One of the pensions announcements in the Budget last week which got less coverage amongst the talk about freedom and the death of the annuity was the one about the minimum age at which pension benefits will be able to be taken in the future. In this respect the Government appears to feel that less freedom is preferable.

Historically the minimum age was 50 except for a list of exempted professions kept by HMRC (or the Inland Revenue as they then were) which included professional footballers. However in 2010 it was increased to 55. From 2028 it is proposed that it is going to be increased again, to 57, thereafter linked to increases in the State Pension Age (SPA).

PwC have projected that, assuming the policy of linking SPA to life expectancy continues into the future, we can expect a SPA of 77 by 2089 and 84 by 2134. If this all sounds a little futuristic, it does highlight a concern about the Government proposal of using SPA minus 10 (or even SPA minus 5 which is also being consulted upon) as a national minimum pension age.

Male HLEFemale HLE

The Office of National Statistics (ONS) have produced an interesting split of both life expectancy at birth (LE) and healthy life expectancy at birth (HLE) by deciles of deprivation. Graphing these with the steadily increasing SPAs shown in black and the minimum pension ages in red we can see that the bottom male and female 10% by deprivation already have a healthy life expectancy below the current minimum pension age, with a further 10% being caught by the increase to 57.

Admittedly we might hope for an increase in both life expectancy and healthy life expectancy at all levels by 2028, but the differentials between the poorest and the richest in this respect have been widening for some time. Certainly if the SPA minus 5 idea is adopted, giving a minimum pension age of 62 by 2028, it is difficult to see the bottom deciles reaching that age in good health. And what about a minimum pension age of 67 by 2089 (72 if SPA minus 5)? Do we think that we have policies in place to increase the healthy life expectancy of the bottom decile by the 15 years (or 20 years if SPA minus 5) that would be required to allow them to retire in good health, even assuming they felt able to do so financially?

As I have mentioned before, I think the Government needs to consider ill health early retirement to a greater extent in its policies towards state pension benefits, but this may be particularly urgent with respect to minimum retirement ages. The main problem as I see it would be the assessment of ill health, bearing in mind the current ATOS fiasco.

One alternative approach might be to try and maintain the minimum pension age as a proportion of SPA rather than a fixed number of years earlier. So, for instance, the current proportion (55/65 or 85%) would give a minimum pension age when SPA reached 77 of around 65.5 rather than the 67 proposed.

Leaving the proposals as they stand, however, is likely to lead to an increasingly ill elderly workforce engaged in the lowest paying and most physically demanding occupations. Not free, and without choices. That doesn’t sound like an election winner to me.

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Source: Wikimedia Commons. A shell of the sea snail species Cymbiola vespertilio, the bat volute. Photo taken by User:Haplochromis

Source: Wikimedia Commons. A shell of the sea snail species Cymbiola vespertilio, the bat volute.
Photo taken by User:Haplochromis

How long am I going to live is, of course, an impossible question to answer precisely in most cases. However estimates about how long people with certain characteristics in common (like age, sex, postcodes and smoking habits for instance) are going to live are used for a wide range of purposes from future population estimates to annuity pricing to pension scheme funding.

Central to making any kind of estimate is working out how you think rates of mortality are going to change in the future. Based on the historical evidence over the last 100 years or more, all the models people use to make projections of future mortality rates in the UK involve them improving, but the consensus tends to end there.

 

There are several ways in which these projections can go wrong:

  • Process or idiosyncratic risk, ie the risk of random fluctuations in mortality experience. The fewer people you have in your pension scheme, the more likely this is to be a big issue.
  • Level or mis-estimation risk, ie you start from the wrong current position.
  • Trend risk, ie risk of underestimating how much longevity will increase in the future.

Some also include another one:

  • Catastrophe risk, ie the occurrence of an unknowable event with large consequences.

But what do these projections look like? Well, the most popular family of projections of future mortality improvements are generated by the CMI Projection Models, a new one of which comes out every year. Giving the rates of mortality improvements for each age in each year a colour produces something called a “heat map”. The colours get progressively “hotter”, moving from yellow to orange to red and then black as the rates of improvement increase, and “cooler” from yellow to green to blue and then purple, as the rates of improvement decrease and ultimately turn negative (ie worsening mortality). One version of this is shown below:

100%S1PMA CMI_2012_M[2.00%]

100%S1PMA CMI_2012_M[2.00%]

Which as you can see is a considerable improvement on this (“cohort” adjustments of this type were used by most pensions actuaries only five years ago):

Source: CMI working paper 39: Actual and projected annual rates of mortality improvement for males: 1991-2005 – estimated actual rates for population of England & Wales; 2006 onwards – projected rates using ‘Medium Cohort_1.0% minimum’

Source: CMI working paper 39: Actual and projected annual rates of mortality improvement for males: 1991-2005 – estimated actual rates for population of England & Wales; 2006 onwards – projected rates using ‘Medium Cohort_1.0% minimum’

However, in my view there is scope to go further.

