Category Archives: Economics

Evidence-based house moving

I live in London. I didn’t really intend to live in London. But I got a job here that seemed to suit me, so I thought maybe it would be OK to live here for a couple of years and then move on.

That was in 1994. Various life events intervened and I sort of got stuck here. But now I’m in the fortunate position where my job is home-based, and my partner also works from home, so we could pretty much live anywhere. So finally, moving out of London is very much on the agenda.

But where should we move to? The main intention is “somewhere more rural than London”, which you will appreciate doesn’t really narrow it down very much. Many people move to a specific location for a convenient commute to work, but we have no such constraints, so we need some other way of deciding.

So I decided to do what all good statisticians do, and use data to come up with the answer.

There is a phenomenal amount of data that can be freely downloaded from the internet these days about various attributes of small geographic areas.

House prices are obviously one of the big considerations. You can download data from the Land Registry on every single residential property transaction going back many years. This needs a bit of work before it becomes usable, but it’s nothing a 3-level mixed effects model with random coefficients at both middle-layer super output area and local authority area can’t sort out (the model actually took about 2 days to run: it’s quite a big dataset).

Although I don’t have to commute to work every day, I’m not completely free of geographic constraints. I travel for work quite a bit, so I don’t want to be too far away from the nearest international airport. My parents, who are not as young as they used to be, live in Sussex, and I don’t want to be too many hours’ drive away from them. My partner also has family in the southeast of England and would like to remain in easy visiting distance. And we both love going on holiday to the Lake District, so somewhere closer to there would be nice (which is of course not all that easy to reconcile with being close to Sussex).

Fortunately, you can download extensive data on journey times from many bits of the  country to many other bits, so that can be easily added to the data.

We’d like to live somewhere more rural than London, but don’t want to be absolutely in the middle of nowhere. Somewhere with a few shops and a couple of takeaways and pubs would be good. So I also downloaded data on population density.  I figured about 2500 people/square km would be a good compromise between escaping to somewhere more rural and not being in the middle of nowhere, and gave areas more points the closer they came to that ideal.

I’d like to have a big garden, so we also give points to places that have a high ratio of garden space to house space, which can easily be calculated from land use data. Plenty of green space in the area would also be welcome, and we can calculate that from the same dataset.

One of the problems with choosing places with low house prices is that they might turn out to be rather run-down and unpleasant places to live. So I’ve also downloaded data on crime rates and deprivation indices, so that run-down and crime-ridden areas can be penalised.

In addition to all that, I also found data on flood risk, political leanings, education levels, and life satisfaction, which I figured are probably also relevant.

I dare say there are probably other things that could be downloaded and taken into account, though that’s all I can think of for now. Suggestions for other things very welcome via the commenst below.

I then calculate a score for each of those things for each middle-layer super output area (an area of approximately 7000 people), weight each of those things by how important I think it is, and take a weighted average. Anything that scores too badly on an item I figured was important (this was just house prices and distance to my parents) automatically gets a score of zero.

The result is a database of a score for every middle-layer super output area in England and Wales (I figured Scotland was just too far away from Sussex), which I then mapped using the wonderful QGIS mapping software.

The results are actually quite sensitive to the weightings applied to each attribute, so I allowed some of the weightings to vary over reasonable ranges, and then picked the areas that consistently performed well.

The final map looks like this:

map

Red areas are those with low scores, green areas are those with high scores.

Not surprisingly, setting a constraint on house prices ruled out almost all of the southeast of England. Setting a constraint on travelling time to visit my parents ruled out most of the north of England. What is left is mainly a little band around the midlands.

And which is the best place to live, taking all that into account? Turns out that it’s Stafford. I’ve actually never been to Stafford. I wonder if it’s a nice place to live? I suppose I should go and visit it sometime and see how well my model did.

Sugar tax

One of the most newsworthy features of yesterday’s budget was the announcement that the UK will introduce a tax on sugary drinks.

There is reason to think this may have primarily been done as a dead cat move, to draw attention away from the fact the the Chancellor is missing all his deficit reduction targets and cutting disability benefits (though apparently he can still afford tax cuts for higher rate tax payers).

