Exactly!

The ones that always annoy me are along the lines of "£70 a year...that's only 20p a day!" That always sounds more significant to me than the annual cost, not less!

We will deposit these surveys on the Secure Research Service, and hope that researchers will link the microdata more widely and uncover further insights. PSMPS was funded by the Public Services Productivity Review. Many thanks to my team and previous work by collaborators at @escoeorg.bsky.social

For example, researchers could apply their own judgement to whether limited financial incentives in the public sector reflect weaker management practices, or are appropriate to the nature of the work in that sector. Differences in characteristics, such as size, might also affect comparisons.

It is worth reflecting on the trade-offs of applying the same management practices standards to public and private sectors. It is helpful to be able to compare results directly, but different organisational characteristics and cultures might affect how questions are interpreted.

We also asked about average days worked from home by managers and non-managers. In all subsectors, managers were at least as likely as non-managers to use hybrid work, and in most cases were more likely. Average management practice scores were usually higher in organisations that use hybrid working.

Scores on underperformance were strongly influenced by the proportion of organisations that reported having no underperformance. These scored zero under our scoring metric, because we assume that all organisations must have some examples of underperforming staff.

While the public sector generally scored well on KPIs, Targets scored similarly to the private sector, with the lack of financial incentives for achieving targets reducing public sector scores. Despite prior question testing, the concept of KPIs was not consistently understood across subsectors.

Given the pilot nature of the PSMPS and the diversity of subsectors, it is worthwhile looking at the responses in more detail. Responses on Employment Practices were more likely to score highly across most of the public sector, but in Education the lack of promotions lowered the score.

There are similarities and differences between and with the sectors. The mean management practice scores were close: 0.56 for the public sector and 0.55 for the private sector. But the spread was wider in the private sector, where firms were more likely to receive the highest or lowest scores.

We combined the 2023 Management and Expectations Survey (MES) with our pilot 2023 Public Sector Management Practices Survey (PSMPS). We kept scoring management practices questions in the PSMPS identical to MES to allow direct comparison. More info on MES methods here: www.ons.gov.uk/economy/econ...

The article and data pack contain more results on uses of AI, barriers to adoption, and the relationship with productivity. Please let us know if you have feedback on the analysis. The dataset will be available to accredited researchers in the Secure Research Service. www.ons.gov.uk/economy/econ...

By linking waves of the Business Insights and Conditions Survey in 2024 onto MES 2023, we also found that among firms who hadn't adopted AI in 2023 but said they planned to in 2024, the firms with stronger management practices were more likely to follow through on those plans.

The positive relationship between management practices quality and technology adoption remained after controlling for other business characteristics such as size and industry.

Firms with higher management practice scores were more likely to adopt advanced technologies. 20% of firms in the top tenth of firms by management practices quality used AI in their methods or processes in 2023, compared to 2% of firms in the lowest tenth.

Conditional adoption rates give clues about which advanced technologies might be required or complementary to adopting others. 91% of the firms using AI in 2023 also used cloud-based computing systems and applications, and 83% of the firms using AI also used specialised equipment.

This looks into what supporting investments are needed for businesses to adopt AI, including good quality structured management practices, and complementary technologies. This is important for understanding potential barriers to diffusion of AI to firms throughout the productivity distribution.