Data analysis

Absent on Fridays

Pupils are over 20 per cent more likely to be absent from school on Fridays compared to Wednesdays.

The average rate of absence last term in England’s state-funded schools was 7.5% on Fridays. This compares to 6.7% on Mondays, the next most common day for school absence, and the lower figures for the middle of the week: 6.3% for Tuesdays, 6.2% for Wednesdays and 6.4% for Thursdays.

I have derived these figures by analysing the detailed school attendance data collected and published by the Department for Education.

The issue of school attendance is moving up the political agenda, as levels of absence are now much higher than before the covid pandemic.

The government has today announced what it calls ‘a major national drive to improve school attendance’, with measures targeted at tackling persistent absence. The Labour party is also focusing on the issue this week.

This weekly pattern of absence being highest on Fridays, and second-highest on Mondays, with better attendance mid-week, is a widespread feature of the current school system.

From my analysis of the DfE’s data, it applies in both primary and secondary schools, and also in all regions of England.

It is seen when looking both at authorised and unauthorised absences from school. This includes applying to absence due to illness, which is the most common reason recorded for pupils not attending school.

It was also evident throughout the autumn term, as can be seen in this chart (with a particular peak on the Friday before half-term).

The DfE’s data on school attendance can be downloaded here.

In a previous post I examined how school attendance can be affected by when in the year pupils are born.

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Absence from school and month of birth

For school pupils, does when in the year they are born affect how often they are absent from school?

My analysis of government data suggests that secondary school pupils born in September to December have a somewhat higher absence rate than those born in May to August – which is actually the opposite of what I expected.

Absence from school is now significantly higher compared to before the covid-19 pandemic, and tackling this has been made a target of government educational policy.

Since 2022 the Department for Education (DfE) has been collecting centrally some remarkably detailed and up-to-date data on attendance records for individual pupils from many schools in England, and publishing regular summaries.

The data collated by the department makes it possible to quickly analyse a wide range of factors and potential connections with absences.

Since month of birth is definitely related to other aspects of school life, such as how well pupils do in exams and in sport – the so-called ‘relative age effect‘ – I decided to explore any link with school attendance. Through a freedom of information request I obtained pupil attendance data from the DfE for the school year 2022/23, broken down by type of school, school year and month of birth.

This table shows the percentage of school sessions missed by pupils in selected year groups. It shows that for pupils in years 1 and 2 (aged 5/6 and 6/7), it was the summer-born pupils who had higher rates of absence. This was what I expected, given the well-documented school problems often faced by summer-born children.

But for pupils in years 8 to 11 (aged 12/13 to 15/16), it was those born in September to December who were more likely to be absent.

However the differences within the year groups are not massive, so this pattern (while clear) shouldn’t be overstated. For the intervening ages the data showed very little variation within each year group, so I haven’t presented the figures here. I haven’t obtained data for the reception year.

All this data relates to pupils at about 85% of state-funded schools in England, those which take part in the DfE scheme for automatically submitting daily attendance information.

The following graph shows the same data presented in the form of a line chart.

Persistent absence is a particular problem. This is defined as when pupils are absent for over 10% of school sessions. Analysing the data on persistent absence discloses a similar pattern.

This is indicated in the table below (which involves data from primary and secondary schools, but not special schools).

Generally rates of absence increase as pupils get older and move into higher year groups. Perhaps this trend could help to explain the fact that in secondary schools it’s the older pupils within the year group who tend to be absent more often.

But this can’t be a complete explanation – for example, the frequency of persistent absence is higher for year 10 September births (32.4%) than for the older pupils born in August and in year 11 (30.7%), and similarly for various other data points.

So it looks like there may be some kind of relative age effect involved here, if probably quite mild.

Bear in mind that this is just one year’s data, the period in the wake of the pandemic could be atypical, and there is also the possibility of random variation.

As another potential factor, some illnesses have been associated with when people are born within the year. However, this would not explain the jumps in this data between August and September births.

The DfE data distinguishes authorised and unauthorised absences, but this does not help much in explaining the pattern identified here.

It’s important to note that there are other characteristics which clearly have a bigger impact on school attendance, including levels of disadvantage (poorer pupils are more likely to be absent) and ethnicity (Caribbean and White ethnic groups have higher absence rates than Indian, African and Chinese groups).

The data spreadsheet supplied to me under FOI by the Department for Education is here.

