? Here is what you can do.

 

  • Do not use graphics or tables
  • Follow the formatting rules
  • Include unique keywords

Sound familiar? These are some of the tips which can help resumes get through an application tracking system (ATS) and eventually land them in front of an HR manager. However, when it comes to new AI technology used in the hiring process, these tips can no longer guarantee the resume getting past the ATS.

 

What is an ATS and how does it work?

Over 98% of fortune 500 companies, as well as an increasing number of small to mid-sized businesses, are using application tracking systems to filter resumes. [1] The ATS screens through a large number of resumes and passes the most qualified candidates on to the hiring managers. The principal at HR consulting firm Bersin by Deloitte says, “Most companies have thousands of resumes sitting in a database that they have never looked at.” Actually, 75 % of resumes get lost somewhere in the database and are never looked at by a human. [2]

When applicants apply for online jobs, their personal information, work experiences, skills, education and other relevant information is uploaded to the database. The ATS assists human resource personnel in managing the candidates throughout the whole hiring process, including sending applicants automated messages to let them know their applications have been received, giving online tests, scheduling interviews and sending rejection letters. [3]

 The drawbacks of ATS

The biggest drawback of ATS is that many of the earlier systems are designed to look for specific keywords and titles in resumes that match with the advertised positions. Even though some ATS providers claim their system has AI capabilities, the search and match results are still very disappointing. This means that if a good candidate, who is switching careers, has a very similar skill set to the one required for the new position but doesn’t have the exact job title in their resume, the system would miss the candidate.

Sometimes recruiters search for candidates by combining multiple keywords, such as job titles, important skill sets and experiences. Even so, a keyword-based system is not capable of finding adequate candidates with an acceptable degree of accuracy and precision. Moreover, the majority of all searches look for terms that are common, such as “Java”, “Project Manager” or “MS Excel”. Unfortunately, this is not the right approach, for with keyword searching, the more trivial the keyword, the less effective the search and the broader the results.

Other drawbacks of an ATS are that it may not understand all abbreviations and that it can only read a certain format.  According to a joint survey by CareerArc and Future Workplace, in 62% of companies using ATS, “some qualified candidates are likely being automatically filtered out of the vetting process by mistake.” [3]

 The new technology to upgrade your current ATS

It would be pointless to discuss how to optimize resumes in order to “beat” the ATS. Instead, companies should implement the newest AI technology to optimize their application tracking system for a more efficient and accurate hiring process. JANZZ.technology offers the semantic technology which structures occupations, skills, experiences, functions and many more logically interlinked concepts, which deliver relevant search and match results to hiring managers.

With a semantic ATS, you will never miss talents simply because of wording. For example, when searching for a Chinese coach (e.g. for executive mangers who are going to China regularly to meet clients), a semantically powered system will show results including applicants whose job titles aren’t identical but related, such as, Chinese language tutor, Chinese instructor, Chinese teacher or language tutor specialized in Chinese and Japanese.

A semantic matching engine like JANZZsme! has the most comprehensive, multilingual knowledge graph of occupations and skills at its disposal. When the semantic matching engine does a query expansion, searches or matches job ads and resumes, it accesses the ontology concepts, lexical terms and synonyms, which may appear in CVs and job vacancies in up to 40 languages.

For instance, CEOs (US English) will match with Geschäftsführer (German), 首席执行官 (Chinese) and Managing Directors (UK English). Carpenters will be fully or partly matched with joiners and kitchen unit makers. Design illustrators, animation artists and film animation designers are all fully or partly connected.

Taking programing language as another example; let’s say you are looking for programmers to develop .NET. If programmer A has C# on his resume and programmer B knows Python, the smart matching engine JANZZsme! will successfully match programmer A to your open position, because it knows that C# is a programming language of .NET. This is achieved through the interlinked relationship of the concepts stored in JANZZon!.