But what effect might a tax on sugary drinks have?

Obviously it will reduce consumption of sugary drinks:  it’s economics 101 that when the price of something goes up, consumption falls. But that by itself is not interesting or useful. The question is what effect will that have on health and well-being?

The only honest answer to that is we don’t know, as few countries have tried such a tax, and we do not have good data on what the effects have been in countries that have.

For millionaires such as George Osborne and Jamie Oliver, the tax is unlikely to make much difference. Sugary drinks are such a tiny part of their expenditure, they will probably not notice.

But what about those at the other end of the income scale? While George Osborne may not realise this, there are some people for whom the weekly grocery shop is a significant proportion of their total expenditure. For such people, taxing sugary drinks may well have a noticeable effect.

For a family who currently spends money on sugary drinks, 3 outcomes are possible.

The first possibility is that they continue to buy the same quantity of sugary drinks as before (or sufficiently close to the same quantity that their total expenditure still rises). They will then be worse off, as they will have less money to spend on other things. This is bad in itself, but also one of the strongest determinants of ill health is poverty, so taking money away from people is unlikely to make them healthier.

The second possibility is that they reduce their consumption of sugary drinks by an amount roughly equivalent to the increased price. They will then be no better or worse off in terms of the money left in their pocket after the weekly grocery shopping, but they will be worse off in welfare terms, as they will have less of something that they value (sugary drinks). We know that they value sugary drinks, because if they didn’t, they wouldn’t buy them in the first place.

Proponents of the sugar tax will argue that they will be better off in health terms, as sugary drinks are bad for you, and they are now consuming less of them. Well, maybe. But that really needs a great big [citation needed]. This would be a relatively modest decrease in sugary drink consumption, and personally I would be surprised if it made much difference to health. There is certainly no good evidence that it would have benefits on health, and given that you are harming people by depriving them of something they value, I think it is up to proponents of the sugar tax to come up with evidence that the benefits outweigh those harms. It seems rather simplistic to suppose that obesity, diabetes, and the other things the the sugar tax is supposed to benefit are primarily a function of sugary drink consumption, when there are so many other aspects of diet, and of course exercise, which the sugar tax will not affect.

The third possibility is that they reduce their consumption by more than the amount of the price increase. They will now have more money in their pocket at the end of the weekly grocery shop. Perhaps they will spend that money on vegan tofu health drinks and gym membership, and be healthier as a result, as the supporters of the sugar tax seem to believe. Or maybe they’ll spend it on cigarettes and boiled sweets. We simply don’t know, as there are no data to show what happens here. The supposed health benefits of the sugar tax are at this stage entirely hypothetical.

But whatever they spend it on, they would have preferred to spend it on sugary drinks, so we are again making them worse off in terms of the things that they value.

All these considerations are trivial for people on high incomes. They may not be for people on low incomes. What seems certain is that the costs of the sugar tax will fall disproportionately on the poor.

You may think that’s a good idea. George Osborne obviously does. But personally, I’m not a fan of regressive taxation.

Solving the economics of personalised medicine

It’s a well known fact that many drugs for many diseases don’t work very well in in many patients. If we could identify in advance which patients will benefit from a drug and which won’t, then drugs could be prescribed in a much more targeted manner. That is actually a lot harder to do than it sounds, but it’s an active area of research, and I am confident that over the coming years and decades medical research will make much progress in that direction.

This is the world of personalised medicine.

Although giving people targeted drugs that are likely to be of substantial benefit to them has obvious advantages, there is one major disadvantage. Personalised medicine simply does not fit the economic model that has evolved for the pharmaceutical industry.

Developing new drugs is expensive. It’s really expensive. Coming up with a precise figure for the cost of developing a new drug is controversial, but some reasonable estimates run into billions of dollars.

The economic model of the pharmaceutical industry is based on the idea of a “blockbuster” drug. You develop a drug like Prozac, Losec, or Lipitor that can be used in millions of patients, and the huge costs of that development can be recouped by the  huge sales of the drug.

