JILPT Research Eye
Toward a New Dawn
―The importance and potential of quantifying occupational information

July 20, 2016
(Originally published on March 17, 2016 in Japanese)

photo

Shinsaku MATSUMOTO

Project Researcher


Many will be wondering what is meant by the phrase “quantifying occupational information”. Here, I would like to explain that this “unknown” concept of quantification has an important significance both for individuals and for society as a whole, as well as having potential for various research and development in connection with strong economies and national competitiveness. The US Department of Labor has been spending between 700 million and 1.2 billion yen a year (calculated at 120 yen to the dollar – though the actual amount varies from year to year) on this quantification of occupational information, thus underlining its importance.

1. Occupational information and development by the state

Firstly, the meaning of the phrase “occupational information” is not explained in any dictionary, and although explanations and commentaries can be found in career guidance books and the like, there is no fixed definition. So let’s now consider this concept using specific examples.

Representative occupational information can be found in the Occupational Outlook Handbook (OOH) issued by the US Department of Labor. Although we may debate the precise meaning of “representative occupational information”, the assertion that this OOH is representative occupational information will surely not be contested by researchers in this sector, nor career consultants or career counselors, at least. OOH used to be just a book, but is now also a website. It gives information on some 300 occupations, explaining (1) what people in a particular occupation do (What They Do), (2) the work environment of that occupation (Work Environment), (3) how to join the occupation (How to Become One), (4) salaries (Pay), (5) future prospects (Job Outlook), (6) data by state and area (State & Area Data), and (7) other similar occupations (Similar Occupations). Although the section on “states” in (6) feels unique to the USA, Japan also has considerable differences based on region or area, though they may be smaller than in the USA. For example, people in a particular occupation may be more numerous in certain places, or pay and working conditions may vary depending on the region or area. In fact, an “Occupational Handbook” used to be published in Japan too. This was developed and produced on the model of OOH, which it resembled in its composition. This “Occupational Handbook” could be described as Japan’s representative occupational information. However, partly because it is no longer published, I have taken the US Labor Department’s OOH as representative occupational information that can be accessed instantly. Based on the above, if OOH is representative occupational information, we could describe “occupational information” as the information given under (1) to (7) above.

To digress somewhat, OOH is thought to have started life as a booklet introducing occupations that might be suitable for demobilized servicemen after World War II. This then evolved into the thick book published in revised editions every other year as the Occupational Outlook Handbook, which today is also available on the Internet. The OOH site shows projected change in occupations over the next 10 years, in descending order of fastest growing occupations where employment is increasing, most new jobs, and highest paying occupations. For reference, the three fastest growing occupations currently listed on the site are (1) Wind turbine service technicians (2014-2024 growth rate 108%), (2) Occupational therapy assistants (2014-2024 growth rate 43%), and (3) Physical therapist assistants and aides (2014-2024 growth rate 41%). Statisticians, an occupation coming under attention as “data scientists”, are 9th with a 2014-2024 growth rate of 34%. The volume of data on the OOH site is vast, and it is completely revised every two years. The Department of Labor employs around 70 specialists to gather and analyze information for OOH.

Besides this OOH, the US Department of Labor also gathers, analyzes, organizes and provides information on occupations through O*NET (Occupational Information Network, O*NET OnLineopen a new window, O*NET Resource Centeropen a new window), to be discussed later. The annual budget of this O*NET is the 700 million to 1.2 billion yen mentioned above. But the USA is not the only country that gathers occupational information. Official occupational information is also organized and provided at government level by ROME in France, BerufeNet in Germany, and Job Profiles in the UK.

