Written evidence submitted by Professor Alice Sullivan, Dr Amanda Gosling, Professor Nick Allum, Professor Tarani Chandola, Dr Colin Mills and Professor Lindsay Paterson (GRA0992)

Women and Equalities Committee: Reform of the Gender Recognition Act

Evidence submitted by:

Professor Alice Sullivan (UCL); Dr Amanda Gosling (University of Kent); Professor Nick Allum (University of Essex); Professor Tarani Chandola (University of Manchester); Dr Colin Mills (University of Oxford); Professor Lindsay Paterson (University of Edinburgh).

November 2020

  1. We are a group of social scientists engaged in the statistical analysis of survey and administrative data. Our collective expertise falls within the fields of sociology, economics, education, labour markets, social mobility, social measurement, survey methodology, population and public health and epidemiology. We have a shared research interest in social inequalities, including those due to sex, race/ethnicity and social class.


  1. Our submission concerns the availability of accurate sex-based data. The submission falls under “wider issues concerning transgender equality and current legislation”, specifically “Are there challenges in the way the Gender Recognition Act 2004 and the Equality Act 2010 interact?”.


  1. Sex is a protected characteristic under EA2010. In order to monitor inequalities, discrimination, and the effectiveness or ineffectiveness of policy interventions to tackle disadvantage due to sex, accurate sex-based data is required.


  1. Gender reassignment is also a separate protected characteristic. Monitoring disadvantage due to gender reassignment similarly requires accurate data on this protected characteristic. As we will argue, it also requires accurate data on sex.


  1. Social statisticians are typically not only interested in one characteristic, but often examine the intersection between a number of characteristics, e.g. sex, ethnic group, social class, age, etc. One cannot assume that membership of one category has identical effects across subgroups. For example, being black may increase an individual’s risk of contact with the police, but the increased risk may be higher for males than females.


  1. Similarly, the experiences of people with the protected characteristic of gender reassignment are likely to vary substantially according to their natal sex. We certainly cannot assume that natal males and females who identify as trans (or non-binary) will have the same or similar experiences in any social domain where sex is a factor, e.g. education; the labour market; experiences of domestic violence or sexual assault. Thus, without accurate data on sex, we cannot adequately monitor the differing experiences of males and females who have undergone gender reassignment (this equally applies to the broader group identified as trans or non-binary).


  1. Sex is a fundamental demographic variable. We need accurate data, disaggregated by sex, in order to understand differences in the lives of women and men, and in order to tackle sexism. Sex matters from the start of life, as illustrated by international differences in the sex ratio at birth due to son preference (Chao et al. 2019). Sex is a powerful predictor of almost every dimension of social life: education (Stoet et al. 2016), the labour market (Joshi et al. 2019), political attitudes and behaviour (Green and Prosser 2018), religion (Voas 2015), crime (Ministry of Justice 2017), physical health (Koblinsky, Campbell and Harlow 2018), mental health (Ploubidis et al. 2017), cultural tastes and consumption (Sullivan and Brown 2015) – the list goes on. It is difficult to think of an area of life where sex is not an important dimension for analysis. A lack of sex-disaggregated data often leads to the needs of women and girls being ignored (Perez 2019).


  1. In healthcare settings, failure to ask about natal sex has potentially serious consequences.  In order to improve both demographic survey data and the health care provided to trans people, the recommendation has been made to ask for information on both natal sex and gender identity (Mays et. Al. 2018; Wylie et. Al. 2016).


  1. The availability of accurate sex-based data is already being undermined by the conflation of sex and gender identity (Sullivan 2020). Examples include police forces recording crimes committed by men as though they were committed by women at the request of the perpetrator (Burden 2019), organisational pay-gap data being collected according to gender identity rather than sex (ACAS 2019), with the option to exclude ‘non-binary’ employees from the data;  and the replacement of actual sex with the desired sex on medical records at patients’ request (MBM Policy Analysis 2020b). The removal of sex from data collection by public bodies is part of a process of ‘policy capture’ by organisations who aim to remove sex as a protected characteristic in law (Murray and Hunter Blackburn 2019).


  1. The UK census has collected data on sex since its inception in 1801, yet the census authorities plan to advise respondents to the 2021 census that they may answer the sex question according to their gender identity. This will damage the ability of the census to monitor inequalities between women and men, including assessing the sex-differentiated impacts of Covid-19. Eighty quantitative social scientists have written to the census authorities to contest this guidance (MBM Policy Analysis 2020a).


  1. It is sometimes assumed that the trans population is so small that the impact of self-id on data accuracy will be negligible. However, we currently have no reliable data on the size of the trans population either in the population as a whole or within sub-groups, and crucially, it is impossible to predict how this may change over time. It is unlikely that the trans population will be evenly distributed, for example by age, sex and geography. This means that the effects on data reliability are likely to be greater at the sub-group level. This can have extreme consequences for particular subgroups, e.g. 1 in 50 male prisoners in England and Wales identify as transgender (HM Inspectorate of Prisons 2019). The Tavistock and Portman NHS Trust claims that between 1.2% and 2.7% of children and young people are ‘gender-diverse’ (NIHR 2019).  The trans population is growing rapidly, particularly among young females, and the reasons for this are not well understood, and require investigation (Littman 2018).