But what if you are developing drugs based on personalised medicine for narrowly defined populations?  Perhaps you have developed a drug for patients with a specific variant of a rare cancer, and it is fantastically effective in those patients, but there may be only a few hundred patients worldwide who could benefit. There is no way you’re going to be able to recoup the costs of a billion dollars or more of development by selling the drug to a few hundred patients, without charging sums of money that are crazily unaffordable to each patient.

Although the era of personalised medicine is still very much in its infancy, we have already seen this effect at work with drugs like Kadcyla, which works for only a specific subtype of breast cancer patients, but at £90,000 a pop has been deemed too expensive to fund in the NHS. What happens when even more targeted drugs are developed?

I was discussing this question yesterday evening over a nice bottle of Chilean viognier with Chris Winchester. I think between us we may have come up with a cunning plan.

Our idea is as follows. If a drug is being developed for a suitably narrow patient population that it could be reasonably considered a “personalised medicine”, different licensing rules would apply. You would no longer have to obtain such a convincing body of evidence of efficacy and safety before licensing. You would need some evidence, of course, but the bar would be set much lower. Perhaps some convincing laboratory studies followed by some small clinical trials that could be done much more cheaply than the typical phase III trials that enrol hundreds of patients and cost many millions to run.

At that stage, you would not get a traditional drug license that would allow you to market the drug in the normal way. The license would be provisional, with some conditions attached.

So far, this idea is not new. The EMA has already started a pilot project of “adaptive licensing“, which is designed very much in this spirit.

But here comes the cunning bit.

Under our plan, the drug would be licensed to be marketed as a mixture of the active drug and placebo. Some packs of the drug would contain the active drug, and some would contain placebo. Neither the prescriber nor the patient would know whether they have actually received the drug. Obviously patients would need to be told about this and would then have the choice to take part or not. But I don’t think this is worse than the current situation, where at that stage the drug would not be licensed at all, so patients would either have to find a clinical trial (where they may still get placebo) or not get the drug at all.

In effect, every patient who uses the drug during the period of conditional licensing would be taking part in a randomised, double-blind, placebo-controlled trial.  Prescribers would be required to collect data on patient outcomes, which, along with a code number on the medication pack, could then be fed back to the manufacturer and analysed. The manufacturer would know from the code number whether the patient received the drug or placebo.

Once sufficient numbers of patients had been treated, then the manufacturer could run the analysis and the provisional license could be converted to a full license if the results show good efficacy and safety, or revoked if they don’t.

This wouldn’t work in all cases. There will be times when other drugs are available but would not be compatible with the new drug. You could not then ethically put patients in a position where a drug is available but they get no drug at all. But in cases where no effective treatment is available, or the new drug can be used in addition to standard treatments, use of a placebo in this way is perfectly acceptable from an ethical point of view.

Obviously even when placebo treatment is a reasonable option, there would be logistical challenges with this approach (for example, making sure that the same patient gets the same drug when their first pack of medicine runs out). I don’t pretend it would be easy. But I believe it may be preferable to a system in which the pharmaceutical industry has to abandon working on personalised medicine because it has become unaffordable.

Made up statistics on sugar tax

I woke up this morning to the sound of Radio 4 telling me that Cancer Research UK had done an analysis showing that a 20% tax on sugary drinks could reduce the number of obese people in the UK by 3.7 million by 2025. (That could be the start of the world’s worst ever blues song, but it isn’t.)

My first thought was that was rather surprising, as I wasn’t aware of any evidence on how sugar taxes impact on obesity. So I went hunting for the report with interest.

Bizarrely, Cancer Research UK didn’t link to the full report from their press release (once you’ve read the rest of this post, you may conclude that perhaps they were too embarrassed to let anyone see it), but I tracked it down here. Well, I’m not sure even that is the full report. It says it’s a “technical summary”, but the word “summary” makes me wonder if it is still not the full report. But that’s all that seems to be made publicly available.

There are a number of problems with this report. Christopher Snowdon has blogged about some of them here, but I want to focus on the extent to which the model is based on untested assumptions.

