Head or heart?

Head or heart?


The last 20 years have seen far more effort to base policy-making on “the use of evidence” to help governments understand their policy problems and options to solve them. This practice is not without limitations or even always adhered to, but is more widespread than in the past. More recently there have been signs that the use of analysis to support policy is losing momentum. Indeed, politicians have become acutely aware that pronouncements based on evidence seem less effective than those that touch the public’s emotions. The lesson is that evidence-based policy is more likely to succeed in its objectives, but that the evidence does not speak for itself: it needs to be communicated well to compete with emotions-based policy-making.

The rationale for evidence-based  policy-making is simple enough: all policy  decisions involve trade-offs, and good analysis  is needed to make those decisions efficiently and  effectively. This might seem particularly obvious  when spending is involved: government resources  are limited and evidence is needed to measure  the trade-offs between one use and another.  It would not make sense to expend all resources  on improving the nation’s health; some are  needed to educate people and try to keep  them safe. The question that can surely only  be answered by analysis is: how much should  go in each pot? 

In the first quarter of the 20th century,  the Cambridge economist Arthur Pigou elegantly  described the rational way to approach this  question, with: 

...the postulate that resources should be so  distributed among different uses that the marginal  return of satisfaction is the same for all of them…  Expenditure should be distributed between  battleships and poor relief in such wise that the  last shilling devoted to each of them yields the  same real return. 

Over the following century, governments  and officials have made repeated efforts to  articulate this “postulate”, or principle, in  practical terms. As the Permanent Secretary  to the UK Treasury, Sir Tom Scholar, reminds  us in the 2018 version, the Treasury’s Green Book  – the official how-to guide to the appraisal and  evaluation of proposals for government policy –  has been in publication for over 40 years. In the  UK and many other countries, legislative  proposals have to be accompanied by “impact  assessments”. Cost–benefit analyses are the  common accompaniment to policy-making  in most jurisdictions. 


The Question that can surely only be answered by analysis is: how much should go in each pot?





The Green Book describes in detail the  approach to be followed with respect to proposals  for legislation or public spending in the UK.  It articulates the different elements of the “policy  cycle”, beginning with the identification of the  rationale for intervention, and concludes with  the need for a feedback mechanism, as shown  in Figure 10.1. 

The aim of appraisal, according to the  Green Book, is to help “decision makers to  understand the potential effects, trade-offs  and overall impact of options by providing an  objective evidence base for decision making”.  But if that has long been the purpose of policy  appraisal, the phrase “evidence-based  policy-making” did not begin to trip off  political tongues until the 1990s. 



The desire for robust evidence to  justify the use of public resources has come  from a number of different political angles.  Governments in the 1970s, when the rise in  public spending was beginning to cause unease  in a number of countries, were occasionally  attracted by ideas of “zero-based budgeting” –  making every department argue for every  penny – but the inertia in any public spending  system easily overcame such ambitions. 

In the 1980s, President Ronald Reagan  struck a chord with US voters when he argued  that “government is not the solution to our  problem; government is the problem”, and the  British prime minister, Margaret Thatcher,  began a long programme of returning  government enterprises to the private sector.  In Green Book terms, the first link in the policy  cycle – establishing a rationale for intervention  – became less of a formality: it had to meet the  test of a new political scepticism about the role  of the state. 

By the turn of the millennium, however,  the pendulum had swung back towards a vision  of government as enabler and facilitator of  societal improvement. But if the “rationale”  had again become easier to establish, the  demand for evidence to support policy had  become even stronger, was articulated publicly,  and had a new, avowedly practical focus on  well-managed delivery. 

The British prime minister, Tony Blair,  argued that the job of government was to decide  on the right policies and then “deliver them  effectively…this means learning from mistakes,  seeing what works best”. And in 2009, in his  inaugural address, President Barack Obama  declared that: 

…The question we ask today is not whether  our government is too big or too small, but whether  it works – whether it helps families find jobs at a  decent wage, care they can afford, a retirement that  is dignified. Where the answer is yes, we intend to  move forward. Where the answer is no, programs  will end. And those of us who manage the public’s  dollars will be held to account – to spend wisely,  reform bad habits, and do our business in the light  of day – because only then can we restore the vital  trust between a people and their government. 

