Get rich predicting the next recession – just watch the fertility statistics

… we are told. Or perhaps not. This was the research reported last week, with varying degrees of credulity, by the BBC here and The (London) Times here (£paywall). This turned out to be a press release about some academic research by Kasey Buckles of Notre Dame University and others. You have to pay USD 5 to get the academic paper. I shall come back to that.

The paper’s abstract claims as follows.

Many papers show that aggregate fertility is pro-cyclical over the business cycle. In this paper we do something else: using data on more than 100 million births and focusing on within-year changes in fertility, we show that for recent recessions in the United States, the growth rate for conceptions begins to fall several quarters prior to economic decline. Our findings suggest that fertility behavior is more forward-looking and sensitive to changes in short-run expectations about the economy than previously thought.

Now, here is a chart shared by the BBC.

Pregnancy and recession

The first thing to notice here is that we have exactly three observations. Three recession events with which to learn about any relationship between human sexual activity and macroeconomics. If you are the sort of person obsessed with “sample size”, and I know some of you are, ignore the misleading “100 million births” hold-out. Focus on the fact that n=3.

We are looking for a leading indicator, something capable of predicting a future event or outcome that we are bothered about. We need it to go up/ down before the up/ down event that we anticipate/ fear. Further it needs consistently to go up/ down in the right direction, by the right amount and in sufficient time for us to take action to correct, mitigate or exploit.

There is a similarity here to the hard and sustained thinking we have to do when we are looking for a causal relationship, though there is no claim to cause and effect here (c.f. the Bradford Hill guidelines). One of the most important factors in both is temporality. A leading indicator really needs to lead, and to lead in a regular way. Making predictions like, “There will be a recession some time in the next five years,” would be a shameless attempt to re-imagine the unsurprising as a signal novelty.

Having recognised the paucity of the data and the subtlety of identifying a usefully predictive effect, we move on to the chart. The chart above is pretty useless for the job at hand. Run charts with multiple variables are very weak tools for assessing association between factors, except in the most unambiguous cases. The chart broadly suggests some “association” between fertility and economic growth. It is possible to identify “big falls” both in fertility and growth and to persuade ourselves that the collapses in pregnancy statistics prefigure financial contraction. But the chart is not compelling evidence that one variable tracks the other reliably, even with a time lag. There looks like no evident global relationship between the variation in the two factors. There are big swings in each to which no corresponding event stands out in the other variable.

We have to go back and learn the elementary but universal lessons of simple linear regression. Remember that I told you that simple linear regression is the prototype of all successful statistical modelling and prediction work. We have to know whether we have a system that is sufficiently stable to be predictable. We have to know whether it is worth the effort. We have to understand the uncertainties in any prediction we make.

We do not have to go far to realise that the chart above cannot give a cogent answer to any of those. The exercise would, in any event, be a challenge with three observations. I am slightly resistant to spending GBP 3.63 to see the authors’ analysis. So I will reserve my judgment as to what the authors have actually done. I will stick to commenting on data journalism standards. However, I sense that the authors don’t claim to be able to predict economic growth simpliciter, just some discrete events. Certainly looking at the chart, it is not clear which of the many falls in fertility foreshadow financial and political crisis. With the myriad of factors available to define an “event”, it should not be too difficult, retrospectively, to define some fertility “signal” in the near term of the bull market and fit it astutely to the three data points.

As The Times, but not the BBC, reported:

However … the correlation between conception and recession is far from perfect. The study identified several periods when conceptions fell but the economy did not.

“It might be difficult in practice to determine whether a one-quarter drop in conceptions is really signalling a future downturn. However, this is also an issue with many commonly used economic indicators,” Professor Buckles told the Financial Times.

Think of it this way. There are, at most, three independent data points on your scatter plot. Really. And even then the “correlation … is far from perfect”.

And you have had the opportunity to optimise the time lag to maximise the “correlation”.

This is all probably what we suspected. What we really want is to see the authors put their money where their mouth is on this by wagering on the next recession, a point well made by Nassim Taleb’s new book Skin in the Game. What distinguishes a useful prediction is that the holder can use it to get the better of the crowd. And thinks the risks worth it.

As for the criticisms of economic forecasting generally, we get it. I would have thought though that the objective was to improve forecasting, not to satirise it.

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Data science sold down the Amazon? Jeff Bezos and the culture of rigour

This blog appeared on the Royal Statistical Society website Statslife on 25 August 2015

Jeff Bezos' iconic laugh.jpgThis recent item in the New York Times has catalysed discussion among managers. The article tells of Amazon’s founder, Jeff Bezos, and his pursuit of rigorous data driven management. It also tells employees’ own negative stories of how that felt emotionally.

The New York Times says that Amazon is pervaded with abundant data streams that are used to judge individual human performance and which drive reward and advancement. They inform termination decisions too.