It turns out that the conclusions were indeed not based on any empirical data about how a sugar tax would impact on obesity, but on  a modelling study. This study made various assumptions about various things, principally the following:

  1. The price elasticity of demand for sugary drinks (ie the extent to which an increase in price reduces consumption)
  2. The extent to which a reduction in sugary drink consumption would reduce total calorie intake
  3. The effect of total calorie intake on body mass

The authors get 0/10 for transparent reporting for the first of those, as they don’t actually say what price elasticity they used. That’s pretty basic stuff, and not to report it is somewhat akin to reporting the results of a clinical trial of a new drug and not saying what dose of the drug you used.

However, the report does give a reference for their price elasticity data, namely this paper. I must say I don’t find the methods of that paper easy to follow. It’s not at all clear to me whether the price elasticities they calculated were actually based on empirical data or themselves the results of a modelling exercise. But the data that are used in that paper come from the period 2008 to 2010, when the UK was in the depths of  recession, and when it might be hypothesised that price elasticities were greater than in more economically buoyant times. They don’t give a single figure for price elasticity, but a range of 0.8 to 0.9. In other words, a 20% increase in the price of sugary drinks would be expected to lead to a 16-18% decrease in the quantity that consumers buy. At least in the depths of the worst recession since the 1930s.

That figure for price elasticity is a crucial input to the model, and if it is wrong, then the answers of the model will be wrong.

The next input is the extent to which a reduction in sugary drink consumption reduces total calorie intake.  Here, an assumption is made that total calorie intake is reduced by 60% of the amount of calories not consumed in sugary drinks. Or in other words, that if you forego the calories of a sugary drink, you only make up 40% of those from elsewhere.

Where does that 60% figure come from? Well, they give a reference to this paper. And how did that paper arrive at the 60% figure? Well, they in turn give a reference to this paper. And where did that get it from? As far as I can tell, it didn’t, though I note it reports the results of a clinical study in people trying to lose weight by dieting. Even if that 60% figure is based on actual data from that study, rather than just plucked out of thin air, I very much doubt that data on calorie substitution taken from people trying to lose weight would be applicable to the general population.

What about the third assumption, the weight loss effects of reduced calorie intake? We are told that reducing energy intake by 100 KJ per day results in 1 kg body weight loss. The citation given for that information is this study, which is another modelling study. Are none of the assumptions in this study based on actual empirical data?

A really basic part of making predictions by mathematical modelling is to use sensitivity analyses. The model is based on various assumptions, and sensitivity analyses answer the questions of what happens if those assumptions were wrong. Typically, the inputs to the model are varied over plausible ranges, and then you can see how the results are affected.

Unfortunately, no sensitivity analysis was done. This, folks, is real amateur hour stuff. The reason for the lack of sensitivity analysis is given in the report as follows:

“it was beyond the scope of this project to include an extensive sensitivity analysis. The microsimulation model is complex involving many thousands of calculations; therefore sensitivity analysis would require many thousands of consecutive runs using super computers to undertake this within a realistic time scale.”

That has to be one of the lamest excuses for shoddy methods I’ve seen in a long time. This is 2016. You don’t have to run the analysis on your ZX Spectrum.

So this result is based on a bunch of heroic assumptions which have little basis in reality, and the sensitivity of the model to those assumptions were not tested. Forgive me if I’m not convinced.

 

Equality of opportunity

Although this is primarily a blog about medical stuff, I did warn you that there might be the occasional social science themed post. This is one such post.

In his recent speech to the Conservative Party conference, David Cameron came up with many fine words about equality of opportunity. He led us to believe that he was for it. Here is an extract from the relevant part of his speech:

If we tackle the causes of poverty, we can make our country greater.

But there’s another big social problem we need to fix.

In politicians’ speak: a “lack of social mobility”.

In normal language: people unable to rise from the bottom to the top, or even from the middle to the top, because of their background.

Listen to this: Britain has the lowest social mobility in the developed world.

Here, the salary you earn is more linked to what your father got paid than in any other major country.

I’m sorry, for us Conservatives, the party of aspiration, we cannot accept that.

We know that education is the springboard to opportunity.

Fine words indeed. Cameron is quite right to identify lack of social mobility as a major problem. It cannot be right that your life chances should depend so much on who your parents are.