One criticism which has been made by actuaries when using the core version of the CMI Projection Model is that the initial rates of improvements do not necessarily start to converge to the long term rate of improvement straight away, often diverging initially before starting a convergent path: these are displayed as little islands in the CMI heat map above.

Another potential criticism is that there are obviously many ways of creating a smooth transition to long term rates, but until now within the CMI model this required selecting the advanced features of the model. This allows much more flexibility over choice of:

  • Base rates of mortality
  • Initial rates of mortality improvement
  • Long term rates of improvement that differ by age and year of birth
  • Convergence, again by age and year of birth

However, selection of the advanced features brings its own problems in that it requires a further set of assumptions to be made for which, certainly within the framework of advising a trustee board of a pension scheme and particularly for small schemes with less data, it might be difficult to identify a convincing rationale. There also remains the problem that, even if a large set of additional assumptions can be agreed, it is often difficult to relate these to views held about what will impact future longevity improvements.

This begs the question of how you do go about introducing alternative projections. I think one answer to this may lie in a series of questions posed by Peter S Stevens in his book Patterns in Nature:

Why does nature appear to use only a few fundamental forms in so many different contexts? Why does the branching of trees resemble that of arteries and rivers? Why do crystal grains look like soap bubbles and the plates of a tortoise shell? Why do some fronds and fern tips look like spiral galaxies and hurricanes? Why do meandering rivers and meandering snakes look like the loop patterns in cables? Why do cracks in mud and markings on a giraffe arrange themselves like films in a froth of bubbles?

Patterns turn up again and again in seemingly unrelated areas in the natural world because, as D’Arcy Thompson pointed out long ago, those patterns are as much to do with the physics and chemistry of the world with which organisms are interacting as they are with their biology. It therefore seems reasonable to look at the mathematics underlying patterns which already exist in nature when considering what patterns might develop in future in, for instance, human mortality improvements.

I have chosen the mathematics underlying sea shell patterns, as explored by Hans Meinhardt and others.

By focusing on a graphical approach to setting future mortality improvement projections via heat maps, I believe that the particular features of any specific projection can be more readily linked to views about the impact of specific factors on longevity improvements. The method set out in a very short paper (The misbehaviour of mortality) I have just produced can be used for instance to turn this:

100%S1PMA CMI_2012_M[2.00%]

100%S1PMA CMI_2012_M[2.00%]

Into this:

100%S1PMA SSA_2012_M[220,0.4,23,1.5]

100%S1PMA SSA_2012_M[220,0.4,23,1.5]

And by taking a path through the heat map like this:

Heat map cohort path

We can compare shapes of mortality improvements projected for eg a man aged 63 this year like this:

Mortality improvement path

As you can see a wide variety of shapes can be achieved using this method. It allows features of a given projection to be more easily related to views held about social change, medical advances, etc and their impact on longevity improvements in the short, medium and long term. In particular, it allows future projections to be discussed in more detail, but in a non-technical way. This differs from the current most common approach, which tends to focus solely on a long term rate.

I think this approach holds promise for generating patterns of future mortality projections. The advantages are:

  • It avoids some of the problems associated with the CMI core projection model (eg “islands”).
  • It also avoids the considerable number of additional assumptions which would need to be agreed before the advanced version of the CMI model could be applied. Instead there are only four additional assumptions, each of which has an easily communicated interpretation for a lay audience.
  • It has an aesthetic appeal, building on a considerable body of work into patterns found elsewhere in nature, which have not, as far as I am aware, been exploited in any other area of actuarial science to date.
  • It allows particular features of a given projection to be more easily related to views held about social change, medical advances etc and their impact on longevity improvements in the short, medium and long term.

There is a potential disadvantage in that the applicability of sea shell patterns to mortality improvements may well be questioned by some. However, mathematics has a long tradition of establishing links between areas where none seemed to exist previously. Perhaps this will be another one?