Precision in matching is achieved through structure and context. However, neither CVs nor job offers are structured efficiently or consistently, which makes it difficult for a keyword search engine to identify the right data type.  A matching engine such as JANZZsme! looks at the type of sought-for data and uses deep learning techniques to identify the correct match while disqualifying matches that are the wrong data type.

CV and job description keyword-based search systems and current CV Parsing technology do not have the same capability to produce high occurrences of accurate matches that contextualized semantic searching and matching has. While the results from a keyword-based search overwhelm hiring managers, a semantic matching engine produces a manageable volume of results, letting hiring managers focus on scanning questionable or unclear data and making the final decision. Thus, radically reducing the amount of needed time and effort.

Do you feel limited by your current ATS (e.g. Oracle Taleo, SAP or IBM Kenexa)? Do you want to optimize it with semantic technology and enjoy more advanced capabilities when searching for candidates, matching open positions and conducting skill gap analyses? To find out how to do so, please write now to sales@janzz.technology

 

 

 

[1] Jon Shields. 2017. Over 98% of fortune 500 companies use application tracking systems (ATS). URL: https://www.jobscan.co/blog/fortune-500-use-applicant-tracking-systems/

[2] Terena Bell. 2017. The secrets to beating an applicant tracking system (ATS). URL: https://www.cio.com/article/2398753/careers-staffing-5-insider-secrets-for-beating-applicant-tracking-systems.html

[3] Alison Doyle. 2019. How employers use application tracking systems (ATS). URL: https://www.thebalancecareers.com/what-is-an-applicant-tracking-systems-ats-2061926

[4] CareerArc. 2016. 23 surprising stats on candidate experience-infographic. URL: http://www.careerarc.com/blog/2016/06/candidate-experience-study-infographic/

The world’s most homogeneous society is opening new doors

Japanese society, one of the world’s oldest and most homogeneous, is about to change. In December 2018, Japan’s parliament passed an immigration bill that is intended to boost the economy and to tackle the country’s labor shortage.

More precisely, the law is designed to attract foreign “semiskilled workers.” These workers are to be employed in various industries, among others, construction, the hotel industry, agriculture and nursing care; in the latter case, shortages are most acute. Despite some protests from oppositional parties, Japan’s Prime Minister Shinzo Abe and his government put the bill through by a vote of 161 to 76. Over the course of five years the immigration bill, which is coming into effect in April 2019, aims to attract 345,000 foreign workers to Japan. [1]

Japanese cities worried about taking in more foreign workers

A survey conducted by Kyodo News in February this year shows that Japanese cities are concerned about the accommodation of more foreign workers. The issues cities seem to worry most about are of economic nature and include questions of how new foreign workers can be provided with livelihood support and with salaries on a par with local Japanese workers’. [2]

Both the oppositional protest and the concerns appear to be justified. In 1993, Japan has already once introduced a program concerned with foreign labor, the so-called Technical Intern Training Program. Its purpose was to attract interns from developing countries and to help them acquire technical skills which they could export to their countries of origin. Despite the good intentions behind it, the program has been abused. Many Japanese companies have misused it as a cheap way of employing foreign laborers. This translates to a majority of Japan’s young foreigners doing low-payed “3K jobs” (the three Ks are short for kitsui, kitanai and kiken, the words to describe work that is “dangerous, dirty and difficult”). Many of them receive less than half of the statutory minimum wage, which has a significant impact on the quality of their life and well-being. [3]

According to the Nikkei Asian Review, Japan wants to complement the new program with a range of measures to support foreign workers in adjusting to Japanese life and to encourage smaller cities to take in foreign laborers. Furthermore, foreign workers’ language proficiency will newly be tested with a focus on spoken Japanese.  “People may have various arguments, but if Japan simply continued along the same path, we would find ourselves in a very difficult situation,” Chief Cabinet Secretary Yoshihide Suga says. [4]

Aging problem is forcing Europe to relax immigration regulation

The 2018 Aging Report, published by the European Commission, indicates that Europe’s population is continuing to age rapidly, with Germany having one of the oldest populations among European countries. The German population pyramid indicates a negative demographic growth and predicts the country’s arrival at a scary milestone this year: there will be fewer citizens under the age of 30 than such over 60.