The reason why occupational information is being highlighted in this way is because having all the citizens manifest their respective abilities is felt to be the foundation of a strong economy and the wellspring of national competitiveness. Economist Robert Reich, a policy adviser in the Ford and Carter administrations who later served as US Secretary of Labor, questioned the nature of investment for national competitiveness. Bearing in mind that most leading companies are now multinational, investing in them does not help to strengthen a nation’s competitiveness, as they cross national borders in search of the best locations for factories and equipment. There are also no borders to capital any more, Reich argued. For a nation to strengthen its competitiveness, investment should be targeted at the education and skills of the people, and the IT infrastructure that connect them (The Work of Nations: Preparing Ourselves for 21st Century Capitalism, Diamond Inc.1991 (Japanese edition); US Congressional Testimony by Secretary of Labor Secretary Robert Reich, 1996). This triggered the development of O*NET, as a new database for occupational information that plays an important role in these areas of education, skills and information infrastructure (“The New DOT: A Database of Occupational Titles for the Twenty-First Century – Advisory Panel for the Dictionary of Occupational Titles, Employment and Training Administration, US Department of Labor (APDOT) Final Report”, Material Series No.39, The Japan Institute of Labour, 1994).

Reich’s statements seem to have been made a long time ago, but in both the Grameen Bank, winner of the 2006 Nobel Peace Prize for its “microfinance”, and “impact investment (social contribution investment)”, a new type of investment now coming under some attention, the point seems to be a system in which investment is made in people and is then returned to society as a whole. As such, the perspective of investing in people not only remains unchanged, but also, if anything, has become more important.

2. What is the quantification of occupational information?

So far, we have looked at “occupational information”. Turning next to the “quantification” of occupational information, the aim of this is to express occupational information in numerical terms as far as possible. What exactly is quantification, and why is it necessary?

The quantification of occupational information is nothing particularly new. In the US Labor Department’s O*NET, occupations are quantified from a variety of aspects, and information is provided in the form of a database. Quantification from a variety of aspects makes it possible to provide information in the form of MySQL, SQL Server and Oracle database files, rather than being restricted to simple numerical tables. The latest version is also provided in Excel form (O*NET 20.1), and there are nearly 40 Excel files. These O*NET database and other files can be freely downloaded by anyone.

This quantification in O*NET is based on an average of responses from people engaged in each occupation and the evaluation by expert analysts. The figures are updated and augmented, while the topics and categories included in them are continuously reviewed, with revised database files released annually (or more recently, twice a year). The most recent files at the current time are O*NET 20.1 released in October 2015.

There again, quantification of occupations by the US Labor Department did not start with O*NET. A source of information prior to O*NET was the Dictionary of Occupational Titles (DOT). This was a thick volume rather like a telephone book (something not often seen these days), giving tens of thousands of occupational titles, as well as definitions and commentaries. DOT included minimum scores required for various aptitudes in each occupation (there were nine aptitudes coded G, V, N, Q, S, P, K, F, and M) in the “General Aptitude Test Battery” (GATB), an occupational aptitude test that was developed by the US Labor Department and widely used. Taking GATB made it possible to know whether a person had aptitude for a given occupation based on the result.

To digress once more, the development of GATB and the creation of standard minimum scores required for each aptitude came while the USA was engaged in World War II. It is thought to have been pursued as a national project so that the large numbers of demobilized servicemen expected to return after the end of the war could be effectively re-employed in industry and the postwar economy could make a robust restart.

Topics in the US Labor Department’s O*NET database

Knowledge, Skills, Abilities, Education, Experience, Training, Job Zones (the overall difficulty of the occupation, from those that can be done after a simple explanation to those that require specialist education and training over many years), Interests, Work Values, Work Styles, Tasks, Tools & Technology, Work Activities, and Work Context (relationships with people in other occupations, status within organizations, etc.).

3. Facilitating large-scale information gathering from actual employees

So how should numerical information for each occupation be developed? In the past, specialists in job analysis (Job Analysts) have visited businesses and observed jobs in situ, interviewed the people concerned and gathered information. This method is explained in detail in The Revised Handbook for Analyzing Jobs (U.S. Department of Labor, 1991). However, this is a very labor- and cost-intensive method. Specialists in job analysis have to be trained, after which they go to businesses all over the USA to collect information. This process not only incurs costs but also takes many years to complete. And because it takes so long, the situation could have changed by the time the analysts have gone around the first time, so that the information collected first is already old. In view of this, Job Analysis Centers in five US locations were closed down, and as in the case of information gathering by O*NET described above, questionnaires are now distributed to businesses and the results aggregated, or evaluated and aggregated by specialists. In this way, steps have been taken to reduce both cost and time.