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doctorIn all the talk about annuities and the poor value they currently offer, nearly all of it has been based on standard annuity rates, ie where there is nothing sufficiently medically wrong with you to affect your life expectancy. However this is almost certainly not the rate you should be looking at.

Go to any of the annuity provider or broker websites, sometimes buried away a little, and you will find a link explaining what they can offer in the way of “enhanced” or “impaired lives” annuities. Legal & General’s web page on this looks like the kind of warning notice you find on the wall of your doctor’s surgery waiting room, with headings like Smoking, Type 2 Diabetes and High Blood Pressure. But in the upside-down world of buying annuities these become good things to do or have.

Just Retirement give some handy illustrations of what various conditions could mean for your income: up 20% for minor conditions like obesity and hypertension, up 30% for “moderate” ones like being a heart attack survivor with a bypass and 40% for serious medical conditions like stage 2 bowel cancer one year in. However, you don’t need to get anywhere near the frankly frightening conditions in the moderate and serious boxes to make a big difference to the income you can receive. annuitydiscount.co.uk provide a very long list of medications (covering every letter in the alphabet except J and Y) which could lead to an impaired life annuity if disclosed to the annuity provider.

As the BBC article from 2012 posted by the Better Retirement Group on enhanced annuities says: “At its simplest an annuity is a bet with the insurance company about how long you will live.”

So on that basis, it makes sense to stack the odds in your favour as much as you can. Which makes the 2007 article in the New England Journal of Medicine entitled, rather dully, Incidental Findings on Brain MRI in the General Population, such an interesting read.

They studied 2,000 people (mean age 63.3 years, range 45.7 to 96.7) from the population-based Rotterdam Study in whom high-resolution, structural brain MRI scans had been carried out. Asymptomatic brain infarcts (more commonly known as strokes) were present in 145 people (7.2%). Among other findings, aneurysms (1.8%) were the most frequent. Benign brain tumors also turned up reasonably often (1.6%). The most extreme case was someone with a large, chronic subdural haematoma, who was subsequently found to have had a minor head trauma 4 weeks before the MRI scan. Some of the scans are shown below.

brain scansBut the really amazing thing is this: only 2 of the 2,000 people scanned (the subdural haemotoma mentioned above and another who had a 12 mm aneurysm of the medial cerebral artery) had any idea that there was anything wrong with them!

Another huge area of undiagnosed disease (and on the annuity.co.uk list for enhanced annuities) is prostate cancer. According to a systematic review of prostate cancer biopsy schemes by the University of York in 2005, where they quoted from the NHS Centre for Reviews and Dissemination publication on screening for prostate cancer, Effectiveness Matters:

Post mortem studies show that 30% of men over 50, who had no symptoms of prostate cancer whilst alive, had histological evidence of prostate cancer at the time of death. This percentage rises to 60-70% in men over 80 years of age. In other words, most men with prostate cancer die with, rather than from, the disease.

The main reason these studies have been carried out is to determine whether screening for prostate cancer, which kills 3.8% of men with the disease, has saved many lives. The Prostate Specific Antigen (PSA) test that is commonly used to detect prostate cancer in the absence of symptoms is not only prone to false positives and negatives (ie telling you you have it when you don’t and don’t have it when you do – something all screening suffers from to some extent), but can lead to you being offered treatment which may well be worse than the disease. This is discussed further in the excellent The Norm Chronicles, by Michael Blastland and David Spiegelhalter, which questions whether, overall, screening is particularly effective in saving lives.

Effective in preventing death? Perhaps not. But effective in increasing retirement income? Almost certainly.

The latest Association of British Insurers (ABI) facts and figures on the UK annuity market suggest that enhanced annuities have grown in popularity, to 24% in 2012 from 2% in 2003. There is scope to make further large increases in these figures if more people can be persuaded to have themselves screened for some of the most common undiagnosed conditions before they retire.

So don’t necessarily accept a standard annuity rate. And consider getting yourself tested first.

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We are certainly living longer than ever before. But within that statement lie a number of interesting stories neatly summarised by the Office of National Statistics (ONS) report on average life span in England and Wales, which came out around a year ago.

The first graph below has been constructed by first devising a rather artificial thing called a life table. This starts with 100,000 people at birth for each year and then, based on the probability of dying in the first year of life, works out how many are expected to survive to age 1. Of those, the probability of dying in the second year is applied to the number at year 1 in the table to work out the expected number of deaths during the second year. These are then deducted from the year 1 entry to arrive at the year 2 entry. And so on. Skip the next paragraph if that explanation is enough for you.