To fight these current developments, Germany has taken action. In August and September 2015, the country opened its borders to welcome more than a million refugees. Last year’s employment figures show that since then 400,000 refugees have been integrated in work or training, which seems to vindicate Angela Merkel’s much-criticized approach. “After one year of instruction, most young migrants can speak German well enough to participate in vocational school classes,” the head of BDA (Confederation of German Employers’ Associations), Ingo Kramer, states. [5]

The aging problem also poses challenges for nursing care. Accordingly, many European countries, including Germany, Switzerland, the UK and Finland, are in great shortage of nurses and other professional care providers. To reduce their lack of skilled labor, these countries are introducing relevant policies.

In December 2018, the German government passed a skilled labor immigration law that will make it easier for employers to recruit workforce from outside the EU. In light of Brexit, the UK government is proposing a drastic overhaul of its immigration policy in order to henceforth prioritize high-skilled workers and treat non-EU citizens equally to EU citizens.

A war for skilled migrants

Evidence shows that immigration has played an important role in bringing significant economic benefits, including to the US and Canada. The two countries had the most welcoming immigration policies to attract skilled laborers that aid national businesses in becoming more agile, competitive and profitable in the “war for talent.” Their governments in exchange received more revenue and citizens profited from the momentum created by the influx of high-skilled migrants. [6]

More recently, other countries, too, have expressed their intent to attract skilled foreign workers, which increases the complexity of the skilled migration boom. The most important decision criteria for skilled workers’ choice of country are language and culture. The English-speaking countries of the US, the UK, Canada and Australia are the so-called “Big Four” of skilled migration and take 70% of all high-skilled migrants to OECD states. [7] Countries like Germany and Japan are therefore facing serious competition, even if they increase their policy efforts.

Negative aspects of migration

There are, however, also some negative aspects about large-scale migration. Although concerning a relatively small group of people, these negative consequences will have drastic effects. In essence, (im)migration can create unequal power balances. In John Stuart Mill’s words, it is big governments’ responsibility to ensure that the local and short-term social costs do not overshadow the role of (im)migration “as one of the primary sources of progress.” [6]

Another drawback of migration are the economic losses caused by the “brain drain” in the nations that high-skilled workers leave behind for countries offering higher salaries and better living standards. Most of these left-behind countries are less developed—the departure of their best-trained workers only perpetuates this: not only are they deprived of their high-skilled professionals, thereby they also lose the money invested in these people’s education.

On average, nurses earn 250 to 400 euros a month in Bosnia or Serbia. Compare this to a starting salary of about 1,500 euros in Germany. “We are losing our best experts,” says Zoran Savic, the president of Serbia’s medical workers’ trade union. “Younger doctors will fill in their places, but it takes a minimum of ten years to educate a specialist physician.” [8]

According to data supplied by POEA (Philippine Overseas Employment Administration), between 2012 and 2016 more than 92,277 nurses have left the Philippines. Low salaries have been one of the main push factors. [9] In the Philippines, a Bachelor of Science in Nursing program (BSN) takes four years to complete and costs about 30,000 pesos (576 USD) per semester. If only one third of the deployed nurses mentioned held a public BSN, the country has already lost 140,320,097 dollars that it invested in their education.

Similarly, a Kenyan study shows that in Kenya a doctor’s higher-level education costs are approximately 48,169 dollars. If one adds the preceding costs of primary (10,963 USD) and secondary education (6,868 USD), the total education cost for one single medical doctor amounts to 65,997 dollars. [10] For a country whose economy classifies as lower-middle-income the brain drain caused by the departure of expert workers such as doctors constitutes a major problem.