For background on the government’s impressive automated collection of real-time school attendance data, you can watch a recent talk by Caroline Kempner, the DfE’s head of data transformation, given at one of the regular Institute for Government ‘Data Bites’ events (from 37’25” in the video).

It was hearing this presentation which prompted me to do this analysis.

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A&E: when are waits shortest?

Would you like to know what times of the week have the shortest or longest waits in your local A&E department?

I’ve obtained a spreadsheet from NHS Digital (via a freedom of information request) which reveals just that.

The spreadsheet gives data separately for each provider of urgent and emergency care in England for 2021/22. For patient arrivals in each hour of the week, it shows the average duration of attendance there until discharge or admission – ie, until leaving the hospital or being admitted as an inpatient.

The overall A&E pattern is very much that there are longer waits in the late evening and overnight, shorter waits in the morning, with the afternoons/early evenings in the middle.

Source: Analysis by Martin Rosenbaum from NHS Digital data

In this chart each row going across is a different provider of emergency/urgent care in England (I have excluded those with only partial data or which are not 24-hour services), and each column is an hour of the week, going from 0000-0059 on Monday to 2300-2359 on Sunday. The red cells show longer average waiting times, the green cells shorter waits, and the yellow ones intermediate times.

It makes clear that for almost all providers, patients who arrive just before midnight or in the hours afterwards experience the longest waits on average, while those who arrive in the morning have the shortest waits.

This pattern is the same on every day of the week, including weekends. The very longest waits of all tend to be overnight from Monday to Tuesday.

The exceptions to this are mainly urgent treatment centres rather than A&E departments – their busiest times are often late afternoon or early evening. They appear congregated towards the top of this chart due to the ordering of the NHS provider code system.

Some providers show much greater variation in waiting times across different points of the week than others do.

Overall national statistics about busy times of the day in A&E are published routinely, but as far as I am aware this dataset broken down by different local providers and hour of the week has not been released before.

In a period when there is increasing concern over waiting times for emergency and urgent care, it is important and valuable localised information.

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Scotland’s alphabet effect

Last week’s local election results appear to confirm how a candidate’s chance of getting elected to Scotland’s councils is dramatically influenced by a factor which is nothing to do with their abilities – alphabetical order of surnames.

This arises from the voting system used for Scottish council elections, the Single Transferable Vote (STV), where voters number candidates in their order of preference.

Parties will stand more than one candidate in a multi-member ward if they think they have a chance of getting more than one elected.

But of course lots of voters, who may have strong preferences between the parties, don’t particularly care about preferring one candidate from within a party to another.

It’s well established that under STV many voters have a tendency to number candidates from the same party just in the order they find them on the ballot paper, which is a major advantage for those listed first. In Scotland that is alphabetical order by surname.

To illustrate the striking extent of this I have looked at what happened last week in two Scottish councils, Aberdeen and West Lothian (the first and last councils alphabetically, in a limited attempt to avoid alphabetical bias in my selection).

I examined all the cases in these two councils where a party stood two or more candidates in one ward.

In West Lothian, there were 14 examples. In 13 of these, the candidate who came first alphabetically from that party got more first preference votes than the candidate listed second alphabetically, sometimes by huge margins.

The candidates listed first alphabetically for a party averaged 1,669 first preference votes; the candidates from the same party listed second alphabetically only averaged 745 first preferences – less than half as much.

The result was that the candidates listed first alphabetically for a party had a 100% success rate at getting elected; the candidates from the same party listed second alphabetically only had a 64% success rate of election.

In Aberdeen, there were 16 examples. In 14 of these, the candidate who came first alphabetically from that party got more first preference votes than the candidate listed second alphabetically, again sometimes by huge margins.

The candidates listed first alphabetically for a party averaged 1,223 first preference votes; the candidates from the same party listed second alphabetically only averaged 554 first preferences – again, less than half as much.

The result here was that the candidates listed first alphabetically for a party had an 88% success rate at getting elected; the candidates from the same party listed second alphabetically only had a 56% success rate of election.

Obviously it would be ideal to do this analysis for all the 32 local authorities in Scotland. But given the different locations and formats in which all the results are published, that would be a very laborious exercise which is too time-consuming for me to do right now. If there was one single national database of all Scottish local election results in a convenient format for exporting data then it would be a lot more feasible! (I also haven’t examined the impact in the very different political circumstances of Northern Ireland).