So, for example, taking the data from the England & Wales interim life tables 2009-11, we have 100,000 males at age 0, 99,508.2 at age 1 and 99,475.2 at age 2. This is because the probability of death for males in the first year of life over the 3 year period 2009 to 2011 was 0.004918, so 100,000 x 0.004918 = 491.8 expected deaths and 100,000 – 491.8 = 99,508.2 expected to be left in this imaginary population to celebrate their first birthdays. The probability of death in the second year of life was 0.000331 (notice this is much smaller, we will return to the significance of this later) so that the number of boys getting to blow two candles out on a cake is expected to be 99,508.2 – (0.000331 x 99,508.2) = 99,475.2. This table is nothing like real life of course, as we all move through time as we get older, so that our chance of death at age 20, say, would not be the same as the chance of a 20 year old dying 20 years earlier. However such a table does allow us to illustrate the patterns of deaths in any given year, and then compare these with other years.

The three measures used are based on the three averages you learned at school: the mean, median and mode.

The life expectancy at birth is a form of mean. The probability of reaching each age can be calculated by looking how many people you have at that age in your imaginary life table and dividing that number by the 100,000 you started with. Then each of these probabilities is multiplied by the age reached and then the probability of dying in that year (strictly the ONS life tables are constructed by taking the average probability for each year as the mid point between the start of the year and end of year probability, with a further adjustment in the first year when the probability of death is very much concentrated in the first 4 weeks). This can be shown to be same as all the entries (from year 1, year 2, year 3, etc) in the life table added up and divided by the 100,000 you started with.

The median is the age at which we expect half the population to have died. The mode is the age at which we see the highest number of deaths. The mode here has been adjusted in two further ways: the deaths below age 10 have been removed (otherwise it would have been 0 in a number of years and it is the old age mortality we are looking to compare) and it has also been smoothed to take out year on year fluctuations caused by wars and flu pandemics (again this would lead to modes in the 20s and 30s in some years, which are not the ages we are focused on).

life expectancy

There are many features to this graph, as set out in the ONS paper. The closing down of the gap between the mode on the one hand, and the median and life expectancy at birth on the other, is especially striking. This was mainly due to the massive improvement in survival rates in the first year in birth in particular. It also demonstrates that, contrary to what we might have believed about Victorian England, plenty of people were living into their 70s in the 1840s.

However I want to focus on the race to live longer between men and women because, armed with these three numbers (or six as we are looking at men and women separately) for each year, we can see that men and women have had a rather different journey since 1841.

As we can see the experience was fairly similar in the 1840s, although even then women lived 2 or 3 years longer on the mean and median measures than men. The modal age at death was more variable due to the relatively small numbers at advanced ages in the early years, but was between 25 and 30 years in excess of the median and life expectancy at birth due to the relatively high level of infant and child deaths at the time. The median and life expectancy then steadily advanced on the mode (interrupted by two downward spikes: in the mid 1840s a combined assault of typhus, flu and cholera, and a much larger one in 1919 from the flu pandemic).

In 1951, the female median age at death moved above the male modal age for the first time, marking the start of a 20 year period where life expectancy increases on all measures for women exceeded those of men. While the commonest age of death for men stayed in the mid 70s over this period, that for women increased from 79.5 to 82.5, leading to a peak difference in commonest age of death between men and women of 8.5 years in 1971. A graph of the differences in all three averages is shown below.

Differences life expectancies

Since 1971 the tide has turned, with all six lines steadily, if very gradually, converging. In 2010 the male modal age at death finally crossed back over the female life expectancy at birth, and all three differences fell below 4 years for the first time since 1926. As the Longevity Science Advisory Panel’s second report points out, the average differences between life expectancy by gender at birth of 4.15 between 2005 and 2009 represent, in terms of the percentage of female life expectancy (5.1%), a return to the levels seen at the start of the journey in 1841. In 2010 this percentage fell to 4.7%. It has only fallen below that level four times since 1841, and not since 1857.

So we may be entering a new phase in the expected longevity differences between men and women. And, as the history shows us, those differences can change with surprising speed.

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The latest figures (January 2014) from the European Central Bank (ECB) statistics pocket book have just been issued, providing comparisons between European Union (EU) countries, both in the Eurozone and outside it, on a range of measures. And some of these comparisons are not quite what I expected to see.

