Job quality

This project provides a method of analysing dimensions of job quality as they appear in online job advertisements. We have extracted and classified a range of aspects mentioned within the job adverts, including employment terms (e.g. offers to work flexible hours), benefits (e.g. life assurance) and job design (e.g. mentions of career progression).

We recognise that mentions of these aspects within adverts are likely to be ‘noisy’ indicators, as advertised benefits may not always be accurate descriptions of the actual benefits. Nevertheless, these indicators could still provide a timely and low-cost signal of job quality dimensions. The UK has no official measuring system for job quality, and the main indicator is an annual survey.

The algorithm that we have developed for extracting dimensions of job quality is freely available and can be adapted to suit different needs. We have also used this project to further refine our existing algorithms, which include a skills extractor algorithm and another that detects the industry which the advert sits within.

Our most recent work, using job adverts, is our Green Jobs Explorer which shows how we can use adverts to provide more nuanced insights on the aspects of greenness within occupations.

The project consists of two workstreams (1) extracting selected drivers of job quality and (2) Machine learning (ML) infrastructure & maintenance:

• Job quality: We have extracted benefits and other aspects mentioned in job adverts, evaluated the resulting data and compared a selection of job quality dimensions across occupations, industries and regions. We have also tested our findings and algorithms to a specific sector; the early years careers.
• Machine learning infrastructure: We have developed an ML Infrastructure layer for all our data science and labour market projects. This included building maintainable approaches using python libraries and project refactoring to modify codebases of any existing projects. Our ultimate aim is to create models that are easy to use for our stakeholders. For more information about the ML Infrastructure, read the technical blog.

We applied this approach to a random sample of job adverts from Nesta’s OJO database in order to investigate offered job quality across sectors in the UK. Some example findings include that mentions of Learning and Development are negatively correlated with salary across occupations, that flexible location is mentioned most commonly in the IT, Human Resources, and Marketing sectors, and that the sectors most likely to offer flexible hours are Logistics & Transport, Health & Social Care and Hospitality & Catering.

We have also used this methodology to carry out a detailed analysis of the advertised working conditions in the early years sector and comparable industries. Nesta’s Fairer Start Mission seeks to close the gap in early outcomes between young children growing up in disadvantage and the national average. A key challenge faced by the mission is a shortage of early years practitioners, and it is thought that early years professionals are leaving the sector for better pay and working conditions. Our analysis suggests that the pay for waiting/retail staff is similar to that for early years practitioners, but that those roles have less secure contracts and are less likely to offer Continued Professional Development (CPD). Meanwhile, vacancies for school teachers are advertised with better pay compared to early years practitioners, and similar other benefits (career progression and Learning and Development).

Oxbury, R., ‘Identifying drivers of job quality in online job adverts‘, ESCoE blog, 30 October 2024

Vines, J., ‘Measuring job quality using ojd_daps models: Technical blog‘, ESCoE blog, 30 October 2024

• See the open codebase
• See full analysis
• Read the case study on early years practitioners

While there have been several efforts to define the dimensions of job quality, there have been far fewer efforts to measure these dimensions. Our approach offers a low-cost approach to quickly detect shifts in the prevalence of different benefits within jobs.

By releasing the algorithms, we hope that both the ONS and government departments (who hold their own datasets of job adverts) will be able to monitor aspects of job quality in any industry or occupation, and that these novel measures will provide a useful complement to more traditional approaches.