Today was the day that submissions had to have been sent to the Migration Advisory Committee (MAC) in connection with their partial review of shortage subjects in education. In preparing the TeachVac submission www.teachvac.co.uk we confronted several methodological issues.
In the first instance, there is the issue of collecting continuous data versus collecting data at a single point in time. TeachVac collects new data every weekday, whereas the DfE collects vacancies only in terms of the number of vacancies recorded at the date of the School Workforce Census in November. So long as there is data for several years that method provides information about trends at that point in time, but cannot say anything about what happens during the rest of the year. The DfE can also calculate turnover and that is also important as additional evidence, but not as compelling as it might seem at first sight. Turnover records outcomes and not desires, so if a school advertises for a teacher of physics, but appoints a biologist because no physicist applies, the data has recorded the turnover, but not the fact that it wasn’t an ideal match with the original requirement of the school.
Some other organisations that collect data on teacher vacancies appear to reply upon vacancies advertised on job boards. Even if job boards are studied regularly, the fact that many vacancies aren’t linked to a particular school makes identifying a reliable total more of a challenge. Is this maths vacancy advertised today for a teacher in London the same as the one advertised yesterday or was that filled and another London schools has requested a teacher? Indeed, could there be several vacancies for maths teachers in London hidden behind a single advertisement? This is more doubtful, because presumably job boards want to show they handle a large number of vacancies for many different schools. Hopefully, there are no occurrences of ‘ghost’ vacancies advertised on job boards just to attract applicants to the site.
As TeachVac is now collecting additional data on several man scale vacancies, it is also able to more successfully handle the issue of identifying multiple vacancies advertised at the same time in the same subject. This is quite common in new schools advertising for staff for the first time, and not unusual in subjects such as English and mathematics during the height of the recruitment season during April.
There still remains the issue of re-advertisements that bedevils all those that study vacancies. The only perfect solution is to ensure a vacancy is attached to a unique identification number that follows it until the post is filled. Until then, there must be an element of extrapolation in any statistics that analyse the job market. There is a similar issue with repeat advertisements, especially in print media, but this is essentially the same problem discussed above with jobs that appear on digital job board. TeachVac has a mechanism for coping with this issue as part of its AI routines.
The MAC will no doubt be wise to these issues when it considers the submissions it has received. It will also have to consider why in the past business studies and design and technology weren’t considered as shortage subjects. Finally, there is the issue, advanced by some in the maths world that schools are supressing vacancies because they don’t want to alarm parents. To measure that you need to look at the DfE’s analysis of teacher numbers and the highest level of qualifications of those teaching a subject and how those have changed over time.
TeachVac has now extended its AI to start collect vacancies beyond teaching and it is discovering some of the issues recorded here makes data collection in that sector possibly even more of a challenge.