The use of “web monitors” (Internet users who respond online to web surveys) is currently under the spotlight in Japan as a method of gathering information. The Japan Institute for Labour Policy and Training, for example, has gathered information from tens of thousands of respondents actually working in specific respective occupations, producing the following reports.

In Japan, there are several survey companies that have millions of web monitors, and information can be gathered by specifying detailed occupations (at the occupation sub-category level in the classification of occupations by the Ministry of Health, Labour and Welfare) and selecting 120 web monitors for each occupation, for example. This makes it possible to obtain precise numbers and information for nearly 600 occupations, which was not previously possible. Moreover, information on tens of thousands of people can now be gathered in just a few weeks.

The latest Population Census could also be completed online for the first time, and the Internet response rate is said to have been 36.9% (Ministry of Internal Affairs and Communications press release, September 25, 2015). Actually, I also completed it online, and it was disarmingly simple. Japan has millions of web monitors, a stable online environment is widely established, and computer and Internet literacy are high. It may only be possible to gather occupational information online if these conditions are all in place. As long as information can be gathered efficiently, this should also be possible in the USA, but it has yet to be established as a method there. The USA still relies on the method of distributing questionnaires to businesses, gathering responses from employees and aggregating them for each occupation, then having specialists evaluate these and aggregate the results. Perhaps there is a problem with the online environment, or literacy, or perhaps, having started with the method of distributing questionnaires, collecting responses, and having them evaluated by experts, there is a problem with connections to the data. It seems strange, but perhaps this method in U.S. is only possible because there is a huge budget and many experts who can conduct evaluation.

Gathering information from actual employees used to be impossible, but can now be done, and information on tens of thousands of people can be gathered in a few weeks. On top of that, the method of gathering responses online makes it possible to use various ideas that would not be possible with a paper survey. When gathering information on occupations, as in this case, people have to specify their own occupation among hundreds of others, but they can search in Japanese phonetic order, search from classification of occupations, search by inputting words freely, and so on, enabling them to select their own occupation appropriately. Data science that analyzes the “big data” produced by the advance of the IT society is now a topical subject, and we could certainly say that there have been revolutionary advances in information gathering on occupations. There may be concerns about occupations being fabricated when information is gathered online. Nevertheless, judging from the information gathered so far, this seems to be rare.
Response activity can be checked in various ways by the system. Various improvements can be made to address situations in which the time given for responses is too short, there is too great a bias toward neutral responses (e.g. “Neither one nor the other”), or when a logical check proves that the response is not serious, etc. Carrying out this kind of check leads to a certain percentage of monitors being replaced every year. The quality of responses to surveys is a matter of greatest concern, and the survey companies are adopting various different measures to address this. By virtue of this process, it seems that appropriate responses are being gathered.

While the gathering of occupational information for quantification has already been discussed, the situation and changes of occupations in situ can be ascertained by changing the topics. JILPT Research Report No.176, “Studies on Occupational Structure II: Data Analysis on Present Status, Change, Requirements and Private Life Based on an Online Occupational Trends Survey with 50,000 Working People” (2015) also discusses this kind of significance of a new challenge. Information on the situation and changes of actual sites of occupations can be gathered in the scale of tens of thousands of people in a few weeks, and this will surely serve as a useful reference for policy assessments. Though discussed in the report, this was just around the time that Abenomics started, and it appraises the phenomenon whereby jobs became busier thanks to the stimulation of economic activity, and wages started to rise, albeit only slightly.

