# Damned lies and statistics

A closer look at statistics in the news

## Neighbourhoods and schools

One of my grand theories is that public policy types are generally bad at geography. Or, at the least, they underestimate the importance of where you live. Here, below the fold, are two zoomable maps, coloured by the school performance of local state-educated children. The map is based on where the children live, not where they go to school. To explain:

• The colouring is red for weaker results and blue for better ones. Darker colours mean more extreme results. If you want detail on an area, click on any one of the blobs and it should give you a run down of local statistics, where possible.
• Both maps are coloured according to FT score results: that is the sum of state-educated pupils’ scores in English, maths and their top three other subjects.    Other data, including official measures, are in the boxes that pop up.
• On the first map, the geographical blobs are smaller than on previous maps: the lowest super output area in high density places, and the middle-layer output area in zones of low density (this way, we can show maximum detail).
• That map can be quite frazzling. The second might be more to some people’s tastes. This is exactly the same sort data, just arranged by parliamentary constituency. Since they are bigger lumps, we can include more detailed data.
• For the constituencies, I have given a barrage of results for all local children in state schools. But also the same just for FSM-eligible children, and for children dubbed “middle attainers” – kids who score in the middle tenth of results aged 11.
• (NB – Where statistics are missing, it is prevent people combining data sources to work out something about individual children.)

If you want a tour, I’d recommend scrolling along the coasts. Check out some of the coastal towns, and look at the belt of towns and cities between Hull and Liverpool. Also, take a peek at how few dark red areas there are in London. In-borough variation is interesting, too: look at the massive variation within, say, Kent. Read more

## An inspector calls at schools

Since January, schools have been subject to a new inspection regime. Ofsted, the inspectorate, has changed its criteria. Data released today mean there is one question we can consider: is the new inspection regime any tougher or easier than its predecessor?

This is not a straightforward question: weaker schools get inspected more regularly, so the sample is not randomly selected. What we can do, however, is see whether schools are more likely to be promoted or relegated than in previous years.

This, too, is not simple. The Department for Education changed schools’ ID numbers when they became academies, so I cannot match every new report to the same school’s previous ones so it is a faff to match records, which has taken a bit of tinkering. We have matches for 1,711 schools – both primary and secondary.

Here are the results:

1 2 3 4
1 25% 50% 25% 0%
2 8% 58% 27% 7%
3 2% 44% 40% 14%
4 1% 13% 75% 11%

## Funder blunders in English schools

The cash advantage for converting to become an academy is bigger for schools in more affluent areas. Read more

## Are NHS funds being diverted to the rich? Well, a bit…

There has been speculation recently that the government is planning to divert millions of pounds in NHS funds from deprived urban areas in the north, to leafy, Conservative voting constituencies in the south.

This stems from health secretary Andrew Lansley’s recent comment that “age is the principal determinant of health need” and that distribution of the £100bn budget for the NHS in England should “get progressively to a greater focus on what are the actual determinants of health need.”

Somewhere along the line, those comments were interpreted by a generally cheesed-off medical profession that Mr Lansley intends to introduce an “age-only” NHS allocation formula, switching substantial NHS funds from, generally younger, Labour-voting constituencies in north to the octogenarians who thrive in the Conservative-voting villages of the south.

It’s a good story, which might even contain elements of the truth, but the reality, as ever, is a little more complicated.

At present, five separate allocation formulae are used to divvy up different bits of the £100bn NHS pot to different areas of England. The largest share – the hospital care budget – is divided up using one formula, while four others – mental health, GP prescribing, health inequalities (more on that in a later post) and maternity – are each allocated using their own separate formula. (Think for a second about the demographics driving the demand for maternity services as opposed to, say, hip replacements, and you will grasp why this makes sense.)

Health economists and statisticians frequently tweak and argue over these formula in order to move, hopefully, ever closer to the Holy Grail: a distribution of health resources which is fairly distributed on the basis of health need. Read more

## On grammar schools

Grammar schools are a seductive idea: skim off high performing children at the age of 11 for education together. At the moment, there are 164 such schools in England, in a few counties which did not manage to slough them off. But their success is a myth. Read more

## Two thirds of deaths not counted

The latest World Health Organisation statistics report has thrown a light on the unglamorous but essential backbone of health policy – accurate death reporting.

According to the report, currently only 15 percent of the world’s population lives in a country where more than 90 percent of births and deaths are registered – and unsurprisingly most of these 34 countries are in Europe and the Americas.

It’s not surprising that war torn countries like Afghanistan might have had other concerns than registration data. But the list of countries without comprehensive data include major economic and population centres like China and India – both of whom use sample registration approaches. The full country by country list is on this pdf.

WHO region No death registration
data
Low quality Medium quality High quality Number of WHO
Member States
AFR 42 2 1 1 46
AMR 2 7 13 13 35
SEAR 7 4 0 0 11
EUR 2 11 24 16 53
EMR 9 10 2 0 21
WPR 12 4 7 4 27
Global 74 38 47 34 193