The recollections of former employees are not the best source of evidence about how a company conducts its business. Amazon’s share of the retail market is impressive and they must be doing something right. What everybody else wants to know is, what is it? Amazon are very coy about how they operate and there is a danger that the business world at large takes the wrong messages.

Targets

Targets are essential to business. The marketing director predicts that his new advertising campaign will create demand for 12,000 units next year. The operations director looks at her historical production data. She concludes that the process lacks the capability reliably to produce those volumes. She estimates the budget required to upgrade the process and to achieve 12,000 units annually. The executive board considers the business case and signs off the investment. Both marketing and operations directors now have a target.

Targets communicate improvement priorities. They build confidence between interfacing processes. They provide constraints and parameters that prevent the system causing harm. Harm to others or harm to itself. They allow the pace and substance of multiple business processes, and diverse entities, to be matched and aligned.

But everyone who has worked in business sees it as less simple than that. The marketing and operations directors are people.

Signal and noise

Drawing conclusions from data might be an uncontroversial matter were it not for the most common feature of data, fluctuation. Call it variation if you prefer. Business measures do not stand still. Every month, week, day and hour is different. All data features noise. Sometimes is goes up, sometimes down. A whole ecology of occult causes, weakly characterised, unknown and as yet unsuspected, interact to cause irregular variation. They are what cause a coin variously to fall “heads” or “tails”. That variation may often be stable enough, or if you like “exchangeable“, so as to allow statistical predictions to be made, as in the case of the coin toss.

If all data features noise then some data features signals. A signal is a sign, an indicator that some palpable cause has made the data stand out from the background noise. It is that assignable cause which enables inferences to be drawn about what interventions in the business process have had a tangible effect and what future innovations might cement any gains or lead to bigger prospective wins. Signal and noise lead to wholly different business strategies.

The relevance for business is that people, where not exposed to rigorous decision support, are really bad at telling the difference between signal and noise. Nobel laureate economist and psychologist Daniel Kahneman has amassed a lifetime of experimental and anecdotal data capturing noise misinterpreted as signal and judgments in the face of compelling data, distorted by emotional and contextual distractions.

Signal and accountability

It is a familiar trope of business, and government, that extravagant promises are made, impressive business cases set out and targets signed off. Yet the ultimate scrutiny as to whether that envisaged performance was realised often lacks rigour. Noise, with its irregular ups and downs, allows those seeking solace from failure to pick out select data points and cast self-serving narratives on the evidence.

Our hypothetical marketing director may fail to achieve his target but recount how there were two individual months where sales exceeded 1,000, construct elaborate rationales as to why only they are representative of his efforts and point to purported external factors that frustrated the remaining ten reports. Pairs of individual data points can always be selected to support any story, Don Wheeler’s classic executive time series.

This is where the ability to distinguish signal and noise is critical. To establish whether targets have been achieved requires crisp definition of business measures, not only outcomes but also the leading indicators that provide context and advise judgment as to prediction reliability. Distinguishing signal and noise requires transparent reporting that allows diverse streams of data criticism. It requires a rigorous approach to characterising noise and a systematic approach not only to identifying signals but to reacting to them in an agile and sustainable manner.

Data is essential to celebrating a target successfully achieved and to responding constructively to a failure. But where noise is gifted the status of signal to confirm a fanciful business case, or to protect a heavily invested reputation, then the business is misled, costs increased, profits foregone and investors cheated.

Where employees believe that success and reward is being fudged, whether because of wishful thinking or lack of data skills, or mistakenly through lack of transparency, then cynicism and demotivation will breed virulently. Employees watch the behaviours of their seniors carefully as models of what will lead to their own advancement. Where it is deceit or innumeracy that succeed, that is what will thrive.

Noise and blame

Here is some data of the number of defects caused by production workers last month.

Worker Defects
Al 10
Simone 6
Jose 10
Gabriela 16
Stan 10

What is to be done about Gabriela? Move to an easier job? Perhaps retraining? Or should she be let go? And Simone? Promote to supervisor?

Well, the numbers were just random numbers that I generated. I didn’t add anything in to make Gabriela’s score higher and there was nothing in the way that I generated the data to suggest who would come top or bottom. The data are simply noise. They are the sort of thing that you might observe in a manufacturing plant that presented a “stable system of trouble”. Nothing in the data signals any behaviour, attitude, skill or diligence that Gabriela lacked or wrongly exercised. The next month’s data would likely show a different candidate for dismissal.