Migration, the only way to tackle labor shortage  

According to the World Bank, developed countries could generate global economic gains of 356 billion dollars if they increased immigration by a margin of 3% of the workforce. Some economists predict that if borders were opened completely and labor forces could be allocated freely the world economy would produce gains of even 39 trillion dollars over the course of 25 years. [6]

Oxford University professor Ian Goldin indicates that ensuring a strong labor supply augment with foreign workers will become even more crucial in the future. Therefore, today’s governments need to prepare themselves for the labor market challenges laying ahead of them and they can do so by choosing the right tools and technologies to shape the future.

JANZZ.technology offers exactly what is needed to achieve this. With proven high-tech solutions such as the newly developed Realtime Labour Market Dashboard, its unique expertise in occupation and skills data and extensive know-how about the re-skilling and digitization of employment markets, JANZZ.technology provides an array of effective tools. These tools can be used to analyze and correctly predict both the potential and the demand for specific skills in labor markets, as well as provide policymakers and people in charge with the answers to make the right decisions at the right time.

Please write now to sales@janzz.technology

 

 

 

[1] Simon Denyer and Akiko Kashiwagi. 2018. Japan passes controversial new immigration bill to attract foreign workers. URL: https://www.washingtonpost.com/world/japan-passes-controversial-new-immigration-bill-to-attract-foreign-workers/2018/12/07/a76d8420-f9f3-11e8-863a-8972120646e0_story.html?utm_term=.1f730552bd5d [2019.02.26]

[2] KYODO. 2019. Japanese cities worried about taking in more foreign workers, survey finds. URL: https://www.japantimes.co.jp/news/2019/02/10/national/japanese-cities-worried-taking-foreign-workers-survey-finds/#.XGKdelxKiUk [2019.02.26]

[3] Christoph Neidhart. 2019. Zuwanderer verzweifelt gesucht. URL: https://www.tagesanzeiger.ch/ausland/asien-und-ozeanien/zuwanderer-verzweifelt-gesucht/story/19372917 [2019.02.26]

[4] Hiona Shiraiwa. 2018. Japan prepares support for incoming foreign workers. URL: https://asia.nikkei.com/Spotlight/Japan-Immigration/Japan-prepares-support-for-incoming-foreign-workers [2019.02.26]

[5] Jorg Luyken. 2018. Angela Merkel was right about refugee integration, says German business federation chief. URL: https://www.telegraph.co.uk/news/2018/12/14/angela-merkel-right-integration-figures-show-400000-refugees/ [2019.02.26]

[6] Ian Goldin. 2016. How immigration has changed the world for the better. URL: https://www.weforum.org/agenda/2016/01/how-immigration-has-changed-the-world-for-the-better/[2019.02.26]

[7] INTHEBLACK. 2016. Which countries are winning the global talent war? URL: https://www.intheblack.com/articles/2016/12/01/which-countries-are-winning-the-global-talent-war[2019.02.26]

[8] Daria Sito-Sucic. 2017. Nurses, doctors leave Balkans to work in Germany. URL: https://www.reuters.com/article/us-balkans-healthcare-germany/nurses-doctors-leave-balkans-to-work-in-germany-idUSKBN16G18X [2019.02.26]

[9] Don Kevin Hapal. 2017. Why our nurses are leaving. URL: https://www.rappler.com/move-ph/180918-why-nurses-leave-philippines [2019.02.26]

[10] Yusuf Abdu Misau, Nabilla Al-Sadat and Adamu Bakari Gerei. 2010. Brain-drain and health care delivery in developing countries. URL: https://www.researchgate.net/publication/46179307_Brain-drain_and_health_care_delivery_in_developing_countries [2019.02.26]

Ontology and taxonomy – stop comparing things that are incomparable

To many people, the word ‘ontology’ might sound abstract. It has its origin in Tim Berners-Lee’s dream of inventing the World Wide Web. This dream included the Web becoming capable of defining a so-called ‘Semantic Web’ by analyzing all Web data, including content, links and computer-person transaction. In the Semantic Web, the Resource Description Framework (RDF) and Web Ontology Language (OWL) have been established as standard formats for sharing and integrating both data and knowledge—the latter in the form of rich conceptual schemes called ontologies. [1] In this article the word ontology serves as the working definition, however it is worth mentioning that in today’s IT world there is also a broad use the term ‘knowledge graph’ to refer to this concept.