4. The importance of quantifying occupational information

1) Individual characteristics and the occupations that make the most of them: Optimal allocation of the labor force by the state

As shown above, huge budgets have been spent on quantifying occupational information in the USA, and this has also been a matter of longstanding concern for me personally. For my first job in JILPT more than 30 years ago involved developing occupational aptitude tests. Even then, there were already plenty of occupational aptitude tests, and developing the tests themselves was not such a difficult job. I was able to do various things, such as incorporating new concepts, or creating a version for personal computers, which were starting to become widespread at the time. But a problem in that respect was how to create data that would be a standard for collation with occupations. One possible method would have been to enlist cooperating companies and carry out tests there, then aggregate data for each occupation together with evaluation from the companies. But that would have been virtually impossible. In particular, corporate evaluation would have to involve personnel information, and that would have been confidential. The fact that the USA pursued this as a national project during World War II shows what a large-scale undertaking it is. Such large-scale information gathering could not be done. And even if it could be, it would have been very difficult to gather data from businesses without any omissions, since there were so many hundreds of occupations. What’s more, the work was predicted to take several years. If it took several years, the standards would already be old by the time all the data had been gathered. At the time, therefore, we were collating the test results with occupations, while referring to the standards and others created by the US Labor Department. As already stated above, this has now made it possible to set the target of a certain number of people for each of several hundred occupations and thus to gather information in just a few weeks, even though the total number runs to tens of thousands.

The notion of collating the results of aptitude tests with occupations may sound rather insignificant. However, aptitude tests reveal individual characteristics, and objectively show occupations that can make the most of those individual characteristics, based on data. To expand economic activity in a society with declining birthrate and aging population, we need to fully harness the abilities of each individual and raise productivity for the society as a whole. To this end, we must have objective data on individual characteristics and the occupations that make the most of them. From a national point of view, this could be seen as basic data for optimal allocation of the labor force. As we have seen, a massive budget has been spent on quantifying occupations in the USA and on updating that quantification. This is because quantification is perceived as being very important for the national economy. It is also important for individuals, in the sense of making the most of individual characteristics. Making the most of one’s own characteristics in one’s occupation and being able to express one’s ability freely is not only related to good incomes and stable employment, but also leads to job satisfaction and motivation.

2) Distance between occupations: Important information when changing jobs

Being able to quantify occupations means being able to arrange them in a multidimensional space. As “multidimensional” might not conjure up much of an image, we could consider the three dimensions of ability, interests and working style, for example, with each occupation allocated within these three dimensions. And being able to arrange each respective occupation means that the distance between occupations can be calculated, and data can be used to show which occupations are close to each other and which are far apart.

While our individual working lives have gradually lengthened to 30 years and then to 40, changes in economy and society have also been accelerating, and more people today are changing not only their place of employment but also the content of their work (i.e. their occupations). In that case, if the relationship between occupations can be shown numerically, people will know which occupations are close to them in terms of ability, and which can make the most of their current abilities. Because we have multidimensional data, occupations that make the most of our abilities, suit our interests and are close to our working styles can be listed in order of “closeness.” A specific example of calculation in this case can be found in JILPT Research Report No.146, “The Studies on Occupational Structure – Numerical Analyses of Occupations and an Analysis of Occupational Mobility” (PDF:800KB).

Being able to quantify occupations multidimensionally and knowing the relationship between occupations provides very useful data when planning to reallocate manpower in line with economic and social change. And for individuals who are changing jobs, it is preferable to change to occupations that suit their ability and interests. This is necessary for efficient labor mobility in line with economic and social change, and is also necessary for the happiness of the individual. This is why the USA allocates such a massive budget to it.

5. Potential for various research and development

The quantification of occupational information has potential to expand into various forms of research. The shocking content of “The Future of Employment: How Susceptible Are Jobs to Computerisation?” by Carl B. Frey and Michael A. Osborne (2013) [Note 1] attracted great interest and was cited in various economic journals. The basic thrust was that significant amounts of human labor will be replaced by machines, robots or artificial intelligence (AI) in the near future. The paper’s calculations were based on numerical data from O*NET. From the quantified characteristics of a given occupation, the authors calculated the likelihood of that occupation being taken over by machines, robots or AI. The Nomura Research Institute, in joint research with Osborne, has also calculated which occupations in Japan are more likely to be replaced [Note 2], but this was based on multidimensionally quantified data on occupations gathered and published by JILPT [Note 3]. In Toshie Ikenaga’s paper “Polarization of the Japanese Labor Market: Adoption of ICT and Changes in Tasks Required” (2009) [Note 4], numerical information on skills gathered by JILPT and published in the form of a career matrix is used in the calculation process. And in my own “Capabilities, Aptitudes, Consciousness and Behavior Required in Service Industries: From the Data Analysis of “50,000 Workers Web Occupational Trend Survey” (2016) [Note 5], I have re-gathered the data and analyzed what is required by service industries in terms of ability.