Cameron is also quite right to highlight the important role of education. Inequality of opportunity starts at school. If you have pushy middle class parents who get you into a good school, then you are likely to do better than if you have disadvantaged parents and end up in a poor school.

But it is very hard to reconcile Cameron’s fine words with today’s announcement of a new grammar school. In theory, grammar schools are supposed to aid social mobility by allowing bright kids from disadvantaged backgrounds to have a great education.

But in practice, they do no such thing.

In practice, grammar schools perpetuate social inequalities. Grammar schools are largely the preserve of the middle classes. According to research from the Institute for Fiscal studies, children from disadvantaged backgrounds are less likely than their better off peers to get into grammar schools, even if they have the same level of academic achievement.

It’s almost as if Cameron says one thing but means something else entirely, isn’t it?

If Cameron is serious about equality of opportunity, I have one little trick from the statistician’s toolkit which I think could help, namely randomisation.

My suggestion is this. All children should be randomly allocated to a school. Parents would have no say in which school their child goes to: it would be determined purely by randomisation. The available pool of schools would of course need to be within reasonable travelling distance of where the child lives, but that distance could be defined quite generously, so that you wouldn’t still have cosy middle class schools in cosy middle class neighbourhoods and poor schools in disadvantaged neighbourhoods.

At the moment, it is perfectly accepted by the political classes that some schools are good schools and others are poor. Once the middle classes realise that their own children might have to go to the poor schools, my guess is that the acceptance of the existence of poor schools would rapidly diminish. Political pressure would soon make sure that all schools are good schools.

That way, all children would have an equal start in life, no matter how rich their parents were.

This suggestion is, of course, pure fantasy. There is absolutely no way that our political classes would ever allow it. Under a system like that, their own children might have to go to school with the plebs, and that would never do, would it?

But please don’t expect me to take any politician seriously if they talk about equality of opportunity on the one hand but still support a system in which the school that kids go to is determined mainly by the socioeconomic status of their parents.

Energy prices rip off

Today we have learned that the big six energy providers have been overcharging customers to the tune of over £1 billion per year.

Obviously your first thought on this story is “And what will we learn next week? Which religion the Pope follows? Or perhaps what do bears do in the woods?” But I think it’s worth taking a moment to think about why the energy companies have got away with this, and what might be done about it.

Energy companies were privatised by the Thatcher government back in the late 1980s and early 1990s, based on the ideological belief that competition would make the market more efficient. I’m not sure I’d call overcharging consumers by over £1 billion efficient.

It’s as if Thatcher had read the first few pages of an economics textbook that talks about the advantages of competition and the free market, and then gave up on the book without reading the rest of it to find out what can go wrong with free markets in practice.

Many things can go wrong with free markets, but the big one here is information asymmetry. It’s an important assumption of free market competition that buyers and sellers have perfect information. If buyers do not know how much something is costing them, how can they choose the cheapest supplier?

It is extraordinarily difficult to compare prices among energy suppliers. When I last switched my energy supplier, I spent well over an hour constructing a spreadsheet to figure out which supplier would be cheapest for me. And I’m a professional statistician, so I’m probably better equipped to do that task than most.

Even finding out the prices is a struggle. Here is what I was presented with after I looked on NPower’s website to try to find the prices of their energy:

Screenshot from 2015-07-07 08:24:19

It seems that they want to know everything about me before they’ll reveal their prices. And I’d already had to give them my postcode before I even got that far. Not exactly transparent, is it?

It was similarly impossible to find out Eon’s prices without giving them my entire life history. EDF and SSE were a bit more transparent, though both of them needed to know my postcode before they’d reveal their prices.

Here are EDF’s rates:

Screenshot from 2015-07-07 08:31:06

And here are SSE’s rates:

Screenshot from 2015-07-07 08:30:20

Which of those is cheaper? Without going through that spreadsheet exercise, I have no idea. And that’s just the electricity prices. Obviously I have to do the same calculations for gas, and given that they all give dual fuel discounts, I then have to calculate a total as well as figuring out whether I would be better off going with separate suppliers for gas and electricity to take the cheapest deal on each and whether that would compensate for the dual fuel discount.