Why to care about ontology

With regard to artificial intelligence (AI), the terms ‘big data’, ‘machine learning’ and ‘deep learning’ are slowly replacing the usage of ‘AI’. However, to quote Adrian Bowles, “there is no machine intelligence without (knowledge) representation.” In other words, AI requires some elements of knowledge engineering, information architecture and a significant amount of human work to do its ‘magical neural work’. Fittingly, Alexander Wissner-Gross finds that, perhaps most importantly, we need to recognize that it is intelligent datasets—not algorithms—that are likely to be the key limiting factor in the development of human-level artificial intelligence.

             “there is no machine intelligence without (knowledge) representation.”

An ontology is a structured and formal representation of relative knowledge in a certain domain. This is necessary, because unlike humans it cannot directly rely on human background knowledge about a term’s correct usage. What an ontology can do, however, is to “learn” about the semantic meaning of a term through the interlinks between the concepts in its system. Powerful ontologies already exist in specific domains, examples include the Financial Industry Business Ontology (FIBO) as well as numerous ontologies for healthcare, geography or occupations.

Another important part of AI is semantic reasoning. In addition to identifying potentially fraudulent transactions, determining users’ intent based on their browser history and making product recommendations, AI can also do the following: It can execute tasks that require explicit reasoning based on general and domain-specific knowledge, such as understanding news articles, preparing food or buying a car. Thus, such tasks require information that is not part of the input data but needs to be dynamically combined with knowledge. This type of machine reasoning can only be achieved with ontologies and the way their knowledge is modeled. [2]

Taxonomy and ontology are fundamentally different

Ontology is often confused with taxonomy.  Apart from the fact that both belong to the fields of AI, the Semantic Web and system engineering, there is really not much that would characterize them as synonyms. Taxonomy classifications such as O*NET (Occupational Information Network) and ESCO (European Skills/Competences, qualifications and Occupations) simply cannot be compared to ontologies.  They provide a much simpler approach to classifying objects, as they have a hierarchical structure and utilize only parent-child relations without any additional, more sophisticated links. Ontologies, on the other hand, are a much more complex form of categorization. Speaking metaphorically, a taxonomy equals a tree whereas an ontology comes closer to a forest.

For example: The term ‘golf’ could appear in several taxonomies.  It might be located under a ‘Human Activities’ tree (human activities -> leisure activities -> sports -> golf).  It could also be found under a taxonomy concerning apparel (apparel -> casual/active apparel -> sporting apparel -> golf clothing and accessories). It could even appear in something quite different, for example an automobile taxonomy (automobile -> Germany -> VW -> Golf). Each of these taxonomies can be considered a tree whose branches touch at their ‘golf’-related nodes. [3]

Put differently, taxonomies represent a collection of topics with ‘is-a’-relationships while ontologies allow for much more complex connections, such as ‘has-a’- and ‘use-a’-relations. [4] Hence, if we return to the classification example above, taxonomies lack the capability to compare child concepts.

In the classification of ESCO, almost all medical specialists are grouped under the heading ‘Specialist Medical Practitioners’. Furthermore, specialist skill sets are simply grouped in lists without any links to the respective specialist occupations. Why is that? One reason is that classifications are mainly used for statistical purposes. From this viewpoint there is no need to further classify all individual medical specialists according to their skill sets and training background. Therefore, according to taxonomies, specializations can only be recognized by their job title and one needs to refer to other sources to better understand their individual meaning.

Building an ontology of occupations, qualifications and skills makes it possible to automatically recognize similarities and differences between job titles. For example, pediatricians and neonatologists have similar jobs, both of which concern themselves with the medical care of newborn infants. With the ontology modeling approach, it is possible to determine that a pediatrician has a very high percentage of similar skills to those of a neonatologist. However, pediatricians can only take over the neonatologist’s job after further training. All this information can be represented in an ontology through the interrelationships between concepts. This goes beyond the capacity of a simple taxonomy.