For instance, perhaps surprisingly in view of the current hysteria in the UK about economic migrants from Bulgaria and Romania, we find that unemployment was lower in Romania (7.3%) than it is in the UK (7.4%) for the latest month (September 2013) for which data on both was available (it’s the UK’s that is missing for October and November for reasons unknown).

I have graphed a selection of the data below, Euro countries are to the left:

ECB country data labelled

First to note, which may also surprise some, is that private sector debt in the UK is not particularly big in EU terms: Denmark and Sweden both have considerably higher private sector debt as a percentage of GDP than the UK, as do 7 countries in the Eurozone with Ireland and Luxembourg heading the list.

Government expenditure as a percentage of GDP is the most evenly distributed of all the measures. I have graphed the 2012 data, as the Q2 2013 data omitted France and Germany. The range across all countries is between 36.1% (Lithuania) and 59.5% (Denmark), with the UK’s 47.9% only a little below the Eurozone average of 49.9%. This suggests to me, for all of the political rhetoric we hear, that it is not the total spend which tends to alter much but the distribution of it. Certainly in the UK, there appears to have been a focus on a relatively small section of the welfare budget to make the savings from.

Government debt is much higher as a proportion of GDP in the Eurozone than in the rest of the EU, with no one outside the Eurozone reaching the Eurozone average of 93.4% (although the UK comes closest at 89.8%). There are 5 countries in the Eurozone with debt above 100%: Belgium (perhaps surprisingly), Ireland, Greece, Italy and Portugal. Spain’s debt is actually below the Eurozone average at 92.3%.

Unemployment statistics are unsurprisingly dominated by Greece and Spain, whose unemployment rates are around 50% higher than the next country. Unemployment rates average 12.1% in the Eurozone and 10.9% for the EU as a whole, perhaps demonstrating the advantage of keeping control of your exchange rate during an economic downturn.

The population statistics remind me what an unusual decision it was for the UK to stay out of the Euro. All the other big countries (by which I mean those with populations over 45 million) are in the Eurozone, with the next biggest EU country outside the Euro being Poland at 38.5 million (with the prospect of their joining the Euro receding somewhat last year). Most of the richer countries are too, illustrated by a much higher proportion of GDP (see below) held in Eurozone countries than their relative populations would lead you to expect.

Finally we come to GDP. This looks very differently distributed according to whether you look at amounts in Euros, or per capita, or by capita adjusted for the purchasing power in each country. The first of these is dominated as expected by the big countries of Germany, Spain, France, Italy and the UK. However, the outstanding performer when looking at GDP per capita with or without the purchasing power adjustment is Luxembourg. Eurozone countries have a higher GDP per capita than those outside (€28,500 compared to €25,500, with the gap narrowing slightly when adjusted for purchasing power).

A final thing strikes me about these statistics. As has been pointed out elsewhere, Francois Hollande is having a hell of a time considering that France’s economic performance is not that bad. In fact it is incredibly average: its Government debt sits at 93.5% compared to the Eurozone average of 93.4% and its GDP per capita when adjusted for purchasing power is bang on the Eurozone average of €28,500. France are much more representative Euro members than Germany (remarkable when you consider that the Euro was once referred to as the Deutsche Mark with a few disreputable friends) and, if Hollande’s approval ratings are any indication, the French people seem to hate that.

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It’s a relatively new science, and one which binds together many different academic disciplines: mathematical modelling, economics, sociology and history. In economic terms, it is to what economists in financial institutions spend most of their time focusing on – the short to medium term – as climate science is to weather forecasting. Cliodynamics (from Clio, the Ancient Greek muse or goddess of history (or, sometimes, lyre playing) and dynamics, the study of processes of change with time) looks at the functioning and dynamics of historical societies, ie societies for which the historical data exists to allow analysis. And that includes our own.

Peter Turchin, professor of ecology and mathematics at the University of Connecticut and Editor-in-Chief of Cliodynamics: The Journal of Theoretical and Mathematical History, wrote a book with Sergey Nefedev in 2009 called Secular Cycles. In it they took the ratio of the net wealth of the median US household to the largest fortune in the US (the Phillips Curve) to get a rough estimate of wealth inequality in the US from 1800 to the present. The graph of this analysis shows that the level of inequality in the US measured in this way peaked in World War 1 before falling steadily until 1980 when Reagan became US President, after which it has been rising equally steadily. By 2000,inequality was at levels last seen in the mid 50s, and it has continued to increase markedly since then.

