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Aligning Supply and Demand Using Data Analytics in Further Education

This article explores the complex relationship between supply and demand in education and how to align delivery with economic need

Photo by Surface on Unsplash
Photo by Surface on Unsplash

Background

There are many reasons why further education providers are increasing their focus on the alignment of supply of education vs. the demand for skills and qualifications of local employers and local economic need.

The new Ofsted Education Inspection Framework (EIF) has a focus on the intent, implementation and impact of education and according to FE News curriculum intent can be bespoke to [educational] programmes and employers.

The government’s Further Education Workforce Strategy gives a set of strategic priorities with the "Quantity and quality of teachers and trainers" as the first priority and "Responsiveness to employer need" as the second.

Lastly commercial firms commonly take account of supply vs. demand of goods and services to inform decisions on pricing and production levels and further education providers have many similarities to commercial firms in this respect.

The Challenges

Access to Labour Market Intelligence (LMI) Data

There are several ways providers can accelerate their access to economic data, for example by using the excellent Analyst tool from EMSI (https://www.economicmodeling.com/).

Image by Author (screenshot of www.emsieconomicmodeling.com)
Image by Author (screenshot of www.emsieconomicmodeling.com)

This subscription service gives rapid access to a synthesis of relevant data in an understandable format but this is just the first step to acquiring intelligence and knowledge relating to the alignment of supply and demand.

Supply and Demand are Expressed in Different Units

Educational supply is expressed in "SSA" or "Subject Sector Area" codes (for example SSA2 42 is "Manufacturing Technologies") whereas employer demand is expressed in "SOC" or "Standard Occupation Codes" (for example SOC1 is "Managers Directors and Senior Officials").

In order to make any comparison between supply and demand they must both be expressed in a common unit and this is complicated because there is no clean mapping. For example, SSA2 code "11" has 7 sub-classifications. 6 of them map into SOC1 code "2" and one of them maps to SOC1 code "3".

A complex conversion involving weightings is required with a Data Analytics and data science capability necessary to make an effective conversion.

Once this has been completed a comparison between supply and demand can be visualised as shown in the following example –

Image by Author
Image by Author

The Supply and Demand Model has Several Conflicting Inputs

I developed the model below recently to help visualise the complex interaction between supply and demand and to explain what factors impact on the design choices a further education college faces when considering strategic and tactical curriculum design and the model highlights several challenges –

Image by Author
Image by Author

Students are the customers of the service but they do not pay for it (in the case of 16–18s), rather the government pays. This is what Duncan Brown, Senior Economist at EMSI described to me as the 3rd party payer problem and it creates complexity in the dynamics of supply and demand.

The employers who ultimately have the demand also pay nothing (again in the case of 16–18s) for the product / service and perhaps more tellingly it is other stakeholders like awarding organisations who broadly decide on course content and criteria and not the employers giving rise to further complexity in alignment of supply with demand.

16–18s leaving education to enter the labour market do so typically on the first rung of the career ladder. The full impact of their education will not be realised until the employee has progressed through their career so there can be a long delay between the production of the good (an educated student) and consumption of the service (a highly skilled and productive employee).

Another complexity in this model is that the education and skills students desire may not align with those the employer and the economy need creating further complexity.

This is an instance of deferred impact and it is also important to recognise that education providers are only one source of new labour for an economy. The others are existing workers moving around, skills improvement by employees at work and workers moving into the economy (for example seasonal farm workers).

The model has "Historic Curriculum Design" as the first input and this indicates that colleges will have a strong pull to roll existing curriculum forward and not to make fundamental changes to reflect changes in demand. Colleges will have significant investment in staff and capital resources and pivoting supply is time consuming, complex and expensive hence alignment with demand is likely to take place incrementally.

The last complexity identified is what Duncan Brown, Senior Economist at EMSI described to me to as education being a "credence good". Unlike a tin of beans that can be tasted or a car that can be test-driven, the impact of education cannot be directly and immediately evaluated by consumers. This contributes to the significant impact of the brand of educational institutions, particularly in the higher education space which makes up a significant proportion of delivery in many further education providers.

Conclusions

Photo by Zach Lucero on Unsplash
Photo by Zach Lucero on Unsplash

Key Points

The alignment of supply and demand in Further Education is complex and multi-faceted, but there are many reasons why FE colleges face an increasing need to align their delivery with local employer and economic need and there are ways that providers can begin to break down this complex task –

· Data analytics and Data Science are key enablers. There is a wealth of data including private, public and subscription datasets that can be developed into information, intelligence and knowledge.

· Collaboration with other regional providers is also important. Further Education colleges do not tend to be in direct competition with each other hence acquiring, manipulating and interpreting the data will be faster and more accurate if done in collaboration rather than in isolation.

· Labour Market Intelligence (LMI) data is just one aspect. Other data like historical learner behaviour, progression data, demographic data, travel distances etc. all have important parts to play and many different "lenses" are needed to build the overall picture.

· External stakeholder relationships are also key. Relationships with Local Enterprise Partnerships, Councils and others are necessary to build strategic intelligence. The best data model in the world cannot know, for example, if a big player like Amazon is about to build a new local fulfilment centre that will disrupt the local labour market but strong external stakeholder relationships can.

· Providers should strive to continually improve their understanding of Supply And Demand and to ensure the intelligence can be gathered and then embedded in all aspects of the strategic curriculum design and planning processes.

Final Word

My two favourite quotations about data analytics and data science are –

"Things get done only if the data we gather can inform and inspire those in a position to make a difference" (Dr. Mike Schmoker)

and …

"If you torture the data long enough, it will confess to anything" (Ronald H. Coase)

Somewhere between those two extremes there is a need to support education leaders to understand and adopt the insightful but often complex messages that data models provide and to act on the conclusions whilst avoiding any over-confidence biases that can come with over-examination of those models or with over-reliance on intuition.

Thank-you for Reading!

If you enjoyed reading this article, why not check out my other articles at https://grahamharrison-86487.medium.com/?

Also, I would love to hear from you to get your thoughts on this piece, any of my other articles or anything else related to data science and data analytics.

If you would like to get in touch to discuss any of these topics please look me up on LinkedIn – https://www.linkedin.com/in/grahamharrison1 or feel free to e-mail me at [email protected].


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