Inclusion by nature of employment

Permanency of employment

Overall, IT specialists from minority groups more likely to be forced into non-permanent jobs

In general IT specialists are less likely to be in non-permanent employment than those within the workforce as a whole (with relative proportions of 3% and 6% respectively) and this is also the case for those within each of the minority groups analysed within this report. A more detailed analysis of data for IT specialists shows however that non-permanent employment is more common for female as opposed to male workers, disabled as opposed to non-disabled and non-white versus white IT specialists.

Figure 9: IT inclusion and the incidence of non-permanent employment, 2016
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Figure 9: IT inclusion and the incidence of non-permanent employment, 2016
Source: Analysis of ONS Quarterly Labour Force Survey by BCS

Not only are IT specialists from minority groups more likely to be in non-permanent positions than other workers, but they are also more likely to be forced into this kind of work as they are unable to find a permanent job (comparison figures of 33% and 21% respectively)[1].

This finding is not common to all minority groups however, and whilst female IT specialists and those from non-white groups are more likely to be unable to find permanent work than their male / white counterparts, the reverse appears true for the disabled / those aged 50 and above.

Figure 10: IT specialists in non-permanent posts unable to find permanent jobs, 2014-16
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Figure 10: IT specialists in non-permanent posts unable to find permanent jobs, 2014-16
Source: Analysis of ONS Quarterly Labour Force Survey by BCS

Inclusion by IT occupation

Inclusion levels are worse for disabled people in all IT specialisms bar IT Engineers

The issue of inclusion amongst IT specialists varies dramatically according to the nature of occupation in question, though in absolute terms, the levels of inclusion for disabled people are lower than that for other groups in all areas of IT work bar IT Engineering. That said, the disparity between the level of inclusion within the IT professions and the level observed for all occupations is higher for women in all IT occupational groups.

Figure 11: Inclusion by IT occupation, 2016
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Figure 11: Inclusion by IT occupation, 2016
Source: Analysis of ONS Quarterly Labour Force Survey by BCS

Inclusion levels in other recognised professions often worse than in IT

Inclusion is not an issue solely for the IT professions though and, as illustrated in the subsequent table, the situation is often actually worse in other well recognised professions - notably amongst the Police and Civil Engineers. More specifically in 2016 it was observed that there was a lower proportion of females working as Civil Engineers than IT specialists, a lower proportion of disabled people working as Doctors and Solicitors, a lower proportion of Police Officers aged 50 or over and lower proportions of non-white workers employed as Civil Engineers, Police Officers, Nurses, Teachers and Solicitor.

Figure 12: Inclusion by selected professions, 2016
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Figure 12: Inclusion by selected professions, 2016
Source: Analysis of ONS Quarterly Labour Force Survey by BCS

Inclusion and unemployment

The unemployment rate for IT specialists in minority groups is higher than the norm

Sadly, whilst the minority groups analysed in this report tend to exhibit lower levels of inclusion within the IT workforce, they are more likely to be unemployed, and with the exception of female IT specialists, each group is associated with an unemployment rate[2] higher than that for IT specialists as a whole or of their peers of ‘non-minority status’. This was particularly apparent for disabled IT specialists who were associated with an unemployment rate of 4.2% over the past 2 years compared with a figure of 2.0% of IT specialists not so classified.

Figure 13: IT inclusion and associated unemployment rates for IT specialists, (2015-16)
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Figure 13: IT inclusion and associated unemployment rates for IT specialists, (2015-16)
Source: Analysis of ONS Quarterly Labour Force Survey by BCS

[1] Three year averages, treat with caution low base sizes.
[2] Unemployment rates are calculated by dividing the estimated number of people classed as unemployed whose previous job was in an IT position by the total number of people working / previously working in an IT position.