The other side of Turchin’s and Nefedev’s analysis combines four measures of wellbeing: economic (the fraction of economic growth that is paid to workers as wages), health (life expectancy and the average height of native-born population) and social optimism (average age of first marriage). This seems to me to be a slightly flaky way of measuring this, particularly if using this measure to draw conclusions about recent history: the link between average heights in the US and other health indicators are not fully understood, and there are a lot of possible explanations for later marriages (eg greater economic opportunities for women) which would not support it as a measure of reduced optimism. However, it does give a curve which looks remarkably like a mirror image of the Phillips Curve.

The Office of National Statistics (ONS) are currently developing their own measure of national well-being for the UK, which has dropped both height and late marriage as indicators, but unfortunately has expanded to cover 40 indicators organised into 10 areas. The interactive graphic is embedded below.

Graphic by Office for National Statistics (ONS)

I don’t think many would argue with many of these constituents except that any model should only be as complicated as it needs to be. The weightings will be very important.

Putting all of this together, Turchin argues that societies can only tolerate a certain level of inequality before they start finding more cooperative ways of governing and cites examples from the end of the Roman civil wars (first century BC) onwards. He believes the current patterns in the US point towards such a turning point around 2020, with extreme social upheaval a strong possibility.

I am unconvinced that time is that short based solely on societal inequality: in my view further aggravating factors will be required, which resource depletion in several key areas may provide later in the century. But Turchin’s analysis of 20th century change in the US is certainly coherent, with many connections I had not made before. What is clear is that social change can happen very quickly at times and an economic-political system that cannot adapt equally quickly is likely to end up in trouble.

And in the UK? Inequality is certainly increasing, by pretty much any measure. And, as Richard Murphy points out, our tax system appears to encourage this more than is often realised. Cliodynamics seems to me to be an important area for further research in the UK.

And a perfect one for actuaries to get involved in.

 

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When I started writing this blog in April, one of its main purposes was to highlight how poor we are at forecasting things, and suggest that our decision-making would improve if we acknowledged this fact. The best example I could find at the time to illustrate this point were the Office of Budget Responsibility (OBR) Gross Domestic Product (GDP) growth forecasts over the previous 3 years.

Eight months on it therefore feels like we have come full circle with the publication of the December 2013 OBR forecasts in conjunction with the Chancellor’s Autumn Statement. Little appears to have changed in the interim, the coloured lines on the chart below of their various forecasts now joined by the latest one all display similar shapes steadily moving to the right, advising extreme caution in framing any decision based on what the current crop of forecasts suggest.

OBR update

However, the worse the forecasts are revealed to be, the keener it seems politicians of all the three main parties are to base policy upon them. The Autumn Statement ran to 7,000 words, of which 18 were references to the OBR, with details of their forecasts taking up at least a quarter of the speech. In every area of economic policy, from economic growth to employment to government debt, it seemed that the starting point was what the OBR predicted on the subject. The Shadow Chancellor appears equally convinced that the OBR lends credibility to forecasting, pleading for Labour’s own tax and spending plans to be assessed by them in the run up to the next election.

I am a little mystified by all of this. The updated graph of the OBR’s performance since 2010 does not look any better than it did in April, the lines always go up in the future and so far they have always been wrong. If they turn out to be right (or, more likely, a bit less wrong) this time, then that does not seem to me to tell us anything much about their predictive skill. It takes great skill, as Les Dawson showed, to unerringly hit the wrong notes every time. It just takes average luck to hit them occasionally.

For another bit of crystal ball gazing in his Statement, the Chancellor abandoned the OBR to talk about state pension ages. These were going to go up to 68 by 2046. Now they are going to go up to 68 by the mid 2030s and then to 69 by the late 2040s. There will still be people alive now who were born when the state retirement age (for the “Old Age Pension” as it was then called) was 70. It looks like we are heading back in that direction again.

The State Pension Age (SPA) was introduced in 1908 as 70 years for men and women, when life expectancy at birth was below 55 for both. In 1925 it was reduced to 65, at which time life expectancy at birth had increased to 60.4 for women and 56.5 for men. In 1940, a SPA below life expectancy at birth was introduced for the first time, with women allowed to retire from age 60 despite a life expectancy of 63.5. Men, with a life expectancy of 58.2 years were still expected to continue working until they were 65. Male life expectancy at birth did not exceed SPA until 1948 (source: Human Mortality Database).