Nor was this only an Anglo-American  preoccupation. In Germany in 2003, a series  of active labour market programmes were  introduced to try to bring down the country’s  high unemployment rate. This depended on  the findings of an expert commission chaired  by Peter Hartz, the Personnel Director of  Volkswagen. 

In France, social policy experimentation has also grown in popularity. Several largescale randomised control trials have been implemented, including a quasi-experimental trial of the new French Minimum Income Scheme in 2009. All these examples demonstrated an appetite for “evidence-based policy-making”. But what precisely was meant by this catchphrase? Was it just a fashionable label to attach to different policy approaches? Or did it have a hard core of must-have elements?

In France, social policy experimentation has also grown in popularity. Several large-scale randomised control trials have been implemented, including a quasi-experimental trial of the new French Minimum Income scheme in 2009. All these examples demonstrated an appetite for "evidence-based policy-making". But what precisely was meant by this catchphrase? Was it just a fashionable label to attach to different policy approaches? Or did it have a hard core of must-have elements? 



Some useful lessons about the making of  complex policies can be drawn from a  comparison of two recent social programmes  in Germany and the UK: the Hartz reforms and  the introduction of Universal Credit. Both had  as one of their key objectives an increase in work  incentives, although this element in the Hartz  reforms was part of a wider package of labour  market measures, while the proposal for  Universal Credit was an ambitious plan to  swallow up the full range of means-tested  benefits and disgorge them as a single payment. 

Germany’s reforms were launched in 2003,  after a period in which painfully slow economic  growth and a sclerotic labour market had driven  the country’s unemployment rate up into double  figures – on one measure, to over 13%. The  chancellor, Gerhard Schroeder, had appointed  an expert commission chaired by Peter Hartz,  the Personnel Director of Volkswagen, and  moved swiftly to implement its findings. 

There were no pre-implementation policy trials,  but the reforms were introduced over three  years, and post-implementation expert reviews  were used to weed out unsuccessful elements  (of which the make-work schemes were the most  notable). These reviews helped to demonstrate  that the reforms had succeeded in reducing  Germany’s underlying unemployment rate by  three percentage points, and Germany’s more  flexible labour market proved remarkably  resilient to the subsequent financial crisis. 

The institutional and benefits changes were,  in technical terms, executed smoothly. But with  the final phase (Hartz IV), which sharply  reduced payments to the long-term unemployed,  income inequality in Germany increased and the  centre-left government began to lose popular  support. The Social Democrats suffered for  this in the 2005 federal election, a lesson on  the extent to which even “successful” policies  need to be constantly recalibrated and resold  to the electorate. 

By comparison, in the UK, Universal Credit has  got off to a very rocky start. It was the personal  project of a departmental minister, or secretary  of state, formerly (and briefly) leader of the  Conservative Party, Iain Duncan Smith, and  was passed into law in 2012 with the intention  that it should be fully rolled out by 2017. 

A dramatic simplification of the welfare system  may have been a laudable ambition, but it was  an extremely expensive one, in conflict with the  government’s efforts to cut the welfare budget.  The secretary of state failed to secure Treasury  buy-in as the costs of Universal Credit mounted  and criticisms of design flaws went unheeded.  He resigned in 2016, claiming the welfare budget  had been short-changed.

Criticisms of the Universal Credit scheme  multiplied, both with respect to costs (the UK’s  National Audit Office concluded it would be  more expensive to administer than the six  benefits schemes it replaced) and to design,  particularly the long delay before those moved  on to the new benefit received their first payment. 

Subsequent ministers secured extra funding  from the Treasury and began redesigning the  scheme. Fortunately it was being “trialled” with  selected groups and in selected areas, and the  period for such trials was soon extended. In 2018  a further £4.5 billion was pumped into the  scheme and its conditions softened. By 2019  the latest secretary of state, Amber Rudd, had  announced that another “careful” trial migrating  10,000 people to Universal Credit would be  started the following July, and there would be  another parliamentary vote before the decision  was taken to roll the scheme out in full. 

It is, of course, far too soon to reach conclusions  on the effectiveness of this reform. But unlike  the Hartz reforms, Universal Credit does not  seem to be cutting welfare costs, even though it  is dogged by stories of hardship caused to losers,  and the use of food banks is reported to have  increased in areas where it was being trialled.  Unemployment has continued to fall to levels not  seen in the UK since the 1970s, but it is far too  soon to say whether anticipation of Universal  Credit has had any part to play in this shift. 