Mistaking signal for noise is, like mistaking noise for signal, the path to business under performance and employee disillusionment. It has a particularly corrosive effect where used, as it might be in Gabriela’s case, to justify termination. The remaining staff will be bemused as to what Gabriela was actually doing wrong and start to attach myriad and irrational doubts to all sorts of things in the business. There may be a resort to magical thinking. The survivors will be less open and less willing to share problems with their supervisors. The business itself has the costs of recruitment to replace Gabriela. The saddest aspect of the whole business is the likelihood that Gabriela’s replacement will perform better than did Gabriela, vindicating the dismissal in the mind of her supervisor. This is the familiar statistical artefact of regression to the mean. An extreme event is likely to be followed by one less extreme. Again, Kahneman has collected sundry examples of managers so deceived by singular human performance and disappointed by its modest follow-up.

It was W Edwards Deming who observed that every time you recruit a new employee you take a random sample from the pool of job seekers. That’s why you get the regression to the mean. It must be true at Amazon too as their human resources executive Mr Tony Galbato explains their termination statistics by admitting that “We don’t always get it right.” Of course, everybody thinks that their recruitment procedures are better than average. That’s a management claim that could well do with rigorous testing by data.

Further, mistaking noise for signal brings the additional business expense of over adjustment, spending money to add costly variation while degrading customer satisfaction. Nobody in the business feels good about that.

Target quality, data quality

I admitted above that the evidence we have about Amazon’s operations is not of the highest quality. I’m not in a position to judge what goes on at Amazon. But all should fix in their minds that setting targets demands rigorous risk assessment, analysis of perverse incentives and intense customer focus.

It is a sad reality that, if you set incentives perversely enough,some individuals will find ways of misreporting data. BNFL’s embarrassment with Kansai Electric and Steven Eaton’s criminal conviction were not isolated incidents.

One thing that especially bothered me about the Amazon report was the soi-disant Anytime Feedback Tool that allowed unsolicited anonymous peer appraisal. Apparently, this formed part of the “data” that determined individual advancement or termination. The description was unchallenged by Amazon’s spokesman (sic) Mr Craig Berman. I’m afraid, and I say this as a practising lawyer, unsourced and unchallenged “evidence” carries the spoor of the Star Chamber and the party purge. I would have thought that a pretty reliable method for generating unreliable data would be to maximise the personal incentives for distortion while protecting it from scrutiny or governance.

Kahneman observed that:

… we pay more attention to the content of messages than to information about their reliability, and as a result end up with a view of the world around us that is simpler and more coherent than the data justify.

It is the perverse confluence of fluctuations and individual psychology that makes statistical science essential, data analytics interesting and business, law and government difficult.

A personal brush with existential risk

Blutdruck.jpgI visited my GP (family physician) last week on a minor matter which I am glad to say is now cleared up totally. However, the receptionist was very eager to point out that I had not taken up my earlier invitation to a cardiovascular assessment. I suspect there was some financial incentive for the practice. I responded that I was uninterested. I knew the general lifestyle advice being handed out and how I felt about it. However, she insisted and it seemed she would never book me in for my substantive complaint unless I agreed. So I agreed.

I had my blood pressure measured (ok), and good and bad cholesterol (both ok which was a surprise). Finally, the nurse gave me a percentage risk of cardiovascular disease. The number wasn’t explained and I had to ask if the number quoted was the annual risk of contracting cardiovascular disease (that’s what I had assumed) or something else. However, it turned out to be the total risk over the next decade. The quoted risk was much lower than I would have guessed so I feel emboldened in my lifestyle. The campaign’s efforts to get me to mend my ways backfired.

Of course, I should not take this sort of thing at face value. The nurse was unable to provide me with any pseudo-R2 for the logistic regression or even the Hosmer–Lemeshow statistic for that matter.

I make light of the matter but logistic regression is very much in vogue at the moment. It provides some of the trickiest issues in analysing model quality and any user would be naïve to rely on it as a basis for action without understanding whether it really was explaining any variation in outcome. Issues of stability and predictability (see Rear View tab at the head of this page) get even less attention because of their difficulty. However, issues of model quality and exchangeability do not go away because they are alien to the analysis.

When governments offer statistics such as this, we risk cynicism and disengagement if we ask the public to take them more glibly than we would ourselves.

 

Another driving metaphor

Welcome – I think this is going to be a shadow blog for a while until everything is in place.

I note that in today’s (London) Times Stephan Shakespeare purports to quote Sir Terry Leahy as saying:

An enterprise without data is like a car with no headlights at night.

Actually, I couldn’t find it on the web, not even with Google. I wonder if Mr Shakespeare has misremembered this famous quote.

In any event, I never encountered an enterprise without data. The problem faced by most enterprises is turning that data into information and exploiting it in making business decisions. I fear that all those decisions are made under besetting uncertainties that have little in common with a halogen beam into the future.

Data, no matter how Big, is not enough. It will not help you steer the car unless you know how to add value to it. In doing that, awareness of uncertainty and variation is a key insight.