In this way, the quantification of occupational information has potential to expand into a variety of research. Numerical research has not been possible until now, because occupational information consisted of commentaries on the content of occupations, i.e. written descriptions. But quantifying occupations multidimensionally allows the numerical data to be re-aggregated and re-analyzed in various kinds of research. The research by Frey and Osborne and the research by Nomura predict a replacement of human labor by machines, robots and IT in future based on the quantified characteristics of occupations. Even besides this, however, various research is possible on wage level determinants, factors stabilizing employment and determining job satisfaction, and the relationship between occupations and lifestyles, etc. for example. The quantification of occupations could be seen as providing a new basis for research on occupations, and the existence of this basic information is taking research on related sectors toward a new dawn.

There is also the possibility of various developments in the quantification of occupations. Because these are objective numerical data that match occupations with the characteristics of individuals, if a tool could be made to measure aspects such as skill, knowledge, experience, interests and values on the individual’s side, it could show objectively which occupations are suited to that individual, based on numerical data. As stated above, it is not difficult to develop aptitude tests and others to measure individual characteristics, but there were previously no data linking the results to occupations, and this was a major stumbling block. This was removed by the use of numerical data on occupations. In fact, the US Labor Department has developed tools for measuring abilities, interests, values and other aspects using the numerical standards for occupations (O*NET Career Exploration Tools). Even besides the Department of Labor, other computerized support systems for right job diagnoses, right job searches, and others have been developed so far, by adding independently gathered data to the numerical standards published by the Labor Department (System of Integrated Guidance and Information of Educational Testing Service, DISCOVER of ACT, Inc., and others). And using information from O*NET, the Labor Department itself provides tools for human resource management (Toolkit for Business). The quantification of occupational information could be said to have potential for explosive development.

In the foregoing, I have discussed the importance and potential offered by the quantification of occupational information. It has an important significance, as also seen here, as well as potential for opening up new horizons of research and making a succession of brilliant developments. Meanwhile, the data for this quantification of occupational information can be gathered efficiently, at low cost, and moreover in a short time by using the Internet. This is not only about occupational information, but, by applying various creative ideas on web surveys, it is now possible to gather other information that could not be gathered using conventional paper questionnaires.

Footnotes

Note 1. Carl B. Frey & Michael A. Osborne (2014), “The Future of Employment: How Susceptible Are Jobs to Computerisation?”(PDF:1.0MB)open a new window, Oxford University

Note 2. Nomura Research Institute (2016) “49% of Japan’s Working Population Could Be Replaced by AI or Robots: Calculating the Probability of Replacement by Computerized Technology for Each of 601 Occupations”open a new window (in Japanese only).

Note 3. The Japan Institute for Labour Policy and Training (2012), “The Studies on Occupational Structure – Numerical Analyses of Occupations and an Analysis of Occupational Mobility” (PDF:800KB), Research Report No.146.

Note 4. Toshie Ikenaga (2009), “Polarization of the Japanese Labor Market: Adoption of ICT and Changes in Tasks Required”, The Japanese Journal of Labour Studies No. 584 (PDF:32.2KB) (Abstract in English).

Note 5. Shinsaku Matsumoto (2016), “Capabilities, Aptitudes, Consciousness and Behavior Required in Service Industries: From the Data Analysis of “50,000 Workers Web Occupational Trend Survey”, The Japanese Journal of Labour Studies No. 666 (PDF:534KB) (Abstract in English).


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