The scheme certainly illustrated the benefit  of pre-implementation trials, but also their  limitations: trials are not without cost when  real people’s lives are affected. Accurate costing  and full government buy-in before policies are  launched remain essential. 



A broad definition of evidence-based  policy-making was provided in a UK Cabinet  Office paper in 1999 – it is an approach that:

…helps people make well informed decisions  about policies, programmes and projects by putting  the best available evidence from research at the  heart of policy development and implementation. 

Beyond such generalities, a powerful  influence on the understanding of what was  needed to fulfil such an agenda was the  American psychologist Donald Campbell,  who had refined his vision of The Experimenting  Society between the 1970s and the 1990s: 

…The US and other modern nations should  be ready for an experimental approach to social  reform, an approach in which we try out new  programs designed to cure specific problems, in  which we learn whether or not these programs are  effective, and in which we retain, imitate or discard  them on the basis of their apparent effectiveness  on the multiple imperfect criteria available. 

Thus the key elements in the approach  were seen to be the collection of relevant  evidence, and its refreshment through  experimentation to discover “what works”.

In what was possibly the high point of  faith in experts, the UK Cabinet Office attempted  to define what could be counted as “evidence”  in its 1999 white paper Modernising Government.  The list runs as follows: 

…Expert knowledge; published research;  existing research; stakeholder consultations;  previous policy evaluations; the Internet; outcomes  from consultations; costings of policy options;  output from economic and statistical modelling. 

Evidence can play a role at various stages  in policy-making, as the table below illustrates: 

The pendulum had swung back towards a vision of government as enabler and facilitator of societal improvement





The final category takes the list into  the second key element – experimentation.  The most powerful influence on this part of the  approach to evidence-based policy-making has  been the development by, among others, the US  criminologist Lawrence Sherman of the five-level  Maryland Scientific Methods Scale (SMS).  The screening of policy evaluations  carried out by What Works Centres in the UK,  which were launched in 2013, makes use of this  approach. The version of the scale used by one  of these centres is summarised below: 

The Maryland SMS (What Works Centre for Local Economic Growth)

Level 1: 

Either (a) a cross-sectional comparison  of treated groups with untreated groups, or (b)  a before-and-after comparison of treated group,  without an untreated comparison group. No use  of control variables in statistical analysis to adjust  for differences between treated and untreated  groups or periods. 

Level 2: 

Use of adequate control variables and  either (a) a cross-sectional comparison of  treated groups with untreated groups, or (b)  a before-and-after comparison of treated group,  without an untreated comparison group. In (a),  control variables or matching techniques used to  account for cross-sectional differences between  treated and control groups. In (b), control variables  are used to account for before-and-after changes  in macro-level factors. 

Level 3: 

Comparison of outcomes in treated  group after an intervention, with outcomes  in the treated group before the intervention,  and a comparison group used to provide a  counterfactual (e.g. difference in difference).  Justification given to choice of comparator  group that is argued to be similar to the  treatment group. Evidence presented on  comparability of treatment and control groups.  Techniques such as regression and propensity  score matching may be used to adjust for  difference between treated and untreated  groups, but there are likely to be important  unobserved differences remaining. 

Level 4: 

Quasi-randomness in treatment is  exploited, so that it can be credibly held that  treatment and control groups differ only in  their exposure to the random allocation of  treatment. This often entails the use of an  instrument or discontinuity in treatment,  the suitability of which should be adequately  demonstrated and defended. 

Level 5: 

Reserved for research designs that  involve explicit randomisation into treatment  and control groups, with Randomised Control  Trials (RCTs) providing the definitive example.  Extensive evidence provided on comparability  of treatment and control groups, showing no  significant differences in terms of levels or  trends. Control variables may be used to adjust  for treatment and control group differences, but  this adjustment should not have a large impact  on the main results. Attention paid to problems  of selective attrition from randomly assigned  groups, which is shown to be of negligible  importance. There should be limited or,  ideally, no occurrence of “contamination”  of the control group with the treatment. 

The What Works Centres, focusing  on education, crime prevention, local growth  initiatives, children’s social care and early  years and ageing, are gaining a strong reputation  for their ability to filter vast quantities of  research for the nuggets of evidence useful  to policy-makers. They had an impressive  forerunner in the National Institute for Clinical  Excellence, which was created in 1999. This has  shown that it is possible to detach difficult  decision-making from the political process  and rest it on expert evidence and careful  trialling – for at least part of the time. 