And then of course I also have to take into account how long prices are fixed for, what the exit charges are, etc etc.

Seriously, if I as a professional statistician find this impossibly difficult, how is anyone else supposed to figure it out? There are price comparison websites that are supposed to help people compare prices, but of course they have to make a living, and have their own problems.

It’s no wonder that competition is not working for the benefit of consumers.

So what is to be done about it?

I think there is a simple solution here. All suppliers should be required to charge in a simple and transparent way. The standing charge should go. Suppliers should be required simply to quote a price per unit, and should also be required to publish those prices prominently on their website without consumers having to give their inside leg measurements first. If different rates are given for day and night use, a common ratio of day rate to night rate should be required (the ratio used could be reviewed annually in response to market conditions).

Suppliers will no doubt argue that a flat price per unit is inefficient, as there are costs involved in simply having a customer even before any energy is used, and a customer who uses twice as much energy as another does not cost them twice as much.

Tough. The energy companies have had over 20 years to sort out their act, and have failed. While I’m not a fan of governments intervening in markets as a general principle, there are times when it is useful, and this is one of them. I don’t see how anyone can argue that an industry that overcharges consumers by over £1 billion per year is efficient. No one energy company would be at  a disadvantage, as all their competitors would be in the same position.

There would be a further benefit to this idea, in that it would add an element of progressiveness to energy pricing. At the moment, poor people who don’t use much energy pay more per unit than rich people. That doesn’t really seem fair, does it?

This is such a simple and workable idea it is hard to understand why it hasn’t already been implemented. Unless, of course, recent governments were somehow on the side of big business and cared far less about ordinary consumers.

But that can’t be true, can it?

Is smoking plunging children into poverty?

If we feel it necessary to characterise ourselves as being “pro” or “anti” certain things, I would unambiguously say that I am anti-smoking. Smoking is a vile habit. I don’t like being around people who are smoking. And as a medical statistician, I am very well aware of the immense harm that smoking does to the health of smokers and those unfortunate enough to be exposed to their smoke.

So it comes as a slight surprise to me that I find myself writing what might be seen as a pro-smoking blogpost for the second time in just a few weeks.

But this blogpost is not intended to be pro-smoking: it is merely anti the misuse of statistics by some people in the anti-smoking lobby. Just because you are campaigning against a bad thing does not give you a free pass to throw all notions of scientific rigour and social responsibility to the four winds.

An article appeared yesterday on the Daily Mail website with the headline:

“Smoking not only kills, it plunges children into POVERTY because parents ‘prioritise cigarettes over food'”

and a similar, though slightly less extreme, version appeared in the Independent:

“Smoking parents plunging nearly half a million children into poverty, says new research”

According to the Daily Mail, parents are failing to feed their children because they are spending money on cigarettes instead of food. The Independent is not quite so explicit in claiming that, but it’s certainly implied.

Regular readers of this blog will no doubt already have guessed that those articles are based on some research which may have been vaguely related to smoking and poverty, but which absolutely did not show that any children were going hungry because of their parents’ smoking habits. And they would be right.

The research behind these stories is this paper by Belvin et al. There are a number of problems with it, and particularly with the way their findings have been represented in the media.

The idea of children being “plunged into poverty” came from looking at the number of families with at least one smoker who were just above the poverty line. Poverty in this case is defined as a household income less than 60% of the median household income (taking into account family size). If the amount families above the poverty line spent on cigarettes took their remaining income after deducting their cigarette expenditure below the poverty line, then they were regarded as being taken into poverty by smoking.

Now, for a start, Belvin et al did not actually measure how much any family just above the poverty line spent on smoking. They made a whole bunch of estimates and extrapolations from surveys that were done for different purposes. So that’s one problem for a start.

Another problem is that absolutely nowhere did Belvin et al look at expenditure on food. There is no evidence whatsoever from their study that any family left their children hungry, and certainly not that smoking was the cause. Claiming that parents were prioritising smoking over food is not even remotely supported by the study, as it’s just not something that was measured at all.