In 1995 the transition arrangements to put the SPA for women back up to 65 began, at which stage male life expectancy was 73.9 and female 79.2 years. In 2007 we all started the transition to a new SPA of 68. In 2011 this was speeded up and last week the destination was extended to 69.

SPAs

Where might it go next? If the OBR had a SPA modeller anything like their GDP modeller it would probably say up, in about another 2 years (just look again at the forecasts in the first graph to see what I mean). Ministers have hit the airwaves to say that the increasing SPA is a good news story, reflecting our increasingly long lives. And the life expectancies bear this out, with the 2011 figures showing life expectancy at birth for males at 78.8 and for females at 82.7, with all pension schemes and insurers building in further big increases to those life expectancies into their assumptions over the decades ahead.

And yet. The ONS statistical bulletin in September on healthy life expectancy at birth tells a different story which is not good news at all. Healthy life expectancies for men and women (ie the maximum age at which respondents would be expected to regard themselves as in good or very good health) at birth are only 63.2 and 64.2 years respectively. If people are going to have to drag themselves to work for 5 or 6 years on average in poor health before reaching SPA under current plans, how much further do we really expect SPA to increase?

Some have questioned the one size fits all nature of SPA, suggesting regional differences be introduced. If that ever happened, would we expect to see the mobile better off becoming SPA tourists, pushing up house prices in currently unfashionable corners of the country just as they have with their second homes in Devon and Cornwall? Perhaps. I certainly find it hard to imagine any state pension system which could keep up with the constantly mutating socioeconomics of the UK’s regions.

Perhaps a better approach would be a SPA calculated by HMRC with your tax code. Or some form of ill health early retirement option might be introduced to the state pension. What seems likely to me is that the pressures on the Government to mitigate the impact of a steadily increasing SPA will become one of the key intergenerational battlegrounds in the years ahead. In the meantime, those lines on the chart are going to get harder and harder for some.

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The consultation on the future shape of workplace pensions has been going on for nearly a month now and ends two weeks on Friday. It is littered with errors, from completely repeated questions (Q52 = Q54) to ones which are so similar as makes no difference (Qs 41 and 44 for example) and the thrust of a lot of the questions are quite hard to answer if you do not share some of the underlying assumptions of the DWP about the process, but come on! This is our chance to put a bit of definition into the rather blurry outline of a straw man which some of the newspapers have been tilting at so vigorously!

You don’t have to answer all of the questions, but just to goad you a bit I have done so here. Agree, disagree, I would love to hear from you. But not until you have responded to one of the following addresses:

How to respond to this consultation

Pleasesendyourconsultationresponses,preferablybye-mail,to:definedambition.pensionsconsultation@dwp.gsi.gov.uk

Or by post to:

Defined Ambition Team

Private Pensions Policy and Analysis

1st Floor, Caxton House

6-12 Tothill Street

London

SW1H 9NA

 

Feedback on the consultation process

There have only been 24 posts on the blog. I think the main reason for this was identified early in the process from a contributor referring to herself only as Hannah:

Hannah

I applaud the use of an open blog but it’s obvious that there’s a bit of a problem here! Perhaps, to avoid this becoming sidetracked, you could introduce a drop-down in the comment section so that people could select what aspect of DA reform or the consultation their comment relates to – and if their comment relates instead to concerns about their accrued benefits, you could redirect them to a separate specialised member queries page?

Reply

Sam Gilbert

Thanks for this Hannah, we will look into this once the blog picks up pace.

DA Team, DWP

Of course the blog never did pick up pace because people soon realised that there comments would be lost in a stream of pension benefit queries. Not the way to encourage a consultation. If you want to comment on this or anything else about the process of the consultation, the contact details are as follows:

Elias Koufou

DWP Consultation Coordinator

2nd Floor

Caxton House

Tothill Street

London

SW1H 9NA

Phone: 020 7449 7439

Email:elias.koufou@dwp.gsi.gov.uk

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November 2013 003The latest revelations from Edward Snowden that the US and UK agreed in 2007 to relax the rules governing the mobile phone and fax numbers, emails and IP addresses that the US National Security Agency (NSA) could hold onto (and extending the net to people not the original targets of their surveillance) has increased the pressure on the Government to tighten controls on the activities of the security services. This extension apparently allowed the NSA to venture up to three “hops” away from a person of interest, eg a friend of a friend of a friend on Facebook.

