thank you for sharing this MNHQ, it is a very (below) interesting report and I hadn't realised it had been released.
I agree with the point in 2.25 re a focus on getting women into STEM. The barriers to this seem to be getting ever higher, from adults/teachers presenting STEM as a 'gendered' skill area that girls aren't naturally good at, to the rather misogynistic attitudes of those already working in the area.
5.88 Pregnancy & Maternity Discrimination. This is such a complex area. I personally know several small business owners who have said that they think twice about employing a young, recently married woman in case they need time off for a baby.
Certain types of business attracts greater numbers of young women and in a very small business, if several valuable employees are off on maternity at once it can have a huge impact on the business.
A balance must be found between preventing discrimination on the grounds of pregnancy/maternity (or the assumed intention in the future) and the needs of small businesses whose operations, profitability and chances of survival are challenged by the requirements of maternity provision. Greater government support for such businesses may be helpful.
I agree generally with the proposals and note Section 7 in particular. This report could turn out to extremely important as it may be the last of its type that uses mostly accurate recorded at birth sex data. The widespread trend (and the standard being set by the ONS) for companies to record data based on self-recorded sex or gender rather than birth sex undermines the quality of the data available. People don't change role or pay grade based on identity, the inequalities are based on sex. We have no way of knowing how much impact this will have but it could easily end up masking issues especially when looking at narrow topics.
I also note that gender seems to be used in place of sex when referring to a selection of protected characteristics in para. 130.
Disaggregated data from Government
127.The need for data disaggregated by sex and indeed other protected characteristics, has been made strongly and repeatedly to us.176 We note that there is a disparity not just between departments as to what data is disaggregated and when, but indeed within individual departments.177 We heard from the Office for National Statistics (ONS) that they were committed to providing disaggregated data.178 Other witnesses also stressed how fortunate we were to have robust and reliable data sets, at least in part as a result of the commitment of the ONS.179 Government ministers and officials also pointed to the availability of ONS statistics.180
128.Whilst we are grateful for the work the ONS has done, and note the recent establishment of the Inclusive Data Taskforce,181 this is not a substitute for action by the Government to make data from administrative sources available. ONS data inevitably suffers from a time lag, and we note with concern that the publication of UK labour market statistics is significantly slower than in other countries.182 The covid-19 pandemic is the clearest possible example of real-time policy making requiring real time data. Further, administrative data is not affected by the same sampling concerns that have affected survey results over the pandemic.183
129.Robust equalities data is crucial to effective policy responses. We have been frustrated by the lack of data disaggregated by sex and other protected characteristics. The lack of intersectional data in large government data sets continues to frustrate meaningful analysis.
130.We recommend the Government require all departments to collect and publish data disaggregated by sex and protected characteristics in a way that facilitates reporting and analysis on how, for example, gender, ethnicity, disability, age and socio-economic status interact, and can compound disadvantage.