Perhaps the most pernicious problem is the assumption that poverty was specifically caused by smoking. I expect many families with an income above 60% of the median spend some of their money on something other than feeding their children. Perhaps some spend their money on beer. Perhaps others spend money on mobile phone contracts. Or maybe on going to the cinema. Or economics textbooks. Or pretty much anything else you can think of that is not strictly essential. Any of those things could equally be regarded as “plunging children into poverty” if deducting it from expenditure left you below median income.

So why single out smoking?

I have a big problem with this. I said earlier that I thought smoking was a vile habit. But there is a big difference between believing smoking is a vile habit and believing smokers are vile people. They are not. They are human beings. To try to pin the blame on them for their children’s poverty (especially in the absence of any evidence that their children are actually going hungry) is troubling. I am not comfortable with demonising minority groups. It wouldn’t be OK if the group in question were, say, Muslims, and it’s not OK when the group is smokers.

There are many and complex causes of poverty. But blaming the poor is really not the response of a civilised society.

The way this story was reported in the Daily Mail is, not surprisingly, atrocious. But it’s not entirely their fault. The research was filtered through Nottingham University’s press office before it got to the mainstream media, and I’m afraid to say that Nottingham University are just as guilty here. Their press release states

“The reserch [sic] suggests that parents are likely to forgo basic household and food necessities in order to fund their smoking addiction.”

No, the research absolutely does not suggest that, because the researchers didn’t measure it. In fact I think Nottingham University are far more guilty than the Daily Mail. An academic institution really ought to know better than to misrepresent the findings of their research in this socially irresponsible way.

Student tuition fees and disadvantaged applicants

Those of you who have known me for a while will remember that I used to blog on the now defunct Dianthus Medical website. The Internet Archive has kept some of those blogposts for posterity, but sadly not all of them. As I promised when I started this blog, I will get round to putting all those posts back on the internet one of these days, but I’m afraid I haven’t got round to that just yet.

But in the meantime, I’m going to repost one of those blogposts here, as it has just become beautifully relevant again. About this time last year, UCAS (the body responsible for university admissions in the UK) published a report which claimed to show that applications to university from disadvantaged young people  were increasing proportionately more than applications from the more affluent, or in other words, the gap between rich and poor was narrowing.

Sadly, the report showed no such thing. The claim was based on a schoolboy error in statistics.

Anyway, UCAS have recently published their next annual report. Again, this claims to show that the gap between rich and poor is narrowing, but doesn’t. Again, we see the same inaccurate headlines in the media that naively take the report’s conclusions at face value, and we see exactly the same schoolboy error in the way the statistics were analysed in the report.

So as what I wrote last year is still completely relevant today, here goes…

One of the most significant political events of the current Parliament has been the huge increase in student tuition fees, which mean that most university students now need to pay £9000 per year for their education.

One of the arguments against this rise used by its opponents was that it would put off young people from disadvantaged backgrounds from applying to university. Supporters of the new system argued that it would not, as students can borrow the money via a student loan to be paid back over a period of decades, so no-one would have to find the money up front.

The new fees came into effect in 2012, so we should now have some empirical data that should allow us to find out who was right. So what do the statistics show? Have people from disadvantaged backgrounds been deterred from applying to university?

A report was published earlier this year by UCAS, the organisation responsible for handling applications to university. This specifically addresses the question of applications from disadvantaged areas. This shows (see page 17 of the report) that although there was a small drop in application rates from the most disadvantaged areas immediately after the new fees came into effect, from 18.0% in 2011 to 17.5% in 2012, the rates have since risen to 20.5% in 2014. And the ratio of the rate of applications from the most advantaged areas to the most disadvantaged areas fell from 3.0 in 2011 to 2.5 in 2014.

So, case closed, then? Clearly the new fees have not stopped people from disadvantaged areas applying to university?

Actually, no. It’s really not that simple. You see, there is a big statistical problem with the data.

That problem is known as regression to the mean. This is a tendency of characteristics with particularly high or low values to become more like average values over time. It’s something we know all about in clinical trials, and is one of the reasons why clinical trials need to include control groups if they are going to give reliable data. For example, in a trial of a medication for high blood pressure, you would expect patients’ blood pressure to decrease during the trial no matter what you do to them, as they had to have high blood pressure at the start of the trial or they wouldn’t have been included in it in the first place.