I have an issue with the Guardian analysis here. They say that three hops from a typical Facebook user would rope in 5 million people. However, using actual ratios from the network in their source (43 friends have 3,975 friends of friends have 1,328,361 friends of friends of friends) and the median number of friends of 99 from the original study, would lead to a number closer to 3 million. Still, it is clearly altogether too many people to be treated as guilty by association.

So it might seem like a strange time for me to be advocating that we give the Government more of our data.

The Office for National Statistics (ONS) is currently consulting on the form of the next census and the future of population statistics generally. The two options they have come down to are:

1. Keep the 2021 census pretty much as it was for 2011, although with perhaps slight changes to the questions and a greater push for people to complete them online; or
2. Using administrative data already held by the Government in its various departments to produce an annual estimate of the population in local areas. In addition there would be separate compulsory surveys of 1% and 4% of the population for checking the overall population figures and some of the sub-grouping respectively, and the ‘residents of “communal establishments” such as university halls of residence and military bases’ which are difficult to reach by other means.

In my response to the survey, I suggested that they do both, increase the compulsory surveys each year to 10% of the population and reduce the time between full censuses to 5 years. This is why.

First of all, everybody needs this data to be available. If the Government does not provide it, someone else will. Not by asking you overt questions, but by buying information about your buying preferences or search engine activities or any number of other transactions without your informed consent (eg you ticked agreement to their terms and conditions on their website) and without your knowledge. I would prefer to give my data to the ONS.

The ONS is part of the UK Statistics Authority, which is an independent body at arm’s length from government. It reports directly to Parliament rather than to Government Ministers and has a strong track record of challenging the Government’s misuse of statistics. With the exception of requests received for personal information (which are filtered off to become Subject Access Requests under the Data Protection Act), they have provided copies of all information disclosed by the ONS under the Freedom of Information Act on their website. In my view the ONS has demonstrated that it is a safe custodian of our data. They are everything the NSA is not: overt, apolitical and committed to the appropriate use of statistics.

But there are problems with the current data, which brings me onto my second point. Ten years is too long to wait for updated information. As the ONS points out in its consultation document, because of the ten year gap between censuses, the population growth resulting from expansion of the European Union in 2004 was not fully understood until 2012. There were other problems with the population data everyone had been working with before 2011, 30,000 fewer people in their 90s than expected for instance, which had serious implications for all involved in services to the elderly and those constructing mortality tables too.

So we do need more frequent census information. Five years seems about right to me, provided the annual updates can be made more rigorous. I think the ONS are right to suggest that they need to be compulsory to achieve this, but 5% of the population does not seem a large enough sample to be confident about this to me. I would prefer to see 10% completing annual surveys. This would allow 50% of the population to be covered over every 5 year census period, or 40% if the requirement was dropped in census year. There are many recent examples (see Schonberger and Cukier below) to suggest that the gains in accuracy due to increased coverage would be far greater than the losses due to the ‘messiness’ of incomplete responses.

There is a lot in the consultation document about the relative costs of the different options, but nothing about the commercial value of the data being collected. Indeed the reduction of the consultation to these two, to my mind, inadequate options seems to be very greatly influenced by the question of costs and the current cuts in budgets seen throughout the public sector. This seems to me to be very short-sighted.

However, I think this displays a failure of imagination. According to Viktor Mayer-Schonberger and Kenneth Cukier in their book Big Data, data is set to be the greatest source of wealth and economic growth looking forward. Many others agree. By taking a fully accountable and carefully controlled approach to licensing the data in its care, the ONS should be able to finance its own activities, even at the level I am suggesting, at the very least.

The ONS is very nervous about becoming more intrusive in its collection methods, citing the 35% increase in cost of the 2011 census in achieving the same level of response. It also refers to the response rates to its voluntary surveys which have dropped from around 80% 30 years ago to around 60% today. The main reasons for this in my view are the incessant requests from companies’ marketing departments masquerading as surveys on everything from phone usage to our views on banking to the relentless demands for feedback on every online purchase making us all subject to survey fatigue. This makes it all the more necessary that an organisation which is not trying to sell you anything and which is scrupulous about the protection of your data should be attempting to increase its scope and maintaining its position as the go to place for statistical data rather than falling behind its commercial rivals.

So let’s not fall into the trap of conflating all official data with the mountains of bitty fragments collected by our intelligence agencies from their shady sources. That has nothing to do with the proper, accountable collection of information to allow government and governed alike access to what they need to make better decisions.

So take part in the consultation, it matters. And when the time comes give the ONS your data. You know it makes census.

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