The growing role of evidence in the  policy-making process has stimulated the  collection and storage of relevant data by  researchers, while the rapid growth in  computing capabilities, information and  communication technologies, and statistical  sciences has led to the better use of such data.  Randomised control trials, deemed by such  frameworks as the Maryland SMS to set the  highest standards for policy evaluations,  are far from universal. But a large number  of quasi-experimental methods as well as  qualitative research methods have become  the norm in evidence-based policy-making. 

Similar bodies to the What Works  Centres have been set up in many other  countries. The US now has the What Works Cities  network funded by Bloomberg; Canada is setting  up its own centre; and countries from France  to Japan are developing their own initiatives to  embed evidence, sometimes drawing on the UK  experience. There are many ways in which these  institutions can evolve: making better use of  vastly greater flows of data; connecting to  neighbouring fields like impact measurement;  and sharing global experience. 

Such approaches have clearly had an  impact. Professor Sherman’s development of  evidence-based policing in the US is an obvious  example. His randomised control trials of such  innovations as the wearing of body cameras by  police officers, or of “hot spots” policing,  together with his development of the “Triple T”  framework – targeting, tracking and trialling  – have had worldwide impact on the use of police  resources. In the UK, notable evidenced-based  policies in other fields include the Sure Start  programme, the Educational Maintenance  Allowance and the Employment and Retention  Advancement (ERA) Demonstration Project,  among others.  

But the UK is a long way from a model  of government where evidence would have  primacy in terms of influence on policy  decisions, and a cursory glance at the news  flow in other countries, democratic or otherwise,  demonstrates the same influences of emotion or  opinion on major decisions. It is time to weigh up  these factors, and ask which way the balance of  policy-making is leaning at the start of the 2020s. 


John Maynard Keynes put the other side  of the story of policy-making to the elegant  “postulate” of his contemporary, Arthur Pigou: 

…There is nothing a government hates more  than to be well-informed; for it makes the process  of arriving at decisions much more complicated  and difficult. 

Policy-making is complicated by  the pressure of different objectives: not only  political imperatives (parties’ manifestos,  opinion polls) but also the differing agendas  of civil servants and their departments, as well  as the deep inertia in resource allocation that  constrains dramatic shifts. 

There is, moreover, a further set of  pressures that distracts from the use of  evidence, or rather which confuses evidence of  inputs with evidence of outputs. Success, in the  eyes of ministers and their departments, lobby  groups or political parties, is often articulated  in terms of the amount of money spent, or the  rate at which it is increasing – X billion more  on defence, welfare, health or education. The  nonsensical extreme to which such an approach  can lead is illustrated by the story of the UK’s  “National Programme for IT” for its National  Health Service. 

In the first decade of the new  millennium, something of the order of  £20 billion was proudly spent on this project –  a sum that would have paid for perhaps 30 new  hospitals. Eventually, the scheme had to be  abandoned, with little or no cost recovery;  one of the most spectacular failures in a long  history of disastrous government IT projects. 

This was also an example of a common  weakness of governments for engaging in grand  legacy projects that prove to be spectacularly  undercosted or whose costs are poorly  controlled (see Box 2). In the UK, the Treasury  specifically warns against “optimism bias”  in proposal costing, but probably only succeeds  in scraping the tip of the iceberg.

In a 2018 article for the Journal of  European Political Research, Messrs Jennings,  Lodge and Ryan analysed 23 policy “blunders”  committed in a variety of different countries  that fall into this category, including such  well-known extravaganzas as the Millennium  Dome and the Hamburg Concert Hall. A common  feature of these projects is that their reversal  costs are high, both financially and politically.

Few politicians relish the U-turn.  Searching for other common elements or  causes, the authors highlight “over-excited  politics” and a lack of administrative capacity,  sometimes exacerbated by the choice of the  wrong instrument. 

Policy-making is, in short, beset by  biases in favour of over-spending, intended  or unintended, rather than output-focused  analysis. It was, after all, the arch-proponent  of evidence-based policy among British prime  ministers, Tony Blair, who gave the final  go-ahead to the Millennium Dome. 

