Charity workers’ skills overlooked in government data, says think tank

20 Oct 2022 News

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HMRC should change how it collects data on employees, so the skills within the charity sector are not overlooked, according to sector think tank NPC.

In response to an HMRC consultation on improving the data it collects, NPC said civil society organisations were “largely invisible” in its current figures.

NPC argued that a lack of good data about civil society can lead to inefficiency and result in government policy underplaying the role and potential of the charity sector.

Civil society ‘invisible’ in HMRC data

HMRC’s data currently allows companies to select a category to describe the nature of their business, but NPC has highlighted there is no obvious way to identify charitable organisations. 

James Noble, associate director for data and learning at NPC, said: “We agree with HMRC’s view that data can drive innovation and productivity, enhance public services, and improve people’s lives. The problem is that civil society is largely invisible within this data.”

The think tank suggests HMRC adopt an accurate ‘civil society’ or ‘not-for-profit’ identifier across all its work and products. 

Noble said an accurate and exclusive civil society or non-profit identifier “would make charity employees more visible in government data, thereby bolstering the place of civil society in government thinking and increasing its potential to play a meaningful role in delivering policy which improves people’s lives.”

The submission also states its support for HMRC’s proposal to start collecting occupational data and the number of hours worked.

“The arguments made in support of the idea in the consultation brief apply equally to civil society: It would be a step forward to better understand the occupational make-up of the civil sector workforce, for example comparing ‘back room’ to ‘front line’ roles, or the number of people in digital roles compared to other sectors and over time.”

However, the submission highlights that the value of occupational data will depend on whether the sector can identify itself within the data, hence the importance of these categorisations.

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