  1. We are concerned that change to the Gender Recognition Act, particularly any move to ‘gender self-id’, may result in a substantial increase in individuals whose legal sex is different from their biological sex. There is a risk that this will further erode the collection of sex-based data. This in turn will undermine the work of the Women and Equalities Committee, and all evidence-based work on inequalities. We suggest the following actions to safeguard sex-based data collection and analysis.


1)      That the committee explicitly recognise the legitimacy and importance of analyzing data based on biological/natal sex.

2)      That data providers should be encouraged to collect data on respondent sex, as distinct from gender identity or legal sex, potentially alongside gender identity and/or gender reassignment as additional variables.

3)      That publicly funded or mandated data (such as the census or equal pay monitoring data) should always include a biological/natal sex variable.


ACAS. 2019. Managing gender pay reporting. https://archive.acas.org.uk/media/4764/Managing-gender-pay-reporting/pdf/Managing_gender_pay_reporting_07.02.19.pdf: Government Equalities Office.

Burden, E. 2019. "Letting criminals self-identify gender ‘putting women at risk’." The Times https://www.thetimes.co.uk/article/letting-criminals-self-identify-gender-putting-women-at-risk-8560wzkqt (14th March 2019).

Chao, Fengqing, Patrick Gerland, Alex R Cook, and Leontine Alkema. 2019. "Systematic assessment of the sex ratio at birth for all countries and estimation of national imbalances and regional reference levels." Proceedings of the National Academy of Sciences 116(19):9303-11.

Joshi, Heather, Alex Bryson, David Wilkinson, and Kelly Ward. 2019. "The gender gap in wages over the life course: evidence from a British cohort born in 1958." IZA DP No. 12725.

Koblinsky, Marjorie A, Oona MR Campbell, and Siobán D Harlow. 2018. "Mother and more: a broader perspective on women’s health." Pp. 33-62 in The Health Of Women: Routledge.

HM Inspectorate of Prisons. 2019. HM Chief Inspector of Prisons for England and Wales: Annual Report 2018–19. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/814689/hmip-annual-report-2018-19.pdf: House of Commons.

Littman, Lisa. 2018. "Rapid-onset gender dysphoria in adolescents and young adults: A study of parental reports." PloS one 13(8):e0202330-e30.

Mays, J. A., Greene, D. N., Metcalf, R. A., & Pagano, M. B. (2018). Transfusion support for transgender men of childbearing age. Transfusion, 58(3), 823-825.

MBM Policy Analysis. 2020a. "MBM view on proposals for the sex question in the UK 2021 census." https://murrayblackburnmackenzie.org/2020/02/07/mbm-view-on-proposals-for-the-sex-question-in-the-uk-2021-census/.

—. 2020b. Recording sex on medical records: a case study of NHS Scotland. https://murrayblackburnmackenzie.org/2020/01/12/recording-sex-on-medical-records-a-case-study-of-nhs-scotland/

Ministry of Justice. 2017. "Statistics on Women and the Criminal Justice System 2017." https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/759770/women-criminal-justice-system-2017..pdf.

Murray, Kath, and Lucy Hunter Blackburn. 2019. "Losing sight of women's rights: the unregulated introduction of gender self-identification as a case study of policy capture in Scotland." Scottish Affairs 28(3):262-89.

NIHR. 2019. "Outcomes and Predictors of Outcome for Children and Young People Referred to UK Gender Identity Development Services: A longitudinal Investigation, award 17/51/19." in NIHR Funding and Awards. https://fundingawards.nihr.ac.uk/award/17/51/19: National Institute for Health Research.

Perez, Caroline Criado. 2019. Invisible Women: Exposing data bias in a world designed for men: Random House.

Ploubidis, GB, A Sullivan, M Brown, and A Goodman. 2017. "Psychological distress in mid-life: evidence from the 1958 and 1970 British birth cohorts." Psychological Medicine 47(2):291-303.

Stoet, Gijsbert, Drew H Bailey, Alex M Moore, and David C Geary. 2016. "Countries with higher levels of gender equality show larger national sex differences in mathematics anxiety and relatively lower parental mathematics valuation for girls." PloS one 11(4).

Sullivan, Alice, and Matthew Brown. 2015. "Vocabulary from adolescence to middle age." Longitudinal and Life Course Studies 6(2):173-89.

Sullivan, A. (2020). Sex and the census: why surveys should not conflate sex and gender identity. International Journal of Social Research Methodology, 23 (5) 517-524.

Voas, David. 2015. "Religious involvement over the life course: problems of measurement and classification." Longitudinal and Life Course Studies 6(2):212-27.

Wylie, K., Knudson, G., Khan, S. I., Bonierbale, M., Watanyusakul, S., & Baral, S. (2016). Serving transgender people: clinical care considerations and service delivery models in transgender health. The Lancet, 388(10042), 401-411.


November 2020