In the case of the university admission statistics, the specific problem is the precise way in which “disadvantaged areas” and “advantaged areas” were defined.

The advantage or disadvantage of an area was defined by the proportion of young people participating in higher education during the period 2000 to 2004. Since the “disadvantaged” areas were specifically defined as those areas that had previously had the lowest participation rates, it is pretty much inevitable that those rates would increase, no matter what the underlying trends were.

Similarly, the most advantaged areas were almost certain to see decreases in participation rates (at least relatively speaking, though this is somewhat complicated by the fact that overall participation rates have increased since 2004).

So the finding that the ratio of applications from most advantaged areas to those from least advantaged areas has decreased was exactly what we would expect from regression to the mean. I’m afraid this does not provide evidence that the new tuition fee regime has been beneficial to people from disadvantaged backgrounds. It is very had to disentangle any real changes in participation rates from different backgrounds from the effects of regression to the mean.

Unless anyone can point me to any better statistics on university applications from disadvantaged backgrounds, I think the question of whether the new tuition fee regime has helped or hindered social inequalities in higher education remains open.

Plain packaging for tobacco

Plain packaging for tobacco is in the news today. The idea behind it is that requiring tobacco manufacturers to sell cigarettes in unbranded packages, where all the branding has been replaced by prominent health warnings, will reduce the number of people who smoke, and thereby benefit public health.

But will it work?

That’s an interesting question. There’s a lot of research that’s been done, though it’s fair to say none of it is conclusive. For example, there has been research on how it affects young people’s perceptions of cigarettes and on what happened to the number of people looking for help with quitting smoking after plain packaging was introduced in Australia.

But for me, those are not the most interesting pieces of evidence.

What tells me that plain packaging is overwhelmingly likely to be an extremely effective public health measure is that the tobacco industry are strongly opposed to it. They probably know far more about the likely effects than the rest of us: after all, for me, it’s just a matter of idle curiosity, but for them, millions of pounds of their income depends on it. So the fact they are against it tells us plenty.

Let’s look in a little more detail at exactly what it tells us. Advertising and branding generally has 2 related but distinguishable aims for a company that sells something. One aim is to increase their share of the market, in other words to sell more of their stuff than their competitors in the same market. The other is to increase the overall size of the market, so that they sell more, and their competitors sell more as well. Both those things can be perfectly good reasons for a company to spend their money on advertising and branding.

But the difference between those 2 aims is crucial here.

If the point of cigarette branding were just to increase market share without affecting the overall size of the market, then the tobacco industry should be thoroughly in favour of a ban. Advertising and branding budgets, when the overall size of the market is constant, are a classic prisoner’s dilemma. If all tobacco companies spend money on branding, they will all have pretty much the same share as if no-one did, so they will gain nothing, but they will spend money on branding, so they’re worse off than if they didn’t. However, they can’t afford not to spend money on branding, as then they would lose market share to their competitors, who are still spending money on it.

The ideal situation for the tobacco industry in that case would be that no-one would spend any money on branding. But how can you achieve that? For all the companies to agree not to spend money on branding might be an illegal cartel, and there’s always a risk that someone would break the agreement to increase their market share.

A government-mandated ban solves that problem nicely. If all your competitors are forced not to spend money on branding, then you don’t have to either. All the tobacco companies win.

So if that were really the situation, then you would expect the tobacco companies to be thoroughly in favour of it. But they’re not. So that tells me that we are not in the situation where the total market size is constant.

The tobacco companies must believe, and I’m going to assume here that they know what they’re doing, that cigarette branding affects the overall size of the market. If branding could increase the overall size of the market (or more realistically when smoking rates in the UK are on a long-term decline, stop it shrinking quite as fast), then it would be entirely rational for the tobacco companies to oppose mandatory plain packaging.

I don’t know about you, but that’s all the evidence I need to convince me that plain packaging is overwhelmingly likely to be an effective public health measure.