Browse the full results of the Impact Rankings 2024

This ranking focuses on universities¡¯ role as engines of?economic growth and their responsibilities as employers. It explores institutions¡¯ economic research, their employment practices and the share of students taking work placements.

View the?methodology for the Impact Rankings 2024?to find out how these data are used in the overall ranking.

Metrics

Research on economic growth and employment?(27%)

• Proportion of papers in the top 10?per cent of?journals as defined by Citescore (14%)
• Number of publications (13%)

This focuses on research that is relevant to economic growth and employment, measuring the proportion of publications in the top 10?per cent of journals and the volume of research produced.

The data are provided by Elsevier¡¯s Scopus dataset, based on a query of keywords associated with SDG 8 (decent work and economic growth) and supplemented by additional publications identified by artificial intelligence. The data include all indexed publications between 2018 and?2022. The data are normalised across the range using Z-scoring.

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Employment practices (19.6%)

• Payment of a living wage to staff and faculty (2.45%)
• Recognition of union and labour rights (2.45%)
• Policy on ending discrimination in the workplace (2.45%)
• Policies against modern slavery, forced labour, human trafficking and child labour (2.45%)
• Guarantees of equal rights for outsourced?labour (2.45%)
• Policy on pay scale equity and gender pay?gaps (2.45%)
• Measuring and tracking pay scale gender equity (2.45%)
• Processes for employees to appeal decisions on?rights and/or pay (2.45%)

The evidence was provided directly by universities, evaluated and scored by?THE?and not normalised.

Expenditure per employee (15.4%)

This metric is calculated by dividing the university expenditure by the number of employees. It is then normalised by the regional GDP per capita. It aims to?explore the extent to which the university is a significant economic driver locally.

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The data were provided directly by universities and normalised across the range using Z-scoring.

Proportion of students taking work placements (19%)

To understand if universities are preparing students for?the world of work, we asked for the number of students with an employment placement of more than a month required as part of their studies, divided by?the total number of students. All data are provided as full-time equivalents.

The data were provided directly by universities and normalised across the range using Z-scoring.

Proportion of employees on secure contracts (19%)

The casualisation of the university workforce is a growing concern, so we asked universities to supply the?number of employees (both academic and non?academic) on contracts of more than 24 months, divided by the total number of employees. All numbers are provided as full-time equivalents. This explicitly excludes short-term contracts required to?cover for maternity or paternity leave.

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The data were provided directly by universities and normalised across the range using Z-scoring.

Evidence

When we ask about policies and initiatives ¨C for example, the existence of mentoring programmes ¨C our metrics require universities to provide the evidence to support their claims. In these cases, we give credit for the evidence, and for the evidence being public. These metrics are not usually size-normalised.

Evidence is evaluated against a set of criteria, and decisions are cross-validated where there is uncertainty. Evidence need not be exhaustive ¨C we are looking for examples that demonstrate best practice at the institutions concerned.

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Time frame

In general, the data used refer to the closest academic year to January to December 2022. The date range for each metric is specified in the full methodology document.

Exclusions

The ranking is open to any university that teaches at undergraduate or postgraduate level. Although research activities form part of the method?ology, there is no?minimum research requirement for participation.

THE?reserves the right to exclude universities that it believes have falsified data, or are no longer in good standing.

Data collection

Institutions provide and sign off their institutional data for use in the rankings. On the rare occasions when a particular data point is not provided, we enter a value of zero.

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