It seems clear that the current position in Scotland represents a form of institutionalised systemic discrimination. A council seat is often a step towards building a powerful political career on a bigger stage.

In the past the Scottish government has considered various means of ameliorating this situation but has not implemented any change. Potential options would include randomising the ballot paper order or listing candidates in reverse alphabetical order on half the ballot papers.

Parties could counteract the effect if they had loyal, disciplined voters who would order candidates as instructed, with different instructions issued to different subsets of voters. Roughly equalising the number of first preferences would help to get more than one of their candidates elected.

There has been some evidence of alphabetical voting affecting results in English and Welsh elections, but this is to a much lesser extent because of the different voting systems. Alphabetical voting is also an international phenomenon.

And alphabetical bias also exists in other contexts – here’s an interesting paper on its impact in an academic discipline where co-authors of papers were listed alphabetically.

By the way, when drafting this piece I noticed I had automatically defaulted to providing the Aberdeen data before that for West Lothian, so I went back and reversed that. But I did leave Aberdeen first in the chart.

The acceptance of alphabetical order as an apparently natural and unproblematic method may have a deeper and more insidious grip on our minds, and more important consequences, than we may consciously realise.

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From Morgan to Frankie

The most popular gender-neutral first names given to babies in England and Wales in 2020 were Frankie, River and Harley.

Looking back at a longer period, the most common gender-neutral first names over the past 25 years were Morgan, Charlie and Taylor.

This is according to my analysis of the baby name datasets for England and Wales issued by the Office for National Statistics, who released their figures for 2020 a few days ago.

The ONS compiles separate datasets for the names of boys and girls. Their annual lists of most popular boys’ and girls’ names are always widely reported. I decided to examine something they don’t analyse – the frequency of gender-neutral or unisex names.

In 2020 there were just 10 first names given at birth to both over 100 girls and over 100 boys. They are listed in this table:

They are ordered according to how often they were used for whichever sex they were less popular for. This measure is mine. As it reflects the frequency of the names in both cases, it seems to me to capture gender-neutrality or ‘unisexness’ better than any other criterion I came up with, although other approaches are possible.

Here is a comparable table compiled on the same basis for the past 25 years in total (the published ONS data goes back to 1996), featuring the 12 first names given at birth both to over 2,000 girls and over 2,000 boys:

So Morgan is the leading unisex first name over this time range, the only name to have been given to over 9,000 girls and also over 9,000 boys in the 25-year period from 1996 to 2020. However it has declined considerably in popularity in recent years, as have some other names in this table.

It’s often said that there has been a long-term phenomenon of unisex names becoming ‘feminised’. Some traditional boys’ names start to become popular for girls too, and then parents apparently no longer want to give them to boys (classic examples include Evelyn and Shirley).

However there seems to be little evidence of such a trend in the ONS data over the past 25 years.

As one way to get an overall impression of this, each line on this chart below represents one of the 50 most popular gender-neutral names, and each column is a year, going chronologically from 1996 on the left to 2020 on the right. For each name, cells are coloured more in red for years when they were more popular for girls and more in blue when more popular for boys. (The colour-coding may be stereotypical, but it does make the chart more intuitive to grasp easily).

As time advances, the names move more from the redder/pinker areas to bluer ones than in the opposite way (although by no means uniformly).

That suggests these gender neutral names are not becoming feminised; if anything they appeared to get a bit more popular for boys (ie bluer) and less popular for girls.

However looking at the data in more detail it seems that what is happening is mainly a trend amongst girls: in particular it’s becoming less common to give girls names like Charlie and Jamie, which are largely boys’ names but which 15 to 25 years ago were also used for a fair number of girls.

What this does mean is that unisex names now are more likely to be broadly similar in popularity for both girls and boys, rather than include various predominantly boys’ names which are also given to some girls.

Finally, it’s important to note that generally these unisex or gender-neutral names aren’t very popular at all. So from my list of top 10 unisex names in 2020, Frankie, the highest for boys, is only 61st in popularity for boys’ names overall that year; and Eden, the highest for girls, only just squeezes into the top 100 girls’ names at 98th.

Parents do seem to prefer to give their children names which are clearly recognisable as belonging to either a girl or a boy.

Note: The ONS data (and therefore this analysis) is based on the specific spellings of names on birth certificates and does not take account of similar names. In other words, Charlie and Charley, for example, are treated as entirely different names.

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