I’m Alex Sheridan. I’ve been a research assistant at the French Institute for Demographic Studies (Ined) for the past three years. I’m interested in childhood inequalities.
In October 2023, as part of the Transnational Access Visits, I spent three weeks at the Policy Evaluation and Research Unit at Manchester Metropolitan University. I was very warmly welcomed by Lee Bentley and by the rest of the GUIDE team, and it was a great opportunity to talk about my comparative work involving MCS data. It was also lovely to meet and spend time with another early career researcher, who was visiting at the same time as me. We had fun discovering Manchester and its whereabouts… lots of great stuff to do and to see!
My current research in Sociology focuses on gender inequalities in early childhood. My stay coincided with the early days of my paper on hygiene practices. In this paper, I ask whether parents have different expectations and practices with respect to their child's hygiene, depending on the child's sex. I also ask whether institutional settings influence the development of children's autonomy, and whether gendered gaps evolve differently as a result. Using data from the French cohort Etude Longitudinale Française depuis l’Enfance and from the British cohort Millennium Cohort Study, I pay particular attention to what happens when children start school at different ages: in France, children start school around age 3, while they start around 4-5 in the UK.
When I was in Manchester, I was trying to compare toilet training in France and in the UK. Both cohorts provide information on this, as well as on the child's socio-economic background. I faced a challenge common to most comparative work using data from national studies: it requires a lot of data harmonisation because variables can be measured at different ages, and they can measure significantly different aspects of one same concept. Applied to toilet training variables, parents were asked about their child's hygiene much later in the UK compared to France, and the questions were very different. For instance, asking about a child's cleanliness produces very different answers whether you ask about daytime or night-time cleanliness, making comparisons more difficult.
Lee made lots of time for us to discuss how to best use the MCS data. I was able to make sure I had fully understood its basic handling—merging the different datasets, which weights to use when, dealing with missing values, etc.—, and to ask more specific questions with regards to my paper. It gave me a chance to make sure I was doing my best in terms of data harmonisation, in particular for my covariates. I was surprised to find differences in the measurement of seemingly basic characteristics, such as parents' origins and migration background. To make sure the analyses matched for both countries, I therefore had to find where information overlapped and resort to more general descriptions of families, leading to loss of information from both countries.
Finding how to code variables similarly across countries isn't the only challenge in comparative work using national datasets. A lot of thought needs to go into cultural differences to make sure you’re comparing comparable things. For instance, because education systems are country-specific, the baccalauréat and the A-levels don’t necessarily bear the same social and economic meaning, even though these national exams take place at similar ages. One needs to answer this type of question when coding education variables across countries.
Addressing these questions at the conception stage of a European cohort, among a network of experts across Europe, would allow researchers using this data to focus on their field of expertise, confident in knowing that the variables they use translate well one country to the next.
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