Even where lip service, at least, is paid  to the need to base proposals on evidence, it is  not difficult to find examples of the following  common weaknesses:

  • The evidence that has been developed  is not well matched to the particular  policy being proposed. It may be  outdated or collected in relation to  a different environment, geography,  cohort or community.
  • The resources that have been put aside  for a robust evaluation are insufficient.  Only some of the critical policy questions  have been answered.
  • The objectives are intangible (e.g. the  “Big Society”). This does not make them  worthless, but it does make it hard to  appraise the value of policies proposed  to deliver them.
  • The production of evidence is an  afterthought, produced just in time  to meet statutory requirements  (e.g. regulatory impact assessments). 

In short, even within evidence-based  frameworks, objective evidence may not have  proved decisive against the demands of  policy-makers and/or their political masters.  And this may as often have been caused by  “cognitive biases” as by the pursuit of  conflicting objectives. 


So how do we weigh up the “evidence”  on the direction of policy-making, as we  approach the third decade of a new millennium?  Until recently, it seemed possible to argue that,  despite frequent and inevitable dips in the  road, we were on a long upward journey, on  which each new stage in the development  of evidence-based policy-making had led  to further improvements in the available  techniques, and further acceptance of its  importance. However, the recent trends in  global and national politics have brought this  optimism into question. 

Three worrying phenomena are much  in evidence. In some ways, these could be seen  as problems of success; but they also look like  kinks in the road.

  • A wide range of evidence is available to  politicians, from competing research  organisations/think tanks to internet  posts. It is not difficult to find a study  that can support a particular position,  allowing policy-makers to cherry-pick.  They also tend to weigh more highly, or  come to rely on, those parts of the media  that share their prejudices – an obvious  example of “confirmation bias”. However,  the public’s difficulty in sifting the  evidence may be compounded by:
  • A misguided attempt by “impartial”  media to demonstrate balance by  giving equal weight to unequal sources  of information. In their attempts to  appear even-handed, public service  broadcasters frequently give equal  prominence to minority voices whose  research may be far less thorough than  mainstream conclusions. News  programmes have long relied on  argument between opposing “talking  heads” and are only gradually waking  up to the need to give listeners and  viewers “fact checks”.
  • A resentment of experts, fed by  populists, which has made voters  keener to listen to non-expert voices  that resonate with their own concerns.

Meanwhile, neuroscientists and  psychologists have been building up our  knowledge about human responses to evidence.  The nub of it is that human beings don’t really  like changing their minds, and are certainly not  very willing to have their minds changed by data. 

A range of psychological and  neuroscientific studies suggest that people are  not much influenced by facts, figures or data.  According to conclusions reached in 2018 in  The Influential Mind: What the Brain Reveals  About our Power to Change Others by Professor  Sharot, an accomplished neuroscientist at  University College London: 

…the problem with an approach that  prioritises information and logic is that it ignores  what makes you and me human: our motives, our  fears, our hopes and desires…data has only a limited  capacity to alter the strong opinion of others. 

The reluctance of political leaders to  change their minds or abandon their projects  has already been noted. In fairness, it must be  acknowledged that they often got to where they  are by battling against the “evidence” – that is  to say, contrary to the expectations of the  prevailing consensus – with strong instincts  that they then naturally tend to rely on in  preference to evidence. 

The election of Donald Trump, the  economic policies of Margaret Thatcher,  arguably even Winston Churchill’s refusal to  make terms with the Nazi regime in 1940, all took  place in defiance of the judgement of “wiser  heads”. But the best leaders know how to weigh  up the evidence before deciding to ignore it:  Churchill had been avid for information about  German rearmament throughout the pre-war  period, and it was only when Mrs Thatcher  became cavalier about evidence that she  embarked on the policy that cost her the  premiership (see Box 2). 

What, however, these neuroscientific and psychological studies demonstrate is the equal resistance of those outside the public eye to data-based arguments intended to make them change their minds. It follows that politicians are more likely to succeed by appealing to emotions than by relying purely on reason. A vivid example of this, cited by Professor Sharot, is given by the responses of two aspirants for the Republican candidacy for the US presidential elections in 2016: the paediatric neurosurgeon Ben Carson and the successful candidate Donald Trump. In one of the primary debates, the discussion turned to the link between childhood vaccines and autism. Mr Carson, the expert, pointed out that:

…the fact of the matter is that we have extremely well-documented proof there’s no autism associated with vaccinations.