Ontologies enable matching datasets

When it comes to matching, say the matching of CVs with vacancies, there is no better way than to use an ontology. All too often, simple keyword-based matching or fuzzy machine learning methods are used for this, which means that many similarities go undetected and cannot be matched, such as keyword variations, synonyms and alternative phrases. When matching, it is important to compare the semantics (the underlying meaning) of two items rather than the wording. This is where ontologies come into play. They can provide a semantic modeling that can detect the underlying meanings and similarities in CVs and job descriptions.

The ontology matching technique represents a fundamental technique in many areas, such as ontology merging. In domains with very complex rules (and complex interactions between rules) there’s no substitute for ontologies. This is shown, for instance, when you consider integrating disparate domains. Let’s say there are two separate ontologies, a weather ontology and a geographic ontology, when considering navigation or insurance risks, to create a third ontology which integrates and leverages the other two is a manageable proposition. [5]

 True value of ontologies

The semantic system relies on explicit, human-understandable representations of concepts, relationships, and rules to develop the desired domain knowledge. It is impossible to rely solely on programmers to build such a system based on machine learning, as they lack the knowledge needed to define relationships between concepts in the specific domains. Therefore, the domain knowledge must be learned from domain experts with various backgrounds (e.g. intellectual property law, fluid dynamics, car repair, open-heart surgery, or educational and vocational systems). This process is crucial for creating a comprehensive knowledge representation.

For the multi-lingual JANZZ ontology language skills are a key point. In many cases, a one-to-one translation of a concept into multiple languages isn’t possible, however, thanks to Switzerland being small and integrated, all the JANZZ ontology curators are fluent in at least two languages and some even speak more than four (including Chinese and Arabic). This advantage guarantees the ontology’s consistency and quality across different languages.

About a decade ago, JANZZ started building its ontology on various occupation taxonomies, namely ISCO-08, ESCO and all country-specific classifications. Over the years, JANZZ has added thousands of new professions and functions (e.g. Market Research Data Miner, Millennial Generational Expert and Social Media Manager) to the JANZZ ontology, which didn’t exist before in any of the known taxonomies. Besides job titles, also up-to-date skills, education, experience and specializations have been included in the ontology. It is the right tool for HR and Public Employment Services, which recognizes the similarities and ambiguities among job titles, rather than being a collection of terms like a taxonomy. Today, the JANZZ ontology is by far the largest, most complicated and most complete occupation data ontology in the world.

For private corporations and public employment services trying to choose between a classification system based on a taxonomy and a classification system based on an ontology, we hope this article helps you make the right decision and helps you realize that investing in a non-semantic system (without content) will not get you any further. Luckily, some governments and corporations have chosen the right path and have already benefited from our newest technology. If you would like to know more about the JANZZ ontology, please write now to sales@janzz.technology

 

 

[1] Ian Horrocks. 2008. Ontologies and the Semantic Web. URL: http://www.cs.ox.ac.uk/ian.horrocks/Publications/download/2008/Horr08a.pdf [2019.02.01 ]

[2] Larry Lefkowitz. 2018. Semantic Reasoning: The (Almost) Forgotten Half of AI. URL: https://aibusiness.com/semantic-reasoning-ai/ [2019.02.01]

[3] New Idea Engineering. 2018. What’s the difference between Taxonomies and Ontologies? URL: http://www.ideaeng.com/taxonomies-ontologies-0602 [2019.02.01]

[4] Daniel Tunkelang. 2017. Taxonomies and Ontologies. URL: https://queryunderstanding.com/taxonomies-and-ontologies-8e4812a79cb2 [2019.02.01]

[5] Nathan Winant. 2014. What are the advantages of semantic reasoning over machine learning? URL: https://www.quora.com/What-are-the-advantages-of-semantic-reasoning-over-machine-learning [2019.02.01 ]