Adzuna is a search engine for job advertisements and provides data about the job market.

Adzuna searches thousands of websites and brings together millions of advertisements on their website.

The dataset consists of full point-in-time snapshots with details of all advertisements which were on adzuna.co.uk.

Download the full Adzuna data profile (PDF 0.2MB) and watch the video of our 'Introduction to Adzuna job market data' webinar for further information about this dataset.

Details of projects that have already been approved to use this data are outlined below.

Effects of Artificial Intelligence on the British labour market

Researcher: Dr Maria Garcia de la Vega

The recent increase of artificial intelligence (AI) opens unimaginable opportunities to businesses and organizations, but it has also raised concerns among policymakers, academics, unions, and wider society for their potential impact on employment and on the organization of labour.

The proposed research will measure the effects of AI on the British labour market. We will construct a measure of adoption of AI at the firm level using information of online job postings from the dataset Adzuna. With information of technology adoption as revealed by desired job characteristics, and additional firm level data, we can analyse the characteristics of the firms, sectors and regions that are adopting the new technologies. These data will also allow us to study how the adoption of new technologies affects the labour force of firms adopting new technology. Moreover, we will analyse the characteristics of the firms that increase and those that reduce their labour force after the adoption of new technologies.

Corporates’ response to climate change via the lens of job posting

Researcher: Dr Xiaoxia Ye

This research looks at how companies have been responding to the climate change risk. One of the key challenges in the quantification of the corporate responses to climate risk is the credibility of the measured responses.

The credibility of the measures inferred from traditional sources is subject to corporates’ commitment to what they have said on paper. The job posting data provide a perfect alternative to the traditional text data that overcomes the credibility issue. In this project, the researchers will employ cutting-edge natural language processing (NLP) and machine learning (ML) techniques to identify climate risk-related jobs posted by companies in various industries and construct firm level as well as aggregate indices quantifying corporates’ responses to climate risk. A statistical model will be developed for the construction of the indices. It is hoped that the resulting indices will be more representative of corporates’ real actions.

Understanding clusters and innovation through a data-led taxonomy of firms and roles

Researchers: Dr Jonathan Reades, Dr Max Nathan

This project will use text from job listings to identify areas of innovative industrial activity across the UK with unprecedented geographical resolution. In looking at how companies describe themselves and the jobs for which they are recruiting, the researchers expect to find some firms presenting themselves in unusual ways, and searching for unusual or ‘novel’ skills, that distinguish them from a common ‘core’ of terms and roles used across a sector as a whole. Drawing on cutting-edge Natural Language Processing (NLP) and data visualisation techniques, the researchers anticipate that subtle but statistically significant differences in word choice will enable them to create a data-driven ‘taxonomy’ focussed on the distinction between ‘core’ and ‘novel’ activities, and to then locate those activities geographically in order to identify areas where the latter predominate.

Impact of COVID-19 on unemployment and earnings inequality

Researcher: Camila Comunello

This research will document how individuals search for jobs across occupations/industries using longitudinal data on job search available through the Understanding Society COVID19 study. Informed by these data, the researchers will develop and estimate multi-sector business cycle models in which workers' occupation/industry mobility decisions trade off their career prospects against the relative abundance of vacancies across sectors. This framework will allow them to quantify the effectiveness of e.g. job seekers assistance, re-training and job retention schemes on unemployment and earnings inequality through their effects on workers' reallocation and firms' layoff and job creation decisions. This will provide a new perspective to the current debate on how best to bring people back to work after the COVID19 pandemic.

Examining the effect of labour market slack on wages during periods of reduced mobility

Researchers: Dr Dafni Papoutsaki (with Dr Jason Hilton)

The supply of labour in the UK has been constrained since the Spring of 2020 by internal movement restrictions set in place to control the pandemic and by international movement restrictions both due to the pandemic and the exit of the country from the EU. This project will investigate how disparities in the supply and the respective demand for labour affect wages. The implications of these labour market scarcities for the labour market outcomes of those at the lower end of the earnings distribution will also be analysed.

Learning about labour demand during the Covid recovery from vacancy data

Researchers: Professor Julia Darby (with Professor Graeme Roy and Dr Stuart McIntyre)

This project aims to investigate changing trends in labour demand as the economy emerges from Covid. Two aspects are of interest:

  1. To what extent is the shift to remote working/working from home likely to continue once guidance that “everyone who can work from home must do so” ends? We are interested in investigating the extent to which opportunities for remote working are more prevalent in job adverts, and how the characteristics of job openings that fall into the “remote working” category have changed since Covid restrictions were first introduced, relaxed, reintroduced and are eventually removed.
  2. Can vacancy data shed light on the acceleration in the shift to e-commerce during the Covid-19 pandemic and the extent to which this is seen in a rise in demand for warehouse and delivery related workers and a persistent decline in traditional retail jobs.

The Impact of Labour Market Opportunities on Wages

Researcher: Professor Alan Manning

There are large and persistent differences in labour market outcomes across areas which partly represent differences in economic opportunity. Understanding these disparities is central to ensuring that growth benefits all. In this project we will use fine-grained data on job vacancies to investigate how the number and type of vacancies vary across areas and whether this variation can explain labour market outcomes.

Growth, resilience and recovery in local and city economies

Researchers: Professor Paul Sissons, Professor Donald Houston

This project will utilise job vacancy data (combined with other sources) to examine the implications of Covid-19 for local economic resilience and recovery. The research will provide detailed analysis of the impacts of the Covid-19 shock on employment, providing new insights of policy relevance (including emerging issues such as hard-to-fill vacancies), and contributing to wider debates about the concept of regional resilience. The project will also develop learning on the distribution and importance of economic clusters in urban areas for local employment. Taken together the project will develop new evidence to support policy around post-Covid economic development and employment support needs.

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