Mr Trump’s response was:

…Autism has become an epidemic…out of control…you take this little beautiful baby and you pump – I mean it looks like it’s meant for a horse not a child…just the other day a beautiful child went to have the vaccine and came back, a week later got a tremendous fever…now is autistic.

As we have all learnt since, it is that appeal to the emotions, however incoherent, that gets through.




Britain’s brief and disastrous experiment with local government finance in the late 1980s provides a vivid illustration of the clash between emotions and evidence in policy-making. The change was introduced after a surge of political emotion against a local tax loosely based on property rental values called the “rates”. Many of the Thatcher Government’s supporters saw “rates” as unfair to elderly single occupants, compared with households which contained multiple income-earning (and local service-using) residents but paid no more tax. A flat-rate “Community Charge” would instead be levied on every resident.

Arguments that such a tax would prove hard to collect, put by the Treasury, were ignored by the prime minister, although a “trial” was undertaken by introducing the charge first in Scotland. This added to the political disaster by leading the Scots to complain their country had been used as a guinea pig by English Tories. And as what was rapidly rechristened the “poll tax” was rolled out in England too, opposition erupted nationwide and its weaknesses became evident.

Property, which can’t move, is not difficult to tax: people (especially the young) proved much harder, disappearing rapidly off the register. Exceptions had to be made for those unable to pay, particularly as the shrinking tax base drove up the level of charge that local authorities had to levy, in a vicious spiral of policy failure. The anger MPs experienced in their constituencies was a more powerful factor in the downfall of Mrs Thatcher than any disagreement with her over Europe. Local revenue collapsed.

The new prime minister, John Major, moved quickly to plug the hole in government finances with a 2.5% increase in VAT, and announced a review of alternative systems of local government finance. A local income tax was seen to be impractical in a densely populated country, where many people live in one local authority area and work in another. A local VAT rate would have suffered from the same border difficulty. But although a property base looked the best option, emotions were still running high against any risk of a “return to the rates”.

So the department responsible for local government came up with a scheme for a tax made up of two different elements – one based on property values, the other, “personal”, element being levied on each of the residents in the property. Analysis by the Treasury and No. 10 Policy Unit showed this to be extremely expensive to administer. They also conducted demographic analysis to demonstrate that the number of households containing more than two adults who could be expected to pay a personal tax of any size – that is to say, adults who were not students, non-earning and/or on benefits – was small. Too small, that is, to be worth the construction and maintenance of a complex personal register to try to capture as many of them as possible.

This paved the way for the introduction of a straightforward property tax with a discount for lone occupants – a discount which had to be claimed by the occupant, thus obviating the need for a full register to be maintained. The tax would be levied at rates covering broad bands of property values, avoiding the tax spikes which characterised “the rates”. The result – the council tax – was introduced in 1992 and remains in place to this day.


When the information changes, I change my conclusions. What do you do, Sir?






What does this mean for evidence-based policy-making? While the use of analysis to support policy has increased immeasurably in the past 20 years, there is reason to be concerned that, for the most important policies, this approach is losing momentum. In areas such as trade, for example, there seems little appetite for evidence to support policy. Instead, it appears that politicians have become increasingly aware that pronouncements based on evidence are less effective than those that touch public emotions.

The all-important lesson is that evidence does not always speak for itself. It remains fundamentally important to build frameworks within which as much public policy as possible is grounded in robust evidence, and constantly retested on the journey from proposal to implementation. Where high or heated politics are not involved, that may be all that is needed. But for all policies, and especially for those that are contentious, policy-makers need to be constantly aware of the need to persuade and retain support.

Dry explanations of the research evidence will not meet the need. Another dimension is required, and that is to understand how the public thinks, and how to deliver research in the most compelling way imaginable. Effective communication of objectives, engagement with the emotions of those whose support is needed, a continuous taking of the public temperature and anticipation of likely reactions may be as critical to the completion of a successful “policy cycle” as the analysis of the academic data and statistical results of control trials.

Both appraisal and communication have one thing in common: the need to remain sensitive, flexible and responsive. Evidence-based policy-makers, in short, are the one group who cannot afford to be resistant to new information. Whether apocryphal or not, there is much to be said for the story of Keynes’s response to a criticism of inconsistency:

When the information changes, I change my conclusions. What do you do, Sir.

Photo: Dave Sinclair – poll tax protests